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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
2 E
mis
sio
ns
(gig
ato
nn
es)
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
Recommended