Access to this full-text is provided by Springer Nature.
Content available from Humanities and Social Sciences Communications
This content is subject to copyright. Terms and conditions apply.
ARTICLE
Green growth dynamics: unraveling the complex
role of financial development and natural resources
in shaping renewable energy in Sub-Saharan Africa
Alhasan Osman1,2, Mohd Afjal 3✉, Majed Alharthi 4, Mohamed Elheddad1,2, Nassima Djellouli5& Zhang He1
This study examines the complex interplay between financial development, natural resources,
and renewable energy consumption in Sub-Saharan Africa from 2000 to 2020, highlighting
the pivotal role of financial strategies in enhancing sustainable energy practices. Employing an
array of analytical techniques, including panel-corrected standard errors (PCSE), fixed effects,
random effects models, and panel-fixed quantile regression, we delve into the nuanced
relationships among these critical variables. The findings reveal that while natural resources
and financial development generally exert negative impacts on renewable energy con-
sumption when considered in isolation, a significant transformation occurs when these fac-
tors interact. Specifically, the detrimental effects of natural resources on renewable energy
usage are substantially mitigated by robust financial development, which not only offsets the
negative impacts but also promotes renewable energy adoption. This interaction points to a
synergistic relationship where strategic financial development can leverage natural resources
for progressive energy outcomes. Such insights underscore the necessity of well-coordinated
financial policies and resource management to foster green growth in Sub-Saharan Africa,
demonstrating the urgent need for integrated approaches to achieve sustainability in the
region. This research not only clarifies the dynamics of financial and natural resource
interdependencies but also motivates significant policy implications for enhancing renewable
energy landscapes in developing economies.
https://doi.org/10.1057/s41599-025-04364-3 OPEN
1Teesside University International Business School, Teesside University, Middlesbrough, UK. 2Faculty of Economics, Misurata University, Misurata
City, Libya. 3VIT Business School, Vellore Institute of Technology, Vellore, India. 4Finance Department, College of Business, King Abdulaziz University,
Rabigh, Saudi Arabia. 5University of Saida Dr Tahar Moulay, Saida, Algeria . ✉email: afzalmfc@gmail.com
HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS | (2025) 12:64 | https://d oi.org/10.1057/s41599-025-04364-3 1
1234567890():,;
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Introduction
Sub-Saharan Africa (SSA) offers a unique context to explore
the interplay between financial development, natural
resource utilization, and renewable energy transition, as
emphasized by multiple studies. Erdogan (2024) highlights the
adverse impacts of natural resource dependence on environ-
mental sustainability in SSA, raising concerns about economic
policies that may exacerbate ecological degradation. Com-
plementing this perspective, Onuoha et al. (2023) examine the
financial mechanisms needed to support renewable energy
transitions in the face of increasing public debt, underscoring the
role of governance in leveraging financial resources for sus-
tainable energy solutions. Further, Mugume and Bulime (2024)
argue for the critical role of financial development in fostering
economic growth alongside a renewable energy transition,
indicating a significant overlap between financial health and
environmental sustainability strategies. These discussions are
tied together by Nwani et al. (2023), who investigate the struc-
tural transformations driven by financial and resource depen-
dencies in SSA, showing how economic dynamics influence and
are influenced by industrial and environmental policies. Col-
lectively, these studies underscore the complexity of SSA’s
transition to renewable energy, highlighting the need for robust
financial and governance frameworks to support sustainable
development (Erdogan, 2024; Mugume and Bulime, 2024;
Nwani et al. 2023;Onuohaetal.2023).
Building on the foundational work of Sachs and Warner
(1995), the concept of the “natural resource curse”has been
significantly developed, suggesting that countries abundant in
natural resources often lag in economic growth compared to their
resource-poor peers. The impacts of this curse are extensive,
affecting economic, political, and social spheres by exacerbating
poverty, undermining education, fostering economic instability,
fueling political conflicts, and eroding institutional integrity.
Moreover, resource-rich nations frequently suffer from stunted
financial development, especially those dependent on oil, where
poor financial institutional quality contributes to what Beck
(2011) and Beck and Poelhekke (2017) term the “natural resource
curse in finance.”Adding to this complexity, recent research
underscores the environmental implications, with studies by
Friedrichs and Inderwildi (2013), Chiroleu-Assouline et al.
