THE NEXUS BETWEEN RENEWABLE ENERGY CONSUMPTION,

ENVIRONMENTAL POLLUTION, AND SOCIO-ECONOMIC VARIABLES IN

AFRICA: AN ECONOMETRIC APPROACH

A THESIS SUBMITTED TO THE BOARD OF GRADUATE PROGRAMS

OF

MIDDLE EAST TECHNICAL UNIVERSITY, NORTHERN CYPRUS CAMPUS

BY

PHEBE ASANTEWAA OWUSU

IN PARTIAL FULFILLMENT OF THE REQUIREMENTS

FOR

THE DEGREE OF MASTER OF SCIENCE

IN

SUSTAINABLE ENVIRONMENT AND ENERGY SYSTEMS

July, 2018

Approval of the Board of Graduate Programs

_______________________

“Prof. Dr. Gürkan Karakaş

“Chairperson”

“I certify that this thesis satisfies all the requirements as a thesis for the degree of

Master of Science.

______________________

Assist. Prof. Dr. Ceren İnce Derogar

Program Coordinator

This is to certify that we have read this thesis and that in our opinion, it is fully

adequate, in scope and quality, as a thesis for the degree of Master of Science.

___________________

Assoc. Prof. Dr. M. Fernanda Rivas Assist. Prof. Dr. Selim Jürgen Ergun

Co-Supervisor Supervisor

Examining Committee Members

Assist. Prof. Dr. Selim Jürgen Ergun Economics Prog.

METU NCC

Assoc. Prof. Dr. Murat Fahrioğlu Electrical and Electronics Engineering Prog.

METU NCC

Assoc. Prof. Dr. Bernard Musyck Business Administration Dept.

Frederick University

iii

I hereby declare that all information in this document has been obtained and

presented in accordance with academic rules and ethical conduct. I also declare that,

as required by these rules and conduct, I have fully cited and referenced all material

and results that are not original to this work.

Name, Last name: Phebe Asantewaa Owusu

Signature:

iv

ABSTRACT

“THE NEXUS BETWEEN RENEWABLE ENERGY CONSUMPTION,

ENVIRONMENTAL POLLUTION, AND SOCIO-ECONOMIC VARIABLES IN

AFRICA: AN ECONOMETRIC APPROACH

Phebe Asantewaa Owusu

M.Sc. Sustainable Environment and Energy Systems

Supervisor: Assist. Prof. Dr. Selim Jürgen Ergun

Co-Supervisor: Assoc. Prof. Dr. M. Fernanda Rivas

July 2018, 128 pages

This thesis explores the nexus between renewable energy consumption, the

environment, the society and the economy in Africa. Twenty-one African countries

are studied for the period of 1990 – 2013. This thesis tries to achieve two things:

first, to examine the validity of the environmental Kuznets curve (EKC) hypothesis

for Africa controlling for renewable energy usage, and second, to ascertain the

effects of some economic and societal variables on renewable energy consumption.

The results of the study rejected the EKC hypothesis with environmental pollution

(measured by carbon dioxide emissions per capita) as the dependent variable. When

environmental degradation (measured by ecological footprint vs biocapacity, EFC) is

the dependent variable, it invalidates the EKC hypothesis. GDP per capita appears to

decline environmental degradation in the long-term. The second analysis investigated

the effects of societal (Human Development Index, HDI, and Institutional quality)

and economic factors (Foreign Direct Investment, Trade openness and GDP per

v

capita) on renewable energy consumption. The findings showed that a higher HDI

reduces renewable energy consumption. A decrease is observed in renewable energy

usage with an increase in GDP per capita while foreign direct investment increases

its usage. Good institutional quality has no significant impact on renewable energy

usage. The findings are discussed in the light of the review of the energy mix of the

21 selected African countries.

Keywords: Environmental Kuznets Curve, Renewable energy consumption, Societal

factors, Economic variables

vi

ÖZ

AFRİKA'DA YENİLENEBİLİR ENERJİ TÜKETİMİ, ÇEVRE KİRLİLİĞİ VE

SOSYO-EKONOMİK DEĞİŞKENLER ARASINDAKİ İLİŞKİ: EKONOMETRİK

BİR YAKLAŞIM

Phebe Asantewaa Owusu

Yüksek Lisans, Sürdürülebilir Çevre ve Enerji Sistemleri Programı

Tez Yöneticisi: Dr. Öğr. Üy. Selim Jürgen Ergun

Eş Danışman: Doç. Dr. M. Fernanda Rivas

Temmuz 2018, 128 sayfa

Bu tez, Afrika’da sürdürülebilir enerji tüketimi, çevre, toplum ve ekonomi arasındaki

ilişkiyi araştırmaktadır. 1990-2013 arası dönem için yirmi bir Afrika ülkesi

incelenmektedir. Bu tez iki şeyi başarmayı amaçlamaktadır: öncelikle, çevresel

Kuznets eğrisi (EKC) hipotezinin Afrika için geçerliliğini yenilenebilir enerji

kullanımını kontrol ederek incelemek, ve ikincisi de, bazı iktisadi ve sosyal

değişkenlerin yenilenebilir enerji tüketimine etkilerini anlamaktır. Bu çalışmanın

sonuçları, bağımlı değişken çevresel kirlilik (kişi başına karbondioksit emisyonu

olarak ölçülmüştür) olduğunda EKC hipotezini reddetmiştir. Bağımlı değişken

çevresel bozulma (ekolojik ayak izine karşı biokapasite, EFC, olarak ölçülmüştür)

olarak alındığında, EKC hipotezi geçersiz kılınmaktadır. Kişi başına gayri safi yurtiçi

hasılanın (GSYH) uzun vadede çevresel bozulmayı azalttığı görülmektedir. İkinci

analiz, toplumsal (İnsani Gelişim İndeksi, HDI, ve Kurumsal kalite) ve iktisadi

(Doğrudan Yabancı Yatırım, Ticaret açıklığı, ve Kişi başına GSYH) faktörlerin

vii

yenilenebilir enerji tüketimine etkilerini incelemiştir. Bulgular daha yüksek bir HDI

değerinin yenilenebilir enerji tüketimini azalttığını göstermiştir. Yenilenebilir enerji

tüketiminde kişi başına GSYH’daki bir artışa bağlı olarak azalma gözlemlenirken,

doğrudan yabancı yatırım yenilenebilir enerji tüketimini arttırmaktadır. İyi bir

kurumsal kalitenin yenilenebilir enerji tüketimine anlamlı bir etkisi yoktur. Bulgular,

seçilmiş olan 21 Afrika ülkesinin enerji karışımının gözden geçirilmesi ışığında

tartışılmıştır.

Anahtar kelimeler: Çevresel Kuznets eğrisi, Yenilenebilir enerji tüketimi, Toplumsal

faktörler, İktisadi değişkenler

viii

DEDICATION

Praise be to God. This is dedicated to my husband, son, parents, the Owusu family,

and the Asumadu-Sarkodie family.

ix

ACKNOWLEDGEMENT

Honour, praise and adoration be unto the Most High LORD for His grace, blessings,

and wisdom through this graduate study.

I wish to express my profound gratitude to my Supervisor, Assist. Prof. Dr. Selim

Jurgen Ergun and Co-Supervisor, Assoc. Prof. Dr. Fernanda M. Rivas for their

guidance, support and diverse contributions throughout this dissertation write-up.

I am grateful to the Coordinator, and the past two coordinators of the SEES program,

the Graduate board of, Middle East Technical University, Northern Cyprus Campus,

the Coordinator of Chemistry Department, Assist. Prof. Dr. Mustafa Erkut Ozser, my

previous adviser, Dr. Umut Oguz, and the members of the Chemistry Department

(Assist. Prof. Dr. Erdal Onurhan, Assist. Prof. Dr. Cemal Albayrak, and Dr. Banu

Kandemir) for the opportunity to pursue my M.Sc. in the Sustainable Environment

and Energy Systems program as well as their encouragement and assistance.

Sincere thanks to my colleague Teaching Assistants in the Chemistry Laboratory

(Kemal, Hope, Yashfeen, and Sana) and CyFES METU-NCC Bible Club members.

Finally, I gratefully acknowledge my husband, Samuel Asumadu-Sarkodie, my late

Dad, Mr. Obed Osei-Owusu, mum, Mrs. Charity Achiaa Marfo, in-laws, Mr. & Mrs.

Asumadu-Sarkodie, Stephen Asumadu-Sarkodie, siblings (Pepertual, Jesse, and

Edward), my son Supernatural Emmanuel Asumadu-Sarkodie and all other family

members for their love, encouragement and prayers.

x

TABLE OF CONTENTS

ABSTRACT ................................................................................................................ iv

ÖZ ................................................................................................................................ vi

DEDICATION .......................................................................................................... viii

ACKNOWLEDGEMENT .......................................................................................... ix

TABLE OF CONTENTS ............................................................................................. x

LIST OF TABLES .................................................................................................... xiii

LIST OF FIGURES .................................................................................................... xv

1.0 INTRODUCTION ................................................................................................ 16

1.1 Background Statement ..................................................................................... 16

1.2 Africa and Renewable Energy .......................................................................... 18

1.3 Problem Statement ........................................................................................... 19

1.4 Goal and Objectives ......................................................................................... 20

1.5 Contribution and Organization of the Study .................................................... 20

2.0 BACKGROUND INFORMATION AND EVOLUTION OF ENERGY

SOURCES .................................................................................................................. 22

2.1 Energy Sources Alternatives ............................................................................ 22

2.1.1 Why should fossil fuels be replaced? ........................................................ 22

2.1.2 Possible Sustainable Substitute Sources of Energy ................................... 23

2.1.3 Renewable Technologies ........................................................................... 24

2.2 The 21 Countries and their Renewable Energy Development. ........................ 31

2.2.1 Background ............................................................................................... 31

2.2.2 Northern Africa ......................................................................................... 32

2.2.3 Western Africa .......................................................................................... 34

xi

2.2.4 Eastern Africa............................................................................................ 39

2.2.5 Central Africa ............................................................................................ 43

2.2.6 Southern Africa ......................................................................................... 44

3.0 LITERATURE REVIEW .................................................................................... 46

3.1 Review of some Econometric Studies and findings for Africa ........................ 46

4.0 METHOD OF THE STUDY ............................................................................... 53

4.1 Data and Countries Selection ........................................................................... 53

4.2 Methods ............................................................................................................ 57

4.2.1 EKC hypothesis and the U-shaped Relationship Test............................... 60

4.2.2 The Westerlund Error-Correction Model (WECM) .................................. 61

4.2.3 Causality Test for Panel Data .................................................................... 63

4.2.4 Fixed Effect Estimators and Variance Components Model (random

effects) ................................................................................................................ 64

5.0 EMPIRICAL ANALYSIS AND RESULTS ....................................................... 66

5.1 Descriptive Analysis of Data ........................................................................... 66

5.2 Unit Root Test .................................................................................................. 72

5.3 Utest Estimation ............................................................................................... 74

5.4 Cointegration Analysis (WECM) ..................................................................... 75

5.5 Panel Causality Estimation .............................................................................. 82

5.6 Estimation of Fixed and Random Effects ........................................................ 83

6.0 DISCUSSION ...................................................................................................... 86

7.0 CONCLUSION .................................................................................................... 90

REFERENCES ........................................................................................................... 92

APPENDICES ......................................................................................................... 106

Appendix A: Descriptive Statistics of Individual Countries ................................ 106

xii

Appendix B: Utest Analysis for individual countries .......................................... 112

Appendix C: WECM Panel Cointegration Tests by Country for Environmental

Pollution ............................................................................................................... 114

Appendix D: WECM Panel Cointegration Tests by Country for Environmental

Degradation .......................................................................................................... 121

Appendix E: Pearson’s Correlation Coefficient Results ...................................... 128

xiii

LIST OF TABLES

Table 1-1. Estimated technical potential of RES in Africa [12-14] ……………...…19

Table 2-1. Renewable Energy Sources and their usage form [28] …………………24

Table 2-2. Primary Energy Consumption by fuel source in 2016 ………………….33

Table 2-3. RES potentials in Benin …………………………………………………35

Table 2-4. RES potential in Sierra Leone and installed capacity [64] ……………...38

Table 2-5. Potential of RES in Cameroon ………………………………………….43

Table 2-6. Swaziland’s RES potentials by source ………………………………….45

Table 3-1. Summary of Literature ………………………………………………….51

Table 4-1. Description of Variables ………………………………………………...54

Table 4-2. Country Description …………………………………………………….55

Table 5-1. All countries Descriptive Statistics ……………………………………..67

Table 5-2. Northern Africa Descriptive Statistics …………………………………..68

Table 5-3. Western Africa Descriptive Statistics …………………………………...69

Table 5-4. Eastern Africa Descriptive Statistics ……………………………………70

Table 5-5. Central Africa Descriptive Statistics (Cameroon) ………………………70

Table 5-6. Southern Africa Descriptive Statistics …………………………………..71

Table 5-7. Unit Root Test …………………………………………………………..73

Table 5-8. Utest Analysis for Africa ………………………………………………..74

Table 5-9. Nexus of Environmental Pollution (CO2Epc), Renewable Energy

Consumption, and GDP growth …………………………………………………….78

Table 5-10. Nexus of Environmental Degradation (EFC), Renewable Energy

Consumption, and GDP growth …………………………………………………….79

Table 5-11. Granger non-causality panel test results ……………………………….82

Table 5-12. Robust Fixed-effects (within) regression and Random-effects GLS

regression of societal effects ………………………………………………………..84

Table 5-13. Robust Fixed-effects (within) regression and Random-effects GLS

regression of economic status effects …………………………………………...….85

xiv

Table 5-14. Robust Fixed-effects (within) regression and Random-effects GLS

regression of all independent variables except HDI …………………………….….85

xv

LIST OF FIGURES

Figure 1-1. Carbon-dioxide Emissions from Fuel Combustion, 1971 – 2015 [5].….17

Figure 2-1. Renewable energy share estimation of total global final energy

consumption, 2015 [29] …………………………………………………………….25

Figure 2-2. Generation of electricity by thermal solar irradiation [35,36] …...…….27

Figure 2-3. World energy installed capacity by continent, 2016……………...…….28

Figure 2-4. The processes for the conversion of biomass to fuels, chemicals and heat

from various application [40] ………………………………………………...……..30

Figure 4-1. Diagrammatic Representation of the Model Estimation Process ………59

16

1.0 INTRODUCTION

1.1 Background Statement

The commercialization of coal in the 1750s, which was first extracted near

Richmond, Virginia, US, soon made it the preferred fuel for steam engines because

when combusted, it produced more energy (heat) than the firewood and charcoal

(biomass-based fuel) of equivalent quantities. During that era, the discovery of coal

was seen as a ‘cleaner’ option compared to biomass fuel [1]. After coal’s discovery,

Crude Oil and Natural gas were also discovered and are still used today. These forms

of energy are termed as fossil fuels. These discoveries and their uneven distribution

around the globe gave rise to concerns as the price of petroleum in the 1970s was

elevated by the OPEC (Organization of Petroleum Exporting Countries) [1]. In later

years, it was realised that the use of fossil energy was degrading and polluting our

environment.

With the world’s growing need for energy to support and improve living conditions,

there is the need to provide ‘clean energy’ sources, which will not destroy the

environment. Considering the above statement and the population increase, the

increased rate in the continual use of fossil fuel (coal, oil, and gas) has brought forth

several challenges namely: depletion of fossil fuel reserves; global warming

(greenhouse gas emissions – CO2, CH4, NO2, NO, etc.) and climate change;

geopolitical and military conflicts; continual fuel price fluctuations and the increased

occurrence of some health conditions (Cardiovascular diseases, etc.) associated with

emissions. Also, it should not be forgotten that the earth in its natural form cannot

change, and if destroyed cannot be repaired [2-4]. Figure 1-1 depicts the carbon

dioxide emissions from fuel combustion for the period 1971-2015 by the

Organisation for Economic Co-operation and Development (OECD).

17

Figure 1-1. Carbon-dioxide Emissions from Fuel Combustion, 1971-2015 [5].

An unsustainable situation was created by some of the above problems. Renewable

energy is the most probable alternative to the growing challenges. Several

researchers have found evidence that renewable sources of energy (Solar, Wind,

Hydropower, Geothermal and Bioenergy) can help address the challenges we are

encountered with [2, 6]. According to the World Bank data on energy consumption

from Renewable Energy Sources (RES) by percentage, the consumption by the end

of 2014 was 18.9% which was not so two decades ago (less than 16%), whereas in

2016 the power generating capacity from renewable sources saw its highest annual

increase (161 GW estimated capacity added) [7, 8]. Even with these opportunities,

there are challenges that tend to hinder the sustainability of renewable energy

sources. These are as follows: “market failures, lack of information, access to raw

materials for future renewable resource deployment, and most importantly our

(humans) way of utilizing energy in an inefficient way” [9]. Despite the growing

CO

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18

modern [using advanced technologies such as solar photovoltaic plants and not the

traditional form of usage such as the burning of wood-Biomass] renewable energy

usage in the world, most African countries are lagging. The percentage of renewable

energy consumption in this continent is averaging around 62% of the total final

energy consumption, with the majority coming from hydropower and traditional

combustion of biomass.

1.2 Africa and Renewable Energy

Most African countries lack full access to electricity especially in the rural areas of

the Sub-Sahara region with 37.4% of the population having access in 2015. With the

increasing population (annual growth of 2.7% in the Sub-Sahara region and 1.8% in

the Northern region in 2016), fuel price fluctuations and lack of financial resources

in most countries in Africa, renewable energy is a potential way out [10]. Africa as a

continent is endowed with various sources of renewable energy. This potential has

not been fully harnessed mainly due to lack of economic capital (research and

development cost) and the institutional framework of the countries [11]. The

renewable energy sources (RES) that are abundant and can be harnessed to its

maximum potential in Africa are solar energy, hydropower, bio-energy (wood,

biogas, and biofuel), wind energy and geothermal sources. Table 1-1 shows the

estimated potential of RES in Africa.

19

Table 1-1. Estimated technical potential of RES in Africa [12-14]

Energy Source Estimated Potential

Hydro 1750 TWh

Geothermal 9000 MW

Solar 80% of continent receiving about 2000

kWh/m2 per year

Wind Onshore: North Africa – 11963 TWh/yr.,

South Africa region – 6971 TWh/yr., East

Africa – 6694 TWh/yr., West Africa – 5152

TWh/yr. and Central Africa – 239 TWh/yr.

Biomass Energy Crops: 0 PJ/yr. – 13900 PJ/yr.

(current period) and 76508 PJ/yr. – 131800

PJ/yr. (forecast 2050)

Forestry biomass: 0 PJ/yr. (current period) –

75000 PJ/yr. by 2100

Residue and Waste: 1000 PJ/yr. – 3000 PJ/yr.

(current period) and 2190 PJ/yr. – 20000

PJ/yr. (forecast)

1.3 Problem Statement

Kraft and Kraft’s [15] empirical study of the nexus between “gross energy

consumption” and “gross national product” using econometrics and the inferences

from their analysis showing a bi-directional causality spear-headed various studies in

this field. Research involving the study of energy consumption (renewable and non-

renewable), trade, carbon dioxide emissions and economic growth relationship has

been growing for the past decades with varying models and outcomes across various

time periods [16-21].

20

The Sustainable Development Goal (SDG) 7, which aims to obtain affordable and

clean energy for all, is one goal that if careful attention and research are not invested

into it, will affect the attainment of a sustainably developed world [22]. The SDG 7

can be linked in one way or another to economic sustainability, environmental

sustainability and social sustainability. Most studies have focused on the relationship

between energy consumption (renewable and non-renewable) and economic growth,

however, the social and environmental aspects of sustainability have received little

attention in Africa.

1.4 Goal and Objectives

Against the backdrop, this thesis attempts to investigate the major drivers of

renewable energy consumption in Africa. It also aims to add to the findings and

discussions regarding the environmental Kuznets curve hypothesis while controlling

for total renewable energy consumption and to understand the effects of some

‘societal’ and economic variables using econometric methods.

1.5 Contribution and Organization of the Study

This thesis contributes to the existing literature by first, studying on a theme that is of

scientific significance, the EKC hypothesis controlling for renewable energy

consumption role in environmental protection in Africa. Secondly, the study

incorporates the concept of sustainable development to the research hypothesis, thus,

considering societal and economic factors effects on renewable energy consumption.

Thirdly, this thesis attempts to find ample representation from the five regions of the

continent with the available data. Finally, based on the empirical findings, the thesis

proposes some policy recommendations which will be relevant to policymakers and

stakeholders in environmental sustainability and energy management.

This thesis is organized as follows. The next section presents some background

information and describes the evolution of energy sources technologies and presents

the energy consumption mix in each of the countries studied. Section 3 reviews

21

recent literature regarding EKC hypothesis study developments in Africa. Section 4

describes the data and methodology used, which is followed by the empirical

analysis and results (section 5), discussion (section 6), and conclusion (section 7).

22

2.0 BACKGROUND INFORMATION AND EVOLUTION OF ENERGY

SOURCES

2.1 Energy Sources Alternatives

2.1.1 Why should fossil fuels be replaced?

Humankind’s curiosity and need for survival led humanity to various discoveries,

which were energy driven. Gaining energy from food (nutrients) brought man to

finding ways to make some inedible raw foods edible and gaining access to food

easily. This led man to find energy (burning wood) to cook food and for production.

After getting energy for the body, humanity wanted more to improve their standard

of living. This led to new discoveries of different and ‘easy’ energy generation

sources.

Fossil fuels are naturally occurring hydrocarbons, obtained from the environment

because of the accumulation of decay matter of living organisms over millions of

years. The first of the fossil energy discovered was ‘black stone’ (coal), in the

thirteenth century, while natural gas, petroleum and oil shale were discovered later

(nineteenth century commercially) [23, 24]. The combustion of the fossil fuel is

mainly used in the transportation, industrial and electricity generation sectors [23-

25].

The consumption of fossil energy in a steady manner (such that, its formation and

usage cancel-out) will not cause much damage to the environment as compared to an

increased rate in usage. As at the end of 1947, there was a cumulative production of

81 billion metric tons of coal. The fact that half of this was consumed within a 27-

year span, brought concerns about the damage it was causing to the environment

[23]. According to Thomas Malthus’ principle, an exponential increase of population

leads to poverty. On the other hand, Adam Smith argues that individuals or societies

act collectively to increase wealth [26]. These principles affirm the above statement

that humankind’s nature is causing harm to the environment.

23

The increase in population coupled with the exponential increase in use of fossil

energy brought rise to concerns about global warming and climate change [27]. This

is majorly due to anthropogenic activities and the combustion of fossil fuel which

releases greenhouse gases into the atmosphere. In addition, since fossil fuels are

formed over time (centuries) its extinction without an alternative will be detrimental

to the survival of human race.

With the increasing temperature of the atmosphere and climate change, the effect on

the environment can be seen in the loss of biodiversity due to excessive flooding,

storms, some volcano eruptions, landslides, etc. This has caused the extinction of

some useful organisms and plant species.

Even though fossil energy has improved humanity’s standard of living and is still

doing so, the need to find other alternative sources of energy is essential considering

the above concerns.

2.1.2 Possible Sustainable Substitute Sources of Energy

The attempt to make energy supply secure and decrease the contribution of energy to

climate change are mostly known as the two “over-riding challenges” of the energy

sector in search of a sustainable future [2-4]. Secure energy supply is vital, and we

should not forget that its form and usage are sustainable. This gave rise to various

researches that lead to the return to Renewable Energy Resources (RES), which are

sustainable sources of energy. ‘Return’ because humankind was using bio-energy

(for cooking and warming) before the discovery of fossil energy.

One of the first reasons in favour of shifting from fossil fuels to renewables came in

the early 1970s when the Oil Producing and Exporting Countries (OPEC) had

suddenly and unilaterally increased the price of crude petroleum and it was at this

same period that the world was becoming increasingly aware of the environmental

pollution [1]. Renewable energy in this last decade has become an eye-catching and

developing sector. The main renewable energy sources are hydropower, geothermal

24

energy, solar energy, ocean (tide and wave) energy, wind energy and bioenergy: they

can restock themselves naturally without its depletion. Table 2-1 shows the main

usage forms and conversion of the above-mentioned RES.

