adams2019

Science of the Total Environment 693 (2019) 133288
Contents lists available at ScienceDirect
Science of the Total Environment
journal homepage: www.elsevier.com/locate/scitotenv
Reducing carbon dioxide emissions; Does renewable energy matter?
Samuel Adams a,⁎, Christian Nsiah b
a
b
School of Public Service and Governance, Ghana Institute of Management and Public Administration, AH 50, Achimota, Accra, Ghana
Baldwin Wallace University Berea, OH, United States
H I G H L I G H T S
G R A P H I C A L
A B S T R A C T
• The role of renewable energy in reducing carbon emissions is examined.
• Both renewable and nonrenewable energy contribute to carbon dioxide emissions.
• Urbanization has a negative effect on
CO2 emissions.
• Less democratic countries are more
likely than democratic countries to pollute the environment.
a r t i c l e
i n f o
Article history:
Received 20 March 2019
Received in revised form 5 July 2019
Accepted 6 July 2019
Available online 22 July 2019
Editor: Huu Hao Ngo
Keywords:
Carbon dioxide emissions
Pollution
Environmental degradation
Renewable energy
Nonrenewable energy
Urbanization
a b s t r a c t
The study employed panel cointegration techniques to investigate the relationship between renewable energy
and carbon dioxide emissions for 28 Sub-Sahara African countries spanning the period 1980–2014. The findings
based on the Fully Modified OLS and GMM estimation techniques show that both renewable and nonrenewable
energy contribute to carbon dioxide emissions in the countries studied in the long run but only nonrenewable
energy has a significant positive effect on carbon dioxide emissions in the short run. The results show that a percentage increase in nonrenewable energy consumption leads to an increase of 1.07% and 1.9% in CO2 emissions in
the short and long run respectively. Additionally, economic growth contributes to environmental degradation
while urbanization has a negative effect on carbon dioxide emissions. A percentage increase in GDP leads to
1.3% and 1.82% increase in emissions in the short and long run respectively. The results also show that less democratic states are more likely to pollute the environment than more democratic states. Further, there is no statistically significant effect of non-renewable energy in the short-run for more democratic nations.
© 2019 Elsevier B.V. All rights reserved.
1. Introduction
Energy availability is critical to economic development and therefore
energy poverty which is the highest in Africa is known to limit its
growth potential. The growth in population and the concomitant use
⁎ Corresponding author.
E-mail address: [email protected] (S. Adams).
https://doi.org/10.1016/j.scitotenv.2019.07.094
0048-9697/© 2019 Elsevier B.V. All rights reserved.
of fossil fuels as the main source of energy in the region has led to the
release of more carbon dioxide into the atmosphere with negative consequences on the environment. The Climate Resilience Handbook
(2018) reports that 2017 was a record year for natural disasters including hurricanes, wildfires, heat waves, and droughts which led to losses
of 31 billion-dollar globally. The World Energy Outlook (2017) reports
that nearly three million premature deaths are related to pollution
from firewood. Currently, nearly two in three Africans do not have
2
S. Adams, C. Nsiah / Science of the Total Environment 693 (2019) 133288
electricity (over 500 million people) and it is estimated that the region
will need about US $ 12 billion to ensure universal access by 2025
(Africa Progress Panel [APP], 2015; African Development Bank, 2012).
The situation is more serious for the African region because of poverty,
and more importantly over half of the population depends on climate
driven businesses such as smallholder farming, peasant mining, agriculture and hawking (Ngwenya et al., 2018; Frimpong et al., 2017; Fawcett
et al., 2015).
The abundance of renewable energy sources has led many international donor organizations and analysts to advocate for the use of renewable energy (RENE) which produces less carbon emissions.
Indeed, the Sustainable Development Goals and especially SDG seven
(7) advocated by the United Nations encourage the use of clean energy
and the use of cleaner fossil fuel technology by 2030. The International
Renewable Energy Agency [IRENA] (2017) and the Africa Progress
Panel (2015, 2017) reports indicate that the use of renewable energy
in Africa will help to limit global warming. Obviously, the abundance
of renewable energy sources (especially wind and solar) makes it imperative (Wesseh and Lin, 2017). For example, the installed capacity
of solar energy increased nearly 30, 000 times between 2000 and
2015, while the SSA region increased from 500 MW to 2100 for the period 2013–2015, with largest capacity being installed in South Africa
(Surroop and Raghoo, 2018).
In the past few years, the decline in the generation cost of renewable
energy especially for Solar panels, which is the most abundant saw a reduction of N60% while the cost of generation of wind and battery cost
also reduced over 25% which have all helped to boost interest in renewable energy (World Energy Outlook, 2017; Apergis et al., 2018; World
Energy Outlook, 2013, 2015, 2017; Sarkodie and Adams, 2018; Pueyo,
2018). The problem, however, is that most of its RENE is untapped
and the sector has been stifled by funding constraints (Pueyo et al.,
2015) due mainly to shortages of capital, skills and governance deficits
(Collier and Venables, 2012).
Though renewables have increased over 60%, its relative share is
only about 2%, which is an indication of Africa's vast potential for renewable energy production and therefore it is important to examine its benefits. Obviously, the great increase in renewable energy development
use necessitates an investigation into its benefits to the environment
to provide evidence informed policy to countries whose economies
are driven by fossil fuels in the region. This gap motivates this study.
The main objective of the study therefore is to explore the effect of renewable energy on carbon dioxide emissions and more importantly examine the differential effects, if any, between renewable energy (RENE)
and nonrenewable (NRENE) on carbon dioxide emissions. To reduce the
problem of omitted variable bias and to improve the consistency in our
estimates, we control for two variables (urbanization and regime type
or level of democracy). Urbanization is considered not only as a socioeconomic concept but also as a social process in SSA which has environmental consequences (Franco et al., 2017; Foresight Africa, 2017; Wang
and Dong, 2019: Zoundi, 2017). Furthermore, climate change and energy use are politically determined (Surroop and Raghoo, 2018). The assumption is that different political institutional environments are likely
to lead to different incentives for the implementation of environmental
energy policies (Bernauer and Koubi, 2009; Sarkodie and Adams, 2018).
Evidently, the focus on Africa is understandable because the sustainable development goals have a high chance of success if they succeed in
Africa. Furthermore, Wesseh and Lin (2017) note that many of the SSA
citizenry would not be able to pay for air conditioning or respond appropriately to flooding associated with global warming. Inglesi-Lotz and
Dogan (2018) also explain that the traditional and agriculturally oriented focus of the SSA countries deepen their vulnerability to changing
weather conditions. The paper is relevant to the environmental pollution literature in four ways. First, the differential effects of RENE and
NRENE are examined. Second, the influence of the political system on
the climate change is explored. Third, the facilitating role of urbanization in the energy – emissions linkage is investigated for SSA countries
spanning the 1980–2014 period. Fourth, the panel cointegration techniques which control for heterogeneity and endogeneity are employed
to improve the consistency of the estimates.
