Search options

Search operator:
Find:
At least one word (OR)
All words (AND)
Exact expression (Phrase)
Semantic search & fuzzy search
Also find:


2021-07-06T16:21:00Z
Republic of Ethiopia (2011) Ethiopia’s Climate Resilient Green Economy Green economy strategy.pdf
:

Republic of Ethiopia (2011) Ethiopia’s Climate Resilient Green Economy Green economy strategy


Ethiopia’s Climate-Resilient
Green Economy
Green economy strategy


FEDERAL DEMOCRATIC REPUBLIC OF ETHIOPIA


CONFIDENTIAL


Federal Democratic Republic of Ethiopia I


Contents


Foreword III

Executive summary 1

The vision: Achieve middle-income status by 2025 in a
climate-resilient green economy 5

The challenge: Realise economic development goals in a
sustainable way 11

The plan: Follow a green growth path that fosters development
and sustainability 19

The ambition is to build a green economy 19

The development of a green economy will be based on
four pillars 20

Agriculture: Improving crop and livestock production
practices for higher food security and farmer income
while reducing emissions 22

Forestry: Protecting and re-establishing forests for
their economic and ecosystem services, including as
carbon stocks 24

Power: Expanding electricity generation form
renewable energy for domestic and regional markets 25

Transport, industrial sectors and buildings:
Leapfrogging to modern and energy efficient
technologies 26

Building a green economy offers cost-efficient abatement
potential while promoting GTP targets 28

Making it happen: Ethiopia’s action plan to create a green
economy 45


Federal Democratic Republic of Ethiopia II


Appendices 59

Approach and methodology 61

Electric Power 78

Green Cities and Buildings 91

Forestry 101

Livestock 117

Soil 134

Industry 151

Transport 165

List of References 181


Federal Democratic Republic of Ethiopia III


Foreword

Ethiopia is experiencing the effects of climate change. Besides the direct effects
such as an increase in average temperature or a change in rainfall patterns, climate
change also presents the necessity and opportunity to switch to a new, sustainable
development model. The Government of the Federal Democratic Republic of
Ethiopia has therefore initiated the Climate-Resilient Green Economy (CRGE)
initiative to protect the country from the adverse effects of climate change and
to build a green economy that will help realise its ambition of reaching middle-
income status before 2025.

Since February 2011, the CRGE initiative, under the leadership of the Prime
Minister’s Office, the Environmental Protection Authority, and the Ethiopian
Development Research Institute, has been developing a strategy to build a green
economy. Seven sectoral teams involving more than 50 experts from more than
20 leading government institutions have been driving the initiative. The objective
is to identify green economy opportunities that could help Ethiopia reach its ambi-
tious growth targets while keeping greenhouse gas emissions low. The government
intends to attract development partners to help implement this new and sustainable
growth model.

This report summarises the findings of the CRGE initiative, and particularly
focuses on outlining the plan to develop a green economy. The document does not
cover climate resilience, which will be added over the coming months. This strat-
egy has been extensively discussed during the previous two months of regional
and sectoral consultations to ensure national alignment on priorities, confirm
initial findings, create awareness, and join forces. This document reflects the work
of the CRGE initiative as well as the outcome of the consultation process.


Addis Ababa, November 2011


Federal Democratic Republic of Ethiopia V


List of abbreviations

Abbreviation Stands for

B5 5% biodiesel content of transport diesel

BAU Business-as-usual (scenario)

BRT Bus-rail transit

CAPEX Capital expenditure or investment rather than a cost

CDM Clean Development Mechanism of the Kyoto Protocol

CEM IV/B Grade IV cement

CFL Compact florescent lights

CO2 Carbon dioxide, the most important greenhouse gas

CO2e Carbon dioxide equivalent

COP Conference of the Parties, the annual summit of UNFCCC

CRGE Ethiopia’s Climate-Resilient Green Economy initiative

CCS Carbon capture and storage

CSA Central Statistical Authority

E15 15% ethanol content of transport gasoline

EDRI Ethiopian Development Research Institute

EEPCo Ethiopian Electric Power Corporation

EIAR Ethiopian Institute of Agricultural Research

EPA Environmental Protection Authority

ETS European Trading Scheme

EWCA Ethiopian Wildlife Conservation Authority

FAO Food and Agriculture Organisation

FDI Foreign Direct Investment

FES Fuel efficiency standards

FRC Forestry Research Centre

GDP Gross domestic product

GGGI Global Green Growth Institute

GHG Greenhouse gases (mainly CO2, N2O, and methane)
GIZ Deutsche Gesellschaft für Internationale Zusammenarbeit

GoE Government of Ethiopia
Gt Gigatonne (i.e., billon metric tonnes)

GTP Growth and Transformation Plan

GVA Gross value added

Ha Hectare (ha)

IBC Institute of Biodiversity Conservation

IPCC Intergovernmental Panel on Climate Change


Federal Democratic Republic of Ethiopia VI


Abbreviation Stands for

KWh Kilowatt hour of electricity

LPG Liquefied petroleum gas

LRT Light-rail transit

MIC Middle income country

MoA Ministry of Agriculture

MoFED Ministry of Finance and Economic Development

MoI Ministry of Industry

MoT Ministry of Trade

MoUDC Ministry of Urban Development and Construction

MoWE Ministry of Water and Energy

MRV Measuring, reporting, and verification

MSC Ministerial Steering Committee of CRGE initiative

Mt CO2e Million metric tonnes of carbon dioxide equivalent

Mt Megatonne (i.e., million metric tonnes)

NAMA Nationally appropriate mitigation action

N2O Nitrous oxide, a greenhouse gas

NGO Nongovernmental organisation

NPV Net Present Value

OPC Ordinary Portland Cement

Opex Operating expenditure or cost

PASDEP Plan for accelerated and sustained development to end poverty

PPC Pozzolana Portland Cement

Q1/Q2/Q3/Q4 First/second/third/fourth quarter of the year

REDD+ Reducing Emissions from Deforestation and Forest Degradation

RELS Reducing Emissions from the Livestock Sector

R-PP Readiness Preparation Proposal

STC Sub-Technical Committee of CRGE initiative

t Tonne

TC Technical Committee of CRGE initiative

TCO Total cost of ownership

TWh Terawatt hour of electricity (tera = one trillion)

UNDP United Nations Development Programme

UNFCCC United Nations Framework Convention on Climate Change

WBISPP Woody Biomass Inventory and Strategic Planning Project


Federal Democratic Republic of Ethiopia VII


Federal Democratic Republic of Ethiopia VIII


Technical note
This strategy is based on the data and sources of information available to the
sub-technical committees as of August 2011. The BAU projections and
calculation of abatement potential and abatement cost follow a consistent
methodology as described in the appendix. As most of the calculations are
performed on a sectoral level, they do not necessarily follow specific project-
level protocols of setting baseline emission scenarios and abatement outcomes,
as is for example done in the context of carbon finance schemes. Rather, the
BAU calculations should be understood as a strategic emission projection
against which the sectoral mitigation programmes are drafted. The sectoral
mitigation initiatives and the associated costs should be understood as an initial
identification and estimation of sectoral abatement potential and a base for
strategic decisions regarding their implementation and required support.
Although they form part of an overall strategy in building a climate-resilient
green economy, and the government is committed to creating a supportive
environment for them, the individual initiatives should not be understood as
immediately mandatory government policies.


Federal Democratic Republic of Ethiopia 1


Executive summary

Ethiopia aims to achieve middle-income status by 2025 while developing a green
economy. Following the conventional development path would, among other
adverse effects, result in a sharp increase in GHG emissions and unsustainable
use of natural resources. To avoid such negative effects, the government has
developed a strategy to build a green economy. It is now starting to transform the
strategy into action and welcomes collaboration with domestic and international
partners.

The vision: Achieve middle-income status by 2025 in a climate-resilient green
economy

Both the government and the International Monetary Fund expect Ethiopia’s
economy to continue as one of the world’s fastest growing over the coming years.
Building on its positive recent development record, Ethiopia intends to reach
middle-income status before 2025. As set forth in the Growth and Transformation
Plan (GTP), reaching this goal will require boosting agricultural productivity,
strengthening the industrial base, and fostering export growth.

As a responsible member of the world, Ethiopia is also aware of the important role
that developing countries play in fighting climate change, and has consequently
taken on a constructive role in international climate negotiations. Ethiopia’s ambi-
tion to become a “green economy front-runner” is an expression of its potential for
and belief in a sustainable model of growth.

The challenge: To achieve economic development goals in a sustainable way

If Ethiopia were to pursue a conventional economic development path to achieve
its ambitious targets, the resulting negative environmental impacts would follow
the patterns observed all around the globe. Under current practices, greenhouse gas
(GHG) emissions would more than double from 150 Mt CO2e in 2010 to 400 Mt
CO2e in 2030. Its development path could also face resource constraints: for
example, it could reach the carrying capacity for cattle. Furthermore, it could lock
its economy into outdated technologies.

A conventional development path could also be financially challenging. For
example, a significant share of GDP might need to be spent on fuel imports,
putting pressure on foreign currency reserves.

Finally, according to the GTP, more than USD 50 billion will be needed over the
coming five years for infrastructure development. More than 50% will have to be


Federal Democratic Republic of Ethiopia 2


in foreign currency. Current and projected domestic savings and foreign direct
investments, grants, and transfers will not be sufficient to finance these invest-
ments, leading to a significant finance gap.

The plan: To follow a green growth path that fosters development and
sustainability

The Climate-Resilient Green Economy (CRGE) initiative follows a sectoral
approach and has so far identified and prioritised more than 60 initiatives, which
could help the country achieve its development goals while limiting 2030 GHG
emissions to around today’s 150 Mt CO2e – around 250 Mt CO2e less than esti-
mated under a conventional development path. The green economy plan is based
on four pillars:

1. Improving crop and livestock production practices for higher food security and
farmer income while reducing emissions

2. Protecting and re-establishing forests for their economic and ecosystem
services, including as carbon stocks

3. Expanding electricity generation from renewable sources of energy for
domestic and regional markets

4. Leapfrogging to modern and energy-efficient technologies in transport, indus-
trial sectors, and buildings.

For more than 80% of the abatement potential, abatement costs are less than USD
15 per t CO2e.1 Many of the initiatives offer positive returns on investments, thus
directly promoting economic growth and creating additional jobs with high value-
added.

Building the green economy requires an estimated total expenditure of around
USD 150 billion over the next 20 years. By developing a green economy, we could
exchange GHG emissions abatement for climate finance to fund some of the
required investment.

Implementing the initiatives would also offer important co-benefits. For example,
it would improve public health, through better air and water quality, and would
promote rural economic development by increasing soil fertility and food security.


1 USD 15 per t roughly equals the price of carbon credits under the European Trading Scheme in 2011.


Federal Democratic Republic of Ethiopia 3


Making it happen: The action plan to create a green economy

We have developed a strategy for transforming our ambition into reality. Under
the leadership of Prime Minister Meles Zenawi, our government has dedicated sig-
nificant resources to the inter-ministerial CRGE initiative. More than 50 experts
from 20 leading governmental institutions were engaged in seven committees,
directed by an inter-ministerial steering group.

As part of the strategy, the government has selected four initiatives for fast-track
implementation: exploiting the vast hydropower potential; large-scale promotion
of advanced rural cooking technologies; efficiency improvements to the livestock
value chain; and Reducing Emissions from Deforestation and Forest Degradation
(REDD). These initiatives have the best chances of promoting growth
immediately, capturing large abatement potentials, and attracting climate finance
for their implementation. To ensure a comprehensive programme, initiatives from
all other sectors will also be developed into concrete proposals.

The CRGE initiative also outlines the structure of a permanent institutional setup
to drive implementation, and to promote the participation of a broad set of
stakeholders.

We are dedicating significant resources to building our green economy. To capture
the full potential of our plan, we welcome emerging climate finance schemes,
which will compensate developing countries for the provision of environmental
services to the world. Bi- and multilateral development partners as well as the
private sector can help us achieve our ambitious goals.


Federal Democratic Republic of Ethiopia 5


The vision: Achieve middle-income
status by 2025 in a climate-resilient
green economy

Economically, Ethiopia is one of the world’s fastest-growing countries. Building
on its positive recent development, it intends to reach middle-income status
before 2025. It aims to do so by building a green economy.

ECONOMICALLY, ETHIOPIA IS ONE OF THE WORLD’S FASTEST-
GROWING COUNTRIES

Despite the challenges of being one of the world’s poorest countries, Ethiopia has
good prospects for growth. The International Monetary Fund forecasts for Ethiopia
a real gross domestic product (GDP) growth of more than 8% p.a. over the next
five years. Among countries with more than 10 million inhabitants, only China and
India will grow at a faster pace (Figure 1). The government is even more opti-
mistic and it projects a growth rate of 11%.

FIGURE 1

Ten fastest-growing economies1 (2011-2015)

Average real GDP growth p.a., percent

1 Excluding countries with less than 10 million population

SOURCE: IMF; The Economist

6,8

6,9

7,0

7,0

7,2

7,2

7,7

8,1

8,2

9,5

Nigeria

Zambia

Ghana

Congo

Vietnam

Tanzania

Mozambique

Ethiopia

India

China

Africa Non-Africa

Over the coming years, Ethiopia will continue to be one of
the world’s fastest-growing countries


Federal Democratic Republic of Ethiopia 6


Ethiopia’s recent track record demonstrates that it can achieve double-digit growth
rates. Between 2005 and 2010, the country’s real GDP grew by 11% p.a., with the
service sector accounting for the highest growth (15%), agriculture for more than
8%, and industrial development falling slightly short of expectations (the actual
growth rate was 10% rather than being in the expected range of 11 to 18%). This
economic success has multiple drivers. A 15% expansion of agricultural land and a
40% yield increase account for growth in the agricultural sector over the last five
years. Major export products include coffee, sesame-seed, leather, flowers, and
gold, and Ethiopia is the 10th largest producer of livestock in the world. Over the
same five-year period, Ethiopia managed to significantly improve infrastructure,
more than doubling electric power generation capacity, increasing the capacity of
the telecommunication network from 0.5 million users to 25 million, opening 40
new federal roads, and adding more than 11,000 kilometres of road to the existing
network.

To support its growth, Ethiopia has managed to attract more foreign investment.
Foreign investment has increased from less than USD 820 million in 2007/08 to
more than USD 2 billion in the first half of the 2010/11 fiscal year. Among other
factors, this is a result of a comparably good investment climate as measured by
various indicators: the World Bank’s 2011 Doing Business report ranks Ethiopia’s
overall business environment as better than that of Brazil or India, for example.
Ethiopia also received higher marks for criteria such as the business tax rate and
enforcement of contracts (Figure 2).

Ethiopia must continue to grow: with a GDP per capita of around USD 380,
Ethiopia is still one of Africa's poorest countries. Ethiopia’s economy is not diver-
sified enough: agriculture and the service sector each contribute more than 40% to
GDP, and 80% of employment is still concentrated in agriculture. Because of its
small manufacturing sector, the economy is not yet in a position to absorb
significant increases in productivity in agriculture. Ethiopia’s trade deficit amounts
to almost 20% of GDP: while exports of merchandise account for around 4%,
goods worth more than 23% of GDP have to be imported (2009/10). In particular,
to sustain its high growth rate, Ethiopia relies heavily on imports of oil, cement,
and other primary goods.


Federal Democratic Republic of Ethiopia 7


FIGURE 2

Good overall business
environment

98

79

58

Burundi 181

Djibouti 158

Sudan 154

India 134

Brazil 127

Russia 123

Ethiopia 104

Kenya

China

Rwanda

Overall ranking Doing Business
2011 (1=Singapore; 183=Chad)

Comparison with BRIC and East African countries

Ethiopia provides a favorable environment for investments –
even compared with BRIC countries

SOURCE: Doing Business 2011

Low tax rates

Total tax rate (% of profit)

Efficient enforcement of
contracts

Cost to enforce contracts (cost
of claim as % of disputed value)

64

63

50

47

39

36

31

31

>100

Brazil >69

China

Burundi

India

Kenya

Russia

Djibouti

Sudan

Rwanda

Ethiopia

79

47

40

39

34

20

17

15

13

11

India

Rwanda

Kenya

Burundi

Djibouti

Sudan

Brazil

Ethiopia

Russia

China


ETHIOPIA HAS SET THE TARGET OF REACHING MIDDLE-INCOME
STATUS BEFORE 2025

Building on the positive development of recent years, Ethiopia intends to reach
middle-income status (GDP per capita of around USD 1,000)2 within 15 years.

Boosting agricultural productivity and strengthening the industrial base will be
essential to reach this goal.

Ethiopia’s Growth and Transformation Plan (GTP) is an ambitious development
plan that lays out growth, development, and industrialisation targets up to 2015. It
reflects the government’s ambition to lift the country to middle-income status by
2025.


2 World Bank classifies economies according to 2009 gross national income per capita, calculated using
the World Bank Atlas method. Lower middle income starts at USD 996. This report uses GDP per capita
equaling USD 1000 to define middle income. 2009 GDP per capita and gross national income per capita
in Ethiopia differ by 5%.


Federal Democratic Republic of Ethiopia 8


Profile of Ethiopia

The population – With more than 80 million inhabitants (2010), Ethiopia is the
most populous nation in Eastern Africa and the second-most populous in Africa
after Nigeria. The average age of the population is 17 years. With an annual
population growth of more than 2%, Ethiopia will have more than 120 million
people by 2030. Orthodox Christians (>40%) and Muslims (~35%) peacefully
live side-by-side in this multi-ethnic country. Altogether there are around 80
different ethnic groups today. While only 17% of the Ethiopians live in urban
centers, nearly half of them live in the capital, Addis Ababa.

The geography – Ethiopia is a land of natural contrasts. It stretches over more
than 1.1 million square kilometers and has a wide variety of climate zones and
soil conditions. Large parts of the country are at high altitude; Addis Ababa is at
an elevation of more than 2000m. With the Afar depression, Ethiopia also
features one of the lowest points of the continent. Ethiopia is a landlocked
country with sea-access primarily via its neighbour, Djibouti.

Politics – Ethiopia is a Federal Democratic Republic that is currently being
ruled by the Ethiopian People’s Revolutionary Democratic Front (EPRDF)
party. Prime Minister Meles Zenawi heads the government and was re-elected
for a five-year term in 2010.


Federal Democratic Republic of Ethiopia 9


To meet the middle-income target, the base case outlined in the GTP sets the
following rates of growth for the period 2010-2015: GDP has to increase by more
than 10% p.a., exports need to grow from 14% of GDP to 23%, and the domestic
savings rate from 5.5% to 16%. These high rates are based on the following
sectoral projections for the same five-year period:

■ Agricultural development will continue to be the basis for economic growth.
The overall targeted growth rate for the sector is 8.6%. Production of major
food crops (e.g., teff, wheat, maize) is targeted to increase from 19 million
tonnes to 27 million tonnes. Fruit and vegetable production is projected to
increase fourfold to 5 million tonnes. This implies increasing crop
productivity from 19 quintal per hectare to 22. The total value of coffee
exports, by far the most important cash crop, is to increase from USD 0.5
billion today to more than USD 2 billion in 2015, while the export of live
animals is projected to grow from USD 0.1 billion to USD 1 billion.

■ Development of the industrial sector is crucial to reach the GTP targets. The
GTP projects that the industrial sector will grow at a rate of 20% p.a. or twice
the annual increase achieved over the last five years. The GTP expects the
industry sector’s share of the GDP to rise from 13% to 19% within five years,
while the service sector remains at around 45%. To reach these targets, light
manufacturing must be significantly scaled up. The GTP assumes foreign
currency earnings from textiles to increase from USD 22 million to USD
1 billion in 2015. Over the same period, cement production is to increase by a
factor of 10, and the market share of domestically produced pharmaceutical
and medical products from 15% to 50%.

To achieve middle-income status before 2025, these five-year growth rates must
be sustained for 15 years. The growth will result in a significant shift in GDP
shares: In 2025, agriculture would contribute only 29% to the GDP, industry 32%,
and services the remaining 39% (Figure 3).


Federal Democratic Republic of Ethiopia 10


FIGURE 3

2010

30

147

2025 projected

39%

32%

29%~10% p.a.

2015 GTP

51

GTP and long-term targets translate into a transition of the
Ethiopian economy

Source: GoE GTP; EDRI

GDP, billion USD

Population
mln (mid-year)

79 89 116

GDP/cap.
In USD

378 565 1.271

▪Diminishing weight of
agriculture from 42% to
29% of GDP

▪Migration from agriculture
jobs to services and
industry

▪Reaching of middle-
income status before 2025

Key transitions

Services

Industry

Agriculture


The GTP explicitly addresses the sustainability of growth: “Environmental con-
servation plays a vital role in sustainable development. Building a ‘Green Econ-
omy’ and ongoing implementation of environmental laws are among the key stra-
tegic directions to be pursued during the plan period.” (GTP, 2011: p. 119).


Federal Democratic Republic of Ethiopia 11


The challenge: Realise economic
development goals in a sustainable way

If Ethiopia were to pursue a conventional economic development path to achieve
its ambition of reaching middle-income status by 2025, GHG emissions would
more than double from 150 Mt CO2e today to 400 Mt CO2e in 2030. Ethiopia’s
development could result in unsustainable use of natural resources, in being
locked into outdated technologies, and in losing an ever-increasing share of GDP
to fuel imports. Ethiopia would lose the opportunity of making its development
sustainable.

Regardless of whether the development path is a conventional or sustainable
one, Ethiopia faces a critical challenge in attracting the investment needed to
support the projected growth. Current and expected domestic savings and
foreign direct investments, grants, and transfers will not be sufficient to fund
these investments.

CONVENTIONAL ECONOMIC DEVELOPMENT WOULD MORE THAN
DOUBLE GHG EMISSIONS

Ethiopia’s contribution to GHG emissions is very low on a global scale. However,
the projected environmental impact of conventional economic development in
Ethiopia risks following the pattern observed around the globe. If current practices
prevail, GHG emissions in Ethiopia will more than double from 150 Mt CO2e to
400 Mt CO2e in 2030. On a per capita basis, emissions are set to increase by more
than 50% to 3.0 t CO2e – and will thus exceed the global target to keep per capita
emissions between 1 t and 2 t per capita in order to limit the negative effects on
climate change.

Current level and sectoral breakdown of emissions

Ethiopia’s current contribution to the global increase in GHG emissions since the
industrial revolution has been practically negligible. Even after years of rapid eco-
nomic expansion, today’s per capita emissions of less than 2 t CO2e are modest
compared with the more than 10 t per capita on average in the EU and more than
20 t per capita in the US and Australia. Overall, Ethiopia’s total emissions of
around 150 Mt CO2e represent less than 0.3% of global emissions.


Federal Democratic Republic of Ethiopia 12


Of the 150 Mt CO2e in 2010, more than 85% of GHG emissions came from the
agricultural and forestry sectors. They are followed by power, transport, industry
and buildings, which contributed 3% each (Figure 4).

FIGURE 4

More than 85% of GHG emissions in Ethiopia come from
forestry and agriculture

50%

3%

BuildingsIndustry

3%
Power

3%

Forestry
37%

Agriculture

3%
Transport

Share of GHG emissions, 2010

Total GHG emissions of ~150 Mt CO2e in 2010


Major sources of emissions within agriculture and forestry:

■ In agriculture, GHG emissions are attributable to livestock and crops in that
order. The current cattle population is more than 50 million and other
livestock nearly 100 million. Livestock generate greenhouse gases mainly in
the form of methane emissions arising from digestion processes and nitrous
oxide emissions arising from excretions. Livestock emissions are estimated to
amount to 65 Mt CO2e in 2010 – more than 40% of total emissions today. The
cultivation of crops contributes to the concentration of greenhouse gases
mainly by requiring the use of fertiliser (~10 Mt CO2e) as well as by emitting
N2O from crop residues reintroduced into the ground (~3 Mt CO2e).

■ In forestry, the impact of human activities is a large source of CO2 emissions
amounting to almost 55 Mt CO2e in 2010. Forestry emissions are driven by
deforestation for agricultural land (50% of all forestry-related emissions) and


Federal Democratic Republic of Ethiopia 13


forest degradation due to fuelwood consumption (46%) as well as formal and
informal logging (4%).

Minor sources of emissions today are transport, power, industry, and buildings, as
described below.

■ In transport, ~75% of the emissions come from road transport, particularly
freight and construction vehicles, and to a lesser extent private passenger
vehicles. Air transport also contributes a significant share (23% of transport-
related emissions). Emissions from inland water transport are minimal.

■ The electric power sector only accounts for very low emissions as it is
largely based on renewable energy, with hydro power accounting for more
than 90% of total power generation capacity, supplemented by the use of on-
and off-grid diesel generators administered by the Ethiopian Electric Power
Corporation (EEPCo). Current emissions in the energy sector amount to
below 5 Mt CO2e or a share of 3% of the country’s total emissions. (The
global average for electric power generation’s share of a country’s GHG
emissions is more than 25%.)

■ Given the comparably small share of organised industrial economic activity
overall, industry accounts for only 3% of GHG emissions. At nearly 2 Mt
CO2e or 50% of the 4 Mt CO2e emissions from industry, cement is the single-
largest industrial source of emissions, followed by mining (32%), and the
textile and leather (17%) industry. Emissions from steel, other types of
engineering, the chemicals industry (incl. fertiliser), pulp and paper industry
and food processing together account for only around 2% of industrial GHG
emissions.

■ Buildings contribute around 5 Mt CO2e or 3% to today’s emissions. Main
drivers are emissions related to solid and liquid waste (3 Mt of CO2e) and the
use of private off-grid power generators in cities (2 Mt of CO2e).

Change of GHG emissions with time under business-
as-usual scenario

In conventional paths to growth, GHG emissions are both strongly and positively
correlated with economic development and population growth. Therefore the
ambitious growth targets as well as the projected increase of the population will
lead to higher emissions if the conventional growth path is followed. The CRGE
initiative has estimated the expected development of GHG emissions from these
sectors based on the current model of economic development. This development is
represented in the Business-as-usual (BAU) scenario.


Federal Democratic Republic of Ethiopia 14


The results of the BAU estimate show that the current pathway for economic
development will increase GHG emissions from 150 Mt CO2e today to 400 Mt in
2030 – an increase of more than 150% (Figure 5). On a per capita basis, emissions
are projected to increase from 1.8 t today to 3.0 t in 2030. In absolute terms, the
highest increase – adding around 110 Mt CO2e in GHG emissions – will come
from agriculture, followed by industry at 65 Mt and forestry at 35 Mt. In relative
terms, the emerging industrialisation will manifest itself in an annual emission
increase of more than 15% from the industrial sector and around 11% from
transport. Industry emissions under BAU assumptions are therefore projected to
increase more than 12-fold, while transport emissions are projected to increase 7-
fold.

Definition of the business-as-usual scenario – The business-as-usual (BAU)
estimation of GHG emissions forms the baseline for the development of a green
economy strategy. The estimation answers the question: how would domestic
GHG emissions develop if no actions to limit emissions were taken? The BAU
is thus not the most likely and definitely not the desired scenario, but a
theoretical case assuming a country would act as if there were no need for a
sustainable growth agenda, because of the absence of either economic interest
or funding. The main assumption in developing a BAU baseline is that a
country is acting only in its economic self-interest. Actions to reduce or prevent
emissions are therefore only included in the BAU baseline if they are already
under development or they represent the economically most viable and feasible
option. Ethiopia’s BAU assumes that power generation will continue to be
largely based on hydropower and other renewable energies. The two main
drivers of BAU emissions are typically economic and population growth and –
to a lesser extent – urbanisation.
It should be noted that the BAU in this strategy is calculated as an emissions
trajectory following the overall approach described here. Hence, it does not
follow specific project-level protocols of setting baseline emission scenarios,
e.g., for carbon finance schemes. Rather, the BAU should be understood as a
strategic emission projection against which the sectoral mitigation action
programmes are drafted.


Federal Democratic Republic of Ethiopia 15


FIGURE 5

If a typical development path were followed, emissions
would increase from 150 Mt to 400 Mt (2010 to 2030)

5
5

75

150

5 5

400

+167%

Buildings

Industry

Transport

Forestry

Agriculture

2030 – BAU

10

70

40

5

90

185

2010

55

1 Compound average growth rate

4.4%

2.6%

-

11.2%

15.7%

3.9%

▪ Average growth of cropland (estimated to reach
3.9% per year)

▪ Increase in population leading to higher fuelwood
consumption

▪ Livestock: Increase in cattle population and other
species (doubling from 2010-30)

▪ Soil: Increase in cultivated land (crops
production) and synthetic fertilizer

▪ Switch of remaining fossil fuel capacity to 100%
clean/renewable generation for on-grid (2014)

▪ Increase in passenger-km traveled projected
based on elasticity to real GDP

▪ Increase in ton-km of cargo transported based on
elasticity to real GDP

▪ Cement production (steep increase in GTP,
thereafter approach to MIC-level)

▪ Establishment and scale-up of industries in
textile, steel, fertilizer, mining and others

▪ Buildings and solid/liquid waste emissions

t CO2e/capita

1.8

3.0

BAU emissions development
Mt CO2e per year

CAGR1

Percent Drivers and rationale

Power


Main drivers for this projected development are:

■ Agriculture

– Livestock – The cattle population is expected to increase from close to
50 million today to more than 90 million in 2030. This will increase
emissions from 65 Mt CO2e today to almost 125 Mt in 2030.

– Soil – Agricultural crop production will increase from around 19 million
tonnes today to more than 71 million tonnes in 2030. This is primarily due
to the increased fertiliser usage and an increase in land used for agriculture.
This will increase emissions from 12 Mt CO2e today to more than 60 Mt in
2030.

■ Forestry

– Deforestation leads to CO2 emissions, and is mostly caused by the conver-
sion of forested areas to agricultural land. Emissions are projected to grow
from 25 Mt CO2e in 2010 to almost 45 Mt in 2030.

– Forest degradation leads to CO2 emissions, and is primarily caused by fuel-
wood consumption and logging in excess of the natural yield of the forests,


Federal Democratic Republic of Ethiopia 16


with the major driver being population growth. Emissions are projected to
grow from around 25 Mt CO2e in 2010 to almost 45 Mt in 2030.

■ Electric power – The power sector in Ethiopia is an exception as it is the
only sector in which emissions will stay very low. Emissions are projected to
remain below 5 Mt CO2e in the BAU scenario. The total power demand is
projected to grow from 4 TWh in 2010 to more than 75 TWh in 2030. EEPCo
plans to switch off current diesel power plants and off-grid generators in
2012-2014 (according to its master plan) and to generate power exclusively
from clean or renewable sources from 2015 onwards. Residential off-grid
fossil fuel based generation in rural areas will account for the only remaining
emissions.

■ Transport – Emissions from transport are projected to grow from around
5 Mt CO2e in 2010 to 40 Mt CO2e in 2030. The increased emissions are
driven first by higher emissions from freight transport (+13% p.a.) and also
by higher emissions from passenger transport (+9% p.a.).

■ Industry – Industries are expected to grow at annual rates of up to 20%.
Output from the largest industrial GHG emitter, cement production, is
projected to increase from 2.7 Mt of cement today to 27 Mt in 2015 and more
than 65 Mt in 2030. The industry sector shows the highest emission growth
rates of all sectors, as its output is rapidly growing and its processes are very
emission intense: Overall industrial emissions are projected to grow by 16%
p.a. from 4 Mt CO2e today to 71 Mt in 2030.

■ Buildings – An increasing urban population drives increasing waste genera-
tion and (off-grid) energy consumption. Total buildings-related emissions are
expected to increase from 5 Mt CO2e today to 10 Mt in 2030, with around
25% of the emissions in 2030 related to off-grid energy consumption, 75% to
waste.

CURRENT DEVELOPMENT PATH WOULD LEAD TO FURTHER
CHALLENGES

Besides increasing GHG contributions to global emissions, rapid economic growth
will lead to other challenges, if not carefully managed and planned.

■ It may jeopardise the very resources it is based on and lead to unsus-tainable
levels of use (i.e., preventing the current generation from passing on an
equivalent level of resources to the next generation): The combination of
growing demand for agricultural products and inefficient agricultural
practices may result in an over-exploitation of natural resources. In the period


Federal Democratic Republic of Ethiopia 17


2001-2009, cropland increased at a ratio of 0.7 ha of deforestation for 1 ha of
cropland. Assuming a decrease of this ratio to 0.55 ha by 2030, and a crop-
land increase from 12.6 million ha today to 27 million ha in 2030, this would
require the deforestation of nearly 9 million ha of forest land. Furthermore,
with a projected increase in the cattle population from more than 50 million
today to more than 90 million in 2030, Ethiopia will reach its overall cattle-
carrying capacity within 20 years and put additional pressure on forests for
expansion of grazing land.

■ It would bind significant resources and put pressure on foreign currency
reserves as fossil fuel demand – already today more than 4% of GDP, which
roughly equals the foreign currency and gold reserves3 – would increase to
around 7% of GDP in 2030.

■ It would lead to a lock-in into outdated technologies if Ethiopia continues to
import technologies that have the lowest upfront investment requirements –
for example, outdated second-hand technologies in the cement sector.

Beyond the economic impact, the conventional development path would lead to a
lower quality of life and health problems, for example, from air polluting exhaust
from old and inefficient vehicles and the inhalation of fuelwood smoke due to
inefficient cooking technologies.

FUNDING NOT READILY AVAILABLE FOR INVESTMENTS REQUIRED
TO REACH GROWTH TARGETS

Funding the investments required to support the projected growth will be a chal-
lenge. Even in the conventional scenario, the country will need more than USD 50
billion over the coming five years for infrastructure development – more than 50%
of which will need to be in foreign exchange. The development of power infra-
structure alone will require almost USD 38 billion over the next 20 years, the
development of water supply and sanitation infrastructure requires USD 1.2 billion
p.a. (World Bank, 2011: p. 18).

Ethiopia’s current savings-investment gap is large. While the country expects to
invest 27.5% of GDP over the coming five years, average domestic savings will
equal only 11.9%. The projected levels of foreign direct investment, grants, and
transfers will not be sufficient to fund the required additional investments. More-
over, 55% of the investment will be denominated in foreign currency, requiring a
large inflow of international capital.


3 Source: Economy Watch, foreign currency and gold reserves figure for 31 December 2009


Federal Democratic Republic of Ethiopia 18


Consequently, finance mobilisation is identified in the GTP as one of the major
constraints on economic development: ‘Low mobilisation of domestic financial
resources was another implementation challenge encountered’ (GTP, 2010: p. 19).
Mobilising private international capital will play a fundamental role, but public
finance – such as climate finance – can also contribute significantly to close the
funding gap. Attracting international capital will not be easy. International compe-
tition for scarce capital increases the challenges for least-developed countries in
accessing such funding.

The capital constraint is also an immediate threat to sustainable growth: Infra-
structure development projects required for economic growth, especially for trans-
port and power supply infrastructure, have high capital costs and long lives. Many
existing carbon-inefficient solutions – such as road transport as opposed to rail
transport – often require less upfront investment than their low-carbon alternatives.
Capital-constrained developing countries such as Ethiopia are often inclined to
invest in low-CAPEX alternatives and thereby lock themselves into solutions that
are inefficient and ultimately less sustainable, although more climate-compatible
alternatives exist and might offer higher social and economic benefits in the long
run.


Federal Democratic Republic of Ethiopia 19


The plan: Follow a green growth path
that fosters development and
sustainability

Ethiopia has the ambition to develop along a green economic trajectory. It has
consequently outlined a strategy to build this green economy. So far, it has
identified and prioritised more than 60 initiatives that could help the country to
achieve its economic development goals while at the same time limiting net GHG
emissions in 2030 to below today’s 150 Mt CO2e – around 250 Mt CO2e less than
estimated for the current development path (BAU). Building a green economy will
lead to further socio-economic benefits and allow Ethiopia to tap climate finance.

THE AMBITION IS TO BUILD A GREEN ECONOMY

Political leaders worldwide realise the need for immediate and effective action to
respond to climate change. These responses include actions to reduce GHG emis-
sions as well as adaptation initiatives to reduce the vulnerability of the population
and the economy to the effects of climate change. At the same time, leaders –
especially in developing countries – have the obligation to promote economic
development to improve living standards. Achieving economic development goals
requires significant funds and binds a large share of government capacity. If cli-
mate change mitigation and adaptation are seen as goals in conflict with economic
development, they risk being de-prioritised and under-funded.

It is to avoid such conflicts, that the Climate-Resilient Green Economy (CRGE)
initiative was started in 2011, giving the initiative three complementary objectives:

■ Fostering economic development and growth

■ Ensuring abatement and avoidance of future emissions, i.e., transition to a
green economy

■ Improving resilience to climate change.

Building a green economy – which is in the focus of this strategy – offers an
opportunity to achieve its economic development targets sustainably. It represents
the ambition to achieve economic development targets in a resource-efficient way
that overcomes the possible conflict between economic growth and fighting
climate change. This would be achieved by emphasising good stewardship of
resources and seizing opportunities for innovation based on the latest production
platforms (“leapfrogging” to the newest and best technology rather than


Federal Democratic Republic of Ethiopia 20


reproducing each evolutionary stage undergone by already-developed economies).
Building a green economy should thus result in the creation of a competitive
advantage out of a focus on the sustainable use of resources and a higher
productivity growth.

The government is aware of the important role that developing countries play in
fighting climate change. They represent a large share of the world’s GHG
abatement potential and they can therefore be essential contributors to limiting
global warming to 1.5 degrees Celsius compared to the beginning of industrial age.
Consequently, Prime Minister Meles Zenawi has taken a leading role in the
international climate negotiations. He is co-chairing the High-Level Advisory
Group on Climate Change Financing of the United Nations Framework
Convention on Climate Change’s (UNFCCC). Addis Ababa is part of the C40, a
group of 40 large cities committed to tackling climate change.

The ambition to build a green economy is grounded in the country’s potential for
and belief in a sustainable growth model for developing countries. Ethiopia has
already followed a relatively green and sustainable development path, and most of
the power generated in the country already comes from renewable sources, mainly
hydropower.

THE DEVELOPMENT OF A GREEN ECONOMY WILL BE BASED ON
FOUR PILLARS

The CRGE initiative follows a sectoral approach and aims at overcoming the
challenges of developing a green economy. This strategy focuses on four pillars
that will support Ethiopia’s developing green economy:

■ Adoption of agricultural and land use efficiency measures

■ Increased GHG sequestration in forestry, i.e., protecting and re-establishing
forests for their economic and ecosystem services including as carbon stocks

■ Deployment of renewable and clean power generation

■ Use of appropriate advanced technologies in industry, transport, and
buildings.

Establishing these pillars within the relevant parts of the economic development
plan will prevent the economy from being locked into an unsustainable pathway
and can help to attract the investment required for their development (Figure 6).

The CRGE initiative analysed 150 potential green economy initiatives across
seven sectors, taking into account their potential to simultaneously enable/support


Federal Democratic Republic of Ethiopia 21


the country in reaching its GTP targets and reduce/avoid GHG emissions in a cost-
efficient way. Current development practices were compared and contrasted with
alternatives that have proven successful elsewhere as well as with green economy
options newly developed and adapted to the Ethiopian situation. The long list of
initiatives that was generated has been rigorously assessed to select and prioritise
those that can form a green economy programme for Ethiopia.

FIGURE 6

Developing a green economy requires the integration
of economic development and GHG abatement/avoidance

Resilience
initiatives

Green economy can help to avoid lock-in in old technologies, unsustainable
growth and land use

Combining eco-
nomic growth with
low GHG
emissions, e.g.

▪Sustainable land
use via efficient
agriculture

▪Sequestration in
forests

▪Expansion of
renewable energy

▪Resource efficient
advanced
technologies

Abatement/
avoidance
initiatives

Development
initiatives

Green
economy

CRGE

Resilient
economy


For an initiative to be retained as a ‘prioritised measure’ within the green economy
plan, the following criteria had to be met:

■ Pass an initial assessment of relevance and feasibility to be implemented in
the local context,

■ Enable a positive contribution to reaching the targets of the GTP,

■ Provide significant abatement potential at reasonable cost for the respective
sectors.

More than 60 priority initiatives, split across the seven different sectors passed this
test based on the analyses made by the CRGE initiative. For each sector, at least
three initiatives are available (Figure 7).


Federal Democratic Republic of Ethiopia 22


FIGURE 7

Long list of
potential
green growth
initiatives –
150+ initiatives

Abatement/
avoidance
potential –
GHG emissions
in case of
implementation
as compared to
BAU

Effects on
GTP – potential
to contribute to
reaching targets
as outlined in
GTP

Cost effec-
tiveness
checked –
costs to reduce
or avoid one t of
CO2e

Feasibility in
local context –
technical and
institutional
implementability

Prioritized
measures for
inclusion in
CRGE plan –
>60 initiatives

150 potential green growth initiatives were screened, >60
have been shortlisted for inclusion in the CRGE strategy


Each of these initiatives will support one or several of the four pillars of the green
economy mentioned above, and will complement existing programmes and policy
measures aiming at increasing resource efficiency.

The following sections give an overview of all four pillars. A detailed account of
each of the individual initiatives is given in the appendix.

Agriculture: Improving crop and livestock production
practices for higher food security and farmer income
while reducing emissions

Well into the foreseeable future, agriculture will remain the core sector of the
economy and provide employment for the vast majority of. Sustained high growth
rates of the agricultural sector – the GTP projects more than 8% over the next five
years – are needed not only to increase household income of most families, but
also to provide food security for a growing population and support the growth of
direct exports of agricultural products and/or the establishment of more light
manufacturing, which often requires agricultural input.


Federal Democratic Republic of Ethiopia 23


The traditional economic development path could deliver the required growth, but
at the cost of significant agriculture land expansion (inducing pursuing and accel-
erating deforestation), soil erosion, and higher emissions as well as at the risk of
reaching the limits to further development, e.g., by exceeding the carrying capacity
for cattle of Ethiopia.

Building a green economy will require an increase the productivity of farmland
and livestock rather than increasing the land area cultivated or cattle headcount. In
order to offer a viable alternative to the conventional development path without
foregoing growth in the short term and significant advantages thereafter, a set of
initiatives has been identified that can provide the required increase in agricultural
productivity and resource efficiency.

The CRGE initiative has prioritised the following initiatives to limit the soil-based
emissions from agriculture and limit the pressure on forests from the expansion of
land under cultivation:

■ Intensify agriculture through usage of improved inputs and better residue
management resulting in a decreased requirement for additional agricultural
land that would primarily be taken from forests,

■ Create new agricultural land in degraded areas through small-, medium-, and
large-scale irrigation to reduce the pressure on forests if expansion of the cul-
tivated area becomes necessary,

■ Introduce lower-emission agricultural techniques, ranging from the use of car-
bon- and nitrogen-efficient crop cultivars to the promotion of organic fertiliz-
ers. These measures would reduce emissions from already cultivated areas.

To increase the productivity and resource efficiency of the Livestock sector, the
following initiatives have been prioritised:

■ Increase animal value chain efficiency to improve productivity, i.e., output
per head of cattle via higher production per animal and an increased off-take
rate, led by better health and marketing,

■ Support consumption of lower-emitting sources of protein, e.g., poultry. An
increase of the share of meat consumption from poultry to up to 30% appears
realistic and will help to reduce emissions from domestic animals,

■ Mechanise draft power, i.e., introduce mechanical equipment for
ploughing/tillage that could substitute around 50% of animal draft power,
which – despite burning fuels – results in a net reduction of GHG emissions.

■ Manage rangeland to increase its carbon content and improve the productivity
of the land.


Federal Democratic Republic of Ethiopia 24


These initiatives offer the combined benefit of supporting economic growth,
increasing farmers’/pastoralists’ income and limiting emissions and should be
integrated into the plan of activities for implementing the transformation plan
under development by the Ministry of Agriculture.

Forestry: Protecting and re-establishing forests for
their economic and ecosystem services, including as
carbon stocks

Deforestation and forest degradation must be reversed to support the continued
provision of economic and ecosystem services and growth in GDP. Fuelwood
accounts for more than 80% of households’ energy supply today – particularly in
rural areas. Furthermore, forests contribute an estimated 4% to GDP through the
production of honey, forest coffee, and timber. They also provide significant and
precious eco-system services: they protect soil and water resources by controlling
the discharge of water to streams and rivers, preserve biodiversity, function as a
carbon sink, clean the air to create important health benefits, and boost land
fertility.

Despite their economic and environmental value, Ethiopian forests are under
threat. The growing population requires more fuelwood and more agricultural pro-
duction, in turn creating needs for new farmland – both of which accelerate defor-
estation and forest degradation. Projections indicate that unless action is taken to
change the traditional development path, an area of 9 million ha might be defor-
ested between 2010 and 2030. Over the same period, annual fuelwood consump-
tion will rise by 65% – leading to forest degradation of more than 22 million
tonnes of woody biomass.

Besides the agricultural initiatives to reduce the pressure on forests (see above),
the CRGE initiative has prioritised two strategies that could help to develop sus-
tainable forestry and reduce fuelwood demand:

■ Reduce demand for fuelwood via the dissemination and usage of fuel-effi-
cient stoves and/or alternative-fuel cooking and baking techniques (such as
electric, LPG, or biogas stoves) leading to reduced forest degradation,

■ Increase afforestation, reforestation, and forest management to increase car-
bon sequestration in forests and woodlands. These initiatives would result in
an increased storage of carbon in Ethiopia’s forests, provide a basis for sus-
tainable forestry, and even allow the forestry sector to yield negative emis-
sions, i.e., store more carbon in growing forests than are emitted from defor-
estation and forest degradation.


