Master of Science Thesis

KTH School of Industrial Engineering and Management

Energy Technology EGI_2017-0057-MSC

Division of Energy and Climate Studies

SE-100 44 STOCKHOLM

Clean cooking in sub-Saharan Africa:

modeling the cooking fuel mix to

2050

Henri Casteleyn

KTH ROYAL INSTITUTE OF TECHNOLOGY

Clean cooking in sub-Saharan Africa:

modeling the cooking fuel mix to 2050

Submitted by

Henri Casteleyn

Supervisor: Prof. Semida Silveira

Examiner: Prof. Semida Silveira

A thesis submitted in fulfillment for the degree of

Master of Science

in the

Division of Energy and Climate Studies

Department of Energy Technology

School of Industrial Engineering and Management

June 2017

“There is no substitute for hard work.”

Thomas A. Edison

Abstract

As of 2014, 81% of sub-Saharan population or 792 million people rely on the traditional use of biomass

to provide in their cooking needs. This situation causes harmful health, environmental, and

development hazards with a substantial annual economic cost of USD58.2 billion. The concern about

the issue of access to clean cooking facilities is growing as international organizations and national

governments define steps to transform the existing situation.

Literature provides a good view on determinants for the cooking fuel choice in developing regions, but

comprehensive outlooks for the future cooking fuel mix in sub-Saharan countries are limited. To this

extent, the presented master's thesis aims to shed light on a history-inspired pathway for the evolution

of the biomass dominated cooking fuel mix in sub-Saharan countries to 2050.

A quantitative model was developed to estimate the future uptake of various cooking technologies,

from which the fuel mix can be derived using energy intensities. Projections were constructed for urban

and rural areas in 45 countries. Economic development, population expansion, urbanization, and to a

certain extent policies are the key drivers of the model.

Despite a moderate improvement in the share of population relying on traditional biomass, 808 million

people in sub-Saharan Africa are expected to make use of traditional three-stone fires in 2050, an

increase compared to 2014. Biomass remains the dominant cooking fuel as a result of limited switching

and the low efficiency of employed stoves. Driven by higher incomes and a better developed

infrastructure, urban areas experience a faster shift to modern fuels. Demand for LPG grows at an

annual rate of 6% across sub-Saharan Africa, in sharp contrast with the phase out of kerosene and the

limited uptake of electric cookstoves. The speed of evolutions is dissimilar across countries because of

differences in economic growth and urbanization, and non-homogeneous starting points. The results

demonstrate the vast size of the challenge to improve living conditions in sub-Saharan Africa and

suggest that universal access by 2030, a target stated by several international organizations, is rather

unrealistic.

Keywords: clean cooking, biomass, sub-Saharan Africa

Master of Science Thesis EGI_2017-0057-MSC

Clean cooking in sub-Saharan Africa: modeling

the cooking fuel mix to 2050

Henri Casteleyn

Approved

Examiner

Prof. Semida Silveira

Supervisor

Prof. Semida Silveira

Commissioner

Contact person

Acknowledgements

The study was carried out during a 6-months internship at McKinsey Energy Insights in Poland.

I am grateful for the incredible learning opportunity and the daily coaching I received. In par-

ticular, the intellectual support of Bram Smeets, Matt Frank, Arjan Keizer, and Magdalena

Wlodarczak proved of great value. Without their commitment, this master’s thesis would not

stand where it is today. Furthermore, the broader Energy Insights team ensured that I had an

unforgettable experience during this period.

I would like to thank my supervisor and examiner Prof. Semida Silveira, head of the Unit

of Energy and Climate Studies at KTH, for the guidance and interesting discussions during the

course of the thesis work.

Finally, I would like to thank my family for their continuous support and encouragement.

This thesis was typeset in LATEX using a template provided by Sunil Patel, who himself modified a templateprovided by Steven Gunn. The template was published under CC BY-NC-SA 3.0 and has been abridged andaltered by the author.

vi

Contents

Abstract iii

Acknowledgements vi

List of Figures xi

List of Tables xiii

Abbreviations xv

Physical Constants xvii

Symbols xix

1 Introduction 1

1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Purpose of the master’s thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

1.3 Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

1.4 Organization of report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6

2 Frame of reference 7

2.1 Factors influencing cooking energy choice . . . . . . . . . . . . . . . . . . . . . . 7

2.1.1 Socioeconomic variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.1.2 Cultural and behavioral habits . . . . . . . . . . . . . . . . . . . . . . . . 8

2.1.3 Product-specific attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . 8

2.1.4 External factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.2 Modeling cooking fuel mix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.2.1 Two distinct types of models . . . . . . . . . . . . . . . . . . . . . . . . . 10

2.2.2 Quantifying cooking fuel choice . . . . . . . . . . . . . . . . . . . . . . . . 10

2.2.3 Projecting cooking fuel mix . . . . . . . . . . . . . . . . . . . . . . . . . . 11

2.3 National and international organizations . . . . . . . . . . . . . . . . . . . . . . . 14

3 Methodology 19

3.1 Literature study . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.2 Develop calculation logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.2.1 High-level calculation logic . . . . . . . . . . . . . . . . . . . . . . . . . . 20

vii

Contents

3.2.2 Deep dive in calculations for urban fuel penetration . . . . . . . . . . . . 24

3.3 Implementation of model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

3.4 Process results and derive insights . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4 Implementation 31

4.1 Model inputs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4.1.1 Gross domestic product and population . . . . . . . . . . . . . . . . . . . 31

4.1.2 Urbanization rates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

4.1.3 Cooking fuel penetration . . . . . . . . . . . . . . . . . . . . . . . . . . . 32

4.1.4 Energy intensity of technologies . . . . . . . . . . . . . . . . . . . . . . . . 32

4.1.5 Number of households . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.2 Rationale behind model rules for fuel penetration projections in urban and ruralareas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

4.2.1 Approach taken for analysis of fuel penetration data . . . . . . . . . . . . 34

4.2.2 As households become wealthier, they shift away from wood for cookingpurposes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

4.2.3 Initially, charcoal presents itself as an attractive alternative for wood . . . 38

4.2.4 When a certain wealth level is reached, households shift to LPG . . . . . 43

4.2.5 Kerosene is set to be phased out . . . . . . . . . . . . . . . . . . . . . . . 45

4.2.6 The uptake of electric cookstoves will remain limited . . . . . . . . . . . . 49

4.2.7 Exemptions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

5 Results 55

5.1 Benchmarking against external perspectives . . . . . . . . . . . . . . . . . . . . . 55

5.2 Despite the upgrade in cooking technologies, biomass remains the dominant cook-ing fuel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

5.3 Urban areas experience a faster shift to modern fuels than rural villages . . . . . 59

5.4 Demand for LPG increases sharply in urban areas, as opposed to kerosene andelectricity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

5.5 The shift away from traditional biomass cooking triggers a decrease in averagecooking energy intensity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

5.6 Dissimilar economic growth results in the emergence of a two-speed Africa . . . . 63

6 Discussion 65

6.1 Discussion of results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

6.1.1 Drivers of a changing cooking fuel penetration . . . . . . . . . . . . . . . 65

6.1.2 Business as usual falls short of energy access targets . . . . . . . . . . . . 66

6.1.3 Effective policies are crucial to speed up the phase out of traditionalbiomass cooking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66

6.1.4 Specific areas of attention need to be addressed . . . . . . . . . . . . . . . 68

6.1.5 Data availability should be a priority of government bodies and interna-tional organizations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

6.2 Recommendation for future work . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

Bibliography 73

viii

Contents

A List of modeled countries 81

B Model results for fuel penetration 83

C Model results for technology penetration 87

D Model results for fuel mix 91

ix

List of Figures

3.1 Link between modeled cooking fuels and technologies . . . . . . . . . . . . . . . . 20

3.2 High-level calculation logic for estimating future cooking fuel mix . . . . . . . . . 21

3.3 Methodology to distinguish between the use of improved wood stoves, traditionalwood stoves, improved charcoal stoves, and traditional charcoal stoves . . . . . . 22

3.4 IEA projections for cooking technology penetrations to 2040 . . . . . . . . . . . . 23

3.5 Calculation logic to project future urban fuel penetrations . . . . . . . . . . . . . 25

3.6 Example of general methodology to project fuel penetrations . . . . . . . . . . . 28

4.1 Regression analysis: household size versus GDP per capita . . . . . . . . . . . . . 34

4.2 Technical analysis for urban wood penetration . . . . . . . . . . . . . . . . . . . . 36

4.3 Technical analysis for rural wood penetration . . . . . . . . . . . . . . . . . . . . 38

4.4 Technical analysis for urban charcoal penetration . . . . . . . . . . . . . . . . . . 40

4.5 Technical analysis for rural charcoal penetration . . . . . . . . . . . . . . . . . . 42

4.6 Technical analysis for urban LPG/natural gas/biogas penetration . . . . . . . . . 44

4.7 Technical analysis for rural LPG/natural gas/biogas penetration . . . . . . . . . 46

4.8 Technical analysis for urban kerosene penetration . . . . . . . . . . . . . . . . . . 48

4.9 Technical analysis for rural kerosene penetration . . . . . . . . . . . . . . . . . . 50

4.10 Link between urban electricity access and penetration of electric cooking . . . . . 51

4.11 Technical analysis for urban electricity penetration . . . . . . . . . . . . . . . . . 52

5.1 Comparison of model results for fuel penetration in urban areas against IEAprojections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56

5.2 Comparison of model results for fuel penetration in rural areas against IEA pro-jections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

5.3 Cooking technology mix in sub-Saharan Africa to 2050 . . . . . . . . . . . . . . . 58

5.4 Number of people in sub-Saharan Africa cooking with biomass by technology, to2050 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59

5.5 Number of people cooking with biomass in 2050, by region and technology . . . . 59

5.6 Cooking fuel mix in sub-Saharan Africa to 2050 . . . . . . . . . . . . . . . . . . . 60

5.7 Comparison of technology penetrations in urban and rural areas, by region, in2016 and 2050 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

5.8 Number of people cooking with LPG/natural gas/biogas in 2016 and 2050, byregion and residence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

5.9 LPG/natural gas/biogas cooking energy demand in sub-Saharan Africa to 2050,by region . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62

5.10 Impact of variation in economic growth on change in cooking energy intensity . . 63

B.1 Projected urban fuel penetration for East Africa . . . . . . . . . . . . . . . . . . 83

B.2 Projected urban fuel penetration for West Africa . . . . . . . . . . . . . . . . . . 83

xi

List of Figures

B.3 Projected urban fuel penetration for Central Africa . . . . . . . . . . . . . . . . . 83

B.4 Projected urban fuel penetration for Southern Africa . . . . . . . . . . . . . . . . 83

B.5 Projected rural fuel penetration for East Africa . . . . . . . . . . . . . . . . . . . 84

B.6 Projected rural fuel penetration for West Africa . . . . . . . . . . . . . . . . . . . 84

B.7 Projected rural fuel penetration for Central Africa . . . . . . . . . . . . . . . . . 84

B.8 Projected rural fuel penetration for Southern Africa . . . . . . . . . . . . . . . . 84

C.1 Projected urban technology penetration for East Africa . . . . . . . . . . . . . . 87

C.2 Projected urban technology penetration for West Africa . . . . . . . . . . . . . . 87

C.3 Projected urban technology penetration for Central Africa . . . . . . . . . . . . . 87

C.4 Projected urban technology penetration for Southern Africa . . . . . . . . . . . . 87

C.5 Projected rural technology penetration for East Africa . . . . . . . . . . . . . . . 88

C.6 Projected rural technology penetration for West Africa . . . . . . . . . . . . . . . 88

C.7 Projected rural technology penetration for Central Africa . . . . . . . . . . . . . 88

C.8 Projected rural technology penetration for Southern Africa . . . . . . . . . . . . 88

D.1 Projected urban cooking fuel mix for East Africa . . . . . . . . . . . . . . . . . . 91

D.2 Projected urban cooking fuel mix for West Africa . . . . . . . . . . . . . . . . . . 91

D.3 Projected urban cooking fuel mix for Central Africa . . . . . . . . . . . . . . . . 91

D.4 Projected urban cooking fuel mix for Southern Africa . . . . . . . . . . . . . . . 91

D.5 Projected rural cooking fuel mix for East Africa . . . . . . . . . . . . . . . . . . . 92

D.6 Projected rural cooking fuel mix for West Africa . . . . . . . . . . . . . . . . . . 92

D.7 Projected rural cooking fuel mix for Central Africa . . . . . . . . . . . . . . . . . 92

D.8 Projected rural cooking fuel mix for Southern Africa . . . . . . . . . . . . . . . . 92

xii

List of Tables

2.1 2030 scale-up targets for clean cookstoves in Ethiopia . . . . . . . . . . . . . . . 16

4.1 List of cooking technologies and their range of energy intensity, expressed intoe/year per household . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

4.2 Definition of model rules for the wood penetration in cities . . . . . . . . . . . . 37

4.3 Definition of model rules for the wood penetration in rural areas . . . . . . . . . 39

4.4 Definition of model rules for the charcoal penetration in urban areas . . . . . . . 41

4.5 Definition of model rules for the charcoal penetration in rural areas . . . . . . . . 42

4.6 Definition of model rules for the LPG/natural gas/biogas penetration in urbanareas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

4.7 Definition of model rules for the LPG/natural gas/biogas penetration in rural areas 45

4.8 Definition of model rules for the kerosene penetration in urban areas . . . . . . . 48

4.9 Definition of model rules for the kerosene penetration in rural areas . . . . . . . . 49

4.10 Definition of model rules for the electricity penetration in urban and rural areas . 52

4.11 Summary of the exceptions to the general model rules . . . . . . . . . . . . . . . 53

A.1 List of modeled countries and their respective region in sub-Saharan Africa . . . 82

B.1 Relative fuel penetration in urban and rural areas in East Africa, as percent ofurban/rural population cooking with a particular fuel . . . . . . . . . . . . . . . 84

B.2 Relative fuel penetration in urban and rural areas in West Africa, as percent ofurban/rural population cooking with a particular fuel . . . . . . . . . . . . . . . 85

B.3 Relative fuel penetration in urban and rural areas in Central Africa, as percentof urban/rural population cooking with a particular fuel . . . . . . . . . . . . . . 85

B.4 Relative fuel penetration in urban and rural areas in Southern Africa, as percentof urban/rural population cooking with a particular fuel . . . . . . . . . . . . . . 85

C.1 Relative technology penetration in urban and rural areas in East Africa, as per-cent of urban/rural population cooking with a particular technology . . . . . . . 88

C.2 Relative technology penetration in urban and rural areas in West Africa, as per-cent of urban/rural population cooking with a particular technology . . . . . . . 89

C.3 Relative technology penetration in urban and rural areas in Central Africa, aspercent of urban/rural population cooking with a particular technology . . . . . 89

C.4 Relative technology penetration in urban and rural areas in Southern Africa, aspercent of urban/rural population cooking with a particular technology . . . . . 89

D.1 Relative fuel mix in urban and rural areas in East Africa, as percent of urban/ru-ral cooking energy demand supplied by a particular fuel . . . . . . . . . . . . . . 92

D.2 Relative fuel mix in urban and rural areas in West Africa, as percent of urban/ru-ral cooking energy demand supplied by a particular fuel . . . . . . . . . . . . . . 93

xiii

List of Tables

D.3 Relative fuel mix in urban and rural areas in Central Africa, as percent of ur-ban/rural cooking energy demand supplied by a particular fuel . . . . . . . . . . 93

D.4 Relative fuel mix in urban and rural areas in Southern Africa, as percent ofurban/rural cooking energy demand supplied by a particular fuel . . . . . . . . . 93

xiv

Abbreviations

IEA International Energy Agency

GDP Gross Domestic Product

UN United Nations

GACC Global Alliance for Clean Cookstoves

SDG Sustainable Development Goal

SE4ALL Sustainable Energy for ALL

GHG GreenHouse Gas

BAU Business As Usual

GTF Global Tracking Framework

CRGE Climate Resilient Green Economy

WEO World Energy Outlook

MGI McKinsey Global Institute

toe Tonnes of Oil Equivalent

xv

Physical Constants

Million tonnes of oil equivalent1 Mtoe = 41.868 · 1015 Joule

1As different types of crude oil have different calorific values, the exact value of one Mtoe is defined only byconvention. Here, the value defined by the IEA is used.

xvii

Symbols

◦C Temperature Degrees Celsius

xix

To my parents.

Chapter 1

Introduction

1.1 Background

Access to modern forms of energy is fundamental to meet basic human needs. A lack of ac-

cess to affordable and reliable energy services results in poor healthcare and education, and

a lagging economic growth (International Energy Agency, 2014). Moreover, increased energy

access can trigger reduced poverty, improved health, increased productivity, a competitive eco-

nomic system, and an overall accelerated economic growth (International Energy Agency, 2011).

Therefore, it is of uttermost importance to be aware of the energy access situation and its future

in developing countries.

Energy access is a broad concept and literature does not bring forward a single definition. The

International Energy Agency (further abbreviated as IEA) summarizes and identifies four key

attributes that are commonly found across definitions (International Energy Agency, 2016b):

� Access to a minimum level of electricity

� Access to cooking fuels and technologies that are safe and sustainable, and pose only

minimal harmful health effects

� Access to modern energy for productive activities

� Access to modern energy for public services

In addition to these important aspects, one needs to be aware that other issues are connected to

energy access as well, including the safety, affordability, reliability, and adequacy of the energy

system. Due to data constraints however, most literature pays attention to the topics of access

to electricity and modern ways of cooking (International Energy Agency, 2016b).

When taking a closer look at sub-Saharan Africa, the global epicenter of energy poverty, one

observes that the current state of affairs related to these two aspects of energy access is far from

satisfactory. Firstly, 620 million people in the region do not have access to electricity (Interna-

tional Energy Agency, 2014). Although a very stunning situation, this master’s thesis focuses

on the second aspect of energy poverty, namely access to clean cooking facilities. In sub-Saharan

Africa, 792 million people use traditional biomass for cooking (International Energy Agency,

1

Chapter 1. Introduction

2016a). Traditional biomass cooking refers to the use of highly inefficient three-stone fires fueled

with fuelwood, charcoal, dung, agricultural waste, and other waste. With population growth

outgrowing the number of people gaining access, the absolute number of people without access

to clean cooking technologies increased during the period 2012-2014. Sub-Saharan Africa repre-

sented 33% of the global population without access to clean fuels and technologies for cooking

in 2014 (Sustainable Energy for All, 2017a). Even though the number of people using solid

biomass for cooking is larger in developing Asia, the share of the population in sub-Saharan

Africa is bigger, namely 80% in sub-Saharan Africa, compared to 50% in developing Asia (In-

ternational Energy Agency, 2015). In 23 countries, more than 90% of population cooks with

biomass, driven by extreme poverty (Lambe et al., 2015). Moreover, the pace of improvement

has been extremely slow since 1990 (Sustainable Energy for All, 2017a). The described situation

is problematic since the high penetration of traditional biomass cooking brings about a variety

of social, environmental, and economic hazards.

Traditional biomass cooking causes severe indoor air pollution, the effects of which are broadly

described in literature. The incomplete combustion of biomass is accompanied by the release of

particulate matter, carbon monoxide, and other products. The inhalation of these harmful pol-

lutants causes many diseases, among others respiratory infections (e.g. acute lower respiratory

infections, chronic obstructive pulmonary disease), cancer, vascular diseases, cataract, and low

birth weights. Estimates show that 600,000 people die each year as a result of the exposure to

biomass smoke in sub-Saharan Africa, with girls having a higher chance to die from hazardous

fumes than from malaria or malnutrition (Lambe et al., 2015; The World Bank, 2014; United

Nations Environment Programme, 2017).