Table 2-1. “Renewable Energy Sources and their usage form ” [28].

Energy Sources Energy Conversion and Usage Options

Hydropower Power generation

Modern biomass Heat and power generation, pyrolysis,

gasification, digestion

Geothermal Urban heating, power generation,

hydrothermal, hot dry rock

Solar Solar home systems, solar dryers, solar

cookers

Direct solar Photovoltaic, thermal power generation,

water heaters

Wind Power generation, wind generators,

windmills, water pump

Wave and Tide Numerous designs, barrage, tidal stream

2.1.3 Renewable Technologies

Renewables are sources of energy drawn from nature. Since energy from nature is in

persistent flow, these sources of energy can be reliable, provided its technologies are

well developed. There is continuous research to improve them with changing trends.

Renewable energy consumption has been improving for the past decade. Figure 2-1

depicts the renewable energy share estimation of total global final energy

consumption as of the end of 2015. The renewables consist of Modern Renewables

(MR) and Traditional Biomass being 10.2% and 9.1% respectively. The MR are

25

further categorized: biomass/geothermal/solar heat 4.2%, hydropower 3.6%,

wind/solar/biomass/geothermal power 1.6% and biofuel for transport 0.8% [29].

Figure 2-1. Renewable energy share estimation of total global final energy

consumption, 2015 [29].

In what follows, a brief description of the technological development and usage of

the various renewable energy sources is presented:

A. Wind Energy

Wind energy is an essential source of energy harnessed from moving air creating

kinetic energy. Wind exists all around us on planet Earth, in some areas with

considerable energy density. The utilization of wind power dates back to several

centuries ago, when it was used to pump water, to sailboat or to grind grain using

mechanical power [2, 30-32]. As discussed earlier, the discovery of fossil energy saw

a shift to its use. With the return to renewables, the focus of wind energy moved

from mechanical to electrical energy.

26

At the end of the 19th century, the first wind turbines were developed for electricity

generation by the Dane Poul LaCour in 1891. The conversion of wind into electrical

energy is achieved with the help of turbines [30]. This energy is the second-ranked in

terms of installed capacity after hydroelectric energy among the RES. This

technology has little or no impact on the environment. The Levelised Cost of

Electricity (LCOE) of wind energy is lower in monetary terms than it used to be a

decade ago. This is due to the improvement of technology installation. This cost

varies depending on the countries’ economic structures [28, 30].

B. Sun’s Energy Source

This source of energy can be termed as the ‘ultimate’ of all the RES because some of

the other renewable energy sources draw their energy from the sun (indirectly).

Examples of these energy sources include: wind, ocean thermal and bioenergy. These

RES uses the sun’s energy in various forms (such as Photosynthesis for bioenergy)

after it has been absorbed onto the surface of the earth and convert it to the other

forms mentioned above to produce the energy.

The energy harnessed from solar irradiance to generate electricity is obtained using

Photovoltaic (PV) panels, direct thermal use and Concentrating Solar Power (CSP)

panels to produce thermal energy. According to the World Energy Council (WEC,

2007: 381) “the total energy from solar radiation falling on the earth was more than

7500 times the World’s total annual primary energy consumption of 450 EJ”. The

most remote areas in developing countries where there is no connection to the

general transmission electricity grid, have the potential of having access to electricity

via solar power [2, 33, 34].

The electricity generated by thermal solar irradiation is obtained through a systemic

process device (see Figure 2-2) [35-37]. Photovoltaic panels ensure the direct

conversion of the sun’s energy conventionally to generate electricity. CSP cells

consist of a solar “block” and “conventional power block”, absorbing energy from

27

the sun, converting it into heat to generate electric power using gas or steam turbines.

Power tower (Central receiver), Dish-Stirling systems and Parabolic trough

technologies are the commercialised developed CSP cells known today [28, 38]. The

cost associated with solar energy technology is still falling with continual research

into materials and technologies that can help.

Figure 2-2. Generation of electricity by thermal solar irradiation [35, 36]

C. Hydro-energy Source

Water is an essential source of life. Its use as an energy source has proven to be

beneficial and so far, among the RES technologies, it ranks first in-terms of installed

capacity (1.21 TW, in 2016. Source: World Energy Council).

This energy source harnesses energy from moving water flowing from an upper

gradient level to a lower gradient level, by the principle of acceleration due to

gravity. Hydro-energy projects include, ‘impoundment hydropower’ (Dam project

28

with reservoirs), run-of-river or diversion/in-stream project, and ‘pumped-storage’.

Hydro’s source of energy technology technically, is more advanced than the other

RES. Electrical energy obtained from water is the result of water flowing through

turbines to fuel generators [2, 39, 40]. In 2016, East Asia had the largest installed

capacity of hydropower. Figure 2-3 depicts the world’s installed capacity of

hydropower by continent.

Figure 2-3. World energy installed capacity by continent, 2016. Source: World

Energy Council (2016).

D. Geo-heat Source Energy

Geo-heat energy source is drawn from the earth’s interior core in the form of heat

energy occurring naturally as steam or hot water. This energy source had been

harnessed from ‘the beginning of recorded history’ for cooking and bathing.

31.7%

24.4%

16.1%

13.3%

6%

4.8%

1.9% 1.7%

East Asia

Europe

North America

Latin America & The Caribbean

South & Central Asia

South East Asia & Pacific

Africa

Middle East & North Africa

(31.7%)

(24.4%)

(16.1%)(13.3%)

(6%)

(4.8%)

(1.9%)

(1.7%)

29

Geo-heat energy can be obtained from various places around the globe but this

source is unevenly distributed. Even if places of concentrated depths are depleted it

is restored rapidly. This energy heat gradient averages about 30°C/km [40, 41]. In the

early years of the nineteenth century, this source of energy began its use in the

industrial sector. The first electrical energy generated from geo-heat energy was in

1902 at Larderello, Italy. There are two conditions to be met for viable geo-heat

energy, which are the accessibility of the heat source and sufficiency of reservoir

productivity [40].

This energy is mined from in-depth crust of the earth reservoirs using mainly wells

and other means. The reservoirs that are naturally adequately hot and permeable are

called “hydrothermal reservoirs”, however, reservoirs that are adequately hot but are

upgraded with hydraulic stimulation are called “enhanced geothermal systems”

(ESG). Once this energy is mined to the surface, fluids of various temperatures can

be used to generate electricity and for other purposes that require the use of heat

energy [2].

E. Bio-energy source

In reference to the name of this energy source ‘bio’, it is obtained from biological

components. This is an essential energy source because it is the only renewable

energy that can provide liquid fuel for the transport sector. Apart from the transport

sector, its energy is also used for cooking, heating and the generation of electrical

energy. Energy from biological components can be obtained from a wide range of

components which includes: some edible crops (sugarcane, soyabean, rape seed,

corn), agricultural waste and residue, forest residue/wood shrubs and animal

husbandry (cow dung). Recently, Jatropha curcas and Algae sources are being

exploited [2, 34, 42, 43]. Research in this RES keeps on developing over the years.

Traditional use of this energy source if not used in a controlled manner, causes

various adverse health conditions (such as cardiovascular and respiratory diseases)

30

mostly affecting women and children [40]. Figure 2-4 shows the various conversion

processes of biological components to either fuel, chemicals or heat.

Re

Figure 2-4. The processes for the conversion of biomass to fuels, chemicals and heat

from various applications [40]. Source: OTA (1980).

Biomass

Airblown gasification

Pyrolysis or oxygen-

blown gasification

Hydrolysis and

fermentation to

ethanol

Anaerobic digestion

Process heat (e.g., crop dying), Steam,

Cooking, Electricity, Space and water

heating, Stationary engines

Direct

combustion

Natural gas pipeline

Process heat, Steam, Cooking,

Electricity, Space and water heating

Regional fuel gas pipeline

Mobile engines, Stationary engines,

Gas turbines, Process heat, Steam,

Cooking, Space and water heating

Methanol

synthesis

Electricity, Space and water heating,

Process heat, Steam, Mobile

engines, Stationary engines

Remove

CO2

31

2.2 The 21 Countries and their Renewable Energy Development.

2.2.1 Background

Africa as a continent consists of five regions: North, West, East, Central and South.

It consists of fifty-four self-governing countries. Each region is endowed with great

geographical settings and rich cultures (language, visual arts and indigenous

traditions). Africa geographically is the second largest continent and it is surrounded

by the Mediterranean Sea and the Red Sea in the North, the Indian Ocean to the

East/South and South Atlantic Ocean to the West/South. African countries, except

Ethiopia, suffering slave trade and colonialism had to fight for their independence.

Republic of South Africa (Union of South Africa) was the first African country to

gain independence in 1931.

With Africa having the largest river in the world, river ‘Nile’ in Egypt, which can

provide hydropower and the largest desert, ‘Sahara’, with solar energy potential,

there is no doubt that its climatic conditions will vary across regions. Africa has a

vast range of climatic conditions and they include tropical rainforest mostly in the

Western, Central and Eastern regions; Humid sub-tropical in South-west; the

Mediterranean in South-east and North-west and Desert and Semi-desert climate in

the Sahara and Sub-Sahara region in the North. The continent is still developing

while some countries are developed and others still in poverty. The climatic

conditions that were known in the 1700s are not what we see today. Research papers

[44-47] have indicated that climate change is already present in Africa. This can be

observed in the extreme weather patterns of prolonged drought and an increase in

rainfall intensity causing floods, some regions becoming hotter destroying

agricultural produce and livelihood. These changes can be associated with the

increase in pollution, the use of fossil energy so that most parts can gain access to

electricity, and deforestation for development. While trying to develop the countries,

the ways to attain this is also destroying the environment and in turn, reduces

economic growth and development they are trying to achieve. Hence, with the

abundance of renewable energy resources, most countries in Africa are trying to

32

develop policies and plans that will develop them in a sustainable manner [11]. The

following subsections will look at the development of renewable energy

consumption in the twenty-one countries that are being considered in this paper. The

countries are: Benin, Burundi, Botswana, Cameroon, Egypt, Ghana, Guinea, Kenya,

Malawi, Mali, Mauritius, Morocco, Rwanda, Senegal, Sierra Leone, South Africa,

Swaziland, Tanzania, Togo, Tunisia, and Uganda.

2.2.2 Northern Africa

2.2.2.1 Egypt

With Egypt bridging the gap between North Africa and the Middle East, it produces

a significant amount of oil and natural gas (second largest after Algeria). Over 80%

of Egypt’s primary energy consumption is from fossil energy (oil, natural gas and

coal) while the remainder comprises of hydroelectricity and other renewables [48,

49]. Table 2-2 depicts the primary energy consumption of Egypt by fuel source in

2016. Because of the location of Egypt, the most significant RES is the ‘sun’ (solar

energy) and wind energy.

In 1986, the New and Renewable Energy Authority (NREA) was established to

oversee and promote the use of renewable energy in the country so that their fossil

energy use can be reduced to protect the environment. Over the years, NREA has

kept on improving the Research and Development in the Solar and Wind potential

energy sectors with various future projects planned. Egypt’s direct solar radiation

potential ranges between 1970 – 2600 kWh/m2 annually, with an average of 9-11

hours of sunlight daily [50]. According to Shata and Hanitsch [51], the best three

locations for wind stations in Egypt are Sidi Barrani, Mersa Matruh, and EI Dabaa.

These stations are expected to give off an annual mean of greater than 5.0 m/s wind

speed along Egypt’s Mediterranean coast. Since the enactment of NREA it had seen

its fair share of challenges and opportunities and now it plays a significant role in the

economy and energy security of the country.

33

Table 2-2. Primary Energy Consumption by fuel source in 2016.

Oil Natural

gas

Coal Nuclear

energy

Hydro-

electricity

Renewables Total

40.6

(44.6%)

46.1

(50.7%)

0.4

(0.4%)

- 3.2 (3.5%) 0.6 (0.7%) 91.0

Source: BP Statistical Review of World Energy, 2017

Note: Oil measured in million tonnes. Other fuels; million tonnes of oil equivalent

2.2.2.2 Tunisia

Even though there had been RES usage in Tunisia for some decades now, up until

January 2016, there was no specific regulatory body (Ministry of Industry, Energy

and Mines) that solely took renewable energy up. The new Mining, Energy and

Renewables sector, will help with the focus and aid in the reduction of the import of

fossil energy (over 90%) which began in 2001 [52, 53].

There are various renewable energy potentials in Tunisia and the noteworthy one of

them is solar energy, with annual radiation rates between 1800 – 2600 kWh/m2

approximately. In 2015, Tunisia saw a 3% of renewable energy addition from the

two wind farms in the country to the connected-grid generation capacity. Currently,

the TuNur project underway aims to create Tunisia’s first large-scale photovoltaic

farm and 10 MW Concentrated Solar Power Plant. Despite these gradual

achievements, there are still some regulatory issues that are yet to be addressed to

enable the new ministry to attain its goals [54].

34

2.2.2.3 Morocco

Morocco’s position in the South-Mediterranean region gives the country significant

solar and wind energy potential (1300 MW estimated power capacity, 2012). Also,

the country has hydro-power potential. In 2008, the Moroccan government

implemented a National Renewable Energy and Efficiency Plan. This plan’s aim is to

increase renewable energy shares in the fossil-dominated energy mix to 42% (solar –

14%, wind – 14% and hydro – 14% installed capacity) at the end of 2020 and 52%

by 2030. It also aims to reduce its energy consumption by 12% by 2020 to improve

energy efficiency [14, 55-57]. By far in North Africa and Africa at large, Morocco is

leading in the implementation and adoption of renewable energy plans for

sustainable development.

2.2.3 Western Africa

2.2.3.1 Benin

The Republic of Benin is located between Togo to the West and Nigeria to the East,

and it also borders with Burkina Faso and Niger. The country is endowed with

various renewable energy potentials but the most significant of them is hydropower

and bio-energy followed by solar and wind. The potential for geothermal is not

empirically studied. Table 2-3 elucidates the various RES in Benin and their

potentials. In 2015 there was a 5 GWh and 14 GWh gross generation of electricity

from solar photovoltaic and hydro, respectively. Also, there was a final consumption

of 60584 TJ from primary solid biofuels [58].

35

Table 2-3. RES potentials in Benin

RES Potential Estimate

Solar 3.9 kWh/m2 – 6.2 kWh/m2

Wind 3 – 6 m/s

Bio-energy Biomass; Traditional wood and 5

million tons from agricultural residue.

Bio-fuel; 116 million litres in 2015 and

229 million litres by 2020

Geothermal No empirical study

Hydro Oueme River: total capacity of 760 MW

with an annual output of more than 280

GWh

Source: REEP policy Database (contributed by SERN for REEEP)

2.2.3.2 Ghana

Ghana’s neighbouring countries are Togo, Burkina Faso, and Ivory Coast. Ghana’s

fuel energy mix comprises of mainly traditional biomass (charcoal and firewood,

about 18%), fossil fuel, and hydropower (over 20%). The country in the past decade

has seen an increase in power outages and load shedding due to the lower electricity

generation from the hydropower station (hydrologic shocks in Akosombo and

Kpong). With this electricity crisis, other RES have the potential to mitigate it. The

RES potential in Ghana includes solar, wind, hydropower, and bio-energy (modern

biomass) [59, 60].

Ghana since the late 90s tried to factor in policies that could promote renewable

energy consumption in the energy mix and in 2011 the Renewable Energy Act was

enacted. This Act’s policies include feed-in tariffs, renewable energy purchase

obligations, renewable energy fund establishment and tax exemptions. The solar

irradiation potential in the country averages daily between 4 – 6 kWh/m2 and there is

36

an estimated wind speed potential of 6 m/s at 50-meter height in some locations

across the country [60].

2.2.3.3 Guinea

Guinea, a West African country, borders with Mali, Senegal and Guinea-Bissau to

the North, and Liberia, Cote d’Ivoire and Sierra Leone to the South. In 2013, only

26% of the population had access to electricity. Considering the situation and the fact

that the country is blessed with twelve major rivers and other renewable sources of

energy, the Ministry of Energy and government is taking strides to improve the

situation. Hydropower and biomass is significant among the RES in the country. In

2015, the government commissioned a 240 MW hydropower plant (Kaleta

Hydropower Plant) to improve the energy mix in the country [61].

2.2.3.4 Mali

Mali borders with Algeria to the north, Mauritania to the west, Niger to the east,

Senegal, Guinea-Bissau, Guinea, and Cote d’Ivoire to the north, and Burkina Faso to

the north-east. 27% of the population had access to electricity in 2016 and this power

was from hydro-source and imported fossil energy. Other RES can help mitigate the

situation [10]. RES that can improve the Malians energy mix includes hydropower,

solar energy, wind energy and bio-energy.

According to a study done by Nygaard et al.[62], the modelled annual mean wind

speed is at 4.46 m/s at a height of 50 meters and a 5.3 m/s observed value at a height

of 41 meters. The intensity of wind speed is greater in the northern region compared

to the south. The solar radiation in the north is approximately between 4000 – 7000

kWh/m2/day throughout the year. With daily variations in the south greater and

steady than the north, it ranges between 5000 – 6000 kWh/m2/day particularly from

May to September.

37

2.2.3.5 Senegal

The country’s energy mix is fossil fuel dominated and not all the population has

access to electricity. With the fossil energy price fluctuations and environmental

pollution and degradation, RES has the potential to improve the situation.

In Senegal, wind energy for more than two decades has seen various developments

compared to the other RES in the country. An empirical study conducted by Haritza

et al. [63] in three regions, Kaolack, Fatick and Thies, found that wind speed

averages between 3 – 5 m/s, mostly more than 4 m/s in a year. Even though wind

speed in the studied regions is not convincing, it is highest near water bodies (river or

sea) and mountainous areas in the country. The geographical location of Senegal

provides the country with a maximum potential of 5.8 kWh/m2/day. The irradiations

get to its peak in April and May, with relatively high radiations throughout the year

except for January and December (minimal irradiations). According to this study,

bio-energy (especially biogas from cow dung) has potential in the country but is not

feasible in these regions for electricity generation.

2.2.3.6 Sierra Leone

The Republic of Sierra Leone government enacted a renewable energy policy in May

2016. It aims to reduce the over-reliance on fossil fuel and improve energy access

and production in the country. The targets for 2020 and 2030 are 4703 ktoe/annum

and 8950 ktoe/annum respectively from the contribution of RES contribution to the

final energy mix consumption. Wind, hydro, solar and bio-energy are potential RES

in the country.

In 2011 there was a 1276 ktoe/annum of energy added to the final consumption from

RES (wood fuel and hydro). Table 2-4 depicts the RES potential in the country as

well as the installed capacity [64].

38

Table 2-4. RES potential in Sierra Leone and installed capacity [64]

Energy Source Estimated Potential Installed Capacity

Hydro 5000 MW from 300 sites

countrywide, 2013

6 MW in Dodo, 1982

50 MW in Bumbuna, 2009

Bioenergy 2706 GWh Addax ethanol project from

sugarcane in Makani- 5 MW

power supplied to Bumbuna-

Freetown grid

Solar 1460 – 1800 kWh/m2

annually

2.5 MW Solar photovoltaic,

2014.

Solar street lighting in 14

districts.

Wind Average 3 m/s – 5 m/s

wind speed countrywide. 8

m/s in hilly areas.

_

2.2.3.7 Togo

The country’s energy mix comprises of thermal plants, imported energy from Ghana

and Nigeria, and hydro-energy. Solar, bioenergy, wind and hydropower are the RES

potentials the country possesses. Estimated solar radiation is between 1700 – 2100

kWh/m2 annually and the country has an average wind speed potential that can

exceed 5 m/s in coastal regions. In 2015, 56 GWh gross power was generated from

hydro and 5 GWh gross electricity from primary solid biofuel [65, 66].

39

2.2.4 Eastern Africa

2.2.4.1 Burundi

Burundi is a country in East Africa surrounded by land. Their energy mix is

predominantly of traditional biomass (over 90%), hydropower, and fossil energy.

Hydro (majority) and about 10% of bagasse contribute to the electric power

generation. Less than 10% of the population has access to electricity which does not

foster the growth of the economy [67].

Currently, out of the RES potential in the country only hydro (32 MW is exploited

from the 1700 MW estimated potential capacity) and bioenergy (bagasse and wood

fuel) are exploited. The solar energy potential in the country averages between 4 – 5

kWh/m2 daily and a wind speed average potential of 4 – 6 m/s [68].

2.2.4.2 Kenya

Kenya shares borders with Uganda to the west, Sudan to the northwest, Ethiopia to

the north, Somalia to the east, and Tanzania to the south. It is the first country on the

east coast of the continent to tap and harness their geothermal resources and the

country is readily developing its RES. The country’s primary energy consumption

mix is made up of traditional biomass (majority), petroleum, electricity, and coal.

Kenya’s installed capacity for power generation is from hydro, fossil fuel,

geothermal, bioenergy (bagasse), and wind.

The Kenya Renewable Energy Association (KREA) which was institutionalized in

2002, aims to see to the growth and development of RES in the country. The RES

potential includes solar, wind, geothermal, bioenergy and hydro. Wind energy

potential in the country is between 5 – 7 m/s at a height of 50 meters in the coastal

areas and as high as 9 m/s in the north-western and hilly (Rift Valley) areas in the

country. Hydro (small, mini and micro) potential is estimated countrywide at 3000

MW. Estimated geo-energy potential is between 7000 – 10000 MW in several sites

40

across the country (Menegei – 1600 MW potential, Baringo-Silali – 3000 MW

potential, Suswa – 750 MW potential and Olkaria – 412 MW potential, etc.) [69, 70].

2.2.4.3 Malawi

Malawi, located in the southern part of the region shares its borders with Tanzania

(north), Mozambique (south and east) and Zambia (west). Primary energy

consumption is mainly from traditional biomass (89% as of 2014), hydropower

(Shire River), fossil fuel, and bioenergy (ethanol). The country is endowed with

solar, wind, hydro, geothermal, and bioenergy resources [71].

The daily estimated average solar energy potential is 5.8 kWh/m2. In September

2013, 850kW grid-connected photovoltaic system was commissioned at Kamuzu

International airport in Lilongwe. In 2008, the average monthly wind speed at a

height of two metres was above 2 m/s. With adequate hydropower potential from

rivers across the country, 1.478 GW is not being exploited. Geothermal energy

potential is sited in over fifty known hot springs nationwide (18 having an average

surface temperature above 50 °C and as high as 82 °C in Mphizi – Rumphi) [71].

2.2.4.4 Mauritius

Mauritius, an island in the south-west of the Indian Ocean, is endowed with a

beautiful scenery, it is rich in biodiversity and has remarkable mountains. Even

though the government of Mauritius gives priority to the development of energy

projects some members of its populace lacks access to electricity. Mauritius energy

mix comprises of fossil fuel (petroleum and coal), bioenergy (bagasse and wood

fuel), and hydropower. RES can help mitigate the electrification deficit, thus the

government (Ministry of Public Utilities) has put policies in place and hopes to

increase RES usage from 22% to 35% [72-74]. RES potential in the country includes

hydro, solar, wind and bioenergy. The annual mean wind speed potential at 30

metres height and above is between 3 – 7 m/s [72].

41

2.2.4.5 Rwanda

Rwanda is a non-coastal country with Uganda to the north, Tanzania to the north-east

and eastern part, the Democratic Republic of Congo to the north-west and western

region of the country, and Burundi to the south. After the genocide which occurred in

the mid-90s, the country has lots of strides to develop the nation. With adequate

access to energy, socio-economic development will be improved. Rwanda’s

electrification rate is less than half of the population. The nation’s primary energy

consumption is drawn from fossil energy (petroleum products), traditional biomass

(wood fuel and charcoal), and crop residue and peat [75].