Federal Democratic Republic of Ethiopia 25


■ Promoting area closure via rehabilitation of degraded pastureland and
farmland, leading to enhanced soil fertility and thereby ensuring additional
carbon sequestration (above and below ground).

Power: Expanding electricity generation form
renewable energy for domestic and regional markets

Electricity is a fundamental enabler of modern economic development, from
powering cities and fuelling industrial activity to pumping water for irrigation
purposes in agriculture. If not adequately scaled up to support economic
development, it also risks becoming a fundamental bottleneck to growth. To
support economic development at an annual growth rate of more than 10% that the
government aspires to, it is necessary to expand electric power supply at a rate of
more than 14% per year.

Ethiopia is endowed with ample natural resources to meet this demand, primarily
by exploiting its vast potential for hydro, geothermal, solar and wind power – all
of which would deliver electricity at virtually zero GHG emissions. If adequately
captured, the projected power supply could even exceed the growing domestic
demand: while the demand is projected to be nearly 70 TWh in 2030, energy effi-
ciency measures exists to decrease the demand by 19 TWh. Hence, increasing the
supply and at the same time maximizing energy efficiency offers the possibility to
export clean energy to neighbouring countries. These exports, in turn, provide the
opportunity to replace electric power generated from fossil fuels, which has
significantly higher average costs and significantly higher emissions.

Developing the necessary electric power capacity from renewable energy will be
an enormous challenge as the pace of growth required is high. The total investment
in expanding electric power generation capacity through 2030 would be
approximately USD 38 billion over 20 years or around USD 2 billion annually.
This requires a doubling of the current expenditure of USD 1 billion, which could
be achieved via a combination of tariff adjustments and the attraction of private
capital, climate finance and sovereign wealth funds. The latter could be obtained
by exporting clean energy to neighbouring countries and capturing a share of the
monetisation of their reduced emissions or by mobilising international assistance
in the form of grants.

Taken together, the generation of clean and renewable electric power also allows
for green development of other sectors of the economy, such as the replacement of
trucks by electric rail or diesel pumps by electric pumps for irrigation. Moreover,
via electricity exports, Ethiopia can share its green development to other countries
in the region while contributing positively to its trade balance.


Federal Democratic Republic of Ethiopia 26


Transport, industrial sectors and buildings:
Leapfrogging to modern and energy efficient
technologies

A short planning horizon as well as the lack of required funds for expensive tech-
nologies often lead to the adoption of technologies that require the lowest upfront
investment. However, these technologies are usually less resource efficient, hence
offering lower economic, social, and environmental benefits than alternative tech-
nologies in the medium to long term.

The transport sector is a prime example of this. The total cost for export ship-
ments, for example, could be significantly reduced by revamping the railway con-
necting Addis Ababa with the seaport of neighbouring Djibouti. However, main-
taining the road connecting both cities in good condition requires much less capital
investment than revamping the railway. Shifting transport from road to rail would
not only decrease transport costs and improve the trade balance through reduced
import of fossil fuels (economic benefits), but would also lower emissions, con-
gestion, air pollution, and traffic accidents (social and environmental benefits).

The government sees the opportunity to gear the development of the transport
sector to contribute to a sustainable development pathway. Therefore, it plans to:

■ Introduce stricter fuel efficiency standards for passenger and cargo transporta-
tion and promote the purchase of hybrid and electric vehicles to counter the
low efficiency of the existing vehicle fleet

■ Construct an electric rail network – powered by renewable energy – to substi-
tute road freight transport

■ Improve urban transport in Addis Ababa by introducing urban electric rail,
and enabling fast and efficient bus transit

■ Substitute imported fossil fuels with domestically produced biodiesel and bio-
ethanol.

The urban population is expanding at 4.4% annually, and will surpass 30 million
people by 2030. Rapid growth of cities will require large scale investment in urban
infrastructure, including the development of management systems for solid and
liquid waste, two of the largest sources of emissions in this sector. Off-grid fossil
fuel energy use (e.g., diesel generators, kerosene lamps) is the largest source of
GHG emissions in the buildings sector in 2010, but the rise of inexpensive elec-
tricity generated from renewable energy will help to curtail the growth of this
emissions source. The three major green economy initiatives identified in this
sector are:


Federal Democratic Republic of Ethiopia 27


■ Accelerated transition to high efficiency light bulbs for residential, commer-
cial, and institutional buildings

■ Use of landfill gas management technologies (e.g., flaring) to reduce emis-
sions from solid waste

■ Reduction of methane production from liquid waste.

Among the industrial sub-sectors, cement will be one of the fastest growing, also
causing the vast majority of GHG emissions from the industry sector. Output will
increase tenfold from 2.7 Mt in 2010 to 27 Mt in 2015. Some cement factories use
outdated technology that is not only energy inefficient, but also causes high
emissions from the production process. The CRGE initiative has identified a series
of initiatives that could help to increase the competitiveness of the cement industry
by reducing production cost and – at the same time – would yield significant
environmental and health benefits:

■ Improved energy efficiency of the process by converting the technology used
from dry to precalciner kilns and from rotary to grate coolers and by intro-
ducing computerized energy management and control systems, which can
decrease the energy demand and hence the cost of and emissions from cement
production

■ Substitution of clinker by increasing the pumice content leading to a decrease
in both variable production costs and emissions

■ Increased share of biomass in the mix of energy for production in cement
factories, potentially decreasing costs and emissions

Although the cement sub-sector has been highlighted in this report because it
represents the most GHG emitting industry and its GHG abatement initiatives have
high chances of implementation, the government will take action to put the other
industrial sub-sectors also on a sustainable economic development path. The
textile, leather, and fertiliser industries are important parts of the envisaged
economic development model. The government aims to promote – among other
initiatives – energy efficiency and the usage of alternative fuels in these sub-
sectors. Further initiatives have also been identified for the steel, chemicals, and
mining sub-sectors.


Federal Democratic Republic of Ethiopia 28


BUILDING A GREEN ECONOMY OFFERS COST-EFFICIENT
ABATEMENT POTENTIAL WHILE PROMOTING GTP TARGETS

Ethiopia’s green economy offers GHG abatement potential of nearly 250 Mt
domestically. Of the total abatement opportunities, more than 80% cost less than
15 USD per ton. Adopting the green economy path promotes socio-economic
targets such as rural development, health, and the creation of employment in high
value-added production.

Ethiopia’s green economy offers GHG abatement
potential of 250 Mt to the global community

The priority initiatives that form the foundation of the green economy concept
could help to curb the increase in the global emissions projected in the BAU
scenario. While contributing to reaching economic and social development targets,
we have the domestic potential to contribute to the global effort by abating around
250 Mt CO2e in 2030 as compared to conventional development practices – this
equals a decrease in GHG emissions of up to 64% compared to BAU in 2030.4
Given the projected population growth, emissions on a per capita basis would
decrease from 1.8 t of CO2e to 1.1 – a decrease of around 35% – while multiplying
GDP per capita from USD 380 to more than USD 1,800.

Ethiopia and the global community have finite human, technological, and financial
resources. The CRGE strategy must make choices about the levers not only to
capture a large share of the abatement potential but also to boost economic and
social development at the same time.

Two sectors – agriculture and forestry – should receive particular attention: they
contribute around 45% and 25% respectively to projected GHG emission levels
under business-as-usual assumptions and together account for around 80% of the
total abatement potential (Figure 8).


4 More GHG abatement available beyond the one of the initiatives considered in the low carbon scenario
through afforestation, reforestation and forest management on additional land. However, this comes with
incremental costs and more stringent requirements on land use management across the different needs
through the country.


Federal Democratic Republic of Ethiopia 29


FIGURE 8

CRGE implementation could ensure a low-carbon economic
development pathway, decreasing per capita emissions by 60%

Additional
abatement
potential of
~19 Mt CO2e
from exporting
green power
to regional
markets

90

400

-64%

Green
Economy
2030

145

Buil-
dings

5

Industry

20

Trans-
port

10

Forestry

130

Agri-
culture

75

5
5

2030
BAU

400

10
70

40
5

90

185

2010

150

5 5
55

Emissions per year1, Mt CO2e Agriculture
Forestry

Power

Transport

Industry

Others

1 Rounded numbers
2 Currently estimated emissions form buildings and waste

t CO2e/capita

1.8 3.0 1.1


The magnitude and relative importance of the initiatives identified to reduce GHG
emissions vary significantly. The following section gives a brief overview of the
abatement potential identified (in Mt CO2e abatement potential in 2030 as com-
pared to the BAU level of emission). Table 1 and Table 2 display the key
assumptions that were taken to project the abatement potential in each sector.
A more detailed account on assumptions and calculations can be found in the
appendix.


Federal Democratic Republic of Ethiopia 30


TABLE 1

Core assumptions for abatement initiatives (1/2)

Sectors

Gross
abatement
potential,
Mt CO2eAbatement levers Core assumptions (2030)

Forestry1

▪ Fuelwood-efficient stoves ▪ Household reach2 (million): 15.7/0.3 34.3
▪ LPG stoves ▪ Household reach2 (million): 0/0.3 0.6
▪ Biogas stoves ▪ Household reach2 (million): 1.0/0.1 2.3
▪ Electric stoves and mitads ▪ Household reach2 (million): 1.0/up to 4.9 14.0
▪ Afforestation/Reforestation ▪ Area in million ha: 2 (A) and 1 (R) 32.3
▪ Forest Management (forest/woodland) ▪ Area in million ha: 2 (F) and 2 (W) 9.7

1 Initiatives for reduced deforestation (agricultural intensification and irrigation) stated under soil-based levers
2 Household reach for rural / urban households
3 Abatement potential from reduced deforestation (agricultural intensification and irrigation) counted under forestry sector

Soil3

▪ Lower-emitting techniques 40.1
▪ Yield increasing techniques 27.2

▪ Irrigation

▪ Household reach2: 13.2/0.0
▪ Only 1.7% growth in cropland needed

under intensification to achieve 9.5%
crops GDP growth due to 3.5% yield
growth and 4.0% crops value growth

▪ Area in million ha: 1.4 (large scale); 0.3
(small scale)

10.6

Live-
stock

▪ Value chain efficiency

▪ Household reach2 : 13.2/0.0
▪ Enhancing diversification of animal mix
▪ Mechanisation
▪ Pastureland improvement

▪ Household reach2 : 19.5/0.0
▪ Target share of chicken: 30%

▪ Area in million ha: 5

16.1

17.7

11.2

3.0


■ Forestry in 5 million ha of forest and 2 million ha of woodland alone
represents around 50% of the total domestic abatement potential (or 130 Mt
CO2e) and, as a sector, can even yield ‘negative emissions’ via sequestration,
i.e., storage of carbon in the form of wood, at a level that surpasses emissions
from deforestation and forest degradation. The single most important lever is
to reduce demand for fuelwood through fuelwood efficient stoves, offering a
potential of almost 35 Mt CO2e reduction, while other advanced cooking and
baking technologies (electric, biogas, and LPG stoves) offer an additional
combined potential of more than 15 Mt CO2e. Capturing this abatement
potential requires the switch of more than 20 million households to more
efficient stoves. In addition, afforestation (2 million ha), reforestation (1
million ha), and forest management (2 million ha of forests and 2 million ha
of woodlands) can help to increase sequestration by more than 40 Mt CO2e
and hence even surpass any remaining emissions from the forestry sector.
Pressure from agriculture on forests can be reduced by agriculture
intensification on existing land or unlocking degraded land thanks to
irrigation, with the potential to lower deforestation and thus the associated
emissions by nearly 40 Mt CO2e in 2030.


Federal Democratic Republic of Ethiopia 31


■ The agriculture sector has a total abatement potential for soil- and livestock-
related emissions of 90 Mt CO2e, representing around 35% of the total
domestic abatement potential

– Soil. The introduction of lower-emitting techniques, such as conservation
agriculture (including applying zero or minimum tillage), watershed man-
agement, and nutrient and crop management, could reduce emissions by
40 Mt CO2e in 2030. The introduction and enhancement of these lower-
emitting techniques will form a priority for the soil sector in the coming
years and the initiative will target 75% of rural households by 2030.
Moreover, through agricultural intensification and capture of new
agricultural land in arid areas through irrigation, techniques from crop
production help to increase the abatement potential from saved forests. In
fact, these initiatives increase the sequestration from forests by 38 MT
CO2e in 2030.

– Livestock. There is ample potential to increase the efficiency of the cattle
value chain via higher productivity of cattle (for both meat and milk) and
an increased off-take rate (decreasing the age at which livestock are sold).
Several initiatives would fall underneath this umbrella, including improv-
ing the market infrastructure, health facilities, and feeding for livestock.
This could reduce emissions by more than 15 Mt CO2e in 2030.
Furthermore, a partial shift towards lower-emitting sources of protein –
e.g., poultry – could yield another emission reduction of nearly 20 Mt
CO2e, assuming the share of chicken in the protein mix will change from
15 to 30%. Finally, the mechanisation of draft power, i.e., the introduction
of mechanical equipment for ploughing/tillage, could help to substitute
about 50% of animal draft power and lower emissions by more than 10 Mt
CO2e in 2030, while the improvement of pastureland lowers emissions by
3 Mt CO2e in 2030.


Federal Democratic Republic of Ethiopia 32


TABLE 2

Core assumptions for abatement initiatives (2/2)

Power

Sectors

Buil-
dings &
Green
cities

Gross
abatement
potential,
Mt CO2e

▪ Clean power exports
Abatement levers

▪ High-efficiency lighting

▪ Domestic surplus capacity: 28 TWh
▪ Substitution of power generation at

carbon intensity of 0.7 kg CO2e/kWh

Core assumptions (2030)

▪ Efficiency improvement: 60-77%

19.31

5.12

▪ Improved landfill gas management ▪ Adoption in all towns above 20,000
inhabitants (271) up to 2030

0.9

▪ Improved liquid waste management ▪ Adoption in all towns above 100,000
inhabitants (34) up to 2030

0.9
1 Not counted as domestic abatement potential 2 Accounted in power

Industry
(cement
only)

▪ Clinker substitution (e.g. by pumice) ▪ Share of additives: 32% to 55%
▪ Share grade IV cement: 36%

5.2

▪ Biomass (agri-residues) usage ▪ Share of energy substituted: 20% 4.3
▪ Energy efficiency equipment

(Precalciner kiln; grate cooler;
computerized process control)

▪ Energy reduction potential of 12%; 8%;
4.5%

5.3

▪ Waste heat recovery ▪ Energy reduction potential: 4.5% 1.0

Trans-
port

▪ Electric rail ▪ Total km of track: 5,196 8.9
▪ Fuel efficiency standards ▪ Programme reach: 30% for passenger

vehicles; 10% for freight vehicles
3.1

▪ Light rail and bus rapid transit ▪ Shift in passenger-km: 7% for LRT; 3%
for BRT

0.2

▪ Hybrid and electric vehicles ▪ Decreasing cost of ownership 0.1
▪ Mixing ethanol and biodiesel ▪ Maximum blends: 15% and 5% 1.0


■ The electric power sector projects below 5 Mt CO2e domestic emissions for
2030. However, one important initiative can be identified: If the installed
electric power generation capacity exceeds domestic demand as planned,
Ethiopia will have capacity to export electricity generated from renewable
energy to countries in the region (up to 28 TWh). This will substitute for their
conventional electric power generation and hence decrease GHG emissions
by nearly 20 Mt CO2e (which could come on top of the around 250 Mt CO2e
identified in other sectors).

■ Of the identified industry abatement potential, around 70% is concentrated in
the cement industry. The main lever, clinker substitution, would increase the
share of additives in cement, particularly pumice (5 Mt CO2e of abatement).
The upgrade to more energy efficient technologies and waste heat recovery
can reduce up to 6 Mt CO2e in 2030, while the usage of biomass (mainly agri-
residues) will help to reduce GHG emissions by 4 Mt CO2e. All other indus-
trial sectors that were analysed (e.g., chemicals, fertiliser, textile, leather,
paper and pulp) account for an abatement potential of around 6 Mt CO2e in
2030.


Federal Democratic Republic of Ethiopia 33


■ Transport offers various opportunities to decrease emissions. All of these
opportunities achieve their abatement potential through increased efficiency
or a shift to lower-emitting fuel sources. The largest initiatives with the great-
est abatement potential are the construction of an electric rail network (9 Mt
CO2e) followed by the introduction of fuel efficiency standards for all vehi-
cles (3 Mt CO2e). This assumes the construction of more than 5000 km of rail
tracks and new fuel efficiency standards for 30% of passenger vehicles and
10% of freight vehicles by 2030. Although the abatement potential is not as
large, the introduction of bio-fuels will also form a priority. The combined
abatement potential of increasing the use of ethanol and biodiesel in the fuel
mix is 1 Mt CO2e.

■ The main abatement levers identified for buildings will result in an acceler-
ated transition to high efficiency light bulbs (leading to increased power
export potential reducing around 5 Mt CO2e abroad) and an improved han-
dling of solid and liquid waste. The total abatement potential of improved
waste handling (for liquid and solid waste) amounts to around 2 Mt CO2e.

More than 80% of the abatement opportunities cost
less than USD 15 per ton

Like many other developing countries who have not yet ‘locked in’ their fast
growing economy into carbon intensive infrastructure, Ethiopia could provide to
the international community a cost efficient contribution to the global effort to
abate GHG emissions: More than 80% of the green economy initiatives’ abate-
ment potential is priced at less than USD 15 per t CO2e (before potential carbon
revenue), i.e., more cost competitive than most abatement initiatives in developed
economies, and 16 initiatives have zero or negative costs of abatement, i.e., eco-
nomically attractive initiatives albeit a significant initial investment often difficult
to bear by the entity responsible for its implementation.

The CRGE initiative has conducted a quantitative assessment of the economics of
the prioritised abatement opportunities, including estimating the abatement cost to
be incurred for the measures within each sector (expressed in USD/t CO2e abate-
ment).5 (The text box at the end of this section provides a description of the
method for determining GHG emission abatement cost curve.)

The outcome of the cost analysis for the prioritised green economy initiatives testi-
fies to a good starting position for establishing a green economy: more than 45%


5 Understanding the costs of GHG mitigation is a critical step in the development of a green economy plan
as it helps to identify and prioritise the most cost-efficient ways to reduce GHG emissions


Federal Democratic Republic of Ethiopia 34


of the abatement potential (16 initiatives) comes at zero or negative costs – these
initiatives would not only lead to lower emissions, but would also save costs as
compared to their conventional alternatives (i.e., the net present value of their cash
flows is positive). Of the remaining 12 initiatives that have been costed, 5 have
abatement costs lower than USD 15 per ton, i.e., abatement costs would still be
lower than the average market price for CO2 emission certificates traded via the
European Trading Scheme (ETS). Although these initiatives come at higher costs
than the traditional development pathway, they might offer the possibility to fully
fund the incremental costs via a monetisation of the emission reduction. In a global
comparison, many of Ethiopia’s initiatives are comparatively inexpensive – which
can be crucial in giving the country a competitive advantage in attracting climate
finance. The majority of abatement potential is concentrated on few initiatives –
about 55% of the total abatement potential can be captured by 5 initiatives: lower
emitting techniques in agriculture, fuelwood efficient stoves, afforestation/
reforestation, yield increasing and power exports (Figure 9).

The total abatement potential as displayed on the horizontal axis of the cost curve
in Figure 9 (264 Mt CO2e) is not equivalent to the total abatement potential dis-
played in Figure 8. This is due to three reasons: first, the non-domestic abatement
potential from power exports is displayed in the cost curve, but not shown as a part
of the total domestic abatement potential in Figure 8; second, the total abatement
potential of all initiatives is not equal to the sum of the abatement potential of each
individual initiative, e.g., introducing fuel-efficiency standards in the absence of
hybrid cars has a higher abatement potential than if both initiatives are introduced
at the same time. The total net potential of the initiatives included in the cost curve
after accounting for non-additivities is around 261 Mt CO2e. Last, some initiatives
with very small abatement potential have not been evaluated with regard to their
cost and are hence not included in the cost curve.


Federal Democratic Republic of Ethiopia 35


FIGURE 9

2008060

Abatement potential1

MtCO2e per year-30

-80

-90

-230

240

Abatement cost
USD per tCO2e

40

120

280

15

26022020

30

-20

-10

0

10

20

100 120 140 160 1800 40

Most green growth initiatives are economically viable
and could reduce GHG emissions at relatively low cost

1 Represents total identified gross potential, some measures are not additive (total net potential is less than sum of all gross potentials)
2 Non-domestic potential (will arise only in importing countries)
3 Assuming full implementation of all levers where cost has been evaluated (excluding buildings/green cities and industry other than cement)

Ethiopia’s abatement opportunities cost curve

Net potential after
accounting for non-
additive levers3

261

86% of abatement
potential below 15
USD per tCO2e

Fuelwood
efficient
stoves

Power
exports2

Agricultural
intensification

Shift of
animal mix

Lower emitting
techniques

Afforestation/
reforestation

Agriculture

Forestry

Power

Transport

Industry

Electric rail


FIGURE 10

Abatement Cost Curve: General overview of methodology

4

2

1

Abatement cost
USD per tCO2e of reduced
emissions in 2030

Each option for reducing emissions is represented by a bar on the cost curve.

The width of each bar shows the abatement potential – the tons of annual emissions that
would be reduced in 2030 if we implemented this option fully.

The sum of the width of all bars shows the sum of the abatement potential of all initiatives
– in reality the aggregated abatement potential will be lower than the sum of each
initiatives as it might not be feasible to implement some initiatives at the same time

The height of each bar shows the abatement cost – the cost of implementing this option
fully in terms of dollars per ton of reduced annual emissions.

The bars on the right represent costly options, while the bars that face downward
represent options that actually have negative cost: they save money as well as emissions.

The Abatement Cost Curve allows us to view and compare all the available options
for reducing emissions along two key dimensions at once: How much can each option
contribute to emissions abatement, and at what cost does it do so?

2

1

4

Abatement potential
Reduced emissions in
2030, Mt CO2e

3

3


Federal Democratic Republic of Ethiopia 36


Method for calculating the GHG emission abatement cost curve – The cost
curve describes green economy initiatives based on two characteristics: the
annual potential of abating GHG emissions in a given year and the costs per
tonne abated (Figure 10). The underlying assumption is full implementation of
the initiative; the reference year is 2030. The abatement cost curve visualises
two important pieces of information concerning each initiative:

■ What is the cost of abatement? The answer is reflected in column height,
sorted by the most cost efficient, from the left

■ What is the potential volume of GHG abatement? The answer is displayed
as column width – the wider the column, the more potential the initiative
offers.

The abatement cost of each initiative is defined as the incremental cost (positive
if more expensive, negative if more cost economical) of a low-emission path
compared to the required cost or benefits of the conventional alternative under-
lying the BAU scenario. Costs are measured in USD/t CO2e of abated emissions
in a given year in the future (here always referring to year 2030). It includes
both the incremental capital expenditure (investment) required for the imple-
mentation of the abatement lever compared with the BAU scenario, the incre-
mental operating cost required for the abatement lever and potential benefits
(e.g., lower costs or higher revenues) compared with the BAU scenario. The
capital expenditure is taken into account in the form of an annualised invest-
ment cost. The annualised cost is calculated with an economic amortisation
period (usually between 20 and 50 years, depending on type of investment)
and a real capital cost of 6%. Costs and benefits are estimated from a societal
perspective, i.e., irrespective of who bears costs or who benefits. The costs do
not include any subsidies, taxes, or external costs that are incurred indirectly
and that largely depend on the exact form of implementation, such as
communication cost and transaction cost.

The columns that extend upwards represent measures with a cost higher than
USD 0 per tonne of reduced emissions, while the columns that extend
downwards represent measures that have a negative cost per tonne of reduced
emissions: they save money as well as emissions. Therefore, initiatives with a
negative abatement cost are economically advantageous in any case.


Federal Democratic Republic of Ethiopia 37


Green economy will unlock economic growth, create
employment, and provide additional socio-economic
benefits

Moving our economy forward on the green pathway will require a trans-
formational shift in current economic development practices, will touch most sec-
tors of its economy, will contribute to the welfare of the population and to the
increased quality of our environment, and will stimulate economic benefits in
several sectors.

The CRGE effort has estimated that its selected initiatives would reach up to two-
thirds of the whole economy (by 2030) and move them onto a more sustainable
pathway (Figure 11). Some of the initiatives also support the creation and growth
of new business opportunities, e.g., the local production of efficient stoves. The
initiatives have the highest reach within agriculture by creating a green agricultural
sector that generates increased output originating from higher yields rather than
from an expansion of agricultural land or the cattle population. As initiatives have
been identified for most of the industrial sub-sectors, a high share of these sub-
sectors is also likely to be positively affected by the green economy. In addition, a
smaller part of the service sector will also be reached by the green economy
through initiatives identified in transportation and buildings.

Adopting a green economy development path would have benefits for the
population, the environment, and the economy: it would improve public health
through better air and water quality and accelerate rural development by increasing
soil fertility, food security, and rural employment. Households would benefit from
higher energy efficiency – especially from more efficient cooking/baking and
transport – with savings worth up to 10% of household income (particularly in
rural areas). This would lead to an increase in domestic savings and hence result in
an enhanced investment capacity.

From a macroeconomic perspective, green economy initiatives would also
improve the balance of payments by reducing dependency on imports of, e.g., fos-
sil fuels, and create a more secure power supply, an essential prerequisite for sus-
tainable economic development. This effect alone could improve the balance of
payments by several billion USD (in 2030). The low-carbon supply of goods and
services (e.g., manufactured goods, power) can easily be marketed as a major
competitive advantage for Ethiopia’s exports. Moreover, the decision to commit to
sustainable economic development opens the door to different sources of
international environmental funding, such as “Fast Start” funding, CDMs, and
voluntary markets, that could complement the funds earmarked for development.


Federal Democratic Republic of Ethiopia 38


FIGURE 11

Up to two-thirds of the economy would be affected by
moving to a green growth path

23%

28%
(Services)

4% (Industry)
4% (Agriculture)

Services

11% Industry

30%

Agriculture

Share of GDP affected (2030) and examples of economic impact/benefits from green economy

Total share of the
economy affected
by green economy

~64%

Share of GDP
not affected

Agriculture and forestry

▪ Creating USD 1 bn yearly savings from
fuelwood expenditure for rural
population

▪ Increasing productivity of up to
40 million head of livestock

Industry

▪ Total fuel cost savings of more than
USD 1 bn p.a. in 2030

▪ Enabling renewable power generation
of more than 67 TWh, opening high
potential for power exports

Service

▪ Savings of around 1/3 of fuel imports
for transportation


BUILDING A GREEN ECONOMY REQUIRES MORE THAN USD
150 BILLION OVER 20 YEARS, BUT PROVIDES ACCESS TO CLIMATE
FINANCE

Developing the green economy will require an estimated expenditure of around
USD 150 billion over the coming 20 years – around USD 80 billion of which is
capital investment and the remaining USD 70 billion operating and programme
expenses. Of the total expenditure, almost USD 30 billion are projected to occur in
the short term up to 2015, with almost USD 22 billion of this being capital
expenditure (Figure 12). These figures underline the significant funding needed to
build a green economy despite the overall low average cost of abatement, and the
need to mobilize capital investment in the early years of the development of the
green economy. However, not all of this expenditure is necessarily additional to
current investment plans – rather, a large part of this expenditure, e.g., for power
generation infrastructure or transport infrastructure, would also occur in a
conventional growth scenario.


Federal Democratic Republic of Ethiopia 39


FIGURE 12

Building a green economy requires around
USD 150 billion up to 2030

Billion USD
Short-term: 2011-2015 Long-term 2011-2030

CAPEX1 additional

OPEX additional
(excl. programme cost)

Programme cost

Savings

Abatement expenditure
(without carbon revenue)

13.7

-3.4

12.3

2.2

5.6

7.9

12.0

37.5

124.8

14.0

57.9

40.9

314922722

1 Full capital expenditure, not amortized
2 Aggregated abatement potential; expenditure per t CO2e not equivalent to abatement cost in cost curve, as the CAPEX abatement expenditure is not

annualized via amortization (rather: cash-flow perspective)

Mt CO2e total
aggregated
abatement potential

Also required for CRGE:
Baseload (BAU) CAPEX
(e.g. hydropower)


The largest share of the total investment of USD 80 billion will be required for the
development of power generation and transmission infrastructure (48%), followed
by the transport sector (29%) and financial requirements for the transformation of
the agricultural sector (2% for soil and 3% for livestock) as well as the forestry
sector (12%, including agricultural intensification and irrigation initiatives that
ultimately create GHG abatement in the forest sector). Upgrading technology in
the cement sector will require investments equal to nearly USD 5 billion over the
next 20 years – or 6% of the total estimated green economy capital investment.

The power generation investment, however, has to be considered as part, not as an
addition, of the ‘conventional development path’ because the renewables-based
development of the electric power sector is part of the existing development path.
Indeed, the scale-up of renewable energy infrastructure builds on existing
competitive advantages and represents the most viable pathway economically,
socially, and environmentally, and the Ministry of Water and Energy through the
Ethiopian Electric Power Corporation has consequently built its development
master plan very strongly on hydro, solar, geothermal, and wind power. Hence,
this expenditure is displayed as a BAU expenditure in Figure 12.


Federal Democratic Republic of Ethiopia 40


In order to analyse the required type of financing for the respective initiatives,
their expenditure is grouped into 3 distinct categories (Figure 13):

FIGURE 13

More than 50% of expenditure will have positive returns −
out of that, more than 20% in the short to medium term

53.3

Category C
No positive return,
grants/performance

pay requirement

Category B
Positive return, but
long term financing

requirement

34.5

Category A
Positive return,

short term financing
requirement

25.0

Agriculture

Forestry

Power

Transport

IndustryBillion USD, total cost1

1 Including additional CAPEX, additional OPEX, and programme cost (not containing baseload/BAU expenditure)
2 NPV calculated with 6% discount rate; societal perspective, the implementing agency might face higher net expenditure when benefits

(i.e. savings or revenues) are captured by different parties

Definition Positive NPV2 of first
five years of cash flow
(from start of initiative)

Positive NPV2 of
overall initiative (from
start of initiative up to
2030), but not after first
five years

Negative NPV2 of
overall initiative (from
start of initiative up to
2030)

Percent of total cost

22% 31% 47%


■ Category A: Expenditure for initiatives that have positive return and only
require short-term financing. These are defined as yielding a positive Net
Present Value (NPV6) from the first five years of cash-flow (from start of
implementation of the initiative).

■ Category B: Expenditure for initiatives that have a positive return, but
require long-term financing. These are defined as yielding a positive NPV
from the overall initiative (from start of implementation of the initiative) up to
2030, but not during the first five years.

■ Category C: Expenditure for initiatives that do not yield a positive (financial)
return, hence they require grants or performance payments for GHG abate-
ment. These are defined as yielding a negative NPV from the overall initiative


6 The NPV is calculated with 6% discount rate (real, derived according to usual market-based risk-free
interest rate and risk premium) and takes into account all expenditure and benefits (taking the societal
perspective). It should be noted that the implementing agency might face higher net expenditure when
benefits (i.e., savings or income) are captured by different parties.


Federal Democratic Republic of Ethiopia 41


from start of implementation of the initiative up to 2030. This does, however,
not necessarily mean that the initiative does not yield a positive NPV at all.
The construction of electric rail, for example, has been calculated with a
much longer depreciation period and generates positive returns from the ini-
tial investment even beyond 2030, which can eventually make the overall
return positive.

This categorization shows that more than half of the expenditure of the proposed
initiatives will have positive returns, i.e., the green economy initiatives are less
expensive – over the 20-year horizon – than the conventional alternatives (Figure
13). This also translates into negative GHG abatement cost as displayed in the cost
curve in Figure 9.

More than 20% of the expenditure for green economy initiatives will already have
positive returns and pay back in the short run (i.e., five years or less after start of
the implementation). However, the profile of expenditure of the green initiatives
typically has a bulge at the beginning due mainly to upfront capital investment.
Upfront investments for green economy initiatives are usually higher due to the
higher investment required in modern and efficient technology, compared to the
one of the traditional path, as well as the investment required to set up the different
scale-up programmes. On the other hand, the medium- to long-term running costs
are typically lower due to the combined effects of fuel savings and efficient use of
other resources. This effect is reflected in a large part of the expenditure only
paying off in the long run.

On the one hand, the green path for 2010 to 2030 is more capital intensive. For
some initiatives, accounting for 47% of the expenditure, the green path could be
even more expensive than the conventional development path.7 The implementa-
tion of these green economy initiatives will require the support of international
funding. On the other hand, potential support from climate-related sources of
funding comes as a complement and hence helps to fund initiatives that would
otherwise not be financed. They provide the additional support required to steer
the economy towards sustainable growth instead of developing along a traditional
path, and will reinforce the robustness of many sectors, especially in agriculture.

A funding pool of at least USD 20 billion annually should be obtained from
various climate finance schemes set up to foster the green economy initiatives of
developing countries like Ethiopia (Figure 14). These funds are typically available
only for initiatives that reduce GHG emissions, i.e., only if the receiving party


7 This is not necessarily the case; please refer to description of category C expenditure.


Federal Democratic Republic of Ethiopia 42


proves reduced GHG emissions as compared with BAU development. In the short
term, support from climate finance can take the following forms:

■ Bi-/multilateral grants primarily for project setup, capacity building, technol-
ogy development, and dissemination

■ Bi-/multilateral pay-for-performance deals, i.e., payments linked to verified
GHG abatement

■ Trading schemes or offset markets, i.e., emission reduction, for example result-
ing from Clean Development Mechanisms (CDMs), sold to companies (in ETS)
or committed countries (cap and trade) or via voluntary carbon markets.

At the 2009 Conference of Parties in Copenhagen, developed countries formally
committed “Fast Start Funding” of USD 30 billion for 2010-2012, half of which is
to be spent on GHG abatement. Beyond 2020, the same countries have pledged
USD 100 billion p.a. for abatement and adaptation, but the sources of these funds
have not yet been specified. Trading of emission certificates offers an additional
USD 10 billion to USD 20 billion p.a. under the Kyoto Protocol or the European
Trading Scheme (ETS).

Ethiopia will divide all prioritised green economy initiatives into three categories:

■ Own initiatives that are planned and fully funded by the government

■ Supported initiatives that are planned by the government but require support
in implementation

■ Market-based initiatives for which Ethiopia might be able to monetise car-
bon credits in exchange for GHG abatement.


Federal Democratic Republic of Ethiopia 43


FIGURE 14

Ethiopia can have access to a vast pool of climate funds
resources totalling at least USD 20 billion p.a.

2020 Total 1001

Total 20-31

Others 1

Norway 1

Japan 5

EU 3

ETS 8-11

Cap and trade 2-10
Pledged bilateral funds (grants
and pay for performance)

Trading schemes/offset markets

Climate fund resources
Billion USD, annual average 2010–20 (rounded)

ESTIMATION

S
h

o
rt

-t
e

rm
(

u
p

t
o

2
0

1
2

)
2

0
2

0

▪ Formal
commitment of
USD 30 billion
for 2010-12 by
developed
countries

▪ 50% for
mitigation

▪ Focus so far on
REDD

▪ Large funds are
available from
climate finance
schemes that could
help to finance
green growth
initiatives

▪ These funds are
available only for
initiatives that reduce
GHG emissions, i.e.
if receiving party
proves reduced GHG
emissions as
compared with
business-as-usual
development

▪ Goal of USD 100
bn p.a. by 2020

▪ Sources not yet
specified

1 Bilateral and multilateral funding pledge, does not include carbon markets


All of the prioritised green economy initiatives could potentially be candidates to
access the emerging climate finance pool in exchange for GHG abatement. The
value given to each tonne of GHG abated differs with the ‘monetisation’ scheme.
For example, existing bilateral deals targeted at reducing and avoiding emissions
as well as increasing sequestration in the forestry sector were valued at around
USD 5 per t of CO2e abated while the average market price of a t of CO2e in the
European Trading Scheme (ETS) is three times higher, at USD 15 per t. If fully
monetised, the total technical abatement potential of Ethiopia’s green economy of
around 250 Mt CO2e by 2030, using these reference prices, could be worth
between USD 1.2 billion and USD 3.6 billion p.a. by 2030. However, due to the
uncertainty concerning the future of the global climate finance regime (particularly
the extension of the Kyoto Protocol), there is uncertainty about how much of this
potential can indeed be monetised.

While it is not realistic for Ethiopia to capture the full technical abatement poten-
tial, nor to monetise every single initiative, the indicative market value of its
abatement potential reflects the importance for the nation to deploy all its effort to
embrace the green economy plan.


Federal Democratic Republic of Ethiopia 45


Making it happen: Ethiopia’s action plan
to create a green economy

We are starting to put in place the building blocks necessary to implement its
green economy initiative. The government has developed an action plan to set up
a permanent financial mechanism, initiate the stakeholder engagement process,
and set priorities for implementation of initiatives. Four initiatives have been
selected for fast-track implementation: attracting the investment required to
exploit hydropower potential; promoting advanced rural cooking technologies on
a large scale; improving the efficiency of the livestock value chain; and Reducing
Emissions from Deforestation and Forest Degradation (REDD).

The government is using significant resources to build and implement its green
economy, but to capture the full potential of the plan, it welcomes the partnership
with bilateral and multilateral development partners as well as contributions by
the private sector.

GEARING UP: PERMANENT COMMITMENT, AN EMERGING
INSTITUTIONAL SETUP, AND STAKEHOLDER MOBILISATION

To achieve the Climate-Resilient Green Economy (CRGE) vision, the government
can draw on its demonstrated track record of commitment to developing a green
economy.

Strong commitment

Ethiopia has repeatedly demonstrated its commitment to developing a green econ-
omy. Besides Prime Minister Meles Zenawi’s leadership role in international cli-
mate negotiations, we have launched the CRGE initiative, which is led by the
Prime Minister’s Office, the Environmental Protection Authority (EPA), the
Ethiopian Development Research Institute (EDRI), and six ministries.

These institutions and the relevant ministries have dedicated significant resources
and have organised a robust and participatory process to develop the green
economy initiative.

As shown in Figure 15, seven sectoral sub-technical committees (STCs) have been
established to work on these plans and see them through to successful implemen-
tation. Since February 2011, more than 50 experts from about 20 leading govern-
mental institutions have dedicated time, effort, and resources to developing sec-


Federal Democratic Republic of Ethiopia 46


toral plans and an integrated federal plan. The results of this work have been dis-
cussed in the biweekly Technical Committee meetings that have been chaired and
administered by the Environmental Protection Authority. The Ministerial Steering
Committee – chaired by H.E. Ato Newai Gebre-Ab and composed of the State
Ministers and senior officials from the participating institutions – represents the
most senior body in the CRGE effort and has decided on the overall direction of
the work as well as discussed and approved the sectoral and overall results.

As its first major deliverable, the CRGE initiative has conducted a comprehensive
investigation of the current development path and options for building a green
economy as outlined in this report. The government has thereby started a process
that will be pursued and improved in the coming years.

In addition to the green economy initiative, which is oriented to GHG mitigation,
the economy will be made climate resilient. As part of the CRGE initiative, the
threats related to climate change have been identified and a cost effective
adaptation programme has been developed.

FIGURE 15

PM’s office’s leadership and inter-ministerial approach ensure
national commitment and alignment across government

1 Not operative, yet

Sub-Technical Committees

Technical Committee
(chair: Ato Dessalegne, EPA)

Environmental
Council

Building
& green
cities

Soil
Electric
power
supply

Forestry
Live-
stock

Trans-
port

Industry Health1

Agriculture

Ministerial Steering
Committee
(chair: Ato Newai, EDRI)


Federal Democratic Republic of Ethiopia 47


Emerging institutional setup

The current setup outlined above has proved instrumental to kick-start the CRGE
initiative – however, responsibility for further development and implementation of
this crucial undertaking ought to be transformed into a permanent setting. To
establish this lasting platform, the government has started to develop a permanent
setup and to identify the required personnel capacity.

Overall responsibility and oversight lies with Ethiopia’s Environmental Council.
The council is chaired by the Prime Minister and comprises members drawn from
Federal Ministries, Presidents of National Regional States, and representatives of
non-governmental bodies, the private sector, and trade unions. The Environmental
Council is responsible for recommending laws and regulations for approval by the
Council of Ministers. The Environmental Council can approve environmental
standards and directives independently. It is envisaged that the Environmental
Council installs a subsidiary body to directly oversee the CRGE initiative. This
subsidiary body will be the already established Ministerial Steering Committee,
granted the required legal status of a permanent public institution. The appoint-
ment of its chair would then be under the responsibility of the Environmental
Council.

The government plans to govern the CRGE initiative under the co-responsibility of
the EPA and the Ministry of Finance and Economic Development (MoFED), with
the following roles and responsibilities:

The EPA supervises and regulates implementation of the technical components of
the CRGE initiative. To this end, it will have a team of experts working on each
economic sector to monitor projects so as to ensure their effectiveness, measure,
report, and verify (MRV) project outcomes, and provide appropriate access to
information on projects and outcomes to the public. It will maintain close links
with all relevant ministries including by fostering the establishment of environ-
mental units within those ministries and other relevant sectoral agencies that do
not already have them. The EPA is accountable to the Environmental Council and
will collaborate under the Council’s direction with all institutions relevant for the
CRGE process – such as the MSC and the TC that are responsible for the
alignment and approval of technical content.

Specifically, the EPA will be responsible for (a) deciding on proposals to be sub-
mitted for financial support or carbon credit; (b) organising and conducting inde-
pendent measurement, review and verification; and (c) adopting guidelines,
procedures; and templates. The latter includes, inter alia, templates and guidelines
for preparing proposals for financial support or access to carbon credit as well as


Federal Democratic Republic of Ethiopia 48


monitoring reports of their implementation. The EPA will develop procedures for
the review of green economy initiatives as well as provide relevant methodological
guidance on determining geographical and sectoral boundaries, on setting
baselines for the quantification of credits, and on measuring GHG emissions.
Addressing – among others – the confidentiality of information, the EPA will
develop a code of conduct and procedures. For transparency, the EPA will
maintain and upload on a web-site a register with up-to-date information on
decisions on and implementation of all green economy initiatives.

The MoFED, in collaboration with the EPA, will solicit financial support from
international sources and channel the available funds in the form of advance sup-
port or ex-post payment. The MoFED will ensure transparency, objectivity, con-
sistency, and professionalism in its operations in compliance with international
agreements. The UNDP has offered its support in establishing a Multi-Donor Trust
Fund within this ministry through which funds could be channelled. The govern-
ment will eventually fully and independently run the facility – regardless of the
concrete organisational design.

At the federal level, ministries and other sectoral agencies will participate and en-
courage the participation of other entities in their respective sectors in the formu-
lation and implementation of the components of the green economy. These
ministries and other sectoral agencies are responsible for:

■ Formulating proposals of green economy initiatives for financial support or
carbon credit

■ Coordinating the implementation of sectoral or sub-sectoral green economy
initiatives

■ Preparing and submitting monitoring reports

■ Designing, establishing and staffing their respective environmental units.

National regional states – in collaboration with the relevant federal level institu-
tions – are responsible for implementing the majority of the initiatives outlined in
the CRGE strategy. The coordination between regional and federal levels will be
under the responsibility of the respective environmental agencies of the national
regional states. This collaboration has proved efficient in numerous other
undertakings.

A key design principle for the permanent institutional setup is to use existing
institutions and responsibilities in order to minimize requirements for funding and
organisational reform. The EPA plans to largely deploy people who are already


Federal Democratic Republic of Ethiopia 49


involved in the CRGE initiative today. This shortens the time needed for recruiting
and ensures the high quality and fit of the staff.

Stakeholder mobilisation

To kick-start implementation and build widespread awareness and support, the
initiative has conducted and will continue to conduct extensive stakeholder con-
sultation. Around 300 stakeholders have already been identified and consulted by
the STCs. Consultation was conducted under the co-responsibility of the STCs/
ministries and the EPA between July and September 2011 and primarily focused
on governmental and public stakeholders.

■ Sectoral consultation was organised and conducted by the STCs/ministries.
These events focused on the presentation, discussion, and improvement of the
sectoral work on green economy initiatives. Consultation events focused on
workshops involving experts from ministries and other public sector
organisations as well as selected experts from academia.

■ In addition to this sectoral consultation, a national consultation was led and
organised by the EPA and the EDRI. National consultation involved regional
governments, standing committees of the parliament, and workshops with
selected researchers.

PROVIDING A FOCUS FOR ACTION: CRGE HAS ALREADY FAST-
TRACKED FOUR INITIATIVES FOR IMMEDIATE IMPLEMENTATION

Four initiatives have been fast-tracked for implementation: attracting financing to
exploit Ethiopia’s vast hydropower potential, promoting advanced cooking technolo-
gies on a large scale, monetising reduced emissions from livestock, and Reducing
Emissions from Deforestation and Forest Degradation (Table 3). Each of these ini-
tiatives offers the chance to immediately promote growth, capture large abatement
potential, and attract available climate finance for implementation. Moreover, they are
important enablers for the country’s economic development, and their implementation
is feasible and considered as a priority by the government.