The vast use of biomass, both fuelwood and charcoal, for cooking has a negative effect on the

environment, causing forest degradation and biodiversity loss (Rivard and Reay, 2012; Beyene

and Koch, 2013). With a depletion rate that is larger than the rate of growth, East Africa can

be considered the center of unsustainable biomass use. In addition, the burning of fuelwood is

expected to make up 5.6% of Africa’s projected business as usual (BAU) greenhouse gas emis-

sions (The World Bank, 2014).

As biomass is often sourced by gathering the cooking fuel, productive time is lost. The bur-

den is typically placed on women and children, who are responsible for this task. On average,

women spend 2.1 hours per day gathering wood for cooking, with a maximum of 5 hours in

Sierra Leone (Lambe et al., 2015). After fuel is gathered, women spend another couple of hours

cooking with the traditional three-stone fires. These practices prevent women and children to

generate income for the household, pursue an education, or fulfill other household tasks. In

addition to the waste of productive time, women are vulnerable to physical and sexual violence

outside their communities (United Nations Environment Programme, 2017).

The above mentioned negative effects on society cause a substantial opportunity cost of USD58.2

billion per year, which represents 4.4% of gross domestic product, further abbreviated as GDP

(high estimate values) (Lambe et al., 2015). This high value suggests that cost-savings could

be achieved when citizens switch to modern cooking technologies, a statement supported in the

Climate Resilient Green Economy plan of Ethiopia (Federal Democratic Republic of Ethiopia,

2011).

2

Section 1.1 Background

The gravity of this topic is demonstrated by the attention paid by influential international

bodies, among others the IEA, the United Nations (UN), the G7, and the G20. Moreover, mul-

tiple national governments started issuing policies on clean cooking facilities, even though the

focus of governments in the past decades was on electricity access, in particular grid extensions.

The concern about the issue of energy access is clearly growing and steps are taken to transform

the existing situation. In the following paragraphs, some examples of meaningful international

and national actions are presented.

The UN has been actively working on the issue of energy access for years. In 2010, the Global

Alliance for Clean Cookstoves (GACC) was established, with an aim to provide 100 million

households with access to clean and efficient stoves by 2020 (Global Alliance for Clean Cook-

stoves, 2017). The UN also declared 2012 the International Year of Sustainable Energy for All

(International Energy Agency, 2011). In 2015, the UN adopted a goal on energy in the Sustain-

able Development Goals (SDGs), namely to ensure access to affordable, reliable, sustainable

and modern energy for all. A cornerstone of this goal is to provide access to modern energy in

developing regions, including sub-Saharan Africa (United Nations, 2017). Another noteworthy

action of the UN is the Sustainable Energy for All (SE4ALL) initiative. This initiative targets

universal access to modern energy services, improved efficiency, and an increased use of renew-

ables (Sustainable Energy for All, 2017b). Very recently, the World Bank and the IEA released

the Global Tracking Framework, the key take-away being that the progress on the objectives for

2030 is not fast enough (Sustainable Energy for All, 2017a). Furthermore, the G7 committed

to accelerate access to renewable energy in Africa and other regions, and the G20 launched

the Energy Access Action Plan for sub-Saharan Africa and developed a plan for Asia and the

Pacific as well (International Energy Agency, 2015, 2016a). In 2015, the African Development

Bank launched A New Deal on Energy for Africa, aiming to achieve universal access to energy

in Africa by 2025 (International Energy Agency, 2016a).

Other noteworthy initiatives that touch upon access to clean cooking are GIZs Energizing De-

velopment Program, the West African Clean Cooking Alliance by the Economic Community of

West Africa States, Energy+ hosted by Norway, United Kingdom and others, and the EnDev

program by Germany, Norway and others (International Energy Agency, 2014).

As mentioned, also national governments are committing to the case of clean cooking. Some

countries with recent policies on clean cooking are (International Energy Agency, 2014, 2013):

� Indonesia: successfully implemented a kerosene-to-LPG program in 2007 and continues to

make efforts to promote clean cooking

� Nigeria: set a target to provide 20 million households with access to clean cooking facilities

by 2020

� Senegal: implemented a national LPG program

� Ghana: implemented a national LPG program

� Cote d’Ivoire: implemented a national LPG program

� Kenya: plans to eliminate kerosene by 2022

3

Chapter 1. Introduction

� Ethiopia: aimed to disseminate 9 million improved cookstoves by 2015 and develops

Biomass Energy Strategy Plans in collaboration with the European Union Energy Ini-

tiative Partnership Dialogue Facility

� Rwanda: plans to reduce the share of bioenergy in primary energy demand to 50% by

2020

� Mozambique: develops Biomass Energy Strategy Plans in collaboration with the European

Union Energy Initiative Partnership Dialogue Facility

� Liberia: develops Biomass Energy Strategy Plans in collaboration with the European

Union Energy Initiative Partnership Dialogue Facility

� Sierra Leone: develops Biomass Energy Strategy Plans in collaboration with the European

Union Energy Initiative Partnership Dialogue Facility

Also, multiple countries, such as Ethiopia, Nigeria, Kenya, Central African Republic, Burkina

Faso etc. state in their Intended Nationally Determined Contributions that the promotion of

clean and improved cookstoves is an action area to increase access to energy among citizens and

cut greenhouse gas (GHG) emissions (United Nations Environment Programme, 2017; Federal

Democratic Republic of Ethiopia, 2015; Ministry of Environment and Natural Resources Kenya,

2015; Government of Nigeria, 2015).

A last example of the growing attention is the emerging involvement of multinationals, illus-

trated by partnerships between Philips and the Industrial Development Corporation of South

Africa, and between the firms General Electric, Burn Manufacturing, and the US Overseas Pri-

vate Investment Corporation (International Energy Agency, 2013).

The previous paragraphs clearly described the issues related to a lack of access to clean cooking

facilities and the growing attention paid by national and international organizations. Relevant

literature has mapped the negative effects of traditional biomass cooking and does touch upon

drivers for cooking fuel choice in developing regions. However, research papers do not provide

a comprehensive, quantitative, and transparent outlook for the future cooking fuel mix in sub-

Saharan countries, a research gap to be filled in order to develop a view on the future situation,

and identify opportunities for increased efforts.

1.2 Purpose of the master’s thesis

Starting from the observation that cooking energy consumption averages approximately 80% of

residential energy use in sub-Saharan Africa, a good understanding of this service is essential to

assess the dynamics in the overall energy system (International Energy Agency, 2014). More-

over, modeling the cooking energy consumption is desirable as the model insights can be used

to guide policy decisions. Governments and international aid organizations can benefit from

having a BAU perspective on the future cooking fuel mix to identify priorities, which is what

this report aims to deliver.

This master’s thesis report aims to fill a gap in the existing research literature by providing

a driver-based quantitative outlook for the cooking fuel mix in urban, rural, and national areas

4

Section 1.3 Limitations

for 45 countries in sub-Saharan Africa, the global epicenter of energy poverty. It is crucial to

understand that differences in used cooking fuel among cities and rural villages are consider-

able. To the author’s best knowledge, transparent estimates of the future fuel mix on an urban,

rural, and/or national level are not available in scientific literature for this geographical scope.

By investigating past evolutions and historical datasets, the author hopes to develop a BAU

scenario to 2050.

To this extent, the following research question is defined:

What is a history-inspired pathway for the evolution of the biomass dom-

inated cooking fuel mix in sub-Saharan countries to 2050?

The research question is further broken down into three objectives:

1. Develop a view on the drivers behind the fuel switch in residential cooking in sub-Saharan

Africa

2. Create a quantitative model that captures the changes in the use of traditional biomass

for cooking, based on the uptake of a number of cooking technologies, in order to establish

a perspective on the future cooking fuel mix on urban and rural level in 45 sub-Saharan

countries

3. Explore the connection between the clean cooking evolution and change in cooking energy

demand intensity

In order to be able to formulate an answer to the above stated research question and achieve

the objectives, a quantitative model was developed. The model estimates the future uptake of

several cooking technologies in 45 sub-Saharan countries (see appendix A for a list of countries).

Projections for urban and rural areas are formulated separately to account for significantly dif-

ferent dynamics in the two residences. Examples of the different circumstances are the difference

in level of income and availability of substitute fuels for biomass. The model rules are based on

regression analyses (penetration of cooking fuel against income), insights from literature, and

expert interviews. Once projections for the diffusion of technologies are obtained, the fuel mix

can be calculated, given assumptions for the energy intensity of each cooking technology.

1.3 Limitations

However, rather than pretending to predict the future with certainty, it is acknowledged that

the produced model is per definition an approximation of the reality. Therefore, it is useful to

understand the limitations associated with its use.

This master’s thesis project aims to provide a driver-based outlook for the cooking fuel mix

in sub-Saharan Africa to 2050. Making long-term projections is challenging because a long time

horizon increases the uncertainty of projections. As one tries to capture fuel dynamics, it is

possible to miss the impact of certain disruptions, such as the uptake of solar cooking or an

accelerated development of the power system.

5

Chapter 1. Introduction

Another inherent limitation of this project is the strong connection between the projections

and historical evolutions, as observed in the available data. The outlook for the future uptake

of technologies is largely based on the speed of trends in the past. As a result, the obtained

model results display a BAU case, without fully accounting for drastic changes in policies and

other developments. When observing the model results, it is crucial to be aware that the future

situation might very well be totally different from what is presented.

As described in chapter 2, literature shows that cooking stove/fuel choice is influenced by many

determinants, broadly categorized as socioeconomic variables, cultural or behavioral habits,

product-specific attributes, and external factors. The developed model focuses on economic de-

velopment, population growth, and urbanization as drivers. The rationale behind this approach

is given by severe data constraints and the belief that the above mentioned macroeconomic

variables are the key drivers of past and future evolutions in cooking energy consumption.

The model is based on the assumption that households use only one fuel for cooking. How-

ever, in reality, households often use multiple fuels as the transition to clean cooking fuels is

accompanied by the simultaneous use of biomass and a more modern cooking fuel. This phe-

nomenon is known as fuel stacking and provides households with flexibility in case of disturbed

fuel supply or high prices. Due to lack of data and complexity of the model, this phenomenon

is not captured by the presented model.

The thesis work has been characterized by severe data limitations. Residential energy con-

sumption data is often hard to find because of privacy issues that limit the availability of data,

and the costs of detailed energy metering. This is especially the case for sub-Saharan Africa,

which is known for fairly poor data quality. As a result, the model often employs strong as-

sumptions and pragmatic actions. The data that was used for representing the cooking fuel

penetration is based on surveys. Inherently, surveys are not always a good representation of

reality because of mistakes during data collection, respondents that are not representative for

the broader population, or the poor design of the survey. Several different surveys were used

to obtain data for as much countries as possible. The scarcity of data called for a pragmatic

approach, resulting in different countries having a different base year for the projections.

1.4 Organization of report

Chapter 2 further elaborates on the research gap by describing relevant work that has been

produced by others. Both academic literature and publications from international organizations

are covered to give the reader a good idea of the overall clean cooking environment. In chapter

3, the methodology is discussed in a brief and concise way. Chapter 4 covers in detail the model

rules that determine the results of the modeling exercise. Some specific aspects of the results are

described and presented graphically in chapter 5. This chapter also contains a benchmarking

of the results. Due to the vast size of the model, results are mostly presented for urban and

rural areas on a regional level. The report ends with a discussion, and a description of potential

future work in chapter 6.

6

Chapter 2

Frame of reference

This chapter aims to provide the reader with a synopsis of literature on clean cooking in de-

veloping regions. In the first two sections, academic research papers are covered. In the third

section, some attention is reserved for the efforts of national and international organizations to

alter the current dominance of traditional biomass cooking.

2.1 Factors influencing cooking energy choice

A number of studies have been conducted to understand the factors that affect cooking stove

choices and fuel consumption patterns. Even though determining or quantifying determinants

of fuel choice is not the aim of this thesis, this research is discussed as it can provide a view on

the drivers behind increased access to clean cooking facilities, which is one of the objectives of

this report. The starting point of these studies is mostly a conducted survey, to which statistical

manipulations are applied. Understanding the determinants of the uptake of improved or clean

cookstoves is crucial for the design of effective policies that aim to expand access to clean cooking

facilities in a cost-efficient way. One can group the identified variables in four groups, namely

socioeconomic variables, cultural or behavioral habits, product-specific attributes, and external

factors.

2.1.1 Socioeconomic variables

This group of determinants characterizes the households or individuals deciding on the stove

type that will be used for cooking activities. Examples of factors include income, education,

and the size of households. This group of factors has received most attention in literature.

The influence of income on the cooking fuel choice is a widely discussed topic. Takama et al.

(2012) provides prove for the rationale that households switch to more advanced and cleaner

cooking technologies when their wealth level increases. This concept is known as the energy lad-

der. Other studies confirm the influence of income on fuel choices, provide empirical evidence,

and state that rising income is a key driver to climb the energy ladder (Barnes et al., 2010;

7

Chapter 2. Frame of reference

Yu-Ting Lee, 2013; Ouedraogo, 2006; Rehfuess et al., 2010; Beyene and Koch, 2013; Karimu,

2015; Rahut et al., 2016).

A different perspective on the fuel switching behavior of households is presented by the fuel

stacking model, which states that households make use of a portfolio of techniques and fuels

for cooking (van der Kroon et al., 2013; Masera et al., 2000). The use of multiple fuels allows

households to be flexible and react to supply shortages, price fluctuations, or irregular income

flows. Income is still accepted to be a key determinant of fuel choice, but the impact of other

influences may not be overseen. Therefore, the authors describe the fuel choice in a household

decision environment which includes variables such as the age of occupants and their education

level.

Several papers agree that education is a significant factor in the fuel choice decision process,

making improved education an important lever to foster a transition towards modern cooking

facilities (Heltberg, 2005; Rao and Reddy, 2007; Yu-Ting Lee, 2013; Ouedraogo, 2006; Rehfuess

et al., 2010; Rahut et al., 2016; Nlom and Karimov, 2015). These sources also recognize the

influence of human capital.

A number of other factors that impact the decision of cooking fuel were identified in litera-

ture. Examples include occupation of household members, household size, age of household

members, availability of public infrastructure, and housing standards (Rao and Reddy, 2007;

Yu-Ting Lee, 2013; Ouedraogo, 2006; Nlom and Karimov, 2015).

Note that it becomes clear why the cooking situation in cities is distinct from the one in rural

villages. As factors such as level of income, education, occupation of household members, avail-

ability of infrastructure, and housing standards differ among the two residences, the penetration

of modern fuels versus traditional biomass is different. This observation supports the decision

to model the fuel mix in both residences separately.

2.1.2 Cultural and behavioral habits

Less academic work has focused on cultural or behavioral habits that affect the choice for a

particular cooking fuel. However, household characteristics such as the frequency of cooking

certain meals (e.g. rice), the position of women, and the religion of household members have an

impact on the used cooking technologies (van der Kroon et al., 2013; Ouedraogo, 2006; Masera

et al., 2000; Heltberg, 2005; Urmee and Gyamfi, 2014; Ruijven et al., 2011).

2.1.3 Product-specific attributes

As opposed to previously discussed considerations, product-specific attributes cover a rather

different set of variables with an influence on the cooking fuel choice decision, namely the char-

acteristics of the cooking stoves and the associated fuels. This research focuses more on the

elements to which consumers pay attention and the behavior of household consumers in switch-

ing away from traditional biomass cooking.

8

Section 2.1 Factors influencing cooking energy choice

One study which reflects on stove attributes is Takama et al. (2012). The authors researched

the importance of several product-specific characteristics in influencing the stove choice, aiming

to understand how households make the trade-off between different attributes. To do so, a

choice experiment was implemented, which is a behavior modeling technique aiming to assess

the strength of various influences. Through a survey of 200 households in July 2008 in Addis

Ababa, Ethiopia, the preferences of consumers were identified as respondents chose the pre-

ferred option from a series of products. Afterwards, the value of the attributes was estimated to

convey the relative strength of product features in influencing households’ choices. The report

covers three cooking fuels, namely wood, kerosene, and ethanol, and four stove attributes; stove

price, monthly usage costs, indoor smoke emission level, and safety. Moreover, by comparing

responses from three different wealth groups, the impact of wealth could be evaluated. The

investigated product-specific factors significantly affect stove and fuel choices and differences

across wealth groups are considerable.

As expected, the research paper finds that stove price and usage cost reduce the utility of

a cookstove, except for high-wealth groups, suggesting that other factors such as convenience

play a significant role for those households. The results show that the low-wealth group is more

sensitive to the stove price than the middle-wealth group, illustrating that as the wealth level

of a household increases, the stove price becomes less important in the decision process. As

with stove price, the low-wealth group is most sensitive to usage cost, which is affected by the

efficiency of a stove and the fuel price. In comparison to low-wealth and middle-wealth groups,

the high-wealth group shows the strongest aversion for smoke, illustrating that rich households

pay attention to user convenience. Finally, the safety characteristics of a stove prove to be

a determinant of fuel choice across wealth groups, with higher wealth groups willing to pay

more for increased safety than lower wealth groups. The results of this research point to the

energy ladder model, in which households shift to more advanced cooking options when income

increases. Wealthy consumers prefer clean fuels and stoves and are willing to pay for it.

Another important influence is the price level of fuels. Higher fuel prices lead to a reduced

consumption of that particular fuel and switching to substitutes, as described by Yu-Ting Lee

(2013); van der Kroon et al. (2013); Nlom and Karimov (2015).

2.1.4 External factors

The last broad category of determinants can be called external factors, as these are not deter-

mined by household characteristics or directly influenced by cookstove suppliers. Often cited

determinants include fuel availability, reliability of supply, policies or clean cookstove initiatives

(if well targeted and executed), and the market structure for clean cooking technologies (The

World Bank, 2013; Bacon et al., 2010; Urmee and Gyamfi, 2014; Mainali et al., 2012).

9

Chapter 2. Frame of reference

2.2 Modeling cooking fuel mix

Several studies have aimed to quantify the process of cooking fuel choice. This master’s thesis

aims to produce a perspective on the future cooking fuel mix in sub-Saharan Africa. In order

to do so, the decision was made to build a bottom-up model that quantifies important trends

as a function of input parameters. In this section, other relevant academic work related to the

research objective is discussed.

2.2.1 Two distinct types of models

Swan and Ugursal (2009) provides a comprehensive overview of the different modeling techniques

to determine end-use energy consumption in the residential sector. The authors distinguish be-

tween top-down and bottom-up models, each characterized by advantages and drawbacks. The

paper provides a review of each technique and pays attention to strengths, weaknesses, and

purposes. The two most critical differences between the two archetypes of models are the depth

of required input data and the range of modeled scenarios. Top-down approaches are easier

to develop because the data requirements are less stringent. On the other hand, these models

do not allow to account for technological breakthroughs or shocks. Bottom-up models, being

inherently more detailed, allow for a deeper investigation of consumer behavior and the impact

of new technologies. The authors exemplify their statements by briefly describing models that

are present in literature.

Even though the model which was developed to support this master’s thesis is largely based on

macroeconomic variables, one can categorize the model as bottom-up. The reasons for this are

the detailed consideration of the different cooking technologies and their characteristics from

an end-user perspective, the assessment of adoptions for individual fuels, and the distinction

between urban and rural areas. Moreover, rather than regressing the national cooking energy

demand, combining projected adoption rates for several technologies and technology ratings

allows to disaggregate cooking energy consumption among fuels and technologies, thereby also

paying attention to consumer behavior.

2.2.2 Quantifying cooking fuel choice

Discrete choice modeling is a commonly used technique to analyze fuel choices (Takama et al.,

2012, 2011). A stated preference survey is conducted to understand consumer preferences as

they choose the preferred option from a number of cooking technologies. In a next step, con-

sumer choices are correlated with characteristics of correspondents to quantify the influence

of attributes and assess the trade-offs in choice of cooking stoves and fuels. Takama et al.

(2011) employs the estimated attribute coefficients to simulate the impact of a change in stove

attributes on the stove market share. This allows to understand the potential effect of policy

measures. However, this approach cannot be used to develop a plausible perspective on the

future (Mainali et al., 2012; Takama et al., 2011).

