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Renewable Energy Consumption and Carbon Emissions in Sub-
Saharan Africa. The Moderating Role of Institutional Quality
Peter Mwai Kinuthia
1,2
, Issacs K Kemboi
1
, James Onyango
1
, Muyiwa Samuel Adaramola
1,3
1
Department of Economics School of Business and Economics, Moi University, Kenya
2
Faculty of Environmental Sciences and Natural Resources Management Norwegian University of Life
Sciences, As, Norway
3
Department of Mechanical, Production and Energy Engineering School of Engineering, Moi University,
Kenya
DOI: https://dx.doi.org/10.47772/IJRISS.2025.905000324
Received: 06 May 2025; Accepted: 11 May 2025; Published: 13 June 2025
ABSTRACT
This paper empirically investigates the effect of renewable energy consumption, institutional quality, and their
interaction on carbon emissions in the Sub-Saharan Africa region, while controlling for foreign direct
investment, industrialization, and population growth. The study analyzed a balanced panel of 839 country-year
observations from 24 countries covering the period 2000–2023. A fixed effects model was employed to examine
the relationships and test the moderating role of institutional quality. Results indicated that renewable energy
consumption had a significant negative relationship with carbon emissions, reinforcing its role in mitigating
environmental degradation. Institutional quality also demonstrated a strong negative relationship with emissions,
underscoring the role of governance in environmental performance. Importantly, the interaction between
renewable energy and institutional quality further enhanced the negative effect on carbon emissions, indicating
that the effectiveness of renewable energy initiatives is significantly enhanced in the presence of strong
institutions. The findings of this study contribute to the growing empirical literature on the relationship between
renewable energy consumption, institutional quality, and environmental sustainability in Sub-Saharan Africa.
Moreover, they offer critical policy insights for governments, development partners, and energy stakeholders.
Given the significant negative relationship between renewable energy consumption and carbon emissions,
policymakers should intensify efforts to expand renewable energy infrastructure and adoption. The amplifying
effect of institutional quality highlights the urgency of governance reforms to support enforcement, transparency,
and accountability in energy transitions.
Key words: Renewable energy consumption, institutional quality, sub-Saharan Africa, carbon emissions
INTRODUCTION
The demand for economic and environmental sustainability has recently necessitated investigations on the
effects of renewable energy usage on both economic and environmental sustainability. The issue has been a
subject of discourse among policymakers and researchers. In 2015, both 'The Paris Agreement' and the Agenda
for Sustainable Development-2030 were ratified as integral components of a strategic approach to energy access
and climate change mitigation (UNFCC, 2015; United Nations, 2015). In the agreed pact, global economies
committed to uniting to eradicate poverty in all aspects of life. Furthermore, the global economies consented to
collaborate synergistically to comprehensively promote sustainable development and address the challenge of
climate change integrally. Nevertheless, a vision of a clean energy system is the sole method to attain the
articulated objectives of both pledges, essential for conquering energy poverty and ensuring environmental
sustainability.
The SSA nations, in accordance with the 2015 Paris Climate Change Conference and subsequent meetings, have
recently committed to and adopted the objectives of the Renewable Energy Initiative (REI) for the year 2030.
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This REI program establishes unorthodox objectives for the economy of the regional bloc. The primary objective
is to ensure that by 2030, the share of renewable energy in the consumption mix reaches a target of 15%. The
primary concerns regarding the realization of this objective include transportation targets for various sectors, as
well as issues related to electricity and temperature regulation. Additional elements encompass the integration
of various renewable technologies through structured energy policy frameworks, alongside corporate utilization
targets to establish collaborative support systems and initiatives among the nations within the regional bloc.
Researchers have recently highlighted the role of institutions, noting that fossil fuel potential and energy use are
not the exclusive determinants of environmental degradation; rather, the quality of institutions within society
also plays a significant role (Uzar, 2020). Ndulu & O'Connell (1999) indicate that a significant research initiative
conducted by the AERC highlighted the substantial emphasis placed on institutions in elucidating the growth of
African economies. A substantial body of literature has empirically examined the effects of population, economic
growth, and energy consumption on carbon emissions within the realms of energy and environmental economics;
however, the influence of institutions on carbon emissions remains a relatively underexplored topic (Adegboye
et al, 2020).
Certain academics assert that the significance of institutional variables on the environment must not be
overlooked, since institutions pertain to the rules and norms governing conduct that shape repetitive human
interactions (Vatn, 2007). Institutional factors, including corruption, political stability, governmental regulation,
the rule of law, and governmental efficacy, significantly influence environmental policies and strategies aimed
at reducing carbon emissions (Muhammad & Long, 2021). According to Bhattacharya, Churchill, and Paramati
(2017), institutions that uphold property rights and facilitate voluntary exchange empower the government to
implement preferred environmental policies. In other words, environmental activities aimed at reducing carbon
emissions may be compromised if institutions lack functionality and efficiency. Although institutions serve as
indicators and precursors to various developmental outcomes, it is crucial to understand their influence on carbon
emissions.