The key assumption behind the importance of RENE is related to its
low carbon content and therefore lower polluting capability. Accordingly, many analysts advocate for its use as an alternative to fossil
fuels in reducing energy poverty (Elliot, 2007). The African Progress
Panel (2017) notes that renewable energy could be described as the
‘golden thread’ to all the SDGs as it helps to connect growth, equity
and environmental sustainability. Kivyiro and Arminen (2014) examine
the drivers of environmental pollution for six SSA countries and find
support for the argument that openness to trade promotes environmental pollution. Wang et al. (2018) studied the causes of carbon emissions
for 170 countries and report that urbanization and energy consumption
contribute to environmental pollution. A few other studies go beyond
total energy to decompose into the renewable and nonrenewable energy shares to identify their differential effects. For example, InglesiLotz and Dogan (2018) employ panel estimation techniques on the
energy–carbon dioxide relationship for the ten biggest electricity producers in SSA over the period 1980–2011 and report that while RENE
reduces emissions, NRENE had the opposite effect. Similarly, Jebli et al.
(2015) investigate the case for 24 SSA countries over a 31 year period
(1980–2010) and demonstrate that RENE and trade have a positive effect on environmental quality. Sinha et al. (2017) investigate drivers
of carbon dioxide emission sin N-11 countries over a fifteen year period
(1990–2014) and find support for N-shaped EKC. Additionally, the results show that RENE and trade have positive effects on environmental
quality but biomass, NRENE and urbanization have a negative effect.
Likewise, Balsalobre-Lorente et al. (2018) show N-shaped EKC for his
study on the five major European countries. The results show that renewable electricity consumption improves environmental quality, but
economic growth had the opposite effect.
Some studies, however, do not find significant impact of RENE on
carbon dioxide emissions, while some studies do not find any difference
between RENE and NRENE in terms of their effect on environmental
quality. For example, Bilgili et al. (2016) show that both RENE and
NRENE have adverse effect on carbon environmental degradation in
the MENA region, while Jebli and Youssef (2017) demonstrate similar
results for the North African countries, even though agricultural production contributed positively to environmental quality. Mert & Bölük
(2016) find similar results for the EU region, however the authors
note that NRENE contributed two times the amount of carbon emissions
generated by RENE. On the other hand, Jebli and Youssef (2015) investigate the case of Tunisia during the period 1980–2009 and report that
RENE does not have a significant impact on emissions but trade and
NRENE do have detrimental effects on environmental quality. Pata
(2018) demonstrated support for the EKC with structural breaks for
Turkey over the period 1974–2014 and also found that urbanization
had adverse effects on the environment but RENE did not have a significant impact.
Some studies have investigated how the institutional or governance
infrastructure affects the energy-carbon emissions link. For example,
Sarkodie and Adams (2018) explore the impact of energy consumption
on carbon emissions in South Africa for the period 1971–2017 and find
that while fossil fuel increases carbon dioxide emissions, RENE and political institutional variables had a contrary effect. Bhattacharya et al.
(2017) also report that both RENE and political institutional quality
(measured as economic freedom) have positive effect on environmental
quality. The results based on FMOLS and the GMM techniques show that
institutions facilitate the speed of adoption of technologies for RENE.
Employing the FMOLS estimation technique, Al-Mulali and Ozturk
(2015) show that political stability lessens pollution of the environment
for MENA countries. These studies are supportive of the importance of
political dynamics in explaining the carbon dioxide emissions and environmental policy more broadly. Accordingly, the study seeks to examine how renewable energy affects CO2 after controlling for the
S. Adams, C. Nsiah / Science of the Total Environment 693 (2019) 133288
institutional environment and urbanization to improve the consistency
in the estimates. The methodology and data used to achieve the research objectives are discussed next.
2. Estimation models and data
2.1. Models
To thoroughly analyze the impact of renewable energy on CO2
emisions, we first employ Fisher-type Unit-root test to check for the stationarity of the time series data. After ensuring that our data are stationary, we use panel cointegration techniques to test for the presence of a
long-run relationship between the pollution and the explanatory variables. Further, we employ panel causality tests to cover all angles and
ensure the use of the best fit estimation models to analyze the direction
of causality between our variables of interest and pollution.
2.2. Unit-root tests
Stationarity of the data was investigated using Stata's suite of unitroot tests. The suite of tests allows one to employ a variety of unit root
tests for panel data where we specifically estimate model with the
Fisher-type options Dickey-Fuller (dfuller) tests, which require the estimation algorithm on each panel to be performed by the ADF unit-root
tests. The model employed is panel estimator with a first-order
autoregressive component of the type presented in Eq. (1).
ΔY it ¼ ∅i Y i;t−1 þ z0it Y i þ εit
ð1Þ
where i denotes country and t denotes time; yit is variable being tested for
stationarity, and εit is stationary error term. Depending on options selected
by the analysis, the Zit can represent four possibilities including panel specific mans with a time trend, only panel-specific means, or nothing at all.
To ensure cross sectional independence, we use Levin et al.'s (2002) methodology in demeaning our series by subtracting the cross-sectional averages from our data series. For this estimation, our null hypothesis is Ho:
ρi = 1 for all i cross sections versus the alternative Ha: ρi b 1. Note that depending on the type of test, our alternate hypothesis may be true for a
country, some of the countries, or all of the countries represented in our
dataset. The alternate hypothesis for the Fisher-type test we employ is
that the data for at least one country in our panel is stationary. Note that
the Fisher-type test we employ provides four unit-root testes analogous
to those from Choi (2002) including the tests that uses the inversenormal, inverse-logit, modified χ2 or inverse χ2 transformations of the associated p-values. We test for stationarity at levels and also in first difference noting that the modified inverse chi-square transformation is
appropriate only when the number of periods approaches infinity.
2.3. Cointegration test
Regressing two non-stationary variables can result in a spurious outcome, however, if the two variables are proven to be cointegrated, then
spurious regression is no longer an issue. In spite of the fact that we
checked for stationarity of our variables, we want to ensure that our variables do have a stable long-run relationship before performing any
other analysis. As such we employ STATA's xtcointtest estimation algorithm to test for cointegration. This STATA function allows us to specify
three different cointegration tests including the Kao, Pedroni &
Westerlund cointegration tests. All three criteria are derived from the
panel estimation model presented in Eq. (2) where it is assumed that
yit is stationary after first differencing (I(1)).
yit ¼ x0it βi þ z0it γi þ εit
ð2Þ
whereas Xit denotes all the covariates, βi is the cointegration factor.
Therefore, we assume that for all our countries in our panels, all the
3
variables represented by Xit are I(1) series. The model requires that for
panel i, each of our covariates are I(1) series. Further, γi represents a
vector of coefficients for our deterministic terms (zit,) which limits linear time trends and panel-specific effects. Finally, εit presents our error
term. We will tests several scenarios including running separate tests
to analyze separately, the link between NRENE and RENE consumption
and pollution on one hand, while modeling with and without the proxy
for governance, on the other hand.