Rwanda is endowed with solar energy, with the average daily estimated irradiation

being 5.2 kWh/m2. With this potential, there is an 8.5 MW photovoltaic plant located

in Agahozo-Shalom Youth Village (Rwamagana District). There exists the potential

for geo-energy with temperatures as high as 150 °C and about 170 – 320 MW of

power generation capacity. Wind potential in the country is not very feasible with

only the Eastern county having some potential. Hydropower in the country is

exploited for the generation of electricity (28 MW installed capacity). The

government of Rwanda has set a target of 100% access of its populace to electricity

by 2020 and renewable energy has the potential to make that a reality [75, 76].

2.2.4.6 Tanzania

The country is known for its large forest areas and wildlife conservation. The country

shares its borders with neighbouring countries Uganda, Rwanda, and Burundi to the

north-west, Kenya to the north, the Democratic Republic of Congo to the west,

Zambia, Malawi, and Mozambique to south with the Indian Ocean to east. Hydro,

traditional biomass and imported fossil fuel make-up their primary energy

consumption. Although energy demand in the country keeps on increasing with an

increase in population and industrial development, access is still low (15.5% of the

population as of 2014) [77].

42

Even though hydro is the major source of power generation in Tanzania, RES

potential is not exploited majorly. Large hydro installed capacity as of 2015 was 562

MW but has an estimated potential of 4000 MW with small hydro estimated at 480

MW mainly developed by private organisations. The geothermal potential has not

been empirically studied but is estimated (analogue methods) to exceed 650 MW

from hot springs with as high as 200 °C in the Mbeya region. The country’s wind

potential in most areas of the country can generate power. Kititimo – Singida and

Makambako – Iringa have a mean speed of 9.9 m/s and 8.9 m/s at 30 metres,

respectively. Solar irradiation averages between 4 – 6 kWh/m2/day with annual

sunshine hours between 2800 – 3500 for photovoltaic and thermal potentials. The

Government of Tanzania is putting policies in place to improve the contribution of

modern RES usage, especially bioenergy, to the energy mix [77].

2.2.4.7 Uganda

Uganda is endowed with a very diverse landscape (mountains and Lake Victoria) and

wildlife. It is also known for its 43-metre-tall waterfall (Murchison Falls National

Park). The country is landlocked with Sudan to the north, Kenya to the east, Congo

to the west, and Tanzania to the south.

The electrification rate in the country is low with 20.4% of the population with

access as of 2014 and the generation of power is from hydro (80%), fossil oil (diesel)

and bioenergy (bagasse) [10, 78]. As energy demand increases in Uganda at a 10 –

12% rate annually, RES with an estimated potential of 5300 MW can mitigate the

challenge. The renewable energy policy established in 2007 by the Ministry of

Energy, Minerals and Development (MEMD) proves the government’s commitment

to effectively tap their RES. With over 2000 MW hydropower potential, less than

50% (783 MW large-scale project to be completed in 2018) of it is being exploited.

The mean of 8 hours sunlight is estimated to give off between 1825 kWh/m2 – 2500

kWh/m2 of radiation yearly. Sustainable bioenergy potential for the country is

estimated at 45 million tons. An empirical study of geo-energy is ongoing with

43

potential estimates at 450 MW. As wind speed is generally low (mean measurements

of 3.7 m/s at 20 m in Mukono and Kabale) large-scale power generation is not

economically viable [78].

2.2.5 Central Africa

2.2.5.1 Cameroon

Cameroon is endowed with forest areas and diverse wildlife. The country is

surrounded by bordering countries Nigeria to the north-west and west, Chad to the

north-east, the Central African Republic to the east, and Gabon and Congo to the

south. The country’s south-west borders with the Atlantic Ocean [79].

The energy ministry hopes to increase the electrification rate from 55% to 75% by

2020, however, power outages are on the rise. The power generation is

predominantly from hydro (60%), fossil fuel (oil and gas), and biomass. Table 2-5

shows the renewable energy potential of Cameroon [79, 80]. Geothermal energy is

yet to be empirically estimated but the potential exists due to volcanic activities in

some regions of the country.

Table 2-5. Potential of RES in Cameroon

Energy Source Estimated Potential Main Region/Location

Hydro 23 GW Sanaga basin

Solar (irradiation) 5.8 kWh/m2/day

4.5 kWh/m2/day

Northern

Southern

Wind 5-7 m/s

2-4 m/s at 100 m

North and Coastal areas

Most regions

Bioenergy (biomass

residue)

Approximately 1 GWh

44

2.2.6 Southern Africa

2.2.6.1 Botswana

Botswana, being a non-coastal country, is bordered to the south by South Africa,

west by Namibia, east by Zimbabwe, and north by Angola and Zambia. The country

is endowed with vast green forests (Kalahari Game Reserve) and diverse wildlife.

The country’s primary energy consumption is from electricity and traditional

biomass. Since the country is endowed with coal, it is the major source for power

generation [81].

The RES of significant potential in Botswana is solar and bioenergy (biogas from

bovine waste) with wind having some minimal potential. Solar radiation potential is

estimated at 21 MJ/m2/day with about 3200 hrs of sunshine annually. Wind speed in

the country is generally low at a height of 10 metres with a mean of 3 m/s [82]. Due

to the topography and lack of rainfall, hydropower in the country for electricity

generation is not practicable. The Botswana government is motivated to improve its

modern usage of RES (especially bioenergy) to its energy mix [81].

2.2.6.2 South Africa

The country is located at the tail end of the African continent with its coast at the

South Atlantic Ocean and the Indian Ocean. The nation’s topography and wildlife

attract tourists all year round. With an electrification rate of about 90%, South Africa

is among the few countries in the continent with adequate power access. The power

generated in the country is from fossil energy (coal and gas), hydro, nuclear, and

wind [83].

Solar and wind energy are the most developed of the RES in the country. With a

mean of over 2500 hrs/yr of sunlight, solar irradiation averages between 4.5 – 6.5

kWh/m2/day. The maximum potential solar energy in the country is obtained in the

northern area. The development of this RES is mostly done by private organizations

or in partnership with the government. The maximum wind speed potential is located

45

along the coastal areas of South Africa and there is moderate wind speed in some

inland areas. Mean wind speed at a height of 100 metres is between 4 – 8 m/s. South

Africa has a 38 MW installed capacity of hydro energy. Some recent estimates (247

MW) of small-scale hydro exist in some areas (Free State, KwaZulu-Natal,

Mpumalanga and Eastern Cape). Bioenergy potential research and development is

still ongoing and expected to be implemented in full scale by 2022 [83].

2.2.6.3 Swaziland

Swaziland is a non-coastal country bordered by South Africa to its south, west and

north, and Mozambique to the east. In 1999, a National Development Strategy (NDS)

was enacted with the objectives of improving research and development, energy

access, and energy efficiency. This strategy suggests that the government of

Swaziland is driven to improve its socio-economic development and the energy

sector. The nation’s usage of and reliance on traditional bioenergy (firewood,

charcoal) and imported power (from Mozambique and South Africa) make the nation

prone to price shocks, power outages and a 100% electrification rate difficult to

achieve. RES (especially modern usage of bioenergy sources and solar power) can

improve energy access and efficiency. Table 2-6 depicts Swaziland’s RES potentials

[84].

Table 2-6. Swaziland’s RES potentials by source

Source Estimated Potential

Solar 4 – 6 kWh/m2 daily

Wind 3 m/s at 10 metre height

Hydro Limited and variable nationwide

Bioenergy Cogeneration: Agricultural waste (bagasse) and wood mill

waste.

Biofuels (ethanol): Molasses from sugar

46

3.0 LITERATURE REVIEW

3.1 Review of some Econometric Studies and findings for Africa

The discussion and analysis of the relationships between economic and energy

variables using econometric methods, began with the study by Kraft and Kraft in

1978. These early studies were mostly centred on developed countries because data

(availability and accessibility) was ‘easy’ to obtain compared to developing and

underdeveloped countries. These studies’ aim was to establish both the short-run and

long-run impacts between, mostly, the environment and the economy. The most

established hypothesis in this area of study is the environmental Kuznets Curve

(EKC) [85]. This hypothesis tells us that an increase in Gross Domestic Product

(GDP) per capita leads to an increase in environmental pollution in the developing

stages of an economy, but pollution decreases over time as the country’s economy

further grows. This is also known as the inverted U-shaped relation. In this section, a

review of the literature is done based on studies on the African continent. Table 3-1

summarizes the literature review.

The aim of Wolde-Rufael’s [86] study, focusing on 19 countries, was to explore the

possibility of a long-run relationship between GDP per capita and energy

consumption per capita. The cointegration bounds test approach was used to analyse

the data for the period 1971 – 2001. He found a long-run relationship between GDP

per capita and energy consumption per capita for only eight countries, a causal

relationship for ten of the countries under study and some countries having no causal

relationship.

Ebohon [87] examined the causal relationship between energy consumption, and

GDP and gross national product (the last two as a proxy for economic growth) in

Nigeria from 1960 – 1984 and Tanzania from 1960 – 1981 using the Granger

causality test. The results showed a bidirectional causality among the variables under

study, for both countries.

47

Mbarek et al. [88] study Tunisia for the period 1980 – 2010 using Granger causality

test and variance decomposition. Their aim was to establish the nexus between CO2

emissions, energy consumption, and GDP. The results showed a positive relationship

between energy consumption and economic growth (measured by GDP growth).

Also, the study found that the causality between GDP and CO2 emissions was

unidirectional in the short-run: from GDP to carbon-dioxide emissions.

Kais and Ben Mbarek [89] studied three selected Northern African countries

(Algeria, Tunisia, and Egypt) for the period 1980 – 2012, using the panel vector error

correction model. Using the variables GDP per capita (dependent variable proxied

economic growth), CO2 emissions, and energy consumption, the results show a one-

directional causality from energy consumption to CO2 emissions in both the long and

short term, a one-directional causality from GDP to energy consumption, and from

GDP to CO2 emissions in the short-term. Energy consumption and GDP show a

bidirectional causality in the long-term, meaning that a change in one will affect the

other.

While the objective of Ali et al.’s [90] paper moved beyond the EKC, Chuku’s [91]

research established the existence of EKC in Nigeria using the standard EKC model

and the nested-EKC decomposition model, for the period 1960 – 2008. The empirical

results showed no evidence of the existence of an inverted-U relationship when the

standard model was used while the nested model presents a strong N-shaped

relationship between GDP per capita and CO2 emissions. Considering the research of

both [90] and [91] even though different models and time periods were used, their

inferences seem similar and can help policymakers draw out appropriate policies to

this effect.

As renewable energy came into the scene for the sustainability of our environment,

econometric studies in the same began. Jebli et al. [92] empirically studied the role

of renewable energy consumption and trade on the hypothesis of Environmental

Kuznets Curve (EKC) for the Sub-Saharan region. The study was done for 24

countries and was for the period 1980 – 2010. They used a panel cointegration model

for the following variables: carbon dioxide (CO2) emissions, GDP, renewable energy

48

consumption, and trade. Their results showed that renewable energy consumption

has a mixed effect on the environment. There are short-run relations among the

variables with different directions of causality, while the long-run estimates of EKC

were not supported.

Wesseh and Lin’s [93] objective was to establish if Africa can build its economy

efficiently on renewable energy consumption. The study employed the translog

production model with data for the period 1980 – 2011 for 34 countries. Variables

investigated in the study include real GDP, total renewable electric power

consumption, total non-renewable electric power consumption (calculated as the

difference between total electric power and total renewable electric power), labour

and capital. They found that renewable energy consumption seemed to be a stronger

drive for growth as compared to the fossil energy, but it comes with challenges

(issues of scale, economics (capital investment) and some sitting problems such as

the availability of raw materials for conversion of energy to power) associated with

the transition. All the four variables under study drive output (real GDP) in Africa.

Zoundi [94] used a panel cointegration model for 25 countries for the period of 1980

– 2012. The variables under study were CO2 emissions, GDP per capita, and

renewable energy consumption. The results showed no evidence of EKC present, but

CO2 emissions increased with an increase in GDP per capita. They also found that a

reduction in carbon dioxide emission is associated with renewable energy usage.

Alabi et al. [95] studied three oil producing and exporting countries in Africa

(Angola, Algeria and Nigeria) for the period 1971 – 2011. Their goal was to

investigate the causality between renewable energy consumption and economic

growth. The fully modified ordinary least squares (FMOLS) approach for panel

cointegration was employed in the empirical analysis. Their findings revealed a

bidirectional causality between renewable energy consumption and GDP per capita

for both the short and long term. It was also found that for the short and long term,

there exists a bidirectional causality between non-renewable energy consumption and

GDP per capita, as well as between GDP per capita and CO2 emissions. The

49

causality between CO2 emissions and non-renewable energy consumption (NREC)

was only one-directional from NREC to CO2.

Attiaoui et al. [96] study 22 countries for the period 1990 – 2011. The method for the

analysis was the autoregressive distribution lags-pooled mean group. The variables

under study were renewable energy consumption (REC), NREC, GDP per capita,

and CO2 emissions. The results showed that causality in the short term runs from

CO2 emissions to GDP; from REC to GDP; and no causality between CO2 emissions

and REC was found. In the long-term, an increase in NREC and GDP increases CO2

emissions, while REC reduces CO2 emissions.

Amri [97] investigated the causality between energy consumption (renewable and

non-renewable), GDP, and CO2 emissions in Algeria from 1980 – 2011 using the

autoregressive distribution lags approach (ARDL). The empirical results proved the

existence of EKC, with a positive effect of non-renewable energy consumption

(NREC) on CO2 emissions, thus NREC increases environmental pollution. The effect

of using renewable energy was found to be insignificant in reducing environmental

pollution.

Ali, Law and Zannah [90] examined the EKC hypothesis in Nigeria by using the

ARDL approach for 1971 – 2011. They included urbanization and trade openness in

the study. Their study showed that urbanization has no significant impact on CO2

emissions, while an increase in GDP and energy consumption impacted positively on

CO2 emissions. Also, trade openness was observed to impact negatively on carbon

dioxide emissions.

As the number of EKC hypothesis studies grew there was a shift away from the mere

analysis of the relation between CO2 emissions and growth. Osabuohien et al. [98]

decided to move beyond the EKC analysis, incorporating other variables such as

Institutional quality (mean value of rule of law, regulatory quality, and government

effectiveness) because previous studies showed that effective and stable governance

improves an economy (growth and development), while poor institutional quality

leads to stagnation and does not improve the economy. For their empirical analysis,

50

they use a panel cointegration test and vector autoregressive approach in fifty

countries. The variables for the analysis were CO2 emissions per capita, real GDP per

capita, Institutional quality, and trade for the period 1995 – 2010. The empirical

results showed the presence of a long-term causality between emissions and all the

other variables. The existence of EKC was proven with GDP growth increasing

pollution and the squared GDP reducing pollution. Institutional quality and trade

reduce pollution, but the effects were not statistically significant.

Abid [99] working with 25 Sub-Saharan African countries for the period 1996 -

2010, used the dynamic panel “difference” General Method of Moments (GMM-

DIFF) estimator and “system” General Method of Moments (GMM-SYST) for his

empirical study. He also included institutional factors and trade openness variables.

In line with Jebli et al.’s [92] findings, Abid found no evidence of an inverted-U

shaped relationship but found that, having good institutional factors increases CO2

emissions, GDP, and trade openness, which proves that institutional factors and trade

can affect the environment and growth of an economy in both ways (positively or

negatively).

Considering the studies that have been done on the continent, various methods,

different time periods and different variables were employed. The conclusions vary

depending on the country(ies), time frame, methodology, and the type of data used.

Also, according to Marques and Fuinhas [100], the relationship between renewable

energy and the growth of an economy (dependent variable) seems to vary across

countries and study periods. Their results showed that renewable energy

consumption does not seem to improve economic growth. Based on the inferences of

different studies in Europe, they argued that this could be a result of possibly omitted

variable bias of non-inclusion of simultaneous consumption of fossil energy in the

analysis. For this reason, the major oil producing and exporting countries (Nigeria,

Angola, and Algeria) in Africa are not included in this study. This thesis has some

additions to the literature: the first, the use of renewable energy consumption as a

percentage of total energy consumption as a variable, which as far as I know has not

been used for analysis in Africa. Second, to the best of my knowledge, the Human

51

Development Index as a variable has not been used in any econometric analysis in

Africa, but a modified version of it was used by Gürlük for an analysis in the

Mediterranean region [101]. The above variables and some already used variables in

the literature are used to explore the relationship among them and its impact on the

economy, society and environment. Thirdly, this thesis attempts to find ample

representation from the five regions of the continent with the available data. This is

because the regions have similar societal norms, behaviours, and similar

governmental policies. Finally, based on the empirical evidence, the thesis proposes

policy recommendations that can assist governments and policymakers.

Table 3-1. Summary of Literature

Author Period Country(ies)

Under Study

Results

Wolde-Rufael [86] 1971 – 2001 19 LR relationship between GDP per

capita and EC per capita for only 8

countries and causality (unilateral

and bi-directional) for 10 countries.

Ebohon [87] 1960 – 1981 2 Simultaneous causality between EC,

GDP and gross national product for

Nigeria and Tanzania.

Mbarek et al. [88] 1980 – 2010 1 Tunisia: + relationship between

energy consumption and GDP

growth.

SR causality: GDP → CO2

emissions (CO2E).

Kais and Ben

Mbarek [89]

1980 – 2012 3 SR causality: EC → CO2E, GDP →

EC and GDP → CO2 emissions.

LR causality: GDP ↔ EC

LR: EC ↑ CO2E.

Jebli et al. [92] 1980 – 2010 24 REC has + and – effects on

environment (CO2E).

SR causality relations exist among

the variables with different

directions.

LR: EKC not supported

52

Wesseh and Lin [93] 1980 – 2011 34 REC is a greater drive for GDP

compared to fossil fuel.

REC, NREC, labor and capital drive

GDP output.

Zoundi [94] 1980 – 2012 25 An ↑ in GDP ↑ CO2E. REC ↓ CO2E.

No evidence of EKC.

Alabi et al. [95] 1971 – 2011 3 In both LR and SR causality: REC

↔ GDP; NREC ↔ GDP; GDP ↔

CO2E and NREC → CO2E.

(Angola, Algeria and Nigeria)

Attiaoui et al. [96] 1990 – 2011 22 SR causality: CO2E → GDP; REC

→ GDP; and none between CO2E

and REC.

LR: ↑ in NREC and GDP, ↑ CO2E,

while REC ↓ CO2E.

Amri [97] 1980 – 2011 1 Algeria: + effect of NREC on

CO2E. REC insignificant in

reducing environmental pollution

and degradation.

EKC existence proven.

Ali et al. [90] 1971 – 2011 1 Nigeria: GDP and EC have + effect

on CO2E. Trade openness has –

effect on CO2E. Urbanization: no

significant effect on CO2E.

Chuku [91] 1960 – 2008 1 Nigeria: standard EKC model,

showed no existence of EKC while,

the nested-EKC decomposition

model showed a strong N-shaped

nexus between GDP and CO2E.

Osabuohien, et al.

[98]

1995 – 2010 50 LR relationship between CO2E and

GDP. Thus GDP ↓ CO2E, EKC

existence proven

Abid [99] 1996 – 2010 25 Strong institutional factors impact

positively on CO2 emissions, GDP

and trade openness. EKC existence

not proven.

Legend: LR: long term; SR: short term; +: positive (increase); -: negative (decrease); →:

unidirectional; ↔: bidirectional; ↑: increase; ↓: decrease; EC: energy consumption; REC: renewable

energy consumption and NREC: non-renewable energy consumption.

53

4.0 METHOD OF THE STUDY

4.1 Data and Countries Selection

The empirical analysis of this study comprises of 9 variables (see Table 4-1) namely:

Renewable energy consumption (RNEW%), Carbon dioxide emissions per capita

(CO2Epc), GDP per capita (GDPpc and GDP per capita Squared), Trade openness

(TOgdp), Foreign direct investment inflows (FDI), Urban population (URB) from the

World Bank Development Indicators [10]; Ecological footprint versus Biocapacity

(EFC) from the National Footprint Accounts [102]; Human Development Index

(HDI) from the Human Development Report [103] and Institutional Quality (INSQ)

from Freedom House as a measure of freedom in the world (mean score of Political

rights and Civil liberties rating) [104]. These variables were selected to establish the

nexus between renewable energy consumption and the above variables under study

in Africa. Twenty-one countries were selected based on their locations and

availability of data spanning from 1990 to 2013 (see Table 4-2).

Renewable energy consumption (share of renewable energy in total final energy

consumption [10]) is included in this study as one of the dependent and independent

variables. RES in this thesis are considered as “sources of energy that replenish itself

naturally” [105]. RNEW% includes the various RES usage from all sectors

(industrial and household). Renewable energy is in abundance depending on location

and resources available to the country compared to fossil fuel. Renewable energy

consumptions’ aim is to provide a sustainable energy option and to protect the

environment but there are discussions about some of the renewable energy

production technologies which are considered unsustainable.

Carbon dioxide (CO2) emissions per capita is used as a dependent variable for the

analysis of one of the models. The increase in the quantities of CO2 emissions in the

past two to three decades has escalated the concerns about environment pollution,

depletion of the ozone layer, and climate change. Hence, several studies attempt to

ascertain how the role of economic activities and the interaction between humans and

54

the environment contribute to CO2 emissions. As human activities contribute either

directly or indirectly to CO2 emissions, the resultant effect in turn affects the human

race. As such, the study employs CO2 emissions as a proxy for environmental

pollution.

Environmental degradation is also used as a dependent variable. In simple terms,

environmental degradation is the reduction in the ability of environmental resources

to support life (humans, plants, and animals). In the light of this, I choose ecological

footprint as a measure of environmental degradation: the quantification of the

“biologically productive land and water needed to generate all resources used-up and

how the ecosystem absorbs the waste produced using predominant technologies and

resource management practices” [106].

Table 4-1. Description of Variable

Indicator Code Indicator Name Mean Minimum Maximum

RNEW% Renewable energy consumption

(% of total final energy

consumption)

62.70 5.58 97.29

CO2Epc CO2 emissions (metric tons per

capita)

1.0507

0.0207 10.0407

GDPpc GDP per capita (constant 2010

US$)

1756.69 204.77 8848.89

GDPpc2 Squared GDP per capita (constant

2010 US$)

_ _ _

EFC Ecological footprint vs

Biocapacity (gha per person)

1.51 0.60 3.84

FDI Foreign direct investment, net

(BoP, current US$)

-500000000 -13800000000 5310000000

TOgdp Trade Openness (% of GDP) 65.49 19.68 170.41

HDI Human Development Index (Very 0.47 0.19 0.77

55

high: 0.8 – 1, High: 0.7 – 0.799,

Medium: 0.550 – 0.699, Low: Below

0.550)

URB Urban population (% of total) 34.92 5.42 66.46

INSQ Institutional Quality (Before 2003,

combined average ratings for Political

rights and Civil liberties fell between

1.0 – 2.5 were designated Free;

between 3.0 – 5.5 Partly Free, and

between 5.5 – 7.0 Not Free; from

2003 onwards, combined average

ratings fall between 3.0 – 5.0 are

Partly Free, and those from 5.5 – 7.0

are Not Free)

4.20 1.00 7.00

Note: gha: global hectare

Table 4-2. Country Description

Region Country Country code

North Africa Egypt, Arab Rep. EGY

Tunisia TUN

Morocco MAR

West Africa Benin BEN

Ghana GHA

Guinea GIN

Mali MLI

Senegal SEN

Sierra Leone SLE

Togo TGO

East Africa Burundi BDI

Kenya KEN

56

Malawi MWI

Mauritius MUS

Rwanda RWA

Tanzania TZA

Uganda UGA

Central Africa Cameroon CMR

South Africa Botswana BWA

South Africa ZAF

Swaziland SWZ

In the literature, GDP growth is the known driver for environmental pollution and

degradation. Nonetheless, when GDP per capita increases to a certain level (peak) it

starts to reduce environmental pollution and degradation. This is because, after

economies have developed, investment in research and development of new

technologies increases, which reduces environmental damage [107]. This hypothesis

is the Environmental Kuznets Curve (EKC) [85, 108]. Grossman and Krueger [16],

Shafik and Bandyopadhyay [109], and Panayotou [85] studies were among the

pioneering studies to prove the existence of EKC. Since then, other studies have

either proved or failed to show EKC’s existence.