The research employed a fixed effects framework to estimate a dynamic cross-sectional environmental model,
aiming to assess the consistency and robustness of the results. The study analyzes a cross-section of 23 chosen
Sub-Saharan African (SSA) nations from 2000 to 2023. This research utilizes a panel dataset to investigate the
moderating effect of institutional quality on the link between renewable energy consumption and carbon
emissions. The document is structured as follows: Section 2 presents a review of pertinent literature review
material subsequent to the introduction in Section 1. Section 3 delineates the approach, encompassing verifiable
models; Section 4 discusses the empirical results; and Section 5 concludes the article with policy
recommendations based on the findings.
LITERATURE REVIEW AND HYPOTHESES DEVELOPMENT
Renewable energy consumption and economic growth
Abbasi, et al., (2021) reported that Thailand's Nationally Determined Contribution (NDC) aimed to reduce CO2
emissions by 20-25%. In order to successfully reach the long-term goal of the Paris Agreement, which is to keep
global temperature rise well below 2 °C, it is imperative to take bold and proactive measures for reducing
greenhouse gas emissions beyond the year 2030. The study investigated the influence of the rate at which energy
is depleted, the consumption of renewable energy, the rate at which non-renewable energy is depleted, and the
GDP on CO2 emissions in Thailand from 1980 to 2018, considering their significance. The study employed a
novel dynamic ARDL simulations model [1] and conducted a frequency domain causality (FDC) test. The
empirical results showed that the rate of depletion had a substantial negative effect on CO2 emissions, both in
the long term and the short term. In addition, our findings indicated that renewable energy had a significant and
negative effect on CO2 emissions in the short term. Nevertheless, the rate at which non-renewable energy is
being used up and the gross domestic product (GDP) have been found to have a significant and positive impact
on carbon dioxide (CO2) emissions, both in the short term and in the long term, according to statistical analysis.
The FDC test has verified the presence of causality in the short, medium, and long term between DR, RE,
DRNRE, and CO2 emission.
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In their study, Yuping, et al., (2021) aimed to assess the impact of globalization, renewable energy consumption,
non-renewable energy consumption, and economic growth on carbon dioxide emissions in Argentina from 1970
to 2018. The econometric methodology employed in this study utilized robust methods to address structural
break issues in the data. The Maki cointegration analysis, which accounted for multiple structural breaks,
identified significant long-term relationships between carbon-dioxide emissions, renewable and non-renewable
energy consumption, globalization, and economic growth. The Autoregressive Distributed Lag analysis of
models revealed that renewable energy consumption and globalization have a negative impact on emissions,
while non-renewable energy consumption has a positive impact on emissions, both in the short- and long-term.
In addition, it was discovered that the combination of globalization and renewable energy consumption led to a
decrease in emissions. Conversely, the combination of globalization and non-renewable energy consumption led
to an increase in emissions, but only in the long-term.
Saidi and Omri (2020) conducted a study to analyze the effects of renewable and nuclear energy consumption
on CO2 emissions in 15 OECD countries from 1990 to 2018. They used two estimation methods, namely the
Fully Modified OLS (FMOLS) and the vector error correction model approach (VECM), to examine both the
short-term and long-term impacts. The findings from the FMOLS analysis indicated that investment in nuclear
energy led to a decrease in CO2 emissions in several countries including Canada, Netherlands, Japan,
Switzerland, Czech Republic, and UK. On the other hand, investment in renewable energy resulted in a decrease
in CO2 emissions in Belgium, Canada, France, Germany, Sweden, UK, US, Japan, Switzerland, Finland, and
Czech Republic, but it led to an increase in CO2 emissions in Netherlands and South Korea. Furthermore, both
nuclear and renewable energy consumption contributed to a reduction in carbon emissions based on the panel
estimations. Furthermore, the findings from the VECM approach demonstrated that nuclear and renewable
energies have a long-term effect in decreasing CO2 emissions.
Chen, et al., (2022) conducted a comprehensive analysis of the relationship between energy and economic
growth and carbon dioxide (CO2) emissions per capita in response to growing concerns about climate change.
This study contributed to the existing body of literature on the subject. Most of the previous studies used different
linear panel estimation methods without considering the possible nonlinear impacts of energy and income on per
capita CO2 emissions. In order to address this deficiency, this research employed panel data encompassing 97
countries from 1995 to 2015. It investigated the non-linear effects of renewable and non-renewable energy
consumption, as well as economic growth, on per capita CO2 emissions. This investigation utilized a dynamic
panel threshold model that is resilient to cross-section dependence. The results of our study showed that there is
a negative and significant relationship between the increase in per capita renewable energy consumption and the
growth of per capita CO2 emissions, but only when countries exceed a certain threshold of renewable energy
consumption.