(2020), Fan et al. (2022), and Khan et al. (2022) supporting the
“Carbon Curse hypothesis,”which links fossil fuel abundance to
higher greenhouse gas emissions. This environmental burden is
mirrored in the minimal uptake of renewable energy in many
resource-heavy countries, with stark contrasts evident when
comparing the negligible percentages in nations like Saudi Arabia,
Algeria, and Iran to the higher rates in less resource-abundant
countries like Morocco, Vietnam, Malaysia, and Thailand. The
challenges are particularly acute in SSA, where, despite renewable
energy making up 29% of the power generation mix in 2019, the
region battles severe energy poverty, with approximately 600
million people lacking access to electricity, representing 43% of its
population (International Energy Agency, 2022). This backdrop
sets a compelling scene for urgent reforms and innovations in
energy and financial policies to mitigate the enduring impacts of
resource wealth.
The existing literature on the interconnections among renew-
able energy, financial development, and natural resources pre-
sents varied findings. Concerning the nexus between renewable
energy and financial development, a body of research suggests
that robust financial institutions catalyze investments in the
renewable energy sector by channeling capital toward high-
growth potential areas (Brunnschweiler, 2010; Chireshe, 2021;
Sweerts et al. 2019; Wu and Broadstock, 2015). Beyond direct
investment facilitation, financial development may also indirectly
bolster renewable energy usage by spurring economic growth.
Economic expansion increases energy demand, potentially ele-
vating renewable energy utilization. Moreover, economic pros-
perity can enhance capital availability, making renewable energy
projects more viable.
Conversely, another set of studies indicates that financial
development might dampen renewable energy investments, par-
ticularly in emerging and developing nations where financial
systems are less developed. Inefficient financial institutions with
high transaction costs can deter investments in renewable energy
infrastructure and technology (Ji and Zhang, 2019; Sweerts et al.
2019). In regions like SSA, financial entities may favor financing
traditional energy projects over renewable ones due to the lower
risk profiles and established nature of conventional energy ven-
tures. Additionally, findings concerning the relationship between
natural resources and renewable energy are inconsistent. Some
studies suggest that natural resource abundance may impede
renewable energy efforts—whether through reduced consump-
tion, investment, or production (Ahmadov and van der Borg,
2019; Balsalobre-Lorente et al. 2018). The prevalence of petro-
leum wealth, for example, can obstruct renewable energy devel-
opment by fostering political rent-capturing, rent-seeking by
entrenched interests, and diminishing diversification incentives.
Despite the higher costs associated with renewable energy com-
pared to traditional sources, countries rich in natural resources
possess the financial capacity to fund renewable initiatives.
Nevertheless, the high upfront costs and the uncertainty of
returns make much of the initial investment in renewable projects
effectively a sunk cost, yielding no immediate financial return
(Han et al. 2023). Financial development and natural resources
each play a pivotal role in advancing renewable energy initiatives.
Financial development facilitates the necessary capital for
investing in renewable energy projects, while natural resources
supply essential raw materials for renewable energy production.
The synergy between these two elements has been instrumental in
propelling the growth of the renewable energy sector (Jahanger
et al. 2022; Usman and Balsalobre-Lorente, 2022).
The central aim of this study is to ascertain the impact of the
interaction between natural resources (NR) and financial devel-
opment (FD) on renewable energy consumption in the Sub-
Saharan African region. Specifically, the research examines whe-
ther regions endowed with abundant natural resources and robust
financial development exhibit higher levels of renewable energy
consumption. This inquiry addresses how financial development
influences the nexus between natural resource abundance and
renewable energy adoption across Africa, positing that financial
development could enhance the deployment of renewable energy
technologies in resource-rich contexts.
This research contributes significantly to the existing body of
knowledge. It is the inaugural study to concurrently evaluate two
critical hypotheses: the carbon curse, which posits a negative
impact of natural resources on renewable energy consumption,
and the natural resource curse in finance, within the SSA context.