Empirical literature shows that Foreign direct investment (FDI) uses up a country’s

natural resources base. On one hand, FDI contributes to biodiversity loss,

deforestation, and greenhouse gas emissions and on the other, contributes to growth

(income and society), thus, reducing environmental pollution. The concept of FDI’s

‘negative’ effect on the environment has brought about empirical studies which are

known as the ‘Pollution Haven’ hypothesis [110, 111], while the concept of FDI’s

‘positive’ effect on the environment and society is known as the ‘Pollution Halo’

hypothesis [112-114].

Another independent variable used in this research is trade openness. There are

different indices for the measurement of ‘trade openness’ (see Yanikkaya, Halit

57

[115]). The most common one, trade as a percentage of Gross Domestic Product, is

the measure for trade openness used in this thesis. Mostly, trade (import and export)

openness is studied to understand the dynamic effect it brings to the environment and

economy (GDP). As reviewed by Harrison [116], most studies showed that it has a

positive relationship with growth. This thesis seeks to study the contribution of trade

openness to renewable energy consumption in Africa. Thus, if trade openness is a

driver for renewable energy consumption its activities can be regulated properly to

bring about development.

HDI is also included in the analysis as an independent variable. Human development

index (HDI) was developed as a measure to reduce the reliance on economic growth

as a measure of development. Ideally, it is mankind’s innate ability (knowledge and

understanding) to thrive and perceive things that helps the economy to grow, even so,

it is arguable. In this paper, HDI is included as a variable to analyse the role it plays

in the option of using a much cleaner energy option (renewable energy sources).

Lastly, institutional quality (INSQ) is adopted in this thesis as a ‘societal’ variable

and comprises of the aggregated weights of political rights (PR) and civil liberties

(CL) ratings. The PR rating is mainly based on “functioning of government, political

diversity and participation, and electoral process”. The CL rating is centred on “rule

of law, freedom of expression and belief, personal autonomy and individual rights,

and associational and organizational rights”. Basically, it reports about a country’s

position when it comes to being free [104]. Democracy is known as an important

‘tool’ in growth and development discussions and it cannot be overlooked.

Institutional quality is studied in this thesis because, I want to know its impact on

renewable energy usage.

4.2 Methods

Both panel data and time series data analysis using econometric methods have been

used in various research studies. Both have advantages and disadvantages. In this

research, panel data analysis is used for robust statistical analysis, considering that

58

the pros of panel data compensate for the cons. Some of the advantages of panel data

are [100, 117]:

➢ Comparing it to a single set of time series or cross-sectional data analysis, it

enables us to better answer questions of substantial interest.

➢ The degrees of freedom are increased, and the explanatory variables’

collinearity is reduced, thus the econometric estimates’ efficiency is

improved.

➢ By increasing the efficiency and stability of estimators, it allows the choice

of suitable estimation methods.

➢ Due to the amplification of the number of observations, it warrants the

asymptotic properties of estimators.

The challenges that can be associated with panel data are [117, 118]:

➢ The modeling of cross-sectional dependence.

➢ The modeling of heterogeneity across individual groups over a period that is

not seen or captured.

➢ The merging of a single times series data or a single cross-sectional data to

form the panel data, which can bring about some bias.

Figure 4-1 below shows a diagrammatic representation of the model estimation

process for this research. The details of the models are explained later (in sections

4.2.1 – 4.2.4), while a brief description follows. First, a descriptive analysis of the

variables is done to understand the characteristics of the data, in order to make

informed decisions on data pre-processing and the selection of an estimation method

(step 1). Secondly, the unit root test is applied to examine the stationarity of data

series. It is expected that variables will be integrated of order one, before proceeding

to testing cointegration among data variables (step 2). For this, the study employs

seven panel unit tests for robust statistical analysis. Thirdly, a Utest estimation is

done to compare the results of the EKC hypothesis to that of the Westerlund model

that follows. In addition, the Utest estimation technique reveals the shape of the

59

relationship and the income levels at the turning points in the results of EKC

hypothesis (step 3). Later, the Westerlund error-correction model is used for

cointegration and regression analysis based on 1,000 bootstrapping samples to

mitigate the challenges of cross-sectional dependence (steps 4 and 5). The

Westerlund error-correction model is subsequently used to test the validity of the

EKC hypothesis. The dependent variables are CO2Epc and EFC, and the independent

variables are RNEW% and GDPpc. Fourthly, panel causality test (step 6) is used to

investigate the direction of causality in a bivariate relationship. The results can be

unidirectional, bi-directional or none. Lastly, fixed and random effects estimators are

used to estimate the long-run equilibrium relationships between the treatment

variables (CO2Epc, GDPpc, TOgdp, FDI, HDI, INSQ) with RNEW% as the

dependent variable (step 7). Subsequently, the Hausman’s test is employed as a post-

estimation method after the application of the fixed and random effects estimators to

choose the appropriate method (step 8).

Figure 4-1. Diagrammatic Representation of the Model Estimation Process

Descriptive Analysis (1)

Panel Unit Root Tests (2)

Utest Estimation (3)

WECM Cointegration (4)

Model Estimation Process

(1) Descriptive Analysis: If Some variables are not

normally distributed, application of logarithmic

transformation.

(2) Panel Unit Root Test: To check whether Variables

are integrated of order one, I(1).

(3) UTest Estimation: Estimation, Verification and

Validation of EKC Hypothesis.

(4) Cointegration Analysis: Examine if variables are

co-integrated or not.

Diagnostic and Stability Tests

(8)Panel Causality Estimation (6)

WECM Regression (5)

Dependent variable: CO2E and

EFC

Model Estimation Process (5) Regression Analysis: Error correction must be

negative and significant. Examine the long-run and

short-run relationship.

(6) Panel Causality: Estimation of the direction of

causality

(7) Fixed & Random Effects Estimators: Estimate

long-run relationships using Fixed & Random Effects

Estimators

(8) Diagnostic and Stability Test: Cross-sectional

dependence test, Bootstrapping for robustness,

Hausman's test, Durbin Watson test, Goodness of fit

test.

Long-run & Short-run

Relationship (5a)

Fixed & Random Effects

Estimation (7)

Dependent variable: RNEW%

60

The following sub-sections give a detailed description of the methods and models

used in the estimation process.

4.2.1 EKC hypothesis and the U-shaped Relationship Test

The theory of EKC is that the relationship between the environment (quality) and the

GDP per capita (income) of an economy is not steady or monotone and can change

directions (upward or downward). The environmental Kuznets curve (EKC)

hypothesis, states that, an increase in GDP per capita increases the pollution in the

environment but after reaching the desired income level ‘better’ technologies and

decisions are sought after to protect the environment reducing pollution, thus

providing an inverted U-shaped relationship. The standard EKC hypothesis equation

is as follows [88]:

(𝐸/𝑃)𝑡 = 𝛼0 + 𝛼1𝑡 + 𝛽1(𝐺𝐷𝑃/𝑃)𝑡 + 𝛽2(𝐺𝐷𝑃/𝑃)𝑡2

+ 𝛽3(𝐺𝐷𝑃/𝑃)𝑡3

+ 𝛾𝑋𝑡 +

휀𝑡 (1)

Where 𝐸 is environmental degradation captured by the variable CO2 emissions; 𝑃 is

population size, therefore, 𝐸/𝑃 is CO2 emissions per capita; GDP is gross domestic

product, thus 𝐺𝐷𝑃/𝑃 is GDP per capita; 𝑋𝑡 is a vector of variables that may affect

environmental quality, and 𝑡 is the deterministic time trend. For various reasons such

as data availability the vector 𝑋𝑡 is often omitted and the restriction 𝛾 = 0 is

assumed.

In view of the quadratic equation of the EKC model for the analysis of the inverted

U-shape relationship testing, Lind and Mehlum [119] value the relationship testing as

weak. That is, creating erroneous inferences when the relationship is “convex but

monotone over relevant data value”. Their U-test proposes a solution to the

identified weakness. The software Stata package is used for this analysis. The

command ‘Utest’ is used to validate the quadratic term of the EKC hypothesis. The

relationships (curves) that can be produced when ‘Utest’ is examined are monotone,

U-shaped or inverted U-shaped. The testing of the U-shaped relationship takes the

following equation form [119]:

61

𝑦𝑖 = 𝛼 + 𝛽𝑥𝑖 + 𝛾𝑓(𝑥𝑖) + 𝜉′ 𝑧𝑖 + 𝑒𝑖 𝑖 = 1, … , 𝑁 (24) (2)

Where y denotes the dependent variable, 𝑥 represents the regressors, 𝑒 represents the

white noise, 𝑧 represents the vector of control variables, 𝛽, 𝛾, 𝜉 are used for the

parameterisation of the model, and 𝑓 is adopted in the model to give a curvature

function. It is expected that the curvature function will produce a turning point that

falls within the data series of the regressors [𝑥𝑖 , 𝑥𝑛]. To obtain a U-shaped

relationship equation (3) must agree with the test results and any deviation to that

effect will lead to either a monotone or inverted-U relationship [119].

𝛽 + 𝛾𝑓′(𝑥𝑖) < 0 < 𝛽 + 𝛾𝑓′(𝑥𝑛) (3)

The null hypothesis (𝐻0) and alternative hypothesis (𝐻1) of the U-test are expressed

as:

𝐻0 : 𝛽 + 𝛾𝑓′(𝑥𝑖) ≥ 0 and ∕or 𝛽 + 𝛾𝑓′(𝑥𝑛) ≤ 0

𝐻1 : 𝛽 + 𝛾𝑓′(𝑥𝑖) < 0 and 𝛽 + 𝛾𝑓′ (𝑥𝑛) > 0

Lind and Mehlum [119] provide readers with a comprehensive explanation for the

development of this U-test relationship study.

In my model estimation, CO2Epc and EFC are the dependent variables, GDPpc and

GDPpc2 the independent variables. The results can be seen in section 5.3.

4.2.2 The Westerlund Error-Correction Model (WECM)

Various panel cointegration test and error correction models [Fully-Modified

Ordinary Least Squares (FMOLS), Dynamic Ordinary Least Squares (DOLS), Panel

Vector Error Correction Model, Autoregressive distribution lag-Pooled Mean Group

(ARDL-PMG), etc.] have been used in the existing literature, but they are unable to

mitigate the challenge associated with cross-sectional dependence that influences

62

panel data regression analysis, as such, the study adopts the WECM with 1,000

bootstrapping samples to overcome the challenge.

According to Persyn and Westerlund [120], many econometric models fail to reject

the null hypothesis of no cointegration because “most residual-based cointegration

tests, both in pure time series and panels, require that the long-run parameters for

the variables in their levels are equal to the short-run parameters for the variables in

their differences”. To correct this, they developed a cointegration model with the

focus on structural dynamics and not residual dynamics. Also, this econometric

method does not impose common factor restriction in contrast to other panel

cointegration techniques. The WECM consists of four-panel cointegration tests for

the alternative hypothesis; the first two are designed to test whether the panel is

cointegrated when considered wholly and the other two test if at least one unit is

cointegrated [120]. The data generating process estimated for the error-correction test

is expressed as:

𝛥𝑦𝑖𝑡 = 𝛿𝑖′𝑑𝑡 + 𝛼𝑖(𝑦𝑖,𝑡−1 − 𝛽′𝑖𝑥𝑖,𝑡−1) + ∑ 𝛼𝑖𝑗

𝑝𝑖𝑗=1 ∆𝑦𝑖,𝑡−𝑗 + ∑ 𝛾𝑖𝑗

𝑝𝑖𝑗=−𝑞𝑖 Δ𝑥𝑖,𝑡−𝑗 + 𝜇𝑖𝑡

(4)

Where y is the dependent variable and 𝑥 are the independent variables; 𝑡 =

1, … , 24 represents time; 𝑖 = 1, … , 21 represents cross-sectional units (countries); 𝛼𝑖

the error correction parameter; 𝛽 the vector for long-run equilibrium relationship

between x and y; 𝑝𝑖 the individual lag order; 𝑑𝑡 contains the deterministic elements

and there are three cases. The first case where 𝑑𝑡 is equal to zero, equation (4) has no

deterministic terms; the second case where 𝑑𝑡 is equal to one, Δ𝑦𝑖𝑡 is generated with

a constant; and the last case, where 𝑑𝑡 = (1, t)′ implies that Δ𝑦𝑖𝑡 is generated with

trend and a constant. With 𝑥𝑖𝑡 modelled as a pure random walk, it makes Δ𝑥𝑖𝑡

independent of 𝜇𝑖𝑡, where 𝜇 is the error term, with the assumption that the errors are

independent across i and t [120]. The bootstrap method used to check for cross-

sectional dependence of i, derived from equation (4) is expressed as:

63

Δ𝑦𝑖𝑡 = 𝛿𝑖′𝑑𝑡 + 𝛼𝑖𝑦𝑖,𝑡−1 + 𝜆′𝑖𝑥𝑖,𝑡−1 + ∑ 𝛼𝑖𝑗

𝑝𝑖𝑗=1 ∆𝑦𝑖,𝑡−𝑗 + ∑ 𝛾𝑖𝑗

𝑝𝑖𝑗=−𝑞𝑖 Δ𝑥𝑖,𝑡−𝑗 + 𝜇𝑖𝑡

(5)

In equation (5) 𝜆′𝑖 = −𝛼𝑖𝛽′𝑖 in equation (4). After a sudden shock 𝛼𝑖 determines the

speed at which the system corrects back to a steady relationship 𝑦𝑖,𝑡−1 − 𝛽′𝑖𝑥𝑖,𝑡−1. If

𝛼𝑖 < 0, the dependent (𝑦𝑖𝑡) and independent (𝑥𝑖𝑡) variables are cointegrated; and if

𝛼𝑖 = 0, then there is no cointegration with no error correction. The null hypothesis of

no cointegration is expressed as 𝐻0: 𝛼𝑖 = 0 for all i and the alternative hypothesis is

as stated by Persyn and Westerlund [120]. Thus, the first two alternative tests are

that the panel is cointegrated when considered wholly (all countries pooled together)

and the other two test that, at least one country is cointegrated. Computation and

details involved in the development of the command (xtwest) in Stata and model can

be seen in Persyn and Westerlund [120].

In my model, the effect of renewable energy consumption (RNEW%) and gross

domestic product per capita (GDPpc) on environmental pollution (CO2Epc) and

environmental degradation (EFC) is estimated. Also, the validity of the EKC is tested

with a second order polynomial of GDPpc (GDPpc2). Findings are shown and

explained in section 5.4.

4.2.3 Causality Test for Panel Data

The well-known Granger causality tests by Granger [121] is not used for causality

testing in this thesis, but rather an augmented version of it by Dumitrescu and Hurlin

[122] is employed and modeled in equation (6). This is because, in the presence of

cross-sectional dependence, the standardized panel statistics have good small sample

properties. Also, this Granger non-causality test can be implemented easily in

balanced and unbalanced panels.

𝑦𝑖.𝑡 = 𝛼𝑖 ∑ 𝛾𝑖 (𝑘)𝐾

𝑘=1 𝑦𝑖,𝑡−𝑘 + ∑ 𝛽𝑖(𝑘)𝐾

𝑘=1 𝑥𝑖,𝑡−𝑘 + 𝑒𝑖,𝑡 (6)

64

Where the lag order of 𝐾 𝑖𝑛 𝑡ℎ𝑒 𝑒𝑙𝑒𝑚𝑒𝑛𝑡 𝑜𝑓 𝑁∗ (cross sectional units) is the same

for all the individual groups for the panel analysis. 𝛽𝑖 equal to (𝛽𝑖(1),

𝛽𝑖(2), 𝛽𝑖

(3), … , 𝛽𝑖

(𝑘))′ denotes the coefficients at a constant time and it can differ

across individual countries in the panel. The null hypothesis for this augmented

version is: 𝐻0: 𝛽𝑖 = 0, ∀𝑖= 1, 2, 3, … , 𝑁 which states that there is no causality,

against the alternative, 𝐻1 : {𝛽𝑖 = 0, ∀𝑖= 1, 2, 3, … , 𝑁1

𝛽𝑖 ≠ 0, ∀𝑖= 𝑁1 + 1, 𝑁1 + 2, … , 𝑁 where the known 𝑁1

is conditioned as 0 ≤𝑁1

𝑁≤ 1. If the alternative hypothesis is satisfied, then causality

exists among at least one panel form [122]. The interpretation of the test results

includes an average Wald statistic across the individual groups in the panel and the

probability value (P-value) of the �̅� (Z-bar). This bivariate analysis is between

renewable energy consumption and the other variables considered in this thesis if

they are integrated of order one. The results are presented in section 5.5.

4.2.4 Fixed Effect Estimators and Variance Components Model (random effects)

The Specification test is essential in econometric studies but it has one major

challenge: the alternative hypothesis is not stated specifically [123]. Fixed effects

(FE) and random effect (RE) methods are used to mitigate the individual groups

(countries) effects in the presence of cross-section and period specific effect terms.

The specification test of FE uses orthogonal projections or first differences while that

of RE assumes that the individual cross-section effects 𝛿𝑖 and the period specific

effects 𝛾𝑖 are realizations of independent random variables with zero mean and finite

variance. Essentially, RE assumes that the effect is uncorrelated with the

idiosyncratic residual of the error term for the individual groups and time [124-127].

Three different models are estimated using renewable energy consumption as the

dependent variable. The first model is used to check the effects of societal variables

(HDI, URB, and INSQ) on the consumption of renewable energy. The second model

checks the effect of economic variables (GDPpc, FDI, and TOgdp) on renewable

65

energy consumption and the third model is a combination of the first two models as a

check of the results. The random effect and fixed effect estimators in this thesis

follow the expression of the standard panel data model:

𝑅𝑁𝐸𝑊%𝑖,𝑡 = 𝛼 + 𝛽1𝐻𝐷𝐼𝑖,𝑡 + 𝛽2𝑈𝑅𝐵𝑖,𝑡 + 𝛽3𝐼𝑁𝑆𝑄𝑖,𝑡 + 휀𝑖,𝑡 (7)

𝑅𝑁𝐸𝑊%𝑖,𝑡 = 𝛼 + 𝛽1𝑙𝑛𝐺𝐷𝑃𝑝𝑐𝑖,𝑡 + 𝛽2𝑙𝑛𝐹𝐷𝐼𝑐𝑜𝑛𝑠𝑖,𝑡 + 𝛽3𝑇𝑂𝑔𝑑𝑝𝑖,𝑡 + 휀𝑖,𝑡 (8)

𝑅𝑁𝐸𝑊%𝑖,𝑡 = 𝛼 + 𝛽1𝑙𝑛𝐺𝐷𝑃𝑝𝑐𝑖,𝑡 + 𝛽2𝑙𝑛𝐹𝐷𝐼𝑐𝑜𝑛𝑠𝑖,𝑡 + 𝛽3𝑇𝑂𝑔𝑑𝑝𝑖,𝑡 + 𝛽4𝐻𝐷𝐼𝑖,𝑡 +

𝛽5𝐼𝑁𝑆𝑄𝑖,𝑡 + 𝛽6𝑈𝑅𝐵𝑖,𝑡 + 휀𝑖,𝑡 (9)

Where 𝑅𝑁𝐸𝑊% is the dependent variable (renewable energy consumption), 𝛼

represents the time-invariant nuisance parameter, lnGDPpc and lnFDIcons are in

logarithmic transformations, while HDI, TOgdp, URB and INSQ are not (see table 4-

1), 휀 represents the independent and identical error term distributed across the cross-

sectional units 𝑖 (countries) in time 𝑡. The null hypothesis is [𝐻0: 𝑝𝑖,𝑗 = 𝑝𝑗,𝑖 =

𝑐𝑜𝑟(휀𝑖,𝑡, 휀𝑗,𝑡) = 0 𝑓𝑜𝑟 𝑖 ≠ 𝑗] and the alternative hypothesis is [𝐻1: 𝑝𝑖,𝑗 = 𝑝𝑗,𝑖 ≠

0 𝑓𝑜𝑟 𝑖 ≠ 𝑗] where 𝑝𝑖,𝑗 is the “product moment of correlation coefficient of the

disturbances”.

The Hausman’s specification test is used to compare the random and fixed effect

estimators [123]:

𝐻 = (𝛽𝑎 − 𝛽𝑏)′(𝑉𝑎 − 𝑉𝑏)−1(𝛽𝑎 − 𝛽𝑏) (10)

The Hausman’s test statistic (see equation (10)) follows a chi-square (𝑥2) distribution

under the null hypothesis that the difference in coefficients is not systematic.

𝛽𝑎 𝑎𝑛𝑑 𝛽𝑏 are the consistent and efficient estimator’s coefficient vectors

respectively; and 𝑉𝑎 𝑎𝑛𝑑 𝑉𝑏 are the consistent and efficient estimator’s covariance

matrices [123]. Results are shown and explained in section 5.6.

66

5.0 EMPIRICAL ANALYSIS AND RESULTS

5.1 Descriptive Analysis of Data

Studying the raw data series through descriptive statistics provides relevant

information about the characteristics of the data such as distribution, tendency,

frequency, etc. Table 5-1 shows the descriptive analysis for all the countries together.

The average of RNEW% for the 21 countries for the span of 24 years (1990 – 2013)

is 62.7% of the final energy consumption. As can be observed in Appendix A the

following countries: Burundi, Benin, Cameroon, Ghana, Guinea, Kenya, Mali,

Malawi, Rwanda, Sierra Leone, Swaziland, Togo, Tanzania, and Uganda, have their

average RNEW% above the mean (62.7%) of all the countries under study, hence

they are considered to have an intensive use of renewable energy sources. The

descriptive statistics for the countries in the Northern African region from 1990 –

2013 are shown in Table 5-2. The mean CO2Epc of the region is 1.79 metric tons

(mt) per capita which is considerably higher than the mean of the continent (1.05

mt), when all the 21 countries are considered. In this region, Tunisia has the highest

emissions per capita (2.09 mt), with Egypt second (1.93 mt), and Morocco the least

with 1.35 mt of carbon dioxide emissions in the region. The average GDPpc of the

region, 2482.72 US$ (constant 2010) is relatively high compared to the continent,

with Tunisia having a maximum of 4196.75 US$ which is the highest in the region;

Morocco with a maximum of 3077.32 US$ and Egypt 2602.48 US$ maximum.

Egypt has the lowest GDPpc in the region. Also, the region’s renewable energy

consumption (average, 12.76%) is low for the period of the study compared to the

other regions. The institutional quality of the region averaging 5.2 means that the

region is partly free in their PR and CL which does not foster free will and

development of populace. This region’s average HDI is the highest (0.61) in the

67

continent. The average FDI1 for all countries under study is -500000000 US$ echoed

in all the sub-regions of Africa, which means that, the disinvestment of foreign

investors was more than the investments, which translates into limited technology

spill over, limited transfer of labour and managerial skills. Considering the p-value of

the Jarque Bera test, the null hypothesis of normal distribution is rejected though

some variables in some regions are normally distributed. Thus, overall the variables

under consideration are not normally distributed, as such, logarithmic transformation

is applied to some of the study variables to have consistent variance.

Table 5-1. All Countries Descriptive Statistics

Note: All individual countries' descriptive analysis is presented in the Appendix A.

1 The countries Egypt, Senegal, Sierra Leone, Togo, Burundi, Malawi, Cameroon, Mauritius,

Botswana, South Africa, and Swaziland have a positive FDI for at least one year in the period of

study.