The following subchapters outline each of these four initiatives – highlighting key
findings from the detailed analyses and describing the tactical plan developed to trans-
late them into investment-ready projects that attract finance and get implemented.

To ensure a comprehensive programme, initiatives from all other sectors will be
developed into concrete proposals. These initiatives will be detailed over the coming


Federal Democratic Republic of Ethiopia 50


months. The main criteria for selection as a priority initiative are the initiatives’ effect
on reaching GTP targets, their abatement potential, and their ease of implementation.

TABLE 3

STCs have started to translate green economy opportunities
into investment-ready projects in 4 sectors

Efficient
livestock
sector

▪Livestock accounts for around 11% of the formal GDP and is also the
largest source of GHG emissions in the country

▪Sectoral growth can be achieved while reducing the projected emissions
of the sector by up to 45 Mt CO2e per year in 2030

▪Ethiopia could possibly monetise these reduced emissions to support GDP
growth in Livestock

Rationale for importance of green economy initiative

▪Electric power generation is a critical component to realize growth and
economic development and a condition for green growth in other sectors
– Fundamental to meet growing domestic demand
– Offers significant export potential

▪Securing the financing enables scale-up of clean/renewable power
generation capacity

Power
infrastructure
financing

Rural energy-
efficient
stoves

▪Fuelwood usage is the largest source of rural energy supply and one of
the largest contributors to GHG emissions

▪Efficient stoves can have massive benefits by increasing rural household
income, health, women’s empowerment, and education while decreasing
emissions by around 50 Mt CO2e in 2030

REDD

▪Forests account for 1/3 of total emissions today and offer huge abatement
potential through less deforestation and less forest degradation

▪ In addition, already today Ethiopia has the second-largest afforestation
and reforestation programme in the world


Initiative 1 – Electric power financing

Electric power generation has been identified as one of the most critical
components to capture growth and economic development and a condition for
building a green economy in other sectors. Making use of the vast renewable
energy potential (particularly in hydropower), is not only fundamental to meeting
growing domestic demand but also offers significant export potential. Securing
appropriate financing has been identified as one of the major challenges to the
scale-up in power generation capacity (Figure 16):


Federal Democratic Republic of Ethiopia 51


FIGURE 16

Electric power financing – preparing a roadmap for tapping
external financing for power infrastructure development

Huge financing need over the coming years
creates a significant gap

Total investment and finance,
Billion USD, real

18

20

Financing
gap

Available
finance1

Total

38
Hydro

Wind

Geothermal

Transmission

1 Assuming constant domestic tariffs; projections assume that financing from existing debt and equity sources remains roughly constant

30%

max
24%1

46%

45%

max
9%

46%

▪ Required, as cost
reduction and
increased tariffs not
sufficient to close
the financing gap

28%

up to
24%1

48%

up to
>52%

48%

▪ Not necessarily
required, if cost
reduction and
increased tariffs
fully implemented

External funding will be required,
at least in intermediate years

Several opportunities to reduce the funding gap exist – tapping
external funding sources will be required

Funding 2010-20 Funding 2021-30

Cost
optimization

Tariff
increase
for internal
financing

1

2

External
funding
sources

3

Available Decrease in gap Remaining gap


In order to build the power generation and transmission infrastructure necessary to
fulfil the supply projections for the electric power sector, a financing need of USD
38 billion in capital expenditures over the coming 20 years has been forecasted.
If the current sources of financing remain constant, however, there will be a
financing gap of more than 50% (around USD 20 billion). To close the financing
gap, the deep-dive analysis on power sector financing identified and analysed three
options:

■ Cost optimization

■ Increasing internal funding capability through tariff adjustments

■ Tapping external funding sources.

While cost optimization and an increase in internal funding capability can partly
close the financing gap, they will not be sufficient, making it vital to obtain fund-
ing from external sources (e.g., from the private sector, sovereign wealth funds),
particularly in the early years. To tap external funding sources, it will be necessary
to offer a convincing proposal for project financing. A first version of this proposal
has already been drafted in the deep-dive work, leading into both the return and
the risk elements of electric power generation investments.


Federal Democratic Republic of Ethiopia 52


Initiative 2 – Rural energy and efficient stoves

Fuelwood usage – by far the largest source of rural energy and the second-largest
contributor to GHG emissions – can be reduced with efficient stoves. With a
sufficiently large scale-up, the use of efficient stoves will have a massive impact
on the green economic development by increasing rural household income by
10%, creating an industry worth USD 15 million in gross value added (GVA),
decreasing GHG emissions by 50 Mt CO2e8 in 2030, and increasing health and
gender equality (Figure 17).

FIGURE 17

Rural energy – reducing emissions from fuelwood
consumption through efficient stoves

As fuelwood is largest source of rural
energy, efficient stoves can have massive
benefits …
TWh

1 Only rural areas

… that can be captured through a massive scale-up
programme

65

74

296

Required
household
usage of
fuels

Rural
electrifi-
cation
(off-grid)

5

Rural
electrifi-
cation
(grid)

25

Rural
energy
demand
2030

460

In order to capture the benefits of efficient stoves,
several dissemination models are analyzed to
define a scale-up programme containing

▪ Technology choice
▪ Dissemination approach
▪ Financing (incl. role of climate finance)

Technology

Fuelwood effi-
cient stoves

30Up to 80 15.5 (80)

Alternative fuel
stoves

6Up to 10 2 (10)

Total 36
Up to 90 17.5 (90)

Households
(people)
Millions

Rural
penetration
Percent

Abatement
potential1

Mt CO2e

Cooking/
baking

Other
residential
purposes

Commercial


The analysis conducted in the deep-dive work on rural energy has focused on both
the impact of improved cooking/baking technologies on rural energy and on the
choice of technology, current and improved dissemination approaches, and
financing options.

For the required scale-up to 9 million stoves in 2015, the current dissemination value
chain is inefficient and will lead to a very high programme cost of USD 300 million –


8 Referring to abatement potential in both rural and urban areas, Figure 17 focuses on rural areas only.


Federal Democratic Republic of Ethiopia 53


equalling 10-15 times current budgets for this purpose, for which there is currently no
appropriate financing mechanism available.

Nevertheless, we can achieve the required scale-up by using best-practice
approaches to reduce the scale-up cost to USD 170 million and by mobilizing
international climate funds to obtain the necessary financing. The tactical plan,
which foresees the start of the implementation of the programme for the beginning
of 2012, has already been drafted. Its full execution is necessary to get the required
support and ensure timely implementation.

Initiative 3 – RELS: Reduced emissions from livestock

Livestock accounts for about 11% of the formal GDP, and is also the largest
source of GHG emissions in the country. We have identified initiatives that help to
achieve sectoral growth while reducing the projected emissions of the sector by up
to 45 Mt CO2e per year in 2030. While doing so, we also aim at establishing a
mechanism to monetise these reduced emissions from the Livestock sector (RELS)
– which could be based on existing mechanisms like REDD and unlock funds for
implementing the initiatives (Figure 18).

Therefore, the deep-dive analysis has focused on the prerequisites for establishing
such a mechanism and the steps to follow in order to eventually monetise the
reduced GHG emissions. Briefly, it will be necessary to:

■ Prove the feasibility of reducing GHG emissions from the Livestock sector
through research and pilot projects

■ Set up the necessary institutional framework, including a system for meas-
uring, reporting, and verifying (MRV) livestock-related GHG emissions

■ Identify and engage development partners and investors to finance the
implementation.

A more detailed description of each of the tasks has been developed in the deep-
dive work that will enable us to push forward on this topic and establish us as a
thought leader on abating GHG emissions from livestock.


Federal Democratic Republic of Ethiopia 54


FIGURE 18

Livestock – preparing a REDD-like mechanism for reduced
emissions from the livestock sector

STCs identified initiatives to increase
the GDP of the livestock sector and
reduce emissions at the same time

GHG emissions from livestock
Mt in CO2e, 2030

Ethiopia aims at establishing a REDD-like mechanism to
monetise reduced emissions from livestock, which could
unlock climate funds for implementation of initiatives

Green
growth

80

Abatement
initatives

45

BAU

125 Mechanization

Animal mix

Productivity

Monetisation of reduced emissions requires

▪ To prove feasibility of efficiency
initiatives and effect on GHG
emissions via research and pilot
projects

▪ To set up the necessary institutional
framework including a system for
measurement, reporting and
verification of livestock related GHG
emissions

▪ To identify and engage development
partners and investors to finance the
implementation of the efficiency
initiatives


Initiative 4 – Reducing Emissions from Deforestation
and Forest Degradation

Deforestation and forest degradation account for one third of total emissions today.
However, the forestry sector also offers huge abatement potential through reduced
deforestation and forest degradation. In addition, it holds large potential for
sequestration – which is underlined by the fact that already today Ethiopia has one
of the largest afforestation and reforestation programmes in the world.

REDD+ offers the opportunity to implement forestry abatement levers and
monetise the respective abatement potential in a structured way. Hence, we have
already prepared a Readiness Preparation Proposal (R-PP) that lays out its plan to
prepare for REDD+ implementation. This R-PP has been accepted and we are now
ready for its REDD+ preparation. The preparation phase will include the setup of
an organisational structure, the definition of a REDD+ strategy, as well as the
preparation for implementation of concrete mitigation actions within REDD+.

The development of the REDD+ strategy builds on the existing experience and
structures developed locally, and will enable a broader learning experience for all
affected stakeholders. It will target to leverage the assessments of the main initia-


Federal Democratic Republic of Ethiopia 55


tives to mitigate deforestation and forest degradation, to identify implementing
options, and to define the key enablers required at regulatory and institutional
level.

The mitigation levers identified based on the work carried out by the CRGE initia-
tive focus on addressing the main two drivers of deforestation and degradation
(conversion to agricultural land and unsustainable fuelwood consumption),
through a combination of proposed measures to increase agricultural yields,
manage soils and forests better, and adopt alternative energy sources and energy-
efficient cooking technologies (Table 4). Particularly for the latter initiative,
REDD+ will strongly interact with initiative 2 (rural energy).

TABLE 4

REDD+ – Identified levers for GHG mitigation

▪ Reduce demand
for fuelwood

▪ Increase
sequestration

▪ Reduce pressure
from agriculture
on forests

▪ Decrease requirements for new agricultural
land by increasing yield and value of crops

▪ Agriculture intensification on
existing land

▪ Shift of new agricultural land from forest to
degraded land brought into production due
to irrigation and use of natural fertiliser

▪ Prepare new land for
agriculture through medium-
and large-scale irrigation

▪ Shift of new agricultural land from forest to
degraded land brought into production due
to irrigation and use of natural fertiliser

▪ Prepare new land for
agriculture through
small-scale irrigation

▪ Reduce wood requirements thanks to
efficient stoves (mostly in rural areas)

▪ Fuelwood efficient stoves

▪ Switch to electric stoves (in urban areas
mostly)

▪ Electric stoves

▪ Switch to LPG stoves▪ LPG stoves

▪ Switch to biogas stoves (in rural areas)▪ Biogas stoves

▪ Large-scale afforestation and reforestation
of degraded areas

▪ Afforestation and reforestation

▪ Large-scale forest management
programmes

▪ Forest management

Macro levers DescriptionLevers


Based on the previous work conducted in the field and the assessment of the miti-
gation levers, a series of REDD+ pilots will be identified. This could range from
Participatory Forest Management and Conservation approaches, which support
strengthened local user rights and sustainable forest management, to various ini-
tiatives designed to take pressure off the forest resources; including better man-
agement of previous plantations, and support for bamboo growth and use as well
as intensified agro-forestry. All pilots will be assessed at the end of the R-PP


Federal Democratic Republic of Ethiopia 56


implementation according to various criteria, including effectiveness, efficiency,
and social justice. The better-performing strategies will be selected for scale up.
Other key activities of this work are the development of a REDD+ learning net-
work and a REDD+ good-governance project that supports the development of
good governance around REDD+ pilots.

Main changes in the regulatory environment to enable the proposed mitigation
mechanisms to be implemented should, according to the consultations made in the
preparation phase, focus on local people’s rights, develop a dedicated forestry
institution, and better coordinate land-use planning.

Taken together, REDD+ and the associated activities are intended to help capture
the mitigation potential from forestry that has been estimated to be up to 130 Mt
CO2e in 2030. The REDD+ initiative will help not only to put an institutional
structure in place that supports the implementation of abatement levers in forestry,
but also to finance these levers, e.g., by monetising abatement potential and put-
ting in place the necessary prerequisites such as a reference scenario and an MRV
(monitoring, review, and verification) system.

WE WELCOME GLOBAL COLLABORATION TO TACKLE CLIMATE
CHANGE

Our resources commitment to building its green economy has been described. To
capture the full potential of our green economy plan, we welcome emerging
climate finance programmes designed to compensate developing countries for the
provision of environmental services to the world. Gaining support from
international partners is essential to prepare and implement our green economy.
Addressing the technology, expertise, and financial needs is a fundamental
element of such support. Bi- and multilateral development partners as well as the
private sector can help us achieve our ambitious goals and inspire other green
economy efforts around the world at the same time.

WE ARE PLANNING AHEAD TO IMPLEMENT THE GREEN ECONOMY
STRATEGY

The CRGE initiative has developed an action plan for the coming years that details
the next steps to be taken in order to put the green economy strategy into motion:

■ Institution and capacity building. As outlined under the heading ‘Emerging
institutional setup’, the government has started to develop a permanent
institutional setup in order to establish a lasting platform for CRGE. The


Federal Democratic Republic of Ethiopia 57


focus over the coming months will be on finalising an organisational
structure, identifying the additional required personnel, and building up
institutional capacity.

■ Getting started on early action. Fast-tracked implementation of the priori-
tised initiatives will help us to rapidly capture some of the biggest green
economy opportunities and demonstrate its example of the alternative green
economy growth path. These initiatives will also provide lessons that we can
quickly apply to design and roll out further green economy initiatives in all
other sectors.

■ Completing sectoral green economy programmes. When the formulation of
the CRGE strategy has been completed and estimates verified as far as possi-
ble, green economy programmes for all relevant sectors will be developed to
ensure that the programme is comprehensive. This work will include piloting
and policy design in accordance with the initiatives and goals of the strategy,
at both federal and regional levels.

■ MRV and benefit sharing: We will develop the enablers required to
monetise carbon credits. This includes primarily the setup of appropriate
measuring, reporting, and verification (MRV) systems, which are needed to
provide proof of GHG abatement. It also includes a definition of benefit
sharing, i.e., specification of the stakeholders who will benefit from the pro-
ceedings of the sale of carbon credits.

■ Funding: To implement green economy initiatives, the government will
commit the country’s own funds, but it will be also necessary to gain support
of international private and public partners. The CRGE initiative will
therefore systematically engage in discussions with targeted development
partners. This also requires establishing the appropriate funding mechanisms
for receiving and distributing funds.

♦ ♦ ♦


Our vision of a Climate-Resilient Green Economy does represent a major shift
away from conventional development approaches and will require significant
international support. We are eager to take up this challenge and have created the
CRGE initiative in order to identify sustainable and climate-resilient paths to
economic growth. It builds on our strengths and has the potential to deliver high
returns to its people, its economy, and its environment. In the short term,
immediate action on financing hydropower production, implementing efficient
stoves, reducing emissions from livestock, and REDD+ can noticeably improve


Federal Democratic Republic of Ethiopia 58


the quality of life and create the momentum and funding streams necessary to see
the other CRGE initiatives through to successful completion. By aspiring to – and
achieving – a constructive contribution to the green economy, we are also laying
the longer-term foundation for reaching middle-income status by or before 2025.


Appendices


Federal Democratic Republic of Ethiopia 61


Approach and methodology

OVERALL APPROACH

The development of a green economy strategy starts from an assessment of a
country’s economic and growth targets. In our case, these are explicitly written
down up to 2015 in the Growth and Transformation Plan (GTP). Beyond 2015, the
plans are much less detailed, but the ambition of reaching middle-income status
before 2025 is clearly stated by the government and guides the forecast of growth
rates beyond 2015.

The attractiveness of developing a green economy plan lies in the substantial con-
tributions it can make to economic advancement by offering a new organising
principle for identifying opportunities, by laying the foundations of a new and
sustainable model of development, and by mobilising international capital to fund
the necessary investments and projects.

Because the immediate monetisation of building a green economy depends on
verified emissions reductions, the Ministerial Steering Committee and the Techni-
cal Committee (described in the chapter ‘Making it happen’) have focused the
analytic work of developing a green economy strategy on initiatives that contribute
to reducing emissions. Other parts of the climate-resilient green economy strategy,
specifically ‘climate resilience’ initiatives, will be incorporated subsequently.

The Sub-Technical Committees (STCs) – inter-ministerial working groups
focusing on specific sectors such as power, agriculture, and forestry, amongst
others – have been tasked with the core analytic work. The STCs followed a four-
step process to determine the preliminary green economy strategy (Figure 19).

1. On the basis of the GTP targets and long-term economic objectives, the STCs
developed a BAU projection of economic growth and associated emissions for
their respective sectors. This projection extends to 2030 to allow enough time
to include long-term infrastructural investments and achieve the middle-income
status the country aspires to.

2. The second step was to identify and analyse the potential of green economy
initiatives or levers. It was understood from the outset that potential initiatives
have to contribute to growth and development targets as well as to the reduc-
tion of GHG emissions as compared with BAU development. Abatement
potential was chosen as a main criterion for selecting the green growth initia-


Federal Democratic Republic of Ethiopia 62


tives as it is a prerequisite for tapping funds available in the context of the
international negotiations on climate change.

3. The third step was to evaluate the initiatives in terms of abatement cost
(expressed in USD/t CO2e), investment and finance requirements, feasibility,
and other implementation requirements. The initiatives were then prioritised
accordingly.

4. The last step was to document and summarise the findings as well as to daft the
preliminary green economy strategy, which ultimately resulted in this report.

FIGURE 19

2030
Green
growth

#4#3#2#1

Process used to develop preliminary green economy
strategy

2030
BAU

2010

Forestry

Crops

Power

Other

Livest.

GE strategy

Estimate current
emission level
and BAU 2030

Evaluate cost,
feasibility, etc.

Develop
preliminary GE
strategy

▪ Lower-emitting
techniques

▪ Higher animal
productivity

▪ Change in animal
mix

▪ Agriculture
intensification

▪ Introduction of
efficient stoves

▪ Power exports

March JuneApril May

Analyse the
potential of green
economy initiatives

GE initiatives Evaluation

1 2 3 4


METHODOLOGY OF THE ANALYSIS

The analytic backbone of the project was a sectoral analysis of GHG emissions, of
initiatives (potential impact and cost), and implementation requirements, including
financing. The CRGE initiative identified seven sectors with high relevance for the
sustainability of Ethiopia’s growth model. One STC working group was set up for
each of these sectors.


Federal Democratic Republic of Ethiopia 63


Identification of relevant sectors

The CRGE initiative selected seven sectors for detailed investigation based on the
GTP and findings of previous studies on the sustainability of Ethiopia’s develop-
ment path – in particular the ‘Green Growth’ study conducted by EDRI and the
Global Green Growth Institute (GGGI). The two criteria for the selection of these
sectors were (a) the importance of the sector for the economy and (b) the sector’s
current/expected future GHG emissions. The following sectors were selected:

Agriculture-related sectors

■ Forestry. Avoiding deforestation is an important development objective, as it
will preserve the natural ecosystem endowment and also contribute to a
sustainable development of agriculture. Potential measures in this category
include the improvement in efficiency and productivity of existing cultivated
land and land to be cultivated to reduce the pressure on forests, as well as the
substitution of traditional cooking techniques with efficient appliances, thus
reducing fuelwood consumption and increasing carbon sequestration by
forests.

■ Soil. Ethiopia has over thirty million hectares of cultivable land and over
eleven million hectares of cultivated land. Historical practices, rapid expan-
sion, and inappropriate agricultural techniques have resulted in poor soil
quality. Soil management is therefore a fundamental component of the
country’s agricultural strategy and, because of the carbon content of land, can
also be an important initiative to control and manage carbon emissions as the
country grows. Additionally, careful soil management can stem the growth of
cropland needed and avoid further deforestation.

■ Livestock. At over 140 million head today, livestock are a critical part of the
economy, both commercial and subsistence: livestock are an important source
of livelihood for over ten million pastoralists and millions of farmers.
Livestock are also the principal source of GHG emissions in the country and a
significant contributor to emissions globally. Because livestock management
is also a high priority for the GTP, the adoption of green economy initiatives
in this area is highly compatible with the country’s agricultural strategy and
its broader development strategy. Measures here include improving the pro-
ductivity of the herds, reducing headcount, and establishing mechanisms to
monetise the abatement potential in livestock with the help of development
partners.


Federal Democratic Republic of Ethiopia 64


Other sectors

■ Electric power. Ethiopia has ample potential in renewable energy generation,
most prominently in hydroelectric power, as well as other renewables such as
wind and geothermal power generation. It also has significant export potential
into the Eastern African Power Pool and further regional markets. These
initiatives are obviously important from a green economy perspective because
they can significantly de-carbonise the regional energy profile as well as
contribute to Ethiopia’s capital stock formation, a critical enabler of industrial
and urban growth and a key priority for the growth and transformation
programme through rural electrification.

■ Buildings/Green Cities. This category includes all the initiatives that contrib-
ute to the creation of new more sustainable urban environments. They range
from the adoption of efficient lighting in urban settings and efficient appli-
ances in the domestic sector to improved waste management of the growing
cities. There are significant energy and carbon emissions savings available in
the built environment as well as important development targets, including the
health of the urban population. An additional point of relevance is that Addis
Ababa is not just the capital of Ethiopia but the political capital of Africa, the
seat of the African Union, and a recognised cultural and economic centre in
Eastern Africa. Urban development that successfully leapfrogs traditional
forms of development of metropolitan cities and their associated problems
could be an important element of sustainability.

■ Transport. The improvement of Ethiopia’s transport networks is an
important part of the country’s growth plans. As a vast and landlocked coun-
try with a large share of the economy dependent on perishable agricultural
products, and given the critical role exports will play in the growth and devel-
opment of the economy, Ethiopia needs efficient and reliable transportation
networks. Multiple initiatives are considered in this category, including the
expansion of the electric rail freight capacity, the adoption of fuel efficiency
standards, and the adoption of cleaner fuel mixes such as the use of biodiesel
and ethanol.

■ Industry. The economy is on an agriculturally led development path.
However, the development of an industrial sector, both to serve domestic
demand and to support exports, is a stated priority of the GTP. Green
economy initiatives in this sector represent an opportunity to create an indus-
trial base that is more sustainable than the ones that have evolved from the
industrial revolution in Europe and the US. In order to take this opportunity,
the Industry STC considered a number of initiatives ranging from changes to


Federal Democratic Republic of Ethiopia 65


products and technologies in key sectors such as cement, to the adoption of
energy efficiency, alternative fuels, and alternative production processes in
manufacturing and other industrial sub-sectors.

For each of these sectors, the respective STCs worked on establishing an accurate
‘business-as-usual’ baseline, identifying a set of green growth initiatives, and car-
rying out their evaluation and prioritisation.

Forecasting economic development and related
emissions in the ‘business-as-usual’ scenario

The forecasting of the ‘business-as-usual’ (BAU) scenario is based on two steps.
The first step is to forecast the country’s economic development; the second step is
to compute the associated emissions. This section explains both steps and gives an
overview of the methodology that was used for the forecast.

Using the GTP targets, past performance, and the ambition to reach middle-
income status before 2025, the CRGE initiative developed a realistic forecast for
economic development over the next 20 years. The development of this forecast
involved the following steps:

■ Analysing GTP forecasts and PASDEP performance regarding contributions
of different sectors to GDP and employment; compiling information on
population growth, urbanisation, and other potential drivers of emissions such
as cattle population and deforestation.

■ Prioritising on those sectors with largest contribution to both GDP and current
emissions for the CRGE strategy.

■ Combining different data sources to identify and compile a base case for
development of economic drivers (of growth and GHG emissions) and other
relevant variables; matching projections with the growth ambitions outlined
by the government (i.e., reaching middle-income status before 2025).

■ Reviewing the BAU scenario with the Ministry of Finance and Economic
Development as well as comparing it with development path of other
countries for robustness of estimation.

In a second step, the projected economic growth was translated into the business-
as-usual development of GHG emissions. The BAU estimation of GHG emis-
sions forms the baseline for the development of a green economy strategy. The
estimation answers the questions: how would domestic GHG emissions develop if
no actions were taken to limit emissions? The BAU is thus not the most likely or
even desired scenario, but a theoretical case assuming a country would act as if


Federal Democratic Republic of Ethiopia 66


there was no need to develop a sustainable growth agenda, either because of the
absence of economic interest or funding. The main assumption in developing a
BAU baseline is that a country (or its government) is acting in its economic self-
interest. Actions to reduce or prevent emissions are therefore only included in the
BAU baseline if they already are under development or if they represent the eco-
nomically most viable and feasible future options without the need to secure extra
funding. Therefore, the BAU assumes that electricity generation will continue to
be largely based on hydropower and other renewable energy sources.

CRGE follows a sectoral approach: each STC first developed a sectoral BAU,
which was then combined with the BAUs of other sectors into a national BAU.

Selection of green economy initiatives

Having developed a baseline for both economic development and a BAU
emissions trajectory, the next step was to identify appropriate initiatives to divert
from the conventional development path to a low GHG-emitting growth model
that combines both economic growth and the reduction of GHG emissions. This
involved the selection of appropriate green economy initiatives out of a long list of
initiatives that have proved successful elsewhere as well as options newly
developed and adapted to the Ethiopian situation – around 150 potential initiatives
in total. The long list of initiatives that was generated has meanwhile been rigor-
ously assessed to select and prioritise those that can be used to form our
comprehensive green economy programme.

For an initiative to be retained as a ‘prioritised initiative’ within the green econ-
omy plan, the following criteria had to be met:

■ Pass an initial assessment of relevance and feasibility to be implemented in
the local context

■ Provide a positive contribution to reaching the targets of the GTP

■ Provide significant abatement potential at a reasonable cost for the respective
sectors.

More than 60 initiatives, split across the seven different sectors, have been classi-
fied as priority levers based on the analyses made in the CRGE effort. For each
sector, at least three initiatives have been chosen (Figure 20). The sectoral STCs
conducted the selection of appropriate initiatives independently, but discussed
them across sectors to ensure alignment and avoid duplication of effort.


Federal Democratic Republic of Ethiopia 67


FIGURE 20

Long list of
potential
green growth
initiatives –
150+ initiatives

Abatement/
avoidance
potential –
GHG emissions
compared with
BAU if
initiative is
implemented

Effects on
GTP – potential
to contribute to
reaching targets
as outlined in
GTP

Cost effec-
tiveness
checked –
costs to reduce
or avoid one t of
CO2e

Feasibility in
local context –
technical and
institutional
implementability

Prioritised
measures for
inclusion in
CRGE plan –
>60 initiatives

150 potential green growth initiatives were screened, >60
have been shortlisted for inclusion in the CRGE strategy


Criteria for initiative assessment

The assessment of green growth initiatives needs to reflect the breadth of concerns
that guides the development of the green economy plan. Hence, the criteria to con-
sider must take into account of both the medium-term objectives of the Growth
and Transformation Plan and the long-term objectives of reaching middle-income
status before 2025. Therefore, the individual initiatives were evaluated against the
following criteria:

■ Abatement potential. Given that the scope of this project is focused on the
green economy, abatement potential is a critical characteristic and provides an
opportunity for monetisation of a country’s contribution to curtailing GHG
emissions. It is therefore a critical component of the evaluation of the
initiatives.

■ Cost and funding requirements. Financial sustainability and cost are para-
mount for our financial situation. The cost and, in particular, the funding
requirements of individual initiatives are an important element for evaluation.

■ Suitability to GTP. The Growth and Transformation Plan describes detailed
targets for multiple sectors and sub-sectors. In some cases, the targets are spe-
cific and numeric and refer to outcomes. In other cases, they refer to inputs


Federal Democratic Republic of Ethiopia 68


such as investments. Individual targets number in the hundreds. The STCs
developed a set of main criteria to evaluate the initiatives. These criteria are:

– Impact on poverty reduction – Ethiopia still remains one of the Least
Developed Countries, with a GDP per capita of less than 300 dollars per
year. Furthermore, over 80% of the population is employed in agriculture,
and the issue is a lack of alternatives. Having an impact on poverty
reduction is thus a critical strategic objective of the initiatives for the
country.

– Food security – According to the GTP, over 7 million people are still food
insecure. The drought of 2003 showed once again how vulnerable the
population is in an agricultural system that is primarily rainfed in an area
that is drought prone. Hence, food security is a critical objective for the
government.

– Increase in real GDP – GDP growth is a necessary objective for the
development of the country. In the coming years, large amounts of produc-
tive investments are expected in business infrastructure. Labour productiv-
ity is expected to grow, and the population is also growing – by 2030
another 50 million people are expected to be added to the population. In
this context, inflation has been a recurring challenge despite the proactive
management by the government. Achieving the ambitious targets of
growth in real GDP is hence a key objective.

– Increase in domestic capital formation – We still have most of the
investments in basic infrastructure ahead of us. In the coming 20 years,
tens of billions of USD are expected to be spent on capital formation, to
enable the increase of labour and land productivity. This serves as a
necessary prerequisite for sustained economic growth.

– Increase in exports – Export-led growth is the rapid-growth paradigm of
the twentieth century and one that we – with a currently small domestic
market – expect to follow. Because the trade balance today is still negative
and enters negatively into the balance of payments, increased exports, par-
ticularly of agricultural and related products, is a key priority.

– Benefit to public finance – With low levels of savings and limited domes-
tic income, the government’s fiscal position is not strong. Foreign reserves
are limited. At the same time, the investment requirements are very high
and are partly to be financed by public money. It is therefore critical that
initiatives not only benefit the wider economy of the country, but also sup-
port the operations and investments of the state (at federal as well as
regional and local levels).


Federal Democratic Republic of Ethiopia 69


– Increase in employment – With an increasing young population,
productive employment is a crucial objective for the government,
particularly because generating additional employment outside of the
agricultural sector is critical to enable the targeted increase in
mechanisation. Moreover, new employment opportunities need to be
created for the rapidly growing population.

■ Feasibility. In a country with limited implementation capacity, criteria of
feasibility are crucial. That is not to say that feasibility is necessarily a reason
for exclusion, but it is an important factor in assessing initiatives as it influ-
ences the sequencing, timing, and resourcing of the implementation. Feasibil-
ity has been evaluated with regard to technical as well as institutional barriers
and additional hindrances to implementation.

Methodology for initiative assessment

Each initiative was assessed against the above criteria. The STCs have drawn on
their own expertise and that of their departments as well as external experts to
work through all of the initiatives. In doing so, they have employed the following
methodologies:

Emissions abatement

The abatement potential was calculated using a comparison with the BAU projec-
tion. The STCs reviewed individual sectoral projections and created BAU emis-
sions projections which served as the basis for all abatement potential calculations.

The level of detail of the business-as-usual projection constrains the level of detail
at which the initiatives can be calculated. For this reason, the STCs went to the
highest possible level of detail for those sources of emissions that represent the
majority of the carbon footprint of the country. In the case of methane emissions
from cattle, for example, the STCs had to estimate the current cattle population,
including the split between indigenous cattle and cross-breed cattle. These were
then projected out to 2030 based on assumptions and data on growth rates and,
where possible, associated with GTP targets. The resulting projection was then
converted into a CO2 equivalent emission (in this case using the conversion factor
for methane) based on international methodology (e.g., IPCC) and based on
domestic expertise and knowledge.

Abatement initiatives were then quantified assuming an emissions reduction per
unit and the scaling up over time. These assumptions allowed the STCs to calcu-
late an abatement potential from each of the initiatives for each of the years under


Federal Democratic Republic of Ethiopia 70


consideration (Figure 21). They were calculated using the best available data,
government estimates, and international benchmarks.

FIGURE 21

Time

GHG emissions
after implementa-
tion of abatement
options

Explanation

CONCEPTUAL

2010 2020 2030

▪ “BAU” reference scenario as
a basis for assessment of
mitigation levers and carbon
finance negotiations

▪BAU is not the most likely
scenario, but a theoretical
case assuming a country
acts in its economic self-
interest only

▪Does not include additional
action for avoiding GHG
emissions (e.g., renewables
are only added if cost
competitive with fossil fuels)

BAU GHG emissions baseline serves as the basis for
building the green economy growth scenario and seeking
carbon finance

Business-as-
usual reference
scenario

Business-as-
usual reference
scenario

GHG emissions
Mt CO2e per year


Abatement cost

The abatement cost of each initiative is defined as the incremental cost (positive if
more expensive, negative if more cost economical) of a low-emission path com-
pared with the required cost or benefits of the conventional alternative underlying
the BAU scenario. Costs are measured in USD/t CO2e of abated emissions in a
given year in the future (here always referring to year 2030). That is, the abate-
ment costs for a given year are divided by the abatement potential in that year to
arrive at the actual abatement cost. The abatement cost includes both the incre-
mental capital expenditure (investment) required for the implementation of the
abatement initiatives compared with the BAU scenario, the incremental operating
cost required for the abatement lever, and potential benefits (e.g., lower costs due
to increased fuel efficiency or higher revenues) compared with the BAU scenario.
The capital expenditure is taken into account in the form of an annualised invest-
ment cost. The annualised cost was calculated with an economic amortisation
period (usually between 20 and 50 years, depending on the type of investment) and
a capital cost of 6% (real). The operational expenditure is taken into account in the


Federal Democratic Republic of Ethiopia 71


respective year of occurrence. For initiatives that create a carbon stock effect but
also running operating expenditure (e.g., avoided deforestation), the perpetuity of
operating expenditure is taken into account alongside the capital expenditure of the
respective year in which the effect is created.

Costs and benefits are estimated from a societal perspective, i.e., irrespective of
who bears costs or benefits. The costs do not include any subsidies, taxes, or
external costs that are caused indirectly and that largely depend on the exact form
of implementation, such as communication cost or transaction cost.

Abatement costs can be displayed in an abatement cost curve. This cost curve
describes green economy initiatives based on two characteristics: the annual
potential of abating GHG emissions in a given year and the costs per tonne abated
in a given year (Figure 22). The underlying assumption is full implementation of
the initiative (i.e., the cost curve displays full technical potential); the reference
year is 2030. Taken together, the abatement cost curve visualises two important
pieces of information concerning each initiative:

■ What is the cost of abatement? The answer is reflected in column height,
sorted by the most cost efficient, from the left.

■ What is the potential volume of GHG abatement? The answer is displayed as
column width – the wider the column, the more potential the initiative offers.

The columns that extend upwards represent measures with a cost higher than
USD 0 per tonne of reduced emissions, while the columns that extend downwards
represent measures that have a negative cost per tonne of reduced emissions: they
save money and reduce emissions. Therefore, initiatives with a negative abatement
cost are economically advantageous in any case.


Federal Democratic Republic of Ethiopia 72


FIGURE 22

Abatement Cost Curve: General overview of methodology

4

2

1

Abatement cost
USD per t CO2e of reduced
emissions in 2030

Each option for reducing emissions is represented by a bar on the cost curve.

The width of each bar shows the abatement potential – the tonnes of annual emissions
that would be reduced in 2030 if we implemented this option fully.

The sum of the width of all bars shows the sum of the abatement potential of all initiatives
– in reality the aggregated abatement potential will be lower than the sum of each
initiatives as it might not be feasible to implement some initiatives at the same time

The height of each bar shows the abatement cost – the cost of implementing this option
fully in terms of dollars per ton of reduced annual emissions.

The bars on the right represent costly options, while the bars that face downward
represent options that actually have negative cost: they save money as well as emissions.

The Abatement Cost Curve allows us to view and compare all the available options
for reducing emissions along two key dimensions at once: How much can each option
contribute to emissions abatement, and at what cost does it do so?

2

1

4

Abatement potential
Reduced emissions in
2030, Mt CO2e

3

3


Suitability to the GTP

The assessment of the suitability of the initiatives to the Growth and Transforma-
tion Plan (GTP) is a more delicate matter, as few models exist for macroeconomic
impacts and some of the criteria are difficult to quantify analytically.

The STCs decided to conduct a qualitative multiple criteria assessment to assess
suitability to the GTP. Each element of the suitability to GTP was rated according
to whether the initiative increases the chances of achieving the GTP objectives,
decreases them, or is neutral. Given this simplification, it is natural to expect some
contradictions. This also included relevant considerations of cross-benefits with
other initiatives (see example in Table 5).


Federal Democratic Republic of Ethiopia 73


TABLE 5

1 Not explicitly in Economic Growth targets of GTP

Assessment of economic impact – Clinker substitution
by pumice as an example

▪ No direct impact on decreasing poverty
▪ But: making cement more affordable to population

=

▪ Making cement more affordable for small-/medium-scale
irrigation (scale-up of irrigation)

Increase food securityII = / +

▪ Availability of cement at lower price will encourage
construction industry

Increase real GDPIII = / +

▪ Indirect via stimulating capital formation in the form of
construction

Increase domestic capital
formation

IV = / +

▪ No impact as long as domestic demand unfulfilled
▪ Thereafter, increasing price-competitiveness

Increase exportsV = / +

▪ No direct impactIncrease public finance (revenue
as % of GDP)

VI =

▪ Availability of cement at lower price will encourage
construction industry

Increase employment1VII = / +

I Decrease poverty (percentage of
citizens living below poverty line)

GTP targets RationaleImpact

Positive None Negative


Feasibility

The assessment of feasibility was done as a rapid assessment to point out the
potential implementation barriers and to understand whether there were critical
issues that needed to be addressed or that could disrupt implementation. The
assessment was primarily conducted around technical barriers – for example, the
availability, applicability, or accessibility of the technology required – and institu-
tional/organisational barriers – for example, the existence of pilot programmes, the
support by stakeholders and others.

Each feasibility category was evaluated through discussion, expert interviews, and
feedback from existing pilots. A detailed evaluation was reported along the dimen-
sions of the framework and a partly quantitative ranking was provided.

Funding requirements

The assessment of expenditure (and benefits) for the green economy initiatives is
largely based on the aggregation of the data used for the calculation of abatement
cost. The expenditure (and benefits) can be systematically split into its components
and aggregated on a yearly or periodical (e.g., 2011-2030) basis:


Federal Democratic Republic of Ethiopia 74


■ Additional CAPEX (capital expenditure) required to implement the respective
green economy initiative

■ OPEX (operating expenditure) required to implement the respective green
economy initiative

■ Programme cost to implement the respective green economy initiative

■ Benefits (savings or income) incurred from green economy initiative
(regarding the societal effect, not necessary implementer‘s savings)

Taken together, these components form the total net expenditure (incl. benefits)
for the respective green economy initiative (N.B.: not including carbon revenues).

In order to analyse the required type of financing for the respective initiatives, they
are grouped into three distinct categories based on net present value (NPV). The
NPV of an initiative shows the current value of the return of that initiative over a
number of years taking into account the time value of money. A positive NPV
means that the benefits outweigh the costs for a specified number of years, while a
negative NPV means that the costs outweigh the benefits for a specified number of
years:

■ Category A: Initiatives that have positive return and only require short term
financing. These are defined as yielding a positive NPV in the first five years
of cash-flow (from start of implementation of the initiative).

■ Category B: Initiatives that have a positive return, but require long term
financing. These are defined as yielding a positive NPV of the overall initia-
tive (from start of implementation of the initiative) up to 2030, but not during
the first five years.

■ Category C: Initiatives that do not yield a positive (financial) return, hence
they require grants or performance payments. These are defined as yielding a
negative NPV of overall initiative (from start of implementation of the initia-
tive up to 2030).

The NPV is calculated with 6% discount rate and takes into account all expendi-
tures and benefits (from a societal perspective as defined above). It should be
noted that the implementing agency might face higher net expenditure when
benefits (i.e., savings or income) are captured by different parties.

Sources of data

As many institutions are building up their capacity, collection of high-quality data
is sometimes a challenge in Ethiopia. For many of the sectors included in the pre-
liminary CRGE strategy, data were not readily available or were of poor quality.


Federal Democratic Republic of Ethiopia 75


As a result, the STCs had to take a pragmatic approach to compiling the fact base
required to support the process, combining domestically available data with inter-
national benchmarks, experiences from other countries, expert interviews, and
making own assumptions. In general, the data were taken from official sources
such as the CSA, the GTP, or MoFED as well as the statistical departments at the
respective ministries and research institutes. International methodology on GHG
emissions (e.g., IPCC) was used wherever possible and appropriate. A detailed
description of the data sources can be found in the sectoral chapters in the follow-
ing appendices.

IMPLEMENTING THE GREEN ECONOMY STRATEGY

The green economy strategy provides the base for the development of a concrete
action plan. Since implementation is very likely to be constrained by existing
financial, institutional, and technical capacities, the initiatives need to be
sequenced. This section explains the criteria for sequencing. Moreover, it outlines
the process that needs to be followed in the overall effort on the green economy to
make sure that the strategy and its individual initiatives are implemented.

Sequencing and fast-tracked initiatives

Although we are planning to implement all prioritised initiatives, given capacity
and financial constraints, it is imperative to sequence them. The government has
therefore selected some initiatives for fast-tracked implementation as they offer the
chance to accomplish several important goals:

■ Immediately promote growth

■ Capture large abatement potential

■ Attract available climate finance for implementation.

Moreover, they are important enablers for the country’s economic development,
and their implementation is feasible and considered as a priority by the govern-
ment.

The government has selected the following initiatives (detailed description and
analyses are available in the main body of the document and in the respective
appendices):

■ Developing a financing strategy for the electric power sector

■ Promoting advanced cooking technologies on a large scale


Federal Democratic Republic of Ethiopia 76


■ Increasing efficiency of livestock handling, including upgrading of the meat
value chain and mechanisation of draught power

■ Reducing Emissions from Deforestation and forest Degradation

Responsibilities have already been assigned for the fast-tracked initiatives and
detailed planning for implementation is underway.

Process for Implementation of the strategy

The green economy strategy covers the projection of a business-as-usual scenario,
the calculation of abatement potential and abatement cost, and evaluation of
feasibility and economic impact for each sector and initiative respectively. These
steps were completed successfully by the STCs between March and June 2011.
Afterwards, the results were subjected to consultation with regional and sectoral
institutional stakeholders to ensure the accuracy of the numbers and to gain
national support for the strategy from relevant stakeholders. Sectoral consultation
focused on the presentation, discussion, and improvement of the sectoral work on
green economy initiatives in collaboration with sectoral experts. Regional
consultation involved regional governments, standing committees of parliament,
and workshops with selected leading regional researchers. This process was
completed in August 2011. Following consultation, the STCs – under the guidance
of the Ministerial Steering Committee and the Technical Committee –
implemented the input to upgrade and complement their original assumptions and
calculations.

In order to ensure the successful implementation of the strategy, the following
steps are scheduled to be taken over the coming months:

■ Inclusion in sectoral development plans – In order to integrate the green
economy initiatives into the development policies of the government, the
respective ministries will include them in their sectoral development plans.

■ Identification and selection of priority initiatives – The STCs will select
some of their initiatives for immediate implementation in addition to those
already chosen. These will serve as fast-track initiatives to gain and demon-
strate immediate impact. The main criteria for selection as a priority initiative
for implementation are the initiatives’ effect on reaching GTP targets, their
abatement potential, and their ease of implementation.

■ Preparation of implementation, resource, and investment plans –
To ensure a comprehensive programme, fast-track initiatives and additional
initiatives from all other sectors will be developed into concrete proposals.
These proposals will contain implementation plans, resource requirements


Federal Democratic Republic of Ethiopia 77


(including financial, human and technical resources), investment plans, and
the assignment of responsibilities. A suggestion of potential development
partners and investors should complement the proposals.

By completing these tasks, we aim to lay the best foundation possible for the
successful implementation of the CRGE strategy.


Federal Democratic Republic of Ethiopia 78


Electric Power

In the business-as-usual (BAU) scenario, Ethiopia will use hydropower and
renewable sources of energy to create a near-zero GHG emission electric power
supply by 2030. While all on-grid power generation capacity is planned to be from
renewable sources (i.e., zero emissions), there are still some off-grid power
generation facilities that create GHG emissions. Most of the emissions from off-
grid electric power generation are taken into account in other sectors9 with the
exception of rural residential fossil-fuel-based generation, which is accounted for
in the Electric Power sector and causes the emissions to be slightly above zero.
Taken together, the Electric Power sector represents an exception in the usual
pattern of emissions development, as the BAU scenario already presents the
characteristics of a green growth plan.

On the other hand, the planned scaling up of domestic power production capacity,
combined with a successful implementation of energy efficiency measures, offers
opportunities for electric power exports. These exports could reduce neighbouring
countries’ emissions with clean electric power generated in Ethiopia and represent
the single most important abatement lever compared with BAU for the Electric
Power sector. The projected domestic supply-and-demand balance indicates an
average export potential of around 25 TWh p.a. between 2011 and 2030, which
would result in an annual abatement potential of 17 Mt CO2e on average and
nearly 20 Mt CO2e in 2030.

To materialise the supply potential projected, the most significant barrier to be
overcome is potentially the financing of the incremental electric power generation,
alongside the need to gain neighbouring countries’ support for importing power
from Ethiopia at the right price.