In their study, Jamil, et al., (2022) aimed to conduct a comparative analysis of CO2 emissions, renewable energy
usage, trade openness, gross domestic product (GDP), financial development (FD), and remittance in a specific
group of G-20 countries. The study utilized fully modified ordinary least squares (FMOLS) and dynamic
ordinary least squares (DOLS) models to estimate annual data from 1990 to 2019. The LM tests identified the
presence of cross-section dependability, while the stationarity of the variables was assessed using the Levin-Lin-
Chu and Im-Pesaran-Shin tests, as well as Hansen's Covariate-Augmented Dickey Fuller (CADF) test,
considering the existence of cross-section dependency. The panel unit root tests indicated that all variables
achieved stationarity after being transformed into their first differences. The Panel Cointegration and Wester-
Lund test investigated whether there is a long-term equilibrium relationship among specific variables in the G-
20 countries. The study's results revealed a substantial and adverse correlation between renewable energy and
CO2 emissions. Two models demonstrated a positive correlation between the economic growth of specific G-20
countries and their CO2 emissions. Moreover, the results revealed that the financial development coefficient has
a positive and significant effect on CO2 emissions. The remittances had a substantial and beneficial impact on
CO2 emissions, while trade openness had a negligible effect on CO2 emissions across the two models.
Namahoro, et al., (2021) conducted a study to investigate the relationship between energy consumption,
economic growth, and CO2 emissions at both regional and global levels, taking into account different levels of
development. Only a small number of studies conducted at the African level failed to consider the variations in
regions and income levels among the countries included in the research. This study conducted an empirical
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analysis to investigate the long-term effects of energy intensity, renewable energy consumption, and economic
growth on CO2 emissions in 50 African countries across different regions and income levels from 1980 to 2018.
State-of-the-art panel estimators, causality tests, and analysis of impulse response and variance decomposition
were utilized. The panel estimators' findings indicated that the consumption of renewable energy significantly
helped to reduce CO2 emissions. However, energy intensity was found to increase emissions across different
regions and income levels, including at the African level. Economic growth had a detrimental negative impact
on CO2 emissions at the African level, although the effect varied across regions and income levels. The causality
test has confirmed the presence of bi-directional causations between CO2 emissions and its determinants in
various regions and income levels in Africa.
Dong, et al., (2020) argue that previous studies often overlooked the substantial disparity in the relationship
between emissions and renewable energy across countries with varying income levels. An empirical study was
conducted to examine whether the impact of renewable energy consumption on carbon dioxide (CO2) emissions
varies among countries with different income levels. The study analyzed a global panel of 120 countries and
divided them into four subpanels based on income levels. The data covered the period from 1995 to 2015. By
taking into account the possible cross-sectional dependence and variation in slopes, a range of econometric
methods that accommodate cross-sectional dependence and slope heterogeneity are employed. Both the global
panel and all four subpanels exhibit confirmed cross-sectional dependence and slope heterogeneity. The validity
of the environmental Kuznets curve (EKC) hypothesis was observed only in the global panel, high-income
subpanel, and upper-middle-income subpanel. While renewable energy consumption does have a negative
impact on CO2 emissions, its influence is not substantial.
Saidi and Omri (2020) did a study to analyze the impact of renewable energy on economic growth and carbon
emissions in 15 major renewable energy-consuming countries. This will be done using fully modified ordinary
least square (FMOLS) and vector error correction model (VECM) estimation techniques. The FMOLS technique
results demonstrated the effectiveness of renewable energy in enhancing economic growth and decreasing
carbon emissions. The VECM Granger causality test revealed bidirectional causality between economic growth
and renewable energy in both short- and long-run for both estimated functions, confirming the feedback
hypothesis. Additionally, it shows there is no causal connection between the emissions of carbon dioxide and
renewable energy in the long-run, but there is a bidirectional causal connection between the two variables in the
short-run.
Adams & Acheampong (2019) did a study to investigate how democracy and renewable energy affect carbon
emissions in 46 sub-Saharan African nations utilizing imbalanced data from 1980 to 2015. The study utilized an
instrumental variable generalized method of moments to determine that democracy and renewable energy had
an improving impact on carbon emissions. Foreign direct investment, trade openness, population, and economic
growth were the factors driving carbon emissions in sub-Saharan Africa. Accounting for democracy leads to a
decrease in carbon emissions despite economic development. No evidence supporting the existence of the
Environmental Kuznets curve was discovered. The studies also showed that the influence of urbanization on
carbon emissions is uncertain.
According to Dogan and Seker (2016), recent decades have seen a significant rise in carbon dioxide (CO2)
emissions, leading to several research in the field of energy-growth-environment that aim to pinpoint the factors
influencing CO2 emissions. The study acknowledged a significant critique about the choice of panel estimation
methods in the current investigations. Most studies utilized panel methods that overlook cross-sectional
dependency, despite the high probability of heterogeneity and cross-sectional dependence among nations in the
panel. Most current studies analyzed overall energy use and do not distinguish the environmental implications
of energy consumption from different sources. This empirical study examined how real income, renewable
energy consumption, non-renewable energy consumption, trade openness, and financial development impact
CO2 emissions in the EKC model for top countries in the Renewable Energy Country Attractiveness Index. The
study uses heterogeneous panel estimation techniques with cross-section dependence to address gaps in the
literature. After doing the CADF and CIPS unit root tests, the study sawed that the variables achieved
stationarity in their initial differences. Additionally, the LM bootstrap cointegration test indicated that the
variables are cointegrated. FMOLS and DOLS analyses reveal that higher levels of renewable energy
consumption, trade openness, and financial development lead to a decrease in carbon emissions. Conversely, an
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increase in non-renewable energy consumption is associated with higher emissions. The EKC hypothesis is
confirmed for the leading renewable energy countries.