Statistic CO2Epc EFC FDI GDPpc HDI INSQ RNEW% TOgdp URB

Mean 1.0507 1.5128 -500000000 1756.69 0.4664 4.2019 62.6952 65.4874 34.9183

Median 0.2830 1.2715 -83657529 841.44 0.4545 4.5000 79.2725 57.0292 36.5915

Maximum 10.0407 3.8363 5.31E+09 8848.89 0.7690 7.0000 97.2914 170.4072 66.4560

Minimum 0.0207 0.5979 -1.38E+10 204.77 0.1940 1.0000 5.5846 19.6842 5.4160

Std. Dev. 1.9174 0.7168 1.48E+09 1952.77 0.1269 1.6181 30.4897 28.3976 15.5879

Skewness 3.2259 1.4717 -5.0464 1.6174 0.2176 -0.3289 -0.6419 1.0927 0.0032

Kurtosis 13.4559 4.4258 36.7815 4.6673 2.2857 1.7468 1.8257 3.7586 2.0921

Jarque-

Bera

3169.97 224.62 26104.14 278.13 14.69 42.07 63.56 112.37 17.31

Probability 0.0000 0.0000 0.0000 0.0000 0.0006 0.0000 0.0000 0.0000 0.0002

68

Table 5-2. Northern Africa Descriptive Statistics

Statistic CO2Epc EFC FDI GDPpc HDI INSQ RNEW% TOgdp URB

Mean 1.7912 1.6633 -1.54E+09 2482.72 0.6061 5.1875 12.7590 68.5296 53.4553

Median 1.7705 1.6453 -7.36E+08 2373.06 0.6100 5.5000 14.1979 63.2820 53.8260

Maximum 2.5997 2.2739 1.11E+09 4196.75 0.7220 6.0000 23.6188 115.3961 66.4560

Minimum 0.9463 1.1039 -1.09E+10 1540.94 0.4580 3.0000 5.5846 38.3615 42.6580

Std. Dev. 0.4638 0.2973 2.11E+09 719.81 0.0685 0.6578 4.2808 20.6644 8.7059

Skewness -0.0220 0.0444 -2.6967 0.8620 -0.3006 -0.9409 -0.0925 0.3482 0.0106

Kurtosis 1.9201 2.2020 10.7918 2.9349 2.4101 3.8458 2.2805 1.9805 1.5149

Jarque-

Bera

3.5045 1.9341 269.4018 8.9297 2.1283 12.7691 1.6555 4.5734 6.6175

Probability 0.1734 0.3802 0.0000 0.0115 0.3450 0.0017 0.4370 0.1016 0.0366

Table 5-3 shows the descriptive statistics for the Western African region. The

average CO2Epc emissions in this region were relatively low (0.25 mt) as compared

to other regions. The country with the lowest emissions is Sierra Leone with 0.11 mt

per capita and Ghana the highest with 0.36 mt per capita on average in the period

1990-2013. The average annual GDPpc at constant 2010 US$ and HDI of this region

is the lowest in the continent with 650.64 US$ and 0.39 respectively. The RNEW%

of this region for the period of study (74%) is higher on average than that of the

continent (63%), with an institutional quality of free and partly free countries.

69

Table 5-3. Western Africa Descriptive Statistics

Statistic CO2Epc EFC FDI GDPpc HDI INSQ RNEW% TOgdp URB

Mean 0.2499 1.2400 -2.16E+08 650.6434 0.3892 3.7545 74.2585 64.3555 36.5445

Median 0.2222 1.2140 -6.1E+07 609.9483 0.3950 3.5000 80.4642 60.2224 36.5915

Maximum 0.6291 1.9405 5.36E+08 1633.494 0.5760 6.5000 94.9888 125.0334 52.7350

Minimum 0.0489 0.9088 -3.29E+09 271.6896 0.2220 1.5000 40.4668 28.2780 23.3220

Std. Dev. 0.1511 0.2065 5.58E+08 261.1697 0.0770 1.5014 15.6560 17.5574 5.8368

Skewness 0.5701 1.2253 -4.2550 1.0266 -0.045 0.1848 -0.7461 0.8382 0.2163

Kurtosis 2.3381 4.6539 21.47388 4.1240 2.5462 1.6284 2.1679 3.5045 3.1914

Jarque-

Bera

12.1667 61.1885 2895.9250 38.35532 1.4970 14.1254 20.4319 21.4554 1.5664

Probability 0.0023 0.0000 0.0000 0.0000 0.4731 0.0009 0.0000 0.0000 0.4570

In Eastern Africa, Burundi has the lowest CO2 emission (0.04 mt per capita) in the

region and the continent for the period of study. Mauritius has the highest emissions

in this region (2.32 mt per capita of carbon dioxide) which is second to South Africa

in the continent. Mauritius’ average GDPpc is the second (5926.68 US$) in the

continent to South Africa (6425.37 US$). Also, the country with the lowest GDPpc

and urban population percentage in the continent is Burundi with 250.39 US$ and

8.68% respectively. The highest average renewable energy consumption in

percentage of total energy consumption in the continent (95.63%) is observed in

Burundi. See Table 5-4 for detailed descriptive analysis of all variables of east

Africa.

70

Table 5-4. Eastern Africa Descriptive Statistics

Statistic CO2Epc EFC FDI GDPpc HDI INSQ RNEW% TOgdp URB

Mean 0.4226 1.29 -302000000 1270.47 0.43 4.35 79.45 56.73 19.73

Median 0.0782 1.11 -41041270 471.28 0.42 4.50 88.03 49.17 16.35

Maximum 3.2410 3.33 989000000 8848.89 0.77 7.00 97.29 137.11 43.90

Minimum 0.0207 0.60 -13800000000 204.77 0.19 1.00 11.44 19.68 5.42

Std. Dev. 0.8157 0.68 1190000000 2006.52 0.13 1.62 23.59 29.66 10.73

Skewness 2.3425 1.61 -9.3763 2.3718 0.77 -0.41 -1.92 1.37 1.06

Kurtosis 7.0559 4.73 102.5367 7.3752 3.18 2.20 5.34 3.86 3.14

Jarque-

Bera

268.80 93.83 71814.45 291.50 16.66 9.25 141.22 58.02 31.52

Probability 0.0000 0.0000 0.0000 0.0000 0.0002 0.0098 0.0000 0.0000 0.0000

Table 5-5. Central Africa Descriptive Statistics (Cameroon)

Statistic CO2Epc EFC FDI GDPpc HDI INQ RNEW% TOgdp URB

Mean 0.2577 1.0072 -199000000 1140.44 0.4560 5.9792 82.9146 41.3999 46.4503

Median 0.2572 0.9810 -79405862 1171.45 0.4520 6.0000 84.4055 40.5056 46.4385

Maximum 0.3461 1.1602 128000000 1270.78 0.5070 6.5000 86.1299 52.3421 53.2500

Minimum 0.0921 0.8925 -810000000 994.64 0.4330 5.5000 77.5126 31.7452 39.6570

Std. Dev. 0.0640 0.0840 280000000 77.18 0.0230 0.2322 2.7535 4.6738 4.2012

Skewness -0.6330 0.6519 -1.1752 -0.5015 0.9238 -0.1679 -0.7969 0.4096 0.0035

Kurtosis 3.0191 1.9860 3.0587 2.3810 2.6437 4.7689 2.1060 3.2012 1.7827

Jarque-

Bera

1.6033 2.7282 5.5278 1.3893 3.5404 3.2420 3.3396 0.7116 1.4819

Probability 0.4486 0.2556 0.0630 0.4993 0.1703 0.1977 0.1883 0.7006 0.4767

Cameroon’s average carbon dioxide emissions per capita for the 24-year period is

0.26 mt (See Table 5-5). The country’s average GDPpc at constant 2010 US$ is

1140.44 for the period of study. The country is considered as not free with regards to

their average ratings of PR and CL (5.98).

71

The Southern African region descriptive analysis is shown in Table 5-6. This region

has the highest emissions of carbon dioxide per capita averaging 3.91 mt for the 24-

year span, mainly from South Africa (8.83 mt per capita). South Africa also has the

highest GDPpc (6425.37 US$) on the continent and their HDI (0.63) is second to

Tunisia.

Table 5-6. Southern Africa Descriptive Statistics

Statistic CO2Epc EFC FDI GDPpc HDI INSQ RNEW% TOGDP URB

Mean 3.9088 2.6828 -6.82E+08 4951.37 0.5886 3.3333 39.8255 93.5600 44.1836

Median 2.1299 3.0070 -79307850 5035.34 0.5885 2.5000 34.9580 93.2600 52.7960

Maximum 10.0407 3.8363 5.31E+09 7617.82 0.6970 6.0000 92.2630 170.407 63.7880

Minimum 0.1429 0.9565 -1.20E+10 2692.12 0.4920 1.5000 15.5799 37.4875 21.3370

Std. Dev. 3.5648 0.7617 2.36E+09 1548.63 0.0558 1.8193 22.4148 35.9823 16.0166

Skewness 0.6555 -0.3522 -2.8184 0.0767 -0.0096 0.4630 0.8152 0.1650 -0.5374

Kurtosis 1.5828 1.8142 13.8029 1.7306 1.9099 1.3987 2.7894 2.0353 1.4947

Jarque-

Bera

11.1819 5.7066 445.4308 4.9044 3.5658 10.265 8.1067 3.1186 10.2631

Probability 0.0037 0.0577 0.0000 0.0861 0.1681 0.0059 0.0174 0.2103 0.0059

72

5.2 Unit Root Test

The various tests for stationarity of variables have strengths and weaknesses. For

robust statistical analysis, this thesis uses seven (7) different panel unit root tests.

Breitung [128]; Fisher type (Phillips-Perron, PP and augmented Dickey-Fuller)

[129]; Im-Pesaran-Sin, IPS [130]; Harris-Tzavalis, HT [131]; and Levin-Lin-Chu,

LLC [132] unit root tests have their null hypothesis that panels contain unit root

against the alternative that panels are stationary. The results of the study reject the

null hypothesis at 10% significance level. The null hypothesis of the seventh test

(Hadri [133]), states that all panels are stationary and the alternative states that some

panels contain unit roots. Therefore, rejecting the null hypothesis in the first 6 tests

implies that panel is stationary, while in the seventh (Hadri) rejecting the null implies

that some panels contain unit roots.

Table 5-7 shows the results for the unit root test. The majority of the unit root tests

fail to reject the null hypothesis at level but reject the null hypothesis at first

difference. Thus, the results show that RNEW%, CO2Epc, EFC, FDI, GDPpc, HDI,

INSQ, and TOgdp are stationary at their first difference. Therefore, the above

variables are integrated of order one (I (1)) and meet the statistical requirement for

the Westerlund cointegration analysis. For urbanization, a minority of the tests (3 out

of 7) state that it has no unit roots. Hence, urbanization is not considered in the

subsequent analysis.

73

Table 5-7. Unit Root Test

Variable Breitung Fischer

type

IPS Hadri

LM*

LLC HT

lambda ADF chi2 PP chi2 z-t stats z-stats t-stats z-stats

level 1st Diff. level 1st Diff. level 1st Diff. level 1st Diff. level 1st Diff. level 1st Diff. level 1st Diff.

FDI -2.4659 -15.1284 137.8204 823.4700 137.8204 823.4700 -2.3161 -12.8502 15.8264 -4.2823 1.5552 -9.0195 -20.4358 -47.5857

Prob. 0.0068 0.0000 0.0000 0.0000 0.0000 0.0000 0.0103 0.0000 0.0000 1.0000 0.9400 0.0000 0.0000 0.0000

GDPpc 9.4521 -5.8794 11.6212 299.6814 11.6212 299.6814 10.8761 -8.3896 63.3660 5.5867 4.2411 -4.6222 5.6589 -29.6495

Prob. 1.0000 0.0000 1.0000 0.0000 1.0000 0.0000 1.0000 0.0000 0.0000 0.0000 1.0000 0.0000 1.0000 0.0000

RNEW% 3.1080 -10.6386 39.6260 489.1022 39.6260 489.1022 1.9358 -11.5595 49.8086 -1.0263 0.6450 -9.2964 1.0692 -32.9766

Prob. 0.9991 0.0000 0.5757 0.0000 0.5757 0.0000 0.9736 0.0000 0.0000 0.8476 0.7405 0.0000 0.8575 0.0000

EFC 0.8898 -11.2018 53.7299 629.6301 53.7299 629.6301 -0.4987 -12.6737 44.0201 -3.1475 -0.7113 -11.4159 -3.3373 -40.0207

Prob. 0.8132 0.0000 0.1060 0.0000 0.1060 0.0000 0.3090 0.0000 0.0000 0.9992 0.2384 0.0000 0.0004 0.0000

HDI 13.5292 -5.2193 14.8186 202.3559 14.8186 202.3559 13.7895 -5.6510 61.8275 14.2546 1.1698 -3.0898 5.2346 -15.7853

Prob. 1.0000 0.0000 1.0000 0.0000 1.0000 0.0000 1.0000 0.0000 0.0000 0.0000 0.8790 0.0010 1.0000 0.0000

URB 16.9093 7.1608 191.1762 134.9854 191.1762 134.9854 16.1214 4.7144 64.5219 29.3144 -6.1125 1.9461 4.1304 -13.2531

Prob. 1.0000 1.0000 0.0000 0.0000 0.0000 0.0000 1.0000 1.0000 0.0000 0.0000 0.0000 0.9742 1.0000 0.0000

CO2Epc 2.1453 -11.0028 38.4964 541.0063 38.4964 541.0063 1.4874 -11.9177 49.1486 -2.9137 0.0184 -7.1928 -1.0967 -36.5604

Prob. 0.9840 0.0000 0.6256 0.0000 0.6256 0.0000 0.9315 0.0000 0.0000 0.9982 0.5073 0.0000 0.1364 0.0000

INSQ -0.5379 -7.2288 140.8757 547.1646 140.8757 547.1646 -3.0270 -11.7497 34.4012 0.5436 -5.4482 -9.5770 -3.8858 -34.9341

Prob. 0.2953 0.0000 0.0000 0.0000 0.0000 0.0000 0.0012 0.0000 0.0000 0.2934 0.0000 0.0000 0.0001 0.0000

TOgdp -3.3014 -11.2787 65.8469 529.1217 65.8469 529.1217 -1.2514 -11.9427 24.3263 -0.9703 -0.8619 -9.8572 -4.3871 -37.1449

Prob. 0.0005 0.0000 0.0108 0.0000 0.0108 0.0000 0.1054 0.0000 0.0000 0.8341 0.1944 0.0000 0.0000 0.0000

Hadri* test: H0 – All panels are stationary Other tests: H0 – Panels contains unit roots

H1 – Some panels contain unit roots H1 – At least one or some panels is/are stationary

74

5.3 Utest Estimation

To check the validation of the EKC hypothesis for this panel data (Africa) a Utest

estimation was conducted. The results in Table 5-8 show that for this analysis and for

the period of 1990 – 2013, EKC for environmental pollution (CO2Epc) exists in

Africa as opposed to the studies done by Jebli et al., Zoundi, and Abid [92, 94, 99]

which failed to prove the validation of EKC on the African continent. The result on

the degradation of the environment is monotone, which indicates that an increase in

GDP will degrade the environment if proper measures are not put in place.

Sarkodie’s paper agrees with the confirmation of the EKC hypothesis for

environmental pollution but for degradation he finds a U shape relationship [134].

The results for the individual countries in Appendix B show that, for environmental

pollution, the EKC hypothesis is validated in Cameroon, Guinea, Kenya, Senegal and

Rwanda. The validation of EKC for the degradation of the environment was

observed in the following countries: Burundi, Benin, Cameroon, Rwanda, Sierra

Leone, Swaziland and Tanzania. The above countries in which the EKC hypothesis

is validated in the long term with increased income level (GDPpc2) have the potential

of continual protection of their environment for future generations.

Table 5-8. Utest Analysis for Africa

Statistic CO2Epc EFC

LB UB TP Structure LB UB TP Structure

Interval 204.77 8848.89 7246.47 Inverse U

shape

204.77 8848.89 10757.06 Monotone

Slope 0.0010 -0.0002 0.0003 0.0001

NB: LB denotes Lower Bound; UB denotes Upper Bound; TP denotes Extreme point

(turning point)

75

5.4 Cointegration Analysis (WECM)

Considering the results of the unit root test, one of the WECM requirements

(variables should be stationary at first difference) is met. Hence, I use the model to

examine the long-run and short-run nexus between the variables. As stated before in

the model description of WECM, the test has four alternative test statistics. The first

two (Gt and Ga test statistics), estimate the grouped individuals (countries), and the

other two (Pt and Pa test statistics), estimate the pooled information of all countries,

thus considering it as one. At 10% significance level, the null hypothesis of no

cointegration is rejected. To check for cross-sectional dependency in the panel, a

bootstrapping method for 1,000 samples is done. This produces robust probability

values that certify the decision of rejecting the null hypothesis. To interpret the

results, the error-correction coefficient also known as the speed of adjustment, should

be negative and significant. Table 5-9 and 5-10 show the results of the Westerlund

error-correction based model for the entire panel. Individual country results are

presented in Appendices, C and D.

The dependent variable in this analysis (lnCO2Epc), is a measure of environmental

pollution (Table 5-9). The results show an error-correction of 81% (-0.81), which is

the ability of the model to correct the previous disturbance in environmental

pollution. The results are statistically significant. In the long-term, a 1% increase in

renewable energy consumption reduces environmental pollution by 2.72% and in the

short-term reduces pollution by 3.14% in Africa. The relationship between GDP per

capita and pollution is statistically insignificant in Africa according to the WECM,

thus, the model does not validate the EKC hypothesis.

The findings of this thesis coincide with Attiaoui and Wesseh et al. [93, 96] in that,

renewable energy consumption in Africa reduces pollution. This validates the idea

that renewable energy sources are a more suitable source of energy compared to

fossil fuel that can sustainably protect the environment.

Table 5-10 shows the ecological footprint’s relationship with renewable energy

consumption and the impact of GDP per capita on the degradation of the

76

environment results. The error-correction is 76% (-0.76) and significant. Thus, 76%

of the deviation in the previous year is adjusted to equilibrium within one year. In the

long-term a one percent increase of GDPpc reduces environmental degradation by

19.3% while GDPpc2 increases degradation by 1.4% and a one percent increase in

renewable energy consumption increases degradation by 0.83%. In the short-term,

the nexus between environmental degradation, renewable energy consumption, and

GDPpc is not statistically significant. This could be due to the fact that

environmental degradation is conspicuously observed after decades of the actual

destruction since it is a gradual process.

The results of renewable energy increasing degradation can be explained, in that,

some of the technologies associated with renewable energy consumption pose some

threats to the environment. For instance, the production of energy from hydrological

sources, either run-off-river or dam type, biodiesel feedstock, and wind farms

threatens biodiversity loss [135, 136]. Examples are, the clearing of land and the

cutting of some tree shrubs for flooding areas for hydropower without replanting in

other areas, etc. disrupting the ecosystem habitation causing some species to be

extinct. The hypothesis of GDPpc increase in the long term reducing degradation of

the environment is not validated. This is because the relationship is not linear and at

the turning point degradation begins to increase. Though an increase in GDP per

capita reduces degradation GDPpc2 increases it. Contrary to the inversed U-shaped

hypothesis where environmental degradation increases at the initial stages of

economic development and declines thereafter reaching a threshold in economic

development, the results prove otherwise. This means that the initial stages of

Africa’s economic growth were characterized by low environmental degradation but

after reaching a turning point in economic development, they begun to increase the

exploitation of the natural resource base to meet the growing energy demand.

Africa’s initial economy was basically agrarian, however, some regions in sub-

Saharan Africa have gradually switched to or increased industrialization in sectors

such as mining, among others. Thus, it echoes the recent challenges associated with

oil exploration, illegal timber logging, illegal mining of minerals such as gold,

77

diamond, bauxite, the expansion in infrastructural and developmental projects,

among others.

In a nutshell, the empirical study for degradation validates the U-shape hypothesis

for WECM which controlled for RNEW% while the Utest results showed a

monotone curve. While the Utest results for pollution validated the EKC hypothesis

by showing an inverted-U relationship, the WECM did not.

78

Table 5-9. Nexus of Environmental Pollution (CO2Epc), Renewable Energy

Consumption, and GDP growth

lnCO2Epc

Mean-group error-correction

Coef. Std. Err. z P>z

lnRNEW% L1. -2.1402 0.7251 -2.9500 0.0030

lnGDPpc L1. 7.2017 10.9371 0.6600 0.5100

lnGDPpc2 L1. -0.6252 0.8447 -0.7400 0.4590

_cons -28.4396 32.1017 -0.8900 0.3760

Trend 0.0082 0.0077 1.0800 0.2820

lnCO2Epc L1. (_ec) -0.8095 0.0812 -9.9700 0.0000

lnRNEW% D1. -3.1393 0.8791 -3.5700 0.0000

lnGDPpc D1. -24.3119 25.9144 -0.9400 0.3480

lnGDPpc2 D1. 1.9701 2.0817 0.9500 0.3440

Long- and Short- run Equilibrium Relationship

Coef. Std. Err. z P>z

LR lnRNEW% -2.7194 0.9339 -2.9100 0.0040

lnGDPpc 0.5460 17.2753 0.0300 0.9750

lnGDPpc2 -0.0880 1.3298 -0.0700 0.9470

_cons -10.3916 50.2266 -0.2100 0.8360

Trend 0.0106 0.0091 1.1600 0.2480

_ec -0.8095 0.0812 -9.9700 0.0000

SR lnRNEW% D1. -3.1393 0.8791 -3.5700 0.0000

lnGDPpc D1. -24.3119 25.9144 -0.9400 0.3480

lnGDPpc2 D1 1.9701 2.0817 0.9500 0.3440

Statistic Value Z value P value Robust P value

Gt -3.3100 -3.1870 0.0010 0.0100

Ga -11.6400 2.2170 0.9870 0.0150

Pt -16.3510 -5.3790 0.0000 0.0310

Pa -12.9620 -0.4780 0.3160 0.0280

79

Table 5-10. Nexus of Environmental Degradation (EFC), Renewable Energy

Consumption, and GDP growth

LnEFC

Mean-group error-correction

Coef. Std. Err. z P>z

lnRNEW% L1. 0.7299 0.5310 1.3700 0.1690

lnGDPpc L1. -11.8287 6.4613 -1.8300 0.0670

lnGDPpc2 L1. 0.8802 0.4546 1.9400 0.0530

_cons 28.5113 22.9321 1.2400 0.2140

Trend 0.0042 0.0052 0.8000 0.4210

lnEFC L1. (_ec) -0.7618 0.0694 -10.9900 0.0000

lnRNEW% D1. 0.3785 0.4280 0.8800 0.3770

lnGDPpc D1. -10.7419 10.2404 -1.0500 0.2940

lnGDPpc2 D1. 0.9661 0.7770 1.2400 0.2140

Long- and Short- run Equilibrium Relationship

Coef. Std. Err. z P>z

LR lnRNEW% 0.8302 0.4453 1.8600 0.0620

lnGDPpc -19.3118 9.6272 -2.0100 0.0450

lnGDPpc2 1.4028 0.6732 2.0800 0.0370

_cons 47.8104 32.2504 1.4800 0.1380

Trend 0.0079 0.0077 1.0300 0.3040

_ec -0.7618 0.0694 -10.9900 0.0000

SR lnRNEW% D1. 0.3785 0.4280 0.8800 0.3770

lnGDPpc D1. -10.7419 10.2404 -1.0500 0.2940

lnGDPpc2 0.9661 0.7770 1.2400 0.2140

Statistic Value Z value P value Robust P value

Gt -3.1920 -2.5700 0.0050 0.0230

Ga -11.6320 2.2210 0.9870 0.0270

Pt -13.3690 -2.2110 0.0140 0.1250

Pa -12.4630 -0.1780 0.4290 0.0450

80

For the individual countries, in the long-term a one percentage increase in the

consumption of renewable energy reduces pollution in Tunisia (0.5%), Mali (6.4%),

Sierra Leone (1.7%), Togo (2.3%), Burundi (8.4%), Malawi (3.7%), Uganda

(11.9%), Cameroon (3.8%), Botswana (0.6%), South Africa (1.2%), and Swaziland

(1.9%). Pollution is increased in Morocco (0.2%) and Senegal (1.3%) with every 1%

increase in renewable energy consumption in the long-term. In the short-term a 1%

increase in renewable energy consumption reduces environmental pollution in

Tunisia (0.4%), Mali (9.9%), Sierra Leone (5.7%), Togo (2.5%), Burundi (9%),

Malawi (4.3%), Tanzania (9.2%), Uganda (12.6%), South Africa (0.5%) and

Swaziland (1.9%).

The above individual countries findings are also in agreement with Jebli et al. [92]

findings of renewable energy consumption having mixed effects on the environment.