SCOPE AND INSTITUTIONAL SETUP

The expected electric power demand and supply in Ethiopia as well as of the
sector’s potential GHG emissions were reviewed and calculated by the Electric
Power STC (Table 6). The STC is composed of experts from the Ministry of
Water and Energy, EEPCo, the Ethiopian Energy Agency, the Ministry of Mines,
and the Central Statistics Agency.


Federal Democratic Republic of Ethiopia 79


The scope of this chapter is particularly focused on electric power generation and
consumption.10

In the business-as-usual (BAU) scenario, Ethiopia will use its available natural
resources (mainly hydropower and, to a lesser extent, wind and geothermal) to cre-
ate an electric power supply infrastructure with zero GHG emissions by 2030. The
Electric Power sector thus represents an exception from the usual pattern of
emissions development, as its BAU scenario is already on a green growth path.

TABLE 6


9 The emissions from commercial power generation are accounted for in the Industry and Agriculture
sectors. With regard to residential energy consumption, the Green Cities sector takes into account GHG
emissions from urban power generation by fossil fuels and the Forestry sector accounts for all emissions
from power generation from woody biomass (urban and rural).

10 Emissions from water supply and irrigation have also been checked. According to the research by the
STC, water works and distribution in most areas (particularly urban) are either electricity powered or
electricity will be used for all water works by 2020 (when 100% area coverage by the grid is planned). As
grid electricity approaches zero emissions by 2015, water supply will not constitute a significant source of
emissions for the year 2030. As for irrigation schemes, medium- and large-scale schemes use mostly
gravity and hydropower (multipurpose) and hence do not represent a major emission source. Small-scale
irrigation consumes partly electricity, partly fossil fuels (mostly diesel), and these emissions are accounted
for in the Agriculture sector.


Federal Democratic Republic of Ethiopia 80


GHG EMISSIONS BASELINE IN 2010 AND BAU UP TO 2030

Drivers of GHG emissions and evolution of the Electric
Power sector

The GHG emissions from the Electric Power sector are determined by the growing
demand and supply of electricity as well as by the source of power generation.

As shown in Figure 23, total electric power demand is projected to grow from
4 TWh in 2010 to a maximum of nearly 77 TWh in 2030. This forecast is based on
projections for energy demand by the different sectors contributing to the
economic growth (based on GTP data until 2015 and projection to reach middle-
income status by 2022), intensity of energy use and projections of increase in
energy efficiency for each of these sectors contributing to the demand.

FIGURE 23

Electric power generation is essential to meet demand and
create export potential

Demand

Supply Hydro

Supply Wind &
Geothermal
Supply Diesel

Maximum
export potential

73

20
56

4 1

Add. ge-
neration
capacity1

2010 supply

0
6

7

67

10
1

Total
supply
2030

831

74

10
0

Export
potential

28

28

Total
demand
2030

Energy
efficiency

BAU
demand
growth

2010
demand

1 Current EEPCo masterplan plus additional capacity estimation (based on economic potential and building capacity) by Power STC; not
including up to 450 MW cogeneration capacity from coal plants (fertilizer production)

TWh (2030)

▪ Due to strong economic growth (esp. industry),
power demand will grow almost 20 times by 2030

▪ Increased energy efficiency can help to decrease
domestic demand in 2030 by almost 30%

▪ Ethiopia has potential to produce >80
TWh from renewable energy in 2030

▪ Power can become an export-oriented
sector and therefore a source of
foreign exchange

Power demand and supply in 2030


According to this reference scenario, the steep increase in demand, reaching
77 TWh by 2030, reflects both the growing electrification – the target for 2020 is
to expand access to grid connection to nearly 100% of the country (measured by
area, not by households) – and rapid growth of electricity-intensive industries –


Federal Democratic Republic of Ethiopia 81


projected at a rate of more than 16% a year, outpacing by far the overall GDP
growth rate and the growth of other sectors.

Even if we can capture the entire energy efficiency potential that has been identi-
fied, the increase in demand will be reduced to 56 TWh by 2030. This would still
represent a more than a tenfold increase over today’s demand.

EEPCo’s master plan and an extrapolation based on the total energy generation
potential and capability to build generation capacity have been used to project the
development of the supply capacity. All major existing power infrastructure
projects have been taken into account. According to these forecasts – also depicted
in Figure 23 – the supply capacity will increase from 7 TWh in 2010 to more than
80 TWh in 2030. According to EEPCo’s master plan, the current diesel power
plants and off-grid diesel generators will be switched off between 2012 and 2014.
From 2015 onwards, EEPCo plans to generate power exclusively from clean or
renewable sources11 (on average around 90% from hydro, 6% geothermal and 4%
wind) – while retaining some diesel generators as standby solutions. However,
since the plan is to establish a more reliable and stable power supply throughout
the country, the use of such standby facilities is expected to decrease dramatically
to reach virtually 0% by 2030.

While these are the grid-related power demand and supply projections, the Electric
Power sector also accounts for off-grid rural residential fossil-fuel-based
generation.12 Figure 24 depicts this generation as well as the on-grid power
generation from non-renewable sources under conventional power generation,
which is the major GHG emission driver for the Electric Power sector as defined
in this strategy and projected to grow from 8.5 to 9.8 TWh in 2030. In addition, it
also shows the renewable power generation, which is forecasted to increase to 98
TWh in 2030.

While EEPCo’s conventional power generation is less than 1 TWh in 2010, it is
planned to be decreased to zero after 2014. On the other hand, off-grid rural resi-
dential fossil-fuel-based generation will increase from 7.8 TWh to 9.8 TWh – the
effect of a growing population and consecutively higher power demand is partly
offset by rural electrification and households switching to on-grid power supply.


11 There is a plan to include capacity from cogeneration plants (e.g., power generation from coal in fertiliser
plants). These emissions are accounted for in the respective industry segments.

12 The emissions from commercial power generation are accounted for in the Industry and Agriculture
sectors. With regard to residential energy consumption, the Green Cities sector takes into account GHG
emissions from urban power generation by fossil fuels and the Forestry sector accounts for all emissions
from power generation from woody biomass (urban and rural).


Federal Democratic Republic of Ethiopia 82


FIGURE 24

Clean/renewable
power generation1

TWh

Electric power – Estimation of changes with time of the main
emission drivers

Output overview

2030

9.8

2020

8.5

2010

8.5

Key emissions
drivers Projected change with time Rationale

98

62

8

203020202010

1 Total generation (before T&D losses)
2 Includes rural domestic off-grid fossil fuel based generation

Conventional
power generation1

(fossil fuel)2

TWh

▪ Includes all on-grid power
generation and rural
residential off-grid fossil fuel
based generation (in
conventional power
generation)

▪Current and future
generation capacity
according to EEPCo
generation capacity
masterplan and
economically viable
potential

▪EEPCo: Plan to switch off
diesel generation capacity
(grid/off-grid) after 2014


GHG emissions baseline and BAU projection for 2030

The Electric Power sector as defined in this strategy currently emits around 3 Mt
CO2e per year, mainly caused by electricity production from fossil fuel facilities.
In the BAU scenario, annual emissions will stay at around 3 Mt CO2e, as the
EEPCo conventional power generation is switched off, but rural households with
increasing population continue to use fossil-fuel-based generation (Figure 25).
Current emission estimates are based on EEPCo’s generation and consumption
data (which includes grid and off-grid diesel generators) as well as an assessment
of existing standby diesel generators and off-grid rural residential fossil-fuel-based
generation. The emissions are calculated with the general emission factors of
diesel power generation provided by EEPCo and adjusted against international
benchmarks.


Federal Democratic Republic of Ethiopia 83


FIGURE 25

Electric power – Current level of GHG emissions of 3 Mt
CO2e is projected to stay at a similar level up to 2030

Mt CO2e/ year

3

Electric power generation
(all on-grid electric power
generation and rural residential
off-grid fossil fuel based generation)

BAU
2030

31

BAU
2020

21

Baseline
2010

BAU emissions estimations

1 Not including emissions from up to 450 MW cogeneration capacity from coal plants (fertilizer production, emissions from coal consumption accounted
for in Industry emissions)


ABATEMENT LEVERS – POTENTIAL AND COST CURVE

By 2030 (and even before), the Electric Power sector could even have a negative
net contribution to GHG emissions. The surplus power supply could be exported,
not only generating income for Ethiopia but also helping neighbouring countries to
reduce emissions from conventional power generation. The opportunity to reduce
neighbouring countries’ emissions by substituting their electric power generation
from fossil fuel with clean electric power generated in Ethiopia represents the
single most important abatement lever for the Ethiopian Electric Power sector.13

Figure 26 shows the resulting GHG abatement from capturing the full export
potential, which will be around 19 Mt CO2e in 2030 – one of the largest individual
abatement levers across all sectors. This export potential is calculated assuming
full capture of energy efficiency levers identified by the STC.


13 The construction of the necessary supply capacity of clean and renewable electric power – although a
challenging task in itself – is not counted as an abatement lever in this regard because it is already part of
Ethiopia’s business-as-usual scenario as it is economically the most appropriate option for the country’s
power sector development (as well as being ecologically sound).


Federal Democratic Republic of Ethiopia 84


As there is only one lever that has been identified in the Electric Power sector, the
estimated abatement costs from electric power exports are negative. This indicates
that each metric tonne of CO2e abatement realised through electric power exports
reducing emissions in neighbouring countries will have a net benefit of around
5 USD for Ethiopia. This figure does not include any potential revenues from
climate funds that might be paid for the reduction of emissions in importing
countries (potentially indirectly through benefit-sharing agreements with importers
or rent-capturing by increased export tariffs).

The total investment cost that is required for the power generation capacity build-
up has been calculated based on average unit capital cost (around 1,100 USD/kW
for hydro, 2,000 USD/kW for wind, and 4,600 USD/kW for geothermal) and on
transmission and distribution infrastructure costs. The total sum amounts to
USD 38 billion up to 2030. A more detailed account of the investment cost and
potential financing options is given in the deep-dive analysis on power financing.

FIGURE 26

19 Power exports1

Mt CO2e reduction
potential in 2030

BAU After abatement

Electric power – Average abatement potential is estimated
to be around 19 Mt CO2e p.a.

1 Emission reductions will occur outside of Ethiopia

Abatement measures1

Mt CO2e/ year in 2030
BAU and abatement potential
Mt CO2e/ year

BAU emissions: 3 Mt in 2030

-30

-25

-20

-15

-10

-5

0

5

20302025202020152010


Federal Democratic Republic of Ethiopia 85


Electric power lever 1 – Electric power exports

The opportunity of power exports faces some constraints. As shown in Figure 27,
which is based on data provided and analysed by EEPCo and regional power
organisations, the total potential for power exports has been analysed for the
period 2010 to 2030 against three constraints:

■ Domestic export capacity, which is determined by the surplus of electric
power generation capacity over domestic demand (net of energy efficiency).
In the period 2020 to 2030, the need to satisfy exponentially growing
domestic demand will impose some constraints on the volume of possible
exports.

■ Interconnection capacity, reflecting the technical limits of the cross-border
transmission line capacity to regional markets. The construction of intercon-
nection capacity will impose a constraint on power exports up to around
2020.

■ Foreign demand, which is determined by planned imports of neighbouring
countries, any still existing electric power generation deficit, and the amount
of conventional electric power generation that comes at generation costs that
are higher than the potential cost for imports of Ethiopian electric power. This
theoretical foreign demand is not expected to represent a binding constraint at
any point in time.

The abatement potential is calculated from the export potential and the average
GHG emissions from conventional power generation in neighbouring countries.
The average carbon intensity of avoided capacity is assumed to be 0.7 kg
CO2e/kWh (based on the average carbon intensity of electricity generation in
neighbouring countries). An average export potential of nearly 25 TWh p.a.
between 2011 and 2030 results in an annual abatement potential of 17 Mt CO2e on
average, while the 2030 export potential of nearly 28 TWh results in the abatement
potential of around 19 Mt CO2e.

The abatement cost is composed of average generation cost (0.051 USD/kWh,
taking account of the production-capacity-weighted average of hydro, wind, and
geothermal) plus average transmission cost (0.006 USD/kWh, based on the East-
ern Nile Power Trade Programme study and Ethio-Kenya interconnection study)
as positive costs as well as export income (0.06 USD/kWh) as negative cost. The
export price (i.e., income to Ethiopia from electric power exports) is conserva-
tively assumed to be relatively low in order to make it attractive to markets in the
region, which are currently producing electric power at an average cost of 0.06 to
0.16 USD/kWh.


Federal Democratic Republic of Ethiopia 86


FIGURE 27

Total electric power exports determined by three constraints
– Average abatement potential is 17 Mt CO2e p.a.

Export potential and CO2e abatement

Average annual export potential (TWh)
Average annual abatement potential (Mt CO2e)

Source: EEPCO; EAPP

1 Assuming avg. carbon intensity of avoided capacity to be 0.7 kg CO2e/kWh

343935

6

4444
28

3

151

109

85

63

2021-20252016-20202011-2015 2026-2030

Export capacity

Interconnection
capacity

Foreign demandAbatement potential p.a.1 Binding constraint

Interconnection
constrained

Domestic surplus
constrained

2 19 27 23


ABATEMENT LEVERS – FEASIBILITY AND ECONOMIC IMPACT
ASSESSMENT

In general, capturing the impact of power exports appears to be a viable lever.
With regard to technical feasibility, the build-up of the power generation potential
should not represent a major barrier. Ethiopia is already running major power gen-
eration infrastructure sites of high capacity and is currently developing more. As
far as the technical potential of the country and the build-up capacity of EEPCo are
concerned, it remains to be seen whether the projected pace can be realised. The
transmission lines required for exports are currently planned or are already under
construction and do not represent a particular technical challenge. However,
EEPCo needs to implement rigorous demand- and supply-side management and
particularly a peak-capacity planning system to avoid domestic instability of
supply when exporting surplus power.

On balance, financing the projected electric power generation scale-up has so far
proved to be very challenging and potentially presents the most significant barrier
to be overcome in realising the ambitious plans for the Electric Power sector. A
finance gap of around USD 20 billion has been projected (given that current


Federal Democratic Republic of Ethiopia 87


financing can be extended) – resulting in a gap of around USD 1 billion per year
on average. For this reason, a dedicated detailed analysis has been conducted on
this issue, a summary of which can be found in the main part of this report.

Political willingness of neighbouring countries to support Ethiopia’s electric
power scale-up or to trade power could remain a question mark. At minimum, an
effort is needed to align important regional players behind Ethiopia’s plans and
win their support. However, political unpredictability might pose a potential
challenge –particularly to long-term power purchase agreements.

Yet, although question marks remain, there are no overt barriers to exporting sur-
plus power. The scope of Ethiopia’s future power growth plans goes beyond past
experience, and the actual demand from neighbouring countries as well as the out-
come of negotiations remains to be seen. Nevertheless, EEPCo has proved its
institutional ability to accomplish major generation capacity extensions. Moreover,
hydropower generation is not only clean but also affordable in the regional
context, and the forum and institutions for implementing regional power trade do
already exist, e.g., in the form of the East African Power Pool.14 Since the question
of how Ethiopia can monetise the GHG mitigation as done in other countries is
still open, a suitable mechanism needs to be drafted (e.g., benefit-sharing agree-
ment).

With regard to its socio-economic impact, the export of power to neighbouring
countries – and, more broadly, the build-up of power generation capacity – would
have a significant additional positive impact on our economic development plans.
Electric power exports would not only directly increase Ethiopia’s exports and
generate additional foreign income, they would also contribute to the economic
viability of the plans to build power generation capacity, hence helping to build up
(and eventually finance) the power generation potential, increase employment, and
contribute to GDP growth. Besides, such exports might also increase public
finance directly via increased tax revenue as well as indirectly via revenue
increases for publicly owned EEPCo. However, there might be adverse environ-
mental and social impacts, e.g., population displacement, which need to be evalu-
ated and properly addressed through rigorous assessment of the environmental and
social impacts. The usage of land and natural resources needs to be subject to an
integrated land planning effort to determine the best of alternative land uses. Also,
the implementation of an integrated catchment management system is necessary to


14 There are very positive signs, e.g., an agreement on the Ethio-Sudan export line seems very likely as the
line is already under construction by both parties to the arrangement.


Federal Democratic Republic of Ethiopia 88


prevent adverse effects to the generation potential from hydropower, e.g., by
sedimentation of hydropower facilities.

ABATEMENT LEVERS – IMPLEMENTATION TIMELINE AND RESOURCE
REQUIREMENTS

Implementation timeline

As described above, the export of power from Ethiopia to its neighbouring coun-
tries and the wider region is bound by three constraints: surplus generation capac-
ity, interconnection capacity, and demand. Since it has been evaluated that inter-
connection capacity and surplus generation capacity will constitute the binding
constraints in earlier and later years respectively, the implementation of power
exports very much depends on the construction and opening of interconnections to
neighbouring countries and regional markets. Figure 28 depicts the three important
interconnectors. The Djibouti interconnector is already in operation and used for
power supply to the neighbour. The Ethio-Sudan-Egypt as well as the Ethio-Kenya
interconnectors will follow and start operations in the coming years so that power
exports to these countries can be started as soon as a surplus of power is generated
and contracts are negotiated.

Resource requirements and existing projects

While the generation and transmission of electric power for export purposes will
require a significant investment, the generated export price is envisaged to
overcompensate that expenditure and lead to a net income. While this might not
materialise in the short term, the long-run projection (i.e., up to 2030) is that the
electric power exports will have a positive net contribution of around USD 1.8
billion (Figure 29).

The total capital expenditure for power generation and transmission infrastructure
to support this initiative has been calculated to be more than USD 37.5 billion.
This capital expenditure (CAPEX) will be utilized for all power generation and
transmission capacity, i.e., it will also contribute to satisfying the growing
domestic demand. As stated above, financing this high required capital
expenditure is envisaged to constitute one of the major challenges for the Power
sector expansion.


Federal Democratic Republic of Ethiopia 89


FIGURE 28

Electric power – Overview of timeline for implementation of
initiatives

2012 2013 2014 2015 2016 2017

Ethio-Kenya
interconnector

(1000 MW)

Djibouti
interconnector

(150 MW)

Ethio-Sudan-Egypt
interconnector

(200 MW; up to 3400 MW from 2018)

Activity

Electric
power
exports

Selected as priority initiative


FIGURE 29

Electric power – Financial overview of power exports

Million USD
Short-term: 2011-2015 Long-term 2011-2030

-13,710

-10

500

0

510

0

1 Full capital expenditure, not amortised
2 Aggregated abatement potential; expenditure per t CO2e not equivalent to abatement cost in cost curve, as the CAPEX abatement expenditure is not

annualised via amortisation (rather: cash-flow perspective)

Mt CO2e total aggregated abatement potential

CAPEX1 additional

OPEX additional
(cost of power generation
and export)

Programme cost

Income from power export

Abatement expenditure
before carbon revenue

62

0

-37,540

1,810

29,580

0

27,770

3452
Mt CO2e
abatement
potential

For comparison:
Underlying CAPEX


Federal Democratic Republic of Ethiopia 90


Due to high upfront expenditure and attractive returns, power exports will pay
back, but only in the long run. Hence, this expenditure requires long-term financ-
ing (Figure 20).

The implementation of the power generation and transmission capacity expansion
is led by the MoWE, the EEA, and EEPCo. Strategic plans for the expansion exist,
and these institutions also carry the overall responsibility for their implementation.

FIGURE 30

Electric power – Electricity exports will have a positive
return

Category C
No positive return,
grants/performance

pay requirement

0

Category B
Positive return, but
long term financing

requirement

27,770

Category A
Positive return,

short term financing
requirement

0

Million USD, total cost1 Percent of total cost

0% 100% 0%

1 Including additional CAPEX, additional OPEX, and programme cost
2 NPV calculated with 6% discount rate

Definition Positive NPV2 of first
five years of cash flow
(from start of initiative)

Positive NPV2 of
overall initiative (from
start of initiative up to
2030), but not after first
five years

Negative NPV2 of
overall initiative (from
start of initiative up to
2030)


Federal Democratic Republic of Ethiopia 91


Green Cities and Buildings

Under the BAU scenario, emissions from cities will increase from 4.7 Mt CO2e in
2010 to 10.2 Mt CO2e 2030. Adopting new technologies in lighting and waste
management offers an abatement potential of up to 6.9 Mt CO2e in 2030. The
major initiatives proposed by the STC are: reduction of electricity demand through
efficient lighting, improved landfill gas management (capture gas for flaring), and
liquid waste emissions management (capture gas for flaring). Of these three initia-
tives, efficient lighting has the largest abatement potential: 5.1 Mt CO2e in 2030.

SCOPE AND INSTITUTIONAL SETUP

The Green Cities and Buildings sector covers emissions from three primary cate-
gories: solid waste, liquid waste, and off-grid fossil fuel use (e.g., kerosene lamps,
diesel generators, construction vehicles). Other sources of emissions in cities (e.g.,
transport, industry, grid electricity) are accounted for in other STCs. The Green
Cities and Buildings STC (Table 7) calculated current and future emissions and
analysed three abatement levers. The STC is composed of members from the
Ministry of Urban Development and Construction and the Environmental Protec-
tion Agency.

TABLE 7


Federal Democratic Republic of Ethiopia 92


GHG EMISSIONS BASELINE IN 2010 AND BAU UP TO 2030

Emissions in the scope of the Green Cities and Buildings sector are split evenly
between solid waste, liquid waste, and off-grid fossil fuel energy. Under the BAU
scenario, emissions from cities will increase from 4.7 Mt CO2e in 2010 to 10.2 Mt
CO2e in 2030 (Figure 31).

Main drivers of GHG emissions

The main drivers of GHG emissions in the Green Cities and Buildings sector as
well as the main assumptions about their impact and development with time are
detailed below (Figure 32)

■ Increase in urban population. The urban population will grow rapidly over
the next 20 years. This growth will be driven by a high fertility rate and
strong rural to urban migration. Together these trends produce an annual
urban population growth rate of 4.41% between 2010 and 2030 according to
CSA. This will result in the urban population growing from 13.5 million peo-
ple in 2010 to 32 million people in 2030. Growth of the urban population
drives emissions as the expanding population generates more waste and con-
sumes more energy; also because per capita solid waste generation and energy
consumption rates of urban populations exceed waste generation and energy
consumption of rural populations.

■ Increase in number of towns and cities. The same forces driving the expan-
sion in population will also increase the number of population centres catego-
rised as towns and cities. The number of urban centres with at least 20,000
people will increase from 86 in 2010 to 237 in 2030, according to the STC
analysis of data from CSA and the MoUDC. The transition of small
population centres into larger towns and cities – as indicated above – drives
emissions through the higher per capita solid waste generation and energy
consumption rates of populations in larger towns and cities.

■ Increase in per capita GDP. Per capita GDP is projected to grow at around
8.5% annually between 2010 and 2030, reaching USD 1,846 in 2030 from a
base of USD 380 in 2010 based on the GTP and STC analyses. The increase
in per capita GDP drives emissions due to the higher rates of energy con-
sumption and solid waste generation associated with higher per capita GDP.


Federal Democratic Republic of Ethiopia 93


FIGURE 31

Green cities – In a business-as-usual scenario, emissions
will more than double to 10.2 Mt CO2e per year until 2030

BAU emissions estimations

Mt CO2e/ year

2.1

2010

4.7

1.9

1.6

1.2

Off-grid energy

Liquid waste

Solid waste

2030

10.21

2.7

3.9

3.7

2020

6.9

2.3

2.5

1 Subcomponents do not add up due to rounding up


FIGURE 32

Source: CSA 2010; MoUDC

Output overview

2030

32.0

2020

21.0

2010

13.5

852
378

2030

1,846

20202010

Per capita GDP
(USD/person)

Key emissions drivers

Urban population
(pop. in millions)

Projected change Rationale

▪ Larger population
creates more waste
and consumes more
energy

▪ Growing per capita
GDP increases per
capita solid waste
generation and per
capita energy
consumption

Green cities – Estimation of changes with time of the main
emission drivers

Source

▪ CSA

▪ GTP growth rate
extrapolated to
2030

237

141
86

203020202010

Towns and cities
(number of towns and
cities with at least
20,000 inhabitants)

▪ Higher per capita
waste generation
and energy
consumption of
urban population

▪ CSA
▪ MoUDC


Federal Democratic Republic of Ethiopia 94


GHG emissions baseline and BAU projection for 2030

The emissions estimated by the Green Cities and Buildings STC will increase from
4.7 Mt CO2e in 2010 to 10.2 Mt CO2e in 2030 (see Figure 31), driven equally by
emissions from solid waste, liquid waste, and use of off-grid fossil fuel energy.

■ Solid waste. As the population grows and per capita GDP increases, the rate
of per capita solid waste generation will increase from 0.33 kg/person/day in
2010 to 0.44 kg/person/day in 2030, based on the 2009 Ethiopia Solid Waste
Study and IPCC benchmarks for waste generation in sub-Saharan Africa. This
will result in the generation of 1.5 million tonnes of solid waste annually in
urban areas by 2030. Emissions from solid waste will consequently grow
from 1.2 Mt CO2e in 2010 to 3.7 Mt CO2e in 2030.

■ Liquid waste. Although per capita liquid waste generation is projected to
remain constant between 2010 and 2030, the expansion of the urban popula-
tion will cause an increase in total liquid waste. Emissions from urban liquid
waste will rise from 1.6 Mt CO2e in 2010 to 3.9 Mt CO2e in 2030.

■ Off-grid fossil fuel. The STC projects an urban off-grid fossil fuel use growth
rate of 1.7% between 2010 and 2030 based on statistics from EEPCo and the
Ethiopian Forestry Action Programme. This rate is significantly lower than
the urban population growth rate due to the expected substitution of off-grid
diesel generators with renewable electricity from the grid. Consequently, the
increase in off-grid fossil fuel use will be driven primarily by kerosene for
lamps and gas for cooking. Emissions from off-grid fossil fuel are expected to
increase from 1.9 Mt CO2e in 2010 to 2.7 Mt CO2e in 2030. Here it is impor-
tant to note that the Electric Power and Industry STCs have accounted for the
emissions from electricity generated by fossil fuels and distributed via electric
power grids as well as the emissions from fossil fuel used by the industry.

ABATEMENT LEVERS – POTENTIAL

The Green Cities and Buildings sector includes three abatement levers: reduction
of electricity demand through efficient lighting, improved landfill gas management
(capture gas for flaring), and liquid waste emissions management (capture gas for
flaring). In total, an abatement potential of up to 6.9 Mt CO2e in 2030 has been
identified (Figure 33). Raising public awareness and encouraging public participa-
tion are crucial in realizing this abatement potential.

■ Reducing electricity demand through efficient lighting in the urban resi-
dential and commercial sectors would free up power supply capacity and
enable the export of more electricity to neighbouring countries, where Ethio-


Federal Democratic Republic of Ethiopia 95


pia’s renewable electricity would displace electricity generated from fossil
fuels. This initiative has an abatement potential of approximately 5.1 Mt
CO2e, and is the largest abatement lever in the Green Cities and Buildings
sector. Because the emissions reduction takes place through increased power
exports, the abatement potential from this initiative is accounted for primarily
by the Electric Power STC.

■ Landfill gas management can be improved through the capture of gas for
flaring, thus reducing the amount of landfill greenhouse gases that are to be
released into the atmosphere. This initiative, which is to be implemented at
landfills in towns and cities larger than 20,000 inhabitants, has an abatement
potential of 0.9 Mt CO2e in 2030.

■ Emissions from liquid waste can be captured and used for flaring. Imple-
menting this technology would nearly eliminate emissions from liquid waste
at waste disposal lagoons. If implemented in cities with a population larger
than 100,000, this lever has an abatement potential of 0.9 Mt CO2e in 2030.

FIGURE 33

Abatement measures
Mt CO2e/ year in 2030

BAU and abatement potential
Mt CO2e/ year

0.9 Landfill gas management

Green cities – Abatement and sequestration potential
reaches 1.9 Mt CO2e per year in 2030

0

2

4

6

8

10

12

2015 203020252020

0.9 Liquid waste emissions
reduction

ΣΣΣΣ
1.8

BAU emissions: 10.2 Mt

Green Cities CO2 abatement
accounted for by Power STC

5.1 Efficient lighting


Federal Democratic Republic of Ethiopia 96


Green Cities and Buildings lever 1 – High-efficiency
lighting (residential and commercial/institutional)

Improvements in light bulb technology have greatly increased the efficiency of
bulbs while simultaneously lowering the lifetime costs of bulbs. As many coun-
tries are already doing, it is now possible to completely transition from incandes-
cent bulbs to compact florescent lights (CFLs) in the residential sector, and from a
mix of low-efficiency bulbs (e.g., incandescent, conventional florescent) to higher-
efficiency bulbs (e.g., light-emitting diodes (LEDs), high-efficiency fluorescents)
in commercial and institutional buildings. This initiative builds on the existing
activities related to the promotion of high-efficiency light bulbs (e.g., EEPCo’s
bulb exchange campaign). Although the Green Cities and Buildings STC has only
included urban areas in its analysis, this initiative could be expanded to rural areas.

By switching to higher-efficiency bulbs, buildings could achieve the same level of
lighting while greatly reducing electricity consumption. The abatement potential
of this initiative in 2030 was calculated to be approximately 5.1 Mt CO2e. These
abatement potential calculations are based on the following data and assumptions:

■ Demand for lighting. The STC estimates annual electricity consumption for
lighting in 2030 of 8.3 TWh in residential buildings and 2.0 TWh in commer-
cial/institutional buildings. The STC estimates an average of 1.5 bulbs per
room, and an average bulb use of 3.5 hours per 24 hours based on UNFCCC
benchmarks.

■ Lighting efficiency. Based on international lighting benchmarks, the STC
estimates an efficiency improvement of 77% by switching from incandescent
bulbs to compact florescent lights (CFL), and an efficiency improvement of
60% by switching from the current inefficient mix of bulbs in commercial/
institutional buildings to high-efficiency bulbs (e.g., LED, high-efficiency
fluorescents). These efficiency improvements would result in annual electric-
ity savings of 6.4 TWh in the residential sector and 0.9 TWh in the commer-
cial/institutional sector by 2030.

■ Penetration. The STC proposes a programme start date of 2012, building on
EEPCo’s ongoing promotion of efficient light bulbs. The STC assumed 100%
CFL usage in the residential sector by 2030, and 75% efficient light technol-
ogy adoption in the commercial/institutional sector by 2030, although a more
rapid transition could be possible with appropriate policy support (e.g., with
banning sales of conventional bulbs).

■ Emissions reduction from electricity export. By exporting more power to
neighbouring countries, electricity generated from fossil fuels would be dis-


Federal Democratic Republic of Ethiopia 97


placed by Ethiopia’s renewable energy. The STC used an estimate of 0.7 kg
CO2e/KWh exported, based on the Electric Power STC’s estimates of the
carbon intensity of energy produced in neighbouring countries.

Green Cities and Buildings lever 2 – Improved landfill
gas management (flaring)

Ethiopia has ambitious landfill construction goals that would bring improved
municipal solid waste services to 364 towns and cities by 2015, as stated in the
GTP. Implementing improved landfill gas management techniques such as flaring
would reduce the amount of greenhouse gases released into the atmosphere.
Implementing these technologies has an annual abatement potential of 0.9 Mt
CO2e by 2030.

■ Programme reach. Based on the scale of similar projects implemented in
sub-Saharan Africa, the STC proposes that landfill gas flaring be imple-
mented for all cities with a population of at least 20,000 people. The STC
proposes to phase in the technology beginning in 2014 with 13% of towns
and cities (17) and gradually expanding to 100% of towns and cities by 2030
(237 total sites).

■ Waste collection rates. The STC estimates that 40% of solid waste is depos-
ited at landfills in cities with populations from 20,000 to 100,000 people, and
that 70% is deposited at cities with over 100,000 people based on the Ethiopia
Solid Waste Study (2009).

■ Technical details. The STC assumes a gas capture rate of 60% based on the
Australia Landfill Status Report (2009), and 0.756 kg CO2e per kg of waste
based on IPCC benchmarks for sub-Saharan Africa and STC analysis. Emis-
sions from the CO2 released during gas flaring/combustion are disregarded in
accordance with IPCC methodological guidelines for solid waste.

Green Cities and Buildings levers 3 – Liquid waste
emissions management (flaring)

Liquid waste is a major source of emissions within the scope of the Green Cities
and Buildings sector, but technologies exist that enable very high capture rates of
gas (CH4 and CO2) from liquid waste lagoons. This initiative would implement
improved liquid waste management from waste lagoons used by networked
municipal sanitation systems and vacuum trucks servicing non-networked sanita-
tion facilities (e.g., pit latrines). This abatement lever has an annual abatement
potential of 0.9 Mt CO2e in 2030. The abatement potential of this lever was esti-
mated using the following calculations:


Federal Democratic Republic of Ethiopia 98


■ Programme reach. Based on the scale of similar projects implemented in
sub-Saharan Africa, the STC proposes that liquid waste gas capture be
implemented in all cities with a population greater than 100,000. The STC
proposes a phase-in of the technology beginning in 2014 with 23% of cities
(3 sites) and gradually expanding to 100% of cities by 2030 (34 total sites).

■ Waste collection rate. The STC estimates that 60% of liquid waste in cities
will be deposited in lagoons during the period of 2010-2030 based on
MoUDC statistics.

■ Technical details. The STC assumes a gas capture rate of 90% based on the
IPCC default value for liquid waste gas capture, and a carbon intensity of
0.121 Mt CO2e/year from liquid waste generated by one million people based
on IPCC default values for liquid waste emissions and its own analysis. Emis-
sions from the CO2 released during gas flaring/combustion are disregarded in
accordance with IPCC methodological guidelines for liquid waste.

ABATEMENT LEVERS – FEASIBILITY AND ECONOMIC IMPACT
ASSESSMENT

The three abatement levers in the Green Cities and Buildings sector have moderate
to high feasibility, but only one – efficient lighting – also has high impact. The
ultimate attractiveness of each initiative will depend on its implementation cost.

Feasible levers with high impact

The efficient lighting initiative offers both a large impact (5.1 Mt CO2e in 2030)
and relatively straightforward and feasible implementation. The technology for
this lever (efficient bulbs) already exists and is in use in Ethiopia, the cost of effi-
cient bulbs continues to fall, and the steps needed for implementation (e.g., regula-
tion of bulb imports to prohibit inefficient bulbs) are relatively easy to enact.
Furthermore, this initiative has significant financial benefits to consumers
(building owners, tenants, and households), since most types of efficient bulbs
have a lower total cost of use than inefficient bulbs, i.e., the higher purchase price
of efficient bulbs is more than outweighed by their lower operating cost (lower
electricity consumption) and longer lifespan. With all of this in mind, the STC
recommends prioritisation of this highly attractive initiative.

Other levers

Although they have a smaller abatement potential (combined total of 1.8 Mt CO2e
in 2030), the two waste-related levers investigated by the STC are also attractive


Federal Democratic Republic of Ethiopia 99


and should be pursued. Their benefit is magnified by their socio-economic benefits
(improved safety at landfills, reduced air pollution, etc.). Depending on the avail-
ability of technology, efficient waste management could be complemented by
electricity generation techniques that would provide further benefits for the green
economy.

In addition to these levers, the STC also conducted a preliminary assessment of
other promising levers that should be further investigated: recycling raw materials
(e.g., metal, paper, plastic) and separation of waste; urban greenery and integrated
infrastructure planning; energy-efficient design of new buildings; reuse, compost-
ing, and biogas generation from liquid and solid waste; and a shift to high-effi-
ciency appliances.

ABATEMENT LEVERS – IMPLEMENTATION TIMELINE AND RESOURCE
REQUIREMENTS

Implementation timeline

The Green Cities and Buildings STC has selected high-efficient lighting as a pri-
ority initiative based on the large abatement potential and the positive outcome of
the feasibility assessment. This initiative will receive particular attention and,
building on EEPCo’s ongoing promotion of efficient light bulbs, the STC has
determined a programme start date within 2012 (Figure 34).

The implementation of the two other levers, improved landfill gas management
and liquid waste emissions management, will start in 2014. Based on the ambitious
GTP goal to bring improved municipal solid waste services to 364 towns and cities
by 2015, solid waste technology will be phased in for 13% of towns and cities in
2014 and gradually expanded until 2030. Liquid waste technology will also start in
2014 with 23% of the cities and expanding to 100% of cities by 2030. It is
important to mention that these dates mark the start of the implementation, which
for some initiatives is staged across several years, includes some required
preparatory work (e.g., development of investment plans), and is subject to
approval by the respective authorities. Hence, the full impact of the initiatives will
only occurs later in most cases.

In addition, several other levers with limited abatement potential − such as
recycling or energy-efficient design of buildings − could be considered.


Federal Democratic Republic of Ethiopia 100


FIGURE 34

Green cities – Overview of timeline for implementation of
initiatives

2012 2013 2014

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

High-efficient lighting

Improved landfill gas
management

Other levers currently
under consideration

Activity

Liquid waste emissions
management

Selected as priority initiative

Other initiatives


Resource requirements and existing projects

The cost and resource requirements of the Green Cities and Buildings abatement
levers have not yet been estimated. However, consultations have indicated that the
implementation of the suggested abatement levers will be an attractive investment.

There are several initiatives already in implementation status that are interlinked
with the work of the Green Cities and Buildings STC. A Green City Strategy is
under development that addresses many of the STC’s propositions. Furthermore,
work has been conducted to develop a CDM for solid waste management. In
addition, several Nationally Appropriate Mitigation Actions (NAMAs) on waste
management have been developed by the Ethiopian government.


Federal Democratic Republic of Ethiopia 101


Forestry

The Forestry sector is a significant contributor of GHG emissions, but it also offers
a high abatement potential that even surpasses the estimated increase in emissions
by 2030.

SCOPE AND INSTITUTIONAL SETUP

The Forestry STC (Table 8) calculated current and future emissions and analysed
abatement levers for several segments of the Forestry sector. Team members were
sector experts from the Environmental Protection Authority, the Ministry of
Agriculture, as well as the Forestry Research Centre.

TABLE 8


Before addressing the business-as-usual (BAU) calculations and abatement
potential, however, it is important to reiterate that these estimates have a degree of
uncertainty, given the current lack of reliable and consistent data on land use and
projected carbon stocks. Best available data have been used and improving the
quality of these data will be part of the suggested strategy.

GHG EMISSIONS BASELINE IN 2010 AND BAU UP TO 2030

Emissions from the Forestry sector are mainly caused by human beings, and are
driven by deforestation for agriculture and forest degradation from fuelwood con-
sumption and logging. Under the BAU scenario, emissions from forestry will
increase from 53 Mt CO2e in 2010 to 88 Mt CO2e in 2030 (Figure 35).


Federal Democratic Republic of Ethiopia 102


FIGURE 35

Forestry – Level of GHG emissions will be increasing by more
than 50% up to 2030 under a business-as-usual scenario

Output overview

Mt CO2e/ year

37

31

1

1 1

Deforestation for
agricultural land

Baseline
2010

53

26

24

Degradation
from fuelwood
consumption

Formal logging

Informal logging

Business-as-usual
2030

88

44

41

1 2

Business-as-usual
2020

71
2


Main drivers of GHG emissions

The main drivers of GHG emissions as well as their assumed impacts are mainly
the increase in cropland and the increase in the cutting of fuelwood to meet the
needs of a growing population, as detailed below (Figure 36).

■ Deforestation for agricultural land. Deforestation rates in Ethiopia histori-
cally correlate with the expansion of agricultural land. Based on STC calcula-
tions for soil-based emissions, total cropland is projected to gradually reach
27 million hectares by 2030, with an annual BAU growth rate of 3.9%
between 2010 and 2030. This is the land expansion needed for the crop
growth target of 9.5% p.a. in the GTP, which is essential to ensure food secu-
rity and poverty alleviation in the face of demographic pressure. This 3.9%
p.a. growth rate in agricultural land will be necessary to reach the 9.5% target,
assuming that Ethiopia makes no significant progress in increasing crop yield
and the value of yield beyond historically observed rates of improvement.15
As a consequence, the annual amount of land taken from forests for
agriculture will need to increase gradually over the next 20 years, which will
lead to a higher deforestation rate and more CO2 emissions. Without any


15 Which are discounted to reflect the impact of major investments that were necessary to achieve them.


Federal Democratic Republic of Ethiopia 103


additional intervention, this agricultural expansion will affect the high
woodland more than in the past, while the high forests will be less affected.16
However, it has been assumed that the proportion of new land for agriculture
that is taken from forests will decrease from 70% to 55% (of the total new
land for agriculture) in 2030, also as a result of current government policies,
which are assumed to continue under the BAU scenario. As its main input
sources, the Forestry STC used the GTP, the WBISPP report, and CSA
cropland data as well as IPCC guidelines and benchmarks.

FIGURE 36

Population
Million people

Forestry – Estimation of changes with time of the main
emission drivers

Output overview

2030

27.0

2020

18.5

2011

12.6

Key emissions
drivers Projected change with time Rationale

134
103

80

2020 20302011

▪ Average growth of cropland
estimated to reach 3.9% p.a.,
enabling it to reach GTP targets
if no significant investment is
made to find new ways to
increase agricultural yields

Increase in cropland
area
Million hectares

▪ Growth based on CSA
projections, 2010-2030; average
growth rate 2.62%

▪ Population is direct driver of
fuelwood consumption and
logging (formal and informal)

5562
69

203020202011

▪ Average 2000-08 share of
agricultural land taken from
forest areas, assumed to
decrease under government
policies in BAU (Sources:
WBISPP and CSA)

Share taken from
forests
Percent of new land


■ Degradation from fuelwood consumption. Ethiopia’s rural energy needs are
predominately satisfied by biomass (>90%). This includes traditional energy
sources such as fuelwood, charcoal, and branches, leaves, and twigs. The
development of fuelwood consumption is primarily influenced by population
increase, unless a significant change in the energy mix takes place. The main
sources used for projections were the WBISPP report (on current levels of
degradation due to fuelwood consumption) and CSA population forecasts
used for projecting future fuelwood demand.


16 Assumption made by STC to reflect new government policies.


Federal Democratic Republic of Ethiopia 104


■ Logging. Authorised and unauthorised logging is currently a relatively minor
driver of forest degradation. The STC used the 2010 FAO report that esti-
mates the total amount of industrial logging (authorised) as well as the
research work by Demel Teketay from 2002 that details unauthorised as com-
pared with authorised logging volumes. To project the BAU development, the
STC assumes that logging will increase on average at the same rate as popu-
lation growth (2.6 % per year), reflecting the increasing demographic pressure
on forest resources and experiences made in other developing economies.

GHG emissions baseline and BAU projection for 2030

The increase of emissions to 88 Mt CO2e in 2030 (see Figure 37) will mainly be
driven by deforestation for agricultural use and degradation from fuelwood con-
sumption.

■ Deforestation for agricultural land. Due to a growing need for agricultural
land fuelled by demographic pressure and development needs as described
above, the deforestation rate will progressively increase from around 280,000
hectares in 2010 to around 550,000 hectares in 2030. Emissions will go up
from 26 Mt CO2e in 2010 to 44 Mt CO2e in 2030.

■ Degradation from fuelwood consumption. In line with population growth,
the total amount of woody biomass degradation is projected to increase from
around 14 million tonnes in 2010 to 23 million tonnes in 2030. This will lead
to a rise in GHG emissions from 24 Mt CO2e to 41 Mt CO2e in 2030.

■ Logging. Formal and informal logging has been projected to undergo a simi-
lar growth (i.e., following the needs of a growing population), increasing
GHG emissions from around 2 Mt CO2e in 2010 to 3.5 Mt CO2e in 2030.

ABATEMENT LEVERS – POTENTIAL AND COST CURVE

Thanks to levers such as afforestation and reforestation, the Forestry sector boasts
an abatement potential even higher than the projected increase in emissions under
the BAU scenario. In total, nine levers have been identified with an abatement
potential of up to 131 Mt CO2e (Figure 37). These levers are clustered into three
groups:

■ Reduced deforestation. This includes lowering the pressure that the need for
agricultural land exerts on existing forests. These levers range from agricul-
tural intensification and preparation of new land by means of small-scale
irrigation to medium- and large-scale irrigation schemes. In total, they
account for an abatement potential of nearly 38 Mt CO2e. Since these levers


Federal Democratic Republic of Ethiopia 105


are mainly related to agricultural practices, a more detailed discussion can be
found in the chapter on Soil in this appendix.

■ Reduced forest degradation. This focuses mainly on reducing the demand
for fuelwood through dissemination of a wide range of efficient cooking and
baking technologies. With a total abatement potential of around 50 Mt CO2e,
this is the largest set of levers identified across all sectors.

■ Increased sequestration: This contains mainly large- and small-scale affore-
station/reforestation/area closures and forest management of woodlands and
forests. Covering an area of 7 million ha in total (by 2030), this set of levers
promises an abatement potential of 42 Mt CO2e. Today, several projects to
increase the forest cover by afforestation or reforestation are already ongoing.
In addition to afforestation/reforestation, sustainable agro-forestry and
protected-area management can provide additional levers to increase
sequestration.