Jin & Kim (2018) studied the factors influencing carbon emissions by examining energy consumption statistics
from 30 nations that utilize nuclear energy between 1990 and 2014. Renewable energy and nuclear energy
consumption are considered as factors, while real coal price and real GDP are included as additional variables.
Panel cointegration analysis and Granger causality tests are performed to explore the link between the variables.
The panel cointegration test indicated the presence of a long-term equilibrium link between carbon emissions,
renewable energy consumption, and nuclear energy consumption. The long-term cointegrating vector and
Granger causality tests showed that nuclear energy does not aid in reducing carbon emissions, unlike renewable
energy.
A study by Nguyen and Kakinaka (2019) analyzed the correlation between renewable energy usage and carbon
emissions in 107 countries from 1990 to 2013, using panel cointegration analysis to consider the development
stage. The data demonstrated considerable disparities between the low- and high-income country groupings.
Renewable energy usage in low-income nations had both a positive and negative correlation with carbon
emissions and output, respectively. In high-income countries, there was a mixed relationship between renewable
energy usage and carbon emissions as well as output.
Salem et al., (2021) choose the top ten nations with large carbon emissions to analyze the trends in renewable
energy usage from 1991 to 2018. Developed renewable energy companies were a gradual process that does not
happen quickly. Additionally, it involved a period of infrastructure building that could lead to a temporary
increase in CO2 emissions. Renewable energy consumption, including Wind, Solar, and Hydropower,
were utilized to analyze the non-linear relationship with CO2 and its variations. The empirical findings obtained
by a pooled mean group (PMG) approach suggest that renewable energy consumption and hydropower exhibit
an inverted U-shaped pattern, whereas wind and solar energy consumption display a U-shaped pattern.
H1. Renewable energy consumption has a significant effect on Carbon emissions in SSA
Institutional quality and carbon emissions
In their study, Bakhsh, et al., (2021) investigated how institutional quality and technological innovation influence
the relationship between foreign direct investment (FDI) inflows and four CO2 emissions indicators in 40 Asian
countries from 1996 to 2016. They employed the generalized method of moment (GMM) estimation to analyze
the data. Our empirical findings indicated that FDI inflows have a positive effect on CO2 emissions, particularly
in the context of non-interactive regression. However, we have also determined that the role of institutional
quality and technological innovation is essential in influencing the relationship between FDI and CO2 emissions.
Specifically, the interaction between institutional quality measures and FDI inflows has been found to
significantly decrease the level of CO2 emissions. Moreover, there is a notable moderating impact of
technological innovation on the relationship between foreign direct investment (FDI) and carbon dioxide (CO2)
emissions.
In their study, Khan and Rana (2021) investigated the impact of trade, institutional quality, and their interactions
on carbon dioxide emissions in a panel sample of 40 Sub-Saharan African countries. They employed the system
generalized method of moments to analyze the data. Our findings indicated that institutional reforms have a clear
and undeniable positive impact on the environment. Conversely, the effects of trade on the environment are
contingent upon the institutional framework of a nation. To be more precise, trade openness has a detrimental
effect on the environment in countries that have poor institutional quality, while it has a positive impact on the
environment in countries with high institutional quality. This implies that countries with low institutional quality
must undergo institutional reforms in order to fully realize the positive impact of trade on their environment.
Khan and Rana (2021) While Asian economies have experienced significant economic growth in recent decades,
the increasing levels of pollution emissions are causing policymakers to question the long-term sustainability of
this growth. This study examined the cause-and-effect connection between economic growth, energy usage,
openness to trade, development of the financial sector, foreign direct investment, government spending,
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institutional quality, and pollution emissions in 41 Asian economies from 1996 to 2015. In addition, we conduct
separate tests to assess the influence of political and economic institutions on pollution emissions in the
economies included in our sample. The results obtained from our analysis using the panel cointegration method
and panel vector error correction models (VECM) confirmed the existence of a cointegration relationship among
all the variables we have selected. Although economic development, energy consumption, trade openness, and
foreign direct investment contribute to environmental degradation, financial development and improved
economic institutions assist the selected countries in reducing their pollution emissions. Furthermore, improved
economic and political institutions play a role in mitigating the negative effects of income, trade openness, and
foreign direct investment (FDI) on pollution emissions. According to the VECM model, per capita GDP is the
sole variable that has a causal impact on pollution emissions in all of the models. The causal effect is significant
only in a few cases for all the other variables.