The results show an increasing level of pollution in some countries while pollution

levels decline in others with relatively more renewable energy usage. The variations

in the individual countries results can be associated with the degree of traditional use

of renewable energy, such as, the burning of wood as fuel or charcoal without proper

measures been put in place to replace the fell tree and the regulations that govern

renewable energy consumption in each country.

In the long-term, GDP per capita increases pollution in Mali, Senegal, Burundi, and

Malawi, while, GDPpc2 reduces pollution in Egypt, Mali, Senegal, Burundi and

Malawi. Thus, the relationship between pollution and income (GDP) in Mali,

Senegal, Burundi, and Malawi validates the EKC hypothesis. An increase in GDPpc

reduces pollution in Senegal and Botswana while GDPpc2 increases pollution in the

short-term (shows a U-shape relationship). GDPpc increases pollution in Mali,

Burundi, and South Africa in the short-term while GDPpc2 decreases it (showing an

inverted-U relationship). The results for the other countries not mentioned are

statistically insignificant.

In Egypt, Mali, and Malawi, a 1% increase of renewable energy consumption

increases environmental degradation by 10.6 %, 3% and 1.3% correspondingly in the

long-term. In the short-term renewable energy consumption (a 1% increase)

81

increases degradation in Egypt (7.8%), Mali (3.1%), Malawi (1.8%) Mauritius

(0.4%), and reduces degradation in Benin (0.9%), Uganda (0.5%), Botswana (1.8%),

and Swaziland (0.5%). GDP growth reduces environmental degradation in Tunisia,

Benin, Mali, Burundi, Malawi, and Mauritius in the long-term, but it starts to

increase degradation at the turning point which gives a U-shape relationship. In the

short-term environmental degradation is reduced with an increase of GDPpc and

increases with GDPpc2 in Malawi, Mauritius, and South Africa (shows a U-shape

relationship).

82

5.5 Panel Causality Estimation

Table 5-11 shows the results of the Granger non-causality test (augmented version)

where the null hypothesis is that the independent variable does not Granger-cause the

dependent variable. The findings show that renewable energy consumption has a

bidirectional causality with CO2Epc, GDPpc, TOgdp, HDI, and INSQ for at least one

country. For this period of study, the causality of FDI with RNEW% is

unidirectional, thus renewable energy consumption drives FDI in at least one of the

countries under study in Africa.

Table 5-11. Granger non-causality panel test results

Variable Coef. P-value Direction of causality

lnRNEW%/ lnCO2Epc 2.2920 0.0219 Bidirectional

lnCO2Epc/ lnRNEW% 4.2231 0.0000

lnRNEW%/ lnGDPpc 9.4452 0.0000 Bidirectional

lnGDPpc/ lnRNEW% 4.0959 0.0000

lnRNEW%/ lnFDIcons 1.1254 0.2604 Unidirectional

RNEW% → FDI lnFDIcons/ lnRNEW% 8.6568 0.0000

lnRNEW%/ lnHDI 7.7494 0.0000 Bidirectional

lnHDI/ lnRNEW% 10.6589 0.0000

lnRNEW%/ lnTOgdp 4.9057 0.0000 Bidirectional

lnTOgdp/ lnRNEW% 4.9057 0.0000

lnRNEW%/ lnINSQ 2.4159 0.0157 Bidirectional

lnINSQ/ lnRNEW% 5.3642 0.0000

83

5.6 Estimation of Fixed and Random Effects

Having satisfied the precondition of fixed and random effect models that the

variables under investigation are integrated of order one (see Table 5-7), the study

proceeds with the estimation method using the fixed and random estimators. The

fixed effect and random effect model is estimated with RNEW% as the dependent

variable and lnFDIcons, lnGDPpc, TOgdp, INSQrev, and HDI as the independent

variables. In order to avoid missing data points, a constant is added to the raw values

to obtain a positive value of FDI prior to logarithmic transformation. The INQ values

are also reversed to ease the interpretation of results. Now, a higher average rating

means more freedom. There are three estimations in this section (see section 4.2.4).

The first (equation (7) except URB variable since it contains unit roots), seeks to

understand the relationship between ‘societal’ factors (HDI and INSQ) and

renewable energy consumption. The second (equation (8)), focuses on the

relationship of economic variables (GDPpc, FDI, and TOgdp) with renewable energy

consumption. The last (equation (9)), is run as a check by bringing all the

independent variables together except for HDI because it is highly correlated with

GDPpc (0.81 Pearson’s correlation coefficient). A table with the correlation

coefficients is presented in Appendix E. Tables 5-12, 5-13 and 5-14 show the

estimation results which are interpreted at 10% significance level.

The Hausman’s specification test was used as a post-estimation method to compare

the fixed and random effects estimates. According to the Hausman’s test (Table 5-12,

5-13 and 5-14) the null hypothesis of ‘difference in coefficients is not systematic’

cannot be rejected. Hence, the random effect model estimate is deemed appropriate

in interpreting the results.

The robust statistical analysis results from the random effect in Table 5-12 show that

an increase in HDI by 0.1 points reduces renewable energy consumption by 5.1

percentage points (pp). The nexus between RNEW% and institutional quality is not

statistically significant.

84

Table 5-12. Robust Fixed-effects (within) regression and Random-effects GLS

regression of societal effects

RNEW% Fixed.-effects Random .-effects

Coef. Robust

Std. Err.

t P>t Coef. Robust

Std. Err.

z P>z

HDI -42.7247 23.6881 -1.80 0.0860 -51.0526 23.6137 -2.16 0.0310

INSQrev -1.2562 0.9418 -1.33 0.1970 -1.1458 0.9138 -1.25 0.2100

_cons 87.3925 11.0091 7.94 0.0000 90.8570 11.1952 8.12 0.0000

sigma_u 25.5636 15.5355

sigma_e 6.6769 6.6769

rho 0.9361 0.8441

R-sq (overall) 0.6108 0.6322

Cluster-Robust

Hausman Test

chi2(2) = 4.12

Prob>chi2 = 0.1277

The statistical analysis results from the random effect in Table 5-13 show that a 1%

increase in GDPpc reduces RNEW% by 0.2 pp. A 1% increase in FDI increases

RNEW% by 0.002 pp. Trade openness does not have a statistically significant

relationship with RNEW%. Table 5-14 results concur with the results from Table 5-

12 and 5-13.

85

Table 5-13. Robust Fixed-effects (within) regression and Random-effects GLS

regression of economic status effects

RNEW% Fixed.-effects Random.-effects

Coef. Robust

Std. Err.

t P>t Coef. Robust Std.

Err.

z P>z

lnGDPpc -19.3222 6.9101 -2.80 0.0110 -20.3313 5.5452 -3.67 0.0000

lnFDIcons 0.2001 0.1114 1.79 0.0880 0.1834 0.0931 1.97 0.0490

TOgdp -0.0551 0.0533 -1.03 0.3130 -0.0499 0.0512 -0.97 0.3300

_cons 195.8757 49.6990 3.94 0.0010 202.9294 40.1248 5.06 0.0000

sigma_u 17.4501 16.8090

sigma_e 6.3167 6.3167

rho 0.8841 0.8763

R-sq (overall) 0.6704 0.6726

Cluster-Robust

Hausman Test

chi2(3) = 0.50

Prob>chi2 = 0.9193

Table 5-14. Robust Fixed-effects (within) regression and Random-effects GLS

regression of all independent variables except HDI

RNEW% Fixed.-effects Random.-effects

Coef. Robust

Std. Err.

t P>t Coef. Robust

Std. Err.

Z P>z

lnGDPpc -18.0774 6.6269 -2.73 0.0130 -19.2574 5.2942 -3.64 0.0000

lnFDIcons 0.2126 0.110 1.92 0.0700 0.1932 0.0932 2.07 0.0380

TOgdp -0.0385 0.0560 -0.69 0.4990 -0.0348 0.0536 -0.65 0.5170

INSQrev -1.0614 0.7452 -1.42 0.1700 -1.0001 0.7240 -1.38 0.1670

_cons 189.8846 48.0247 3.95 0.0010 198.0535 39.0116 5.08 0.0000

sigma_u 17.8294 17.1302

sigma_e 6.2695 6.2695

rho 0.8899 0.8819

R-sq (overall) 0.6654 0.6685

Cluster-Robust

Hausman Test

chi2(4) = 0.27

Prob>chi2 = 0.9918

86

6.0 DISCUSSION

The above robust empirical analysis shows that the global debate on renewable

energy playing a significant role in the environmental sector is somehow ‘justified’.

The mitigation of global warming amidst climate change controversies must be dealt

with by all possible means, as such, this thesis contributes to the discussion from the

African perspective and compares the outcome with studies from different continents

(countries).

The results of the thesis reveal that renewable energy usage in Africa reduces

environmental pollution in the short-term and long-term. This agrees with a study on

OECD countries whose results show that an increase in renewable energy

consumption reduces pollution in the long-term [137]. On the other hand, in Senegal

and Morocco results shows a positive effect of renewable energy consumption on

environmental pollution. This can be associated generally, with the governmental

regulations and how individuals collect and use biomass which increases carbon

dioxide emissions.

The U-test results for environmental pollution validate the EKC hypothesis for

Africa and when controlled for renewable energy usage (in WECM) the EKC

hypothesis is no longer validated. Although GDP per capita was statistically

insignificant in the panel for the entire continent, it was statistically significant for

some countries. In Senegal and Botswana an increase in GDP per capita reduces

pollution in the short-term and increases pollution in Mali, Burundi and South

Africa. In the long-term, it increases pollution in Mali, Senegal, Burundi and Malawi.

The U-test proved the existence of EKC in Africa with an extreme point of $7246.47

USD for the period of study. This means that at the initial stages of development

pollution increases and at a turning point of $7246.47 income level environmental

pollution begins to decline. This may be explained by the observation that the

traditional and unsustainable agricultural practices adopted in Africa some years ago

played a role in the increasing levels of pollution, however, the introduction of

modern technologies and sustainable agricultural practices during pre-harvest,

87

harvest and post-harvest seasons have helped in reducing environmental pollution.

This concurs with some studies in Africa [92, 94, 99] and in Europe (Central and

Eastern) for the period 1980 – 2002 by Atici [138].

To leave the environment as a bequest for future generations, environmental

degradation requires the same attention that is given to environmental pollution. To

this effect, this thesis analysed the effect of renewable energy usage and GDP per

capita on degradation. The results showed that in the long-term, renewable energy

consumption increases degradation in Africa. Forest reserves and other land areas are

converted to make space for hydropower dam construction, which is one of the most

established renewable energy technologies on the continent which causes some level

of degradation to the environment. Africa’s major use of energy comes from biomass

in the form of wood fuel for warming and cooking. As such, timber logging, charcoal

production and deforestation are rampant in the sub region which contributes

immensely to environmental degradation (see section 2.2).

GDP per capita was observed to reduce environmental degradation in the long-term.

GDPpc2, on the other hand, increases degradation. This does not agree with the EKC

hypothesis that growth in income (GDP) eventually reduces degradation. While the

WECM shows a U-shaped relationship, the Utest, on the other hand, shows a

monotone structure. Infrastructural development is good for the development of a

nation but if not regulated properly it might, in the end, cause more harm than good

(environmental degradation).

The nexus between renewable energy consumption, carbon dioxide emissions,

income (GDPpc), human development index, trade openness, and institutional

quality in a bivariate analysis show results of a feedback phenomenon. These

variables can help in the prediction of positive effects with each other provided all

things being equal.

The relationship between renewable energy consumption and the ‘social’ (HDI and

INSQ), and ‘economic’ (GDPpc, FDI and TOgdp) variables was also analysed. In

considering how ‘societal’ factors affect renewable energy consumption, the result

88

shows that a higher HDI in Africa reduces renewable energy consumption. Higher

HDI implies higher life expectancy, higher education, and higher income (GDP per

capita). This provides the populace with better living standards, information and

high-income level that could drive their decisions to protect their environment.

However, the results show otherwise. This can be explained by the review of the 21

countries which showed that most of the consumption of renewable energy comes

from using ‘traditional’ methods (e.g. burning wood for cooking) [See section 2.2].

Therefore, with higher HDI the traditional methods of renewable energy

consumption will decline while modern methods (e.g. LPG for cooking), increase in

automobiles that run on biofuel or electric batteries, and development of more

industries, etc. will increase. Considering the economic status across the continent,

traditional energy usage is a relatively cheaper option compared to advanced

renewable energy technologies, hence the reduction in renewable energy

consumption.

In view of the relationship between economic variables and renewable energy

consumption, it is observed that economic growth reduces renewable energy

consumption. This can be discussed from two viewpoints: first, one of the goals of

most developing and underdeveloped countries is to grow economically. As energy

drives economic growth and fossil energy is a cheaper option, its consumption will

be increased to promote income growth. Secondly, a growth in income (GDP) can

prompt the populace to seek for sustainable options and reduce the traditional

methods of renewable energy consumption. The shift is capital intensive hence, the

percentage of renewable energy consumption declines. Contrary to the findings of

this thesis (increase in GDP per capita reduces renewable energy consumption in

Africa), a study (by Sadorsky [139]) in the G7 countries (Canada, France, Germany,

Italy, Japan, the United Kingdom and the United States) shows that GDP per capita

is a driver of renewable energy usage. The differences can be explained considering

the fact that, the G7 countries are developed while most African countries are still

developing. Therefore, after some point, with increased income (GDP growth) on the

89

African continent, an improvement in the ‘modern’ usage of renewable energy can

be expected.

The relationship between renewable energy consumption and foreign direct

investment inflows is positive. In accordance with the Sustainable Development Goal

17 which ensures global partnership, the study reveals that foreign direct investment

inflows remain one of the major sources of external financing coupled with

technological innovation and managerial skill transfers to the continent. Therefore, a

sustained growth in foreign direct investment inflows will assist the region in the

achievement of a long-term reduction in climate change related outcomes while

promoting sustainable development. Thus, foreign direct investment can improve

renewable energy technologies in Africa. As such, foreign direct investment can be

considered as a driver of renewable energy consumption.

90

7.0 CONCLUSION

The quest for better living conditions has moved the standards of living through

various technological stages of development. Though these developments were

essential and beneficial, it has come to our attention that natural resources must be

preserved through a collective effort. Hence, we have to move towards a sustainable

development. Africa still has low percentages of energy access and security.

Renewable energy can help mitigate these challenges while protecting the

environment.

Studies on the EKC hypothesis have improved over the years with the inclusion of

various variables. As such, the first analysis of this thesis examined the validity of

the EKC hypothesis for environmental pollution and degradation controlling for

renewable energy consumption. The Utest results show the existence of an inverse U

relationship with pollution and monotone relationship with degradation. The findings

of WECM established that the EKC hypothesis is invalid for both pollution and

degradation. In the long-term and short-term renewable energy consumption reduces

pollution while in the long-term its usage increases degradation. GDP per capita has

a positive effect on degradation.

As part of the objectives of this thesis, the study examined the major drivers of

renewable energy consumption in Africa by considering the nexus between

renewable energy consumption and some ‘societal’ and economic factors. The

empirical findings show that foreign direct investment is a driver for renewable

energy consumption in Africa. The causality estimation results show that there is a

bidirectional causality between the variables under study and renewable energy

consumption apart from urbanization and unidirectional for foreign direct

investment.

In sum, though renewable energy consumption has a positive effect on the

environment, the literature review showed that most of these renewable energy

sources consumed in Africa are mostly through ‘traditional’ methods which in the

long-term degrades the environment. Considering the empirical findings, it is

91

recommended that policymakers would improve their efforts in educating their

populace on ‘clean’ energy and energy efficiency. Secondly, environmental policies

that can promote re-planting and conservation of forest areas should be researched

and implemented. Thirdly, development cooperation (such as World Bank

partnership with recipient governments) should research into clean and modern

renewable energy alternatives that are affordable and reliable in Africa.

Research into economic policies that can promote income growth and foreign direct

investment is recommended. Future studies should aim at examining the

demographics and urbanization effects of climate change adaptation options on

renewable energy consumption in the region.