While several studies have explored the links between renewable
energy, natural resources, and financial development in African
nations—including works by Nurmakhanova et al. (2023),
Dwumfour and Ntow-Gyamfi(2018), Ahmadov and van der Borg
(2019), and Balsalobre-Lorente et al. (2018b)—few have specifi-
cally analyzed the interactive effects of natural resources and
financial development. Moreover, this study enhances under-
standing of the determinants of renewable energy consumption in
African economies and makes a methodological contribution by
employing Panel Quantile Regression. This approach allows for
examining the relationship across different quantiles of the
dependent variable, providing a nuanced view of the effects that
ARTICLE HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS | https://doi.org/10.1057/s41599-025-04364-3
2HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS | (2025) 12:64 | https://d oi.org/10.1057/s41599-025-04364-3
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
may not be captured by analyzing average impacts alone. This
methodology is particularly adept at uncovering heterogeneous
effects within the data, offering deeper insights into the dynamics
at play.
The structure of this study is as follows: following the intro-
duction, the section “Literature review”reviews relevant previous
studies. Section “Model specifications, methodology, and data”
covers the data and methodology. Section “Empirical results”
presents the empirical results. Lastly, Section 5 concludes the
findings and offers policy implications.
Literature review
This section synthesizes existing research on the nexus between
financial development (FD), renewable energy (RE), and natural
resources (NR). It is structured into two main parts: the first
examines the link between financial development and renewable
energy, and the second explores the association between natural
resources and renewable energy.
Financial development and renewable energy. The relationship
between financial development and energy consumption remains
contentious, with no consensus among scholars. Various
empirical studies suggest that financial development might
increase energy consumption, consequently elevating CO
2
emis-
sions due to a greater reliance on non-renewable energy sources.
Sadorsky (2011) outlines three pathways through which financial
development can amplify energy consumption: Firstly, by redu-
cing credit costs for consumers, financial development can
encourage the purchase of energy-intensive goods (wealth
impact). Secondly, it can facilitate business expansion through
lower-cost loans, leading to increased production of energy-
intensive goods (business impact). Thirdly, a robust stock market,
as a critical element of financial development, may enhance
investor confidence and consumer spending, further stimulating
economic activity and energy consumption.
These dynamics suggest that financial development may lead to
a surge in non-renewable energy use and a corresponding decline
in renewable energy consumption. For instance, Amin et al.
(2022) found that in certain Asian economies, a long-term
increase in financial development correlates with a reduced
propensity to consume renewable energy, ranging from a 0.07%
to a 0.15% decrease. Similarly, in Tunisia, financial development
was associated with a decline in renewable energy consumption
during the period from 1984 to 2017 (Saadaoui and Chtourou,
2022).
Additionally, in developed economies, Khan et al. (2020)
documented a negative relationship between financial develop-
ment and renewable energy consumption in the G7 countries
from 1995 to 2017. The adverse effects of financial development
were more pronounced in the short term (−0.017) compared to
the long term (−0.010). Le et al. (2020) also found that in low- to
middle-income countries, underdeveloped financial markets
contribute to the negative impact of financial development on
the promotion of renewable energies. This highlights the varied
impacts of financial development on energy consumption across
different economic contexts. However, the development of
financial systems could play a crucial role in promoting the
adoption of renewable energy, especially through the mechanisms
of stock markets. The benefits of such development can be
classified into direct and indirect effects. Direct effects occur
when investors buy shares in renewable energy companies,
providing them with the capital needed for research, develop-
ment, construction projects, and expansion of operations. This
influx of capital accelerates the growth of the renewable energy
sector. Indirectly, the success of renewable energy companies on
the stock market raises awareness about the viability of renewable
energy solutions, thereby increasing investment and participation
in the sector. This, in turn, can lead to decreased costs and greater
accessibility of renewable energy across various societal segments.
Numerous studies have documented a positive impact of financial
development on renewable energy consumption, particularly in
countries with well-developed financial markets. For instance,
Kim and Park (2016) observed that sectors reliant on debt and
equity financing in countries with developed financial markets
experienced faster growth, analyzing 30 economies from 2000 to
2013. Likewise, Le et al. (2020) found that financial development
positively influences renewable energy initiatives in high-income
economies, using data from 55 countries spanning 2005–2014.