In their study, Haldar and Sethi (2021) examined how the quality of institutions affects the relationship between
energy consumption and CO2 emissions in 39 developing countries from 1995 to 2017. They also considered
other factors such as trade, capital formation, foreign direct investment (FDI), financial development, and
population. We employed various econometric techniques, namely mean group (MG), augmented mean group
(AMG), common correlated effects mean group (CCEMG) estimator, dynamic system GMM, panel grouped-
mean FMOLS, and panel quantile regression, to analyze the empirical results. Through various estimation
techniques, we have discovered that the quality of institutions plays a moderating role in energy consumption
and enhances its effectiveness in reducing carbon emissions. The joint impact of institutional quality and energy
consumption by sector on emissions is substantial and adverse. Our research also validated the hypothesis of the
Environmental Kuznets curve (EKC) when considering the influence of institutional quality. Renewable energy
usage has been observed to substantially decrease emissions over an extended period of time.
Jahanger, et al., (2023) Although developing economies have achieved economic success, the majority of them
have not yet achieved environmental sustainability. However, if these economies fail to maintain the quality of
the natural environment, they will also fail to sustain their trade and industry growth. The primary objective of
this study was to investigate the impact of natural resources and institutional quality on carbon emissions
throughout the process of globalization. This was achieved by employing two-stage least square and panel
threshold methods on a global panel consisting of 73 countries that were developing from 1990 to 2018. This
study also applied these empirical models to three sub-panel regions: Asia, Africa, and Latin America. The
study's findings indicated that natural resources, globalization, institutional quality, and human capital have had
a detrimental effect on carbon dioxide emissions. On the other hand, foreign investment and consumption of
energy have the potential to increase emission levels. The panel threshold test revealed that economies in
developing nations with natural resources scoring beneath the threshold of 3.0315 points are likely to experience
an increase in environmental pollution. This study emphasized the moderating impact of institutional quality and
natural resources on carbon emissions. The findings of the other study indicated that both the environmental
Kuznets curve and pollution haven hypothesis were present in all countries.
In a study conducted by Yuan, et al., (2022), China's economy is under increasing pressure to decrease carbon
dioxide (CO2) emissions. This study examined the influence of green innovation and institutional quality on
CO2 emissions, and analyzed the moderating influence of institutional quality. The findings indicated that: (1)
Green innovation greatly decreased the amount of CO2 emissions. The presence of high institutional quality had
an adverse impact on the correlation between green innovation and CO2 emissions. Specifically, when
institutional quality is elevated, green innovation exhibited a more pronounced decrease in CO2 emissions. (2)
Green innovation greatly decreased carbon dioxide (CO2) emissions in both the eastern and western regions.
Furthermore, as the quality of institutions improved, there is an increase in the reduction of CO2 emissions
through green innovation in the western region. The impact of green innovation on reducing CO2 emissions was
more significant during the period of 2013-2017 compared to 2005-2012.
In their study, Dada and Ajide (2021) investigated the influence of institutional quality on the relationship
between the shadow economy and environmental pollution in Nigeria from 1984 to 2018. In addition, the study
also identified a particular form of institutional quality which reduced the size of the shadow economy and
mitigated environmental pollution. The shadow economy was quantified as a proportion of the gross domestic
product (GDP) through the utilization of the currency demand approach, while pollution to the environment is
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represented by the per capita emission of carbon dioxide (CO2). The estimation technique employed was
Autoregressive Distributed Lag (ARDL). The study's findings indicated that the shadow economy has a notable
and positive impact on environmental pollution, both in the short and long term. Conversely, the quality of
institutions was found to have an adverse influence on environmental pollution. This study demonstrated that
the presence of a shadow economy has a negative impact on environmental quality, while the quality of
institutions helps to reduce environmental pollution. In the long term, the correlation between the interacting
term of shadow economy and institutional quality had a negative impact on environmental pollution, although
this impact was not statistically significant.
Boussaidi and Hakimi (2025, January) aimed to examine the potential advantages of a more inclusive financial
system for both economic growth and the environment. Additionally, it examined the moderating influence of
institutional quality (IQ) on the relationship between growth and the environment. The study utilized a dataset
consisting of 12 countries in the Middle East and North Africa (MENA) region, covering the period from 2004
to 2021. The analysis employed the seemingly unrelated regression (SUR) model. The results suggested that the
presence of financial inclusion (FI) had a notable impact on the magnitude of CO2 emissions. Therefore, the
presence of a more comprehensive financial system has had a negative impact on the environmental conditions
in the MENA region. Nevertheless, FI does not have a substantial impact on the rate of growth. Moreover, our
research revealed that the correlation between FI and IQ positively influenced development and enhanced the
overall condition of the surroundings. The observed impact is more evident when the variable being measured
is the quality of the environment rather than growth.
H2. Institutional quality has a significant effect on the relationship between renewable energy consumption and
Carbon emissions in SSA
RESEARCH METHODOLOGY
Sample size and data
The target population for this study was all countries in Sub-Saharan Africa. The selection of the countries was
based on the criteria of whether the country was an independent country within the study period, which is from
2000 – 2023, and the availability of complete data that captured all variables used in this study. The dataset used
for this study is secondary data extracted from the World Bank database and countries’ specific Bureau of
Statistics websites. Our final sample was 552 country-year observations representing 24 countries.