92

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APPENDICES

Appendix A: Descriptive Statistics of Individual Countries

Country/

Statistics

CO2Epc EFC FDI GDPpc HDI INSQ RNEW

%

TOgdp URB

BDI

Mean 0.0382 0.8682 -5908792 250.3887 0.3023 5.6042 95.6290 36.1071 8.6795

Median 0.0314 0.9040 -406809 233.0125 0.2745 5.5000 95.2522 38.3244 8.5715

Maximum 0.0609 0.9933 236531 337.7084 0.4040 7.0000 97.2914 54.1518 11.4720

Minimum 0.0207 0.5979 -117000000 219.1866 0.2620 4.0000 93.2257 20.9641 6.2710

Std. Dev. 0.0141 0.1103 23694741 38.2227 0.0490 0.9323 1.2342 10.3004 1.5922

Skewness 0.2174 -0.8984 -4.5119 1.4351 1.1218 0.0604 -0.2521 -0.0977 0.1668

Kurtosis 1.3683 3.0871 21.5794 3.4597 2.6552 1.6847 1.8564 1.7687 1.8358

Jarque-Bera 2.8515 3.2364 426.6223 8.4497 5.1527 1.7445 1.5619 1.5543 1.4666

Probability 0.2403 0.1983 0.0000 0.0146 0.0760 0.4180 0.4580 0.4597 0.4803

BEN

Mean 0.3311 1.1211 -87177738 697.6320 0.4090 2.2708 70.6643 55.9208 38.8908

Median 0.2707 1.0915 -55541440 714.8188 0.4115 2.0000 65.4457 56.5079 38.8095

Maximum 0.5795 1.3142 -1403787 805.9449 0.4750 5.0000 94.9888 69.4615 43.0860

Minimum 0.1422 0.9975 -302000000 609.3456 0.3450 2.0000 50.0962 45.7202 34.4850

Std. Dev. 0.1560 0.0833 85720383 61.6194 0.0406 0.6252 17.1790 6.4083 2.4581

Skewness 0.5159 0.8700 -1.2351 -0.0943 -0.0269 3.6523 0.3314 0.0365 0.0304

Kurtosis 1.6052 2.6823 3.3783 1.6734 1.6313 16.507 1.5393 2.4097 1.9583

Jarque-Bera 3.0101 3.1283 6.2451 1.7956 1.8763 235.81 2.5730 0.3538 1.0888

Probability 0.2220 0.2093 0.0440 0.4075 0.3914 0.0000 0.2762 0.8379 0.5802

BWA

Mean 2.0862 2.8556 -221000000 5237.350 0.6089 2.1042 36.8518 95.5332 52.4003

Median 2.1299 3.0170 -148000000 5035.337 0.5885 2.0000 34.9580 93.2600 54.2180

Maximum 2.5480 3.7752 349000000 7409.421 0.6970 2.5000 48.2732 122.9491 56.9380

Minimum 1.7108 1.8629 -1.38*10^9 3860.009 0.5580 1.5000 27.1608 85.8340 41.9330

Std. Dev. 0.2005 0.5334 344000000 1032.314 0.0476 0.3290 7.5280 9.0861 4.1715

Skewness 0.0467 -0.2719 -1.4124 0.4089 0.7291 -0.2246 0.3174 1.2746 -0.9161

Kurtosis 2.8735 1.8394 6.5843 2.0526 1.9915 2.3142 1.5376 4.4993 2.8282

Jarque-Bera 0.0247 1.6428 20.8270 1.5665 3.1436 0.6722 2.5415 8.7461 3.3867

Probability 0.9877 0.4398 0.0000 0.4569 0.2077 0.7146 0.2806 0.0126 0.1839

107

CMR

Mean 4250.21 1.0072 -199000000 1140.441 0.4560 5.9792 82.9146 41.3999 46.4503

Median 3839.35 0.9810 -79405862 1171.451 0.4520 6.0000 84.4055 40.5056 46.4385

Maximum 6813.29 1.1602 128000000 1270.778 0.5070 6.5000 86.1299 52.3421 53.2500

Minimum 1111.10 0.8925 -810000000 994.6423 0.4330 5.5000 77.5126 31.7452 39.6570

Std. Dev. 1507.27 0.0840 280000000 77.1756 0.0230 0.2322 2.7535 4.6738 4.2012

Skewness 0.1506 0.6519 -1.1752 -0.5015 0.9238 -0.1679 -0.7969 0.4096 0.0035

Kurtosis 2.5605 1.9860 3.0587 2.3810 2.6437 4.7689 2.1060 3.2012 1.7827

Jarque-Bera 0.2840 2.7282 5.5278 1.3893 3.5404 3.2420 3.3396 0.7116 1.4819

Probability 0.8676 0.2556 0.0630 0.4993 0.1703 0.1977 0.1883 0.7006 0.4767

EGY

Mean 1.9322 1.69 -2.54*10^9 2031.124 0.6170 5.6042 7.4761 50.8056 42.9764

Median 1.9136 1.63 -1.03*10^9 1986.552 0.6190 5.5000 7.6253 49.0907 43.0010

Maximum 2.5282 2.00 1110000000 2602.480 0.6860 6.0000 9.8290 71.6806 43.4780

Minimum 1.3228 1.41 -1.09*10^10 1540.944 0.5470 4.5000 5.5846 38.3615 42.6580

Std. Dev. 0.4226 0.2087 3260000000 376.1425 0.0434 0.3895 1.3289 9.7281 0.1970

Skewness -0.0019 0.2364 -1.3488 0.3001 -0.0350 -0.9339 -0.0753 0.4346 0.5261

Kurtosis 1.5760 1.6012 3.6757 1.7245 1.8106 3.8930 1.6055 2.0389 3.4163

Jarque-Bera 2.0278 2.1802 7.7332 1.9873 1.4197 4.2859 1.9672 1.6793 1.2802

Probability 0.3628 0.3362 0.0209 0.3702 0.4917 0.1173 0.3740 0.4319 0.5272

GHA

Mean 0.3573 1.5572 -885000000 1071.090 0.5023 2.8021 66.2436 77.2845 44.7703

Median 0.3547 1.6010 -155000000 992.0853 0.4870 2.5000 67.4951 77.9887 44.9380

Maximum 0.5549 1.9405 -14800000 1633.494 0.5760 6.0000 82.9284 116.0484 52.7350

Minimum 0.2575 1.0779 -3.29*10^9 823.5817 0.4550 1.5000 44.0118 42.4884 36.4410

Std. Dev. 0.0683 0.2626 1230000000 229.1632 0.0380 1.4199 13.0894 20.4309 5.0102

Skewness 0.8763 -0.2671 -1.0993 1.1170 0.6698 0.8516 -0.3028 -0.0139 -0.0629

Kurtosis 4.1929 1.9549 2.4315 3.2810 2.0433 2.5100 1.7122 2.2228 1.8049

Jarque-Bera 4.4943 1.3776 5.1573 5.0697 2.7098 3.1408 2.0251 0.6048 1.4441

Probability 0.1057 0.5022 0.0759 0.0793 0.2580 0.2080 0.3633 0.7391 0.4858

GIN

Mean 0.1671 1.3067 -133000000 421.2474 0.3337 5.5000 84.0846 60.2011 31.7537

Median 0.1553 1.3428 -34385000 428.2842 0.3270 5.5000 84.3682 56.3896 31.5285

Maximum 0.2519 1.4126 0 453.5832 0.4120 6.5000 89.9303 91.6884 36.2090

Minimum 0.1195 1.0902 -955000000 382.6046 0.2710 5.0000 77.5230 42.4151 28.0260

Std. Dev. 0.0376 0.1001 228000000 24.1590 0.0447 0.3612 3.4682 13.7786 2.4981

Skewness 0.9119 -1.1692 -2.4820 -0.2830 0.2156 1.4142 -0.0645 0.7497 0.2126

108

Kurtosis 2.7902 2.9533 8.6847 1.6304 1.7650 6.0000 2.2413 2.5375 1.8543

Jarque-Bera 3.3701 5.4699 56.9570 2.1962 1.7113 17.000 0.5923 2.4619 1.4934

Probability 0.1854 0.0649 0.0000 0.3335 0.4250 0.0002 0.7437 0.2920 0.4739

KEN

Mean 0.2717 1.1236 -118000000 893.5105 0.4815 4.6458 79.4948 57.3143 20.5430

Median 0.2689 1.1114 -53981355 870.1764 0.4695 4.2500 79.5250 55.4171 20.4150

Maximum 0.3371 1.3232 -5302623 1048.269 0.5460 6.5000 83.1830 72.8585 24.7800

Minimum 0.1979 0.9972 -693000000 823.0919 0.4470 3.0000 76.2731 48.1923 16.7480

Std. Dev. 0.0420 0.0998 171000000 62.4913 0.0333 1.2290 1.6109 6.6425 2.4706

Skewness -0.0409 0.5835 -2.2176 1.0408 0.7533 0.0503 0.1043 1.1938 0.1247

Kurtosis 2.0019 2.0927 7.2422 3.1390 2.0797 1.4943 3.0510 3.6704 1.8069

Jarque-Bera 1.0029 2.1850 37.6679 4.3523 3.1168 2.2771 0.0461 6.1503 1.4857

Probability 0.6056 0.3354 0.0000 0.1135 0.2105 0.3203 0.9772 0.0462 0.4758

MAR

Mean 1.3477 1.4126 -1.22*10^9 2236.962 0.5447 4.7083 16.6094 62.5309 53.9768

Median 1.2755 1.4083 -847000000 2110.984 0.5455 4.5000 16.9693 59.2899 53.8260

Maximum 1.8817 1.7898 -165000000 3077.315 0.6400 5.5000 23.6188 85.6728 59.2000

Minimum 0.9463 1.1039 -2.92*10^9 1699.404 0.4580 4.0000 11.4714 47.0955 48.3910

Std. Dev. 0.2847 0.2175 926000000 452.7674 0.0576 0.3269 2.7637 12.9461 3.0463

Skewness 0.3551 0.2005 -0.4706 0.4940 0.0645 0.3133 0.3397 0.5461 -0.0051

Kurtosis 1.7374 1.8917 1.6475 1.8540 1.7278 2.9155 3.3384 1.9007 2.1054

Jarque-Bera 2.0984 1.3891 2.7152 2.2892 1.6351 0.3999 0.5760 2.4016 0.8004

Probability 0.3502 0.4993 0.2573 0.3183 0.4415 0.8188 0.7498 0.3010 0.6702

MLI

Mean 0.0630 1.3327 -159000000 601.7924 0.3184 2.9375 86.3156 54.8609 29.9747

Median 0.0656 1.3399 -89323666 620.4215 0.3130 2.5000 86.1536 55.8334 29.4405

Maximum 0.0757 1.5062 21874497 709.3994 0.4300 6.0000 88.1534 63.7880 38.3630

Minimum 0.0489 1.1598 -749000000 481.2549 0.2220 2.0000 84.7166 46.2456 23.3220

Std. Dev. 0.0105 0.0991 194000000 81.3047 0.0670 1.1259 1.1590 4.8090 4.7606

Skewness -0.2540 0.0054 -1.5844 -0.1403 0.1646 1.6523 0.2823 -0.2560 0.2611

Kurtosis 1.3496 1.8549 4.9534 1.3836 1.7203 4.5003 1.7559 2.1531 1.7727

Jarque-Bera 2.9818 1.3114 13.8567 2.6915 1.7460 13.171 1.8666 0.9794 1.7791

Probability 0.2252 0.5191 0.0010 0.2603 0.4177 0.0014 0.3933 0.6128 0.4109

MUS

Mean 2.3190 2.7830 -879000000 5926.677 0.6900 1.4792 24.7557 121.7674 42.1592

Median 2.3949 2.6647 -37210830 5698.253 0.6850 1.5000 19.5795 122.0175 42.3550

Maximum 3.2410 3.3332 989000000 8848.887 0.7690 2.0000 47.0678 137.1121 43.9000

109

Minimum 1.3819 2.2182 -1.38*10^10 3707.857 0.6200 1.0000 11.4376 104.4297 39.9790