Another approach to model the cooking fuel choice is discussed by Rehfuess et al. (2010).

10

Section 2.2 Modeling cooking fuel mix

The authors aim to quantify the influence of household-level factors and area-level characteris-

tics, including differences between communities, by employing Bayesian hierarchical and spatial

modeling with a geographical focus on Benin, Kenya, and Ethiopia. As found by the authors,

the heterogeneity between areas may point to the considerable influence of contextual effects

and supply-side limitations. As one will understand later, this master’s thesis accounts for these

contextual factors by applying the slope of the calculated regression curves to the starting point

of each country, rather than applying the equation of the regression curve to all countries. As

this work of Rehfuess et al. (2010) concentrates efforts on determining determinants of solid

fuel use in a set of selected countries, it does not provide insights for the future evolution of the

cooking fuel mix.

2.2.3 Projecting cooking fuel mix

As the transition from traditional to modern fuels is a specific dynamic in developing regions,

the literature study focuses on quantitative projections for developing regions. Several academic

papers contain projections for the composition of cooking energy demand by fuel in developing

regions. Often, relevant reports cover the total residential energy sector, of which cooking con-

stitutes an important share, focus on one particular country, and opt for a shorter projection

period compared to what was done for this report. Moreover, performed analyses are often

characterized by the application of a cost-optimization model, thereby assuming rational agents

with perfect foresight. A point of similarity lies in the identification of model drivers, often

being economic growth, urbanization, and population expansion. The author is not aware of a

comprehensive research paper on the future cooking fuel mix to 2050 for the whole geograph-

ical region of sub-Saharan Africa. Furthermore, projections for specific sub-Saharan countries

proved almost impossible to find, except for South Africa. Therefore, one can state that this

lack of knowledge presents a clear research gap, which this master’s thesis hopes to address.

This report aims to deliver a BAU perspective on the future cooking fuel mix in urban and rural

areas of 45 sub-Saharan countries to 2050. In the following paragraphs, relevant literature is

commented to give the reader an idea of what work has been performed.

Howells et al. (2005) aim to compute the optimal energy system in a rural village in South

Africa, namely the village of Nkweletsheni. For this purpose, the authors developed an exten-

sion of the MARKAL energy modeling tool, the TIMES model. TIMES is a linear optimization

model which identifies the least-cost supply options, from a lifetime perspective. While taking

into account details on technology cost and performance, the model assumes perfect foresight

by energy consumers, an assumption which can be questioned.

Rather than focusing on cooking fuel mix only, the model calculates the future energy con-

sumption for six energy services; cooking, space heating, water heating, lighting, refrigeration,

other (radio, TV, etc.). A set of appliances is assumed to satisfy the demand for the six energy

services. The demand for considered fuels (biomass, coal, electricity, LPG, paraffin, candle way)

is estimated from the quantities of final energy required by the set of appliances. Five scenarios

were developed, trying to capture different levels of access to electricity. Two differentiating

aspects of the approach are the disaggregation of energy consumption according to the time of

day through load curves, and the consideration of appliances that can supply more than one

11

Chapter 2. Frame of reference

energy service (e.g. a wood brazier serves as a source for cooking, space heating, and water

heating). The described approach allowed to obtain a holistic view on energy consumption in

rural villages, but lacks a clear focus on cooking. The work is also characterized by a narrow

geographical scope and limited predictive time frame (2003-2018).

The base case scenario projects biomass remaining a dominant cooking fuel to 2018. LPG

fulfills the role of secondary or back-up fuel when wood is not available, supplying only a very

small part of the cooking energy demand. According to the authors, LPG is not economically

competitive when wood is present.

Ruijven et al. (2011) presents a bottom-up model for residential energy use in developing coun-

tries, making a distinction between five end-uses, namely cooking, water heating, space heating,

lighting, and appliances. The model, which was designed to be incorporated in the global energy

system model TIMER, determines energy use and fuel choice.

The authors applied the developed model to India because of good data availability. The

identified drivers of residential energy use are population growth, urbanization, household ex-

penditure, household size, and temperature changes. The analysis employs correlations from

econometric studies and regressions. In a first step, the level of useful energy demand for the

various end-uses is determined. The second step constitutes of assigning fuels to meet this de-

mand, based on economic considerations and fuel availability. To do so for cooking fuel choice,

the authors used a capital vintage model, which determines the market shares of stoves from

assumptions on investment and depreciation rates. The annual perceived cost of technologies

is crucial for this purpose, but the model also accounts for up-front investment challenges and

household preferences not captured by economics. As a result of this modeling approach, the

switch from one fuel to another follows the concept of the energy ladder, which states that

households opt for more efficient and modern fuels as they get richer.

The developed model is used to investigate the impact of the level of rural electrification and

income distribution. The authors conclude that an equal income distribution and adequate rural

electrification play an enhancing role in the diffusion of commercial fuels. However, projections

show that rural areas will continue to rely mainly on the use of traditional fuels to 2050 and

that biomass will make up between 34% and 45% of total national final residential energy use.

Vassilis et al. (2012) applies an extension of the model, now called REMG, to analyze fu-

ture developments of residential energy use in India, China, South East Asia, South Africa,

and Brazil. The projection timescope is reduced from 2050 to 2030. The authors state that

over time, non-cooking end-uses, including space heating, cooling, and appliances, become more

important in the total residential energy demand. At the same time, the shift towards modern

fuels only develops slowly in urban and rural areas, but more explicitly in rural areas where

biomass still shows a strong dominance in 2030 across all five regions. However, the per capita

cooking energy demand decreases to 2030 as a result of moderate fuel switching and increases

in efficiency for any given fuel. Even though this report does not account for improvements

in efficiency for any given technology, the switch to modern and improved cookstoves is also

expected to display a reduction in cooking energy intensity.

12

Section 2.2 Modeling cooking fuel mix

Another paper which shows efforts to estimate the future cooking fuel and stove choices in

developing countries is presented by Mainali et al. (2012). Focusing on China, the authors

project the cooking fuel mix to 2030, thereby making use of the MESSAGE-Access model-

ing tool. Similar to the previously mentioned MARKAL model, MESSAGE is an optimization

model which aims to identify least-cost energy supply options under predefined constraints. The

model is mostly used to construct long-term projections (up to 2100). The MESSAGE-Access

model is an extension of the MESSAGE model and covers the residential sector with a focus on

cooking and electrification related demand.

The analysis of cooking fuels was performed separately for urban and rural areas, based on

the fact that energy consumption patterns differ, as mentioned before. To account for the dis-

tribution of wealth across the population, households were sub-divided into five income quintile

groups. Different quintiles are characterized by different discount rates, technology preferences,

inconvenience costs, and budgets constraints.

The following variables are treated as inputs: per capita final energy consumption in rural

and urban quintiles in the base year (2005), population growth, income growth and distribu-

tion, and cost of stove technologies. The prices of fossil fuel energy sources are determined

endogenously within the model. The model assumes that per capita solid fuel (biomass and

coal) use cannot increase over time. Also, per capita modern fuel consumption is not allowed

to decrease from one year to another.

The total cost of cooking with a particular stove is calculated as the sum of fuel cost, annual-

ized cooking technology cost, and inconvenience cost, which aims to capture the non-monetary

disutility of using traditional solid fuels versus modern cooking technologies. Thus, the outcome

of the optimization process represents the least-cost solution from a lifetime perspective. It is

noted that a similar, detailed determination of the various costs of cookstoves is not required

for this report because the model is not based on a cost-optimization algorithm.

In addition to a base case perspective, the authors developed a number of scenarios, reflecting

different levels of credit access to cover the upfront stove costs and support mechanisms to

reduce fuel cost. Biomass is expected to remain a dominant fuel to 2030 in rural areas in the

BAU scenario. The authors predict that 24% of rural population and 17% of urban population

will rely on solid fuels (biomass and coal) in 2030 under BAU scenario. However, a clear shift

towards LPG in both urban and rural areas can be observed.

A similar approach was followed by Ekholm et al. (2010). The objective of the study was

to analyze the effectiveness of policies that aim to improve the penetration of modern cooking

facilities in India.

The authors developed a choice model based on economic considerations and practical deter-

minants such as consumer preferences, while maximizing end-user utility. As in the previously

discussed model, heterogeneity of households was taken into account by considering fuel choices

separately for populations living in rural and urban areas and by differentiating between income

groups. The choice model was then implemented within the MESSAGE framework to forecast

the diffusion of different fuels to 2020.

13

Chapter 2. Frame of reference

The baseline model results display the domination of biomass in rural areas. According to

these projections, the number of people using traditional biomass would increase with almost

100 million in the period 2000-2020. It is remarkable that the authors project a decrease in the

use of LPG, stating that existing LPG stoves are replaced with biomass stoves after their lifetime.

The baseline scenario is supplemented with additional scenarios, with the purpose to assess

the impact of policies, including fuel subsidies and improved financing opportunities. This anal-

ysis pointed out that high investment costs are a major barrier for the adoption of modern fuels,

especially LPG. Therefore, the authors conclude that fuel subsidies should be combined with

improved access to financing to cover the appliance investment.

2.3 National and international organizations

Several national and international organizations performed studies on cooking patterns in sub-

Saharan Africa. However, most of this work does not include a quantitative view on the future.

The produced publications often focus on past evolutions, success stories of selected countries,

and the design of strategies.

The UN is one of the major institutions that does efforts to improve the uptake of clean cooking

technologies. The SDGs, introduced in 2012 as replacement of the Millennium Development

Goals, aim to meet global environmental, political, and economic challenges. The 7th SDG is

focused on affordable and clean energy. Apart from targeting an increased share of renewable

energy in the global energy mix, a doubling of the improvement rate in energy efficiency, en-

hanced international cooperation, and an expansion of energy infrastructure, the objective is

to ensure universal access to affordable, reliable, and modern energy services (United Nations,

2017). In the reports that assist this ambitious goal, insights are provided in how households

and communities are supported to complete the switch away from traditional biomass cooking.

Examples of activities are developing sustainable charcoal value chains and realizing improved

cookstove projects. Figures on the use of funds and the number of projects are also published.

The project portfolio covers almost 100 projects on energy access in 70 countries and the UN

is involved in USD410 million of grant financing and a further USD1.5 billion of co-financing.

Even though the largest portion of supported projects is related to renewable energy, 19 projects

in 2015 were addressing the uptake of energy efficient cookstoves (United Nations Development

Programme, 2016; United Nations Development Programme Global Environmental Finance

Unit, 2015). 12 of these projects were located in Africa, namely in Burkina Faso, Ethiopia,

Guinea, Guinea Bissau, Kenya, Liberia, Mali, Niger, Tanzania, and Uganda (United Nations

Development Programme Global Environmental Finance Unit, 2015). However, the published

reports do not include projections for the uptake of cooking technologies or cooking fuel mix.

The UN Foundation hosts a partnership called The Global Alliance for Clean Cookstoves

(GACC) (Global Alliance for Clean Cookstoves, 2017). This initiative aims to create a strong

market for clean and improved cooking solutions and set a target of 100 million households

adopting clean and efficient cookstoves by 2020. The GACC concentrates its projects in eight

14

Section 2.3 National and international organizations

countries, four of which in sub-Saharan Africa, namely Ghana, Kenya, Nigeria, and Uganda.

GACC reporting mainly covers insights on stove/fuel production and distribution, progress, and

market assessments. The 2016 Progress report is a good illustration of a publication which tracks

the progress towards cleaner cookstoves and fuels (Global Alliance for Clean Cookstoves, 2016).

This documents includes a high-level historical estimate and forecast to 2020 of the cumulative

global stove distribution, making a distinction between clean or improved cookstoves and tra-

ditional biomass stoves. According to their analysis, 250 million improved or clean cookstoves

will be distributed in the period 2010-2020. This perspective does not differentiate between re-

gions or fuels, and thus contains no information on fuel mix in sub-Saharan Africa, let alone on

country-level. Another key component of the report is a more detailed investigation of the latest

progress and improvements in the eight focus countries. Other relevant publications mainly aim

to develop a more comprehensive market understanding and therefore assess the current market

structure, consumer preferences, and active stakeholders. Examples of work in this area include

a market assessment for Nigeria, an LPG market assessment for Kenya which provides a BAU

scenario for the LPG penetration to 2020, and a consumer segmentation analysis for Ghana

(Accenture, 2011; Dalberg, 2013; AddedValue, 2014). Again, no comprehensive, quantitative

outlook for the future situation is provided in these documents as the focus is on enhancing

market intelligence and characterizing current cooking habits. A last type of document that

is developed under guidance of the GACC is the country action plan, which summarizes the

priority interventions and opportunities on a country level for selected countries and sometimes

includes targets for clean cookstove penetrations. Examples include the Kenya Country Action

Plan and the Ghana Country Action Plan (Ghana Energy Commission, 2013; Global Alliance

for Clean Cookstoves, 2013).

Another global platform on energy access in Sustainable Energy for All (SE4ALL) (Sustain-

able Energy for All, 2017b). This organization aims to achieve universal access to energy while

limiting the impact on the environment, keeping global warming well below 2°C. In this regard,

SE4ALL defined three objectives for 2030, namely universal access to modern energy services,

doubling the share of renewable energy in the global energy mix, and doubling the global rate

of improvement in energy efficiency. To achieve these targets, high impact opportunities were

identified. One of the 11 Action Areas is the universal adoption of clean cooking solutions, which

calls for a close collaboration with the GACC, the Global LPG Partnership, and the World LP

Gas Association. Moreover, national governments were encouraged to complete gap analyses to

determine key challenges and opportunities in achieving the three SE4ALL objectives. Seven

countries, all of them in Africa, already completed the next step in the process, which is to draft

an Action Agenda. These national Action Agendas elaborate on a long-term vision and should

serve as a starting point for donor aid. On a regular base, the SE4ALL organization assesses

past developments at the national, regional, and international level to clarify whether the move-

ment is on track to reach the objectives and identify where more action is required. The tool

that is used for this purpose is called the Global Tracking Framework (GTF) and an update

is produced bi-annually. Each edition delivers insights on trends in energy access, renewable

energy, and energy efficiency. The GTF 2017 represents the latest thinking and claims that 850

million people in sub-Saharan Africa lacked access to clean cooking in 2014, representing 74% of

the population (Sustainable Energy for All, 2017a). To obtain universal access to clean cooking

facilities by 2030, the SE4ALL states that progress in the remaining years to 2030 should be

five times faster than the evolution in 2012-14. In their reports, the SE4ALL does not provide

15

Chapter 2. Frame of reference

Table 2.1: 2030 scale-up targets for clean cookstoves in Ethiopia, as estimated in FederalDemocratic Republic of Ethiopia (2011)

Improved fuelwood LPG Biogas ElectricUrban areas 5% 5% 1% 61%Rural areas 80% 0% 5% 5%

own estimates of likely future outcomes for fuel penetrations. Instead, the organization uses

IEA projections.

In contrast to previously mentioned organizations, the Global Green Growth Institute, which

was established in 2012 and promotes a strong and sustainable economic development, does not

demonstrate a clear focus on clean cooking (Global Green Growth Institute, 2017). However,

this topic is part of their expertise as the organization supports governments of developing coun-

tries to strive for a green growth path, which often involves efforts to cap the inefficient biomass

use for cooking. An example is provided by the involvement in developing the Climate Resilient

Green Economy plan (CRGE) of Ethiopia (Federal Democratic Republic of Ethiopia, 2011).

The CRGE is Ethiopia’s strategy for addressing both climate change adaptation and mitigation

objectives, while realizing its ambition of reaching middle-income status by 2025. One of the

four strategy pillars is protection and rehabilitation of forests for their economic and ecosystem

services. An important goal is to reduce demand for fuelwood via the dissemination of efficient

stoves and alternative fuels. Four initiatives for fast-track implementation were selected, one

of them being the large-scale promotion of advanced rural cooking technologies. The rationale

behind this stress on the switch away from traditional biomass cooking is given by the fact that

the forestry sector accounts for 51% of the GHG abatement potential by 2030, the most impor-

tant lever being an increased uptake of fuelwood efficient cookstoves. According to the report,

switching to more efficient technologies and modern cooking fuels has a negative abatement

cost, meaning that the benefits outweigh the costs. An exception to this general observation is

provided by LPG stoves. With this particularity in mind, ambitious scale-up targets for cooking

technologies in urban and rural areas by 2030 were drafted, as visible in table 2.1.

Compared to other well-known international bodies, the IEA often follows a more quantita-

tive approach, rather than describing visible trends and policies. The IEA has been paying

attention to the topic of energy access for more than a decade. The institution does so by

developing energy access databases on electrification rates and traditional biomass cooking, and

by creating quantitative analyses to estimate the future situation and investment needs (In-

ternational Energy Agency, 2016b). Several annual reports, including the WEO 2013, WEO

2015, and WEO 2016, devoted a chapter to energy access (International Energy Agency, 2013,

2015, 2016a). The latest IEA number of people in sub-Saharan Africa without access to clean

cooking facilities is found in WEO 2016 and equaled 792 million in 2014 (International Energy

Agency, 2016a). The topic of energy access was covered more in depth by the IEA in their

special energy investment focus (International Energy Agency, 2011). This document estimates

the number of people lacking access to electricity and clean cooking and provides insights on

the level of investments in energy access and the financing sources. In 2009, the aggregated

global investments in extending energy access amounted to USD9.1 billion. A large share of

16

Section 2.3 National and international organizations

the funding was directed towards electricity projects. International Energy Agency (2015) in-

cludes an updated investment estimate, namely USD13.1 billion in 2013. Only 3% of the energy

access investments went to extending access to clean cooking facilities. To provide an outlook

for energy access to 2030, the IEA considers existing government policies and a cautious imple-

mentation of intended policies. This scenario is named the New Policies Scenario. According

to these projections, 823 million people in sub-Saharan Africa will lack access to clean cooking

facilities in 2030 (International Energy Agency, 2016a). To achieve universal access by 2030,

both for electricity access and clean cooking, International Energy Agency (2011) states that

an annual average investment of USD48 billion is required. This value represents five times the

investment level of 2009 and demonstrates the size of the challenge.

As opposed to other IEA perspectives, International Energy Agency (2014) presents a more

detailed view on the cooking fuel mix in sub-Saharan Africa, while offering a comprehensive

study of the energy system in Africa. Starting from the 2012 base year, the IEA developed

projections to 2040 for cooking energy consumption by fuel for urban and rural areas for the

whole of sub-Saharan Africa, population with and without clean cooking access in four sub-

regions of sub-Saharan Africa (West Africa, Central Africa, East Africa, Southern Africa), and

fuel/technology penetration rates for urban and rural areas in the four sub-regions. These pro-

jections state that 653 million people will still cook with biomass in an inefficient way by 2040.

It is important to note that this number is lower than the latest estimate of 708 million in

International Energy Agency (2016a). This deviation is an indication for the high uncertainty

of the long-term projections. Unfortunately, the methodology behind the IEA projections is not

transparent. International Energy Agency (2016b) declares that the dependency on traditional

biomass is calculated from panel econometric regressions, but it remains unclear how projec-

tions for different fuels are obtained. Moreover, the results are presented on a regional level.

This level of aggregation makes it impossible to know whether differences among countries are

present.

17

Chapter 3

Methodology

As described before, the purpose of this report is to provide an outlook for the cooking fuel mix

in sub-Saharan Africa to 2050. In order to do so, the following steps were identified:

1. Study literature to understand context and determinants of fuel choice in developing

regions

2. Develop calculation logic for the projections

3. Implement defined calculation logic in Excel

4. Process results and derive insights

The next sections go more into detail for each separate step.

3.1 Literature study

The literature study focused on both academic work and publications from international or

national organizations that are active in the field of clean cooking. The investigated academic

papers covered the identification and quantification of determinants of fuel choice. This allowed

to determine key drivers of the switch away from traditional biomass cooking. Moreover, several

approaches to model the future cooking fuel mix were analyzed and the differences with the

approach behind this report were discussed. In addition, reports from well-known bodies helped

to build an understanding of the increased attention for the case of clean cooking and the efforts

that are being made to accelerate the uptake of clean cookstoves.