FIGURE 37

Mt CO2e reduction
potential up to 2030

Abatement measures
Mt CO2e/ year in 2030

BAU and abatement potential
Mt CO2e/ year

BAU emissions: 88 Mt

34 Fuelwood efficient stoves

Forestry – Abatement and sequestration potential reaches
131 Mt CO2e per year in 2030

-60

-40

-20

0

20

40

60

80

100

202520202015 2030

1 LPG stoves

2 Biogas stoves

14 Electric stoves

32 Afforestation/reforestation

10 Forest management

27 Agriculture intensification

New land through irrigation11

Σ 131


Federal Democratic Republic of Ethiopia 106


FIGURE 38

Forestry – Most of the abatement potential has a cost below
5 USD/t CO2e, more than half of it has a negative abatement cost

Abatement opportunities cost curve

Abatement
potential
MtCO2e
per year

Reduce deforestation Reduce forest degradation Increase sequestration

Output overview

30

20

10

Irrigation small-scale

-13

Electric stoves

-14

Biogas stoves

-19

Intensification

-21

Fuelwood efficient stoves

-21

5

Afforestation/reforestation

47

Irrigation large-scale

120

LPG stoves

0 10

2

20 30

Forest management

1

40 50 60 70 80 90

Abatement cost
USD per tCO2e

Woodlands management

110 120 130 140100

-20

-10

0

120

40

-30

INCLUDES SOCIETAL COSTS


The cost curve depicted in Figure 38 shows a wide range of abatement costs,
which extend across both the negative and the positive areas.

■ Most of the levers aiming at reducing forest degradation and reducing
deforestation by shifting into more efficient stove technologies and
intensified, i.e., higher-yield, agriculture have a negative societal cost: The
benefits (e.g., reduced costs for purchasing or collecting fuelwood) surpass
the cost of implementing and operating these technologies.17

■ All levers aiming at increasing sequestration have a positive cost. For some
levers, the costs are exceptionally high due to the investments needed for set-
ting them up.

The total investment cost for all levers adds up to about USD 10 billion by 2030.
This includes the initiatives to curtail deforestation, i.e., agricultural intensification
and irrigation (which are discussed in more detail in the chapter on Soil-based
emissions) that require the major part of this investment at over USD 6 billion.


17 One exception to this is LPG stoves, as the technology and fuel cost required by far surpass the savings
from reduced fuelwood consumption. A more detailed evaluation of this can be found in the section “Forest
levers 1-4 – Reduced forest degradation from improved cooking/baking technologies.


Federal Democratic Republic of Ethiopia 107


Climate finance can play an important contributing role if the abatement potential
is appropriately monetised, e.g., in a REDD+ arrangement.

Levers reducing deforestation

Since these levers are mainly related to agricultural practices, they are described in
more detail in the Soil chapter of this appendix.

Forestry levers 1-4 – Reduced forest degradation from
improved cooking/baking technologies

Fuelwood consumption is the main source of GHG emissions in Ethiopia. The
wood is mainly used for residential baking and cooking purposes. As most of the
households, particularly in rural areas, use highly energy-inefficient technologies
(e.g., open fire or three-stone technology), the improvement potential here is huge.
The dissemination of technologies leading to a reduction in fuelwood consump-
tion, either by making more efficient use of it or by shifting to other, less carbon-
intense fuels, can be a major lever for GHG abatement. The STC analysed
different technologies:

■ Fuel-efficient stoves

– Baking stoves, such as the mirt for baking injera bread

– Cooking stoves, such as the tekikil for cooking

■ Fuel-shift stoves

– LPG stoves (mostly for cooking)

– Biogas stoves (mostly for cooking)

– Electric stoves and electric mitad (both cooking and baking – in rural areas
without grid access, this can also include off-grid solar-powered stoves).

The pattern of stove usage varies between regions and according to cooking/
baking traditions. One common feature, however, is that most households need
both a stove for cooking (sauces, coffee, etc.) and a stove for baking (injera).
This is reflected in scale-up plans.

The total abatement potential has been calculated for each stove type based on
the following information:

■ Maximum scale-up. In order to reflect differences in access and cost of
alternative fuels/energy sources, the team distinguished between rural and ur-
ban populations. The rates are based on a projection of GTP plans (particu-


Federal Democratic Republic of Ethiopia 108


larly the National Energy Network sectoral GTP review plan), several expert
discussions, and cross-checks with other countries that have successfully dis-
seminated efficient stoves. For 2030, the following scale-up targets were
estimated (in percentage of rural/urban households respectively):

– Fuelwood-efficient stoves: 80% rural/5% urban (both cooking and baking)

– LPG stoves: 0%/5%

– Biogas stoves: 5%/1%

– Electric stoves: 5%/61% (weighted for cooking and baking).

The distribution between the different types of stoves will be refined during
the phase of work detailing the implementation plan for this initiative.

■ Efficiency improvement. This indicates the percentage of fuelwood that can
be saved by employing different technologies. The calculation is based on
efficiency evaluations and testing data of the Ministry of Water and Energy as
well as donor organisations active in the promotion of efficient stoves (e.g.,
GIZ). The potential savings are as follows:

– Fuelwood-efficient stoves: 50% (average for both cooking and baking)

– LPG stoves: 100% (cooking only)

– Biogas stoves: 100% (cooking only)

– Electric stoves: 100% (cooking and baking).

■ Emissions from alternative fuels. This takes into account the GHG emis-
sions from alternative fuels used to substitute fuelwood.

– LPG stoves: Emission reduction of 89% due to the higher efficiency of
LPG stoves (comparison of fuelwood emissions and LPG emissions based
on IPCC combustion emission factors).

– Fuelwood-efficient biogas and electric stoves: Hardly any emissions
(assuming that electricity will have near zero emissions from 2015
onwards when all electricity in the grid will be from renewable sources).

Introducing efficient stoves has two distinct effects on GHG emissions. First of all,
it reduces forest degradation, with an impact of around 0.9 t biomass/year per
household. Secondly, woody biomass acts as carbon sink, amounting to 2.1 t/year
per household (if it is not burned). The effect of reduced degradation can be
counted in at 100% (resulting in an abatement potential of 1.6 t CO2e/stove/year
under the assumption that reduced consumption first decreases direct degradation
before it affects the carbon sink). The reduction of emissions through the carbon
sink effect does, however, need to be discounted by an adjustment factor to cap the


Federal Democratic Republic of Ethiopia 109


total carbon sink potential of all stoves to the maximum estimated forest
regeneration potential (and the gradual realisation of this potential over time).
Employing this factor yields an additional abatement potential of 0.6 – 1.4 t
CO2e/stove/ year, depending on the stove type.

The total abatement potential of stoves is nearly 51 Mt CO2e in 2030. At 34.3 Mt
CO2e, the scale-up of fuelwood-efficient stoves contributes the largest share of this
total potential, 14.0 Mt CO2e can be achieved from electric stoves, 2.3 Mt CO2e
from biogas stoves, and 0.6 Mt CO2e from LPG stoves.

The abatement cost calculation also differentiates among stove types:

■ Stove cost. Stove cost varies by model and has been calculated using average
prices of different quotes from disseminating agencies (e.g., MoWE, GIZ, and
World Vision). The stove cost is accounted for as a capital expenditure and
amortised over the average period of usage, depending on the model as well
(based on experiences in Ethiopia and other countries). Costs and period of
usage were calculated as follows:

– Fuelwood efficient stoves: USD 6 – 8; with an average durability of 5 years

– LPG stoves: USD 107; average durability 7 years

– Biogas stove (including digester infrastructure): USD 912; average dura-
bility of 20 years (of the expensive and more robust digester infrastructure)

– Electric stove and electric mitad: USD 20 – 63; with an average durability
of 7 years.

■ Programme cost. The team estimated the cost of the programme on a per
stove basis. The calculation includes costs for product development and test-
ing, training of manufacturers, promotion of the technology, administrative
overhead, programme evaluation, and follow-up. The actual costs have been
evaluated based on the experience of implementing organisations such as the
Ministry of Water and Energy and GIZ and set at nearly USD 30 per stove on
average.18 The programme costs have been accounted for as operating costs.

■ Fuel cost savings. In order to determine fuel cost savings, the team compared
average fuel expenditure before and after a technology change. The savings
have been accounted for as (negative) cost.


18 Although there is a significant cost reduction potential (to around USD 17 per stove), it has not been
included in the evaluation since it has not been captured, yet. A detailed analysis has been carried out on
the cost reduction and is contained in the detailed discussion document on rural energy.


Federal Democratic Republic of Ethiopia 110


Without accounting for the potential benefits for users of more efficient stoves, the
cost of implementing the stove scale-up would be positive, e.g., around 8 USD/t
CO2e for fuelwood-efficient stoves. Including the benefits, however, the cost
becomes negative (money-saving over their lifetime) for most stoves types, with
the figures ranging from USD -21 to USD -14. The only notable exception is the
abatement cost for LPG stoves, which remains positive at USD 120, due to the
(currently) more expensive fuel.

The cost estimate also confirmed the maximum scale-up assumptions ex-post: the
stoves that deliver the highest negative cost, i.e., net savings, were estimated to
have the highest scale-up rates (e.g., fuelwood-efficient stoves) and stoves with
positive cost the lowest rate of scale-up (e.g., LPG stoves).

Since efficient stoves are such a significant abatement lever, the STC conducted a
detailed analysis of their potential, the implementation cost, and dissemination
models. For the most important results, please refer to the summary of the detailed
analysis in the concluding chapter of the main part of this report.

Forestry lever 5 – Large- and small-scale afforesta-
tion/reforestation and area closure

Afforestation, reforestation, and area closure measures provide additional seques-
tration opportunities. The total abatement potential for the year 2030 comes to
around 32.3 Mt CO2e, with afforestation contributing 21.5 Mt CO2e and refores-
tation 10.8 Mt CO2e.

The calculation of this potential is based on the following data and assumptions:

■ Afforestation/reforestation area. Based on consultations with forestry
experts, existing afforestation/reforestation projects, and discussions in the
STC, it was assumed that 2 million hectares of pastureland will be afforested
up to 2030. At the same time, Ethiopia will reforest 1 million hectares of
degraded areas.

■ Sequestration rate. The sequestration rate for both afforestation and refor-
estation was set at 10.75 t CO2e/ha/year, a number directly taken from the
afforestation/reforestation CDM project in Humbo.

Abatement cost adds up to around 5 USD/t CO2e:

■ Planting material. Planting material costs 300 USD/hectare and is accounted
for as CAPEX with an amortisation period of 30 years (based on GHG stan-
dard methodology for afforestation/reforestation).


Federal Democratic Republic of Ethiopia 111


■ Nurseries. The team assumed that one nursery, costing USD 50,000, will be
needed for every 5,000 hectares. Since a total of 30 nurseries will be needed,
this amounts to a CAPEX investment of USD 1.5 million that will amortise
over 30 years. A nursery also has operating costs of USD 10,000 per year. In
addition, the team estimated USD 1 million in operating expenditures for
annual research and development activities.

■ Operating cost for afforested/reforested areas. The management and care
for afforested/reforested areas is – in consultation with experts – assumed to
cost around 30 USD/ha/year.

■ Programme cost and additional operating expenditure. A programme cost
of around 9 USD/ha/year is incurred over the first three years of afforesting/
reforesting. Other operating expenditures include monitoring costs (around 3
USD/ha/year) for all afforestation/reforestation areas, which was computed
based on experiences from the Bale project.

■ Economic income effect. Sustainable forestry creates an income from timber
and non-timber products, which has been estimated to be around 7 USD/ha
per year (based on a possible value of 14 USD/ha/year as evaluated by
forestry experts and a realisation factor of 50%).

Forestry lever 6 – Forest management

Forest management boasts an abatement potential of nearly 10 Mt CO2e in 2030,
with management of forests contributing 6.5 Mt CO2e and management of wood-
lands 3.2 Mt CO2e. The abatement potential of these two levers was calculated in a
very similar way, albeit using different assumptions on the following parameters:

■ Projected area coverage. Based on interviews with experts, experience from
existing forest management projects, and discussions in the STC, the area for
the management of forests and for the management of woodlands was set at
2 million hectares each.

■ Sequestration rate. Management of forests has a sequestration potential of
3.24 t CO2e/ha/year as international benchmarks indicate. Assuming that the
management of woodlands has about 50% of that impact, the potential for this
activity is around 1.62 t CO2e/ha/year.

These assumptions result in an abatement potential of 6.5 Mt CO2e from the man-
agement of forests and 3.2 Mt CO2e from the management of woodlands.

Abatement costs are around 1 and 2 USD/t CO2e for the management of forests
and woodlands respectively:


Federal Democratic Republic of Ethiopia 112


■ Planting material. The direct cost of planting material will amount to around
30 USD/hectare. It is accounted for as CAPEX with an amortisation period of
50 years (based on standard GHG methodology for forest management).

■ Nurseries. Here it is assumed that one nursery, at the cost of USD 50,000, is
needed for every 50,000 hectares. For a gradual scale-up, four nurseries will
be needed, amounting to a CAPEX investment of USD 200,000. A nursery
also incurs an operating cost of USD 10,000 per year. In addition, operating
expenditures of USD 1 million were taken into account for annual research
and development activities.

■ Operating cost for afforested/reforested areas. The management and care
for project coverage areas cost 2 USD/ha/year.

■ Programme cost and additional operating expenditure. A programme cost
of around 4 USD/ha/year is incurred over the first three years for the
management of forests (50% of the cost for afforestation/reforestation) and 3
USD/ha per year for woodlands (total programme cost of 10 USD/hectare).
Other operating expenditures include monitoring (1 - 2 USD/ha/year) for all
areas.

■ Economic income effect. Sustainable forestry creates an income from timber
and non-timber products, which has been estimated to be about 3.50 USD/ha
per year (50% of benefits assumed in afforestation/reforestation).

ABATEMENT LEVERS – FEASIBILITY AND ECONOMIC IMPACT
ASSESSMENT

Feasible levers with high impact

The initiatives that reduce forest degradation as well the ones that increase
sequestration have comparably low implementation barriers:

■ Initiatives to reduce forest degradation. Most of the efficient cooking-stove
technologies are readily available, have already been tested for applicability,
and have been deployed on a large scale in Ethiopia. A number of govern-
mental and donor organisations as well as the private sector have already been
active in the dissemination of such stoves. This existing institutional infra-
structure and experience, as well as the grassroots level organisation of the
governmental institutions involved, can prove instrumental in scaling up the
production and distribution effort. There are, however, potential barriers to
the adoption of some of the technologies, for cultural reasons or for costs


Federal Democratic Republic of Ethiopia 113


(particularly for LPG and biogas stoves), and the production of large volumes
of high-quality stoves needs to be ensured.

■ Initiatives to increase sequestration. From a technical point of view, both
afforestation/reforestation and forest management are highly feasible: They
do not require any complicated technologies and have already been success-
fully tried in Ethiopia. In fact, there are several large projects for afforesta-
tion/reforestation (e.g., Humbo CDM) and forest management (e.g., Partici-
patory Forest Management projects) already ongoing – making the country
one of the largest afforestation/reforestation areas on the continent. Large
pilot projects for REDD+ are in the preparatory stages. A continuing forest
data inventory might be helpful to ensure long-term success. In general, there
appear to be no cultural or social barriers to implementation.

In addition, reducing forest degradation and increasing sequestration have a neu-
tral or even positive impact on overall economic development.

■ Reduced forest degradation. Efficient stoves increase the available income
of the relatively poor rural population, create employment, and improve
health and gender equality. As the only potential socio-economic disadvan-
tage, LPG stoves may increase dependence on imports of technology and
fuel.

■ Increased sequestration. Afforestation/reforestation as well as forest man-
agement levers might both lead to additional economic benefits by creating
employment, income from sustainable forestry for the managing communi-
ties, a stronger link between forest industry and forest development, and
eventually even increasing exports and public revenues. Additional benefits
such as erosion control and other ecosystem services also speak for the
implementation of these measures.

Taken together, the suggested forestry abatement levers not only appear to be
without major barriers to implementation, but also seem to have strong socio-eco-
nomic benefits beyond GHG abatement. The initiatives discussed should therefore
not only be a prime focus of the CRGE strategy, but also amongst the first initia-
tives for which implementation can start quickly and achieve fast success.

Other levers

The levers related to reduced deforestation (i.e., agricultural intensification (yield-
increasing and emission-reducing techniques and new land through irrigation) are
discussed in the chapter on Soil. They are evaluated as being highly feasible


Federal Democratic Republic of Ethiopia 114


(yield-intensifying techniques and small-scale irrigation) to moderately feasible
(large-scale irrigation).

ABATEMENT LEVERS – IMPLEMENTATION TIMELINE AND RESOURCE
REQUIREMENTS

Implementation timeline

On the basis of the abatement potential and feasibility assessment, the Forestry
STC has selected three priority initiatives for particular attention and immediate
implementation efforts. These initiatives are the scale-up of fuelwood-efficient
stoves, afforestation/reforestation, and forest management (Figure 39). Significant
scale-up of these initiatives is envisaged to start already at the beginning of 2012.

FIGURE 39

Forestry – Overview of timeline for implementation of
initiatives

2012 2013 2014

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Implementation by Soil STC

Implementation by Soil STC

Activity

Irrigation

Agricultural
intensification

Forest
management

Afforestation/
Reforestation

LPG stoves

Biogas stoves

Electric stoves

Fuelwood
efficient stoves

Selected as priority initiative

Other initiatives


In addition, the scale-up programmes for other stoves will also start in the course
of 2012. An exception is the programme for the scale-up of LPG stoves, which is
envisaged to start only in 2013/14 in order to explore the availability of sufficient
amounts of LPG within the country and thereby ideally avoid an increase of costly
fuel imports. The initiatives summarised under agricultural intensification as well


Federal Democratic Republic of Ethiopia 115


as large-, medium- and small-scale irrigation, that are described in more detail in
the Soil chapter, also have planned starting dates in 2012. It is important to
mention that these dates mark the start of the implementation, which for some
initiatives is staged across several years (e.g., afforestation/ reforestation is staged
across all 20 years up to 2030). The estimated project time includes some required
preparatory work (e.g., development of investment plans), and is subject to
approval by the respective authorities and the availability of funding. Hence, the
full impact of the initiatives only occurs later in most cases.

Resource requirements

The forestry initiatives (excluding agricultural intensification and irrigation pro-
jects that are accounted for in the Soil chapter of this appendix) will require a total
expenditure of nearly USD 7.9 billion in the long run (i.e., up to 2030). Out of this
total amount, about USD 3.4 billion is capital expenditure and USD 4.5 billion is
operating and programme expenditure. Around USD 1.2 billion of the expenditure
will be necessary in the short term, i.e., up to 2015 (Figure 40). If the initiatives
that reduce deforestation (agricultural intensification and irrigation) would be
included in the cost estimation, the total expenditure would rise to more than USD
35 billion, USD 9 billion of which would be initial investments (up to 2015).

FIGURE 40

Forestry – Financial overview of all initiatives
(not including soil initiatives)

Million USD
Short-term: 2011-2015 Long-term 2011-2030

CAPEX1 additional

OPEX additional
(excl. programme cost)

Programme cost

Savings/income

Abatement expenditure
before carbon revenue 230

1,420

420

90

680

7,770

15,640

3,410

1,100

3,360

▪Particularly efficient stoves yield returns higher than cost
▪Af-/Reforestation and forest management with high cost

9832702

1 Full capital expenditure, not amortized
2 Aggregated abatement potential; expenditure per t CO2e not equivalent to abatement cost in cost curve, as the CAPEX abatement expenditure is not

annualized via amortization (rather: cash-flow perspective)

Mt CO2e total aggregated abatement potential

Mt CO2e
abatement
potential


Federal Democratic Republic of Ethiopia 116


On the other hand, since most of the initiatives, particularly the scale-up of fuel-
efficient and fuel-shift stoves, entail high savings for the targeted population, the
savings and/or additional income is expected to overcompensate this expenditure
from a societal perspective. More precisely, this means that more than USD 1.4
billion savings/additional income can be generated in the short run up to 2015 and
nearly USD 16 billion of savings/additional income can be achieved by 2030.

Classifying the initiatives by their return profiles (Figure 41), the fuel-efficient and
fuel-shift stove initiatives (with the exception of biogas stoves) fall into the cate-
gory of expenditure that yields a positive return (from a societal perspective) after
less than five years. Due to the high upfront investment cost for the biogas digest-
ers, biogas stoves yield a positive return, but only in the longer run. Although they
might increase income from forestry for rural communities, the afforestation/
reforestation as well as forest management initiatives will not yield a positive
return in the long run due to high investment and operating expenditure. Hence,
these initiatives need to be supported by grant or pay-for-performance schemes.

As a range of programmes is already in progress in the Forestry sector, the scale-
up initiatives should be able to build on a solid experience base.

FIGURE 41

Forestry – 68% of cost will have positive returns in the short
or long run, but 32% will need grants or performance pay

Category C
No positive return,
grants/performance

pay requirement

2,470

Category B
Positive return, but
long term financing

requirement

980

Category A
Positive return,

short term financing
requirement

4,400

Million USD, total cost1 Percent of total cost

56% 12% 32%

1 Including additional CAPEX, additional OPEX, and programme cost; not including soil initiatives (intensification and irrigation)
2 NPV calculated with 6% discount rate

Definition Positive NPV2 of first
five years of cash flow
(from start of initiative)

Positive NPV2 of
overall initiative (from
start of initiative up to
2030), but not after first
five years

Negative NPV2 of
overall initiative (from
start of initiative up to
2030)


Federal Democratic Republic of Ethiopia 117


Livestock

Livestock are a significant contributor to the GDP of Ethiopia and are the main
source of income for a large part of the society. Simultaneously, a large share of
GHG emissions originates in the Livestock sector, and the sector is expected to
expand even faster than population growth. To prevent the projected doubling of
livestock-related emissions to 124 Mt CO2e by 2030, the Livestock STC identified
five main levers that offer an abatement potential of 45 Mt CO2e to which climate
finance projects could make a major contribution. These abatement levers are:
enhancing and intensifying animal mix diversification (e.g., poultry, sheep, goats,
fish, etc), improving value-chain efficiency for livestock belonging to farmers and
pastoralists through regionally appropriate techniques, and increasing the use of
mechanisation (small-scale and tractors) through techniques tailored to each type
of terrain. A sixth, non-costed lever, rangeland management, provides an
additional abatement potential of 3 Mt CO2e, resulting in a total abatement
potential of 48 Mt CO2e in the Livestock sector.

SCOPE AND INSTITUTIONAL SETUP

The Livestock sector is a significant contributor of GHG emissions, with its contri-
bution nearly doubling over the next 20 years if no measures are taken. The
Livestock STC (Table 9) calculated current and future emissions and analysed
abatement levers for several segments of the Livestock sector. The STC is
composed of sectoral experts from the Environmental Protection Authority, the
Ministry of Agriculture, the IBC, and the EWCA as well as the Ethiopian Institute
of Agricultural Research.

TABLE 9


Federal Democratic Republic of Ethiopia 118


GHG EMISSIONS BASELINE IN 2010 AND BAU UP TO 2030

Livestock contribute to the livelihoods of 70% of Ethiopians, and the growth of
this emissions source is thus tightly linked to population growth. Under the BAU
scenario, emissions from livestock will increase from 65 Mt CO2e in 2010 to 124
Mt CO2e in 2030 (Figure 42).

FIGURE 42

Livestock – CH4 emissions from livestock are projected
to grow by over 100% by 2030 in the BAU scenario

BAU emissions estimations

Mt CO2e/ year

N2O from
manure left
on pasture
and range

CH4 from enteric
fermentation
and manure

BAU
2030

124

12

112

BAU
2020

94

11

83

Baseline
2010

65

9

57

1 The emissions from manure used as fertiliser are accounted for in the Soil-based emissions STC


Main drivers of GHG emissions

The main drivers of GHG emissions from the Livestock sector as well as the main
assumptions about their impact and development are detailed below (Figure 43).

The STC separates the cattle population from other livestock populations as 84%
of GHG emissions originates from cattle.


Federal Democratic Republic of Ethiopia 119


FIGURE 43

Increase in other
species
population
Million tropical
livestock units

Livestock – Estimation of changes with time of the main
emission drivers

Output overview

68
50

36

203020202010

Key emissions
drivers Projected change Rationale

24

15
11

203020202010

Increase in cattle
population
Million tropical
livestock units

▪2008-11 growth rates from
CSA projected until 2030

▪Growth projected based on
2008-11 growth rates from
CSA, progressively reduced
to the rate of human
population increase until
2030


■ Increase in cattle population. Over the last decades, the cattle population
has grown at an even faster rate than the expansion of the human population.
The CSA projects the latter to grow at a rate of 2.62% annually, which will
add 54 million people to the population by 2030. The demand from this
growing population is likely to cause the cattle population to almost double
over the next 20 years from 36 to 68 million tropical livestock units (a
common ratio used to compare different species of livestock against each
other) or 51 to 95 million head of cattle, leading to higher GHG emissions.

■ Increase in population of other livestock. The population of other species of
livestock is growing at a slightly faster rate than the cattle population. The
STC included the following species in its analysis: sheep, goats, horses,
mules, asses, poultry, and camels. The combined population of other livestock
is expected to grow from 11 million tropical livestock units in 2010 to 24
million tropical livestock units in 2030.


Federal Democratic Republic of Ethiopia 120


GHG emissions baseline and BAU projection for 2030

Emissions from livestock are projected to increase from 65 Mt CO2e in 2010 to
124 Mt CO2e in 2030, mainly driven by an increase in methane released during
digestion, called enteric fermentation, and the decomposition of manure in storage
– which are likely to account together for 90% of livestock emissions in 2030.
Nitrous oxide released during decomposition of manure left on pasture range and
paddock account for only 10% of livestock emissions in 2030.

■ Enteric fermentation and manure management. Driven primarily by a
growing cattle population (84% of emissions in this category), emissions
from enteric fermentation and decomposition of manure in storage will grow
from 57 Mt CO2e in 2010 to 112 Mt CO2e in 2030.

■ Manure left on pasture range and paddock. Driven by an increase in the
livestock population and dominated specifically by the increase in cattle,
GHG emissions from manure left on pasture range and paddock will increase
from around 8.6 Mt CO2e to 12 Mt CO2e in 2030.

ABATEMENT LEVERS – POTENTIAL AND COST CURVE

In total, an abatement potential of up to 48 Mt CO2e in 2030 has been identified in
six abatement levers (Figure 44). These levers can be clustered into four groups:

■ Enhancing and intensification of animal mix diversification. Low-emitting
animals (poultry, sheep, goat and fishery) are high feed converters and low
GHG emitters as compared to large ruminants (including cattle and camels).
These animals are high protein suppliers as well as high sources of income for
the rural population. This initiative supports the increase in production and
consumption of lower-emitting species by acting both on supply and demand
aspects. The primary element of the animal mix lever is increasing poultry to
30% of meat consumption by 2030. This initiative has the largest abatement
potential in the Livestock sector, amounting to 17.7 Mt CO2e in 2030.

■ Value chain efficiency improvements (pastoralists and farmers). These
two levers aim to increase efficiency across the animal value chain for pastor-
alists and farmers. Increased productivity should be obtained by introducing
more productive breeds, providing high-quality feed and other essential
inputs, improved technology and public infrastructure, and a higher off-take
rate (decreasing the age at which livestock is sold). Currently, Ethiopia's
livestock suffer from low production levels and poor reproductive
performance. This is exemplified by poor feed conversion efficiency, poor
daily weight gain, low milk and meat yield, low off-take rates, low


Federal Democratic Republic of Ethiopia 121


conception and calving rates, longer calving intervals, and high mortality.
Value chain efficiency improvements aim to improve this performance. These
levers have a combined abatement potential of 16.1 Mt CO2e in 2030.

■ Mechanisation (small scale and large scale). These two levers will intro-
duce and promote mechanical equipment (e.g., manual tools and tractors) for
ploughing/tillage to partially substitute for animal draught power among
farmers in the highland plains. New techniques and improved tools will be
introduced to increase work efficiency and thereby reduce demand for oxen.
These levers have a combined abatement potential of 11.2 Mt CO2e in 2030.

■ Rangeland and pastureland management. This lever is aimed at
introducing and promoting appropriate techniques to increase soil carbon
content and the productivity of pastureland in highland areas and rangeland
within pastoral areas. The main activities here include bush clearing,
reseeding, paddocking, rotational grazing, improving and adoption of
traditional ways of managing rangelands, and water point development.
This lever has an abatement potential of 3 Mt CO2e.

FIGURE 44

Livestock – Abatement potential until 2030 is 48 Mt CO2e per
year

0

20

40

60

80

100

120

140

2010 20252020 20302015

Expected emissions
after abatement

Mt CO2e reduction
potential up to 2030

Abatement measures
Mt CO2e/year in 2030

BAU and abatement potential
Mt CO2e/ year

BAU emissions: 124 Mt

5
Pastoralist animal
value chain efficiency

18 Enhancing animal mix

11
Farmer value chain
efficiency improvement

4 Large-scale mechanisation

7 Small-scale mechanisation

ΣΣΣΣ 48

3
Rangeland and pastureland
management


Federal Democratic Republic of Ethiopia 122


FIGURE 45

Livestock – Most of the initiatives have positive costs,
but enhancing animal mix is below the USD 15 mark

19.8

7.6

453525155 40

20.0

10

Abatement potential
Mt CO2e per year

30

25

20

15

10 30

0

20

Abatement cost
USD per tCO2e

0

26.0

29.4

5

Enhancing animal
mix (poultry)

Value chain efficiency
(pastoralists)

Mechanisation
(small scale)

Mechanisation
(tractors)

Value chain efficiency
(farmers)

Value chain efficiency Diversify animal mix (poultry) Mechanisation of draught power

Output overview

Abatement opportunities cost curve


The cost curve in Figure 45 depicts the range of abatement costs for livestock ini-
tiatives.

The total investment cost that is required for all initiatives in the Livestock sector
up to 2030 is about USD 17.5 billion, to which climate finance can make an
important contribution.

Livestock lever 1 – Enhancing and intensification of
diversifying animal mix

Beef is the primary meat consumed in Ethiopia, and the demand for beef is a major
driver of the size of the cattle population. Beef production is far more carbon
intensive than the production of other types of meat. Poultry – specifically chicken
meat – offer a particularly attractive lower-carbon alternative to beef. However, at
only 15% of total meat consumption, chicken consumption is currently low
compared with other countries. The STC estimates that this share could be
increased to 30% by 2030 through both supply- and demand-side activities to
promote poultry, resulting in an abatement potential of 17.7 Mt CO2e in 2030.
This figure is based on the following considerations:


Federal Democratic Republic of Ethiopia 123


■ Programme coverage. The STC proposes a programme covering 17.6 mil-
lion households by 2030.

■ Meat consumption. Meat consumption is expected to be 20 kg/person/year in
2030 based on the elasticity of meat consumption to per capita GDP
according to analysis conducted by the STC using FAO statistics on per
capita meat consumption and World Bank statistics on per capita income.

■ Emissions from meat production. The STC calculated emissions from cattle
and chickens using the following assumptions:

– Emissions per animal (tonnes of CO2e per year): 1.08 (cattle), 0.0056
(chickens).

– Annual off-take rate from population held for meat production: 20%
(cattle), 300% (chickens).

– Average weight of meat per animal (kg): 120 (cattle), 2 (chickens).

■ Animal substitution. To increase the chicken share of meat consumption to
30% by 2030, the chicken population would need to increase by 70 million.
This would substitute for nearly 17 million head of cattle. The additional
emissions in 2030 from the increased chicken population will amount to 0.4
Mt CO2e, which has been factored into the abatement potential of this
initiative.

The abatement cost of this initiative is approximately 8 USD/t CO2e, based on the
following assumptions:

■ Supporting costs include investments in the following areas:

– Feed processing plants: USD 12,500 per plant, on average five plants per
region (45 in total).

– Mini-hatcheries: capacity of 10,000 eggs at a time, costing USD 31,250 per
facility, on average five facilities per region (45 in total).

– Grandparent farm (farm that produces parent chickens for other farms):
one farm at the national level costing USD 1 million to establish.

– Poultry slaughter and processing units: USD 500,000 per unit, on average
five units per region (45 in total).

■ Programme costs of USD 10 million to establish the programme, USD 6 per
household annually to operate the extension programme, USD 1 million
annually to run poultry production and consumption promotion to convince
the population to produce and consume poultry products, and USD 1 per
household annually to maintain and monitor the programme.


Federal Democratic Republic of Ethiopia 124


■ Research expenses of USD 1 million annually.

■ Economic benefits based on new job creation related to poultry of 1,800 full-
time employees by 2030, valued at USD 400 per job/year.

Livestock lever 2 – Value chain efficiency improve-
ments (pastoralists)

This initiative reduces headcount and lowers per animal emissions in pastoralist
herds through higher productivity and off-take rates at early ages. Sub-components
include commercialisation, improved health services, improving market efficiency
and infrastructure, strengthening linkages to neighbouring medium-highlands
feedlot systems, promoting the sale of animals when they are young, improved
early-warning systems for extreme weather conditions, breed improvement
through selection, and improved feed and feeding systems for a sub-group of
pastoralists. This programme will build on the existing government plans to
strengthen the livestock extension system. The abatement potential from this ini-
tiative comes from two sources: the reduced cattle population and lower emissions
per animal due to better feeding, health, and management. The total abatement
potential that has been calculated for the year 2030 is around 4.9 Mt CO2e. The
calculation of this potential is based on the following information:

■ Programme coverage. The proposed productivity-improving programme
would reach 1.95 million households by 2030 (100% of the pastoralist popu-
lation), aside from better feeding techniques, such as improving forage quality
and supplementation, which would reach 20% of pastoralists. This 20% takes
into account the costs and implementability issues related to improved feed
and feeding systems.

■ Programme impact. The proposed programme would increase annual
productivity growth to 4.5%, and product value growth due to quality
improvements to 4.0%, thus reaching the target GDP growth rate of 11.1%
annually with a smaller cattle population (reduction of approximately 4
million head by 2030). Better feeding techniques introduced to 20% of the
pastoralist herd would increase per animal productivity and reduce emissions
from cattle reached by a further 10% per animal.

The abatement cost is calculated at around 20 USD/t CO2e, incorporating:

■ Direct costs related to cattle (e.g., feed, health services) totalling USD
10/head/year, and distribution and marketing costs amounting to USD
0.50/head/year.


Federal Democratic Republic of Ethiopia 125


■ Supporting costs include investments in:

– Abattoirs: USD 1 million per facility, 16 facilities in total.

– Animal Health Facilities: USD 1 million per facility, 32 facilities in total.

■ Programme costs of USD 10 million to set up the programme, USD 3 per
head of cattle/year in ongoing programme expenses, and USD 0.40 per head
of cattle/year in monitoring and programme management costs.

■ Research expenses to establish four research centres at USD 1.8 million each,
and USD 638,000 in annual research costs per centre.

■ Economic benefits based on the reduction of cattle due to the improved
productivity and value growth rates of cattle (4.5% and 4.0% per year
respectively), an average animal sales price of USD 350, and an average
animal lifespan of 10 years.

Livestock lever 3 – Value chain efficiency improve-
ments (farmers)

This initiative reduces headcount and lowers per animal emissions for cattle
belonging to farmers through higher production (e.g., milk, meat) per animal and
will increase off-take rates of non-dairy cattle at an early age. Example sub-com-
ponents include improved feedlots, indigenous livestock cross-breeding (mainly
using artificial insemination techniques), improved health services, improved
market efficiency through the establishment of milk collection and processing
centres in strategically selected milk shed areas, increased dairy value chain
efficiency through aggregation of smallholder production in cooperatives, as well
as supply of improved feed and feeding systems for a sub-group of farmers. This
programme will build on the existing government plans to strengthen the livestock
extension system. The abatement potential from this initiative comes from two
sources: a reduced cattle population and lower emissions per animal due to better
productivity and production. The total abatement potential that has been
calculated for 2030 is around 11.2 Mt CO2e. The calculation of this potential is
based on the following programme coverage and impact assumptions:

■ Programme coverage. The proposed productivity-improving programme
would reach 17.6 million households by 2030 (90% of the rural population),
and 25% of farmers with full impact from better feeding techniques (quality
feed, treatment of crop residue used for feeding, supplementary feed, and
forage crop production). This 25% takes into account the costs and
implementability issues related to improved feed and feeding systems.


Federal Democratic Republic of Ethiopia 126


Among the farmers to be addressed, the main focus group is business-oriented
farmers near market areas.

■ Programme impact. The proposed programme would increase annual
productivity growth to 4.5%, and value growth to 4.0%, thus reaching the
target GDP growth rate of 11.1% annually with a smaller cattle population
(reduction of approximately 13.5 million head by 2030). Although best prac-
tices in feeding (e.g., feedlots, supplementation) may increase daily methane
emissions per animal, emissions per kilogram of product (e.g., meat and milk)
are reduced. The STC estimates that such better feeding techniques would
reduce emissions from cattle reached by 10% through improved per animal
productivity.

The abatement cost is calculated at around 26 USD/t CO2e, incorporating:

■ Direct costs related to cattle (e.g., feed, health, and artificial insemination
services) totalling USD 10/head/year, and distribution and marketing costs
amounting to USD 0.50/head/year.

■ Supporting costs include investments in:

– Abattoirs and refrigerated storage: USD 1 million per facility, 37 facilities
in total by 2030.

– Health facilities: USD 1 million per facility, 73 facilities in total by 2030.

– Artificial insemination centres, including liquid nitrogen production facili-
ties: USD 1.5 million per facility, 73 facilities in total by 2030.

■ Programme costs of USD 10 million to set up the programme, USD 3 per
head of cattle/year in ongoing programme expenses, and USD 0.40 per head
of cattle/year in monitoring and programme management costs.

■ Research expenses to establish 20 research centres at USD 1.8 million per
facility, and USD 3.2 million in total annual research costs.

■ Economic benefits based on the reduction of cattle due to the improved
productivity and value growth rates of cattle (4.5% and 4.0% per year), an
average animal selling price of USD 350, and an average animal lifespan of
10 years.

Livestock lever 4 – Mechanisation (small scale)

This initiative introduces techniques to increase work efficiency so that the
demand for oxen is reduced, and has an abatement potential of 7.3 Mt CO2e in
2030. Small-scale mechanisation (techniques and improved tools) can reduce the


Federal Democratic Republic of Ethiopia 127


need for animal draught power, thereby lowering the oxen population and reducing
emissions from livestock. The abatement potential of this lever was calculated
using the following assumptions:

■ Programme coverage. This initiative will target 50% of total farmer house-
holds, including a large portion of farmers in highland areas.

■ Programme impact. The STC estimated that 77% of farmers hold cattle, and
50% of draught power can be substituted by small-scale mechanisation.
Households are assumed to hold an average of two oxen. This leads to a total
number of oxen substituted of 6.7 million by 2030. Emissions per head of
cattle are estimated at 1.08 t CO2e per year.

The abatement cost is calculated to be around 20 USD/t CO2e. This abatement cost
incorporates:

■ Household expenses, including a one-time capital expenditure of USD 188
per household, USD 9 per household/year for distribution, and USD 19 per
household /year for maintenance and replacement.

■ Supporting costs include the following investments:

– Training centres for manufacturers. USD 10,000 to establish each centre,
USD 4,000 per centre per year in operating costs, one centre per 100,000
households.

– Production facilities. Initial investment of USD 1,000 per facility to kick-
start production, USD 400 per facility per year in operating costs, one
facility per 1,000 households.

– Advertisement. USD 1 million per year.

■ Programme costs of USD 1 million for programme setup, USD 6 per house-
hold/year in operating costs, and USD 1 per household/year in programme
management and monitoring.

■ Economic benefits based on the reduced need for animal draught power of
4 million oxen by 2030, an average animal selling price of USD 350, and
an average ox lifespan of 10 years.

Livestock lever 5 – Mechanisation (large scale)

This initiative introduces techniques to increase work efficiency so that the
demand for oxen is reduced, and has an abatement potential of 3.9 Mt CO2e in
2030. Large-scale mechanisation (tractors) can reduce the need for animal draught
power, thereby lowering the oxen population and reducing emissions from


Federal Democratic Republic of Ethiopia 128


livestock. The abatement potential of this lever was calculated using the following
assumptions:

■ Programme coverage area. This initiative will target 25% of total farmer
households (4.4 million households), drawn primarily from the highlands
where the oxen ownership rate is high, and the topography enables the use of
tractors.

■ Programme impact. The STC estimated that 77% of farmers hold cattle, and
60% of total draught power can be substituted by large-scale mechanisation.
Households are assumed to hold an average of two oxen. This leads to a total
number of oxen substituted of 4.0 million by 2030. Emissions per head of
cattle are estimated at 1.08 t CO2e per year, while emissions per tractor are
estimated to be 10.31 t CO2e per year.

The abatement cost is calculated to be around 29 USD/t CO2e, incorporating four
main components:

■ Household expenses, including a one-time capital expenditure of USD
10,000/unit (100 households per unit), 500/unit/year in distribution costs
(5 households per unit), 2,000/unit/year for running costs including fuel,
insurance, etc. (20 households per unit), and 1,500/unit/year for maintenance
and replacement (15 households per unit).

■ Supporting costs, including investments in tractor maintenance centres, at
USD 50,000 to establish each centre, and one centre per 500 tractors.

■ Programme costs of USD 1 million for programme setup, USD 12/house-
hold/year in operating costs, and USD 2/household/year in programme
management.

■ Economic benefits based on the reduced need for animal draught power of
4 million oxen by 2030, an average animal selling price of USD 350, and
an average ox lifespan of 10 years.

Livestock lever 6 – Rangeland and pastureland
management

This initiative aims to introduce techniques to increase soil carbon content and
improve productivity of pastureland in highland areas and rangeland within
pastoral areas. The main activities include bush clearing, reseeding, paddocking,
rotational grazing, improving and adoption of traditional ways of managing
rangelands, and water point development. The total abatement potential for this
lever is 2.7 Mt CO2e in 2030.


Federal Democratic Republic of Ethiopia 129


■ Programme coverage area. The initiative aims to reach 5 million hectares of
rangeland and pastureland in total, i.e., 2.5 million hectares of pastureland
will be reached in the highlands, while 2.5 million hectares of rangeland will
be reached in the lowlands. Programme coverage will be developed linearly
over the years.

ABATEMENT LEVERS – FEASIBILITY AND ECONOMIC IMPACT
ASSESSMENT

Feasible levers with high impact

Three of the levers in the Livestock sector were evaluated to have comparably low
barriers to implementation. These levers fall into the following two categories:

■ Enhancing and intensification of animal mix diversification. The technolo-
gies related to this initiative are readily available and most have been tested
for applicability in Ethiopia. Feed processing plants are expanding beyond
Addis Ababa, and vaccines are produced locally for most epidemic diseases.
Also, from the institutional perspective, there is a relatively strong capacity
within and support from the extension system, the commercial poultry sector,
and research institutions. Poultry-raising also requires a small initial
investment by producers and therefore entails limited financial risk. One key
barrier is the lack of a chicken grandparent farm in Ethiopia. Another
potential barrier is cultural practices around chicken consumption, which is
currently quite low, especially due to the long preparation time of traditional
chicken recipes. National consumption promotion may overcome this, but the
feasibility of this will have to be tested.

■ Value chain efficiency (pastoralists and farmers). The two initiatives
related to improving animal value chain efficiency (for pastoralists and farm-
ers) are both generally feasible. Most technologies related to these initiatives
are available and can be transferred easily to households through the existing
government agricultural extension system. These abatement levers also enjoy
strong institutional support, including a prioritisation of the sector at the fed-
eral government level. Key barriers include the need to improve local sup-
porting institutions (animal health posts and clinics, regional labs, etc.) and
reluctance among pastoralists to switch to improved breeds or reduce herd
size.

There are some additional challenges with regard to value chain efficiency
implementation for pastoralists. There will need to be a system in place to
increase their awareness of the programme. Existing extension and service deliv-


Federal Democratic Republic of Ethiopia 130


ery systems to pastoralists may be leveraged to ensure their participation. Exist-
ing government programmes such as the Pastoral Community Development Pro-
gramme can also be used.

Regarding socio-economic impact, all three of these levers have been evaluated to
have a positive contribution to overall economic development.

Other levers

The small-scale mechanisation and large-scale mechanisation (tractors) initiatives
were both judged to be moderately feasible due to the lower availability of tech-
nology, the ability of farmers to afford this technology, and the suitability of the
technologies to the land use in Ethiopia. Rangeland management is considered to
be technically feasible but is challenged by relatively low direct economic bene-
fits. Other abatement levers that were considered but not quantified due to limited
expected abatement potential or implementability include:

■ Improved manure management using a wide range of activities, including
proper drying to avoid methane emissions related to aerobic fermentation and
use as fertiliser or biofuel.

■ Improved rumen ecology to lower per animal emissions (via use of additives,
manipulation of rumen flora, and vaccination against methane-producing
organisms). This abatement lever would change the internal digestion process
of the animal. Vaccinations against methane-producing organisms have been
successfully implemented in other countries, and a research budget is
available to observe the local potential in Ethiopia.

■ Switching to lower-emitting cattle breeds.

■ Reduced emissions from equines due to better road networks.

■ Small-scale processing plants/ technologies that could improve livestock pro-
ductivity.

■ Value chain efficiency and productivity improvements in other livestock,
such as sheep, goats, and camels. These initiatives would be similar to the
cattle initiatives above.