Measurement of variables
This section addresses the measurement of the research variables. Carbon emission is the dependent variable,
while renewable energy consumption and financial development are the independent variables, and institutional
quality is used as a moderating variable. The study includes control variables such as foreign direct investment,
population, and industrialisation. Comprehensive definitions and measurements of all research variables are
provided in Table 1.
Table 1: Measurement of variables
Variable
Category
Operationalization
Acronym
CO2 Emissions
Dependent
Variable
The unit of measurement is kt (kiloton) (Boden, Marland &
Andres, 2013).
CE
Renewable
Energy
Consumption
Independe
nt variable
Measured as share of renewable energy (Kilowatt-Hours, kWh) in
total final energy consumption (World bank, 2022).
REC
Institutional
Quality
Moderator
variable
Composite index of political stability, control of corruption,
regulatory quality, the rule of law, voice and accountability, and
government effectiveneness (Kaufmann et al., 2009; Langbein
IQ
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and Knack, 2010)
Foreign Direct
Investment
Control
variable
FDI is the measure of investment inflows (Pattayat, 2016)
FDI
Population
Control
Variable
The population of a given area is the number of people usually
living in that country (Gu, Andreev & Dupre, 2021)
POP
Industrialization
Control
variable
According to Anchi, Aboubakary, and Edoh (2023), the amount
of net output produced by the manufacturing sector in the
economy
IND
Source, Researcher (2025)
Regression models
The following regression equations were adopted to test the proposed hypotheses:
Model 1: testing the effect of control variables on carbon emissions.
……………...……(1)
Model 2: testing the effect of renewable energy consumption and financial development on carbon emissions.
……………(2)
Model 3: testing the effect of institutional quality on carbon emissions.
……………...……(3)
Model 4: Testing the interaction between renewable energy consumption and institutional quality.
…………………………………(4)
EMPIRICAL FINDINGS AND DISCUSSION
Diagnostic tests
Hausman test (p=0.000) for the choice between fixed effect and random effect model indicated that the fixed
effect model was the most appropriate model for the study. Multicollinearity test results indicated that the model
did not suffer from multicollinearity, Breusch-Pagan test indicated that the model did not suffer from
heteroscedasticity and Breusch-Godfrey test results also indicated that the model did not suffer from
autocorrelation. These results confirm the model’s robustness and reliability.
Descriptive statistics
Table 2 shows the descriptive statistics of seven key variables over different observation counts (Obs) across
different countries or regions. Carbon emissions have mean of 47,327.03 metric tons and standard deviation of
157,595.7, which suggests large differences in carbon emissions among countries. Sao Tome and Principe
recorded the lowest carbon emission in Sub-Saharan Africa, while DRC emitted the highest, and the minimum
value of 53.5 and maximum value of 865,283.2 illustrate that (Wang & Feng, 2022). The mean of foreign direct
investment (FDI) is -2.44e+08 (approximately -244 million USD) and the standard deviation is 1.21e+09 (1.21
billion USD). There is great variability in FDI inflows and outflows, the minimum value (-3.51e+09) and
maximum value (1.24e+10) suggesting that some countries receive large investments such as Nigeria (US$5.3
billion), South Africa (US$4.4 billion), Angola (US$2.2 billion), Liberia (US$2 billion) and Ghana (US$0.61
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billion) (World Bank, 2022), while others experience large disinvestment such as Central Africa Republic,
Eritrea, Burundi and Comoros, which may be due to economic or political instability (Okara, 2023). The
population figures range very widely, from a low of 81,131 people to a high of 22,1907,294.4 people. The mean
population is 40.9 million and the standard deviation is 142 million. The large standard deviation indicates that
the sample covers both least populated countries such as Seychelles, Comoros, Sao Tome and Principe and
highly populated countries such as Nigeria (15.4%) · Ethiopia (8.7%) · Egypt (7.7%) · DR Congo (7.1%) ·
Tanzania (4.5%) · South Africa (4.3%) · Kenya (3.7%), as is typical in global datasets (Zhang & Tan, 2016).
The mean of industrialization is 6.72e+09 (6.72 billion USD) with a large standard deviation of 2.49e+10 (24.9
billion USD). Levels of industrial activity show significant differences, wide range from 3,996,971 to 2.36e+11,
which may be due to the developmental stage of various countries (Kuznets, 1973). The average value of
renewable energy consumption with respect to the total energy consumption is 49.62% with the standard
deviation of 34.33. This range of 0.005% to nearly 100% demonstrates a regional move towards sustainable
energy in some regions such as Ethiopia, Kenya, Tanzania, Uganda, Rwanda and South Africa, while other
regions depend on non-renewable sources such as Nigeria, Angola, Ghana among others (Nguyen & Kakinaka,
2019). Institutional quality finally averages at -0.714 (std. dev. = 0.70) with values between -2.22 and 1.04.