3.2 Develop calculation logic

Before diving into the calculation logic, the reader should understand that the model distin-

guishes between five fuel categories and seven cooking technology groups. The connection be-

tween fuel groups and technologies is visualized in figure 3.1. As can be seen, the main difference

between these two terms lies in the fact that technologies differentiate between the traditional

and improved use of biomass, which is relevant for the fuel categories of wood and charcoal.

19

Chapter 3. Methodology

Figure 3.1: The model distinguishes between five fuel categories and seven cooking technologygroups. The wood category contains wood, straw, shrubs, grass, agricultural crop, and dung.

As can be seen, technologies and fuels are closely linked.

The calculation methodology can be broken down into two levels, the first one being the natural

higher level of the second one.

3.2.1 High-level calculation logic

To understand the logic, it is crucial to become familiar with three frequently recurring concepts:

� Fuel mix: breakdown of energy demand by fuel

� Technology penetration: breakdown of population by used cooking technology

� Fuel penetration: breakdown of population by used cooking fuel

A visualization of the high-level calculation tree can be seen in figure 3.2. It is noted that the

three defined concepts are closely linked. In brief, the fuel penetrations are used to determine

urban and rural technology penetrations. The fuel penetrations for urban and rural areas are

projected separately to incorporate the immense differences between the two premises, an ap-

proach widely present in academic literature as well (Howells et al., 2005; Vassilis et al., 2012;

Ruijven et al., 2011; Mainali et al., 2012). This allows to account for a strong urbanization trend,

apart from economic development, population growth, and to a lesser extent assumptions on

future government policies. By supplementing technology penetration values with an energy

intensity per technology, expressed in toe/year per household, the fuel mix can be determined.

As mentioned, once the fuel penetrations in urban and rural areas are calculated, the technol-

ogy penetrations can be derived. The key difference between fuel penetrations and technology

20

Section 3.2 Develop calculation logic

Figure 3.2: The high-level calculation logic illustrates the link between fuel penetration,technology penetration, and fuel mix. The calculation of the cooking fuel mix follows from

projecting the penetration of the five fuel categories in urban and rural areas.

penetrations is the distinction between traditional and improved wood stoves, and between tra-

ditional and improved charcoal stoves, as visualized in figure 3.1. It is important to capture

the increased uptake of improved biomass cookstoves as their diffusion may have a significant

impact on the overall cooking intensity and fuel mix because of their lower energy intensity

(see table 4.1). In order to make this distinction, a two-step process was employed, which is

visualized in figure 3.3.

The first step mainly involves leveraging projections from International Energy Agency (2014).

In their 2014 Africa Energy Outlook, the IEA specifies a perspective on the uptake of cooking

technologies to 2040, from the base year 2012. This IEA view contains a differentiation between

traditional and improved biomass (wood + charcoal) cooking, as observed in figure 3.4. In order

to leverage these estimates, the following actions were undertaken:

1. Calculate the 2012 share of traditional biomass cooking as percentage of total biomass

cooking for the four regions (Southern Africa, East Africa, Central Africa, West Africa)

2. Calculate the 2040 share of traditional biomass cooking as percentage of total biomass

cooking for the four regions

21

Chapter 3. Methodology

Figure 3.3: A two-step approach is employed to derive the penetration of improved woodstoves, traditional wood stoves, improved charcoal stoves, and traditional charcoal stoves. Thefirst step entails adding the penetration of wood and charcoal stoves and distinguishing betweenimproved and traditional biomass cooking. In the second step, the share of improved (tradi-

tional) biomass stoves is divided between improved (traditional) wood and charcoal stoves.

3. Calculate the 2050 share of traditional biomass cooking as percentage of total biomass

cooking for the four regions by linearly extrapolating the 2012-2040 projection

4. Calculate the shares of traditional biomass cooking as percentage of total biomass cooking

for the four regions for the years 2013 to 2049 by linearly interpolating between the 2012

and 2050 values

5. For urban and rural areas in each country, for each year, apply the above determined split

of the respective region to the sum of wood and charcoal penetration

The described procedure results in a split between traditional and improved biomass cooking

for urban and rural areas, for modeled countries to 2050.

In the second step of the process, the model distinguishes between the traditional and im-

proved use of wood and charcoal, starting from the split between improved and traditional

biomass cooking as derived in the first step. To this end, the share of improved biomass cooking

is distributed among improved wood cookstoves and improved charcoal cookstoves, adopting the

ratio of overall wood cooking to overall charcoal cooking, which follows from the fuel penetration

22

Section 3.2 Develop calculation logic

Figure 3.4: The IEA developed a perspective on future penetration of cooking technologiesin urban and rural areas for four regions in sub-Saharan Africa. Figure reproduced from Inter-

national Energy Agency (2014)

projections. The same approach is applied to the penetration of traditional biomass for cooking.

Once the penetration of the various cooking technologies in urban and rural areas is estimated,

the fuel mix can be calculated. The fuel mix captures what share of the cooking final energy

demand is delivered by a particular fuel.

In order to determine the fuel mix, an energy intensity was assigned to each cooking tech-

nology. Values for the various cooking technologies were obtained from International Energy

Agency (2014) and are introduced in table 4.1. The IEA estimate for the energy intensity of

LPG cooking was applied to the LPG/natural gas/biogas technology category as this category is

dominated by LPG. For coal use in Sudan, the same energy intensity as for traditional charcoal

was assumed. Calculations were performed with the middle point of the energy intensity range.

As an example, the following formula was applied to derive the share of wood in the cook-

ing fuel mix:

sWood =tImprovedWood ∗ eImprovedWood + tTraditionalWood ∗ eTraditionalWood∑

i tTechnology,i ∗ eTechnology,i(3.1)

23

Chapter 3. Methodology

with sFuel,i representing the share of fuel i in the fuel mix, tTechnology,i the technology pene-

tration of technology i, and eTechnology,i the energy intensity of technology i. A very similar

approach was followed to calculate the share of charcoal, LPG/natural gas/biogas, kerosene,

and electricity in the fuel mix.

As stated in chapter 1, one of the objectives of the project is to link fuel and technology

switching to a change in overall cooking energy intensity. To do so, cooking energy intensity

is calculated as the ratio of total cooking energy demand to the number of households, which

gives the energy intensity expressed in toe/year per household.

The projections for fuel penetration, technology penetration, and fuel mix were developed on

an urban and rural level for the 45 countries to 2050. At each level, national estimated are

obtained by using estimates of future urbanization levels, according to the following formula:

vNational = vUrban ∗ u + vRural ∗ (1 − u) (3.2)

with vNational the calculated national value of the variable, vUrban the urban value of the variable,

vRural the rural value of the variable, and u the urbanization rate as estimated by the UN (United

Nations - Department of Economic and Social Affairs, 2014). Moreover, figure 3.2 visualizes the

calculation of the relative quantities, expressed in percentages. Projections in absolute terms,

i.e. number of people using a fuel, number of people using a technology, and demand for a fuel,

are derived by employing estimates of the number of future households (see section 4.1).

3.2.2 Deep dive in calculations for urban fuel penetration

The second level, which estimates the future urban and rural fuel penetrations, makes up the

foundation for further calculations and feeds into the high-level logic, as described above.

As seen in figure 3.5, which illustrates the analysis for urban areas, the relative uptake of

the five fuel categories is projected separately for each fuel group. The main driver of the

projections is GDP per capita. The reason for selecting GDP per capita lies in the widely

discussed influence of income on cooking fuel choice. Multiple papers have found quantitative

evidence for the energy ladder theory (Takama et al., 2012; Barnes et al., 2010; Yu-Ting Lee,

2013; Ouedraogo, 2006; Rehfuess et al., 2010; Beyene and Koch, 2013; Karimu, 2015; Rahut

et al., 2016). Patterns in historical data, supplemented with knowledge obtained from literature

and interviews underpin the model rules, which determine how the penetration of a particular

fuel evolves when per capita GDP changes over time.

After having projected the penetration of the five fuel categories in urban and rural areas,

the sum of the penetrations does not add up to 100% because of two reasons:

1. The penetration of each fuel category is projected independently from the other fuels

2. The input data on cooking fuel penetration also includes households who do no cooking

(e.g. eat out), as mentioned in subsection 4.1.3. As a result, the sum of the shares of

households cooking with wood, charcoal, LPG/natural gas/biogas, kerosene, or electricity

do not add up to 100% in the base year

24

Section 3.2 Develop calculation logic

Figure 3.5: The calculation of the penetration for the five fuel categories in urban areas isbased on a regression analysis, supplemented with insights from literature and expert calls. Thepresented charts are illustrative and provide a first idea of the direction of evolution of the fuelpenetration when GDP per capita changes. A normalization step is required to ensure that

projected fuel penetrations add up to 100%.

To circumvent this anomaly and come to a perspective on the cooking fuel mix, a normalization

step is incorporated in the calculation. The aim of this step is to obtain the share of cooking

households that use a particular fuel for cooking. The projected non-normalized fuel penetra-

tions in urban and rural areas are normalized separately, before the derivation of national fuel

uptake. The normalization approach for the fuel categories of kerosene and electricity is differ-

ent from the fuel categories of wood, charcoal, and LPG/natural gas/biogas. The reason behind

this difference lies in the methodology for projecting the future uptake of the fuel. As explained

in section 4.2, the model rules for kerosene and electricity are based on strong assumptions.

Therefore, it is undesirable to let the normalization influence projections too much, as the effect

could be in conflict with the assumptions made.

The electricity penetration was normalized according to the following formula:

pElec,Norm = pElec,NN ∗ 100

100 − pNoCooking(3.3)

25

Chapter 3. Methodology

with pElec,Norm the normalized electricity penetration, pElec,NN the non-normalized electricity

penetration, and pNoCooking the base year share of population that does no cooking. It is im-

portant to note that the variables in the equation differ among countries, years, and residence

(urban vs. rural area). A similar formula was applied to the non-normalized kerosene penetra-

tion in all countries and to coal penetration in Sudan (see table 4.11).

For the wood penetration, the following formula is used to perform the normalization:

pWood,Norm = pWood,NN ∗100 − pElec,Norm − pKerosene,Norm

pWood,NN + pCharcoal,NN + pLPG/NG/biogas,NN(3.4)

with pWood,Norm the normalized wood penetration, pWood,NN the non-normalized wood penetra-

tion, pElec,Norm the normalized electricity penetration, pKerosene,Norm the normalized kerosene

penetration, pCharcoal,NN the non-normalized charcoal penetration, and pLPG/NG/biogas,NN the

non-normalized LPG/natural gas/biogas penetration. Again, the variables in the equation differ

among countries, years, and residence (urban vs. rural area). A similar formula was applied to

normalize the share of charcoal cooking and the share of LPG/natural gas/biogas cooking.

For Sudan, the constant share of coal cooking is included in the formula:

pWood,Norm,Sudan = pWood,NN,Sudan∗100 − pElec,Norm,Sudan − pKerosene,Norm,Sudan − pCoal,Norm,Sudan

pWood,NN,Sudan + pCharcoal,NN,Sudan + pLPG/NG/biogas,NN,Sudan(3.5)

with pWood,Norm,Sudan the normalized wood penetration in Sudan, pWood,NN,Sudan the non-

normalized wood penetration in Sudan, pElec,Norm,Sudan the normalized electricity penetration

in Sudan, pKerosene,Norm,Sudan the normalized kerosene penetration in Sudan, pCoal,Norm,Sudan

the normalized coal penetration in Sudan, pCharcoal,NN,Sudan the non-normalized charcoal pen-

etration in Sudan, and pLPG/NG/biogas,NN,Sudan the non-normalized LPG/natural gas/biogas

penetration in Sudan.

The methodology behind the projections for rural fuel penetrations is very similar to the process

which was just described and is therefore not depicted.

3.3 Implementation of model

In the implementation step, the described calculation logic was employed to obtain a perspec-

tive on the evolution of the cooking fuel mix in sub-Saharan countries. All computations were

performed in the program Excel.

A first task was to gather data for the model inputs. Data on GDP, population, urbaniza-

tion, number of households, cooking fuel penetration, and energy intensities of technologies was

obtained from various sources, as described later in section 4.1. As could be expected, finding

information on cooking fuel penetration proved a most challenging task.

26

Section 3.3 Implementation of model

Panel data, consisting of cooking fuel penetration values and per capita GDP estimates, was

used to construct regression curves. These curves capture the evolution of fuel penetration in

urban and rural areas when income levels change and are used to estimate the future uptake of

cooking fuels by following a number of steps:

1. As starting point for the projections of a country, the last available historical point is

considered. As further described in subsection 4.1.3, the surveys on which the cooking

fuel penetration data is based do not show any consistency in terms of timing. As a

result, the starting years for the different countries are not the same. In terms of modeling

implications, this characteristic of the input data means that countries have a non-uniform

base year

2. The per capita GDP of a country in a particular year is used to categorize the country in

one of the income buckets (poor countries, intermediate countries, rich countries)

3. For each income bucket and fuel in urban and rural areas, a model rule is defined. Model

rules are determined based on regression analysis, literature review, and expert contact.

This analysis results in a GDP multiplier (often the slope of the regression curve), which

reveals how fast the fuel penetration changes with rising income (GDP per capita). In

addition, boundaries are identified below/above which the fuel penetration remains con-

stant as a function of GDP per capita. For a detailed explanation of the various model

rules, refer to section 4.2

4. As the citizens of a country become wealthier, the fuel penetration follows the slope of

the regression curve which was determined for the particular fuel and income bucket. In

effect, the year over year change in per capita GDP is multiplied with the respective GDP

multiplier to calculate the change in penetration. When a predetermined penetration limit

is hit, the share is kept constant until the next GDP per capita boundary is reached

5. When a certain level of per capita GDP is achieved, the GDP multiplier changes because

a different regression curve is applicable

6. To prevent reverse fuel transitions, the fuel penetration is kept constant in case of a

temporarily declining GDP per capita

The above methodology is exemplified for the urban wood penetration in Nigeria, Malawi, and

Mali in figure 3.6.

As stated, the data analysis was complemented by insights from literature and expert calls

to define additional model rules. Interviews were performed to test certain model assumptions.

Three categories of individuals were contacted1:

� Employees of consulting firm McKinsey & Company

� Expert on energy access, employed by the IEA

� Inhabitants of Kenya with expertise in biomass cooking and buildings’ energy consumption

The defined model rules are applied to 45 countries in sub-Saharan Africa to estimate the future

penetration of cooking fuels in urban and rural areas, after which the technology penetration is

determined, from which the fuel mix can be derived with a simple calculation.

1Statements in this report do not necessarily reflect the opinion of involved individuals or organizations.

27

Chapter 3. Methodology

Figure 3.6: The methodology to project wood penetration in urban areas is visually exempli-fied for three countries, namely Nigeria, Malawi, and Mali. The per capita GDP of a countryin a particular year (x-axis) is used to categorize the country in one of the income buckets. Thevertical dotted lines represent the boundaries of the income buckets. Each income bucket ischaracterized by a model rule, consisting of a regression curve (blue full lines) and a predeter-mined penetration boundary (red zone). The slope of the regression curves are used to estimatethe future penetration of cooking fuels. As the citizens of a country become wealthier overtime (GDP per capita increases), the fuel penetration of the country follows the slope of theregression curve. This evolution is captured by the blue dotted lines. These lines are parallel tothe regression curves because the slope of the regression curve determines evolution with risingGDP per capita. When a predetermined penetration limit is hit, represented by the red zones,the share of the fuel is kept constant with changing GDP per capita. When the next GDP percapita bucket is reached, i.e. when a certain GDP per capita value is surpassed, different model

rules (regression curve and penetration limit) apply.

28

Section 3.4 Process results and derive insights

3.4 Process results and derive insights

The final stage of the project aims to discuss the results of the modeling exercise, benchmark

the perspective against external projections, and extract meaningful insights. Also, potential

future additions to the analysis are described.

29

Chapter 4

Implementation

The calculation logic which was described in section 3.2 was implemented as an Excel model.

In the next sections, the various input parameters and model rules to project the urban and

rural fuel penetrations are discussed while assumptions are clarified.

4.1 Model inputs

4.1.1 Gross domestic product and population

GDP and population values, both historical and projected to 2050, were sourced from McKin-

sey Global Institute (further abbreviated as MGI). MGI is the business and economics research

arm of McKinsey & Company and aims to develop a deep understanding of the evolution of

the global economy. Positioned on the intersection between commerce and academics (several

leading economists, including Nobel laureates, act as advisors to the research), MGI is able to

generate unique insights to support commercial, public, and social sectors (McKinsey Global

Institute, 2017).

As GDP and population are important inputs for the model, it is useful to know that MGI

defined a Global Downshift Scenario for economic growth, which reflects the latests views on

key influencing factors, such as levels of investments, consumption, and productivity, and acts

as their BAU Scenario. In this process, they partnered with IHS Markit, a London-based in-

formation services company (IHS, 2017). For estimates of future population, MGI uses the

medium variant population projections from the United Nations (United Nations - Department

of Economic and Social Affairs, 2015).

By dividing the projections for GDP to population forecasts, estimates of future GDP per

capita values are derived, which are used as proxy for the income of citizens.

31

Chapter 4. Implementation

4.1.2 Urbanization rates

As mentioned in chapter 3, urban and rural projections are combined to form national values by

employing the urbanization rate of countries. The urbanization rate captures what percentage

of the population lives in cities in a particular country. The data was sourced from the United

Nations, which produces estimates and projections of urban and rural populations of countries

every two years (United Nations - Department of Economic and Social Affairs, 2014).

4.1.3 Cooking fuel penetration

As one can imagine, data on cooking fuel penetration in sub-Saharan Africa is very limited.

Therefore, it is justified to state that data availability was a constraint for this project.

However, data on the cooking fuel mix for 45 countries in sub-Saharan Africa was obtained

by combining two sources (The World Bank, 2014; USAID, 2017). The sources provide infor-

mation on the cooking habits in urban and rural areas based on surveys. A distinction is made

between households that cook with:

� Electricity

� LPG/natural gas/biogas

� Kerosene

� Charcoal

� Wood

� Straw/shrubs/grass

� Dung

� Other types of fuels

� Households who do no cooking

For each country, one or multiple historical data points could be retrieved. However, there is

no consistency in terms of timing of the surveys, which has consequences for the model, as

previously explained in chapter 3.

Due to lack of data, the model does not cover Cape Verde, Mauritius, Reunion, Sao Tome

and Principe, Seychelles, and South Sudan. These countries have a combined 2015 population

of 15.25 million, which is negligible in comparison to the sub-Saharan total.

4.1.4 Energy intensity of technologies

As discussed in section 3.2, the energy intensities of the various cooking technologies are used

to make the step from technology penetrations to fuel mix. The IEA provides a range for

average energy intensities of different cooking technologies in their 2014 Africa Energy Outlook,

as can be seen in table 4.1 (International Energy Agency, 2014). The calculations in the model

32

Section 4.1 Model inputs

Table 4.1: Each cooking technology is characterized by a particular energy intensity, expressedin toe/year per household (International Energy Agency, 2014)

Cooking technologyEnergy intensity

in toe/year per household

Traditional wood stove 1.0-3.7

Traditional charcoal stove 0.5-1.9

Improved wood stove 0.5-1.6

Improved charcoal stove 0.4-1.5

LPG stove 0.08-0.15

Kerosene stove 0.1-0.2

Electric stove 0.07-0.13

are performed with the middle point of the ranges. It is assumed that the energy intensities

of individual technologies remain constant over time, which is equivalent to stating that the

efficiency of the various cooking technologies does not improve to 2050. This assumption is

reasonable because the report does not focus on advancements in existing technologies, but

on switching between fuels and technologies. However, as households shift to more efficient

cooking technologies, e.g. from traditional wood stoves to improved wood stoves or LPG stoves,

the overall average cooking energy intensity, expressed in toe per household per year, is expected

to decrease over time, as illustrated in chapter 5. This means that, on average, sub-Saharan

households require less primary energy to supply the same energy service as a result of increased

adoption of efficient technologies.