2.4. Causality test
We take another step in building a holistic picture of the link between energy usage and other covariates on one hand, and pollution
on the other by checking for the direction of causality. We deploy the
panel vector autoregression (PVAR) approach as introduced by Abrigo
and Love (2016) with its accompanying PVARGRANGER test. The
PVAR tests through a multivariate panel regression fits homogeneous
panel vector autoregression (VAR) models of our dependent variable
on its own lags and that of all other dependent variables via the GMM
approach. Also, PVAGRANGER routine performs pairwise Granger causality tests after PVAR.
2.5. Long and short-run elasticities
After establishing stationarity of our data and the long run relationship between our covariates and Carbon dioxide emissions which we
are using as our proxy for pollution, we turn our attention to estimating
both the short and long-run elasticities of our covariates. In this case, we
deploy the fully modified ordinary least squares (FMOLS) estimation in
a panel framework. Here we specify a dynamic panel autoregressive distributive lag (ARDL) model presented in Eq. (3) below:
Sit ¼
p
X
γ ij Si;t− j þ
j¼1
q
X
δ0ij xi;t− j þ μ i þ εit
ð3Þ
j¼0
where Sit denotes the level of emissions for a particular country i at time
t respectively. Further, Xit denotes a K x 1vector of our explanatory variables employed. Also, the γij ′ s are scalars, whereas δit is a K x 1 vector of
coefficients. It is assumed in Eq. (3) that if the variables are I(1) and
cointegrated, then the error term is expected to be I(0) process for the
various panels. Essentially, cointegrated variables tend to return to the
long-run steady state after a shock, thus, suggesting the appropriateness
of the use of an error-correcting model which allows short-run changes
in our variables of interest to readjust to its long-run steady state. This
feature of error correction, provides us with the ability to rewrite
Eq. (3) in an error correction format as presented in Eq. (4), where
∅iis the adjustment term of the error-correction speed:
q−1
X
X
p−1
γ it ΔY i;t−1 þ
δ0ij ΔX i;t− j þ μ i þ εit
ΔSit ¼ ∅i Si;t−1 θ0i X it þ
j¼1
ð4Þ
j¼0
∅i can take on three main forms. For example, ∅i=0 denotes cases of no
statistically significant long-run relationship between the variables of
interest. On other hand, if assumption of error-correction holds, then
we expect a significantly negative parameter ∅i. The vector θ′i presents
the long-run relationships (elasticities) between CO2 emissions and
our explanatory variable (relative price difference).1 The Panel Fully
Modified Ordinary Last Squares (PFMOLS) has two main advantages.
First, it is a more dynamic estimation technique unlike the fixed effects
models. Second, there is no need for pretesting for stationarity because
of the underlying ARDL structure. Additionally, the ARDL framework always yields consistent estimates because it does not matter whether the
1
q
∑ j¼0 δij
ð1−∑k γik Þ
p
∅ ¼ −ð1−∑ j¼1 γ it Þ; θi ¼
p
q
; γij ¼ −∑m¼ jþ1 γim j ¼ 1…::P−1; and δij ¼ −∑m¼ jþ1 δim j ¼ 1……::q−1:
4
S. Adams, C. Nsiah / Science of the Total Environment 693 (2019) 133288
variables of interest are I (1) or I (0) process. It is noteworthy to mention
that we suspect that there could be two – way causality between CO2
emissions and energy consumption. Thus, indicating the need to employ estimation models that can handle reverse causality. Another advantage of our model of choice is the fact that so far as the variables in
consideration are I (1), reverse causality will not lead to inconsistent
and biased estimates. This is mainly due to the act that the model exhibits a super consistent property. The panel fully modified Ordinary
Least Squares (PFMOLS) model has three possibilities including the
mean group (MG), pooled mean group (PMG), and dynamic fixed effects (DFE) estimators. In our case, we employ the PMG estimator. An
advantage of the PMG estimator is that unlike other estimators that
eliminate any potential long-run linkages between variables, it estimates dynamic heterogeneous panels due to its consideration of longrun equilibrium relationships. Further, this estimator does not force
the restrictive assumption of homogeneous short-run parameters in
its estimation of long-run parameters.
Concisely, the PFMOLS is preferred over OLS due to its dual advantage of correcting for endogeneity issues, which are difficult or in
some cases impossible to deal with when using OLS type estimators.
Further, we chose this model over the dynamic OLS model since our
focus is to estimate the long and short-run determinants of CO2 emissions to provide policy makers with both short and long-run possible
solutions. In order to investigate whether democracy plays a role in
how our explanatory variables affects CO2 emissions, in addition to
the overall estimation, we separate our sample into high democracy
and low democracy countries (using the mean of our democracy variable as the internal cut-off). Table 5 presents the results of this analysis.
2.6. Data
The goal of this study is to analyze the effect of NRENE, RENE and regime type on environmental pollution as measured by the amount of
Carbon dioxide emissions during 1990–2014 for 28 African countries.
The real GDP (Y) and its squared term (Y2) are used to test for environmental Kuznets hypothesis. Other control variables include urban share
as % of total population (UPOP), the number of persons engaged or
employed (L). The regime type captures the political institutions or
space that is represented by the level of democracy. There are many variables that have been used to measure democracy but the most popular
are the civil liberties and political rights by Freedom House and the Polity 2 data obtained form the Polity IV database. Of the two, however,
many more researchers suggest that the Polity 2 measure is more comprehensive and reliable and have therefore been employed the most
(You et al., 2015; Munck and Verkuilen, 2002; Buitenzorgy and Mol,
2011; Kolcava et al., 2019). Accordingly, we use the polity data that represents the regime authority spectrum on a 21-point scale ranging from
−10 (fully non-democratic) to +10 (fully democratic). Autocracy is an
additive 11 point scale (−10–0) and the Democracy indicator is elevenpoint scale (0–10). There is no perfect autocracy or democracy and
therefore can be said to be highly autocratic or democratic. The higher
the negative value the higher the level of autocracy and the higher the
positive value the higher the level of democracy. Thus, a value of −8
suggest a more autocratic than a value of −6, while a value of 8 is
more democratic than a value of 6. Presented in Table 1 are the variable
descriptions and their respective summary statistics. Tables 1A and 2A
in the Appendix present data sources and also a list of countries represented in our sample.
3. Results and discussion
3.1. Unit root tests
We conducted the Fisher–type unit-root test for the independent
variables and all of our dependent variables. See Table 2.
Choi (2002) recommends the use of normal Z statistic in applied
projects because it presents the best trade-off between power and
size. For our study, we find that all variables under consideration are stationary in levels and first difference regardless of the statistic used.