Federal Democratic Republic of Ethiopia 131


ABATEMENT LEVERS – IMPLEMENTATION TIMELINE AND RESOURCE
REQUIREMENTS

Implementation timeline

The STC has determined five prioritised levers based on abatement potential and
implementability. These levers are enhancing and intensification of diversifying
the animal mix, value chain efficiency improvements for pastoralists and farmers,
and large-scale and small-scale mechanisation (Figure 46). All the initiatives are
envisaged for implementation in the near future. Rangeland management is
envisaged to take off simultaneously. The implementation of the levers is assumed
to follow a phased approach, with the programme reach developing linearly over
the years. While technically feasible, the immediate start of the initiatives is
dependent on sufficient available funds. It is also important to keep in mind that
these dates mark the start of the implementation, which includes some required
preparatory work (e.g., development of investment plans), and is subject to
approval by the respective authorities. Hence, the full impact of the initiatives only
occurs later in most cases.

FIGURE 46

Livestock – Overview of timeline for implementation of initiatives

2012 2013 2014 2015

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4Activity

Small-scale
mechanisation

Rangeland/pastureland
management

Large-scale
mechanisation

Farmer value
chain efficiency

Pastoralist animal
value chain efficiency

Enhancing animal mix

Selected as priority initiative

Other initiatives


Federal Democratic Republic of Ethiopia 132


Resource requirements and existing projects

The livestock initiatives will cost USD 17.5 billion in the period from 2011 to
2030. Most of this is operating expenditure (USD 10.6 billion) and programme
costs (USD 4.4 billion). The expenditure until 2015 is USD 1.8 billion, with all of
the costs occurring after 2011 when all the initiatives are started (Figure 47).

FIGURE 47

Livestock – Financial overview of all initiatives

Million USD
Short-term: 2011-2015 Long-term 2011-2030

CAPEX1 additional

OPEX additional
(excl. programme cost)

Programme cost

Savings/income

Abatement expenditure
before carbon revenue -944

814

344

775

640

-7,566

9,968

4,401

10,590

2,543

▪The expenditure for all the livestock levers outweighs
the savings; external financing is needed

4752352

1 Full capital expenditure, not amortised
2 Aggregated abatement potential for the given period; expenditure per t CO2e here is not equivalent to the abatement cost in the cost curve as the CAPEX

abatement expenditure is not annualised via amortisation (the analysis here takes the cash-flow perspective)

Mt CO2e total aggregated abatement potential

Mt CO2e
abatement
potential


The societal cost savings amount to USD 0.8 billion in the period from 2011 to
2015 and USD 10 billion in the period from 2011 to 2030. Even though these cost
savings are quite large, they do not finance the total expenditures. Long-term
financing will be vital to get these initiatives off the ground as will be clear in the
following classification.

Figure 48 shows a categorisation of the different initiatives. None of the initiatives
within the Livestock sector has a positive net present value (category C). This
means that they cannot be financed by normal loans or other forms of market-
based long-term financing. Instead, they require carbon finance and/or perform-
ance-based grants (similar to those currently considered for the REDD scheme).


Federal Democratic Republic of Ethiopia 133


There are currently multiple programmes in place to tackle sustainable develop-
ment and related issues in the Livestock sector. The suggested initiatives can build
on the positive outcome of these programmes.

FIGURE 48

Livestock – All of the initiatives require carbon finance and
other forms of performance-based grants

Category C
No positive return,
grants/performance

pay requirement

17,534

Category B
Positive return, but
long-term financing

requirement

0

Category A
Positive return,

short-term financing
requirement

0

Million USD, total cost1 Percent of total cost

0% 0% 100%

1 Including additional CAPEX, additional OPEX, and programme cost
2 NPV calculated at 6% discount rate

Definition Positive NPV2 of first
five years of cash flow
(from start of initiative)

Positive NPV2 of
overall initiative
(from start of initiative
up to 2030), but not
after first five years

Negative NPV2 of
overall initiative
(from start of initiative
up to 2030)


Federal Democratic Republic of Ethiopia 134


Soil

Soil-based GHG emissions are significant and come from three main sources: 58%
from using synthetic fertilisers; the rest from applying manure to cropland and
reintroducing crop residues into the soil. In the business-as-usual (BAU) scenario,
emissions from soil will increase to 61 Mt CO2e in 2030. A 9.5% annual growth
rate of crop GDP will be necessary to sustain population growth, provide food
security, and help achieve middle-income status by 2025. Both the business-as-
usual and green growth emission scenarios take this growth in crop GDP as an
assumption. The Soil STC identified an abatement potential of 78 Mt CO2e in
2030 from four levers, which require a total estimated investment of USD 30.5
billion. Two of the soil levers will be implemented jointly: promotion of lower-
emitting techniques for crop cultivation and crop-yield-increasing techniques. The
abatement potential of the third and fourth levers will be achieved through reduced
deforestation by creating new agricultural land from non-forested areas through
small- and large-scale irrigation. The two levers amount to a total of 38 Mt CO2e
in abatement potential, all of which is accounted for in the forestry chapter.

SCOPE AND INSTITUTIONAL SETUP

The Soil sector is a significant contributor of GHG emissions, with its role set to
further increase if no measures are taken. The Soil STC (Table 10) calculated
current and future emissions from growing crops and analysed four abatement
levers. Emissions from deforestation due to agricultural land expansion have been
accounted for by the Forestry STC. The Soil STC is composed of several sectoral
experts from the Environmental Protection Authority, the Ministry of Agriculture,
and the Ethiopian Institute of Agriculture Research.

TABLE 10


Federal Democratic Republic of Ethiopia 135


GHG EMISSIONS BASELINE IN 2010 AND BAU UP TO 2030

The Soil sector includes three sources of emissions: crop reintroduction, synthetic
fertiliser use (direct and indirect emissions), and manure applied to cropland. The
Soil STC projected emissions from these sources based on four main emission
drivers, including growth in total crop production, growth in synthetic fertiliser

used per hectare, growth in hectares cultivated, and growth in the population of
livestock (to estimate the manure applied to cropland). The GDP from crops will
need to grow at 9.5% annually to sustain population growth, provide food security,
and help achieve middle-income status by 2025. This growth rate was used in both
the BAU and the green growth scenarios, but with varying constituent growth rates
for total cultivated area, yield, and value (USD/t of crops). In the BAU scenario,
yield and value growth rates were projected using historical trends in Ethiopia.
Under the BAU scenario, soil-based emissions will increase from 12 Mt CO2e in
2010 to 61 Mt CO2e in 2030 (Figure 49).

FIGURE 49

Soil – Emission levels are projected to increase up to 2030
by 508% under a business-as-usual scenario

Output overview

Mt CO2e/ year

5

10

BAU
2020

N2O from manure
used as fertiliser

N2O from synthetic
fertilisers

N2O from crops
residue

BAU
2030

61

16

35

Baseline
2010

32

10

12

5
4

18

3


Federal Democratic Republic of Ethiopia 136


Main drivers of GHG emissions

The main drivers of GHG emissions from the Soil sector as well as the main
assumptions about their impacts and development are detailed below (Figure 50).
Other emission drivers that could have been included in the Soil sector are wet-
lands, which are a significant source of methane, and tillage practices, which
might form a large source of emissions as well. Due to the nature of the abatement
levers, these emission drivers were excluded from the BAU calculations for the
moment.

FIGURE 50

Synthetic
fertiliser/hectare
kg/hectare

Soil – Estimation of changes with time of the main emission
drivers

Output overview

Key emissions
drivers Projected change Rationale

▪ Crop production projection
based on report by Dorosh
(2007)

Total crop
production
Million tonnes

▪ Fertiliser/hectare targets based
on similar land in India (World
Bank data)

Hectares cultivated
Million hectares

▪ Growth rate based on GTP,
extrapolated to 2030 assuming
no measures taken to increase
agricultural yield/hectare

20302010 2020

71
36

19

247
155

65

203020202010

27
19

13

2010 20302020

92
6547

2010 2020 2030

Increase in livestock
population
Million tropical
livestock units

▪ 2008-11 growth rates from CSA
projected until 2030


■ Total cereal crop production is expected to increase from 19 million tonnes
in 2010 to 71 million tonnes in 2030. Cereal crops residues are regularly
reintroduced in the soil (composting) and therefore form a good proxy for
what drives the total amount of residue reintroduction into the soil. The
growth rate is based on the average of 2011-2015 GTP growth rates for total
cereal crop production. Based on IPCC methodology, the reintroduction
factor for each crop included in the total cereal crop production was
determined to be around 75% resulting in an estimate for total residue
reintroduction. This 75% does not take into account local conditions and will
need to be refined over the coming years to incorporate local practices, such


Federal Democratic Republic of Ethiopia 137


as feeding crop residues to livestock. Total emissions from crop reintroduce-
tion were then calculated using relevant emission factors for each crop.

■ Synthetic fertiliser per hectare and hectares cultivated drive the emissions
from synthetic fertilisers used. Synthetic fertiliser per hectare will grow
from 65 kg/ha in 2010 to 247 kg/ha in 2030. Synthetic fertiliser use in 2010-
2015 was projected based on GTP targets, and usage growth until 2030 was
estimated based on the World Bank 2015 fertiliser application estimate for
India (247 kg/ha).

■ Hectares cultivated will grow at 4% over the years based on projections
from the GTP, which already includes programmes such as improved seeds
and fertiliser use. This will raise the area cultivated from 13 million hectares
in 2010 to 27 million hectares in 2030.

■ Livestock population mainly drives the emissions from manure used on land.
As projected in the Livestock chapter, the population of livestock will grow
from 47 million tropical livestock units in 2010 to 92 million tropical
livestock units in 2030. These figures are based on projections by the CSA.

GHG emissions baseline and BAU projection for 2030

In the BAU scenario, emissions from soil will increase from 12 Mt CO2e in 2010
to 61 Mt CO2e in 2030 (see Figure 49), mainly driven by an increase in the use of
synthetic fertiliser. The use of synthetic fertiliser is considered not to stifle the
growth of crop reintroduction and manure application as the use of fertiliser on
land is expected not to reach the saturation level in the years up to 2030, meaning
that the use of crop residue, synthetic fertiliser and manure will all grow along
with their emission drivers.

■ Emissions from crop residue reintroduction were estimated based on IPCC
methodology and CSA crops data on the crop mix in Ethiopia, and will
increase from 2.6 Mt CO2e in 2010 to 10 Mt CO2e in 2030. A different
percentage for reintroduction of crop residue was considered for every type of
crop based on IPCC data.

■ Emissions from synthetic fertiliser constitute the largest source of soil-based
emissions (58% in 2030), and will increase from 4.3 Mt CO2e in 2010 to 35
Mt CO2e in 2030. They were calculated based on fertiliser use and amount of
hectares cultivated.

■ Emissions from manure applied to agricultural land were projected based on
IPCC methodology and CSA livestock population data, and will increase
from 5.3 Mt CO2e in 2010 to 16 Mt CO2e in 2030.


Federal Democratic Republic of Ethiopia 138


ABATEMENT LEVERS – POTENTIAL AND COST CURVE

In total, the Soil STC identified an abatement potential in 2030 of 40 Mt CO2e of
soil-based emissions and 38 Mt CO2e through agriculture abatement levers that
reduce deforestation, thereby achieving a combined abatement potential of 78 Mt
CO2e (Figure 51). The abatement potential of the crops-related initiatives that
reduce emissions through deforestation is accounted for in the Forestry STC.
The four Soil sector initiatives can be grouped into three categories:

■ Enhancing of lower-emitting techniques for agriculture: By speeding up
the introduction of low-emission techniques and sustainable land management
practices, emissions would be reduced while maintaining production levels.
These techniques include agronomic best soil practices to increase carbon
storage, optimal nutrient management to improve nitrogen use efficiency,
effective tillage and residue management practices, terracing and other water-
harvesting techniques, and agro-forestry practices to prevent soil erosion and
degradation. Within this lever, massive community-based soil conservation
activities on watershed development and natural resources management
through different interventions are highly important. The adoption of lower-
emitting techniques has an abatement potential of 40 Mt CO2e.

■ Enhancing of yield-increasing techniques for agriculture: This initiative
would promote and introduce best practices aimed at increasing agricultural
yield and value per tonne, thereby reducing the need for new agricultural land
created from forest areas. Ethiopia’s farmers could dramatically increase crop
yields by using improved seeds (new varieties and higher quality) and basic,
low-cost irrigation systems, increasing the use of fertiliser and manure, and
adopting agronomic best practices (e.g., harvest and post-harvest
management). The adoption of yield improving techniques has an abatement
potential of 27.2 Mt CO2e in 2030 through reduced deforestation, which has
been accounted for by the Forestry STC.

■ Creation of new agricultural land in arid areas through irrigation:
Through the use of small, medium, and large-scale irrigation schemes, new
agricultural land could be created from un-cultivated non-forest areas, thereby
reducing emissions from the expansion of total cropland. The creation of new
agricultural land in arid areas through irrigation has an abatement potential of
10.6 Mt CO2e in 2030, which is also accounted for by the Forestry STC.


Federal Democratic Republic of Ethiopia 139


FIGURE 51

Abatement potential in Mt
CO2e/ year achieved through
Programme promoting lower-
emitting techniques

0

10

20

30

40

50

60

70

20302025202020152010

Expected emissions
after abatement

40

Additional CO2 abatement
potential (accounted for by
REDD+ STC)

27

ΣΣΣΣ 30

11

Agriculture intensification
on existing cropland

Irrigation initiatives

Soil – Abatement and sequestration potential of 40 Mt per year
in 2030 (in addition to the 30 Mt accounted for in forestry)

Abatement measures
Mt CO2e/ year in 2030

BAU and abatement potential
Mt CO2e/ year

BAU emissions: 60.9 Mt in 2030

Mt CO2e
reduction
potential up to
2030

ΣΣΣΣ 40


FIGURE 52

Soil – Most abatement potential has either negative costs or
is below the average carbon price

5

0

-5

-10

-15

-20

-25

Abatement potential
MtCO2e per year

80757065605550454035

50

2520155050

Large-scale irrigation

46.58

30

6.18

Small-scale irrigation

-12.56

Yield-increasing techniques

-20.89

Abatement cost
USD per tCO2e

45

10

Lower-emitting techniques

Abatement opportunities cost curve


Federal Democratic Republic of Ethiopia 140


The cost curve depicted in Figure 52 shows a wide range of abatement costs, both
in the negative and positive areas.

■ Three of the four Soil sector initiatives have a cost below USD 10/t. This low
cost is due to the substantial economic benefits from preserving forestland for
productive use and increasing the economic return per hectare of cropland.

■ The higher cost of large-scale irrigation (USD 47/ton) is due to the significant
infrastructure investment required by the initiative.

The total investment cost that is required for all levers up to 2030 is about USD
30.5 billion, to which climate finance projects could make an important contribu-
tion.

Soil lever 1– Enhance lower-emitting techniques for
agriculture

Emissions from crops are set to grow rapidly over the next 20 years due to carbon-
intensive crop residue and tillage management practices, and the increasing usage
of manure and synthetic fertiliser. The introduction of lower-emitting techniques
for agriculture offers an opportunity to check this increase while maintaining pro-
duction levels. This initiative includes improved agronomic practices that increase
soil carbon storage, nutrient management to more efficiently use carbon/nitrogen,
improved tillage and soil management, integrated systems (mixed crop-livestock-
agri-forest), and water management (irrigation, terracing, and other water-harvest-
ing techniques). This programme would build on the existing government plans to
strengthen the agriculture extension system.

■ Soil nutrient and crop management. Improved agronomic practices can
lead to increased soil carbon storage. Examples of such practices include:
using improved crop varieties responsive to optimum external inputs
(fertilizers and pesticides); sowing forage legumes in growing cereal crops;
adopting cropping systems with reduced reliance on external inputs such as
green manuring of legume crops, double cropping of cereals, and use of
beneficial microorganisms and earthworms in compost making. Nitrogen
management should also be considered. Nitrogen causes significant leaching
and emissions. Employing techniques that could maximize the efficient use of
nitrogen on crops reduces N2O emissions. Examples of such practices include
adjusting application rates to crop needs and soil test-based nitrogen
application; applying nitrogen at times when loss is minimal; splitting
application rates between crop establishment and critical vegetative growth


Federal Democratic Republic of Ethiopia 141


periods and manipulating soil chemical properties (such as liming) to release
immobilised nutrients by raising soil pH to a neutral range.

■ Tillage/residue management. Soil disturbance tends to hasten
decomposition and erosion whereas reduced tillage results in soil carbon gain
and reduction of CO2 emissions. To achieve the latter effect, conservation
agriculture will be promoted, including the use of zero and minimum tillage
through the application of non-selective herbicides. The level of organic
matter in the soil depends on the inputs from plant growth by reducing the
losses due to erosion, harvesting, and microbial respiration. Even though
returning crop residues into the soil is one of the main emissions drivers,
reintroduction of an increased amount can maintain or enhance soil quality
and productivity through favourable effects on soil properties and life-
supporting processes. While emissions result from the practice, reintroduction
of crop residues increases the carbon stock of soil and, on balance, causes a
reduction of greenhouse gases into the atmosphere as compared with other
uses of crop residues. For example, avoiding burning and over-exploitation as
animal feed may help reduce organic matter loss in soils under cultivation.

■ Watershed-based integrated farming systems. Combining the production
of livestock and food crops on land that also grows trees for timber, firewood,
or other tree products would increase the standing stock of carbon above
ground relative to equivalent land use without trees. Examples of practices of
this type include shelterbelts, introduction of high-value tree crops such as
fruit trees, agri-silvopasture practices like growing fodder trees within crop
fields as source of livestock feeds, live fences, and multi-story crop
production.

■ Water management: This category includes the promotion of terracing,
particularly in hilly regions with high soil erosion hazards, and the improve-
ment of water harvesting and irrigation structures, such as providing supple-
mentary irrigation by focusing on increased water use efficiency, which can
enhance carbon storage in soils through enhanced yields and residue returns.

The lower-emitting techniques programme proposed by the STC would target 75%
of farmers, reaching over 13 million households by 2030 through the government
extension system. Through a combination of lower-emitting techniques tailored to
local soil conditions, weather, and crop-livestock mixes (i.e., different practices in
the highlands than in the lowlands), this initiative would lower emissions per hec-
tare by an average of 3 tonnes of CO2e per year and have an abatement potential
of 40 Mt CO2e in 2030. The estimate of the abatement potential of this initiative is


Federal Democratic Republic of Ethiopia 142


based on the UNIQUE 2010 study ‘Carbon Finance Opportunities in Ethiopia’s
Agricultural Sector’.

The abatement cost calculations for a programme introducing lower-emitting tech-
niques are based on the following set of assumptions:

■ Programme implementation in combination with a programme promoting
yield-increasing techniques (see Soil Lever 2) through the Ministry of
Agriculture extension service.

■ Household expenses totalling USD 62/household to bring households into
the programme, followed by USD 6 per household annually for running costs.
Estimates are based on the Sustainable Land Management project, discounted
by 50% to account for the joint implementation of the lower-emitting
techniques and yield-increasing techniques.

■ Supporting investments including nurseries (one per 20,000 households),
costing USD 50,000 each to set up,

■ Programme expenses amounting to USD 10 million for programme setup,
USD 17/household/year for the first three years that a household is in the
programme, and USD 5/household/year for monitoring and programme
management.

■ Research expenses of USD 11 million annually for federal and regional
research, based on the annual budget of EIAR.

These assumptions result in a cost of around USD 6 per Mt CO2e.

Soil lever 2 – Enhance yield-increasing techniques for
agriculture

There is significant potential to increase agricultural productivity. By boosting
yield per hectare and value per tonne of crops, it is possible to achieve the crop
GDP target of 9.5% per year without rapid expansion of the total land under culti-
vation. Through this initiative, it would be possible to achieve an annual yield
growth rate of 3.5% (as opposed to 2% in the BAU) and a value growth rate of 4%
(as opposed to 3.3% with BAU), thereby reducing the need for expansion of crop-
land to 1.7% per year (compared to 3.9% under BAU). These numbers are based
on averages for lowland and highland areas. The Soil STC estimated yield and
value growth rates under a yield-increasing programme using historical trends for
yield (CSA data) and value (Dorosh and Ahmed cereal price index). This initiative
would reduce the need for new cropland from 14.3 million additional hectares


Federal Democratic Republic of Ethiopia 143


under the BAU scenario to only 5.1 million additional hectares by 2030. The
proposed yield-increasing techniques include:

■ Improved seeds. Introduction of tissue culture, new varieties and high-
quality seeds to lower the incidence of pests and diseases and increase yield

■ Irrigation. Introduction of basic/low-cost irrigation systems to allow con-
tinuity of production, especially in the dry season, reduce variability of
output, and enable a shift to higher-value crops

■ Organic and inorganic fertiliser. Increase usage of slow-release fertilisers
and manure, thereby replenishing soil nutrients to ensure sustainable soil
fertility

■ Best agronomic practices. Introduction of planting, harvest, and post-harvest
management best practices to lower the incidence of pests and disease,
improve quality, and decrease spoilage.

The yield-increasing techniques programme proposed by the STC would target
75% of farmers, reaching over 13 million households by 2030. The programme
would build on the existing government plans to strengthen the agriculture exten-
sion system. This initiative would lower emissions by reducing the need for new
agricultural land by 9.1 million hectares. Given an average carbon sequestration
rate per hectare preserved of 53.5 tonnes of CO2e, this initiative has an abatement
potential of 27.2 Mt CO2e in 2030. This calculation is a conservative estimate of
the abatement potential as it does not count the reduction in soil-based emissions
from crop growing that would have occurred on land cultivated in the BAU
scenario but not in the scenario where this lever is implemented.

The abatement cost calculations for a programme introducing yield-increasing
techniques are based on the following assumptions:

■ Programme implementation in combination with a programme promoting
lower-emitting techniques (see Soil Lever 1) through the Ministry of
Agriculture extension service

■ Household expenses totalling USD 233 per hectare to bring land into the
programme, followed by USD 90 per hectare annually for running costs.

■ Supporting investments including:

– Seed production: six facilities costing USD 2,200,000 each to set up

– Fertiliser manufacturing plants: two facilities costing USD 100 million
each to set up


Federal Democratic Republic of Ethiopia 144


– Irrigation equipment production plants (pumps, agriculture equipment,
etc.): seven facilities costing USD 44.7 million each to set up

– Herbicide/pesticide/fungicide formulation plants: two plants costing
USD 5 million each to set up

– Tissue culture labs: 42 labs costing USD 3 million each to set up

■ Programme expenses amounting to USD 10 million for programme setup,
USD 50/household/year for the first year that a household is in the
programme, USD 25/household/year for the second and third year a
household is in the programme, and USD 10/household/year for monitoring
and programme management

■ Research expenses of USD 11 million annually for federal and regional
research, based on the annual budget of EIAR

■ Economic benefits are based on forestland preserved and improved
productivity from labour on the newly intensified land. First of all, savings on
forestland conserved are USD 7/ha/year. This estimate was discounted from
USD 14/ha/year to account for incomplete monetisation (original estimate
from ‘Ethiopian Forestry at the Crossroads’ report). The value is based on
GDP generated from forestland through foraging and gathering (i.e.,
gathering honey). Labour savings result from higher productivity of labour
per hectare of land and a reduced amount of hectares needed to achieve the
same production. In fact, the labour costs for intensified land might be
slightly higher (222 USD as compared to 192 USD per hectare) due to a
higher share of high value crops, but 4.69 times less land is needed when land
is intensified instead of using deforested areas. This factor is based on such
changes as a shift to higher value crops, better techniques, increased
irrigation, and having several harvests a year. Reduced savings from unsold
timber are minor due to the practice of burning forestland to acquire new
land. Alternatively, the wood is used as personal fuelwood, in which case the
costs of cutting the timber weighs up against the savings of using it as fuel.
Together, this leads to USD 91.3 million a year in cost savings from the
intensification programme.

These assumptions result in a cost of USD -21 per Mt CO2e.

Soil lever 3-4 – Creation of new agricultural land in arid
areas through irrigation (small scale and large scale)

These two initiatives reduce emissions by creating new agricultural land out of
uncultivated non-forest arid areas, thereby reducing the need for deforestation and


Federal Democratic Republic of Ethiopia 145


avoiding the associated emissions. The STC estimates that a total area of 1.7
million hectares of new agricultural land could be created through small- and
large-scale irrigation projects in arid areas based on estimates of total irrigable
land from the Bekele 2009 Irrigation Report and a feasibly factor for irrigation
projects of 64% based on the historic performance of irrigation projects.
Irrigation increases output from the land and avoids deforestation, both of which
constitute economic benefits. The main sources used by the STC include surface
irrigation potential estimates from Bekele 2009 Irrigation Report, expert
interviews, and statistics from MoWE, MoARD, and IWMI.

Given an average carbon sequestration rate per hectare preserved of 53.5 tonnes of
CO2e, the abatement potential in 2030 is 2 Mt CO2e for small-scale irrigation and
9 Mt CO2e for large-scale irrigation.

The abatement cost calculations for these initiatives were based on the following
set of assumptions:

■ Household expenses totalling USD 233 per hectare in capital expenditure for
watershed/irrigation for small-scale schemes and USD 3,552 for large-scale
systems, followed by USD 23/ha in annual operating costs for small-scale and
USD 178/ha for large-scale.

■ Supporting investments, including equipment production (pumps, irrigation
equipment, etc.): setup costs of USD 250,000 and annual operating costs of
USD 100,000 for small-scale irrigation, and setup costs of USD 5 million plus
USD 2 million per year in irrigation equipment/system operating costs for
large-scale systems.

■ Programme expenses amounting to USD 5 million for programme setup of
small-scale irrigation and USD 10 million for programme setup of large-scale
irrigation, extension programme operating expenses of USD 17/ha/year for
the first three years that the small-scale programme is introduced and USD
724/ha for the first three years that the large-scale programme is introduced,
and monitoring and management costs per household of USD 5 and USD 72
for small- and large-scale systems, respectively.

■ Research expenses of USD 2 million annually for small-scale irrigation
research and USD 10 million annually for large-scale irrigation research.

■ Economic benefits are based on both the deforestation that is avoided as well
as reduced labour costs due to improved output from the land. The savings
from deforestation are based on a value of forestland conserved of USD 7/ha/
year as specified in the yield-increasing lever above. The output from the
irrigated land improves by a factor of 2.29, reducing labour costs per hectare.


Federal Democratic Republic of Ethiopia 146


Together, these factors constitute USD 19 million per year in savings for
large-scale irrigation and USD 4 million per year in savings for small-scale
irrigation.

These assumptions result in a negative cost of USD -13 per Mt CO2e for small-
scale irrigation but a positive cost of USD 47 per Mt CO2e for large-scale
irrigation.

Without a doubt, small-scale irrigation has immediate benefits when implemented
and can be considered a profitable short-term investment. As alluded to before,
large-scale irrigation requires significant upfront capital as well as programme
expenditure. The option should only be considered in combination with other
initiatives to improve crop productivity, such as seed and extension programmes,
in order to make the investment more attractive and achieve positive returns in the
long run.

ABATEMENT LEVERS – FEASIBILITY AND ECONOMIC IMPACT
ASSESSMENT

Feasible levers with high impact

Three of the four Soil sector abatement levers (lower-emitting techniques, yield-
increasing techniques, and small-scale irrigation) were evaluated as being highly
feasible, though only the first two had substantial abatement potential.

Lower-emitting and yield-increasing techniques. These abatement levers were
deemed highly feasible due to the necessary technologies’ general availability,
suitability, and track record of success in pilots. In addition to carbon reduction
benefits, the yield-increasing initiative would have substantial socio-economic
benefits (increased household income, greater food security) as it would achieve
the crop GDP while preserving valuable forestland for other economically
beneficial uses (forest ecosystem services and sustainable logging). The key
challenges of implementing these initiatives would be to strengthen the grassroots
agricultural extension system and the regional and federal crops-related
institutional setup, provide financing to farmers for improved inputs and
equipment, overcoming farmer reluctance to change certain longstanding
agricultural practices (e.g., use of undirected flood irrigation, use of unblended
fertiliser), and fragmented land use in the highlands, which hampers community-
level adoption of technologies. Despite these obstacles, the Soil STC evaluated
this high-potential initiative as highly feasible.


Federal Democratic Republic of Ethiopia 147


Small-scale irrigation has a modest abatement potential (2 Mt CO2e in 2030), but
the abatement lever’s strong socio-economic benefits (increased farmer household
income, greater food security, etc.) make it an attractive initiative.

Other levers

Large-scale irrigation was evaluated by the STC as being only moderately feasi-
ble due to substantial technical and institutional implementation challenges –
similar to the challenges faced by large-scale irrigation projects in Ethiopia in the
past. The technical requirements for large-scale irrigation may be an issue in some
scantly visited areas and there are significant equity issues related to the benefits
created by irrigation. The expenditure required for large-scale irrigation may also
be prohibitive. Nevertheless, large-scale irrigation’s abatement potential of 9 Mt
CO2e in 2030 and sizeable socio-economic benefits (increased farmer household
income, greater food security, etc.) also make it an attractive abatement lever.

Additional levers that may be considered in future include indigenous agroforestry
practices as well as wetland management. Agroforestry practices may prove to be
a more sustainable form of farming than traditional land exploitation practices.
Wetlands form a great source of methane, which, if it can be captured, would offer
another great source for GHG abatement. These additional levers may also
generate numerous other societal benefits.

ABATEMENT LEVERS – IMPLEMENTATION TIMELINE AND RESOURCE
REQUIREMENTS

Implementation timeline

The Soil STC has selected two priority initiatives based on implementability,
abatement potential, and cost attractiveness. Yield-increasing and lower-emitting
techniques seem to outscore the other initiatives on these criteria (Figure 53). All
of the initiatives are envisaged to have a kick-off in the near future. The levers will
be implemented in phases with the programme reach developing linearly over the
years. While technically feasible, the immediate start of the initiatives is dependent
on sufficiently available funds. It is also important to mention that these dates
mark the start of the implementation, which includes some required preparatory
work (e.g., development of investment plans), and is subject to approval by the
respective authorities. Hence, the full impact of the initiatives will only occur later
in most cases.


Federal Democratic Republic of Ethiopia 148


FIGURE 53

Soil – Overview of timeline for implementation of initiatives

2012 2013 2014 2015

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Lower-emitting
crops

Yield-increasing
techniques

Large-scale
irrigation

Small-scale
irrigation

Activity

Selected as priority initiative

Other initiatives


Resource requirements and existing projects

The soil initiatives will require an investment of USD 30.5 billion if it is to start
being implemented immediately. Of the total, the largest expenditure is the
running operating costs per year. The expenditure for the period until and
including 2015 is USD 7.4 billion (Figure 54). Operating expenditure dominates
for this period as well. The programme costs are quite significant for each of the
periods as compared to the capital expenditure. Existing programmes may be
leveraged to limit these costs.

The cost savings in both the periods of 2011-2015 and 2011-2030 are quite sig-
nificant as well. In fact, because of these cost savings, the initiatives have a posi-
tive return in the short and the long run on average. These cost savings can be
attributed almost entirely to the yield increasing initiative which has benefits of
USD 600 million each year. These benefits both cover the significant costs of yield
increasing as well as large-scale irrigation.


Federal Democratic Republic of Ethiopia 149


FIGURE 54

Soil – Financial overview of all initiatives

Million USD
Short-term: 2011-2015 Long-term 2011-2030

CAPEX1 additional

OPEX additional
(excl. programme cost)

Programme cost

Savings/income

Abatement expenditure
before carbon revenue 360

7,710

1,390

4,120

1,840

310

30,850

6,190

17,130

7,220

▪Yield intensification accounts for most of the overall
savings

108221482

1 Full capital expenditure, not amortised
2 Aggregated abatement potential; expenditure per t CO2e not equivalent to abatement cost in cost curve, as the CAPEX abatement expenditure is not

annualised via amortisation (rather: cash-flow perspective)

Mt CO2e total aggregated abatement potential

Mt CO2e
abatement
potential


Figure 55 shows a categorisation of the different initiatives.

Category A represents investments that achieve a positive net present value in the
first five years. This category includes both the yield increasing and the small-
scale irrigation initiatives. The bulk of the investment is for yield increases,
namely USD 11.9 billion. The USD 17.7 billion in category C has no positive net
present value. These large-scale irrigation and lower-emitting crop initiatives will
need to be financed through performance-based grants, CDMs, or similar forms of
financing. At USD 14.3 billion, most of the investment in this category is needed
for the large-scale irrigation initiative.

There are currently several programmes running in the Soil sector already,
including the GTP and the Agricultural Growth Plan, which promote the
implementation of irrigation and other soil-related initiatives.


Federal Democratic Republic of Ethiopia 150


FIGURE 55

Soil – 42% of cost will have positive returns in the short run,
but 58% will need some form of performance-based financing

Category C
No positive return,
grants/performance

pay requirement

17,740

Category B
Positive return, but
long term financing

requirement

0

Category A
Positive return,

short term financing
requirement

12,800

Million USD, total cost1 Percent of total cost

42% 0% 58%

1 Including additional CAPEX, additional OPEX, and programme cost
2 NPV calculated with 6% discount rate

Definition Positive NPV2 of first
five years of cash flow
(from start of initiative)

Positive NPV2 of
overall initiative (from
start of initiative up to
2030), but not after first
five years

Negative NPV2 of
overall initiative (from
start of initiative up to
2030)


Federal Democratic Republic of Ethiopia 151


Industry

Industry is the sector with the highest growth in GHG emissions up to 2030. Under
BAU assumptions, emissions will rise from 4 Mt CO2e in 2010 to 71 Mt CO2e in
2030. The Industry STC has identified and evaluated 37 abatement levers for 12
industry segments, with a total gross abatement potential of 22 Mt CO2e in 2030.
The vast majority of the emissions growth and abatement potential is in the cement
industry, which has a gross abatement potential of 16 Mt CO2e.

SCOPE AND INSTITUTIONAL SETUP

The Industry STC (Table 11) is composed of experts from industries and related
institutes of the Ministry of Industry that focus on particular industries and from
the Ministry of Mines. The STC focused on calculating current and future emis-
sions and analysed abatement levers for five sub-sectors.

TABLE 11


The following five sub-sectors comprise 12 individual industries that make up the
major part of Ethiopia’s industrial activities (and hence account for industrial the
country’s GHG emissions) as planned in the GTP:

■ Cement

■ Textile and leather

■ Steel and engineering

■ Chemicals (including fertilizer), paper and pulp, and food processing

■ Mining (including gold, coal, potash and others)


Federal Democratic Republic of Ethiopia 152


GHG EMISSIONS BASELINE IN 2010 AND BAU UP TO 2030

Under BAU assumptions, GHG emissions caused by the Industry sector will
increase from around 4 Mt CO2e in 2010 to 71 Mt CO2e in 2030. The single most
important driver is the cement industry, followed by the chemical industry and
steel/engineering (Figure 56).

FIGURE 56

Industry – GHG emissions are projected to rapidly increase
from 4 Mt CO2e in 2010 to 71 Mt CO2e in 2030

Baseline and BAU overview

Mt CO2e/ year

Cement

Steel/
Engineering

Mining

Others1
Textile/Leather

BAU
2030

71

BAU
2020

38

Baseline
2010

4

1 Chemicals (including fertiliser), food processing, paper and pulp industry


Main drivers of GHG emissions

The emissions of the industry sector are essentially driven by the volume of
production in each industry and the emission factors per unit of production
(Figure 57).


Federal Democratic Republic of Ethiopia 153


FIGURE 57

Industry – Estimation of changes with time of the main
emission drivers

Output overview

65.8
37.7

2.7

359.4

124.3
43.0

30.0

10.4
0.4

10,1009,144
3,907

Steel and engineer-
ing production
Billion USD

Key emissions
drivers Projected change Rationale

▪ Projection based on GTP up to 2015

▪ After 2015 growth to middle-income
country level of cement consump-
tion per capita (0.5t) up to 2030

Cement production
Million tonnes

▪ Projection based on data from GTP
and Textile Institute planning

Gold mining and
processing
Kg

▪ Projection based on GTP
▪ Growth forecast in GTP to 2015
▪ After 2015 1% growth p.a. (naturally

constrained growth)

Textile production
Thousand tonnes

▪ Projection based on GTP up to 2015

▪ After 2015 growth of 11.2% (in line
with assumed economic growth)

Fertiliser production
Million tonnes

▪ Baseline estimated with data from
Metal and Engineering Corporation1

▪ Forecast based on total domestic
fertiliser demand203020202010

6.7

2.4
0

1 Assumption: Actual production starts at 2/3 of production capacity


■ The development of the production volume of major industries is displayed
in Figure 57. Most of the industries are expected to increase their production
volumes significantly between 2010 and 2030. Production in the cement
industry, as the major industrial driver of emissions, is projected to grow
more than 20-fold in 20 years, while steel and engineering as well as fertiliser
production are forecast to grow from a very small or non-existent base to sig-
nificant volumes. The data for the volume of production in each industry
came from the CSA and the GTP as well as from different departments of the
Ministry of Industry and related institutes. For production forecasts, the team
used growth rates indicated in the GTP whenever possible as well as growth
rates for the industry sector as a whole and extrapolated these based on the
assumptions outlined below. Selected expert estimates for maximum produc-
tion volumes in 2030 were also considered.

■ Emission factors per unit of production are assumed to be constant in the
BAU scenario, which does not include replacement of current technologies
with lower-emitting techniques. Data for the emission factors were provided
by the IPCC or drawn from international benchmarks (e.g., US Economics
and Statistics Administration emission factors, scientific studies), and from
studies in Ethiopia (e.g., on emissions of the leather production process).


Federal Democratic Republic of Ethiopia 154


GHG emissions baseline and BAU projection for 2030

In the business-as-usual (BAU) scenario, the expected development of the industry
sectors analysed and the main assumptions for these sectors are as follows:

■ The cement sector is the most important driver of emissions. Its contribution
will increase significantly from nearly 2 Mt CO2e in 2010 to over 45 Mt CO2e
in 2030. Major emission sources are the clinker production process, which
releases significant amounts of CO2 in preparing the input materials for use,
and the fuel (mainly coal) that is consumed during clinker production.19
According to the GTP, cement production will have grown by 10 times or
more by 2015. Thereafter, cement production is assumed to increase up to a
per capita level of cement use typical for a middle-income country (around
500 kg per capita) by 2030. In total, annual cement production will rise from
nearly 3 Mt in 2010 to more than 65 Mt in 2030.

■ Though the textile and leather sector plays a less important role, its volume
of GHG emissions is projected to rise from 0.6 Mt CO2e in 2010 to almost
5 Mt CO2e in 2030. The main drivers of emissions here are the furnace oil
used in the production process and the effluents being discharged. A detailed
evaluation of the processing steps in leather production shows that leather is
responsible for only a minor share of the emissions. The major share is attrib-
utable to the textile industry, which, according to GTP projections, will ini-
tially expand rapidly (increasing the value of production fivefold to more than
USD 2.5 billion by 2015) and will then grow thereafter in line with overall
economic growth.

■ The steel and engineering sector in Ethiopia, while relatively small today, is
expected to emit more than 5 Mt CO2e in 2030. The GTP forecasts that this
industry will have grown by 15 times or more by 2015 and will increase after
that in line with overall economic growth. The team calculated expected
emissions based on the value of output and international benchmark data for
emission factors.

■ Other sectors include the chemical sector (typical products are caustic soda,
soda ash, and fertilizers), food processing, and the paper and pulp industry.
Although these industries individually contribute only a relatively small share
of GHG emissions, (mainly from chemical processes and energy input, e.g.,
fuel oil or coal), they collectively constitute a significant source of emissions.


19 Both of these major emission sources have been estimated conservatively; depending on the weighted
average of the cement factories’ efficiency, the emissions related to cement dust and CaCo3 caused in the
production process could be higher.


Federal Democratic Republic of Ethiopia 155


Overall, GHG emissions from the chemical sector are expected to increase
from 0.1 Mt CO2e in 2010 to 11 Mt CO2e in 2030, with the fertiliser industry
accounting for the bulk (around 9 Mt CO2e in 2030).

■ For the mining sector, the team evaluated several products and processes
such as gold mining and processing, tantalum, potash, and coal. Taken
together, emissions are projected to grow from nearly 1.5 Mt CO2e in 2010 to
almost 4 Mt CO2e in 2030.

ABATEMENT LEVERS – POTENTIAL AND COST CURVE

The Industry STC has identified and evaluated 37 abatement levers for 12 industry
sub-sectors, with a total gross abatement potential of 22 Mt CO2e in 2030 (Figure
58). The vast majority of this potential comes from the cement industry, which has
a gross abatement potential of 16 Mt CO2e.

FIGURE 58

69

Industry – Identified abatement potential until
2030 is up to 22 Mt CO2e p.a.

Mt CO2e reduction
potential in 2030

1 Represents total identified gross potential, some measures are not additive
2 Chemicals (including fertiliser), food processing, paper and pulp industry
3 Assuming full implementation of all levers

0

10

20

30

40

50

60

70

80

20302025202020152010

Expected emissions
after abatement

Σ 22

Abatement measures1

Mt CO2e/ year in 2030
BAU and abatement potential
Mt CO2e/ year

BAU emissions: 71 Mt in 2030

Textile/Leather2

Others2/Mining4

Cement16

Net potential after
accounting for non-
additive levers3

20


Federal Democratic Republic of Ethiopia 156


The majority of the abatement levers falls under the following headings:

■ Energy efficiency (e.g., retrofitting factories with modern production tech-
nologies; improving insulation, recovering waste heat, and using cogenera-
tion)

■ Alternative fuels (e.g., switching from coal/furnace oil to biomass/biofuels or
electricity)

■ Alternative production processes (e.g., replacing chemicals with enzymes,
clinker substitution)

■ Carbon capture and supply to other industries which use carbon as an input
into their production process; mineralisation.

It should be mentioned that some of the levers are not fully mutually exclusive.
Hence, the total net potential (i.e., after accounting for non-additivity) for full
implementation of all levers is 20 Mt CO2e.

As the cement industry offers the largest abatement potential, the Industry STC
concentrated on evaluating the levers in this industry segment. Hence, the cost
curve depicted in Figure 59 displays abatement levers from the cement industry
only. The net potential of all cement initiatives – if they are all fully implemented
– is around 14 Mt CO2e in 2030. In addition to cement, the chemicals sector
(including fertiliser), food processing, and paper and pulp provide abatement
potential of another 4 Mt CO2e in 2030, while the textile and leather industry can
mitigate nearly 2 Mt CO2e in 2030.

As most of the levers are related to energy efficiency or reduction of relatively
expensive input materials, they have a negative abatement cost, i.e., the cost of
installing and operating the respective technologies is more than offset by the
savings (e.g., on fuel), resulting in a net gain for the implementing cement pro-
ducer. However, since most of the levers require some upfront capital investment,
they have not been widely adopted so far.

The total investment cost that is required for all levers – i.e., for a complete green
retrofitting of the cement sector up to 2030 – is about USD 4.9 billion. In the long
run, this will be more than offset by the savings incurred, but this amount
emphasizes the challenge that lies ahead if we want to capture all our emissions
abatement potential.

The following sections describe the six cement levers in more detail, explaining
both the technical rationale as well as the assumptions for the calculation of
abatement potential and abatement cost.


Federal Democratic Republic of Ethiopia 157


FIGURE 59

Industry – All currently feasible abatement levers have a
negative abatement cost, making them attractive to implement

-100

0

Abatement
cost
USD
per tCO2e

-20

-40

-60

-80

-34

1614121086420

Increase biomass
(agri-residues)
content

-1

Energy-efficiency equipment
(grate cooler)

Energy-efficiency equipment
(computerized energy management)

-35

Energy-efficiency equipment
(precalciner kiln)

-75

Waste heat recovery

-94

Clinker
substitution
(e.g. pumice)2

-97

Abatement potential1

MtCO2e per year

1 Represents total identified gross potential, some measures are not additive (total net potential is less than sum of gross potentials, ~24 Mt)
2 Production cost only; does not include potential price decrease for grade IV cement

Abatement opportunities cost curve (cement levers only)


Cement lever 1 – Clinker substitution

GHG emissions in the production of cement are caused not only by the energy that
is used in production but also by the emissions from the clinker production proc-
ess. Reducing the ingredient share of clinker by replacing it with other additives
(e.g., fly ash, pumice) can significantly reduce these emissions. Pumice is poten-
tially the most readily available additive in Ethiopia. With a shift to coal as the
major fuel for the cement industry, fly ash might also become readily available (as
it is a byproduct of burning coal).

For the calculation of the abatement potential, the following assumptions were
made:

■ Of the total cement production, 20% is OPC (no additives), 80% PPC (with
additives). Currently, PPC cement has a clinker content of around 68%, the
rest consists of additives (32% of total volume, composed of around 5% gyp-
sum and 27% pumice).

■ Ethiopian standards for cement (ES1177-1; in line with common European
standards) allow companies to increase additives (for PPC) to up to 55%
(incl. gypsum) for grade IV (CEM IV/B) cement, leaving an incremental 23%


Federal Democratic Republic of Ethiopia 158


of total volume to be substituted by additives such as pumice. This cement
can be used for lower-cost construction (e.g., residential construction), which
is assumed to constitute 45% of total cement consumption.