4.1.5 Number of households

As mentioned in section 3.2, estimates of the number of households in each country are used to

obtain projections in absolute terms (number of people, energy demand), rather than as per-

centages. However, no historical data or projections are available for all sub-Saharan countries

on the number of households. Therefore, a simple calculation was applied to obtain estimates

on the number of households in all 45 modeled countries.

In a first step, data was retrieved from the United Nations Data Retrieval System (United

Nations Statistics Division, 2017). This database contains one or two historical values for the

number of households for 12 countries in sub-Saharan Africa. In a second step, household size

was calculated for these data points, making use of historical population data. This set-up

allowed to enter the third step, which consists of a cross-sectional regression of household size

to GDP per capita. The calculation resulted in the following regression equation with an R2 of

0.51:

HHsize = −0.0002 ∗ GDP

capita+ 5.4401 (4.1)

with HHsize the average size of a household. The equation shows a decrease in household size

with rising income levels, a trend which is described in literature (Ruijven et al., 2011; Lanjouw

and Ravallion, 1995). The data points and regression curve are visualized in figure 4.1. In a

fourth step, household size per country to 2050 was estimated based on the forecasted GDP per

capita. For countries that are included in the UN database, the slope of the regression curve

33

Chapter 4. Implementation

Figure 4.1: Cross-sectional regression of household size versus GDP per capita for 12 coun-tries in sub-Saharan Africa. It is observed that household size decreases when citizens become

wealthier.

was applied to the starting point. For other countries, average household size was calculated

by inputting the GDP per capita forecast in equation 4.1. A lower limit of 3.47 persons per

households, which is the lowest value among sub-Saharan countries in the UN database, was

employed to avoid unreasonable small household size values. The fifth and final step involves

deriving the number of households from population projections and the calculated household

size.

4.2 Rationale behind model rules for fuel penetration projec-

tions in urban and rural areas

Historical panel data, and insights from literature review and expert calls form the base for the

projections. In this section, regression curves and model rules to estimate future fuel penetra-

tions are derived, and a rationale is provided to support assumptions. In section 3.3, one can

read how the defined methodology is applied to obtain estimates of future fuel penetrations.

4.2.1 Approach taken for analysis of fuel penetration data

To derive useful insights from the data on cooking fuel penetration, some preprocessing and

screening of the available data was required. As mentioned in subsection 4.1.3, two data sources

34

Section 4.2 Rationale behind model rules for fuel penetration projections in urban and ruralareas

were combined to cover the largest possible geographical scope. To perform the regression anal-

ysis, the author opted to start with countries for which multiple historical data points were

available. Rationale for this is to build estimates of future fuel penetrations from the track

record of countries for which historical data is accessible. This approach allows to combine a

view on the position of different countries with a perspective on past trends and changes. In

addition, countries for which the past data points demonstrated inconsistent survey results were

not included in the calculation of regression curves to avoid distortion of the results.

This analysis resulted in a list of 16 countries in sub-Saharan Africa with reliable historical

data points that can be used for a regression analysis1. In the next subsections, an explanation

of the various model rules for the five fuel categories in urban and rural areas can be found.

4.2.2 As households become wealthier, they shift away from wood for cook-

ing purposes

The use of wood as cooking fuel is set to decrease over time due to various associated disad-

vantages, discussed below. This trend is seen in the data and incorporated in the model in an

appropriate way.

Firstly, cooking with wood is inconvenient as wood is typically gathered by women and children,

who spend an average of 2.1 hours per day on this burdensome task. After fuel is gathered,

women spend another couple of hours cooking with traditional three-stone fires. As a result,

productive time is lost and children are kept away from school (Lambe et al., 2015).

Secondly, the use of wood for cooking is associated with high levels of indoor air pollution

as burning biomass produces large amounts of particulate matter and toxic chemicals. Women

and children, spending most of their time in the vicinity of the cookstoves, are disproportionately

affected and face respiratory infections (e.g. acute lower respiratory infections, chronic obstruc-

tive pulmonary disease), cancer, vascular diseases, cataract, and low birth weights. Each year,

around 600,000 people in sub-Saharan Africa die due to exposure to biomass smoke (Lambe

et al., 2015; The World Bank, 2014; Fullerton et al., 2008; World Health Organization, 2016).

Apart from the above mentioned user inconvenience issues, intense use of wood as cooking

fuel can bring about environmental concerns. The extensive and unsustainable use of biomass

in sub-Saharan Africa causes forest degradation and biodiversity loss. Ethiopia, Uganda, and

Nigeria are examples of countries with high woodfuel consumption and a strong pressure on the

biomass stock. Furthermore, the burning of firewood accounts for 6% of global black carbon

emissions. Black carbon, the main component of soot, contributes to climate change and is

considered a driver of global warming (The World Bank, 2014).

Urban wood use analysis The available data is consistent with the above explained story-

line. The historical evolution of various countries shows that the penetration of wood in cities

1The countries included in the regression analysis are Benin, Republic of Congo, Ethiopia, Lesotho, Ghana,Kenya, Mali, Malawi, Mozambique, Nigeria, Namibia, Rwanda, Sierra Leone, Tanzania, Madagascar, and Senegal.

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Chapter 4. Implementation

decreases rapidly when income is relatively low and increasing. When a certain wealth level is

reached, the decrease slows down, until the penetration becomes stable. A similar observation

was made by Vassilis et al. (2012). Figure 4.2 visualizes the implemented model rules for the

wood penetration in urban areas. As one can see, the countries are grouped according to their

Figure 4.2: Visualization of technical analysis for penetration of wood cooking in urban areas.Colored dots represent historical data points that capture what share of urban population cookswith wood in a particular country. Connected data points belong to the same country and showthe evolution over time. The colored lines are visualizations of the regression curves/modelrules that are used to project the future penetration of wood for cooking in cities. The verticaldotted lines indicate the boundaries of a particular income bucket. The red areas on thegraph are boundary areas, meaning that the penetration of wood remains constant when acountry is characterized by the respective level of GDP per capita and penetration. The modelrules illustrate that the penetration of wood decreases rapidly when income is relatively lowand increasing. When a certain wealth level is reached, the decrease slows down, until the

penetration becomes stable.

GDP per capita into three income buckets:

� Poor countries with a GDP per capita below USD1,500

� Intermediate countries with a GDP per capita between USD1,500 and USD7,000

� Rich countries with a GDP per capita above USD7,000

36

Section 4.2 Rationale behind model rules for fuel penetration projections in urban and ruralareas

Table 4.2: Definition of model rules for the wood penetration in cities.

Lower limit forGDP per capita

(USD 2010)

Upper limit forGDP per capita

(USD 2010)

Equation forregression curve

R2 Lowerlimit

Poorcountries

0 1500 -0.0463x + 67.6 0.52 15%

Intermediatecountries

1500 7000 -0.0043x + 34.3 0.32 5%

Richcountries

7000 n/a n/a n/a 5%

The regression curve for the poor countries was calculated from the available data points of coun-

tries with the appropriate GDP per capita level and multiple reliable historical data points. In

addition, data for a number of countries in Asia was included, as a similar development is taking

place in the region2. For intermediate countries, with a GDP per capita between USD1,500 and

USD7,000, a similar approach was followed. However, in addition to supplementing the analysis

with data on Asian countries, the data points for Swaziland and Botswana were also included3.

As data scarcity is an issue, this action allows to develop a more comprehensive view on the

evolution of the wood penetration in urban areas. For countries with a GDP per capita above

USD7,000, the share of wood cooking is kept constant with rising income. Additional limits

were added based on the observation that only few countries have a wood penetration in urban

areas below 15% for poor countries, or 5% for intermediate and rich countries. It is assumed

that the use of wood for cooking will always remain present in cities to some extent as the very

poor have no other options, e.g. in slums. The summary of model rules can be observed in

table 4.2.

Rural wood use analysis As can be expected, the same trend is present in rural areas in

sub-Saharan Africa. However, it is important to note that the phasing out of wood occurs much

slower than in cities because of the lack of alternatives, the abundance of free wood, and lower

income levels.

Figure 4.3 graphically shows how the decrease of wood use in rural areas is modeled. A distinc-

tion is made between:

� Poor countries with a GDP per capita below USD1,500

� Rich countries with a GDP per capita above USD1,500

The regression curve for the low income bucket was derived by incorporating all countries with

reliable and multiple historical data points, except for Madagascar as this country has a low

wood penetration for its respective income level. Similar to the regressions performed for the

urban wood penetration, the historical evolution of Bangladesh, Cambodia, and India are taken

into account as well. A slightly different approach was chosen for the rich countries due to the

lack of sufficient data. For this group of countries, the regression curve was calculated on all last

2Historical data for Bangladesh, Cambodia, and India was included in the regression analysis.3Historical data for Indonesia and Philippines was included in the regression analysis.

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Chapter 4. Implementation

Figure 4.3: Visualization of technical analysis for penetration of wood cooking in rural areas.Colored dots represent historical data points that capture what share of rural population cookswith wood in a particular country. Connected data points belong to the same country and showthe evolution over time. The colored lines are visualizations of the regression curves/modelrules that are used to project the future penetration of wood for cooking in rural villages. Thevertical dotted lines indicate the boundaries of a particular income bucket. The red areas onthe graph are boundary areas, meaning that the penetration of wood remains constant whena country is characterized by the respective level of GDP per capita and penetration. As inurban areas, the penetration of wood in rural areas decreases with rising incomes. However,

the phasing out occurs much slower.

available data points of countries with a GDP per capita above USD1,500. Equatorial Guinea

was not taken into account for this analysis because of the exceptionally high income level and

large share of wood cooking, which is inconsistent with the observed trend. A lower limit of

10% was added to the calculation logic. The summary of model rules for wood penetration in

rural areas can be found in table 4.3.

4.2.3 Initially, charcoal presents itself as an attractive alternative for wood

Charcoal is the product of a process in which wood is heated in the absence of air. IRENA

(2015) estimates that 20% of harvested woodfuel is converted to charcoal. In developing coun-

tries, it is often used as a cooking fuel, whereas charcoal is also present in developed regions as

38

Section 4.2 Rationale behind model rules for fuel penetration projections in urban and ruralareas

Table 4.3: Definition of model rules for the wood penetration in rural areas.

Lower limit forGDP per capita

(USD 2010)

Upper limit forGDP per capita

(USD 2010)

Equation forregression curve

R2 Lowerlimit

Poorcountries

0 1500 -0.0198x + 105.4 0.72 10%

Richcountries

1500 n/a -0.0066x + 96.0 0.50 10%

a barbecue fuel (Girard, 2002).

The use of charcoal presents several advantages over wood, mostly related to user convenience

(Fullerton et al., 2008; Girard, 2002; Iwuoha, 2013; Boynton, 2012; Onishi, 2016):

1. Due to the pre-processing, charcoal is cleaner and less smoky than wood, and thus results

in reduced adverse health effects

2. Charcoal has a higher energy density, so it is more convenient to transport

3. The fire is easier to manage because regular check-ups are not required

4. Charcoal allows for shorter cooking times because it burns hotter

5. In contrast with wood, charcoal can be stored for a long period of time without damage

as it does not get attacked by insects and fungi

As a result of the above mentioned attractive characteristics of charcoal, households shift from

wood to charcoal as their income increases, especially in urban areas. Wealth is an important

driver because woodfuel is often gathered, whereas charcoal is purchased from vendors (The

World Bank, 2014). Moreover, the production of charcoal allows poor, often rural, households

to gain extra income, acting as a push for increased charcoal use (Boynton, 2012). Data on the

production of charcoal shows a doubling of volumes in sub-Saharan Africa between 2000 and

2015 (United Nations Environment Programme, 2017). Keeping in mind that a large part of

the charcoal industry is not regulated, this increase might be a severe underestimation of the

actual charcoal production.

However, the use of charcoal presents a challenge for the environment in the form of defor-

estation, especially since charcoal is often produced in inefficient kilns with yields not exceeding

15 to 25%. In other words, 1000 kg of wood is used to obtain 150 kg to 200 kg of charcoal

(Girard, 2002). As a result, the availability of wood, and thus charcoal, around city centers sets

a limit on the penetration. Charcoal producers need to source their commodity from further

away and increase the price, thereby lowering the cost gap with modern cooking fuels, mainly

LPG. Moreover, the production and distribution of charcoal is largely unregulated (Boynton,

2012; Onishi, 2016). As a result, some countries, e.g. Kenya and Tanzania, have attempted to

ban charcoal use (Iwuoha, 2013).

Therefore, it can be stated that charcoal is a transition fuel. Initially, it presents several ad-

vantages over wood and is more easily available (no dedicated infrastructure required, largely

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Chapter 4. Implementation

produced and sold locally) and more affordable than modern cooking fuels such as LPG, nat-

ural gas, or electricity. Charcoal cooking is still accompanied by smoke production and other

inconveniences, which makes people opt for modern cooking fuels when income allows. This

observation was also made by (Yu-Ting Lee, 2013).

Rural villages are expected to use charcoal to a lesser extent because its use does not present

strong benefits. However, some penetration can be assumed due to spillover effects as rural

households produce charcoal from wood and sell it to urban distributors.

Urban charcoal use analysis When looking at the data, one can see a trend that underpins

the above mentioned reasoning. Figure 4.4 and table 4.4 summarize the implementation of

model rules for charcoal penetration in urban areas.

Figure 4.4: Visualization of technical analysis for penetration of charcoal cooking in urbanareas. Colored dots represent historical data points that capture what share of urban popu-lation cooks with charcoal in a particular country. Connected data points belong to the samecountry and show the evolution over time. The colored lines are visualizations of the regressioncurves/model rules that are used to project the future penetration of charcoal for cooking incities. The vertical dotted lines indicate the boundaries of a particular income bucket. Thered areas on the graph are boundary areas, meaning that the penetration of charcoal remainsconstant when a country is characterized by the respective level of GDP per capita and charcoalpenetration. Charcoal is a transition fuel in urban areas. Initially, it presents several advantages

over wood, but people opt for modern cooking fuels when income allows.

40

Section 4.2 Rationale behind model rules for fuel penetration projections in urban and ruralareas

Table 4.4: Definition of model rules for the charcoal penetration in urban areas.

Lower limit forGDP per capita

(USD 2010)

Upper limit forGDP per capita

(USD 2010)

Equation forregression curve

R2 Upperlimit

Poorcountries

0 800 0.0644x + 9.24 0.56 70%

Richcountries

800 n/a -0.0142x + 71.99 0.81 n/a

Lowcharcoalpenetration

n/a n/aConstant, until

trendline is reachedn/a n/a

The regression for the poor countries takes into account all data points for countries with

multiple reliable historical data points, except for Madagascar. Madagascar is characterized by

a high charcoal penetration due to the fact that climate change and consecutive years of drought

disrupted farming and pushed many rural households into the charcoal business (Onishi, 2016).

In addition, 70% was defined as upper limit for charcoal penetration in cities, as it is observed

that only six countries ever reached a share above 70%4. The regression analysis for rich coun-

tries excluded Lesotho, Senegal, and Nigeria. Lesotho and Senegal were not considered because

these countries have skipped charcoal cooking and immediately moved to LPG. On the other

hand, Nigeria is designated by a lagging development for its relatively high GDP per capita,

which is largely driven by the export of petroleum products. The decision to keep the charcoal

penetration constant for countries that are located well below the regression curves in figure 4.4

is build upon the assumption that these countries will not intensify charcoal use for country-

specific reasons (e.g. regulation, scarcity of forest around city centers, etc.). As the income of

these countries rises, penetration is kept constant. Once the regression curve for rich countries

is reached, the charcoal share follows the slope of this curve, until charcoal use is completely

phased out (in the assumption that GDP per capita increases substantially).

Rural charcoal use analysis Charcoal use dynamics in rural areas are somewhat different

than in cities. In general, incomes are lower and time is less valuable from an economic per-

spective. In addition, the production of charcoal from wood for personal use represents an extra

burden for rural households without economic benefit. Therefore, the incentives to switch from

firewood to charcoal are less prevalent in rural villages. One can expect that charcoal is used

by a small share of households because charcoal is produced for use in cities.

As visible in figure 4.5 and table 4.5, charcoal penetration in rural villages increases with ris-

ing incomes. When a certain income level is reached, the model keeps the share of charcoal

constant, mainly because available data does not show a clear trend. The regression for poor

countries was performed on all countries except for Lesotho and Madagascar. These countries

were excluded from the calculation for the same reasons as explained above.

4Countries with a historical charcoal penetration above 70% are Burundi, Guinea, Liberia, Madagascar, So-malia, and Togo.

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Chapter 4. Implementation

Figure 4.5: Visualization of technical analysis for penetration of charcoal cooking in ruralareas. Colored dots represent historical data points that capture what share of rural populationcooks with charcoal in a particular country. Connected data points belong to the same countryand show the evolution over time. The colored lines are visualizations of the regression curves/-model rules that are used to project the future penetration of charcoal for cooking in ruralvillages. The vertical dotted lines indicate the boundaries of a particular income bucket. Thered areas on the graph are boundary areas, meaning that the penetration of charcoal remainsconstant when a country is characterized by the respective level of GDP per capita and charcoalpenetration. Charcoal use in rural areas remains limited as households have less incentive to

switch from wood to charcoal than in cities.

Table 4.5: Definition of model rules for the charcoal penetration in rural areas.

Lower limit forGDP per capita

(USD 2010)

Upper limit forGDP per capita

(USD 2010)

Equation forregression curve

R2 Upperlimit

Poorcountries

0 1500 0.0139x - 3.54 0.85 n/a

Richcountries

1500 n/a Constant penetration n/a n/a

42

Section 4.2 Rationale behind model rules for fuel penetration projections in urban and ruralareas

4.2.4 When a certain wealth level is reached, households shift to LPG

As mentioned in section 3.2, LPG, natural gas, and biogas are treated as one fuel category

because of the structure of input data. The split between these three different fuels is rather

straightforward:

� Natural gas is unlikely to be used by African households on a large scale. The reasons for

this are the lack of a gas grid and the high cost to foresee this infrastructure. Therefore,

natural gas has only a chance to contribute to the clean cooking evolution in the largest

cities of gas-rich countries

� Biogas digesters convert waste to methane, which is then used as cooking fuel. Several

barriers impede a large-scale uptake of the technology, among others the need for water,

high upfront investment costs (between USD600 and USD1,500), and the ongoing mainte-

nance requirements (International Energy Agency, 2014; The World Bank, 2014). These

particularities restrain adoption to the community level in rural areas

� LPG use is set to increase significantly, driven by rising incomes, government programs,

and its ease of use. As an example, both Nigeria and Kenya have stated the intention

to promote the use of LPG as substitution for kerosene and biomass (Federal Republic

of Nigeria, 2016; Kenya Ministry of Energy and Petroleum, 2015). Compared to biomass

cooking, LPG cooking is cleaner, takes less time, and is better controllable (International

Energy Agency, 2014). As a result, LPG makes up the largest part of the fuel category.

As can be expected, the uptake in rural areas is slower than in urban areas because of the

need for a distribution network (roads, refueling of cylinders, etc.) and stove costs

Urban LPG/natural gas/biogas use analysis As seen in figure 4.6, which visualizes the

model rules for the urban penetration of LPG/natural gas/biogas, this fuel category is expected

to claim an important role as cooking fuel in the future.