These figures indicate large variation in governance and institutional effectiveness, where countries such as
Mauritius, Botswana, South Africa, Cape Verde and Namibia are among the top scorers of institutional quality
while Liberia, Congo Democratic and Somalia are the least performers (Karim et al., 2022).
Table 2: Descriptive statistics results
Variable
Obs
Mean
Std. Dev.
Min
Max
Carbon emission
552
47327.03
157595.7
53.5
865283.2
Foreign direct investment
552
-2.44e+08
1.21e+09
-3.51e+09
1.24e+10
Population
552
4.09e+07
1.42e+08
81131
221907294.4
Industrialization
552
6.72e+09
2.49e+10
3996971
2.36e+11
Renewable energy consumption
552
49.61874
34.32933
.0051146
99.97338
Institutional quality
552
-.7145176
.7029199
-2.224527
1.044383
Source: Authors computation
Correlation analysis
Correlation analysis aims at realizing the objective of knowing the nature and degree of relationship that exists
between the variables under study. The pairwise correlation coefficients for the variables under investigation are
shown in Table 3. A positive relationship is established between Carbon Emissions (CE) and Foreign Direct
Investment (FDI) (0.3417), implying that countries with higher levels of FDI have higher levels of carbon
emissions. There is a possibility that the concept of foreign investments in industries like manufacturing and
energy cause a rise in economic activity, which in turn leads to an increase in carbon emissions (Науменко,
2021). Although FDI inflow is beneficial to economic growth, it may cause environmental degradation if FDI is
concentrated in high-emission industries. There is a strong positive correlation between Carbon Emissions (CO2)
and Population (POP) (0.6475), indicating that countries with larger populations also produce more carbon. That
is in line with the idea that higher populations typically require more energy for transportation, industry, and
residential use, resulting in higher emissions (Rahman & Alam, 2021). Population growth can also add pressure
to resources and infrastructure, adding to environmental strain. The strongest correlation is between Carbon
Emissions and Industrialization (IND) (0.6992). This means that as countries become more industrialized, their
carbon emissions increase. Greenhouse gas emissions are mainly due to industrial activities, especially
manufacturing, energy production, and transportation. This relationship is consistent with the large literature that
demonstrates that industrialization increases carbon emissions, especially in developing countries where
industrial processes tend to be carbon-intensive (Raheem & Ogebe, 2017).
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Table 3: Correlation results
CE
FDI
POP
IND
REC
IQ
Carbon emission
1.0000
Foreign direct investment
0.3417*
1.0000
Population
0.6475*
-0.1307*
1.0000
Industrialization
0.6992*
-0.1383*
-0.9466*
1.0000
Renewable energy consumption
-0.2582*
-0.0937*
-0.0955*
-0.1817*
1.0000
Institutional quality
-0.1102*
-0.0921*
-0.1675*
-0.0848*
-0.4147*
1.000
Source: Authors computation
Notes: CE= Carbon Emissions; FDI= Foreign Direct Investment; POP=Population; IND= Industrialization;
REC=Renewable Energy Consumption; 1IQ= Institutional Quality; *<0.05
On the contrary, Renewable Energy Consumption (REC) is negatively correlated with Carbon Emissions (-
0.2582), indicating that greater use of renewable energy sources is linked to lower emissions. The reason for this
is that renewable energy (e.g., solar, wind, hydro) has very low or negligible carbon emissions relative to fossil
fuel-based energy sources (International Energy Agency, 2020). This negative relationship highlights the
significance of renewable energy in helping a country cut its carbon footprint and achieve a low-carbon economy.
Institutional Quality (IQ) has a small negative correlation with Carbon Emissions (-0.1102). Thus, better
governance and stronger institutions lead to slightly lower carbon emissions among countries, as better
governance and stronger institutions often correlate with stronger regulatory frameworks, environmental
policies, and enforcement of sustainability standards (Karim et al., 2022). Strong institutions can help mitigate
the negative environmental impacts of industrialization and population growth by fostering cleaner technologies
and efficient resource management.
Regression results
The four models in the fixed effects regression table analyze the relationship between carbon emissions and
various economic, energy, and institutional factors in Sub-Saharan Africa, with a specific focus on the
moderating role of institutional quality in the link between renewable energy consumption and carbon emissions.
Model 1 presents the baseline relationship using control variables only—foreign direct investment (FDI),
population, and industrialization. FDI shows a strong and positive relationship with carbon emissions (β = 0.478,
p < 0.05), suggesting that inflows are likely directed toward carbon-intensive sectors, such as extractive
industries and manufacturing (Doytch, 2020). Population is negatively and significantly related to emissions (β
= -0.078, p < 0.05), possibly due to efficiency gains in urban infrastructure and shared resource use (Shahbaz et
al., 2017). Industrialization shows a strong positive impact on emissions (β = 0.795, p < 0.01), aligning with the
dominance of fossil-fuel-based industrial growth in the region (Grossman & Krueger, 1995).