■ The team calculated the abatement potential by using the same process emis-
sion factors for the clinker production process that were used to compute
BAU (0.51 t CO2e/t clinker). In addition, the energy requirements of the
clinker production process were counted towards the abatement potential
(4.5 GJ/t clinker).

The substitution of clinker is implementable from 2012 onwards, starting with an
annual gross abatement potential of around 1 Mt CO2e. The potential rises to
nearly 5 Mt CO2e in 2030 (in line with the overall projected increase in cement
production).

The cost of this lever is composed of the initial capital expenditure (per t of pro-
duction capacity), which is depreciated over 50 years, as well as operating and
maintenance costs. Fuel costs, on the other hand, are expected to decline. As total
savings are higher than the investment and operating costs, this lever has a
negative abatement cost of -97 USD/t CO2e.20

In addition to the substitution of clinker by additives, the substitution of conven-
tional limestone as an input material for the production of clinker (e.g., by
limestone with a low carbon content) could be considered to increase the abate-
ment potential.

Cement lever 2 – Waste heat recovery

Since the cement production process is very energy intensive, waste heat recovery
can have a significant effect. The team estimated a potential reduction of (thermal)
energy needed of 0.2 GJ/t, i.e., from 4.5 to 4.3 GJ/t of clinker. This lever can be
put in place starting in 2013 and would reach its full potential (i.e., providing
retrofits to existing cement works and covering all new production capacity) in
2014. The gross abatement potential will grow in line with projected cement
industry growth from 0.1 Mt CO2e in 2013 to around 1 Mt CO2e in 2030.

Initial capital expenditure needed for technology and retrofits (per t of production
capacity) will amortise over 30 years. Annual operation and maintenance are esti-
mated to incur costs of 10% of capital expenditure. Since the energy savings far


20 The savings in increasing additives might have to be passed on to consumers (not separately regarded)


Federal Democratic Republic of Ethiopia 159


exceed the investment and operating costs, this lever offers a negative cost of -94
USD/t CO2e.

Cement levers 3, 4, 5 – Energy efficiency equipment

The Industry STC evaluated three distinct technologies that increase energy effi-
ciency in the cement production process:

■ Converting from preheaters to precalciner kilns, which can reduce energy
requirements by up to 12%

■ Converting from rotary to grate coolers, which can reduce energy require-
ments by up to 8%

■ Introducing computerised process control and energy management, which can
reduce energy requirements by up to 4.5%

Implementation of all three technologies can start in 2012 or 2013 – with a ramp-
up over two years and covering all new cement production capacity thereafter. The
total gross abatement potential is projected to increase in line with cement indus-
try growth from 0.8 Mt CO2e in 2012 to more than 5 Mt CO2e in 2030.

As far as the abatement costs are concerned, all three of these abatement levers
have been attributed capital expenditure that is depreciated over 25 years minus
the savings in energy consumption (in the case of grate coolers, the higher cost for
electricity usage has been included as well). For all technologies, the savings more
than offset the cost in the long run, leading to a negative abatement cost of -75
USD/t CO2e (precalciner kiln), -35 USD/t CO2e (computerised energy manage-
ment), and -34 USD/t CO2e (grate cooler).

Cement lever 6 – Increase biomass (agri-residues)
content

An increase in the content of biomass has been projected to generate an abate-
ment potential of up to 4.2 Mt CO2e in 2030, based on the following assumptions:

■ Up to 20% of the energy needed for cement production can be generated from
biomass instead of conventional fuels (mainly coal).

■ Increased biomass-fuelled energy can be implemented in the cement industry
from 2014 onwards, with a ramp-up to the full 20% potential within three
years.

■ The biomass to be used for the replacement of conventional fuels will come
only from agricultural/wood residues (agri-residues). New areas of land for


Federal Democratic Republic of Ethiopia 160


biomass production will therefore not be required (although the soil might
decrease in fertility if the biomass was used as an organic fertiliser before and
animal feed might be reduced – hence, the impact needs to be tested during
the further development of the initiative).

■ The same emission factors for conventional fuel are used as in the BAU
calculations (0.1 t CO2e/GJ).

The total abatement potential is projected to increase from around 0.3 Mt CO2e in
2014 to 4.2 Mt CO2e in 2030 (in line with cement industry growth).

The abatement cost has been calculated based on a detailed study by UNDP at
Mugher Cement on using biomass in the cement industry. The total cost is
-1 USD/t CO2e. The CAPEX includes all the equipment necessary to make bio-
mass usable (i.e., choppers, briquettors, etc.) and is amortised over 20 years. The
operational cost includes the transport and delivery cost (which is adjusted
upwards from the UNDP report to represent a more conservative collection cost of
more than 5 USD/t) and is netted out against the savings achieved from the
decrease in conventional fuel usage.

ABATEMENT LEVERS – FEASIBILITY AND ECONOMIC IMPACT
ASSESSMENT

Feasible levers with high impact

All abatement levers related to changing cement composition, fuel usage, and
energy efficiency equipment (1 to 6) are generally technically feasible, but need to
be optimized for our context and the existing technical standards. However, since
they require very high capital expenditures as well as decisions by several
public/private cement producers, the barriers to implementation should be further
clarified. A concerted effort seems necessary to convince cement producers of the
economic and ecological benefits of the measures and to offer financing instru-
ments supporting the required investments (e.g., by supporting CDM proposals).
A further feasibility prerequisite, particularly for the clinker substitution initiative,
is the existence of a strong standardization and regulatory policy governing the
production and application of the different types of cement. The current regulatory
setup should be complemented to prevent misuse of the standards or wrong appli-
cation of the respective cement types.

In addition, some capability building will be required, particularly for technically
more advanced measures, such as computerised energy management. Moreover,
more detailed evaluation is needed, particularly of the initiative on increasing


Federal Democratic Republic of Ethiopia 161


biomass, with regard to availability, the collection process and competing with the
use of residues as fertiliser and animal feed to ensure that the initiative is
implemented in a feasible and sustainable way.

If these barriers to implementation can be overcome, the six abatement levers
evaluated by the Industry STC support the targets of economic development and
might even prove a catalyst in surpassing them in several different ways:

■ The reduction in production cost of PPC cement with a higher additives con-
tent, for example, might be partly passed on to the consumer by reducing the
market price for this type of cement. This might in turn stimulate construc-
tion, also in the residential market or by relatively less wealthy consumers,
leading to employment, value added, and capital formation.

■ Besides this effect, the substitution and decrease of the required amount of
fossil fuels (coal and furnace oil) will significantly reduce the otherwise nec-
essary imports and save valuable foreign currency.

■ In addition, selling biomass to cement factories might increase the income of
farmers (although it might reduce soil fertility or animal feed if the biomass
were otherwise used as an organic fertiliser or feed for livestock).

In terms of sequencing the individual initiatives, it makes sense from the point of
view of the cement factories involved to start with clinker substitution and then
sequentially build the more CAPEX-intensive energy efficiency equipment.
However, for all newly built production capacity (which will soon represent the
major part of the capacity), it makes sense to build in this upgraded equipment
straight away, if the necessary financing is available. Increased biomass usage can
be implemented in a ramp-up over several years.

Other levers

Carbon capture and storage (CCS) technologies are still at a nascent stage and are
assumed to represent little or no significant abatement potential for Ethiopian
industry. Also, the cost that is typically found in international benchmarks – regu-
larly estimated to be around 50 USD/t CO2e – is much higher than the cost that has
been computed for other industry-related levers. Hence, carbon capture and
storage was excluded from detailed study.


Federal Democratic Republic of Ethiopia 162


ABATEMENT LEVERS – IMPLEMENTATION TIMELINE AND RESOURCE
REQUIREMENTS

Implementation timeline

On the basis of the abatement potential and feasibility assessment, the Industry
STC has selected initiatives for particular attention and immediate implementation
efforts, including substituting pumice for clinker and the several energy efficiency
levers (Figure 60). Scale-up of all of these measures is envisaged for 2012 – with
the exception of the energy-efficient grate coolers that are scheduled to start
implementation in 2013. The scale-up of these initiatives is assumed to be staged
over two years for existing facilities and to immediately affect any newly built
capacity. Hence, it is important to mention that the starting dates only mark the
beginning of the implementation, which for some initiatives is staged across
several years, includes some required preparatory work (e.g., development of
investment plans), and is subject to approval by the respective authorities and the
availability of funding. Thus, the full impact of the initiatives will only occur later
in most cases. The other initiatives in the cement industry are currently envisaged
for implementation from 2013 (waste heat recovery) and 2014 (increase of
biomass content) onwards.

FIGURE 60

Industry – Overview of timeline for implementation of
initiatives

2012 2013 2014

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Starting at various times between 2012 and 2017Initiatives in
other sectors

Increase biomass
(agri-residues)

Waste heat
recovery

Energy efficiency
(precalciner)

Energy efficiency
(grate cooler)

Energy efficiency
(energy mgmt)

Clinker
substitution

Activity

Selected as priority initiative

Other initiatives


Federal Democratic Republic of Ethiopia 163


Implementation of initiatives in other sub-sectors needs to be started at several
points in time from 2012 onwards (a detailed sequencing of these – around 30 –
initiatives has been conducted by the STC).

Resource requirements and existing projects

The cement industry initiatives (i.e., initiatives for which cost has been evaluated)
will require a total expenditure of around USD 6.2 billion in the long run (i.e., up
to 2030). Of this total, about USD 4.9 billion is capital expenditure and USD 1.3
billion is operating expenditure. Around USD 2.1 billion of the total expenditure
will already be required in the short term, i.e., up to 2015 (Figure 61).

FIGURE 61

Industry – Financial overview of all cement initiatives

Million USD
Short-term: 2011-2015 Long-term 2011-2030

CAPEX1 additional

OPEX additional
(excl. programme cost)

Programme cost

Savings

Abatement expenditure
before carbon revenue -710

1,400

0

110

2,000

9,630

15,840

0

1,340

4,870

▪ Initiatives only start in 2012/13 with high upfront invest
▪Fuel savings will overcompensate cost after 5-7 years

1772132

1 Full capital expenditure, not amortised
2 Aggregated abatement potential; expenditure per t CO2e not equivalent to abatement cost in cost curve, as the CAPEX abatement expenditure is not

annualised via amortisation (rather: cash-flow perspective)

Mt CO2e total aggregated abatement potential

Mt CO2e
abatement
potential


Most of the initiatives offer substantial reduction of energy consumption and
hence of operating expenditure for the implementing cement producers; these
savings are expected to more than offset the initial expenditure. Around
USD 1.4 billion in savings will be generated in the short run up to 2015 and
more than USD 15.8 billion of savings can be generated up to 2030.

The expenditures will have positive returns for all initiatives (Figure 62). Due to
the high upfront investment cost for the energy efficiency technologies, most of


Federal Democratic Republic of Ethiopia 164


the expenditures – particularly for the energy efficiency levers and waste heat
recovery – only pay back in the long run. Hence, most of the expenditure can be
classified in category B – i.e., have a positive return, but only in the long run and
therefore need long-term financing. The remaining levers do not have very high
upfront CAPEX and will pay back in less than five years.

FIGURE 62

Industry – 100% of cost will have positive return, but more
than 90% will need long-term financing

Industry
(all cement
Initiatives,
excluding
CCS)

Category C
No positive return,
grants/performance

pay requirement

0

Category B
Positive return, but
long term financing

requirement

5,710

Category A
Positive return,

short term financing
requirement

500

Million USD, total cost1 Percent of total cost

8% 92% 0%

1 Including additional CAPEX, additional OPEX, and programme cost
2 NPV calculated with 6% discount rate

Definition Positive NPV2 of first
five years of cash flow
(from start of initiative)

Positive NPV2 of
overall initiative (from
start of initiative up to
2030), but not after first
five years

Negative NPV2 of
overall initiative (from
start of initiative up to
2030)


Because the initiatives target corporate cement factories’ operations, most of them
will have to be driven by the public and private sectors working together. Several
of the levers are currently being evaluated or being proposed for CDMs. As
several donor organisations are also actively evaluating green economy levers for
the Ethiopian industry, the scale-up initiatives for efficient technologies may be
able to build on the experience of pilots.


Federal Democratic Republic of Ethiopia 165


Transport

Under the BAU scenario, emissions from the Transport sector will increase from
5 Mt CO2e in 2010 to 41 Mt CO2e in 2030. Leapfrogging to new technologies in
transport offers an abatement potential of up to 13.2 Mt CO2e in 2030. The major
initiatives proposed by the STC are improving Addis Ababa public transit by
building a light-rail transit system and a bus rapid transit system; improving
vehicle efficiency by applying fuel efficiency standards, promoting clean fuel
blends (biodiesel and ethanol), adopting hybrid and plug-in electric vehicles, and
shifting freight transport from road to an electric rail network. Shifting freight to
electric rail is the single largest abatement lever in the Transport sector, with a
potential of 8.9 Mt CO2e.

SCOPE AND INSTITUTIONAL SETUP

The Transport sector includes passenger and cargo transport by road, air, sea, and
rail. The Transport STC (Table 12) calculated current and future emissions for all
transport segments and analysed abatement levers. The STC is composed of mem-
bers from the Ministry of Transport, the Ministry of Water and Energy, the
Ministry of Agriculture, the Environmental Protection Authority, the Addis Ababa
City Administration, and the Ministry of Urban Development and Construction.

TABLE 12

▪Yetmyet Birhanu (Chair)
▪Robel Meseret
▪Dinberu Girma
▪Tesfaye Abebe
▪Dereje Abebe
▪Gebreselassie Gebreamiak
▪Fetiya Ahimed
▪Sebsibe Tadesse

▪MoT
▪MoT
▪MoT
▪MoWE
▪MoA
▪EPA
▪Addis Ababa city admin
▪MoUDC

STC members (role) Institution

▪Yetmyet Birhanu (Chair)
▪Robel Meseret
▪Dinberu Girma
▪Tesfaye Abebe
▪Dereje Abebe
▪Gebreselassie Gebreamiak
▪Fetiya Ahimed
▪Sebsibe Tadesse

▪MoT
▪MoT
▪MoT
▪MoWE
▪MoA
▪EPA
▪Addis Ababa city admin
▪MoUDC

STC members (role) Institution


Federal Democratic Republic of Ethiopia 166


GHG EMISSIONS BASELINE IN 2010 AND BAU UP TO 2030

Emissions from the Transport sector are mainly from road transport, particularly
freight and passenger vehicles, and, to a lesser extent, construction vehicles. Air
transport also contributes a significant share (1.1 Mt CO2e or 23% of transport-
related emissions in 2010). Under the BAU scenario, emissions from transport will
increase from 4.9 Mt CO2e 2010 to 40.7 Mt CO2e 2030 (Figure 63).

FIGURE 63

Transport – Level of GHG emissions increases eightfold
until 2030 under the business-as-usual scenario

Output overview

Mt CO2e/ year

Passenger: Intra-city

Passenger: Inter-city

Passenger: International

Freight: Dry cargo

Freight: Construction
and mining

Freight: Liquid cargo

Freight: International

BAU
2030

41

Baseline
2010

5

BAU
2020

14


Main drivers of GHG emissions

The main drivers of GHG emissions from the Transport sector as well as the main
assumptions about their impacts and development are detailed below (Figure 64).


Federal Democratic Republic of Ethiopia 167


FIGURE 64

Tonne-km of
cargo transported/
year1

Billion tonne-km

Transport – Estimation of changes with time of the main
emission drivers

Output overview

220

95

40

203020202010

Key emissions
drivers Projected change Rationale

313

91
26

203020202010

▪ Increase in passenger-km
travelled projected based on
elasticity of passenger-km
to real GDP, using GTP’s
GDP target extrapolated to
2030

Passenger-km
travelled/year
Billion passenger-
km

▪ Increase in tonne-km of
cargo transported based on
elasticity of passenger-km
to real GDP, using GTP
GDP target extrapolated to
2030

1 Includes construction and mining transport activity


■ Increase in tonne-kilometres of freight transported. Inland freight is trans-
ported almost exclusively by road due to the poor condition of the rail
network. Emissions from dry cargo road transport constitute the majority of
all transport emissions (20.7 Mt CO2e) in 2030. International air and sea
freight, and inland ship-based cargo transport each account for a small share
of emissions. The STC estimates an annual growth rate ranging from 12.4%-
13.7% in tonne-km of freight transported. This estimate was calculated using
the elasticity of diesel imports to real GDP based on National Bank of Ethio-
pia’s statistics and GDP growth rates as projected by the GTP and by
EDRI/MOFED.

■ Increase in passenger-kilometres travelled. Passenger transport emissions
are driven primarily by international air travel, followed by inter-city and
intra-city road transport. Growth in air travel was forecast using Ministry of
Transport statistics. Passenger road transport emissions are driven by an old
and inefficient fleet composed of 240,000 vehicles, with an average age of
15 years. The passenger fleet consumed 0.6 billion litres of imported fossil
fuel in 2010. The increase in road passenger-km travelled was forecast at an
annual growth rate of 8.3%-9.1%. This estimate was calculated using the
elasticity of passenger-km to real GDP based on the Ministry of Transport’s


Federal Democratic Republic of Ethiopia 168


statistics for the past ten years and GDP growth rates as projected by the GTP
and by EDRI/ MOFED.

■ Increase in construction and mining transport. Emissions from construc-
tion and mining were accounted for using Ministry of Transport statistics.
Their annual growth rate was projected at 12.4%-13.7% based on the elastic-
ity of diesel imports to real GDP, which was calculated using the National
Bank of Ethiopia’s statistics and GDP growth rates as projected by
EDRI/MOFED.

GHG emissions baseline and BAU projection for 2030

Emissions from transport will increase from 5 Mt CO2e in 2010 to 41 Mt CO2e in
2030 (see Figure 63), mainly driven by a rapid increase in dry freight road
transport and international passenger transport.

■ Freight transport. The rapidly growing economy will bring with it a strong
need for freight transport, leading to steep growth in tonne-km of freight
transported from 23 billion in 2010 to 279 billion in 2030. This will result in
emissions rising from 2.0 Mt CO2e in 2010 to 24.1 Mt CO2e in 2030. The
BAU scenario assumes that the average fuel efficiency of the freight vehicle
fleet will improve by 3.3% from 2010 to 2030.

■ Passenger transport. Driven by an increasing population, a strong
urbanisation trend, and a growing per capita GDP, total travel measured in
passenger-km will rise from 40 billion in 2010 to 220 billion in 2030. This
will lead to an increase in GHG emissions from 2.5 Mt CO2e to 13.1 Mt
CO2e in 2030. The BAU scenario assumes that the average fuel efficiency of
the passenger vehicle fleet will improve by 10% from 2010 to 2030.

■ Mining and construction. Mining and construction transport activity will
grow from 3 billion tonne-km to 34 billion tonne-km, increasing GHG
emissions from 0.3 Mt CO2e in 2010 to 3.5 Mt CO2e in 2030. The BAU
scenario assumes that the average fuel efficiency of the mining and
construction vehicle fleet will improve by 3.3% from 2010 to 2030.

ABATEMENT LEVERS – POTENTIAL AND COST CURVE

The Transport sector’s eight abatement levers fall into four categories: improving
the public transport system in Addis Ababa, improving vehicle efficiency, chang-
ing the fuel mix, and constructing an electric rail network for efficient freight
transport. A total abatement potential has been identified of up to 12.2 Mt CO2e in
2030 (Figure 65). This is lower than the sum-total of the abatement potential of all


Federal Democratic Republic of Ethiopia 169


the levers added up individually due to the interaction between the levers. For
example, when electric rail is implemented, the savings from applying fuel
efficiency standards to lorries are lower. The abatement potential of four of the
Transport sector’s green growth initiatives has been calculated as follows:

■ Improving Addis Ababa public transit by building a light rail transit system
and a bus rapid transit system has an abatement potential of approximately
0.1 Mt CO2e.

■ Improving vehicle efficiency by enacting fuel efficiency standards has an
abatement potential of approximately 3.1 Mt CO2e.

■ Changing the fuel mix using a combination of adding biodiesel to the diesel
mixture, increasing the amount of ethanol in the gasoline mixture, and
promoting the adoption of hybrid and plug-in electric vehicles has a combined
abatement potential of nearly 1.0 Mt CO2e.

■ Shifting freight transport from road to an electric rail network would
eliminate emissions from the largest source of transport emissions. Shifting
freight from road transport using diesel vehicles to rail transport powered by
renewable electricity has an abatement potential of 8.9 Mt CO2e, and is the
largest abatement lever in the transport sector.

FIGURE 65

Transport – Abatement potential reaches 13.3 Mt CO2e per year in 2030

Business-as-usual
Mt CO2e/ year

BAU emissions: 41 Mt in 2030

0

10

20

30

40

50

2015 2020 2025 20302010

8.9 Electric rail

0.1 Light rail transit (LRT)

0.04 Bus rapid transit (BRT)

0.1 Hybrid vehicles

0.04 Plug-in electric vehicles

Fuel efficiency standards

0.2 E15 fuel blend

0.7 B5 fuel blend

3.1

ΣΣΣΣ 13.2 Net potential after
accounting for non-
additive levers2

1 Represents total identified gross potential, some measures are not additive
2 Assuming full implementation of all levers

Abatement measures1

Mt CO2e/ year in 2030

12.2

Mt CO2e reduction
potential in 2030


Federal Democratic Republic of Ethiopia 170


FIGURE 66

78

Transport – All abatement potential has negative or zero costs

-80

-100

-120

11

-200

-220

-240

Abatement potential
Mt CO2e per year

1312

-140

-160

-180

Abatement cost
USD per t CO2e

0

-20

-40

-223.3

Fuel efficiency standards

-91.4

-60

Electric rail

0
109876543210

0

E15 B5

Output overview

Abatement opportunities cost curve


The cost curve shown in Figure 66 shows a wide range of abatement costs. Fuel
efficiency standards seem to generate a lot of cost savings (USD 220 per t of
CO2e) as does electric rail (USD 91 per t of CO2e). Introducing ethanol and bio-
fuel is relatively cost neutral and has lower abatement potential.

Implementing all the transport levers up to 2030 will require investments totalling
about USD 22.9 billion.

Transport levers 1-2 – Improved public transit in Addis
Ababa: Light Rail Transit and Bus Rapid Transit

The existing public transport network in Addis Ababa consists primarily of mini-
buses and full-length buses running on a mix of gasoline and diesel fuel. Small
taxis and three-wheelers comprise a small share of passenger transport. The STC
identified two public transit improvements – light rail transit (LRT) and bus rapid
transit (BRT) using electric trolley buses.

Both of these abatement levers would reduce road congestion, air pollution, traffic
accidents, and passenger travel times. Their annual abatement potential in 2030
was calculated to be approximately 0.1 Mt CO2e and 0.04 Mt CO2e respectively.


Federal Democratic Republic of Ethiopia 171


These abatement potential calculations are based on the following data and
assumptions:

■ Passenger-kilometres shifted to LRT and BRT. From the STC’s projection
of total passenger-km demanded as well as the Transport Master Plan and the
existing Addis Ababa LRT and BRT proposals, the STC estimated that of the
over 60 billion passenger-km travelled within Addis Ababa in 2030, 7%
(4.4 billion) could be shifted to LRT and 3% (1.9 billion) could be shifted to
BRT. The LRT combined route length (35 km) was taken from the Addis
Ababa LRT feasibility study. The BRT combined route length (32 km) was
taken from the BRT feasibility study.

■ Construction timing and passenger mix. Based on planned route locations
and travel patterns, the STC assumed that the passengers shifted to LRT would
be drawn from mini-, midi-, and maxi-buses, while the passengers shifted to
BRT would be drawn from midi- and maxi-buses. The STC assumed the
routes will be completed by 2015, based on the LRT and BRT Addis Ababa
proposals under consideration by the Ministry of Transport.

■ Emissions from LRT and BRT. Due to the expected availability of renew-
able energy for power, the LRT and BRT were assumed to have zero
emissions.

Transport lever 3 – Fuel efficiency standards for all
vehicle types

The existing vehicle stock is old and highly inefficient, and no fuel efficiency
standards exist for vehicles entering the country. This presents a major opportunity
to reduce transport emissions through fuel efficiency standards (FES), which has
an abatement potential of 3.1 Mt CO2e in 2030. FES would be applied to new and
used vehicles imported into the country after the standards are enacted. The
abatement potential for this lever was estimated using the following assumptions:

■ Vehicle fleet efficiency improvement due to FES. Based on FES pro-
grammes in South Africa and China, STC discussions, and a FES phase-in
start date of 2015, the fleet efficiency improvement by 2030 due to FES was
assumed to be 30% for passenger vehicles and 10% for freight vehicles
(instead of 10% and 3.3% respectively in the BAU scenario).

■ Vehicle stock growth. Based on the growth forecast for passenger-km and on
historic vehicle import data from the Customs Authority, the STC assumed
the vehicle stock would grow from 240,000 passenger, 70,000 freight, and
10,000 construction and mining vehicles to 1,350,000 passenger, 820,000


Federal Democratic Republic of Ethiopia 172


freight, and 140,000 construction and mining vehicles in 2030. This growth
projection is based on a 20-year average vehicle use after importation and
14% and 18.4% annual imports of passenger and freight/construction vehicles
based on Import Authority data.

■ The abatement cost is calculated to be -223 USD/t CO2e. This abatement cost
incorporates the following elements:

– Vehicle costs. The STC assumed that more efficient vehicles meeting the
FES would have a price 15% higher relative to the less efficient vehicles
that would have been imported in the absence of FES. Baseline vehicle
costs are based on the average cost of vehicles imported in 2010, namely:
USD 9,500 (passenger) and USD 40,500 (freight). The STC assumed that
vehicle prices would erode at a rate of -1% annually. Vehicles were
assumed to have an average age of 10 years at time of importation, and an
additional 20 years of use in Ethiopia.

– Fuel cost savings. Fuel cost savings have been computed based on a
comparison of average fuel expenditure before and after the change of
vehicle fleet efficiency and have been accounted for as (negative) cost.
The STC calculations used 2011 prices. The price of fuel was assumed to
increase at the rate of 2.2% per year from the following 2010 figures.

□ Price of gasoline in 2010: USD 0.83/litre.

□ Price of diesel in 2010: USD 0.70/litre.

– Programme cost and additional operating expenditure. Total
programme setup costs are USD 5 million. Ongoing programme costs,
including FES enforcement, total USD 500,000 per year.

Transport levers 4-5 – Biodiesel and ethanol in fuel
mixtures

Incorporating 5% biodiesel into the national diesel fuel mixture has an abatement
potential of 0.7 Mt CO2e in 2030. Increasing the ethanol content of the gasoline
from 10% in the Addis Ababa fuel mix to 15% nationally – the maximum feasible
ethanol mix that does not require mechanical alteration to vehicles – has an abate-
ment potential of 0.2 Mt CO2e in 2030. These initiatives would require about
486,000 hectares of arable land to support bio-diesel and 25,000 hectares of arable
land for ethanol. However, the government plan is to produce biofuels entirely
from crops on marginal land and by-products/residue of crop processing.
Increasing the ethanol mix to 85% is technically feasible but was rejected in this
case due to the high level of infrastructure investments needed in the fleet, storage,


Federal Democratic Republic of Ethiopia 173


and pumping facilities. The abatement potential of these levers was estimated
using the following calculations:

■ Fuel consumption projections. The amount of fossil fuel that would be sub-
stituted by biodiesel and ethanol was based on the STC’s demand forecast for
passenger-km and freight-km, and on the Ministry of Water and Energy’s bio-
fuel production forecasts. Imports of diesel are expected to increase from 1.1
billion litres in 2010 to 11.1 billion litres in 2030, while gasoline imports are
expected to increase from 0.3 billion litres in 2010 to 1.2 in 2030. Due to the
lower caloric value of ethanol, increasing the ethanol content of the fuel blend
to 15% was assumed to lower vehicle fuel efficiency by 10%.

■ Biofuel production. In accordance with the Ethiopian Biofuel Development
and Utilisation Strategy, ethanol would be produced from sugarcane, and bio-
diesel primarily from jatropha as well as from castor oil and palm oil. Imple-
menting 5% biodiesel and 15% ethanol blends would substitute for 0.28 bil-
lion litres of diesel and 0.09 billion litres of gasoline in 2030.

■ Emissions from fuel. The following fuel emissions factors were used:

– Diesel: 2.67 kg CO2e/litre.

– Gasoline: 2.42 kg CO2e/litre.

■ Fleet modification. The STC assumed that all vehicles in the existing stock
could be used with the new fuel mixtures (5% biodiesel and 15% ethanol)
without a need for user-initiated vehicle modification.

The abatement cost of biodiesel and ethanol blends is calculated to be around
USD 0 per USD/t CO2e and –USD 0 per USD/t CO2e respectively. These abate-
ment costs incorporate the following elements:

■ Fuel cost savings. Fuel prices for biodiesel and ethanol were assumed to be at
price parity with diesel and gasoline (adjusted for caloric content). This as-
sumption was based on the equivalence of prices for diesel and gasoline im-
ports. Part of the production of ethanol and biodiesel may take place inside
the country, in which case the fuel price may slightly differ.

■ Programme cost and additional operating expenditure. Implementing
changes to the fuel blend would entail a programme setup cost of USD 5 mil-
lion. Operating costs, including programme management and monitoring,
were estimated at USD 500,000/year. Because the costs are so low relative to
the abatement potential, they appear as zero in the cost curve.


Federal Democratic Republic of Ethiopia 174


Transport levers 6-7 – Hybrid and plug-in electric
vehicles

The low fuel efficiency of the vehicle fleet could be improved through the promo-
tion of hybrid and plug-in electric vehicles. Increasing the fleet share of hybrid and
electric vehicles to 13.0% and 2.2%, respectively, by 2030 would significantly
reduce annual gasoline consumption. These initiatives have a combined abatement
potential of approximately 0.09 Mt CO2e in 2030. The abatement potential of these
levers was estimated using the following assumptions:

■ Fuel efficiency of hybrid and plug-in electric vehicles. The STC assumed a
hybrid vehicle fuel efficiency improvement of 60% relative to existing private
automobiles. The STC estimated the fuel efficiency of private automobiles in
the existing fleet to be 12 litres per 100 km based on the African Public
Transport Association Study for Addis Ababa. The efficiency of plug-in
electric vehicles was assumed to be 21 kWh per 100 km.

■ Penetration rate of hybrid and plug-in electric vehicles. The STC’s pro-
posed import shares for standard internal combustion engine (ICE) vehicles,
hybrid vehicles, and plug-in electric vehicles was determined by the total cost
of ownership (TCO) of each vehicle type corresponding to a particular dis-
tance driven per year. TCO was calculated based on vehicle purchase price,
maintenance, and fuel costs. Vehicle purchase price was assumed to be a one-
off cost, while maintenance and fuel costs were assumed to recur over the
years and progress linearly with the amount of kilometres driven per year.
Maintenance and fuel costs in the years subsequent to purchase were
discounted using a 35% discount rate per year to reflect local purchase
capacity. Based on these assumptions, the total cost of ownership was
calculated for each vehicle type in 2010, 2020 and 2030. Subsequently, the
optimal share of each vehicle type in imports was calculated for 2010, 2020
and 2030 based on these TCO figures and assuming a normal distribution of
kilometres driven per year with an average of 16,000 km. If the average
turnover rate of vehicles is 20 years, it is possible to calculate the penetration
rate of each vehicle type in the vehicle population in 2030 assuming a linear
increase in import shares of the vehicle types over the years.

The resultant fleet composition is 100% ICE until 2014 and 84.8% ICE,
13.0% hybrid, and 2.2% plug-in electric in 2030 (with import rates of 57.9%,
34.0% and 8.1%, respectively). The 2030 import rates reflect 2020 world
production under the radical scenario that a pollution cap of 10 g CO2/km
would be set in 2050. This assumes progressive action by the government to


Federal Democratic Republic of Ethiopia 175


build the right infrastructure and promote the usage of the new types of
vehicles. The penetration rate could be even higher if domestic production of
hybrid and electric vehicles takes off, but this would require the attraction of
automobile manufacturers to Ethiopia. Figure 67 shows the TCO for the
different vehicle types in 2030 based on different distances driven per year.

FIGURE 67

Transport – Hybrid has the lowest TCO for middle distances;
electric cars for longer distances

24.000

26.000

28.000

30.000

32.000

34.000

36.000

38.000

40.000

14000 16000 18000 20000 22000 24000 26000

23000 km

17000 km

Hybrid

ICE

Electric

Total cost of ownership
USD

TCO1 for different vehicle types

Km driven
Km/year

1 TCO = Total cost of ownership


Transport lever 8 – Electric rail network for freight
transport

The STC’s proposed electric rail network consists of seven lines totalling 5,196
km, with the first and primary line of Addis Ababa – Djibouti opening in 2015.
The information sources for this initiative were the Ethiopian Railway Corporation
transport plan, the Transport Sector Nationally Appropriate Mitigation Actions
Plan, Ministry of Transport data, and statistics from the National Bank of Ethiopia.
The abatement potential of shifting freight to electric rail reaches 8.9 Mt CO2e per
year in 2030. The abatement potential was calculated using the following assump-
tions:

■ Fuel efficiency of freight transport by rail and road. The STC expects the
electric rail network to be powered by renewable energy. Thus, freight trans-


Federal Democratic Republic of Ethiopia 176


ported by rail would produce zero emissions. Road cargo was assumed to be
transported by vehicles with the following 2010 fuel efficiencies:

– 5-19 quintals: 40 litres per 100 tonne-km

– 20-34 quintals: 8.3 litres per 100 tonne-km

– 35-69 quintals: 6 litres per 100 tonne-km

– 70+ quintals: 5.7 litres per 100 tonne-km

These rates of fuel efficiency were assumed to improve by 3.3% between 2010
and 2030 due to gradual improvement of the freight vehicle stock through
imports.

■ Amount of freight to be shifted to electric rail. The electric rail network
was assumed to transport 50% of dry and liquid cargo by 2030.

The abatement cost is calculated to be around -91 USD/t CO2e. This abatement
cost incorporates:

■ Savings from substitution away from road transport. The STC assumed a
road cargo transport cost of USD 0.09 per tonne-km based on a transport
master plan for rail. The STC conservatively assumed an electric rail cargo
transport cost of USD 0.06 per tonne-km based on the expected cost of the
electric rail network. This amounts to a savings of USD 0.03 per tonne-km
applied to the 61.3 billion tonne-km shifted from road to rail. This amounts to
more than USD 1.8 billion in annual savings in 2030.

■ Investment cost of electric rail network. Based on total electric rail project
cost estimates and per-km track cost estimates from the Ethiopian Rail Corpo-
ration, the STC estimates the total cost of the electric rail network to be USD
15.6 billion USD for 5,196 km of track. Total track length was taken from the
Transport Sector Nationally Appropriate Mitigation Actions.

ABATEMENT LEVERS – FEASIBILITY AND ECONOMIC IMPACT
ASSESSMENT

Feasible levers with high impact

The Transport sector contains two initiatives with high impact and moderate feasi-
bility: fuel efficiency standards for all vehicle types and construction of an electric
rail network for freight transport.

■ Fuel efficiency standards for all vehicle types. This policy-based initiative
offers a highly feasible opportunity to realise other environmental and socio-


Federal Democratic Republic of Ethiopia 177


economic benefits while also saving drivers’ money over the lifetime of their
vehicles’ utilisation. Fuel economy standards would reduce air and noise
pollution, improve the balance of payments through reduced fossil fuel
imports, and lower the lifetime cost of vehicle ownership, since the higher
price of incrementally more efficient vehicles would be more than out-
weighed by fuel cost savings. Fuel efficiency standards have been success-
fully implemented in other developing countries. The key challenges in
Ethiopia would be related to building the institutional capacity needed to
enforce the standards and monitor the implementation. Lowering import
duties for fuel-efficient cars may be considered to speed up implementation.

■ Electric rail network for freight transport. In addition to having a large
abatement potential, shifting 50% of freight transport to electric rail would
have significant socio-economic benefits. Key amongst these are: improved
balance of payments by reducing the need for imported fossil fuel, improved
industry international competitiveness through lower transport costs,
improved road safety, reduced air and noise pollution, and increased
employment. The large infrastructure required by this initiative (5,196 km of
track) poses a significant but achievable challenge. Extensive technical assis-
tance would be needed from abroad, and careful planning would need to be
carried out to keep possible population displacement by the network at a
minimum. The large financing requirement (USD 15.6 billion) also poses a
hurdle, but these requirements are largely offset by the significant economic
benefits in the future.

Other levers

All the other Transport sector abatement levers were evaluated as being moder-
ately to highly feasible, but their abatement potential is much smaller (combined
value of 1.2 Mt CO2e in 2030). Despite their lower greenhouse gas abatement
potential, these other levers have substantial co-benefits (reduced air and noise
pollution, improved balance of payments through reduced imports of fossil fuel,
improved road safety, improved public transit, reduced road congestion) and are
attractive initiatives in their own right, albeit at a smaller scale.

In addition to the above levers, more ideas for carbon abatement in the Transport
sector are under discussion and could possibly be implemented in the future. Ideas
include changing roads from gravel to asphalt, establishing dry ports, encouraging
the use of telecommunication as well as promoting scooters and bicycles. These
additional possible levers were not quantified as of yet due to limited expected
abatement potential or current constraints in implementability. Nonetheless, they


Federal Democratic Republic of Ethiopia 178


should be considered for future implementation as the initiatives may have
significant societal benefits.

ABATEMENT LEVERS – IMPLEMENTATION TIMELINE AND RESOURCE
REQUIREMENTS

Implementation timeline

The Transport STC has selected three initiatives based on abatement potential and
implementability as priorities for rapid implementation. These initiatives are the
electric rail network, fuel efficiency standards (FES), and ethanol (Figure 68). The
implementation of electric rail is envisaged to commence at the end of 2012, while
its utilization will commence in 2015. The other initiatives will kick off in 2015 to
provide time for approval procedures and required capacity building. It is
important to mention that these dates mark the start of the implementation, which
for some initiatives is staged across several years, includes some required pre-
paratory work (e.g., development of investment plans), and is subject to approval
by the respective authorities and availability of funding. Hence, the full impact of
the initiatives will only occur later in most cases.

FIGURE 68

Transport – Overview of timeline for implementation of initiatives

2012 2013 2014 2015 2016

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

Plug-in electric

Hybrid

Bus rapid
transit

Light rail

B5

E15

Fuel economy
standards

Electric rail

Activity

Selected as priority initiative

Other initiatives


Federal Democratic Republic of Ethiopia 179


In addition, the non-prioritised initiatives are envisaged to start in 2015 consecu-
tively with the FES initiative. The targets are set at 2015 to allow sufficient time
for appropriate technologies to develop and research to be conducted.

Resource requirements and existing projects

Together, the transport initiatives will cost USD 22.9 billion until 2030. This cost
is comprised almost entirely of capital expenditure. The expenditure for the period
until and including 2015 is USD 2.8 billion, mostly representing the capital expen-
diture for the electric rail network starting in 2013 (Figure 69).

FIGURE 69

Transport – Financial overview of all initiatives

Million USD
Short-term: 2011-2015 Long-term 2011-2030

CAPEX1 additional

OPEX additional
(excl. programme cost)

Programme cost

Savings/income

Abatement expenditure
before carbon revenue -2,370

420

0

0

2,790

10

22,920

10

0

22,900

▪FES accounts for most of the savings in the first period;
electric rail contributes significantly in the second

87212

1 Full capital expenditure, not amortised
2 Aggregated abatement potential for the given period; expenditure per t CO2e here is not equivalent to the abatement cost in the cost curve, as the CAPEX

abatement expenditure is not annualised via amortisation (the analysis here takes the cash-flow perspective)

Mt CO2e total aggregated abatement potential

Mt CO2e
abatement
potential


The cost savings in the period 2011-2030 are significant as well, while the savings
are less significant in the period from 2011-2015. The societal costs savings in the
period until and including 2015 is USD 420 million, which leaves the country with
a net cost of USD 2.4 billion. For the 20-year period of 2011-2030, these savings
are even larger at USD 22.9 billion, resulting in a net societal saving of USD 10
million.


Federal Democratic Republic of Ethiopia 180


Figure 70 shows a categorisation of the different initiatives. Category A represents
investments that achieve a positive net present value within the first five years.
This category is comprised of the FES initiative and requires a total investment of
USD 4.1 billion. The USD 15.6 billion in category C has no positive net present
value over the first 20 years. This category includes the electric rail, biodiesel, and
ethanol initiatives. These initiatives will need to be financed with performance-
based grants or other forms of climate finance. It should be noted; however, that
the rail network will have a positive net present value in the long run and will thus
pay back for itself over the course of its investment. The other initiatives still
represent significant societal benefits in the form of improved air quality and
avoided emissions.

FIGURE 70

Transport – 32% of costs will have positive returns in the
short run

Category C
No positive return,
grants/performance

pay requirement

15,590

Category B
Positive return, but
long-term financing

requirement

0

Category A
Positive return,

short-term financing
requirement

7,320

Million USD, total cost1 Percent of total cost

32% 0% 68%

1 Including additional CAPEX, additional OPEX, and programme cost
2 NPV calculated at 6% discount rate

Definition Positive NPV2 of first
five years of cash flow
(from start of initiative)

Positive NPV2 of
overall initiative
(from start of initiative
up to 2030), but not
after first five years

Negative NPV2 of
overall initiative
(from start of initiative
up to 2030)


At present, two initiatives are already in the planning phase, namely the light rail
transit in Addis Ababa and the electric rail for freight. The Ministry of Transport
plays a leading role in both. The ministry aims to play a leading role in the
implementation of the other levers as well.


Federal Democratic Republic of Ethiopia 181


List of References

LITERATURE, RESEARCH PAPERS, AND PRESENTATIONS

Addis Ababa City (2009): Overview of SWM of Addis Ababa, presentation to the
delegation of Ethiopian cities, Addis Ababa, October 2009.

BDA Group (2009): The full cost of landfill disposal in Australia, report prepared
for the Department of the Environment, Water, Heritage and the Arts,
Melbourne.

Bekele, Seleshi; Hagos, Fitsum; Makombe, Godswill; Namara, Regassa (2009):
Importance of Irrigated Agriculture to the Ethiopian Economy: Capturing the
Direct Net Benefits of Irrigation, IWMI Research Report 128, Colombo:
IWMI.

Blackburn, R.S. (2009): Sustainable Textiles, Life Cycle and Environmental
Impact, Cambridge.

Bracmort, K.; Ramseur, J.; McCarthy, J.; Folger, P. and Maples, D. (2011):
Methane Capture: Options for Greenhouse Gas Emission Reduction,
Congressional Research Service, CRS Report for Congress, Washington, DC.

Buttazzoni, M. (2009): GHG Emissions Reductions With Industrial
Biotechnology: Assessing the Opportunities, Palmetto.

Canning, D. and Pedroni, P. (2004): The Effect of Infrastructure on Long Run
Economic Growth, Boston.

CDM Executive Board (2011): Tool to calculate project or leakage CO2 emissions
from fossil fuel combustion, Version 02, EB 28, Meeting Report, Annex 13.

CDM Executive Board (2008): Tool to calculate project or leakage CO2 emissions
from fossil fuel combustion, Version 02, EB 41, Annex 11.

CDM Executive Board (2010): Indicative simplified baseline and monitoring
methodologies for selected small-scale CDM project activity categories, Type
II: Energy Efficiency improvement projects, Version 04, EB 54, Sectoral
Scope 03.

CDM Executive Board (2010): Indicative simplified baseline and monitoring
methodologies for selected small-scale CDM project activity categories,
III.H./Version 15, EB 55, Sectoral Scope 13.


Federal Democratic Republic of Ethiopia 182


CDM Executive Board (2011): Tool to determine methane emissions avoided from
disposal of waste at a solid waste disposal site, Version 05.1.0, EB 61,
Annex 10.

CDM Executive Board (2010): Consolidated Baseline Methodology for Increasing
the Blend in Cement Production, ACM0005, EB 50.

City Government of Addis Ababa (October 2010): Addis Ababa Bus Rapid Transit
Line Feasibility Study, Addis Ababa.

Danish Co-operation for Environment in Eastern Europe (2004): Methane Gas
Capture and Electricity Production at Chisinau Wastewater Treatment Plant,
Moldova, Draft CDM Project Design Document, Danish Ministry of
Environment.

Demissie, Y. (2009): CDM Project opportunities on Landfills Methane Recovery
and Power Generation for Energy Utilization in Seven Cities of Ethiopia
Urban Solid Waste Management, Ethiopia Ministry of Works and Urban
Development, Addis Ababa, 27-28 October 2009.

Dikshita, A.K. and Birthal, Pratap S. (2010), Environmental Value of Draught
Animals: Saving of Fossil-fuel, and Prevention of Greenhouse Gas Emission,
Agricultural Economics Research Review, Vol. 23, pp 227-232.

Dorosh (2007): Agricultural Production, Not published.

East African Power Pool (2011): Master Plan Studies, Dar es Salaam.

Ethiopian Electric Power Corporation (2007, updated 2011): Power System Master
Plan, Addis Ababa.

Ethiopian Railways Corporation (March 2011): Transport Planning for BFS of
Addis Ababa-Djibouti Railway Project Final Report, Civil Engineering Dept.,
Addis Ababa University.

Ethiopian Railways Corporation (July 2009): Addis Ababa E-W & N-S (PHASE I)
Light Rail Transit Project Feasibility Study, Addis Ababa.