As for the wood and charcoal penetration, countries are again grouped according to their GDP

per capita level:

� Poor countries with a GDP per capita below USD500

� Intermediate countries with a GDP per capita between USD500 and USD1,500

� Rich countries with a GDP per capita above USD1,500

As long as a country belongs to the category of poor countries, the penetration of LPG/natural

gas/biogas remains constant as people do not possess the required resources to upgrade from

biomass or kerosene to modern fuels. Starting from a GDP per capita level of USD500, the

historical evolution of countries shows a rapid increase in the uptake of these fuels. A regression

was performed on available data points of countries with a relevant GDP per capita level and

multiple reliable historical data points. Lesotho and Senegal were not included in this regression

as these states are characterized by an above average penetration due to government interven-

tion. Once the USD1,500 GDP per capita level is surpassed, the growth slows down. For the

rate of growth, the average historical GDP multiplier of Angola, Republic of Congo, Namibia,

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Chapter 4. Implementation

Figure 4.6: Visualization of technical analysis for penetration of LPG/natural gas/biogascooking in urban areas. Colored dots represent historical data points that capture what shareof urban population cooks with LPG/natural gas/biogas in a particular country. Connecteddata points belong to the same country and show the evolution over time. The colored linesare visualizations of the regression curves/model rules that are used to project the future pen-etration of LPG/natural gas/biogas for cooking in cities. The vertical dotted lines indicate theboundaries of a particular income bucket. The red areas on the graph are boundary areas,meaning that the penetration of LPG/natural gas/biogas remains constant when a country ischaracterized by the respective level of GDP per capita and LPG/natural gas/biogas penetra-tion. Starting from a GDP per capita level of USD500, the historical evolution of countriesshows a rapid increase in the uptake of LPG/natural gas/biogas, after which growth slows

down.

Nigeria, and Philippines was used. In addition, an upper limit of 70% was applied across all

three income buckets. A summary of the applied model rules can be found in table 4.6.

Rural LPG/natural gas/biogas use analysis For the rural penetration of LPG/natural

gas/biogas, the same income grouping was used as for the urban penetration. Poor countries

are characterized by a constant (and low) penetration. When countries are in the intermediate

income group, the penetration of this fuel category rises slightly with income. For the regression,

Lesotho and Senegal were excluded because of their exceptionally high LPG penetration. Data

for the high income category was scarce and did not provide a uniform rule, so the regression

curve for intermediate countries was extrapolated based on the expectation that the growth in

44

Section 4.2 Rationale behind model rules for fuel penetration projections in urban and ruralareas

Table 4.6: Definition of model rules for the LPG/natural gas/biogas penetration in urbanareas.

Lower limit forGDP per capita

(USD 2010)

Upper limit forGDP per capita

(USD 2010)

Model rule/regression curve

R2 Upperlimit

Poorcountries

0 500 Constant penetration n/a 70%

Intermediatecountries

500 1500 0.0378x - 20.47 0.84 70%

Richcountries

1500 n/a Follow slope = 0.014 n/a 70%

Table 4.7: Definition of model rules for the LPG/natural gas/biogas penetration in ruralareas.

Lower limit forGDP per capita

(USD 2010)

Upper limit forGDP per capita

(USD 2010)

Equation forregression curve

R2 Upperlimit

Poorcountries

0 500 Constant penetration n/a 70%

Intermediatecountries

500 1500 0.004x - 2.45 0.92 70%

Richcountries

1500 n/a 0.004x - 2.45 n/a 70%

LPG use will continue as households get richer. Even though past evolutions do not yet show

this trend, the assumption makes sense as a similar evolution could be observed in Asia. The

same upper limit as for urban areas, namely 70% was applied across all income categories. A

detailed summary can be found in table 4.7. The rules are visualized in figure 4.7.

4.2.5 Kerosene is set to be phased out

The use of kerosene as cooking fuel is dominant in a number of countries in sub-Saharan Africa.

One could think that the penetration of kerosene is linked to the production of petroleum prod-

ucts. However, data contradicts this logic as some countries with a large penetration do not

produce oil or gas. On the other hand, countries such as Angola, Sudan, and Gabon, all of

them large oil producers, do not show a high penetration of kerosene. The use of kerosene by

households for cooking purposes, and by extension for lighting, is largely driven by the presence

of fuel subsidies. Well-targeted kerosene subsidies lower the cost for end-consumers, thereby in-

creasing the attractiveness of kerosene in resource-constrained sub-Saharan Africa. Examples of

countries with a notable use of kerosene and kerosene subsidies are Nigeria, Equatorial Guinea,

Ethiopia, and Kenya (International Energy Agency, 2014).

That being said, the use of kerosene as cooking fuel is expected to be reduced over time, driven

by several considerations:

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Chapter 4. Implementation

Figure 4.7: Visualization of technical analysis for penetration of LPG/natural gas/biogascooking in rural areas. Colored dots represent historical data points that capture what share ofrural population cooks with LPG/natural gas/biogas in a particular country. Connected datapoints belong to the same country and show the evolution over time. The colored lines arevisualizations of the regression curves/model rules that are used to project the future penetra-tion of LPG/natural gas/biogas for cooking in rural villages. The vertical dotted lines indicatethe boundaries of a particular income bucket. The red areas on the graph are boundary areas,meaning that the penetration of LPG/natural gas/biogas remains constant when a country ischaracterized by the respective level of GDP per capita and LPG/natural gas/biogas penetra-tion. The use of LPG/natural gas/biogas is limited in rural areas. An increased uptake is

observed when countries cross the USD500 border for GDP per capita.

� Government interventions aim to phase out the use of kerosene for lighting and cooking.

As an example, Nigeria reduced kerosene subsidies in January 2016 after spending USD1

billion per year, counteracting the false perception of kerosene being a cheap cooking

fuel. The subsidy removal resulted in a price hike from N50 per liter to N83 per liter

(Obasi et al., 2016; NNodim, 2016). In 2013, the government of Ghana raised the price

of kerosene by 15% and in 2015, the decision was made to deregulate fuel prices, meaning

that prices will no longer be determined by government and subsidies were cut (Whitley

and van der Burg, 2015; Ofori, 2015). Angola plans to reduce kerosene subsidies gradually

as fuel subsides made up 3.7% of GDP in 2014 (International Monetary Fund, 2015b).

In September 2014, the price of kerosene increased by 35% (Africa Business, 2014). In

April 2015, the price of kerosene rose by almost 30% (The Economist Intelligence Unit,

46

Section 4.2 Rationale behind model rules for fuel penetration projections in urban and ruralareas

2015). Other countries are likely to follow as a consensus emerges that fuel subsidies are

undesirable because of the high fiscal cost, the risk of over-consumption, the substantial

leakage of subsidies to rich households, incentive for smuggling fuel to neighboring coun-

tries, and rent seeking opportunities (Coady, Unknown; Sunnews, 2016; International

Monetary Fund, 2015a; Whitley and van der Burg, 2015; International Monetary Fund,

2015b). For Ghana, research shows that 78% of fuel subsidies benefited the wealthiest

group of society. Only 3% of subsidies reached the poorest quintile. On average in Africa,

16% of subsidy benefits end up in the poorest quintile (Cooke et al., 2014). At the same

time, Kenya, another country with significant use of kerosene as cooking fuel, stated the

ambition to be kerosene free by 2022 (Sustainable Energy for All, 2016; Kenya Ministry

of Energy and Petroleum, 2015). Kenya increased taxes on kerosene and aims to use the

expected proceeds to support the diffusion of LPG (Wafula, 2016; Anyanzwa, 2014)

� Supply shortages occur from time to time, thereby increasing prices and making kerosene

inconvenient. At peak prices, the weekly fuel cost of kerosene in Nigeria can rise up to

three times the subsidized price and is higher than wood, charcoal, and LPG (Accenture,

2011)

� The use of kerosene in cookstoves brings the risk of explosions leading to injuries or loss

of lives, and physical damage (Kenya Ministry of Energy and Petroleum, 2015)

� Kerosene is often obtained in informal markets at high prices causing adulteration and

black market activity (Accenture, 2011; Wafula, 2016; Anyanzwa, 2014)

� The phase out of kerosene as cooking fuel goes hand in hand with its replacement as

lighting fuel. As an example, the government of Ghana is planning to accelerate the

replacement of kerosene lanterns with solar lighting equipment as a part of its medium

term national development policy framework for renewable energy (Netherlands Enterprise

Agency, 2016). In Angola, the government has set the target to limit kerosene use to

residual utilization in rural areas by 2030, by focusing on renewable energy technologies

(Sustaianble Energy for All, 2015)

Urban kerosene use analysis The above described assumption that kerosene will be phased

out is also heavily present in the model rules. As opposed to the methodology for other fuel

categories, the approach behind the kerosene projections groups countries based on the kerosene

penetration as reported in the last available data point of the respective country. For urban

areas, a penetration above 15% is considered high.

For countries with a high starting penetration of kerosene, it is assumed that the share will

evolve according to the average historical GDP multiplier of the Republic of Congo, Ethiopia,

Kenya, Lesotho, and Nigeria. These countries have a historical kerosene penetration above 15%

and multiple historical data points. The analysis of the mentioned countries validates the as-

sumption of a decreasing kerosene use for cooking. For countries with a low initial penetration,

the model employs a linear phase out by 2030, based on the statement of Kenya to eliminate

household kerosene use by 2022 (Sustainable Energy for All, 2016; Kenya Ministry of Energy

and Petroleum, 2015). Figure 4.8 and table 4.8 visualizes and summarizes the defined model

rules, respectively.

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Chapter 4. Implementation

Figure 4.8: Visualization of technical analysis for penetration of kerosene cooking in urbanareas. Colored dots represent historical data points that capture what share of urban populationcooks with kerosene in a particular country. Connected data points belong to the same countryand show the evolution over time. The colored line is a visualization of the model rule thatis used to project the future penetration of kerosene for cooking in cities. The penetration ofkerosene for countries with a high starting kerosene penetration (higher than 15%) evolves inparallel with this line. The horizontal line indicates the distinction between countries with a highstarting penetration of kerosene and countries with a low starting penetration of kerosene. Thegraph illustrates that the use of kerosene is phased out over time, mainly driven by anticipated

policies.

Table 4.8: Definition of model rules for the kerosene penetration in urban areas.

Lower limit forkerosene penetration

Upper limit forkerosene penetration

Model rule

Low startingpenetration

n/a 15%Linear phaseout to 2030

High startingpenetration

15% n/aShare evolves with

average GDP multiplier= -0.04344

48

Section 4.2 Rationale behind model rules for fuel penetration projections in urban and ruralareas

Table 4.9: Definition of model rules for the kerosene penetration in rural areas.

Lower limit forkerosene penetration

Upper limit forkerosene penetration

Model rule

Low startingpenetration

n/a 10%Linear phaseout to 2030

High startingpenetration

10% n/aShare evolves with

average GDP multiplier= -0.00391

Rural kerosene use analysis As for kerosene use in urban areas, the model for rural kerosene

uptake categorizes countries based on the kerosene penetration. However, the cap, which is 15%

in urban areas, is lowered to 10% for rural villages as the overall kerosene diffusion is substan-

tially lower5.

A very similar approach as for urban kerosene penetration was used for the rural kerosene

analysis. It is assumed that countries with a high rural kerosene diffusion will evolve in accor-

dance with the past evolution of Nigeria, which is the only country with a historical penetration

above 10% and multiple historical data points. Countries with a low starting penetration are

modeled with a linear phase out of kerosene to 2030. Figure 4.9 and table 4.9 summarize the

modeling approach for rural kerosene penetration.

4.2.6 The uptake of electric cookstoves will remain limited

From a developed country perspective, one could think that electric cookstoves would be adopted

by households when the electricity grid is developed. However, data contradicts this logical pre-

sumption. High electricity access rates are required to allow for significant electric cooking, but

this is not a causal relationship. Many countries with high access rates are characterized by a

small share of electric cooking, as visible in figure 4.10.

The developed model assumes that the penetration of electric cooking will remain limited in

the future due to the various associated challenges:

� Electric cooking is the most expensive cooking technology from a total cost perspective.

The World Bank (2014) estimates the average annual cost of using an electric cookstove

at USD310. Moreover, high upfront stove costs limit a widespread adoption by average-

income households. International Energy Agency (2014) quantifies the investment cost

for an electric cookstove at USD300. Even though this value is high compared to other

sources, it demonstrates that electric stoves are expensive (The World Bank, 2014)

� Sub-Saharan Africa still struggles with an underdeveloped power sector. 620 million peo-

ple in the region do not have access to electricity, which represents 48% of the global

population without access to electricity. Moreover, people with access often face an unre-

liable supply and frequent electricity shortages (United Nations Environment Programme,

5Only four countries have a kerosene penetration above 10%, namely Nigeria, Djibouti, South Africa, andEquatorial Guinea.

49

Chapter 4. Implementation

Figure 4.9: Visualization of technical analysis for penetration of kerosene cooking in ruralareas. Colored dots represent historical data points that capture what share of rural populationcooks with kerosene in a particular country. Connected data points belong to the same countryand show the evolution over time. The colored line is a visualization of the model rule thatis used to project the future penetration of kerosene for cooking in rural villages. The slopecaptures the historical evolution of Nigeria and the penetration of kerosene for countries witha high starting kerosene penetration (higher than 10%) evolves in parallel with this line. Thehorizontal line indicates the distinction between countries with a high starting penetration ofkerosene and countries with a low starting penetration of kerosene. The graph illustrates that

the use of kerosene is phased out over time, mainly driven by anticipated policies.

2017; Rivard and Reay, 2012). As a result, many individuals and businesses own genera-

tors to make up for the lack of reliable electricity supply through the power grid6. With

generator power being four times more expensive than grid power, this practice causes a

substantial cost and demonstrates the strong demand for electricity and the insufficient

supply (International Energy Agency, 2014; Castellano et al., 2015)

� Electric cooking is not encouraged by governments because it causes extra stress for the

power system during peak moments. The coincidence of electric cooking and other uses

of electricity could lead to very high costs of service (Howells et al., 2006; Gamos, 2016)

6In Kenya, a generator is owned by 57% of businesses, with numbers reaching 42% for Tanzania and 41% forEthiopia (Castellano et al., 2015).

50

Section 4.2 Rationale behind model rules for fuel penetration projections in urban and ruralareas

Figure 4.10: Relationship between urban electricity access rates and penetration of electriccooking in cities. Each dot represents the data point for a country in sub-Saharan Africa. A

high electricity access rate is necessary but not sufficient for electric cooking.

� Households often do not seen cooking as a priority for electricity use compared to lighting

and appliances, for which fuel substitutes are mostly absent (International Energy Agency,

2014)

Urban electricity use analysis Due to lack of clear trends in the data, it is assumed that

electric cooking in urban areas for all countries in sub-Saharan Africa will evolve in accordance

with the historical evolution of Ghana. Ghana has gone through a growth in GDP per capita

which a lot of other countries will experience in the period to 2050 and could therefore be

considered representative.

Rural electricity use analysis Electric cooking in rural areas will be even more limited than

in cities because of the absence of an electricity grid. Also historically, only four countries have

shown a rural electric cooking penetration above 5%, namely Zimbabwe, Namibia, Swaziland,

and South Africa. South Africa is the only country in sub-Saharan Africa with a penetra-

tion larger than 10%. As no exceptional evolution is to be expected, the model keeps the rural

electric cooking penetration constant at the level of the last available data point for all countries.

The model rules for electric cooking uptake in cities are visualized in figure 4.11. A summary

of the rules for urban and rural areas can be found in table 4.10.

51

Chapter 4. Implementation

Figure 4.11: Visualization of technical analysis for penetration of electric cooking in urbanareas. Colored dots represent historical data points that capture what share of urban populationcooks with electricity in a particular country. Light blue data points belong to Ghana and showthe evolution over time. The colored line is a visualization of the model rule that is used toproject the future penetration of electricity for cooking in cities. The penetration of electricityevolves in parallel with this line. It is expected that electric cooking will remain limited in the

future due to several challenges.

Table 4.10: Definition of model rules for the electricity penetration in urban and rural areas.

Model ruleUpper limit for electric

cooking penetration

Urban areasShare evolves with

historical GDP multiplierof Ghana = 0.00258

70%

Rural areas Share remains constant n/a

52

Section 4.2 Rationale behind model rules for fuel penetration projections in urban and ruralareas

Table 4.11: Summary of the exceptions to the general model rules, as described in section4.2.

Country Exception Explanation

MozambiqueExtrapolate historical evolutionfor urban charcoal penetration

Historical trend is inconflict with regression curve

SenegalReduced GDP multiplier forurban LPG/natural gas/biogas

High starting penetrationfor urban LPG/natural gas/biogas

LesothoReduced GDP multiplier forurban LPG/natural gas/biogas

High starting penetrationfor urban LPG/natural gas/biogas

Constant penetration for ruralcharcoal

Historical penetration of 0%

Burkina FasoReduced GDP multiplier forurban LPG/natural gas/biogas

High starting penetrationfor urban LPG/natural gas/biogas

MauritaniaReduced GDP multiplier forurban LPG/natural gas/biogas

High starting penetrationfor urban LPG/natural gas/biogas

NigeriaIncreased GDP multiplier forurban LPG/natural gas/biogas

Assuming strong uptake of LPGcooking in Africa’s biggest LPGproducer

South AfricaConstant share for urbanLPG/natural gas/biogas

High electric cooking penetration

NamibiaConstant share for urbanLPG/natural gas/biogas

High electric cooking penetration

SudanUrban and rural coalpenetration kept constant

Sudan is characterized by asignificant share of coal cooking

4.2.7 Exemptions

The model includes several exemptions on the above defined model rules. The exemptions are

summarized in table 4.11.

53

Chapter 5

Results

The previous chapter outlined the methodology for projecting the future cooking fuel mix in

45 sub-Saharan countries to 2050. In this chapter, model results are presented. Following the

design of the model, appendices B, C, and D visualize the number of people cooking with a

particular fuel, the number of people cooking with a particular technology, and the absolute

energy demand by fuel, respectively. The relative values, in percentages, are presented in table

format in the respective appendices. Due to the vast size of the model, some level of aggregation

is required in order to present the results in a relatively condense way. Therefore, results are

shown on a regional level.

In this section, some specific aspects of the results are highlighted and covered in more de-

tail. The results are then further discussed in chapter 6.

5.1 Benchmarking against external perspectives

In their 2014 Africa Energy Outlook, the IEA specifies a perspective on the uptake of cooking

technologies to 2040, from the base year 2012 (International Energy Agency, 2014). The per-

spective is incorporated in their New Policies Scenario, which assumes an implementation of

favorable policies for clean cooking. The projections make a distinction between four regions

in sub-Saharan Africa, and between urban and rural areas. The projections were previously

visualized in figure 3.4 and were used to benchmark the results of the developed model.

The IEA values for the penetration of the various technologies are manipulated to obtain

the penetration of five fuel categories, namely electricity, LPG/natural gas/biogas, kerosene,

biomass, and coal. The results of the model are aggregated in the same way in order to com-

pare the two perspectives. The results of this exercise are visualized in figures 5.1 and 5.2.

As one can observe, differences are substantial. The IEA projections show a faster shift away

from traditional fuels to modern fuels, mainly LPG but also electricity to a certain extent.

Moreover, the IEA does not expect a rapid phase out of kerosene. As a result, the dominant

position of biomass is much more reduced than in the developed model. One reason for the

55

Chapter 5. Results

Figure 5.1: Comparison of model results for fuel penetration in urban areas against IEAprojections. The benchmark analysis shows that IEA expects a faster shift to modern cooking

fuels in cities of sub-Saharan countries.

difference is the fact that IEA projections are part of the New Policies Scenario, thereby being

built on the assumption that governments will implement effective policies to increase access to

clean cooking facilities. As the development of the model behind this report is mostly based

on historical data, results present the outcome of a BAU scenario. This implicates a much

slower shift away from biomass cooking, which partly explains differences with the IEA outlook.

However, in International Energy Agency (2016a), estimates were adjusted from 653 million

people without access to clean cooking in 2040 to 708 million, indicating the uncertainty and

challenge associated with this long term forecast, but also suggesting the optimistic projections

in the 2014 Africa Energy Outlook. As biomass is made up of wood and charcoal, the idea of

charcoal as transition fuel could be another explanation for the higher share of biomass cooking

in the model. It is observed that the deviations are much smaller in rural areas than in urban

areas, which is inherently linked to the slower dynamics in rural villages.