Model 2 introduced renewable energy consumption as an independent variable. The coefficient for renewable
energy is negative and significant (β = -0.073, p < 0.01), confirming that increased use of renewable sources
reduces carbon emissions. FDI and industrialization remain positively associated with emissions, while
population switches to a positive and significant coefficient (β = 0.075, p < 0.05), possibly reflecting
demographic pressures in energy-intensive urban centers when other variables are controlled for. Model 3 adds
institutional quality as a moderator. Institutional quality shows a negative and statistically significant relationship
with carbon emissions (β = -0.105, p < 0.05), suggesting that stronger governance can reduce environmental
degradation, likely through policy enforcement and investment guidance (Yeboah et al., 2024). Renewable
energy consumption remains negatively associated with emissions (β = -0.120, p < 0.01), reinforcing the idea
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that renewables are effective in reducing environmental harm in regions with improved institutional capacity.
Model 4 includes the interaction term between renewable energy consumption and institutional quality. The
interaction effect is negative and statistically significant (β = -0.052, p < 0.05), indicating that institutional quality
strengthens the mitigating effect of renewable energy consumption on carbon emissions. In essence, renewable
energy becomes more effective at reducing emissions when implemented in a context of strong institutions—
through mechanisms such as regulatory enforcement, transparent energy governance, and incentive alignment.
Notably, in this model, FDI, population, and industrialization coefficients become negative, which may suggest
that strong institutional frameworks can reverse the environmental damage typically associated with these factors
by guiding them toward greener practices. The incremental increase in R-squared values from Model 1 (0.6877)
to Model 4 (0.7061) further demonstrates the importance of including institutional quality and its interaction
with energy policy variables in explaining carbon emissions dynamics in Sub-Saharan Africa. This supports the
hypothesis that good governance not only directly reduces emissions but also enhances the effectiveness of
renewable energy adoption.
Table 4: Fixed regression results
Fixed effect model
Carbon emissions
Model 1
Model 2
Model 3
Model 4
Coef.
Coef.
Coef.
Coef.
Constant
.022 (0.020)
0.022 (0.021)
0.032 (0.021) **
0.052 (0.022) **
Foreign direct investment
0.478 (0.020) **
0.486 (0.021) **
0.468 (0.021) **
-0.105 (0.023) **
Population
-0.078 (0.022) **
0.075 (0.022) **
-0.105 (0.023) **
0.761 (0.021)**
Industrialization
0.795 (0.020) **
0.783 (0.021) **
0.769 (0.021) **
-0.136 (0.026) **
Renewable energy
consumption
-0.073 (0.023) **
-0.120 (0.025)**
-0.094 (0.025) **
Institutional Quality
-0.105 (0.024)**
-0.106 (0.016) **
Renewable energy
consumption * Institutional
Quality
-0.052 (0.023)**
R-sq:
0.6877
0.6964
0.7036
0.7061
Number of Obs
839
839
839
839
Number of groups
24
24
24
24
F
657.35
472.15
389.53
328.54
Prob>F
0.0000
0.0000
0.0000
0.0000
Source: Authors computation
** ρ < 0.05, *P<0.1 standard errors in parentheses
CONCLUSION AND RECOMMENDATION
The fixed effects model results offer significant insights into the factors influencing carbon emissions in Sub-
Saharan Africa, emphasizing the essential roles of renewable energy consumption and institutional quality. The
positive relationship between FDI and carbon emissions in the first model indicates that investment flows
predominantly target carbon-intensive sectors. The transition to a negative FDI-emissions relationship in the
final model, when influenced by institutional quality, signifies the transformative capacity of robust institutions
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in steering investment towards more sustainable options. Renewable energy usage exhibits a consistent and
substantial inverse relationship with emissions, highlighting its efficacy in alleviating environmental
degradation. The relationship between renewable energy and institutional quality further substantiates that
governance frameworks substantially amplify the environmental advantages of renewable energy
implementation. Moreover, institutional quality independently has a significant negative effect on emissions,
underscoring its pivotal role in environmental governance. Given these findings, it is essential for policymakers,
institutional leaders, and development stakeholders in Sub-Saharan Africa to incorporate governance changes
into climate and energy initiatives. Fortifying institutional frameworks can augment regulatory enforcement,
boost transparency, and stimulate investments in sustainable energy. Renewable energy policies must be
supplemented by the enhancement of institutional capacity to guarantee their efficacy. Policymakers must
prioritize institutional improvements that incorporate environmental responsibility and facilitate the execution
of renewable energy regulations. Ultimately, utilizing institutions to enhance collaboration between the public
and private sectors can magnify the effectiveness of renewable energy programs and advance enduring
environmental sustainability throughout the region.
ACKNOWLEDGMENT
I wish to acknowledge the financial support provided by a collaborative project between the Norwegian
University of Life Sciences in Norway and Moi University in Eldoret, Kenya. This support was granted under
the Norwegian Partnership Programme for Global Academic Cooperation (NORPART) and funded by the
Norwegian Ministry of Education and Research and the Norwegian Ministry of Foreign Affairs.
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