These results present a strong case for the stationarity of variables and
therefore providing compelling support for the sufficient condition of
the weak form of PPP for the independent variables and our covariates.
3.2. Cointegration test results
Results of cointegration are reported in Table 3 based on three different tests including the Kao, Pedroni, and Westerlund tests. It should be
noted that the Kao test also has five different estimation algorithms including the Dickey Fuller, Modified Dickey Fuller, Unadjusted Dickey
Fuller, Unadjusted Modified Dickey Fuller, and the Augmented Dickey
Fuller tests.
As shown by our results, the no cointegration null hypothesis can be
rejected for all three major estimation algorithms and also for all five
sub-Kao tests. The conclusion of rejecting the null hypotheses remains
consistent even for models that both models with and without the
proxy for governance. It can therefore be concluded that results exhibit
robust support for a long-run linkage between the Carbon dioxide emissions and the covariates.
3.3. Panel granger causality test results
We find evidence of a long-run relationship between our dependent
variable and the covariates of choice (see Table 4). However, we did not
determine the magnitude nor direction of the long-run relationship. To
establish the direction of the relationship between our main covariates
and our proxy for pollution, we employed the panel vector
autoregression model (PVAR) along with its Granger causality option
presented by Abrigo and Love (2016).
From our overall sample, similar to Mohiuddin et al. (2016), we find
that there exist unidirectional causality between nonrenewable energy
consumption and emissions. This finding indicates that while energy
consumption may directly impact emissions, it is nonrenewable energy
consumption that directly worsens emissions. Interestingly, however
we find a bidirectional causality between economic activity measured
Table 1
Summary statistic.
Variable Definition
Co2kt
RE
NRE
POPD
UPOP
Y
K
L
Polityu
CO2 emissions (kt)
Renewable energy consumption in Quadrillion Btu (British thermal unit)
Non-renewable energy consumption in Quadrillion Btu
Population density (people per sq. km of land area)
Urban population (% of total)
Real GDP converted with Purchasing Power Parities (PPP) (constant prices mil. 2011 US$)
Capital stock at current PPPs (in mil. 2011 US$)
Number of persons engaged or employed (in millions)
Regime type (Polity) (Polity is from −10 to 10 depending on the autocratic or democratic nature of
the government we recoded to (Polity+11)/21)*100)
Observations Mean
990
990
990
990
990
990
990
990
990
Std. dev.
Min
Max
21,280
70,949
33
503,112
0.02
0.03
0.00
0.16
0.11
0.26
0.00
1.54
82.73
119.19
1.49
618.66
31.74
15.54
4.34
86.37
51,753.80 119,757.80 151.36 919,085.50
92,898.29 216,315.30 106.64 2,020,231.00
6.43
8.37
0.07
53.73
43.33
27.40
4.76
100.00
S. Adams, C. Nsiah / Science of the Total Environment 693 (2019) 133288
5
Table 2
Unit-root test.
Variable Description
Levels
First difference
Inverse
Inverse
chi-squared normal
Z
(60) P
Dependent variables
LCo2kt
Natural logarithm CO2 emissions (kt)
PValue
Independent variables
LNRE
Natural logarithm the non-renewable energy consumption in
Quadrillion Btu
PValue
LRE
Natural logarithm of renewable energy consumption in
Quadrillion Btu (British thermal unit)
PValue
LL
Natural logarithm of number of persons engaged or employed
(in millions)
PValue
LY
Natural logarithm real GDP converted with Purchasing Power
Parities (PPP) (constant prices mil. 2011 US$)
PValue
−10.17
0.00
13.79
0.00
171.86
0.00
−7.88
0.00
−8.46
0.00
10.95
0.00
109.99
−4.03
−4.27
5.10
88.81
−2.78
−2.89
3.10
0.00
0.01
0.01
0.00
0.00
0.00
0.00
0.00
129.08
−4.23
−5.10
6.91
173.00
−7.40
−8.40
11.00
0.00
00
0.00
0.00
0.00
0.00
0.00
0.00
158.22
−5.70
−6.89
9.65
173.00
−7.40
−8.40
11.00
0.00
0
0.00
0.00
0.00
0.00
0.00
0.00
122.09
−4.83
−4.99
6.25
126.10
−5.00
−5.14
6.62
0.00
0.00
0.00
0.00
0.00
0.00
0.00
0.00
241.87
0.00
−0.31
0.38
−2.41
0.01
17.56
0.00
168.98
0.00
−6.17
0.00
−7.30
0.00
10.68
0.00
−11.58
−13.55
19.44
238.38
−11.13
−12.35
17.23
0.00
0.00
0.00
0.00
0.00
0.00
0.00
LGOV
Natural logarithm Regime type (Polity). Polity is from −10 to 10 261.74
depending on the autocratic or democratic nature of the
government we recoded to (Polity+11)/21)*100
0.00
by real GDP and emissions, which is consistent with the findings of
Dritsaki & Dritsaki (2014). This finding confirms the complex relationship between emissions and growth where there is feedback relationship in the growth/emissions nexus in developing countries engaged
in high growth activities, most likely due to the type of production processes at play. We also find unidirectional linkages from urban population and labor force size to emissions, a finding consistent with that of
Babbar & Babbar (2018). Urban population and labor force growth are
good proxies for general and firm level energy demand respectively,
Table 3
Panel cointegration test.
Kao
Modified
Dickey-Fuller t
Dickey-Fuller t
Augmented
Dickey-Fuller t
Unadjusted
modified
Dickey-Fuller t
Unadjusted
Dickey-Fuller t
Pedroni
Modified
Phillips-Perron t
Phillips-Perron t
Westurlund
Variance ratio
Inverse Modified inv.
chi-squared
logit t
(154) L* Pm
−9.22
0.00
Natural logarithm Urban population (% of total)
Model
Inverse
Inverse
chi-squared normal
Z
(60) P
201.97
0.00
LUPOP
PValue
PValue
Inverse Modified inv.
chi-squared
logit t
(154) L* Pm
Model 1
Model 2
Model 3
Model 4
RE & Co2kt
NRE &Co2kt
RE & Co2kt
NRE & Co2kt
−4.843
*** −5.116
*** −4.871
*** −5.134
***
−4.607
−2.934
** −4.736
*** −3.045
*** −4.616
** −2.951
*** −4.743
*** −3.058
***
***
and as such increases in these indicate increased demand for energy
consumption for basic human needs and productivity. This increased
need for energy consumption in developing countries may more than
likely be fulfilled with increased consumption of nonrenewable energy
sources which increase CO2 emissions. In the case of democracy, we do
not find a direct causal relationship between democracy and CO2 emissions. In separating our sample into high and low democratic countries,
however, we see only a one-way relationship from renewable energy
usage to emissions in low-democracy nations. This indicates that some
characteristics of countries with low governance structures, makes
them more susceptible to the negative impact of the consumption of
nonrenewable energy, perhaps due to the usage of much more environmentally unfriendly nonrenewable sources of energy. The bidirectional
relationship between economic activity and emissions remains for both
high and low-governance nations. We also find a unidirectional link between urban population and employment, on the one hand, to emissions on the other. These results indicate that our resource
consumption variables may not be endogenous and therefore, we
shouldn't be overly concerned with dealing with the issue of
endogeneity when it comes to these variables.