56

Section 5.2 Despite the upgrade in cooking technologies, biomass remains the dominant cookingfuel

Figure 5.2: Comparison of model results for fuel penetration in rural areas against IEAprojections. The differences with IEA estimates are smaller compared to urban areas because

of the slower fuel dynamics in rural villages.

5.2 Despite the upgrade in cooking technologies, biomass re-

mains the dominant cooking fuel

As visualized in figure 5.3, a moderate switch away from biomass cooking is expected. A strong

population growth offsets efforts to increase access to modern cooking facilities, resulting in an

increase in number of people cooking with biomass. Overall, the share of population cooking

with wood or charcoal decreases by only 9%-pts, from 80% in 2016 to 71% in 2050. By 2050,

1.5 billion people rely on biomass to foresee in their cooking needs, equivalent to more than two

times the current population of Europe.

The continued reliance on biomass for cooking hides an underlying change in used technol-

ogy, namely the fast uptake of improved biomass cookstoves, as visualized in figure 5.4. Driven

by relatively low upfront investment costs, attractive user features, a large rural population, and

initiatives by clean cooking organizations, an extra 672 million people will make use of improved

biomass stoves by 2050, as compared to 2016.

East Africa is expected to lead the way in terms of diffusion of improved stoves. By 2050,

57

Chapter 5. Results

Figure 5.3: Cooking technology mix in sub-Saharan Africa to 2050. The technology splitshows a continued reliance on biomass for cooking purposes. The share of population cooking

with biomass decreases by only 9%-pts, from 80% in 2016 to 71% in 2050.

96% of urban biomass cooking in the region will happen through efficient biomass cookstoves.

The main reasons are the low forest biomass stock and presence of a market structure. The

shrinking biomass base calls for more efficient technologies as fuelwood and charcoal become

more scarce over time. On top of this consumer demand aspect, the existence of a market for

improved cookstoves and local manufacturers (e.g. in Kenya) provide a good starting point

for the anticipated growth. In other regions, the progress is much slower, especially in Central

Africa where 55% of urban citizens will still rely on the use of traditional biomass by 2050. On

a sub-Saharan level, an extra 318 million people in cities and 354 million in rural areas will

make use of improved biomass cookstoves by 2050.

However, in 2050, still 808 million people are cooking with the highly inefficient three-stone

fires, representing 37% of sub-Saharan population, down from 74% in 2016. The combination of

the limited switching to alternative cooking fuels, and the extremely low efficiency of biomass-

based technologies compared to modern fuels, results in the continued dominance of biomass as

cooking fuel, as visualized in figure 5.6. In 2050, the model indicates that 96% of cooking energy

demand is provided by biomass, as compared to 98% in 2016, despite a reduction of the share

of wood from 87% to 76%. This decrease is offset by a more intense use of charcoal, especially

58

Section 5.3 Urban areas experience a faster shift to modern fuels than rural villages

Figure 5.4: Number of people in sub-Saharan Africa cooking with biomass bytechnology, to 2050. Improved wood andcharcoal stoves experience a fast uptake.An extra 672 million people will make use

of improved biomass stoves by 2050.

Figure 5.5: Number of people cookingwith biomass in 2050, by region and tech-nology. East Africa leads the way in termsof diffusion of improved stoves. In otherregions, the progress is much slower, espe-

cially in Central Africa.

in urban areas, where an extra 250 million people will rely on charcoal by 2050.

5.3 Urban areas experience a faster shift to modern fuels than

rural villages

The immense difference between the situation in cities and rural villages is striking, as displayed

in figure 5.7. As 63% of sub-Saharan population lived in rural villages as of 2014, this group

of people has a significant weight in the overall cooking energy demand. The share of rural

population in the total sub-Saharan population decreases to 45% by 2050.

The two residences differ in both starting point and pace of expected evolution.

� Starting point: In 2016, 94% of rural population relies on biomass for cooking, as compared

to 59% in urban areas

� Pace of evolution: Projections show that still 92% of rural citizens cook with biomass in

2050, down 2%-pts. from the 2016 value. In urban areas, the share of population cooking

with biomass decreases by 5%-pts. to 54%. Focusing on the use of LPG, penetration in

rural areas increases by 3%-pts. and by 14%-pts. in cities

Because of the limited upgrade in cooking fuels for rural villages, it becomes clear that improved

biomass cookstoves are the only possible progress for a large part of rural population. 41% of

rural population cooks with improved biomass stoves in 2050, as compared to 28% in urban

areas.

The continued reliance on biomass in rural villages and the switch away in cities results in

the fact that the demand for biomass by rural households represents 65% of total biomass

59

Chapter 5. Results

Figure 5.6: Cooking fuel mix in sub-Saharan Africa to 2050. Biomass remains the dominantcooking fuel, supplying 96% of cooking energy demand in 2050.

demand for cooking in 2050. As rural households represent only 45% of total population in

2050, the vast use of inefficient biomass stoves and the limited uptake of modern fuels becomes

apparent.

5.4 Demand for LPG increases sharply in urban areas, as op-

posed to kerosene and electricity

Given governments succeed to establish reliable supply chains and implement promoting poli-

cies, 483 million people in sub-Saharan Africa cook with LPG, natural gas, or biogas in 2050.

As can be expected, 89% of this population group lives in cities. By 2050, an extra 368 million

people in cities will use LPG/natural gas/biogas as cooking fuel. This translates into a penetra-

tion of 36% in urban areas and 5% in rural areas, again illustrating the considerable difference

between the two residences.

An increase in penetration, combined with a strong population expansion drives a growing

demand for LPG/natural gas/biogas. The absolute energy demand for this fuel category grows

at an average annual rate of 6% in the period 2016-2050, resulting in a seven-fold increase in

60

Section 5.4 Demand for LPG increases sharply in urban areas, as opposed to kerosene andelectricity

Figure 5.7: Comparison of technology penetrations in urban and rural areas, by region, in2016 and 2050. It is observed that not only the starting point, but also the speed of evolution

varies between the two residences.

demand. However, due to the high efficiency of LPG cookstoves compared to biomass stoves,

demand makes up only 2.4% of total cooking energy demand by 2050.

As can be observed in figures 5.8 and 5.9, the difference between regions in substantial. West

Africa has the highest 2016 and 2050 number of people cooking with LPG/natural gas/biogas.

Demand for the fuel category is predominantly driven by the countries Nigeria and Ghana.

However, the projections show that demand in East Africa is growing fastest in relative terms,

with an average annual growth rate of 7%. Again, within the region there are a number of

countries clearly exhibiting a higher demand than other countries, driven by a large population

and/or a high LPG/natural gas/biogas penetration, namely Ethiopia, Kenya, and Sudan.

On the other hand, kerosene cookstoves, which are especially prevalent in West Africa, are

phased out over time, as a result of the defined model rules (see chapter 4). By 2050, only 0.5%

of sub-Saharan population makes use of kerosene as primary cooking fuel, 76% of them living in

rural villages. In 2016, 75% of kerosene cookstoves were employed in cities, which indicates that

the use of kerosene for cooking decreases much faster in urban areas than in rural areas. This

observation is strengthened by considering the penetration of kerosene over time. In cities, the

61

Chapter 5. Results

Figure 5.8: Number of people cookingwith LPG/natural gas/biogas in 2016 and2050, by region and residence. 483 mil-lion people in sub-Saharan Africa cook withLPG, natural gas, or biogas in 2050. 89%

of this population group lives in cities.

Figure 5.9: LPG/natural gas/biogascooking energy demand in sub-SaharanAfrica to 2050, by region. An increase inpenetration, combined with a strong popu-lation expansion drives a growing demand

for LPG/natural gas/biogas.

penetration decreases from 13% in 2016 to 0.2% in 2050. In rural villages, penetration decreases

from 2.7% to 0.9%.

When considering electric cooking, a remarkable observation is made. The aggregate pene-

tration on sub-Saharan level in both urban and rural areas decreases over time (from 1.4% to

0.8% in rural areas and from 11.4% to 9.3% in urban areas). The reason for this is the fast

population growth in countries with a low penetration of electric cooking. Overall, the penetra-

tion of electric cookstoves barely changes between 2016 and 2050, from 5.2% to 5.5%, thereby

clearly demonstrating the effect of urbanization. However, due to a strong population growth,

the nearly constant overall electric cooking penetration causes a doubling in absolute number

of people cooking with electricity. When comparing regions, one notices that the penetration

of electric cookstoves is remarkably higher in Southern Africa than in other regions, as seen

in figure 5.7. Moreover, it becomes clear that the decrease in share of people cooking with

electricity on urban and rural level is the result of demographic dynamics in Southern Africa.

From the 13 countries in Southern Africa, seven of them have an above 2016 average uptake

of electric cookstoves1. South Africa and Zimbabwe, where 85% and 73% of urban households

cook with electricity, respectively represent 87% of urban households that cook with electricity

in Southern Africa in 2016.

5.5 The shift away from traditional biomass cooking triggers a

decrease in average cooking energy intensity

As previously stated, sub-Saharan households gradually discard traditional biomass cookstoves,

decreasing its penetration from 74% in 2016 to 37% in 2050. As a replacement, they shift to

1Countries with an above average electric cookstove penetration in Southern Africa are Botswana, Lesotho,Namibia, South Africa, Swaziland, Zambia, and Zimbabwe.

62

Section 5.6 Dissimilar economic growth results in the emergence of a two-speed Africa

modern cooking fuels, mainly LPG, and improved biomass cookstoves. This evolution triggers

a 32% decrease in average cooking energy intensity, from 1.63 toe/year per household in 2016 to

1.11 toe/year per household in 2050. It has to be emphasized that this reduction is not a result

of increased efficiencies of existing technologies, but solely from the adoption of less energy

intensive cookstoves. Despite this reduction, total cooking energy demand will increase by

160 Mtoe or 48% due to the expanding population. The additional energy demand is equivalent

to the aggregated 2015 residential energy consumption of the UK, France, Germany, and Italy

(European Commission, 2017).

5.6 Dissimilar economic growth results in the emergence of a

two-speed Africa

In this report, results were mainly presented on a regional level. However, when taking a closer

look at differences among countries, it is observed that sub-Saharan countries are characterized

by a dissimilar economic growth, as can be seen in figure 5.10. This variety in the speed

Figure 5.10: Impact of variation in economic growth on change in cooking energy intensity.Sub-Saharan countries display a wide variety in the speed of economic development, which has

a strong impact on projected fuel mixes and national average energy intensities.

of economic development results in the emergence of a two-speed Africa, where some countries

63

Chapter 5. Results

take the lead and others lag behind. Since economic growth is a key driver of the model, this has

strong implications for the model results. Leading countries, i.e. countries with a sharp increase

in GDP per capita, are expected to quickly move away from traditional biomass use, whereas the

use of traditional biomass remains persistent in countries with much slower growth perspectives.

Projections show that Ethiopia, Zimbabwe, and Mozambique are examples of countries that

rapidly move away from inefficient biomass cookstoves for cooking to LPG, whereas countries

such as Niger and Central African Republic will only experience slow and limited switching.

As technology penetration translates in national energy intensities, the variety in the pace of

economic development has a profound impact on the decrease in cooking energy intensity, as

visualized in figure 5.10.

64

Chapter 6

Discussion

6.1 Discussion of results

Today’s residential energy consumption in sub-Saharan Africa is strongly dominated by cooking,

representing around 80% of residential energy demand (International Energy Agency, 2014).

Within the group of developing regions, sub-Saharan Africa sees a significantly higher share of

cooking than other regions. Due to low income levels and lagging development, many households

have no access to elementary services. In combination with the low efficiency of employed

technologies, cooking is by far the most important lever of energy demand. To provide in

their cooking needs, 792 million people continue to rely on inefficient biomass cookstoves in

2014 (International Energy Agency, 2016a). As discussed in chapter 1, this situation leads to

harmful health and development hazards and a substantial annual economic cost of USD58.2

billion.

6.1.1 Drivers of a changing cooking fuel penetration

As discussed in chapter 2, many influencing factors for cooking fuel choice have been identified in

literature, broadly grouped into four categories; socioeconomic variables, cultural and behavioral

habits, product-specific attributes, external factors. Given the expected developments in sub-

Saharan Africa and data constraints, several macroevolutions will be crucial in driving a shift

in cooking fuel choices. As a result, these factors are incorporated in the model as drivers.

� Increasing wealth levels result in a switch to modern cooking technologies with a higher

efficiency. In the period 2014-2050, average GDP per capita in sub-Saharan Africa will

increase by 52%, allowing households to buy improved and modern cookstoves. Some

countries are expected to experience exceptional growth. An example is Ethiopia, which

will see a tenfold increase of its economy, speeding up access to cleaner cooking facilities

� Population more than doubles through 2050, growing at an annual rate of 2.3%. A boom-

ing population renders the target of universal access to clean cooking even more challenging

� As households move to cities, citizens gain access to cleaner forms of energy. Historical

data shows an increased penetration of modern fuels in cities. An increase in urbanization

65

Chapter 6. Discussion

rate from 37% in 2014 to 55% in 2050 in combination with population growth implicates

an extra 830 million people living in urban areas

� Other drivers, such as policy interventions and fuel availability, play a role as well, but

their impact is more uncertain. However, as opposed to enabling policies, fuel availability

and reliability of supply are potential barriers for the uptake of modern cookstoves

6.1.2 Business as usual falls short of energy access targets

It is clear from chapter 5 that the model results, which account for a business as usual develop-

ment of above mentioned drivers, predict a limited upgrade in cooking technologies. The share

of population cooking with wood or charcoal decreases by only 9%-pts. to 2050. Not only does

the share of people cooking with biomass change slowly over time, the strong population growth

strongly offsets efforts to increase access to modern cooking facilities. According to the model

results, 1.5 billion people in SSA still cook with biomass in 2050. The combination of the slow

progress and the low efficiency of biomass cookstoves compared to modern cooking fuels, makes

biomass the dominant cooking fuel to 2050. Wood and charcoal still represent 96% of cooking

energy demand in 2050.

This slow progress is striking and in contrast with the various ambitious targets found in liter-

ature (e.g. universal access to clean cooking by 2025 according to the New Deal on Energy for

Africa program, universal access by 2030 according to the 7th SDG of the UN (African Devel-

opment Bank Group, 2016; United Nations, 2017)). The results suggest that universal access

is not going to take place any time soon without strong and effective actions. A disruption is

required to come anywhere near the formulated targets. The foreseen economic development

and urbanization trend in sub-Saharan Africa will ease the efforts, but are clearly insufficient

on their own.

6.1.3 Effective policies are crucial to speed up the phase out of traditional

biomass cooking

Throughout the report, several passages pointed towards the importance of government policies

to speed up the use of improved and modern cooking technologies. Countries in sub-Saharan

Africa and other developing regions show that interventions are often a prerequisite to meet

energy access targets.

Lesotho and Senegal are two sub-Saharan countries that exemplify this statement. Senegal

is characterized by a substantial use of LPG as cooking fuel and illustrates that countries with-

out significant hydrocarbon production can successfully promote LPG use over biomass. In

the 1970s, the Senegalese government promoted the deployment of a small LPG cookstove, by

eliminating taxes and duties on LPG equipment and introducing a subsidy for small cylinders.

The goal of the policy was to reduce the pressure on forest cover. As a result, 75% of urban

households was cooking with LPG stoves by 2006. The coincidence of the increased LPG use

and the introduction of beneficial policies, combined with the high penetration as opposed to

other countries in the region, supports the statement that this situation was policy-driven. The

66

Section 6.1 Discussion of results

subsidy was removed in 2010 because of the high fiscal burden on the government budget, de-

spite savings in the national budget due to avoided expensed of reforestation. Moreover, the

sharp drop in LPG consumption and return to firewood and charcoal after the subsidy removal

in 2009-2010 learn that sub-Saharan households are very price sensitive (International Energy

Agency, 2014; Nanasta, 2014).

Indonesia is another example of a country that successfully employed policies to promote the

use of modern cooking fuels. The government of Indonesia has subsidized kerosene for decades

but employed a kerosene to LPG program in 2007. The incentive aimed to support 50 million

Indonesian households in making the switch from kerosene to LPG as primary cooking fuel.

The rationale for this move was the increasingly high burden on the nation’s budget as fuel

subsidies reached 18% of the total state expenditure in 2005. LPG was chosen as alternative

fuel because of the lower required subsidies, cleanliness, relatively limited infrastructure needs,

and success stories in neighboring countries (Malaysia, Thailand). As of 2009, more than 44

million conversion packages were provided to citizens, with only three areas to be converted

in 2011. The short timeline demonstrates that developing countries can transform the state of

affairs in a short period of time trough bold policies and a rapid development of infrastructure

such as LPG terminals and filling stations (Budya and Arofat, 2011).

The introduction of LPG in Brazil began much earlier, around 65 years ago. High subsidies,

price regulations, and the involvement of private investors enabled the replacement of wood

stoves by LPG stoves. As a result, LPG is now used by most households as the dominant

cooking fuel and infrastructure is well developed, even in rural areas. This is remarkable since

more than 80% of residential energy consumption was supplied by wood or charcoal 40 years

ago. The situation led to deforestation and increasing purchase costs for wood and charcoal.

The switch from biomass to LPG occurred in parallel with a rapid economic development and

strong urbanization (Coelho and Goldemberg, 2013).

The discussed country-examples demonstrate that governments do have an impact on the in-

creased diffusion of modern cooking fuels. Strong government policies can significantly accelerate

the switch away from traditional fuels towards modern cooking fuels. The existence of a number

of success stories should inspire sub-Saharan governments to follow the same direction and chal-

lenge the business as usual scenario, thereby accelerating the slow progress towards universal

access to clean cooking facilities. By learning and adopting best practices, and anticipating

encountered challenges, the probability of cost-effective policies could be increased. Across the

discussed government interventions, the strength of policies and legal basis, coordination be-

tween stakeholders, a clear business case, a form of subsidies, and real benefits for end users are

frequently recurring success factors. Therefore, it is crucial that governments pay attention to

the mentioned factors, while being aware of frequently encountered challenges (Federal Repub-

lic of Nigeria, 2016; International Energy Agency, 2014; Budya and Arofat, 2011; Coelho and

Goldemberg, 2013; GNESD, 2008; The World Bank, 2014):

� Lack of a long term vision for the fuel mix

� Inadequate formal monitoring of efforts and progress

� Bad targeting of subsidies with leakage to rich household as a result

67

Chapter 6. Discussion

� Limited business management capacity and financial constraints of local entrepreneurs

� Resistance from population or stakeholders because of asymmetry of information, lim-

ited awareness of benefits, poor fit with consumers’ cooking preferences, and conflicting

interests

� The prevalent perception among households that LPG cooking represents explosion and

safety risks due to accidents with LPG equipment

� Limited fuel availability due to a issues in the value chain and logistics, especially in slums

and rural areas (insufficient number of storage facilities, inadequate road network, lack of

refueling stations, etc).

� Inadequate supply of additional cooking equipment, such as cylinders and stoves, for

consumers

� High upfront stove costs associated with the acquisition of modern cookstoves

� A strong bureaucratic role of government

The complexity and the size of the challenges call for competent governments that are able

and possess the required resources to design and implement an agenda on clean cooking access.

History proves that sub-Saharan nations have often failed to demonstrate these capabilities.

Therefore, not only national governments should play a role, but international organizations

should assist and support through aid programs. Aid programs should encompasses financial

support and operational guidance. If governments succeed to copy success stories of other

countries, the business as usual evolution, presented in this report, gets significantly accelerated

and universal access to clean cooking facilities becomes reachable.

6.1.4 Specific areas of attention need to be addressed

It became clear in chapter 5 that the business as usual case predicts a relatively rapid shift

away from traditional biomass use in countries that are expected to undergo a strong economic

growth and urbanization. On the other side of the spectrum, a number of nations do not see

significant improvements in the cooking fuel mix. These lagging countries are set to continue to

rely on biomass for cooking, with the negative effects as result. Therefore, international aid and

donor organizations should make extra efforts in these countries, in order to strive for equality

across borders.