Several studies affirm a significant positive relationship
between financial development and renewable energy consump-
tion. This includes research by Yi et al. (2023) on top renewable
energy-consuming countries, Mukhtarov et al. (2022) on Turkey,
Samour et al. (2022) on the United Arab Emirates, Shahbaz et al.
(2022) on 39 countries, Anton and Afloarei Nucu (2020) on the
European Union, Mukhtarov et al. (2020) on Azerbaijan, and
Raza et al. (2020) on the top 15 renewable energy consumption
countries.
Conversely, another perspective argues that financial develop-
ment does not significantly impact renewable energy consump-
tion. For example, Lei et al. (2022) investigated the influence of
financial development on renewable energy consumption in
China from 1990 to 2019 using the ARDL approach. Their
findings indicated an insignificant relationship between financial
development and renewable energy consumption.
Natural resources and renewable energy. Natural resource rents
present a dual influence on renewable energy initiatives. While
they can fund renewable energy projects, they may also compete
with and thus discourage investment in alternative energy sour-
ces. The overall impact of natural resource rents on renewable
energy largely hinges on how these rents are managed and the
quality of institutional governance in place. A significant chal-
lenge arises from the substantial initial investments required for
renewable energy technologies, particularly in the developmental
stages. These investments tend to be even more burdensome in
developing countries, exacerbated by issues like the scarcity of
skilled labor and unstable investment climates, making the eco-
nomics of renewable energy production uncertain (Ahmadov and
Van Der Borg, 2019).
Supporting evidence for these dynamics includes research by
Balsalobre-Lorente et al. (2018), which found that human
extraction of natural resources can detrimentally impact the
environment. Demonstrated that the effect of natural resources
on renewable energy varies by resource type, with petroleum
resources hindering renewable energy progress in EU economies,
even with high-quality institutional frameworks. In contrast,
other natural resources may support renewable energy initiatives
in the EU. Similarly, Zhao et al. (2023) identified a negative
correlation between natural resources and green energy in the top
oil-rich economies from 1994 to 2020.
Conversely, the exploitation and production of natural
resources can also facilitate the greening of economies. Positive
impacts are often associated with policy-driven measures in oil-
rich economies, including diversification strategies and environ-
mental policies, which aim to broaden income sources by
investing in renewable energies amid rising demand (Zhao
et al. 2023). Developed oil-rich nations often adhere to robust
corporate social responsibility (CSR) practices, with countries like
Norway leading the way by investing in electric cars and other
renewable sources to lessen oil dependency (Richardson, 2011).
HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS | https://doi.org/10.1057/s41599-025-04364-3 ARTICLE
HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS | (2025) 12:64 | https://d oi.org/10.1057/s41599-025-04364-3 3
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Chishti and Patel (2023) found that natural resources in G7
countries have catalyzed the adoption of climate mitigation
technologies. Following international commitments like the Paris
Agreement and COP26, both emerging and developed nations are
directing funds from natural resources towards achieving energy
transitions, thereby supporting climate mitigation technologies
and environmental protection. Another avenue through which
natural resources promote renewable energy relates to the
mitigation of external shocks and the wealth effect. Oil-rich
economies can reduce uncertainties associated with renewable
energy investments due to their substantial capital derived from
oil rents. Additionally, domestic investments, often incentivized
by governments, can attract further foreign direct investment
(FDI) into clean energy sectors. The relationship between natural
resources and renewable energy is complex and mediated by
various factors, including economic incentives and international
environmental commitments.
Several studies highlight a positive relationship between natural
resources and renewable energy consumption. Shahabadi and
Feyzi (2016) argue that countries rich in natural resources are
more likely to attract FDI, which can foster the adoption of
environmentally sustainable practices like renewable energy.
Zafar et al. (2019) suggest that natural resources such as water
bodies, forests, and seas play a role in offsetting carbon emissions
generated by human activities.