−10.325 *** −10.687 *** −10.342 *** −10.687 ***
3.4. Long and short-run elasticities
−6.622
*** −6.736
*** −6.622
*** −6.733
***
1.780
**
*
*** 2.601
***
−7.005
−4.824
*** −8.828
*** −6.376
*** −8.658
*** −6.358
***
***
−2.189
**
1.675
−2.265
3.059
*** −7.188
*** −4.678
**
−2.114
**
−2.241
**
Note: ***, **,* denotes PValues of b0.01, b0.05, and b 0.10 respectively. Models 1 and 2 include all explanatory variables except proxy for governance (polity2). Models 3, 4, include
all explanatory variables. The emissions proxy in models 1 and 3 is renewable energy consumption, whereas for models 2 and 4 is non-renewable energy consumption. The dependent variable in each model is a proxy for Co2 emissions (the measuring units is Kiloton).
There are 28 countries covering 35 periods.
After checking for stationarity, cointegration, and direction of causality, our attention moves to quantifying the long and short-run impacts
(elasticities) of our explanatory variables concerning emissions. In particular, our interest was on documenting the differential impacts of renewable and non-renewable energy usage by African countries on the
various country's emissions in the long and short-run. We also investigate whether the political regime type of the countries under consideration affects energy consumption's effect. As such apart from estimating
models which look at our overall sample for different energy consumption types (presented in models 1 and 2), we also split up our sample
into high (above average governance index) and low governance countries (Models 3 to 6). The estimated ∅ parameters in Table 5 are allnegative and significantly signed indicating a cointegration relationship.
6
S. Adams, C. Nsiah / Science of the Total Environment 693 (2019) 133288
Table 4
Panel causality test.
Direction of causality
Overall
Energy consumption & pollution
Renewable energy consumption in Quadrillion Btu doesn't cause Co2kt
Non-renewable energy consumption in Quadrillion Btu doesn't cause Co2kt
Co2kt doesn't cause Renewable energy consumption in Quadrillion Btu
Co2kt doesn't cause Non-renewable energy consumption in Quadrillion Btu
Economic activity & pollution
Real GDP doesn't cause Co2kt
Co2kt doesn't cause Real GDP
Governance & pollution
Governance doesn't cause Co2kt
Co2kt doesn't cause governance
Population & pollution
Urban population doesn't cause Co2kt
Co2kt doesn't cause population density
Labor force & pollution
Employment doesn't cause Co2kt
Co2kt doesn't cause employment
Consistent with the finding of Shafiei and Salim (2014), Model 1
shows that nonrenewable energy consumption has significant and positive impact on CO2 emissions in both the short and long run. Particularly, we find that a percentage increase in nonrenewable energy
consumption leads to an increase of 1.07% and 1.9% in CO2 emissions
in the short and long run respectively. This finding suggests that consumption of nonrenewable energy which comes from sources including
natural gas, coal and oil result in increase in CO2 emissions and thus contribute significantly to environmental degradation. The results from
Model 1 show support for the EKC as the log of GDP and its squared
term significantly affect CO2 emissions. We find that a percentage increase in GDP leads to 1.3 and 1.82 percentage increase in emissions
in the short and long respectively, whereas the squared term yields
−0.07% and −0.058% decline in emissions. This result therefore provides support for the Environmental Kuznets Curve similar to the findings of Kivyiro and Arminen (2014), but contrary to that of Shafieia &
Salim (2014). Interestingly, the results show that urban population is
negative and significantly related to carbon dioxide emissions in the
long run, but the short run results show a positive and insignificant effect. This finding is not too surprising since Chen et al. (2018) find an
inverted U-shaped curved relationship between urbanization and CO2
emissions in China. In our case, this finding may support the idea that
initially urbanization increase the demand for energy usage, which is
filled with nonrenewable energy sources, however as income of the
urban population increases, other cleaner energy sources are demanded
which lead to a reduction in greenhouse emissions.
Additionally, while Model 1 and Model 2 yield very similar results,
there are some fascinating, subtle differences between them. Specifically, while nonrenewable (NRENE) and renewable (RENE) have a significant positive impact on emissions in the long run, only NRENE has
substantial effects in the short run. Even more interesting is the observation that RENE‘s impact on carbon dioxide emissions in the long run
is higher compared to NRENE. This finding contradicts the outcome
from some other studies that report otherwise, suggesting that RENE
usage is associated with lower levels of carbon dio1ide emissions.2
However, our result aligns with Apergis et al.'s (2010) result that
show a significant positive relationship between renewable energy
and emissions. Accordingly, the authors report they could not find evidence for the emissions reducing role of RENE in the long run. These results are associated with inadequate storage technology and systematic
power-outages, some of the people resort to emission generating renewable energy consumption such as the open burning of firewood.
2
See (Jebli and Youssef, 2017; Zoundi, 2017; Dogan and Seker, 2016a; Dogan and Seker,
2016b; Jebli et al., 2016; and Shafiei and Salim, 2014).
High governance
Prob N chi2
chi2
chi2
Prob N chi2
Low governance
chi2
Prob N chi2
1.413
2.592
1.590
1.825
0.235
0.086
0.207
0.177
0.098
0.992
0.279
2.148
0.754
0.312
0.597
0.143
2.515
5.467
0.164
0.541
0.113
0.019
0.686
0.462
33.812
6.076
0.000
0.014
26.254
7.831
0.000
0.005
11.595
4.211
0.001
0.040
0.650
2.417
0.420
0.120
2.795
1.034
0.095
0.309
0.212
0.027
0.645
0.793
37.774
0.616
0.000
0.432
30.440
0.615
0.000
0.433
6.856
0.027
0.009
0.870
73.581
1.196
0.000
0.274
38.493
7.778
0.000
0.005
30.099
0.176
0.000
0.675
When we differentiate between high democratic and low democratic nations, we find that though non-renewable energy consumption
contributes positively to emissions in the long-run for both categories of
countries, the impact is larger for low democratic countries than for
high democratic nations. The division into low and high democracies
was based on the Polity data and Freedom House (2018) discussion of
democracy and autocracy, where we used the mean of the scores for
the countries as a midpoint to categorize countries into relatively high
and low democratic nations. These results are consistent with those of
Li and Reuveny (2006), Gellers and Jeffords (2018) and Ghodrati et al.
(2018). Further, we find that there is no statistically significant effect
of non-renewable energy in the short-run for high democratic nations.
Renewable energy, on the other hand, has a significant impact in low
democratic countries.