In chapter 5, the immense difference between the situation in cities and rural villages was

addressed, as displayed in figure 5.7. Not only the starting point, but also the speed of evolu-

tion varies between the two residences. As 67% of sub-Saharan population lived in rural villages

as of 2014, this group of people cannot be forgotten. Moreover, historical data clearly indicates

that improvements in access to clean cooking is much harder to achieve in rural areas because of

the abundance of free biomass, lower incomes, and lack of adequate infrastructure. Projections

show that still 52% of rural population cooks with traditional biomass in 2050 in the business

as usual case. This particularity makes that government policies are even more crucial in rural

villages to alleviate the limited resource availability and incentive to switch away from tradi-

tional biomass cooking.

68

Section 6.1 Discussion of results

Improved biomass cookstoves, being less expensive than LPG stoves, are the only way for-

ward for a large share of the rural population. These improved stoves present an upgrade

compared to three-stone fires because of their higher efficiency and lower emissions. In terms

of absolute energy demand, the demand for biomass is expected to increase with 46% in the

period 2016 to 2050. It is unclear to what extent this evolution will add extra pressure to the

biomass resources in sub-Saharan Africa, but it is reasonable to state that there exists a chance

that the situation becomes problematic. Improved biomass cookstoves are twice as efficient as

traditional biomass cookstoves and therefore have the possibility to counteract the ever-growing

biomass demand in sub-Saharan Africa. A number of international organizations recognize the

potential of improved biomass cookstoves to elevate living conditions in sub-Saharan Africa

and reduce the pressure on biomass stocks. The potential is already visible in the projections,

which show biomass demand flattening out in rural areas in East Africa as a result of a sub-

stantial diffusion of improved biomass stoves (see figure D.5). Across the region, the birth of

regional markets for improved stoves can be observed, enabled by rising incomes, a push by

manufacturers, and the introduction of innovative financing models for the poor. In the period

2009-2014, the number of Africa-based industrial and semi-industrial ICS manufacturers has

more than quadrupled. The new financing models, such as microlending, leasing schemes, and

carbon financing, are especially relevant for improved biomass cookstoves because the targeted

audience cannot afford to switch to modern fuels. However, in order to achieve high penetra-

tions of improved biomass stoves, similar barriers as the ones above should be addressed. When

zooming in on the charcoal demand within the biomass fuel category, which grows at an annual

rate of over 3%, the potential acceleration of deforestation and loss of biodiversity becomes

even more apparent, especially since charcoal is produced from forest wood in highly inefficient

charcoal kilns. Therefore, a primary concern of governments should lie in regulating the largely

underground charcoal industry to make it more sustainable.

6.1.5 Data availability should be a priority of government bodies and inter-

national organizations

A severe lack of data on the use of cooking fuels in developing regions hampers the development

of a good understanding of ongoing trends and their pace of evolution. When data is available,

the quality of it remains questionable because survey data is inherently marked by several

challenges. In addition, only a limited amount of countries have multiple historical data points,

which allow to track back the past evolution and link it with certain events. This situation

makes it challenging to identify strong model rules which can be used to make estimates to

the future, but is generally encountered in research on residential energy consumption. As a

result, work in the field of clean cooking in sub-Saharan Africa is by necessity based on strong

assumptions and is characterized by high levels of uncertainty. To alleviate these drawbacks,

a comprehensive framework for data collection should be in place. The surveys that currently

exist present a starting point, but are lacking consistency of results, granularity, and frequency.

69

Chapter 6. Discussion

6.2 Recommendation for future work

Based on the work done in this report, a recommendation for potential future research is for-

mulated. These suggestions mostly focus on deepening the analysis.

A number of research papers argue that the energy ladder model can only partially explain

cooking fuel choice. Households are said to use multiple cooking technologies in parallel to

counteract temporary shortages in supply or fuel price hikes (van der Kroon et al., 2013; Masera

et al., 2000). This phenomenon is called fuel stacking and is not accounted for in the presented

model due to a lack of quantitative data. As a result, households that use multiple cooking fuels

are now categorized in one fuel category, thereby overestimating the demand for one fuel and

underestimating the use of another fuel. More generally, a limited number of drivers, namely

economic growth, urbanization, population, and to a certain extent policies, is incorporated

in the model. Therefore, the quantitative inclusion of more determinants of fuel choice while

accounting for the fuel stacking theory could be an interesting addition to the model. However,

at present, the required survey data is not available.

The presented results are based on modeling rules that are defined for the whole geographical

region of sub-Saharan Africa, apart from some exemptions. A risk of this approach is the strong

generalization, where it is assumed that the various nations will evolve along a similar path.

This overlooking of country-specific factors could be canceled out by adding country-specific

tweaks. An example of such a tweak could be related to fuel availability, where landlocked

countries should be characterized by a lower LPG penetration than sea-facing countries because

of the cost of extending LPG import infrastructure. The author’s suggestion would be to start

with countries with a large population, thereby covering much of sub-Saharan population in a

time-efficient way. One step further is performing the analysis on certain regions of particular

countries, thereby maximizing the insight for a specific area, but only covering a small part of

the population without access to clean cooking.

Another addition lies in creating various scenarios to obtain a more comprehensive view of

future states. This report presented a perspective based on the historical track record of coun-

tries, but history might be a bad predictor of the future. To address this uncertainty, scenarios

could be added to the analysis. The author recommends to develop scenarios across various

dimensions. One important dimension is presented by government policies, which have proven

to be an important influence for the cooking fuel mix in developing countries. A view on the

evolution of the cooking fuel mix in sub-Saharan Africa in case of a broad and effective design

and implementation of government interventions, contains much value in combination with the

presented BAU scenario. The value lies in the assessment of effectiveness of efforts and associ-

ated investments to achieve universal access to clean cooking facilities, by comparing the results

with the BAU projections. When developing this view on policies, a holistic approach should

be taken. As clean cooking touches many aspects of society, the relevant policies cover energy

access (also electricity access), infrastructure development, fuel subsidies, forest preservation,

biomass stocks, technology standard regulations, etc. The size and complexity of this task are

a source of uncertainty, but the resulting discussions are crucial to many stakeholders. Further-

more, the author believes that policy scenarios should be performed on a country level because

70

Section 6.2 Recommendation for future work

nations with a same starting point, geographical location, or past evolution might express very

distinct views on the ideal future outlook, and therefore implement different policies. A second

important dimension for the scenarios is the use of pre-defined cooking technologies. This report

examines the uptake of technologies that are currently widely used or being introduced. As a

result, the model, and therefore the results, do not account for technological disruptions. An

additional analysis could be carried out, focusing on cooking technologies that have the potential

of significantly affecting the cooking fuel mix, but have a questionable feasibility or maturity.

Two examples of such technologies are solar cookers and biogas cookstoves. Other dimensions

for scenarios are the pace of GDP growth and urbanization. A topic closely related to scenarios

is a sensitivity analysis. The results of the model, namely the cooking fuel mix, is heavily de-

pendent on assumptions for the energy intensities of the cooking technologies. For this report,

values from International Energy Agency (2014) were employed. However, other reports (e.g.

(The World Bank, 2014)) suggest that the employed energy intensity for biomass-based cooking

technologies is rather high. As a result, the relative share of biomass in the cooking fuel mix

is potentially overestimated, and the dominance of biomass might be smaller than presented.

To address this issue, a sensitivity analysis might be performed, in which the model is run for

various values of the energy intensities. This allows to get a perspective on the range of results,

and therefore level of uncertainty.

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—————————————————————-

79

Appendix A

List of modeled countries

81

Appendix A. List of modeled countries Bibliography

Table A.1: List of modeled countries and their respective region in sub-Saharan Africa

Country Region in sub-Saharan Africa

Angola Southern Africa

Benin West Africa

Botswana Southern Africa

Burkina Faso West Africa

Burundi East Africa

Cameroon Central Africa

Central African Republic Central Africa

Chad Central Africa

Comoros Southern Africa

Congo, Dem. Rep. Central Africa

Congo, Rep. Central Africa

Cote d’Ivoire West Africa

Djibouti East Africa

Equatorial Guinea Central Africa

Eritrea East Africa

Ethiopia East Africa

Gabon Central Africa

Gambia West Africa

Ghana West Africa

Guinea West Africa

Guinea-Bissau West Africa

Kenya East Africa

Lesotho Southern Africa

Liberia West Africa

Madagascar Southern Africa

Malawi Southern Africa

Maldives East Africa

Mali West Africa

Mauritania West Africa

Mozambique Southern Africa

Namibia Southern Africa

Niger West Africa

Nigeria West Africa

Rwanda East Africa

Senegal West Africa

Sierra Leone West Africa

Somalia East Africa

South Africa Southern Africa

Sudan East Africa

Swaziland Southern Africa

Tanzania Southern Africa

Togo West Africa

Uganda East Africa

Zambia Southern Africa

Zimbabwe Southern Africa

82

Appendix B

Model results for fuel penetration

Figure B.1: Breakdown of urban popula-tion in East Africa by cooking fuel.

Figure B.2: Breakdown of urban popula-tion in West Africa by cooking fuel.

Figure B.3: Breakdown of urban popula-tion in Central Africa by cooking fuel.

Figure B.4: Breakdown of urban popula-tion in Southern Africa by cooking fuel.

83

Appendix B. Model results for fuel penetration Bibliography

Figure B.5: Breakdown of rural popula-tion in East Africa by cooking fuel.

Figure B.6: Breakdown of rural popula-tion in West Africa by cooking fuel.

Figure B.7: Breakdown of rural popula-tion in Central Africa by cooking fuel.

Figure B.8: Breakdown of rural popula-tion in Southern Africa by cooking fuel.

Table B.1: Relative fuel penetration in urban and rural areas in East Africa, as percent ofurban/rural population cooking with a particular fuel. Numbers might not add up due to

rounding

Urban Rural

2016 2030 2050 2016 2030 2050Wood 33.9 22.7 18.6 90.5 84.3 77.6

Charcoal 37.5 43.8 35.9 5.9 11.1 14.9

LPG/natural gas/biogas 12.2 23.6 35.8 1.1 2.5 5.5

Kerosene 9.4 2.7 1.0 0.5 0.1 0.0

Electricity 1.7 2.7 4.5 0.0 0.0 0.0

Coal 5.2 4.6 4.3 2.0 1.9 1.8

84

Appendix B. Model results for fuel penetration

Table B.2: Relative fuel penetration in urban and rural areas in West Africa, as percentof urban/rural population cooking with a particular fuel. Numbers might not add up due to

rounding

Urban Rural

2016 2030 2050 2016 2030 2050Wood 33.5 32.3 31.6 87.8 86.9 86.3

Charcoal 23.5 22.6 21.7 4.8 5.5 6.0

LPG/natural gas/biogas 17.5 29.5 43.6 2.1 3.3 4.6

Kerosene 24.4 13.6 0.0 5.2 4.1 2.9

Electricity 1.1 2.1 3.1 0.2 0.2 0.2

Coal 0.0 0.0 0.0 0.0 0.0 0.0

Table B.3: Relative fuel penetration in urban and rural areas in Central Africa, as percentof urban/rural population cooking with a particular fuel. Numbers might not add up due to

rounding

Urban Rural

2016 2030 2050 2016 2030 2050Wood 35.5 28.5 22.6 92.8 90.3 85.5

Charcoal 43.1 47.9 45.2 6.4 8.5 12.1

LPG/natural gas/biogas 15.1 18.5 26.0 0.6 1.0 2.3

Kerosene 2.3 0.3 0.3 0.3 0.2 0.1

Electricity 4.0 4.8 5.9 0.0 0.0 0.0

Coal 0.0 0.0 0.0 0.0 0.0 0.0

Table B.4: Relative fuel penetration in urban and rural areas in Southern Africa, as percentof urban/rural population cooking with a particular fuel. Numbers might not add up due to

rounding

Urban Rural

2016 2030 2050 2016 2030 2050Wood 15.3 12.9 11.4 80.3 78.7 75.6

Charcoal 27.1 31.3 32.9 8.0 10.6 13.4

LPG/natural gas/biogas 16.2 23.4 29.7 2.4 4.7 7.8

Kerosene 2.9 0.3 0.0 3.8 1.9 0.0

Electricity 38.6 32.1 26.0 5.4 4.2 3.1

Coal 0.0 0.0 0.0 0.0 0.0 0.0

85

Appendix C

Model results for technology

penetration

Figure C.1: Breakdown of urban popula-tion in East Africa by technology.

Figure C.2: Breakdown of urban popula-tion in West Africa by technology.

Figure C.3: Breakdown of urban popula-tion in Central Africa by technology.

Figure C.4: Breakdown of urban popula-tion in Southern Africa by technology.

87

Appendix C. Model results for technology penetration Bibliography

Figure C.5: Breakdown of rural popula-tion in East Africa by technology.

Figure C.6: Breakdown of rural popula-tion in West Africa by technology.

Figure C.7: Breakdown of rural popula-tion in Central Africa by technology.

Figure C.8: Breakdown of rural popula-tion in Southern Africa by technology.

Table C.1: Relative technology penetration in urban and rural areas in East Africa, as percentof urban/rural population cooking with a particular technology. Numbers might not add up

due to rounding

Urban Rural

2016 2030 2050 2016 2030 2050Traditional wood 28.9 11.7 0.7 79.5 48.7 11.6

Improved wood 5.0 11.0 17.8 11.0 35.5 66.0

Traditional charcoal 32.0 22.6 1.4 5.2 6.4 2.2

Improved charcoal 5.6 21.1 34.5 0.7 4.7 12.7

LPG/natural gas/biogas 12.2 23.6 35.8 1.1 2.5 5.5

Kerosene 9.4 2.7 1.0 0.5 0.1 0.0

Electricity 1.7 2.7 4.5 0.0 0.0 0.0

Coal 5.2 4.6 4.3 2.0 1.9 1.8

88

Appendix C. Model results for technology penetration

Table C.2: Relative technology penetration in urban and rural areas in West Africa, as percentof urban/rural population cooking with a particular technology. Numbers might not add up

due to rounding

Urban Rural

2016 2030 2050 2016 2030 2050Traditional wood 30.8 24.8 17.5 83.0 75.6 65.7

Improved wood 2.7 7.5 14.1 4.8 11.3 20.7

Traditional charcoal 21.6 17.3 12.0 4.5 4.8 4.6

Improved charcoal 1.9 5.2 9.7 0.3 0.7 1.4

LPG/natural gas/biogas 17.5 29.5 43.6 2.1 3.3 4.6

Kerosene 24.4 13.6 0.0 5.2 4.1 2.9

Electricity 1.1 2.1 3.1 0.2 0.2 0.2

Coal 0.0 0.0 0.0 0.0 0.0 0.0

Table C.3: Relative technology penetration in urban and rural areas in Central Africa, aspercent of urban/rural population cooking with a particular technology. Numbers might not

add up due to rounding

Urban Rural

2016 2030 2050 2016 2030 2050Traditional wood 33.7 25.4 18.4 88.8 84.0 76.2

Improved wood 1.8 3.0 4.2 4.0 6.3 9.3

Traditional charcoal 40.9 42.8 36.8 6.1 7.9 10.8

Improved charcoal 2.2 5.1 8.4 0.3 0.6 1.3

LPG/natural gas/biogas 15.1 18.5 26.0 0.6 1.0 2.3

Kerosene 2.3 0.3 0.3 0.3 0.2 0.1

Electricity 4.0 4.8 5.9 0.0 0.0 0.0

Coal 0.0 0.0 0.0 0.0 0.0 0.0

Table C.4: Relative technology penetration in urban and rural areas in Southern Africa, aspercent of urban/rural population cooking with a particular technology. Numbers might not

add up due to rounding

Urban Rural

2016 2030 2050 2016 2030 2050Traditional wood 13.8 8.8 4.2 75.9 66.8 53.8

Improved wood 1.5 4.1 7.2 4.5 11.9 21.8

Traditional charcoal 24.4 21.4 12.2 7.6 9.0 9.5

Improved charcoal 2.7 10.0 20.7 0.4 1.6 3.9

LPG/natural gas/biogas 16.2 23.4 29.7 2.4 4.7 7.8

Kerosene 2.9 0.3 0.0 3.8 1.9 0.0

Electricity 38.6 32.1 26.0 5.4 4.2 3.1

Coal 0.0 0.0 0.0 0.0 0.0 0.0

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Appendix D

Model results for fuel mix

Figure D.1: Breakdown of urban cookingfuel mix in East Africa.

Figure D.2: Breakdown of urban cookingfuel mix in West Africa.

Figure D.3: Breakdown of urban cookingfuel mix in Central Africa.

Figure D.4: Breakdown of urban cookingfuel mix in Southern Africa.

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Appendix D. Model results for fuel mix Bibliography

Figure D.5: Breakdown of rural cookingfuel mix in East Africa.

Figure D.6: Breakdown of rural cookingfuel mix in West Africa.

Figure D.7: Breakdown of rural cookingfuel mix in Central Africa.

Figure D.8: Breakdown of rural cookingfuel mix in Southern Africa.

Table D.1: Relative fuel mix in urban and rural areas in East Africa, as percent of urban/ruralcooking energy demand supplied by a particular fuel. Numbers might not add up due to

rounding

Urban Rural

2016 2030 2050 2016 2030 2050Wood 58.0 40.9 31.4 95.4 91.1 84.5

Charcoal 34.7 49.8 53.5 3.3 7.3 13.1

LPG/natural gas/biogas 1.1 2.9 6.5 0.1 0.2 0.6

Kerosene 1.1 0.4 0.2 0.0 0.0 0.0

Electricity 0.1 0.3 0.7 0.0 0.0 0.0

Coal 4.9 5.7 7.8 1.1 1.4 1.9

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Appendix D. Model results for fuel mix

Table D.2: Relative fuel mix in urban and rural areas in West Africa, as percent of urban/ruralcooking energy demand supplied by a particular fuel. Numbers might not add up due to

rounding

Urban Rural

2016 2030 2050 2016 2030 2050Wood 69.4 68.2 66.6 96.7 96.1 95.7

Charcoal 25.1 25.8 26.8 2.8 3.3 3.7

LPG/natural gas/biogas 1.9 3.6 6.2 0.1 0.2 0.3

Kerosene 3.5 2.2 0.0 0.4 0.3 0.2

Electricity 0.1 0.2 0.4 0.0 0.0 0.0

Coal 0.0 0.0 0.0 0.0 0.0 0.0

Table D.3: Relative fuel mix in urban and rural areas in Central Africa, as percent of ur-ban/rural cooking energy demand supplied by a particular fuel. Numbers might not add up

due to rounding

Urban Rural

2016 2030 2050 2016 2030 2050Wood 60.3 51.9 46.3 96.5 95.3 92.9

Charcoal 37.7 45.8 50.1 3.4 4.7 7.0

LPG/natural gas/biogas 1.4 1.9 3.0 0.0 0.1 0.1

Kerosene 0.3 0.1 0.1 0.0 0.0 0.0

Electricity 0.3 0.4 0.6 0.0 0.0 0.0

Coal 0.0 0.0 0.0 0.0 0.0 0.0

Table D.4: Relative fuel mix in urban and rural areas in Southern Africa, as percent ofurban/rural cooking energy demand supplied by a particular fuel. Numbers might not add up

due to rounding

Urban Rural

2016 2030 2050 2016 2030 2050Wood 48.4 39.5 31.5 94.2 92.4 89.9

Charcoal 41.1 49.7 56.5 4.9 6.7 9.2

LPG/natural gas/biogas 2.8 4.4 6.3 0.2 0.4 0.7

Kerosene 0.7 0.1 0.0 0.4 0.2 0.0

Electricity 7.1 6.3 5.7 0.4 0.3 0.3

Coal 0.0 0.0 0.0 0.0 0.0 0.0

93