Ethiopian Rural Development and Promotion Center (2007): Solar and Wind
Energy Utilization and Project Development Scenarios, Addis Ababa.

Ethio Resource Group (2009): Diversity and Security for the Ethiopian Power
System, Addis Ababa.

European Cement Research Academy (2009): Development of State of the Art
Techniques in Cement Manufacturing: Trying a Look Ahead, Düsseldorf.


Federal Democratic Republic of Ethiopia 183


Fedderke, J.W. and Bogetic, Z. (2010): Infrastructure and Growth in South Africa:
Direct and Indirect Productivity Impacts of 19 Infrastructure Measures, Cape
Town.

Federal Democratic Republic of Ethiopia (2011): Forest Carbon Partnership
Facility, Readiness Preparation Proposal, Addis Ababa.

Figueres, C. and Bosi, M. (2006): Achieving greenhouse gas emission reductions
in developing countries through energy efficient lighting projects in the clean
development mechanism (CDM), The Carbon Finance Unit, World Bank,
Washington, D.C.

Forum for Environment (2009): Ethiopian Forestry at the Crossroads: The Need
for Strengthened Institutional Setup, Addis Ababa: Forum for Environment.

Foster, V. and Morella, E. (2011): Ethiopia’s Infrastructure – A Continental
Perspective, Policy Research Working Paper 5595, World Bank, Washington,
D.C.

Gaul, M. (2009): Subsidy Schemes for the Dissemination of Improved Stoves –
Experiences of GTZ HERA and Energising Development, Eschborn.

GIZ Energy Coordination Office Ethiopia (2011): The Energy Development
Intervention in Ethiopia, March 2011.

Gruenspecht, H. (2010): International Energy Outlook 2010 with Projections to
2035, Center for Strategic and International Studies, Washington, D.C.

GTZ (2010): Energising Development – Report on Impacts, Eschborn.

GTZ Information and Advisory Service on Appropriate Technology (ISAT)
(2009): Biogas – Costs and Benefits and Biogas – Programme
Implementation, Biogas Digest, Volume III.

Hongyang He, O.; Gallagher, K. S.; Donglian, T. and Jinhua, Z. (2009): China’s
Fuel Economy Standards for Passenger Vehicles: Rationale, Policy Process,
and Impacts, Discussion Paper 2009-03, Belfer Center for Science and
International Affairs, Harvard University.

Hussain, I. (2005): Pro-poor Intervention Strategies in Irrigated Agriculture in
Asia, International Water Management Institute, Colombo, Sri Lanka.

Hydrovision (2008): Hydro Finance Handbook, Kansas City.

International Finance Corporation (2011): Doing Business 2011: Making a
Difference for Entrepreneurs, Washington: The World Bank Group.


Federal Democratic Republic of Ethiopia 184


Intergovernmental Panel on Climate Change (2006): 2006 IPCC Guidelines for
National Greenhouse Gas Inventories, Volume 4: Agriculture, Forestry and
Other Land Use, Geneva.

Intergovernmental Panel on Climate Change (2007): Mitigation of Climate
Change, Fourth Assessment Report, Working Group III.

Intergovernmental Panel on Climate Change (2006): 2006 IPCC Guidelines for
National Greenhouse Gas Inventories, Volume 4: Agriculture, Forestry and
Other Land Use, Geneva.

Intergovernmental Panel on Climate Change (2001): IPCC Good Practice
Guidance and Uncertainty Management in National Greenhouse Gas
Inventories, Geneva.

International Energy Agency (2009): Cement Technology Roadmap 2009, Carbon
Emissions Reductions up to 2050, Paris.

International Hydropower Association (2010): Hydropower Sustainability
Assessment Protocol.

International Water Management Institute (2009): Small-scale irrigation in the
Ethiopian highlands. What potential for poverty reduction and climate
adaptation?, Addis Ababa.

Kennedy/Jenks Consultants (2008): City of San Rafael and Central Marin
Sanitation Agency Methane Capture Feasibility Study, KJ 0868015.

Kinfu, M. (2009): Landfill Methane Recovery and Power Generation-CDM
Projects in Ethiopia, HoAREC-AAU, Addis Ababa, 27-28 October 2009.

Masera, O.R.; Saatkamp, B.D. and Kammen, D.M. (2000): From Linear Fuel
Switching to Multiple Cooking Strategies: A Critique and Alternative to the
Energy Ladder Model, World Development, Vol. 28 (12), pp. 2083-2103.

Megen Power (2008): Impact Assessment of Mirt Improved Biomass Injera Stoves
Commercialization, Final Report submitted to MoARD/GTZ SUN Energy
Programme, Addis Ababa.

Mekuriaw, A. and Jembere, K. (2008): Assessment of capacity building needs of
small to medium sized utilities in the water and sanitation sector in Ethiopia,
Final Report submitted to Cap-Net, Addis Ababa.


Federal Democratic Republic of Ethiopia 185


Ministry of Agriculture, Federal Democratic Republic of Ethiopia (2004): Woody
Biomass Inventory and Strategic Planning Project Phase 2, Final Report,
Addis Ababa.

Ministry of Agriculture, Federal Democratic Republic of Ethiopia (2004): Woody
Biomass Inventory and Strategic Planning Project – Forest Resources of
Ethiopia, Addis Ababa.

Ministry of Finance and Economic Development, Federal Democratic Republic of
Ethiopia (2010): Growth and Transformation Plan, Addis Ababa.

Ministry of Mines, Federal Democratic Republic of Ethiopia (2007): The Biofuel
Development and Utilization Strategy of Ethiopia, Addis Ababa.

Ministry of Transport and Communication, Federal Democratic Republic of
Ethiopia (2008): Annual Statistical Bulletin 2007/2008, Transport Services
Summary, Addis Ababa.

Ministry of Transport and Communication, Federal Democratic Republic of
Ethiopia (2007): National Transport Master Plan Study, Vol. 2. Action Plan,
Addis Ababa.

Nwoboshi, L. C. (1981): Soil productivity aspects of agri-silviculture in the West
African rain forest zone, University of Ibadan, Nigeria.

OECD and IEA (2010): Projected Costs of Generating Electricity, Paris.

Quality and Standards Authority of Ethiopia (2005): Ethiopian Standard ES1177-
1:2005, Cement – Composition, specifications and conformity criteria for
common cements, Addis Ababa.

Rayner, S. (2010): Fuel economy/CO2 labeling and taxation: South African Motor
Industry experience, National Association of Automobile Manufacturers of
South Africa/Ford Motor Company.

Regassa, N.; Sundaraa, R. D. and Seboka, B. B. (2011): Challenges and
Opportunities in Municipal Solid Waste Management: The Case of Addis
Ababa City, Central Ethiopia, J Hum Ecol, Vol. 33 (3), pp.: 179-190.

Rural Electrification Executive Secretariat (2006): Off-grid Rural Electrification
Master Plan Study, Addis Ababa.

Sarkar, A. (2008): The Role of Carbon Finance, World Bank, Lighting Africa
2008, Accra, 7 May 2008.


Federal Democratic Republic of Ethiopia 186


Sarkar, A. (2008): Large scale CFL deployment programs: mainstreaming carbon
finance and clean development mechanism (CDM), World Bank, Shanghai,
13 May 2008.

Scottish Environment Protection Agency and UK Environment Agency (2002):
Guidance on Landfill Gas Flaring.

Seyoum, T. (2007): GHG Emission in the Leather Industry, Addis Ababa
University Department of Chemical Engineering, M.Sc. Thesis, Addis Ababa.

Smith, K.R. et al. (1993): One Hundred Million Improved Cookstoves in China:
How Was it Done?, World Development, Vol. 21 (6), pp. 941-961.

Takeshi, T. et al. (2011): Will African Consumers Buy Cleaner Fuels and Stoves?
Research Report, Stockholm Environment Institute, Stockholm.

Tennigkeit, T.; Kahrl, F.; Woelcke, J. and Newcombe, K. (2009): Agricultural
carbon sequestration in Sub-Saharan Africa: Economics and institutions,
World Bank, Washington, DC.

The Economist (2011): Africa's Impressive Growth, January 6, London: The
Economist Group.

The Federal Democratic Republic of Ethiopia (2010): Growth and Transformation
Plan, Addis Ababa.

UNDP (2009): Bio-carbon opportunities in Eastern and Southern Africa,
Harnessing Carbon Finance to Promote Sustainable Forestry, Agro-Forestry
and Bio-Energy, New York.

UNDP (2009): Biomass Energy for Cement Production: Opportunities in Ethiopia,
New York.

UNEP and EcoSecurities (2007): Guidebook to Financing CDM Projects,
The Hague.

UNFCCC (2010): CDM Methodology Booklet, Information including EB 56.

UNIQUE (2010): Carbon Finance Opportunities in Ethiopia’s Agricultural Sector,
Freiburg: UNIQUE.

U.S. Energy Information Administration (2010): International Energy Outlook,
Washington, D.C.


Federal Democratic Republic of Ethiopia 187


Van Benthem, A. and Romani, M. (2009): Fuelling Growth: What Drives Energy
Demand in Developing Countries?, The Energy Journal, Vol. 30 (3),
pp. 91-114.

Vanderschuren, M. and Jobanputra, R. (July 2005): Fuel Efficiency Measures for
South Africa, Proceedings of the 24th Southern African Transport Conference,
Pretoria, South Africa.

Wölcke, J. and Tennigkeit, T. (2009): Harvesting agricultural carbon in Kenya,
The International Journal for Rural Development, Vol. 43 Nr. 1.

Wood, S. and Cowie, A. (2004): A Review of Greenhouse Gas Emission Factors
for Fertiliser Production, New South Wales.

World Bank and ECON (2008): Costing Power Infrastructure Investment Needs in
Southern and Eastern Africa, Oslo.

World Bank (2007): Second Electricity Access Rural Expansion Project,
Washington, D.C.

World Health Organization (2007): Indoor Air Pollution: National Burden of
Disease Estimates, Geneva.

Worrell, E., Galitsky, C., and Price, L. (2008): Energy Efficiency Improvement
Opportunities for the Cement Industries, Berkeley.

INTERNET SOURCES

All Africa (2009): Establish a Fertilizer Manufacturing Plant,
http://allafrica.com/stories/200910190826.html (August 2011).

Central Statistical Agency, Federal Democratic Republic of Ethiopia,
www.csa.gov.et (August 2011).

Economy Watch (2011): Foreign Exchange Reserves,
http://www.economywatch.com/economic-statistics/economic-
indicators/Foreign_Exchange_Reserves/

FAO (2009), the State of Food and Agriculture,
http://www.fao.org/docrep/012/i0680e/i0680e00.htm, (August 2011).

Nonor, D. (21 July 2010): Ghana: Fund to Invest in Improved Seed Production for
Smallholder Farmers, http://allafrica.com/stories/201007210757.html (August
2011).


Federal Democratic Republic of Ethiopia 188


Mutumweno, N. (12 August 2010): NCZ, The sleeping giant, http://www.shout-
africa.com/?p=2066 (August 2011).

U.S. Economics and Statistics Administration (2011): U.S. Carbon Dioxide
Emissions and Intensities Over Time: A Detailed Accounting of Industries,
Government and Households, http://www.esa.doc.gov/Reports/u.s.-carbon-
dioxide (August 2011).

World Bank: GDP Per Capita,
http://data.worldbank.org/indicator/NY.GDP.PCAP.CD (August 2011).


Page 1
Page 2
Page 3
Page 4
Page 5
Page 6
Page 7
Page 8
Page 9
Page 10
Page 11
Page 12
Page 13
Page 14
Page 15
Page 16
Page 17
Page 18
Page 19
Page 20
Page 21
Page 22
Page 23
Page 24
Page 25
Page 26
Page 27
Page 28
Page 29
Page 30
Page 31
Page 32
Page 33
Page 34
Page 35
Page 36
Page 37
Page 38
Page 39
Page 40
Page 41
Page 42
Page 43
Page 44
Page 45
Page 46
Page 47
Page 48
Page 49
Page 50
Page 51
Page 52
Page 53
Page 54
Page 55
Page 56
Page 57
Page 58
Page 59
Page 60
Page 61
Page 62
Page 63
Page 64
Page 65
Page 66
Page 67
Page 68
Page 69
Page 70
Page 71
Page 72
Page 73
Page 74
Page 75
Page 76
Page 77
Page 78
Page 79
Page 80
Page 81
Page 82
Page 83
Page 84
Page 85
Page 86
Page 87
Page 88
Page 89
Page 90
Page 91
Page 92
Page 93
Page 94
Page 95
Page 96
Page 97
Page 98
Page 99
Page 100
Page 101
Page 102
Page 103
Page 104
Page 105
Page 106
Page 107
Page 108
Page 109
Page 110
Page 111
Page 112
Page 113
Page 114
Page 115
Page 116
Page 117
Page 118
Page 119
Page 120
Page 121
Page 122
Page 123
Page 124
Page 125
Page 126
Page 127
Page 128
Page 129
Page 130
Page 131
Page 132
Page 133
Page 134
Page 135
Page 136
Page 137
Page 138
Page 139
Page 140
Page 141
Page 142
Page 143
Page 144
Page 145
Page 146
Page 147
Page 148
Page 149
Page 150
Page 151
Page 152
Page 153
Page 154
Page 155
Page 156
Page 157
Page 158
Page 159
Page 160
Page 161
Page 162
Page 163
Page 164
Page 165
Page 166
Page 167
Page 168
Page 169
Page 170
Page 171
Page 172
Page 173
Page 174
Page 175
Page 176
Page 177
Page 178
Page 179
Page 180
Page 181
Page 182
Page 183
Page 184
Page 185
Page 186
Page 187
Page 188
Page 189
Page 190
Page 191
Page 192
Page 193
Page 194
Page 195
Page 196
Page 197
Page 198
Page 199
Page 200


TF


SOMALIA


Ethiopia’s Climate-Resilient
Green Economy

Preliminary green economy strategy
for government consultation

DRAFT DOCUMENT

FEDERAL DEMOCRATIC REPUBLIC OF ETHIOPIA

a


STC composition

STC members (role)

Institutions


* Tesfaye Abebe (Chair), Berhanu

Wolau, Endale Gorfu, Nega Abrha,

Ketsela Mengistu

* Gebeyehu Likassa, Daniel Mulatu,
Akalu Wondemu

« Habte Berhanu
* Solomon Kebede
« Jemal Abdi

* Ministry of Water and Energy

"EEPCo

"EEA

* Ministry of Mines
"CSA


arom eerit lal

STC members (role)

" Sebsebie Tadesse (Chair)

Institutions

= Ministry of Urban Development

= Adugna Glazgi

* Mesfin Haile

« Yared Tefera

= Shimeles Aragawu
* Yonas Hailemichael

and Construction

* Environmental Protection
Authority (EPA)


STC composition


STC members (role) Institutions


* Shimeles Sima (chair), Abdure "EPA
Seid, Hilina Getachew, Tesfaye
Ayele, Wondwossen Sintayehu

" Serste Sebuh, Melaku Tadesse *MoA,
"Dr. Zewdu Eshetu "FRC


STC composition


STC members (role) Institutions
= Melaku Tadesse (Coordinator) =MoA

= Tadesse Sori (Chair) =MoA

= Moti Cheru = MoA

= Solomon Mengistu " EIAR

= Solomon Abegaz = IBC

= Kumera Wakjira = EWCA


STC composition


STC members (role) Institutions
= Melaku Tadesse (Coordinator) =MoA

= Girma Mamo (Chair) " EIAR

* Alishum Ahmed "IBC

= Ayana Salehu =MoA

= Bayeh Mulatu " EIAR

* Elias Awol = MoA


STC composition

STC members (role)

Institutions


* Amha Bekele (chair), Yibekal

Belay, Tesfaw Wondimu,
Aschalew Wondifraw

« Melkamu Kitefew

* Ministry of Industry and related
institutes (Textile, Chemical,
Leather, Mugher Cement)

* Ministry of Mines


Hashtags

#4#3#2#1

Phone numbers

  • 14000160001800020000220002400026000
  • 20102025202020302015
  • 201220132014201520162017
  • 201020202030
  • 2012201320142015
  • 8015580
  • 203020202010
  • 2015203020252020
  • 9017590
  • 201220132014
  • 100120140160180040
  • 200910190826
  • 3785651271
  • 20212025201620202011201520262030
  • 405060708090
  • 183011
  • 1614121086420
  • 201020302020
  • 2192723
  • 202020302011
  • 203020202011
  • 120112015
  • 110120130140100
  • 109876543210
  • 20122013201420152016
  • 80757065605550454035
  • 201007210757
  • 45352515540
  • 203020102020
  • 20302025202020152010
  • 20152020202520302010
  • 2025202020152030
  • 2520155050

Phone numbers

  • 2012 2013 2014 2015 2016
  • 1 (2011-2015
  • 1.8 3.0 1.1
  • 2012 2013 2014 2015
  • 203020202010
  • 2012 2013 2014 2015 2016 2017
  • 2015 2020 2025 20302010
  • 80 15.5 (80
  • 453525155 40
  • 20302010 2020
  • 200910190826
  • 202520202015 2030
  • 40 50 60 70 80 90
  • 1614121086420
  • 2021-20252016-20202011-2015 2026-2030
  • 378 565 1.271
  • 2015 203020252020
  • 2010 2020 2030
  • 203020202011
  • 2010 20302020
  • 90 17.5 (90
  • 109876543210
  • 80757065605550454035
  • 2012 2013 2014
  • 201007210757
  • 14000 16000 18000 20000 22000 24000 26000
  • 2 19 27 23
  • 20302025202020152010
  • 2010 20252020 20302015
  • 110 120 130 140100
  • 100 120 140 160 1800 40
  • 2020 20302011
  • 2520155050

Law code

Filename extension

pdf

Countries

Chroma_BlackIsZero:
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true


Chroma_ColorSpaceType:
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB
  • RGB


Chroma_NumChannels:
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 4
  • 4
  • 4
  • 4
  • 4
  • 4
  • 4
  • 4
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 4
  • 4
  • 3
  • 3
  • 4
  • 3
  • 4
  • 3
  • 4
  • 3
  • 4
  • 4


Component_1:
  • Y component: Quantization table 0, Sampling factors 2 horiz/2 vert
  • Y component: Quantization table 0, Sampling factors 2 horiz/2 vert
  • Y component: Quantization table 0, Sampling factors 2 horiz/2 vert
  • Y component: Quantization table 0, Sampling factors 2 horiz/2 vert
  • Y component: Quantization table 0, Sampling factors 2 horiz/2 vert
  • Y component: Quantization table 0, Sampling factors 2 horiz/2 vert
  • Y component: Quantization table 0, Sampling factors 2 horiz/2 vert


Component_2:
  • Cb component: Quantization table 1, Sampling factors 1 horiz/1 vert
  • Cb component: Quantization table 1, Sampling factors 1 horiz/1 vert
  • Cb component: Quantization table 1, Sampling factors 1 horiz/1 vert
  • Cb component: Quantization table 1, Sampling factors 1 horiz/1 vert
  • Cb component: Quantization table 1, Sampling factors 1 horiz/1 vert
  • Cb component: Quantization table 1, Sampling factors 1 horiz/1 vert
  • Cb component: Quantization table 1, Sampling factors 1 horiz/1 vert


Component_3:
  • Cr component: Quantization table 1, Sampling factors 1 horiz/1 vert
  • Cr component: Quantization table 1, Sampling factors 1 horiz/1 vert
  • Cr component: Quantization table 1, Sampling factors 1 horiz/1 vert
  • Cr component: Quantization table 1, Sampling factors 1 horiz/1 vert
  • Cr component: Quantization table 1, Sampling factors 1 horiz/1 vert
  • Cr component: Quantization table 1, Sampling factors 1 horiz/1 vert
  • Cr component: Quantization table 1, Sampling factors 1 horiz/1 vert


Compression_CompressionTypeName:
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate
  • deflate


Compression_Lossless:
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true
  • true


Compression_NumProgressiveScans:
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1


Compression_Type:
  • Baseline
  • Baseline
  • Progressive, Huffman
  • Progressive, Huffman
  • Progressive, Huffman
  • Baseline
  • Progressive, Huffman


Creation-Date:
2021-07-05T15:30:26Z

Data_BitsPerSample:
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8 8
  • 8 8 8 8
  • 8 8 8 8
  • 8 8 8 8
  • 8 8 8 8
  • 8 8 8 8
  • 8 8 8 8
  • 8 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8 8
  • 8 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8 8
  • 8 8 8
  • 8 8 8 8
  • 8 8 8
  • 8 8 8 8
  • 8 8 8
  • 8 8 8 8
  • 8 8 8 8


Data_PlanarConfiguration:
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved
  • PixelInterleaved


Data_Precision:
  • 8 bits
  • 8 bits
  • 8 bits
  • 8 bits
  • 8 bits
  • 8 bits
  • 8 bits


Data_SampleFormat:
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral
  • UnsignedIntegral


Dimension_HorizontalPixelSize:
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367


Dimension_ImageOrientation:
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal
  • Normal


Dimension_PixelAspectRatio:
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0
  • 1.0


Dimension_VerticalPixelSize:
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367
  • 0.35273367


File_Modified_Date:
  • Mon Aug 16 08:54:50 +00:00 2021
  • Mon Aug 16 08:54:50 +00:00 2021
  • Mon Aug 16 08:54:57 +00:00 2021
  • Mon Aug 16 08:54:58 +00:00 2021
  • Mon Aug 16 08:54:58 +00:00 2021
  • Mon Aug 16 08:54:58 +00:00 2021
  • Mon Aug 16 08:54:59 +00:00 2021


File_Name:
  • apache-tika-52871064497562150.tmp
  • apache-tika-5590055265001454676.tmp
  • apache-tika-12296267697571368612.tmp
  • apache-tika-1647766672534576144.tmp
  • apache-tika-3059429362405411599.tmp
  • apache-tika-12936689549097415345.tmp
  • apache-tika-13743365024454126989.tmp


File_Size:
  • 1329 bytes
  • 2117 bytes
  • 33930 bytes
  • 6570 bytes
  • 6395 bytes
  • 3587 bytes
  • 26712 bytes


IHDR:
  • width=1106, height=715, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=1106, height=715, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=94, height=60, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=94, height=60, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=94, height=60, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=94, height=60, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=189, height=1, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=94, height=60, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=94, height=60, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=94, height=60, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=125, height=63, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=16, height=16, bitDepth=8, colorType=RGBAlpha, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=16, height=16, bitDepth=8, colorType=RGBAlpha, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=16, height=16, bitDepth=8, colorType=RGBAlpha, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=16, height=16, bitDepth=8, colorType=RGBAlpha, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=16, height=16, bitDepth=8, colorType=RGBAlpha, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=16, height=16, bitDepth=8, colorType=RGBAlpha, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=16, height=16, bitDepth=8, colorType=RGBAlpha, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=16, height=16, bitDepth=8, colorType=RGBAlpha, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=243, height=30, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=243, height=30, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=243, height=30, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=243, height=30, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=243, height=30, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=243, height=30, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=1106, height=715, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=1106, height=715, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=593, height=291, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=16, height=16, bitDepth=8, colorType=RGBAlpha, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=16, height=16, bitDepth=8, colorType=RGBAlpha, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=690, height=339, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=596, height=218, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=16, height=16, bitDepth=8, colorType=RGBAlpha, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=692, height=236, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=16, height=16, bitDepth=8, colorType=RGBAlpha, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=692, height=235, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=16, height=16, bitDepth=8, colorType=RGBAlpha, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=592, height=196, bitDepth=8, colorType=RGB, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=16, height=16, bitDepth=8, colorType=RGBAlpha, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none
  • width=16, height=16, bitDepth=8, colorType=RGBAlpha, compressionMethod=deflate, filterMethod=adaptive, interlaceMethod=none


Image_Height:
  • 60 pixels
  • 60 pixels
  • 349 pixels
  • 92 pixels
  • 92 pixels
  • 74 pixels
  • 451 pixels


Image_Width:
  • 94 pixels
  • 94 pixels
  • 325 pixels
  • 243 pixels
  • 243 pixels
  • 81 pixels
  • 309 pixels


Number_of_Components:
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3
  • 3


Number_of_Tables:
  • 4 Huffman tables
  • 4 Huffman tables
  • 2 Huffman tables
  • 2 Huffman tables
  • 2 Huffman tables
  • 4 Huffman tables
  • 2 Huffman tables


Resolution_Units:
  • inch
  • inch
  • inch
  • inch
  • inch
  • inch
  • inch


Thumbnail_Height_Pixels:
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0


Thumbnail_Width_Pixels:
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0


Transparency_Alpha:
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • nonpremultipled
  • nonpremultipled
  • nonpremultipled
  • nonpremultipled
  • nonpremultipled
  • nonpremultipled
  • nonpremultipled
  • nonpremultipled
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • none
  • nonpremultipled
  • nonpremultipled
  • none
  • none
  • nonpremultipled
  • none
  • nonpremultipled
  • none
  • nonpremultipled
  • none
  • nonpremultipled
  • nonpremultipled


X_Resolution:
  • 71 dots
  • 71 dots
  • 71 dots
  • 72 dots
  • 72 dots
  • 72 dots
  • 71 dots


Y_Resolution:
  • 72 dots
  • 72 dots
  • 72 dots
  • 71 dots
  • 71 dots
  • 71 dots
  • 71 dots


access_permission_assemble_document:
true

access_permission_can_modify:
true

access_permission_can_print_degraded:
true

access_permission_can_print:
true

access_permission_extract_content:
true

access_permission_extract_for_accessibility:
true

access_permission_fill_in_form:
true

access_permission_modify_annotations:
true

countries_ss_taxonomy0:
  • Kenya
  • Kenya
  • Kenya
  • Kenya


created:
2021-07-05T15:30:26Z

dc_format:
application/pdf; version=1.5

dc_title:
Republic of Ethiopia (2011) Ethiopia’s Climate Resilient Green Economy Green economy strategy

dcterms_created:
2021-07-05T15:30:26Z

embeddedResourceType:
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE
  • INLINE


file_modified_dt:
2021-07-06T16:21:00Z

height:
  • 715
  • 715
  • 60
  • 60
  • 60
  • 60
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 60
  • 60
  • 60
  • 63
  • 16
  • 16
  • 16
  • 16
  • 16
  • 16
  • 16
  • 16
  • 30
  • 30
  • 30
  • 30
  • 30
  • 30
  • 715
  • 715
  • 291
  • 16
  • 16
  • 339
  • 218
  • 16
  • 236
  • 16
  • 235
  • 16
  • 196
  • 16
  • 16


id:
https://plasticsdb.surrey.ac.uk/documents/Ethiopia/Republic of Ethiopia (2011) Ethiopia’s Climate Resilient Green Economy Green economy strategy.pdf

law_code_ss_taxonomy0:
  • Corpus Juris Civilis
  • International Building Code


law_code_ssall_labels_stemming_en_ss_tag_ss_taxonomy0:
  • Oregon Revised Statutes
  • Corpus Juris Civilis
  • Swiss Civil Code
  • International Building Code


meta_creation-date:
2021-07-05T15:30:26Z

pHYs:
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter
  • pixelsPerUnitXAxis=2835, pixelsPerUnitYAxis=2835, unitSpecifier=meter


path0:
plasticsdb.surrey.ac.uk

path1:
documents

path2:
Ethiopia

path_basename:
Republic of Ethiopia (2011) Ethiopia’s Climate Resilient Green Economy Green economy strategy.pdf

pdf_PDFVersion:
1.5

pdf_charsPerPage:
  • 119
  • 4
  • 16
  • 4
  • 1077
  • 224
  • 1871
  • 4
  • 1456
  • 1450
  • 48
  • 1245
  • 2413
  • 2026
  • 1633
  • 0
  • 1247
  • 2458
  • 1630
  • 1388
  • 2077
  • 794
  • 2097
  • 1449
  • 2436
  • 2348
  • 2085
  • 2454
  • 2483
  • 1375
  • 2312
  • 2333
  • 1623
  • 1638
  • 2525
  • 2534
  • 2755
  • 2493
  • 2127
  • 2128
  • 1032
  • 2703
  • 1923
  • 2572
  • 2726
  • 2228
  • 2211
  • 2412
  • 2718
  • 1803
  • 2052
  • 2310
  • 2772
  • 1496
  • 2228
  • 0
  • 2152
  • 1573
  • 2788
  • 2501
  • 2408
  • 2215
  • 2211
  • 1976
  • 1974
  • 2224
  • 2427
  • 2402
  • 2466
  • 372
  • 15
  • 4
  • 2381
  • 1462
  • 2491
  • 2754
  • 2438
  • 2404
  • 1860
  • 2609
  • 2515
  • 2090
  • 1837
  • 1984
  • 2111
  • 2399
  • 2140
  • 2617
  • 380
  • 2195
  • 1687
  • 2025
  • 2909
  • 1624
  • 1836
  • 1497
  • 2542
  • 1823
  • 2857
  • 2147
  • 1004
  • 1154
  • 1248
  • 2234
  • 1023
  • 2699
  • 1465
  • 2713
  • 2414
  • 2411
  • 2257
  • 1090
  • 1301
  • 1717
  • 2057
  • 2485
  • 1456
  • 1951
  • 1998
  • 2359
  • 2510
  • 2368
  • 2392
  • 2316
  • 2485
  • 1439
  • 2198
  • 2236
  • 1684
  • 1053
  • 1569
  • 2629
  • 1673
  • 1598
  • 2180
  • 2416
  • 2487
  • 2240
  • 2155
  • 2195
  • 2517
  • 1912
  • 1497
  • 1915
  • 973
  • 1824
  • 1388
  • 1988
  • 2504
  • 2536
  • 1018
  • 2496
  • 2796
  • 2246
  • 2295
  • 2468
  • 2647
  • 2336
  • 2407
  • 1316
  • 1598
  • 791
  • 1255
  • 868
  • 2439
  • 2734
  • 1431
  • 2398
  • 1926
  • 2400
  • 2127
  • 2495
  • 2281
  • 1703
  • 1950
  • 1633
  • 1755
  • 1012
  • 2023
  • 2429
  • 1882
  • 1477
  • 2440
  • 2448
  • 2401
  • 2690
  • 1551
  • 2090
  • 2741
  • 1422
  • 1830
  • 1868
  • 1873
  • 2055
  • 2125
  • 1913
  • 1991
  • 1696
  • 1733
  • 490


pdf_docinfo_created:
2021-07-05T15:30:26Z

pdf_docinfo_creator_tool:
Draw

pdf_docinfo_producer:
LibreOffice 6.4

pdf_docinfo_title:
Republic of Ethiopia (2011) Ethiopia’s Climate Resilient Green Economy Green economy strategy

pdf_encrypted:
false

pdf_hasMarkedContent:
false

pdf_hasXFA:
false

pdf_hasXMP:
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false
  • false


pdf_unmappedUnicodeCharsPerPage:
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0
  • 0


producer:
LibreOffice 6.4

resourceName:
  • b'Republic of Ethiopia (2011) Ethiopia\xe2\x80\x99s Climate Resilient Green Economy Green economy strategy.pdf'
  • image0.png
  • image1.png
  • image2.jpg
  • image3.png
  • image4.jpg
  • image5.png
  • image6.png
  • image7.png
  • image8.png
  • image9.png
  • image10.png
  • image11.png
  • image12.png
  • image13.png
  • image14.png
  • image15.png
  • image16.png
  • image17.png
  • image18.png
  • image19.png
  • image20.png
  • image21.png
  • image22.png
  • image23.png
  • image24.png
  • image25.png
  • image26.png
  • image27.png
  • image28.png
  • image29.png
  • image30.png
  • image31.png
  • image32.png
  • image33.png
  • image34.png
  • image35.png
  • image36.png
  • image37.png
  • image38.png
  • image39.png
  • image40.png
  • image41.png
  • image42.png
  • image43.png
  • image44.png
  • image45.png
  • image46.png
  • image47.png
  • image48.png
  • image49.png
  • image50.png
  • image51.png
  • image52.png
  • image53.png
  • image54.png
  • image55.png
  • image56.png
  • image57.png
  • image58.png
  • image59.png
  • image60.png
  • image61.png
  • image62.png
  • image63.png
  • image64.png
  • image65.png
  • image66.png
  • image67.png
  • image68.png
  • image69.png
  • image70.png
  • image71.png
  • image72.png
  • image73.png
  • image74.png
  • image75.png
  • image76.png
  • image77.png
  • image78.png
  • image79.png
  • image80.png
  • image81.png
  • image82.png
  • image83.png
  • image84.png
  • image85.png
  • image86.png
  • image87.png
  • image88.png
  • image89.png
  • image90.png
  • image91.png
  • image92.png
  • image93.png
  • image94.png
  • image95.png
  • image96.png
  • image97.png
  • image98.png
  • image99.png
  • image100.png
  • image101.png
  • image102.png
  • image103.png
  • image104.png
  • image105.png
  • image106.png
  • image107.png
  • image108.png
  • image109.png
  • image110.png
  • image111.png
  • image112.png
  • image113.png
  • image114.png
  • image115.png
  • image116.png
  • image117.png
  • image118.png
  • image119.png
  • image120.png
  • image121.png
  • image122.png
  • image123.png
  • image124.png
  • image125.png
  • image126.png
  • image127.png
  • image128.png
  • image129.png
  • image130.png
  • image131.jpg
  • image132.png
  • image133.png
  • image134.png
  • image135.png
  • image136.png
  • image137.png
  • image138.png
  • image139.png
  • image140.png
  • image141.jpg
  • image142.jpg
  • image143.png
  • image144.png
  • image145.png
  • image146.png
  • image147.png
  • image148.png
  • image149.jpg
  • image150.png
  • image151.png
  • image152.jpg
  • image153.png
  • image154.png
  • image155.png
  • image156.png
  • image157.png
  • image158.png
  • image159.png
  • image160.png
  • image161.png
  • image162.png
  • image163.png
  • image164.png
  • image165.png


tiff_BitsPerSample:
  • 8 8 8
  • 8 8 8
  • 8
  • 8 8 8
  • 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8
  • 8 8 8
  • 8 8 8 8
  • 8 8 8 8
  • 8 8 8 8
  • 8 8 8 8
  • 8 8 8 8
  • 8 8 8 8
  • 8 8 8 8
  • 8 8 8 8
  • 8
  • 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8
  • 8
  • 8 8 8
  • 8 8 8
  • 8
  • 8 8 8
  • 8 8 8 8
  • 8 8 8 8
  • 8 8 8
  • 8 8 8
  • 8 8 8 8
  • 8 8 8
  • 8 8 8 8
  • 8 8 8
  • 8 8 8 8
  • 8 8 8
  • 8 8 8 8
  • 8 8 8 8


tiff_ImageLength:
  • 715
  • 715
  • 60
  • 60
  • 60
  • 60
  • 60
  • 60
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 60
  • 60
  • 60
  • 349
  • 63
  • 16
  • 16
  • 16
  • 16
  • 16
  • 16
  • 16
  • 16
  • 92
  • 92
  • 30
  • 30
  • 30
  • 30
  • 30
  • 30
  • 74
  • 715
  • 715
  • 451
  • 291
  • 16
  • 16
  • 339
  • 218
  • 16
  • 236
  • 16
  • 235
  • 16
  • 196
  • 16
  • 16


tiff_ImageWidth:
  • 1106
  • 1106
  • 94
  • 94
  • 94
  • 94
  • 94
  • 94
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 94
  • 94
  • 94
  • 325
  • 125
  • 16
  • 16
  • 16
  • 16
  • 16
  • 16
  • 16
  • 16
  • 243
  • 243
  • 243
  • 243
  • 243
  • 243
  • 243
  • 243
  • 81
  • 1106
  • 1106
  • 309
  • 593
  • 16
  • 16
  • 690
  • 596
  • 16
  • 692
  • 16
  • 692
  • 16
  • 592
  • 16
  • 16


width:
  • 1106
  • 1106
  • 94
  • 94
  • 94
  • 94
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 189
  • 94
  • 94
  • 94
  • 125
  • 16
  • 16
  • 16
  • 16
  • 16
  • 16
  • 16
  • 16
  • 243
  • 243
  • 243
  • 243
  • 243
  • 243
  • 1106
  • 1106
  • 593
  • 16
  • 16
  • 690
  • 596
  • 16
  • 692
  • 16
  • 692
  • 16
  • 592
  • 16
  • 16


xmpTPg_NPages:
200

xmp_CreatorTool:
Draw

etl_file_b:
1

etl_enhance_mapping_id_time_millis_i:
0

etl_enhance_mapping_id_b:
1

etl_filter_blacklist_time_millis_i:
0

etl_filter_blacklist_b:
1

etl_filter_file_not_modified_time_millis_i:
10

etl_filter_file_not_modified_b:
1

etl_enhance_file_mtime_time_millis_i:
0

etl_enhance_file_mtime_b:
1

etl_enhance_path_time_millis_i:
0

etl_enhance_path_b:
1

etl_enhance_entity_linking_time_millis_i:
2437

etl_enhance_entity_linking_b:
1

etl_enhance_multilingual_time_millis_i:
2

etl_enhance_multilingual_b:
1

etl_export_solr_time_millis_i:
4

etl_export_solr_b:
1

etl_export_queue_files_time_millis_i:
1

etl_export_queue_files_b:
1

etl_time_millis_i:
21622

etl_enhance_extract_text_tika_server_ocr_enabled_b:
1

etl_count_images_yet_no_ocr_i:
0

X-Parsed-By:
  • org.apache.tika.parser.DefaultParser
  • org.apache.tika.parser.pdf.PDFParser
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.jpeg.JpegParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.jpeg.JpegParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.jpeg.JpegParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.jpeg.JpegParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.jpeg.JpegParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.jpeg.JpegParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.jpeg.JpegParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]
  • [org.apache.tika.parser.DefaultParser, org.apache.tika.parser.ocr.TesseractOCRParser, org.apache.tika.parser.image.ImageParser]


etl_enhance_extract_text_tika_server_time_millis_i:
12497

etl_enhance_extract_text_tika_server_b:
1

etl_enhance_pdf_ocr_time_millis_i:
6

etl_enhance_pdf_ocr_b:
1

etl_enhance_detect_language_tika_server_time_millis_i:
51

etl_enhance_detect_language_tika_server_b:
1

etl_enhance_contenttype_group_time_millis_i:
1

etl_enhance_contenttype_group_b:
1

etl_enhance_pst_time_millis_i:
0

etl_enhance_pst_b:
1

etl_enhance_csv_time_millis_i:
0

etl_enhance_csv_b:
1

etl_enhance_extract_hashtags_time_millis_i:
80

etl_enhance_extract_hashtags_b:
1

etl_enhance_warc_time_millis_i:
5

etl_enhance_warc_b:
1

etl_enhance_zip_time_millis_i:
1

etl_enhance_zip_b:
1

etl_clean_title_time_millis_i:
0

etl_clean_title_b:
1

etl_enhance_rdf_annotations_by_http_request_time_millis_i:
26

etl_enhance_rdf_annotations_by_http_request_b:
1

etl_enhance_rdf_time_millis_i:
0

etl_enhance_rdf_b:
1

etl_enhance_regex_time_millis_i:
1940

etl_enhance_regex_b:
1

etl_enhance_extract_email_time_millis_i:
1109

etl_enhance_extract_email_b:
1

etl_enhance_extract_phone_time_millis_i:
1079

etl_enhance_extract_phone_b:
1

etl_enhance_extract_law_time_millis_i:
1291

etl_enhance_extract_law_b:
1

etl_export_neo4j_time_millis_i:
1014

etl_export_neo4j_b:
1

X-TIKA_content_handler:
  • ToTextContentHandler
  • ToTextContentHandler
  • ToTextContentHandler
  • ToTextContentHandler
  • ToTextContentHandler
  • ToTextContentHandler
  • ToTextContentHandler
  • ToTextContentHandler
  • ToTextContentHandler
  • ToTextContentHandler


X-TIKA_embedded_depth:
  • 0
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1
  • 1


X-TIKA_parse_time_millis:
  • 12420
  • 47
  • 74
  • 90
  • 49
  • 70
  • 61
  • 45
  • 53
  • 46
  • 57
  • 45
  • 45
  • 45
  • 52
  • 46
  • 45
  • 46
  • 44
  • 44
  • 55
  • 48
  • 43
  • 50
  • 57
  • 45
  • 43
  • 43
  • 42
  • 42
  • 42
  • 45
  • 45
  • 44
  • 43
  • 44
  • 41
  • 42
  • 45
  • 44
  • 43
  • 43
  • 45
  • 49
  • 43
  • 44
  • 45
  • 44
  • 44
  • 43
  • 44
  • 47
  • 57
  • 56
  • 91
  • 93
  • 85
  • 44
  • 45
  • 44
  • 46
  • 47
  • 43
  • 46
  • 67
  • 69
  • 106
  • 91
  • 44
  • 105
  • 103
  • 69
  • 102
  • 74
  • 49
  • 48
  • 52
  • 53
  • 48
  • 55
  • 78
  • 139
  • 48
  • 47
  • 51
  • 43
  • 43
  • 44
  • 45
  • 41
  • 42
  • 53
  • 47
  • 43
  • 42
  • 42
  • 42
  • 44
  • 44
  • 50
  • 45
  • 60
  • 61
  • 52
  • 52
  • 44
  • 43
  • 57
  • 51
  • 52
  • 52
  • 49
  • 44
  • 44
  • 44
  • 45
  • 48
  • 44
  • 45
  • 44
  • 44
  • 43
  • 43
  • 43
  • 42
  • 44
  • 45
  • 43
  • 43
  • 43
  • 42
  • 42
  • 48
  • 50
  • 55
  • 44
  • 50
  • 46
  • 49
  • 45
  • 50
  • 54
  • 50
  • 77
  • 62
  • 99
  • 105
  • 125
  • 67
  • 47
  • 46
  • 103
  • 45
  • 46
  • 103
  • 51
  • 64
  • 56
  • 120
  • 57
  • 103
  • 76
  • 121
  • 54
  • 44
  • 46
  • 46


X-TIKA_embedded_resource_path:
  • /image0.png
  • /image1.png
  • /image2.jpg
  • /image3.png
  • /image4.jpg
  • /image5.png
  • /image6.png
  • /image7.png
  • /image8.png
  • /image9.png
  • /image10.png
  • /image11.png
  • /image12.png
  • /image13.png
  • /image14.png
  • /image15.png
  • /image16.png
  • /image17.png
  • /image18.png
  • /image19.png
  • /image20.png
  • /image21.png
  • /image22.png
  • /image23.png
  • /image24.png
  • /image25.png
  • /image26.png
  • /image27.png
  • /image28.png
  • /image29.png
  • /image30.png
  • /image31.png
  • /image32.png
  • /image33.png
  • /image34.png
  • /image35.png
  • /image36.png
  • /image37.png
  • /image38.png
  • /image39.png
  • /image40.png
  • /image41.png
  • /image42.png
  • /image43.png
  • /image44.png
  • /image45.png
  • /image46.png
  • /image47.png
  • /image48.png
  • /image49.png
  • /image50.png
  • /image51.png
  • /image52.png
  • /image53.png
  • /image54.png
  • /image55.png
  • /image56.png
  • /image57.png
  • /image58.png
  • /image59.png
  • /image60.png
  • /image61.png
  • /image62.png
  • /image63.png
  • /image64.png
  • /image65.png
  • /image66.png
  • /image67.png
  • /image68.png
  • /image69.png
  • /image70.png
  • /image71.png
  • /image72.png
  • /image73.png
  • /image74.png
  • /image75.png
  • /image76.png
  • /image77.png
  • /image78.png
  • /image79.png
  • /image80.png
  • /image81.png
  • /image82.png
  • /image83.png
  • /image84.png
  • /image85.png
  • /image86.png
  • /image87.png
  • /image88.png
  • /image89.png
  • /image90.png
  • /image91.png
  • /image92.png
  • /image93.png
  • /image94.png
  • /image95.png
  • /image96.png
  • /image97.png
  • /image98.png
  • /image99.png
  • /image100.png
  • /image101.png
  • /image102.png
  • /image103.png
  • /image104.png
  • /image105.png
  • /image106.png
  • /image107.png
  • /image108.png
  • /image109.png
  • /image110.png
  • /image111.png
  • /image112.png
  • /image113.png
  • /image114.png
  • /image115.png
  • /image116.png
  • /image117.png
  • /image118.png
  • /image119.png
  • /image120.png
  • /image121.png
  • /image122.png
  • /image123.png
  • /image124.png
  • /image125.png
  • /image126.png
  • /image127.png
  • /image128.png
  • /image129.png
  • /image130.png
  • /image131.jpg
  • /image132.png
  • /image133.png
  • /image134.png
  • /image135.png
  • /image136.png
  • /image137.png
  • /image138.png
  • /image139.png
  • /image140.png
  • /image141.jpg
  • /image142.jpg
  • /image143.png
  • /image144.png
  • /image145.png
  • /image146.png
  • /image147.png
  • /image148.png
  • /image149.jpg
  • /image150.png
  • /image151.png
  • /image152.jpg
  • /image153.png
  • /image154.png
  • /image155.png
  • /image156.png
  • /image157.png
  • /image158.png
  • /image159.png
  • /image160.png
  • /image161.png
  • /image162.png
  • /image163.png
  • /image164.png
  • /image165.png





Searching ...