4. Policy implications and conclusion
The role of different types of energy and the political system in mitigating environmental degradation are examined in 28 SSA countries.
The results based on cointegration techniques show that both RENE
and NRENE contribute to carbon dioxide emissions, but urbanization
has the opposite effect. The results also support the finding that democracies pollute less than non-democracies. The results lead to three key
policy implications.
First, the results of the study are supportive of the view that both
RENE and NRENE contribute to carbon dioxide emissions. (Bilgili et al.,
2016; Farhani and Shahbaz, 2014). It is worthy of note that the RENE
contributed more to environmental degradation than NRENE. This
could be attributed to the sporadic nature of RENE and inadequate storage technology for RENE. Many other authors suggest that RENE generation has not reached the threshold required (8.39%) to generate
positive benefits on the environment (Chiu and Chang, 2009). In the
last two decades, many SSA countries have seen an appreciable increase
in RENE development, however, the relative share globally stands at just
about 2% (Chachoua, 2016), which represents a drop of over 30%. This
suggests that the African region has a huge potential for renewable energy development to mitigate the effects of high population growth and
energy poverty and their concomitant negative effect on climate
change. Evidently, with the abundant sunshine, biomass and wind,
RENE can be promoted to ensure energy justice and environmental
quality.
Second, the results show a positive influence of urbanization on carbon dioxide emissions, which support studies that provide evidence of
environmental dividend of urbanization (Behera and Dash, 2017; Li
and Lin, 2015; Liu, 2009). The findings of the study are therefore
***
−0.384
(0.059)
**
**
***
−0.387
(0.061)
*
***
−0.194
(0.038)
***
−0.199
(0.040)
**
***
*
***
−0.258
(0.052)
***
EC
Constant
LPOL
LEMP
LUPOP
LY2
−0.267
(0.052)
*
***
*
**
1.299
(0.609)
−0.058
(0.030)
0.333
(0.658)
−0.035
(0.225)
−0.014
(0.030)
−0.826
(0.659)
***
1.820
(0.111)
−0.067
(0.006)
−0.355
(0.207)
0.246
(0.390)
−0.009
(0.069)
LY
LRE
Note: ***, **,* denotes PValues of b0.01, b0.05, and b0.10 respectively. Models 1 and 2 include our full sample of countries. Models 3 and 4 on one hand, and 5 and 6 on the other, splits our sample into high and low governance nations respectively.
Models 1, 3, and 5 employs non-renewable energy as our proxy for energy consumption, whereas, 2, 4, and 6 employs renewable energy consumption. The dependent variable in each model is a proxy for Co2 emissions (the measuring units is
Kiloton). There are 28 countries covering 35 periods.
***
**
***
***
***
***
***
***
***
4.446
(0.861)
−0.181
(0.037)
0.473
(0.406)
−0.227
(0.554)
−0.044
(0.083)
**
4.644
(1.948)
1.694
(0.123)
−0.059
(0.007)
−0.376
(0.206)
0.287
(0.379)
−0.017
(0.068)
**
1.074
(0.513)
*
1.901
(1.064)
LNRE
Long-run
***
**
−0.207
(0.579)
1.346
(0.662)
−0.061
(0.301)
0.460
(0.633)
−0.010
(0.216)
0.460
(0.633)
−0.579
(0.591)
Short-run
Long-run
Short-run
Model 2
Model 1
Variable
Full sample
Table 5
Long and short-run elasticities.
***
2.220
(0.271)
−0.087
(0.017)
−0.316
(0.216)
0.851
(0.452)
0.048
(0.086)
−1.119
(0.435)
−1.119
(0.338)
−1.122
(1.062)
−0.168
(0331)
−1.122
(1.062)
−2.537
(1.593)
***
0.618
(0.435)
1.663
(0.773)
**
Short-run
Long-run
***
0.995
(1.663)
4.119
(0.987)
−0.166
(0.042)
0.461
(0.411)
−0.119
(0.577)
0.039
(0.111)
Long-run
Model 4
***
−0.950
(0.828)
−1.177
(0.385)
0.058
(0.017)
−1.106
(1.097)
−0.133)
(0.332)
−0.019
(0.031)
−2.175
(1.773)
Short-run
***
3.017
(1.623)
Long-run
***
1.483
(0.379)
−0.070
(0.338)
1.368
(0.674)
−0.236
(0.360)
1.368
(0.674)
3.669
(1.423)
2.254
(1.286)
***
Model 5
Model 3
*
Low governance
High governance
Short-run
*
***
9.694
(4.597)
2.006
(0.181)
−0.073
(0.012)
−0.267
(02.38)
0.696
(0.520)
0.042
(0.090)
Long-run
Model 6
**
0.229
(0.734)
1.480
(0.396)
−0.068
(0.020)
1.233
(0.654)
−0.126
(0.328)
−0.020
(0.040)
3.715
(1.409)
Short-run
***
S. Adams, C. Nsiah / Science of the Total Environment 693 (2019) 133288
7
supportive of the optimist view of urbanization, which suggests that urbanization promotes energy efficiency and consequently improvement
in environmental and health outcomes. This is especially important because the sub-Saharan African region is urbanizing at a rate which is
exceeded only by that of the Asian region. It is important to mention
though that other studies show an insignificant or negative effect of urbanization on the environment (Adams et al., 2016; Foresight Africa,
2016). Further research is needed to examine and validate the findings
on how urbanization affects carbon dioxide emissions.
Third, the results show that democracies are more likely to be proenvironment than non-democracies and this should give some credence to the democratization process going on in the region. Accordingly, democratization should not be seen as mere conditionality to be
achieved to receive donor support but internally driven to help promote
sustainable development. As shown in the study, the effect of renewable
energy is more pronounced in more democratic states than in less democratic states. However, some studies report that the mere transition to
democracy is not enough to ensure the democratic dividend and argue
for consolidation of democracy (Huntington, 1993). This is critical for
the region as the consolidation or deepening of democracy helps to promote the development of both political and economic institutions that
facilitate the proper functioning of society as whole. In concluding, it is
worth reiterating the main findings of the study, which suggest that
both RENE and NRENE have adverse effects on the environment though
the negative effect is more severe in less democratic countries, indicating that democracy is good for the environment. The lack of significant
positive effect of renewable energy on environmental quality has been
associated with its minimal use in the region and more emphasis should
be placed on its use and empirical studies given by future studies to
drive the renewable agenda in helping the continent achieve the win
benefits of economic growth and environmental quality. Obviously,
this energy transition is critical to the SSA region as the continent is
endowed with abundant renewable energy sources (Winkler, 2018).
Othieno and Awange (2016) and Olutola (2018), for example, have argued that the abundance of renewable energy in the region makes it imperative for the countries in the region to invest in renewable energy
technologies to reduce energy poverty on the continent. This is consistent with the idea that the continent cannot achieve the UN Sustainable
Development Goals without a renewed focus on clean energy sources
including both renewable and nuclear energy. The study focuses on a
panel of 28 SSA countries and does not identify country – specific conditions and therefore future studies should be directed at both firm and
country-levels to provide detailed information on the energy consumption - environmental degradation nexus.