Std. Dev. 0.6411 0.3582 3010000000 1594.324 0.0468 0.2322 11.9832 8.0366 1.2378

Skewness -0.0738 0.2145 -3.7094 0.3604 0.1983 -0.1679 0.5187 -0.2388 -0.2878

Kurtosis 1.4957 1.5914 15.9763 1.9213 1.7524 4.7689 1.6986 2.5066 1.7828

Jarque-Bera 2.2848 2.1682 223.4231 1.6831 1.7138 3.2420 2.7700 0.4715 1.8129

Probability 0.3190 0.3382 0.0000 0.4310 0.4245 0.1977 0.2503 0.7900 0.4040

MWI

Mean 0.0733 0.7157 -90659687 387.3869 0.3879 3.8125 82.0725 60.5092 14.3330

Median 0.0732 0.7142 -25496307 381.4782 0.3870 3.5000 82.0300 57.3254 14.7420

Maximum 0.0870 0.8241 4343678 471.8405 0.4660 6.5000 84.2278 91.3780 15.9440

Minimum 0.0649 0.6087 -812000000 315.9296 0.3250 2.5000 79.7350 41.9008 11.5600

Std. Dev. 0.0048 0.0554 182000000 45.4003 0.0398 1.2407 1.2549 11.4902 1.2917

Skewness 0.6840 0.0495 -3.0860 0.5159 0.4634 1.1731 -0.0253 0.8949 -0.8162

Kurtosis 3.8026 2.3111 12.0302 2.3319 2.4311 3.4538 2.2738 3.4673 2.4665

Jarque-Bera 2.5158 0.4844 119.6385 1.5110 1.1824 5.7107 0.5300 3.4220 2.9495

Probability 0.2843 0.7849 0.0000 0.4698 0.5536 0.0575 0.7672 0.1807 0.2288

RWA

Mean 0.0669 0.8237 -40642875 415.2684 0.3471 5.9375 87.8306 35.8399 16.0305

Median 0.0665 0.8370 -7325774 385.9852 0.3510 5.7500 88.9176 33.1112 16.3030

Maximum 0.0788 0.9193 0 640.9474 0.4880 7.0000 91.3019 71.0956 26.8690

Minimum 0.0559 0.7293 -258000000 204.7727 0.1940 5.5000 79.6227 19.6842 5.4160

Std. Dev. 0.0079 0.0638 65374304 114.9911 0.0999 0.4959 3.2169 10.3106 6.7249

Skewness 0.2167 -0.1725 -1.9434 0.4838 -0.1076 0.5249 -1.0685 1.5358 -0.0816

Kurtosis 1.6367 1.5896 6.2616 2.3935 1.6280 1.8116 3.1662 6.7802 1.8505

Jarque-Bera 2.0465 2.1082 25.7456 1.3040 1.9286 2.5145 4.5945 23.7241 1.3480

Probability 0.3594 0.3485 0.0000 0.5210 0.3812 0.2844 0.1005 0.0000 0.5097

SEN

Mean 0.4524 1.2329 -120000000 902.0407 0.4063 3.2708 48.3008 65.7167 40.7058

Median 0.4336 1.2334 -63127044 885.2857 0.3990 3.2500 48.3395 66.0947 40.5630

Maximum 0.6291 1.3665 29959372 1007.410 0.4830 4.5000 55.5510 78.6194 43.0790

Minimum 0.3549 1.0731 -291000000 789.7914 0.3650 2.0000 40.4668 49.6369 38.9000

Std. Dev. 0.0772 0.0728 108000000 77.2283 0.0390 0.7515 5.0718 7.4746 1.2283

Skewness 1.0429 -0.3749 -0.4107 0.0663 0.5294 0.1930 -0.0992 -0.2758 0.3378

Kurtosis 3.0609 2.8009 1.5656 1.4315 1.9083 1.8624 1.7165 2.7164 2.0446

Jarque-Bera 4.3542 0.6017 2.7324 2.4777 2.3128 1.4430 1.6868 0.3847 1.3691

Probability 0.1134 0.7402 0.2551 0.2897 0.3146 0.4860 0.4303 0.8250 0.5043

SLE

110

Mean 0.1143 1.1117 -122000000 362.8952 0.3250 4.3125 87.0971 51.6605 36.0614

Median 0.1114 1.1023 -21424054 348.4224 0.3140 4.0000 87.2118 46.9558 35.9880

Maximum 0.1722 1.2113 7462924 550.8760 0.4260 6.5000 93.9179 93.2741 39.2260

Minimum 0.0826 1.0241 -950000000 271.6896 0.2620 2.5000 75.2248 28.2780 33.2520

Std. Dev. 0.0197 0.0508 243000000 62.1895 0.0536 1.3738 4.5340 16.9470 1.7956

Skewness 1.1329 0.4222 -2.4677 1.1911 0.4009 0.5816 -0.8975 1.0503 0.1294

Kurtosis 4.5758 2.2317 8.0245 4.6624 1.7769 1.8959 3.4750 3.3561 1.8779

Jarque-Bera 7.6173 1.3032 49.6034 8.4382 2.1390 2.5719 3.4476 4.5396 1.3260

Probability 0.0222 0.5212 0.0000 0.0147 0.3432 0.2764 0.1784 0.1033 0.5153

SWZ

Mean 0.8108 1.8034 -54041795 3191.389 0.5246 5.7083 65.3393 133.2709 22.3550

Median 0.9142 1.8851 -55623825 3006.123 0.5245 5.5000 62.8551 133.5522 22.4900

Maximum 1.1980 2.2052 77263421 3994.957 0.5510 6.0000 92.2630 170.4072 23.0810

Minimum 0.1429 0.9565 -132000000 2692.122 0.4920 5.0000 47.8245 99.1588 21.3370

Std. Dev. 0.3094 0.3320 51584102 416.8635 0.0196 0.2918 16.5720 19.0979 0.6414

Skewness -0.7253 -1.0695 0.4988 0.4188 -0.2374 -0.3414 0.4957 -0.0607 -0.3322

Kurtosis 2.3114 3.4015 3.0246 1.6899 1.7204 2.2150 1.7226 2.5467 1.5335

Jarque-Bera 2.5785 4.7363 0.9956 2.4179 1.8628 1.0824 2.6145 0.2202 2.5921

Probability 0.2755 0.0937 0.6079 0.2985 0.3940 0.5821 0.2706 0.8957 0.2736

TGO

Mean 0.2642 1.0159 -5374547 497.8059 0.4299 5.1875 77.1034 84.8442 33.6545

Median 0.2382 1.0223 -35817585 490.5861 0.4280 5.5000 77.4865 83.9392 33.5825

Maximum 0.4397 1.1126 536000000 568.9842 0.4750 6.0000 91.4986 125.0334 38.9790

Minimum 0.1999 0.9088 -204000000 411.1814 0.3890 4.0000 63.3977 56.4784 28.5890

Std. Dev. 0.0641 0.0558 142000000 32.1558 0.0230 0.5278 6.0928 15.9633 3.1969

Skewness 1.3433 -0.2615 2.7629 -0.1513 0.1626 -0.5700 -0.1389 0.3972 0.0553

Kurtosis 3.9800 2.0341 10.8536 4.0525 2.4112 2.3956 3.5413 2.9870 1.7964

Jarque-Bera 8.1778 1.2064 92.2147 1.1992 0.4525 1.6650 0.3701 0.6312 1.4610

Probability 0.0168 0.5471 0.0000 0.5490 0.7975 0.4350 0.8310 0.7293 0.4817

TUN

Mean 2.0939 1.8935 -860000000 3180.077 0.6567 5.2500 14.1915 92.2522 63.4125

Median 2.1211 1.8801 -577000000 3099.062 0.6645 5.5000 14.2775 89.9016 64.0100

Maximum 2.5997 2.2739 -77419355 4196.752 0.7220 6.0000 16.0700 115.3961 66.4560

Minimum 1.6115 1.3337 -3.24*10^9 2227.471 0.5690 3.0000 12.6878 77.9051 57.9460

Std. Dev. 0.2879 0.2464 773000000 685.3288 0.0507 0.8076 0.6571 10.1669 2.5152

Skewness 0.0744 -0.4020 -1.7115 0.1363 -0.3091 -1.7393 0.1193 0.6451 -0.6694

Kurtosis 1.7549 2.3745 5.4803 1.5674 1.7357 4.9100 5.0266 2.4292 2.3401

111

Jarque-Bera 1.5723 1.0375 17.8691 2.1266 1.9807 15.748 4.1640 1.9905 2.2280

Probability 0.4556 0.5953 0.0001 0.3453 0.3714 0.0004 0.1247 0.3696 0.3282

TZA

Mean 0.1181 1.2392 -606000000 567.0953 0.4220 4.1458 92.1750 47.3683 23.6260

Median 0.1074 1.2246 -423000000 523.8325 0.4045 3.7500 92.4305 46.7967 22.8590

Maximum 0.2123 1.4231 0 777.4036 0.5130 6.0000 95.1776 65.6907 30.1960

Minimum 0.0762 1.1496 -2.09*10^9 456.8931 0.3670 3.0000 86.3543 33.4909 18.8840

Std. Dev. 0.0379 0.0629 629000000 105.3468 0.0544 0.9722 2.4524 9.2354 3.4746

Skewness 0.9267 1.1135 -1.0766 0.6262 0.4687 0.4852 -0.8926 0.5707 0.4134

Kurtosis 3.0124 4.2693 2.9854 1.9569 1.6527 1.7861 3.3329 2.5395 1.9435

Jarque-Bera 3.4352 6.5705 4.6369 2.6568 2.6941 2.4154 3.2979 1.5151 1.7996

Probability 0.1795 0.0374 0.0984 0.2649 0.2600 0.2989 0.1923 0.4688 0.4066

UGA

Mean 0.0712 1.4916 -374000000 452.9515 0.3982 4.7917 94.1636 38.1711 12.7362

Median 0.0603 1.5009 -193000000 429.9940 0.4110 4.5000 94.5035 35.7424 12.2100

Maximum 0.1304 1.6882 0 632.5564 0.4830 6.0000 96.9470 56.2583 15.4370

Minimum 0.0356 1.2244 -1.16*10^9 302.1261 0.3030 4.0000 89.9752 26.6095 11.0760

Std. Dev. 0.0291 0.1161 367000000 110.1024 0.0650 0.5500 1.9285 8.4863 1.3097

Skewness 0.6828 -0.4749 -0.9106 0.3020 -0.1980 0.4847 -0.4301 0.6622 0.6977

Kurtosis 2.0835 3.1521 2.4952 1.8422 1.5409 2.3079 2.1376 2.3613 2.1962

Jarque-Bera 2.7048 0.9254 3.5713 1.7054 2.2858 1.4187 1.4835 2.1620 2.5935

Probability 0.2586 0.6296 0.1677 0.4263 0.3189 0.4920 0.4763 0.3393 0.2734

ZFA

Mean 8.8294 3.3894 -1.77*10^9 6425.366 0.6324 2.1875 17.2852 51.8761 57.7953

Median 8.7354 3.3591 -461000000 6129.963 0.6325 1.7500 17.1898 51.4198 57.6330

Maximum 10.0407 3.8363 5310000000 7617.819 0.6600 4.5000 19.1214 72.8654 63.7880

Minimum 7.7774 3.0489 -1.2*10^10 5517.513 0.6090 1.5000 15.5799 37.4875 52.0370

Std. Dev. 0.5881 0.1925 3910000000 761.0297 0.0161 1.0916 1.0088 9.3164 3.6189

Skewness 0.3983 0.3712 -1.0868 0.3881 -0.0090 1.5553 0.0819 0.2279 0.0645

Kurtosis 2.7102 2.7363 4.1479 1.5200 1.6884 3.7413 1.8737 2.3816 1.7904

Jarque-Bera 0.7186 0.6208 6.0422 2.7929 1.7207 10.226 1.2954 0.5903 1.4799

Probability 0.6982 0.7332 0.0487 0.2475 0.4230 0.0060 0.5232 0.7444 0.4771

112

Appendix B: Utest Analysis for individual countries

CO2Epc EFC

Country Statistics LB UB TP Structure LB UB TP Structure

BEN Interval 609.35 805.95 313.51 Monotone 609.35 805.95 637.76 Inverse U

shape Slope 0.023 -0.010 -0.001 0.004

BDI Interval 219.19 337.71 301.11 U shape 219.19 337.71 235.89 Inverse U

shape Slope 0.0056 0.0093 -0.0005 0.0028

BWA Interval 3860 7409.4 721.75 Monotone 3860 7409.4 6391 U shape

Slope 0.00004 0.00009 0.0004 -0.0002

CMR Interval 994.64 1270.78 1191 Inverse U

shape

994.64 1270.78 1026.64 Inverse U

shape Slope -0.0021 0.0009 -0.0002 0.002

EGY Interval 1540.94 2602.48 2735.06 Monotone 1540.94 2602.48 3590.81 Monotone

Slope 0.0011 0.0001 0.0004 0.0002

GHA Interval 823.58 1633.49 2180.42 Monotone 823.58 1633.49 1423.35 U shape

Slope 0.0009 0.0004 0.0017 -0.0006

GIN Interval 382.61 453.58 410.29 Inverse U

shape

382.61 453.58 74.53 Monotone

Slope -0.0114 0.0178 0.0020 0.0025

KEN Interval 823.09 1048.27 862.29 Inverse U

shape

823.09 1048.27 901.23 U shape

Slope -0.0003 0.0014 0.0011 -0.0021

MAR Interval 1699.40 3077.32 3659.34 Monotone 1699.40 3077.32 3525.16 Monotone

Slope 0.0007 0.0002 0.0005 0.0001

MLI Interval 481.26 709.40 623.23 U shape 481.26 709.40 3245.24 Monotone

Slope 0.0072 -0.0044 0.0008 0.0008

MUS Interval 3707.86 8848.89 8359.71 U shape 3707.86 8848.89 14558.7 Monotone

Slope 0.0004 -

0.00004

0.0010 0.0001

MWI Interval 315.93 471.84 428.18 U shape 315.93 471.84 1852.05 Monotone

Slope 0.0015 -0.0006 0.0015 0.0014

RWA Interval 204.77 640.95 507.48 Inverse U

shape

204.77 640.95 403.92 Inverse U

shape Slope -0.0022 0.0010 -0.0007 0.0008

SEN Interval 789.79 1007.41 853.71 Inverse U 789.79 1007.41 800.32 U shape

113

Slope -0.0016 0.0038 shape 0.00002 -0.0003

SLE Interval 271.69 550.88 201.95 Monotone 271.69 550.88 365.14 Inverse U

shape Slope 0.0007 0.0033 -0.0006 0.0012

SWZ Interval 2692.12 3994.96 3389.72 U shape 2692.12 3994.96 3254.89 Inverse U

shape Slope 0.0025 -0.0022 -0.0005 0.0007

TGO Interval 411.18 568.98 498.43 U shape 411.18 568.98 316.75 Monotone

Slope 0.0082 -0.0067 0.0005 0.0014

TUN Interval 2227.47 4196.75 7670.15 Monotone - - - -

Slope 0.0002 0.0002 - -

TZA Interval 456.89 777.40 262.60 Monotone 456.89 777.40 632.91 Inverse U

shape Slope 0.0015 0.0039 -0.0009 0.0008

UGA Interval 302.13 632.56 -6475.3 Monotone 302.13 632.56 -189.28 Monotone

Slope 0.0034 0.0036 -0.0005 -0.0008

ZAF Interval 5517.51 7617.82 8210.84 Monotone 5517.51 7617.82 7568.23 U shape

Slope 0.0001 0.00002 0.0001 -

0.000003

114

Appendix C: WECM Panel Cointegration Tests by Country for Environmental

Pollution

Country/ Statistics lnCO2Epc

Coef. Std. Err. Z P>|z|

EGY

lnRNEW% L1. -0.3323 0.3935 -0.8400 0.3980

lnGDPpc L1. 18.4018 11.8856 1.5500 0.1220

lnGDPpc2 L1. -1.1365 0.7519 -1.5100 0.0131

_cons -73.6999 34.6907 -2.1200 0.0340

Trend 0.0005 0.0190 0.0200 0.980

_ec -1.2262 0.3261 -3.7600 0.0000

lnRNEW% D1. -0.1875 0.2938 -0.6400 0.5230

lnGDPpc D1. 18.5810 45.8758 0.4100 0.6850

lnGDPpc2 D1. -1.0748 2.9976 -0.3600 0.7200

TUN

lnRNEW% L1. -0.5122 0.2660 -1.9300 0.0540

lnGDPpc L1. 0.9857 2.9586 0.3300 0.7390

lnGDPpc2 L1. 0.0216 0.1796 0.1200 0.9040

_cons 35.8940 20.1933 1.7800 0.0750

Trend -0.0215 0.0101 -2.1300 0.0330

_ec -1.2018 0.1950 -6.1600 0.0000

lnRNEW% D1. -0.4352 0.1437 -3.0300 0.0020

lnGDPpc D1. -13.1830 10.4312 -1.2600 0.2060

lnGDPpc2 D1. 0.8474 0.6468 1.3100 0.1900

MAR

lnRNEW% L1. 0.2244 0.1011 2.2200 0.0260

lnGDPpc L1. -13.2024 10.4188 -1.2700 0.2050

lnGDPpc2 L1. 0.8617 0.6594 1.3100 0.1910

_cons -7.3917 0.6594 1.3100 0.7330

Trend 0.0288 0.0152 1.9000 0.0580

_ec -0.9655 0.3315 -2.9100 0.0040

lnRNEW% D1. 0.0700 0.09178 0.7600 0.4470

lnGDPpc D1. -1.8086 20.6446 -0.0900 0.9300

115

lnGDPpc2 D1. 0.1140 1.3657 0.0800 0.9330

BEN

lnRNEW% L1. -1.5972 1.2246 -1.3000 0.1920

lnGDPpc L1. -48.1236 110.9811 -0.4300 0.6650

lnGDPpc2 L1. 3.4192 8.5289 0.4000 0.6880

_cons 142.9798 369.8524 0.3900 0.6990

Trend 0.0159 0.0315 0.5000 0.6150

_ec -0.4588 0.2553 -1.8000 0.0720

lnRNEW% D1. -0.5010 1.3565 -0.3700 0.7120

lnGDPpc D1. -36.2307 125.9496 -0.2900 0.7740

lnGDPpc2 D1. 2.7295 9.5759 0.2900 0.7760

GHA

lnRNEW% L1. 1.6482 1.0919 1.5100 0.1310

lnGDPpc L1. -66.4966 60.6203 -1.1000 0.2730

lnGDPpc2 L1. 4.6530 4.1112 1.1300 0.2580

_cons 11.1311 96.1864 0.1200 0.9080

Trend 0.1091 0.0719 1.5200 0.1290

_ec -0.9026 0.2607 -3.4600 0.0010

lnRNEW% D1. 1.3513 1.0278 1.3100 0.1890

lnGDPpc D1. -5.9696 83.3613 -0.0700 0.9430

lnGDPpc2 D1. 0.3526 5.7835 0.0600 0.9510

GIN

lnRNEW% L1. 1.8376 3.3014 0.5600 0.5780

lnGDPpc L1. -49.6642 286.2694 -0.1700 0.8620

lnGDPpc2 L1. 3.9823 23.8969 0.1700 0.8680

_cons 87.7622 908.0273 0.1000 0.9230

Trend 0.0291 0.0348 0.8400 0.4030

_ec -0.2855 0.1867 -1.5300 0.1260

lnRNEW% D1. -0.2316 2.3538 -0.1000 0.9220

lnGDPpc D1. -473.3757 284.0974 -1.6700 0.0960

lnGDPpc2 D1. 39.1313 23.4459 1.6700 0.0950

MLI

lnRNEW% L1. -6.4013 3.4762 -1.8400 0.0660

lnGDPpc L1. 92.1224 33.53212 2.7500 0.0060

116

lnGDPpc2 L1. -7.1607 2.5966 -2.7600 0.0060

_cons -243.9401 89.8634 -2.71 0.0070

Trend -0.0129 0.0108 -1.1900 0.2320

_ec -0.7741 0.3167 -2.4400 0.0150

lnRNEW% D1. -9.8842 1.6311 -6.0600 0.0000

lnGDPpc D1. 63.0221 25.9887 2.4200 0.0150

lnGDPpc2 D1. -4.8937 2.0336 -2.4100 0.0160

SEN

lnRNEW% L1. 1.2874 0.3004 4.2900 0.0000

lnGDPpc L1. 139.1939 69.4309 2.0000 0.0450

lnGDPpc2 L1. -10.1827 5.1212 -1.9900 0.0470

_cons -507.7959 245.1170 -2.0700 0.0380

Trend 0.0133 0.0092 1.4400 0.1490

_ec -0.6689 0.1367 -4.8900 0.0000

lnRNEW% D1. -0.0534 0.3011 -0.1800 0.8590

lnGDPpc D1. -176.031 95.7459 -1.8400 0.0660

lnGDPpc2 D1. 13.0586 7.0741 1.8500 0.0650

SLE

lnRNEW% L1. -1.7241 2.1390 -0.8100 0.0420

lnGDPpc L1. 14.8278 14.6944 1.0100 0.3130

lnGDPpc2 L1. -1.2638 1.2583 -1.0000 0.3150

_cons -13.2173 57.4236 -0.2300 0.8180

Trend -0.0120 0.0099 -1.2100 0.2240

_ec -0.6464 0.2972 -2.1800 0.0300

lnRNEW% D1. -5.7038 1.2963 -4.4000 0.0000

lnGDPpc D1. -5.3112 9.9933 -0.5300 0.5950

lnGDPpc2 D1. 0.5276 0.8635 0.6100 0. 5410

TGO

lnRNEW% L1. -2.3190 0.7416 -3.1300 0.0020

lnGDPpc L1. -47.5340 43.9648 -1.0800 0.2800

lnGDPpc2 L1. 3.8332 3.5389 1.0800 0.2790

_cons 153.5113 135.2361 1.1400 0.2560

Trend 0.0014 0.0039 0.3600 0.7190

_ec -0.7875 0.2125 -3.7100 0.0000

117

lnRNEW% D1. -2.5262 0.3049 -8.2900 0.0000

lnGDPpc D1. -39.0916 27.7793 -1.4100 0.1590

lnGDPpc2 D1. 3.0735 2.2477 1.3700 0.1720

BDI

lnRNEW% L1. -8.3725 3.4140 -2.4500 0.0140

lnGDPpc L1. 99.8630 30.6026 3.2600 0.0010

lnGDPpc2 L1. -8.8701 2.7218 -3.2600 0.0010

_cons -176.4834 75.2110 -2.3500 0.0190

Trend -0.0346 0.0101 -3.4300 0.0010

_ec -0.9494 0.2542 -3.7300 0.0000

lnRNEW% D1. -9.0094 2.0872 -4.3200 0.0000

lnGDPpc D1. 72.2316 22.1462 3.2600 0.0010

lnGDPpc2 D1. -6.3131 1.9697 -3.2100 0.0010

KEN

lnRNEW% L1. -2.4305 4.1588 -0.5800 0.5590

lnGDPpc L1. -17.6829 115.1729 -0.1500 0.8780

lnGDPpc2 L1. 1.2841 8.4358 0.1500 0.8790

_cons 71.1382 400.0292 0.1800 0.8590

Trend -0.000003 0.0078 -0.0001 0.9970

_ec -0.3276 0.2622 -1.2500 0.2120

lnRNEW% D1. -5.8605 2.7078 -2.1600 0.0300

lnGDPpc D1. 101.6400 195.0736 0.5200 0.6020

lnGDPpc2 D1. -7.4811 14.3732 -0.5200 0.6030

MWI

lnRNEW% L1. -3.7009 1.3412 -2.7600 0.0060

lnGDPpc L1. 22.5820 11.6014 1.9500 0.0520

lnGDPpc2 L1. -1.9175 0.9754 -1.9700 0.0490

_cons -57.9705 33.2771 -1.7400 0.0810

Trend 0.0030 0.0029 1.0300 0.3040

_ec -0.7120 0.2655 -2.6800 0.0070

lnRNEW% D1. -4.2607 0.7716 -5.5200 0.0000

lnGDPpc D1. 2.0563 17.1266 0.1200 0.9040

lnGDPpc2 D1. -1.1733 1.4595 -0.1200 0.9050

MUS

118

lnRNEW% L1. -0.2256 0.1554 -1.4500 0.1470

lnGDPpc L1. 6.5920 4.2616 1.5500 0.1220

lnGDPpc2 L1. -0.4058 0.2202 -1.8400 0.0650

_cons -85.2627 61.3068 -1.3900 0.1640

Trend 0.0299 0.0355 0.8400 0.4000

_ec -0.6502 0.2886 -2.2500 0.0240

lnRNEW% D1. -0.1961 0.1388 -1.4100 0.1580

lnGDPpc D1. 24.3624 45.0337 0.5400 0.5890

lnGDPpc2 D1. -1.3597 2.5937 -0.5200 0.6000

RWA

lnRNEW% L1. -0.5189 0.4895 -1.0600 0.2890

lnGDPpc L1. -4.0897 2.5775 -1.5900 0.1130

lnGDPpc2 L1. 0.3507 0.2155 1.6300 0.1040

_cons 18.6564 15.1201 1.2300 0.2170

Trend -0.0024 0.0055 -0.4400 0.6610

_ec -0.1240 0.2016 -0.6200 0.5380

lnRNEW% D1. -0.8981 0.6543 -1.3700 0.1700

lnGDPpc D1. -1.5894 5.0850 -0.3100 0.7550

lnGDPpc2 D1. 0.1445 0.4471 0.3200 0.7460

TZA

lnRNEW% L1. -2.5406 3.5492 -0.7200 0.4740

lnGDPpc L1. 5.4373 24.8952 0.2200 0.8270

lnGDPpc2 L1. -0.3604 2.0014 -0.1800 0.8570

_cons 23.4421 88.7289 0.2600 0.7920

Trend -0.0165 0.0185 -0.8900 0.3730

_ec -0.4926 0.2121 -2.3200 0.0200

lnRNEW% D1. -9.1485 3.1129 -2.9400 0.0030

lnGDPpc D1. -20.2730 72.8403 -0.2800 0.7810

lnGDPpc2 D1. 1.7429 5.6687 0.3100 0.7580

UGA

lnRNEW% L1. -11.8656 5.6244 -2.1100 0.0350

lnGDPpc L1. -1.1254 3.8458 -0.2900 0.7700

lnGDPpc2 L1. 0.2035 0.3321 0.6100 0.5400

_cons 116.5042 52.6781 2.2100 0.0270

119

Trend -0.0329 0.0188 -1.7500 0.0810

_ec -0.9290 0.3517 -2.6400 0.0080

lnRNEW% D1. -12.6443 2.4593 -5.1400 0.0000

lnGDPpc D1. -22.2907 16.4916 -1.3500 0.1760

lnGDPpc2 D1. 1.9505 1.3578 1.4400 0.1510

CMR

lnRNEW% L1. -3.8089 1.9861 -1.9200 0.0550

lnGDPpc L1. -7.7134 169.9144 -0.0500 0.9640

lnGDPpc2 L1. -0.0840 12.0979 -0.0100 0.9940

_cons -98.3817 578.6773 -0.1700 0.8650

Trend 0.0855 0.0244 3.5000 0.0000

_ec -1.7558 0.2243 -7.8300 0.0000

lnRNEW% D1. -3.2544 2.7202 -1.2000 0.2320

lnGDPpc D1. 20.9886 200.8493 0.1000 0.9170

lnGDPpc2 D1. -2.1970 14.4436 -0.1500 0.8790

BWA

lnRNEW% L1. -0.5525 0.3018 -1.8300 0.0670

lnGDPpc L1. -5.2611 13.3602 -0.3900 0.6940

lnGDPpc2 L1. 0.25294 0.7490 0.3400 0.7360

_cons 4.5655 45.8174 0.1000 0.9210

Trend 0.0123 0.0160 0.7700 0.4440

_ec -0.9366 0.1980 -4.7300 0.0000

lnRNEW% D1. -0.1846 0.3643 -0.5100 0.6120

lnGDPpc D1. -78.1634 16.1405 -4.8400 0.0000

lnGDPpc2 D1. 4.5159 0.9313 4.8500 0.0000

ZFA

lnRNEW% L1. -1.1486 0.3539 -3.2500 0.0010

lnGDPpc L1. -4.5152 18.8457 -0.2400 0.8110

lnGDPpc2 L1. 0.2826 1.0727 0.2600 0.7920

_cons 37.0397 83.5117 0.4400 0.6570

Trend -0.0067 0.0055 -1.2000 0.2290

_ec -1.1695 0.2499 -4.6800 0.0000

lnRNEW% D1. -0.4696 0.2770 -1.7000 0.0900

lnGDPpc D1. 87.3745 41.9406 2.0800 0.0370

120

lnGDPpc2 D1. -4.9330 2.3812 -2.0700 0.0380

SWZ

lnRNEW% L1. -1.8919 0.9545 -1.9800 0.0470

lnGDPpc L1. 16.6390 245.6104 0.0700 0.9460

lnGDPpc2 L1. 0.8926 15.3121 -0.0600 0.9540

_cons -35.7119 1052.4970 -0.0300 0.9730

Trend -0.0164 0.0703 -0.2300 0.8160

_ec -1.0343 0.2637 -3.9200 0.0000

lnRNEW% D1. -1.8970 0.6265 -3.0300 0.0020

lnGDPpc D1. -27.4879 318.2445 -0.0900 0.9310

lnGDPpc2 D1. 1.6102 19.4862 0.0800 0.9340

121

Appendix D: WECM Panel Cointegration Tests by Country for Environmental

Degradation

Country/ Statistics LnEFC

Coef. Std. Err. Z P>|z|

EGY

lnRNEW% L1. 10.5833 1.6120 6.5700 0.0000

lnGDPpc L1. -3.0325 10.3520 -0.2900 0.7700

lnGDPpc2 L1. 0.2594 0.9207 0.2800 0.7780

_cons 28.1551 31.0719 0.9100 0.3650

Trend -0.0339 0.0041 -8.1700 0.0000

_ec -1.5850 0.1880 -8.4300 0.0000

lnRNEW% D1. 7.8173 0.8882 8.8000 0.0000

lnGDPpc D1. 4.2065 9.7416 0.4300 0.6660

lnGDPpc2 D1. -0.3451 0.8660 -0.4000 0.6900

TUN

lnRNEW% L1. -0.2631 0.3383 -0.7800 0.4370

lnGDPpc L1. -66.5325 26.2759 -2.5300 0.0110

lnGDPpc2 L1. 5.0726 2.0246 2.5100 0.0120

_cons 219.0936 91.5098 2.3900 0.0170

trend 0.0001 0.0075 0.0100 0.9890

_ec -0.6229 0.2201 -2.8300 0.0050

lnRNEW% D1. -0.3075 0.3651 -0.8400 0.4000

lnGDPpc D1. 3.1762 33.7076 0.0900 0.9250

lnGDPpc2 D1. -0.1474 2.5626 -0.0600 0.9540

MAR

lnRNEW% L1. -0.1922 0.5353 -0.3600 0.7200

lnGDPpc L1. 26.0285 25.1794 1.0300 0.3010

lnGDPpc2 L1. -1.5282 1.4153 -1.0800 0.2800

_cons -155.7965 89.9963 -1.7300 0.0830

trend 0.0233 0.0310 0.7500 0.4520

_ec -0.9305 0.2342 -3.9700 0.0000

lnRNEW% D1. -0.2246 0.7312 -0.3100 0.7590

lnGDPpc D1. 39.3515 33.3127 1.1800 0.2370

122

lnGDPpc2 D1. -2.2852 1.9222 -1.1900 0.2340

BEN

lnRNEW% L1. -0.1312 0.4501 -0.2900 0.7710

lnGDPpc L1. -44.6497 25.2433 -1.7700 0.0770

lnGDPpc2 L1. 3.1746 1.7979 1.7700 0.0770

_cons 144.6199 85.8008 1.6900 0.0920

Trend 0.0065 0.0041 1.5800 0.1140

_ec -0.4264 0.2562 -1.6600 0.0960

lnRNEW% D1. -0.8674 0.4092 -2.1200 0.0340

lnGDPpc D1. 17.6247 32.2307 0.5500 0.5840

lnGDPpc2 D1. -1.2919 2.3242 -0.5600 0.5780

GHA

lnRNEW% L1. -0.2057 0.2704 -0.7600 0.4470

lnGDPpc L1. -4.5776 7.6228 -0.6000 0.5480

lnGDPpc2 L1. 0.2862 0.4842 0.5900 0.5540

_cons -3.6842 21.4129 -0.1700 0.8630

Trend 0.0113 0.0136 0.8300 0.4060

_ec -0.6050 0.2388 -2.5300 0.0110

lnRNEW% D1. -0.2098 0.1766 -1.1900 0.2350

lnGDPpc D1. 11.3272 31.7290 0.3600 0.7210

lnGDPpc2 D1. -0.6659 2.0686 -0.3200 0.7480

GIN

lnRNEW% L1. 0.5301 0.4577 1.1600 0.2470

lnGDPpc L1. 7.5211 22.6716 0.3300 0.7400

lnGDPpc2 L1. -0.5019 1.5356 -0.3300 0.7440

_cons -68.5723 49.6670 -1.3800 0.1670

Trend 0.0193 0.0253 0.7600 0.4460

_ec -0.7550 0.2656 -2.8400 0.0040

lnRNEW% D1. -0.0655 0.4453 -0.1500 0.8830

lnGDPpc D1. 11.8279 30.7272 0.3800 0.7000

lnGDPpc2 D1. -0.8040 2.1317 -0.3800 0.7060

MLI

lnRNEW% L1. 3.0031 1.2321 2.4400 0.0150

lnGDPpc L1. -18.1877 7.8183 -2.3300 0.0200

123

lnGDPpc2 L1. 1.5347 0.6383 2.4000 0.0160

_cons 0.0000 - - -

trend 0.0204 0.0094 2.1600 0.0310

_ec -0.7368 0.2505 -2.9400 0.0030

lnRNEW% D1. 3.0737 0.5791 5.3100 0.0000

lnGDPpc D1. -150.9878 92.5341 -1.6300 0.1030

lnGDPpc2 D1. 12.4654 7.6326 1.6300 0.1020

SEN

lnRNEW% L1. -0.1936 0.8098 -0.2400 0.8110

lnGDPpc L1. 19.1438 32.9330 0.5800 0.5610

lnGDPpc2 L1. -1.3810 2.4162 -0.5700 0.5680

_cons -42.1088 113.6153 -0.3700 0.7110

trend -0.0116 0.0045 -2.6100 0.0090

_ec -0.8059 0.2712 -2.9700 0.0030

lnRNEW% D1. -0.4463 0.6905 -0.6500 0.5180

lnGDPpc D1. -27.1381 60.9147 -0.4500 0.6560

lnGDPpc2 D1. 2.0416 4.4888 0.4500 0.6490

SLE

lnRNEW% L1. -0.0340 0.0927 -0.3700 0.7130

lnGDPpc L1. -1.4079 8.5083 -0.1700 0.8690

lnGDPpc2 L1. 0.1402 0.5333 0.2600 0.7930

_cons 30.6128 21.2041 1.4400 0.1490

trend -0.0139 0.0103 -1.3500 0.1760

_ec -0.6487 0.2225 -2.9100 0.0040

lnRNEW% D1. 0.0655 0.0867 0.7600 0.4500

lnGDPpc D1. -23.8918 20.9426 -1.1400 0.2540

lnGDPpc2 D1. 1.7484 1.3928 1.2600 0.2090

TGO

lnRNEW% L1. 0.6946 1.7106 0.4100 0.6850

lnGDPpc L1. -0.5383 13.2683 -0.0400 0.9680

lnGDPpc2 L1. 0.0985 1.0440 0.0900 0.9250

_cons 5.7771 51.5700 0.1100 0.9110

trend -0.0046 0.0063 -0.7300 0.4640

_ec -0.9492 0.3183 -2.9800 0.0030

124

lnRNEW% D1. 0.5346 1.5017 0.3600 0.7220

lnGDPpc D1. -12.9562 17.8574 -0.7300 0.4680

lnGDPpc2 D1. 1.0780 1.4090 0.7700 0.4440

BDI

lnRNEW% L1. -0.0371 0.1071 -0.3500 0.7290

lnGDPpc L1. -6.9339 3.9912 -1.7400 0.0820

lnGDPpc2 L1. 0.3437 0.2035 1.6900 0.0910

_cons -53.9849 53.1724 -1.0200 0.3100

trend 0.0446 0.0321 1.3900 0.1650

_ec -0.9010 0.2620 -3.4400 0.0010

lnRNEW% D1. -0.0522 0.1170 -0.4500 0.6550

lnGDPpc D1. -37.4256 41.6025 -0.9000 0.3680

lnGDPpc2 D1. 2.1455 2.3939 0.9000 0.3700

KEN

lnRNEW% L1. 0.3453 1.6176 0.2100 0.8310

lnGDPpc L1. -2.6004 12.5733 -0.2100 0.8360

lnGDPpc2 L1. 0.2579 1.0548 0.2400 0.8070

_cons 3.3525 35.9220 0.0900 0.9260

trend 0.0006 0.0036 0.1700 0.8670

_ec -0.7917 0.3095 -2.5600 0.0110

lnRNEW% D1. -0.3411 1.1034 -0.3100 0.7570

lnGDPpc D1. 12.3326 22.8537 0.5400 0.5890

lnGDPpc2 D1. -1.0122 1.9467 -0.5200 0.6030

MWI

lnRNEW% L1. 1.2536 0.3789 3.3100 0.0010

lnGDPpc L1. -4.2153 1.7833 -2.3600 0.0180

lnGDPpc2 L1. 0.3728 0.1518 2.4600 0.0140

_cons 40.9967 9.8189 4.1800 0.0000

trend -0.0174 0.0035 -4.9300 0.0000

_ec -0.3798 0.1649 -2.3000 0.0210

lnRNEW% D1. 1.7956 0.5049 3.5600 0.0000

lnGDPpc D1. -14.5209 4.0788 -3.5600 0.0000

lnGDPpc2 D1. 1.2833 0.3593 3.5700 0.0000

MUS

125

lnRNEW% L1. -0.2176 0.1523 -1.4300 0.1530

lnGDPpc L1. -90.9733 35.0317 -2.6000 0.0090

lnGDPpc2 L1. 6.7207 2.5837 2.6000 0.0090

_cons 333.1711 123.8442 2.6900 0.0070

trend -0.0122 0.0047 -2.6000 0.0090

_ec -0.8955 0.1698 -5.2800 0.0000

lnRNEW% D1. 0.3585 0.1540 2.3300 0.0200

lnGDPpc D1. -108.8246 49.6043 -2.1900 0.0280

lnGDPpc2 D1. 8.1995 3.6672 2.2400 0.0250

RWA

lnRNEW% L1. -0.0254 0.6123 -0.0400 0.9670

lnGDPpc L1. 3.4450 6.1024 0.5600 0.5720

lnGDPpc2 L1. -0.2909 0.5200 -0.5600 0.5760

_cons -8.5318 18.4602 -0.4600 0.6440

trend -0.0008 0.0033 -0.2300 0.8170

_ec -0.2463 0.2517 -0.9800 0.3280

lnRNEW% D1. -0.8010 0.5720 -1.4000 0.1610

lnGDPpc D1. 0.8797 4.6136 0.1900 0.8490

lnGDPpc2 D1. -0.0633 0.3947 -0.1600 0.8720

TZA

lnRNEW% L1. 0.5022 0.5004 1.0000 0.3160

lnGDPpc L1. -62.6082 164.5450 -0.3800 0.7040

lnGDPpc2 L1. 3.6601 10.2529 0.3600 0.7210

_cons 112.5590 703.6744 0.1600 0.8730

trend 0.0762 0.0473 1.6100 0.1070

_ec -0.5950 0.2536 -2.3500 0.0190

lnRNEW% D1. 0.1103 0.4242 0.2600 0.7950

lnGDPpc D1. 72.5991 214.1190 0.3400 0.7350

lnGDPpc2 D1. -4.5221 13.0896 -0.3500 0.7300

UGA

lnRNEW% L1. -0.1392 0.2441 -0.5700 0.5690

lnGDPpc L1. -6.4345 25.0396 -0.2600 0.7970

lnGDPpc2 L1. 0.5287 2.0212 0.2600 0.7940

_cons 22.0712 76.6630 0.2900 0.7730

126

trend -0.0009 0.0025 -0.3800 0.7060

_ec -0.4336 0.2469 -1.7600 0.0790

lnRNEW% D1. -0.4495 0.1821 -2.4700 0.0140

lnGDPpc D1. -18.5653 17.0501 -1.0900 0.2760

lnGDPpc2 D1. 1.5127 1.3801 1.1000 0.2730

CMR

lnRNEW% L1. -0.1362 0.8117 -0.1700 0.8670

lnGDPpc L1. -0.8756 8.9494 -0.1000 0.9220

lnGDPpc2 L1. 0.1012 0.5571 0.1800 0.8560

_cons 8.0611 70.3558 0.1100 0.9090

trend -0.0033 0.0331 -0.1000 0.9210

_ec -1.0295 0.2646 -3.8900 0.0000

lnRNEW% D1. -0.1802 0.4313 -0.4200 0.6760

lnGDPpc D1. 20.3580 30.2849 0.6700 0.5010

lnGDPpc2 D1. -1.1569 1.8776 -0.6200 0.5380

BWA

lnRNEW% L1. -1.3804 0.8834 -1.5600 0.1180

lnGDPpc L1. -6.5361 7.1964 -0.9100 0.3640

lnGDPpc2 L1. 0.5390 0.5812 0.9300 0.3540

_cons 64.1369 31.1431 2.0600 0.0390

trend -0.0189 0.0079 -2.3800 0.0170

_ec -1.2841 0.2455 -5.2300 0.0000

lnRNEW% D1. -1.7654 0.8697 -2.0300 0.0420

lnGDPpc D1. -5.8623 21.1429 -0.2800 0.7820

lnGDPpc2 D1. 0.5209 1.6439 0.3200 0.7510

ZFA

lnRNEW% L1. 1.8608 1.5260 1.2200 0.2230

lnGDPpc L1. 0.6378 1.4819 0.4300 0.6670

lnGDPpc2 L1. -0.0842 0.1210 -0.7000 0.4870

_cons -30.9737 15.6008 -1.9900 0.0470

trend 0.0109 0.0077 1.4300 0.1540

_ec -0.4000 0.1814 -2.2000 0.0270

lnRNEW% D1. 0.4318 1.1060 0.3900 0.6960

lnGDPpc D1. -19.1723 6.0177 -3.1900 0.0010

127

lnGDPpc2 D1. 1.5481 0.4955 3.1200 0.0020

SWZ

lnRNEW% L1. -0.4890 0.3744 -1.3100 0.1920

lnGDPpc L1. 14.9248 17.8318 0.8400 0.4030

lnGDPpc2 L1. -0.8193 1.0148 -0.8100 0.4190

_cons -50.2172 78.9204 -0.6400 0.5250

trend -0.0075 0.0052 -1.4400 0.1510

_ec -0.9768 0.3282 -2.9800 0.0030

lnRNEW% D1. -0.5293 0.3103 -1.7100 0.0880

lnGDPpc D1. 0.0807 38.3364 0.0000 0.9980

lnGDPpc2 D1. 0.0388 2.1752 0.0200 0.9860

128

Appendix E: Pearson’s Correlation Coefficient Results

RNEW GDPpc CO2Epc FDIcons EFC HDI URB INSQrev TOgdp

RNEW 1

GDPpc -0.7329 1

CO2Epc -0.6530 0.7997 1

FDIcons 0.3603 -0.2871 -0.3171 1

EFC -0.6658 0.9331 0.8060 -0.2902 1

HDI -0.8171 0.8070 0.6064 -0.3515 0.7312 1

URB -0.7661 0.5898 0.5560 0.2644 0.5773 0.6833 1

INSQrev -0.2734 0.4305 0.3465 -0.0997 0.4838 0.3252 0.3374 1

TOgdp -0.3900 0.5180 0.1294 -0.0550 0.4358 0.5392 0.3060 0.2698 1