Appendix A
Table 1A
Sources of data.
Variable Definition
Source
Co2kt
CO2 emissions (kt)
RE
Renewable energy consumption in Quadrillion
Btu (British thermal unit)
NRE
Non-renewable energy consumption in Quadrillion Btu
UPOP
Urban population (% of total)
Y
World Development
Indicators
U.S. Energy
Information
Administration
U.S. Energy
Information
Administration
World Development
Indicators
PENN World Tables
Real GDP converted with Purchasing Power
Parities (PPP) (constant prices mil. 2011 US$)
Number of persons engaged or employed (in
PENN World Tables
millions)
Regime type (Polity) (Polity is from −10 to 10 Polity IV Database
depending on the autocratic or democratic
nature of the government we recoded to (Polity
+11)/21)*100)
L
GOV
8
S. Adams, C. Nsiah / Science of the Total Environment 693 (2019) 133288
Table 2A
Country list.
Angola
Burkina Faso
Burundi
Cameroon
The Central African Republic
Comoros
Congo (Rep)
Cote d'Ivoire (Ivory Coast)
Egypt
Equatorial Guinea
Ethiopia
Gabon
Ghana
Guinea
Kenya
Madagascar
Malawi
Mali
Mauritania
Mauritius
Mozambique
Nigeria
Rwanda
South Africa
Tanzania
Togo
Zambia
Zimbabwe
References
Abrigo, M.K., Love, I., 2016. Estimation of panel vector autoregression in Stata. Stata Journal, 2016 16 (3), 778–804.
Adams, S., Adom, P.K., Klobodu, E.K.M., 2016. Urbanization, regime type and durability,
and environmental degradation in Ghana. Environ. Sci. Pollut. Res. 23 (23),
23825–23839.
Africa Progress Panel, 2015. Power People Planet: Seizing Africa's Energy and Climate
Opportunities.
Africa Progress Panel, 2017. Lights, Power, Action: Electrifying Africa.
African Development Bank, 2012. Income Inequality in Africa. Briefing Notes for AfDB’s
Long Term Strategy Briefing. Note 5. African Development Bank, Tunis.
Al-Mulali, U., Ozturk, I., 2015. The effect of energy consumption, urbanization, trade openness, industrial output, and the political stability on the environmental degradation in
the MENA (Middle East and North African) region. Energy 84, 382–389.
Apergis, N., Payne, J.E., Menyah, K., Wolde-Rufael, Y., 2010. On the causal dynamics between emissions, nuclear energy, renewable energy, and economic growth. Ecol.
Econ. 69 (11), 2255–2260.
Apergis, N., Jebli, M.B., Youssef, S.B., 2018. Does renewable energy consumption and
health expenditures decrease carbon dioxide emissions? Evidence for sub-Saharan
Africa countries. Renew. Energy 127, 1011–1016.
Babbar, S., Babbar, R., 2018. Causal dynamics of CO 2 source emissions and population in
India using Bayesian approach. Model. Earth Syst. Environ. 4 (1), 339–348.
Balsalobre-Lorente, D., Shahbaz, M., Roubaud, D., Farhani, S., 2018. How economic growth,
renewable electricity and natural resources contribute to CO 2 emissions? Energy
Policy 113, 356–367.
Behera, S.R., Dash, D.P., 2017. The effect of urbanization, energy consumption, and foreign
direct investment on the carbon dioxide emission in the SSEA (South and Southeast
Asian) region. Renew. Sust. Energ. Rev. 70, 96–106.
Bernauer, T., Koubi, V., 2009. Effects of political institutions on air quality. Ecol. Econ. 68
(5), 1355–1365.
Bhattacharya, M., Churchill, S.A., Paramati, S.R., 2017. The dynamic impact of renewable
energy and institutions on economic output and CO 2 emissions across regions.
Renew. Energy 111, 157–167.
Bilgili, F., Koçak, E., Bulut, Ü., 2016. The dynamic impact of renewable energy consumption
on CO2 emissions: a revisited Environmental Kuznets Curve approach. Renew. Sust.
Energ. Rev. 54, 838–845.
Buitenzorgy, M., Mol, A.P., 2011. Does democracy lead to a better environment? Deforestation and the democratic transition peak. Environ. Resour. Econ. 48 (1), 59–70.
Chachoua, E. 2016. Power Up: Delivering Renewable Energy in Africa. The Economist
Intelligence Unit, https://www.eiuperspectives.economist.com/sites/default/files/
Power%20Up.pdf.
Chen, B., Han, M.Y., Peng, K., Zhou, S.L., Shao, L., Wu, X.F., ... Chen, G.Q., 2018. Global landwater nexus: agricultural land and freshwater use embodied in worldwide supply
chains. Sci. Total Environ. 613, 931–943.
Chiu, C.L., Chang, T.H., 2009. What proportion of renewable energy supplies is needed to
initially mitigate CO2 emissions in OECD member countries? Renew. Sust. Energ. Rev.
13 (6), 1669–1674.
Choi, I., 2002. Combination unit root tests for cross‐sectionally correlated panels. Mimeo,
Hong Kong University of Science and Technology.
Collier, P., Venables, A.J., 2012. Greening Africa? Technologies, endowments and the latecomer effect. Energy Econ. 34, S75–S84.
Dogan, E., Seker, F., 2016. The influence of real output, renewable and non-renewable energy, trade and financial development on carbon emissions in the top renewable energy countries. Renew. Sust. Energ. Rev. 60, 1074–1085.
Dritsaki, C., Dritsaki, M., 2014. Causal relationship between energy consumption, economic growth and CO2 emissions: A dynamic panel data approach. Int. J. Energy
Econ. Policy 4 (2), 125–136.
Elliot, S., 2007. Environmentally sustainable ICT: a critical topic for IS research? PACIS
2007 Proceedings, p. 114
Farhani, S., Shahbaz, M., 2014. What role of renewable and non-renewable electricity consumption and output is needed to initially mitigate CO2 emissions in MENA region?
Renew. Sust. Energ. Rev. 40, 80–90.
Fawcett, A.A., Iyer, G.C., Clarke, L.E., Edmonds, J.A., Hultman, N.E., McJeon, H.C., ... Creason,
J., 2015. Can Paris pledges avert severe climate change? Science 350 (6265),
1168–1169.
Foresight Africa, 2016. Top priorities for the continent in 2016. Brookings Institute.
Foresight Africa, 2017. Top Priorities for the Continent in 2016. Brookings Institute,
Washington DC.
Franco, S., Mandla, V.R., Rao, K.R.M., 2017. Urbanization, energy consumption and urbanization, energy consumption and emissions in the Indian context A review. Renew.
Sust. Energ. Rev. 71, 898–907.
Freedom House, 2018. Freedom in the World 2018: Democracy in Crisis.
Frimpong, K., Eddie Van Etten, E.J., Oosthuzien, J., Nunfam, V.F., 2017. Heat exposure on
farmers in northeast Ghana. Int. J. Biometeorol. 61 (3), 397–406.
Gellers, J.C., Jeffords, C., 2018. Toward environmental democracy? Procedural environmental rights and environmental justice. Global Environmental Politics 18 (1),
99–121.
Ghodrati, S., Harati, J., Nazari, A., 2018. Democracy and environment quality in selected
countries: an application of panel data. Iranian Economic Review 22 (1), 21–49.
Huntington, S., 1993. The Third Wave of Democratization in the Late Twentieth Century.
3rd ed. University of Oklahoma Press, Norman.
Inglesi-Lotz, R., Dogan, E., 2018. The role of renewable versus non-renewable energy to
the level of CO2 emissions a panel analysis of sub-Saharan Africa's Βig 10 electricity
generators. Renew. Energy 123, 36–43.
International Renewable Energy Agency (IRENA), 2017. Rethinking Energy. 2017. IRENA,
Abu Dhabi.
Jebli, M.B., Youssef, S.B., 2015. The environmental Kuznets curve, economic growth, renewable and non-renewable energy, and trade in Tunisia. Renew. Sust. Energ. Rev.
47, 173–185.
Jebli, M.B., Youssef, S.B., 2017. The role of renewable energy and agriculture in reducing
CO2 emissions: evidence for North Africa countries. Ecol. Indic. 74, 295–301.
Jebli, M.B., Youssef, S., Ozturk, I., 2015. The role of renewable energy consumption and
trade: environmental Kuznets curve analysis for sub-Saharan Africa countries. Afr.
Dev. Rev. 27 (3), 288–300.
Kivyiro, P., Arminen, H., 2014. Carbon dioxide emissions, energy consumption, economic
growth, and foreign direct investment: Causality analysis for Sub-Saharan Africa. Energy 74, 595–606.
Kolcava, D., Nguyen, Q., Bernauer, T., 2019. Does trade liberalization lead to environmental burden shifting in the global economy? Ecol. Econ. 163, 98–112.
Levin, A., Lin, C.-F., Chu, C.-S.J., 2002. Unit root tests in panel data: asymptotic and finitesample properties. J. Econ. 108, 1–24.
Li, K., Lin, B., 2015. Impacts of urbanization and industrialization on energy consumption/
CO2 emissions: does the level of development matter? Renew. Sust. Energ. Rev. 52,
1107–1122.
Li, Q., Reuveny, R., 2006. Democracy and environmental degradation. Int. Stud. Q. 50 (4),
935–956.
Liu, B., 2009. Some research problems in uncertainty theory. J. Uncertain Syst. 3 (1), 3–10.
Mert, M., Bölük, G., 2016. Do foreign direct investment and renewable energy consumption affect the CO2 emissions? New evidence from a panel ARDL approach to Kyoto
Annex countries. Environ. Sci. Pollut. Res. 23 (21), 21669–21681.
Mohiuddin, O., Asumadu-Sarkodie, S., Obaidullah, M., 2016. The relationship between carbon dioxide emissions, energy consumption, and GDP: A recent evidence from
Pakistan. Cogent Eng. 3 (1), 1210491.
Munck, G.L., Verkuilen, J., 2002. Conceptualizing and measuring democracy: evaluating alternative indices. Comparative political studies 35 (1), 5–34.
Ngwenya, B., Oosthuizen, J., Cross, M., Frimpong, K., Chaibva, C.N., 2018. A review of heat
stress policies in the context of climate change and its impacts on outdoor workers:
evidence from Zimbabwe. Int. J. Soc. Ecol. Sustain. Dev. 9 (1), 1–11 (IJSESD).
Olutola, O., 2018. Addressing nuclear in the Africa–European Union energy partnership:
issues and perspectives through an African lens. Politikon 1–17.
Othieno, H., Awange, J., 2016. Energy Resources in Africa. Springer International Publishing, Basel, Switzerland.
Pata, U.K., 2018. Renewable energy consumption, urbanization, financial development,
income and CO2 emissions in Turkey: testing EKC hypothesis with structural breaks.
J. Clean. Prod. 187, 770–779.
Pueyo, A., 2018. What constrains renewable energy investment in Sub-Saharan Africa? A
comparison of Kenya and Ghana. World Dev. 109, 85–100.
Pueyo, A., Orraca, P., Godfrey-Wood, R., 2015. What Explains the Allocation of Aid and Private Investment for Electrification? (No. IDS Evidence Report; 123). IDS
Sarkodie, S.A., Adams, S., 2018. Renewable energy, nuclear energy, and environmental
pollution: accounting for political institutional quality in South Africa. Sci. Total Environ. 643, 1590–1601.
Shafiei, S., Salim, R.A., 2014. Non-renewable and renewable energy consumption and CO 2
emissions in OECD countries: a comparative analysis. Energy Policy 66, 547–556.
Sinha, A., Shahbaz, M., Balsalobre, D., 2017. Exploring the relationship between energy
usage segregation and environmental degradation in N-11 countries. J. Clean. Prod.
168, 1217–1229 Stata Panel-data unit-root tests. https://www.stata.com/manuals/
xtxtunitroot.pdf.
Surroop, D., Raghoo, P., 2018. Renewable energy to improve energy situation in African
island states. Renew. Sust. Energ. Rev. 88, 176–183.
Wang, J., Dong, K., 2019. What drives environmental degradation? Evidence from 14 subSaharan African countries. Sci. Total Environ. 656, 165–173.
Wang, S., Li, G., Fang, C., 2018. Urbanization, economic growth, energy consumption, and
CO2 emissions: empirical evidence from countries with different income levels.
Renew. Sust. Energ. Rev. 81, 2144–2159.
Wesseh Jr., P.K., Lin, B., 2017. Is renewable energy a model for powering Eastern African
countries transition to industrialization and urbanization? Renew. Sust. Energ. Rev.
75, 909–917.
S. Adams, C. Nsiah / Science of the Total Environment 693 (2019) 133288
Winkler, M., 2018. Why nuclear power does not make sense for Africa. Conversation May
15, 2018.
World Energy Outlook.
World Energy Outlook (2015).
World Energy Outlook, 2017. International Energy Agency.
9
You, W.H., Zhu, H.M., Yu, K., Peng, C., 2015. Democracy, financial openness, and global carbon dioxide emissions: heterogeneity across existing emission levels. World Dev. 66,
189–207.
Zoundi, Z., 2017. CO2 emissions, renewable energy and the environmental Kuznets curve,
a panel cointegration approach. Renew. Sust. Energ. Rev. 72, 1067–1075.