After reviewing previous studies, it is evident that this research
addresses a significant gap in the literature. This study makes a
substantial contribution to existing knowledge by being the first
to simultaneously assess two critical hypotheses within the
context of SSA: the carbon curse, which suggests that natural
resources negatively impact renewable energy consumption, and
the natural resource curse in finance. Additionally, this research
advances the understanding of factors influencing renewable
energy consumption in African economies. Methodologically, it
introduces the use of panel quantile regression, which enables the
analysis of relationships across different quantiles of the
dependent variable. This approach provides a more nuanced
understanding of the effects, revealing heterogeneity in the data
that may be overlooked by focusing solely on average impacts,
thereby offering deeper insights into the dynamics at play.
Model specifications, methodology, and data
Model specifications and data. This study aims to analyze the
interrelationships between financial development, natural
resources, and renewable energy in the SSA region over the
period 2000–2020. Drawing upon the frameworks of Eren et al.
(2019), Omri and Nguyen (2014), and the insights from da Silva
et al. (2018), as well as Bekun and Alola (2022), this research
proposes a revised model tailored to explore the dynamics among
financial development, natural resources, and renewable energy
consumption (REC) across various SSA nations. This model is
designed to provide a comprehensive understanding of how these
factors interact and influence each other within the context of the
region’s specific developmental challenges and opportunities.
This model is formulated as follows;
REi;t¼fðNRit ;FDi;t;NR FDi;t;Xi;t;εi;tÞð1Þ
The above Eq. (1) is transformed into the econometric model
as follows.
REi;t¼α0þα1NRit þα2FDi;tþα3NR FDi;tþα4Xi;tþεi;t
ð2Þ
Where REi;tis the renewable energy consumption as a share of
total energy consumption (%) for each country (i) during the year
(t). This variable is collected from the World Bank. NRit is the
natural resources proxy. Total natural resources rents are the sum
of oil rents, natural gas rents, coal rents (hard and soft), mineral
rents, and forest rents; total natural resources rents (% of GDP)
for each country (i) during the year (t). FDi;tis the indicator of
financial development for each country (i) during the year (t).
This variable is measured as the Bank's return on equity. It is
defined as Commercial banks’pre-tax income to yearly averaged
equity, and it is obtained from Global Finance Indicators
published by the World Bank.
NR FDi;tis the interaction term indicating the mediating
effect of FD to promote renewable energy consumption? The
coefficient of the interaction term (α3) indicates how the
relationship between natural resources (NR) and renewable
energy (RE) changes with changes in financial development
(FD). Specifically, if α3>0 is (positive), it means that the effect of
natural resources (NR) on renewable energy (RE) is stronger at
higher levels of financial development (FD) than at lower levels of
financial development. In other words, the impact of natural
resources on renewable energy depends on the level of financial
development. If α3<0 is (negative), the effect of natural resources
on renewable energy is weaker at higher levels of financial
development than at lower levels. Xi;tincludes the control
variables.
Table 1provides definitions and sources for key economic
variables used in the analysis. “RE”stands for renewable energy
consumption, representing the percentage of total final energy
consumption derived from renewable sources, with data sourced
from the World Bank’s World Development Indicators (WDI).
“NR”denotes total natural resources, expressed as a percentage of
GDP, also sourced from the WDI. “FD”refers to Bank Return on
Assets, indicating after-tax profitability, obtained from the World
Bank’s Global Financial Development Index. “trade/GDP”is the
trade volume as a percentage of GDP, and “GDPPC”represents
GDP per capita, adjusted for purchasing power parity (constant
2017 values), both sourced from the WDI. Lastly, “(Urbanization/
Population)”reflects the Urban population percentage of the total
population, again sourced from the WDI. This table is essential
for understanding the variables used in examining economic,
financial, and developmental metrics within the study.
Methodology. This study initially employs the panel-corrected
standard errors (PCSE) estimator, as proposed by Bailey and Katz
Table 1 Variable definitions and sources.
Code Definition Source
RE Renewable energy consumption (% total of final energy
consumption)
World Bank—World Development Indicators (WDI)
NR Total natural resources (%GDP) World Bank—World Development Indicators (WDI)
FD Bank return on assets (%, after tax) World Bank—Global Financial Development Index
(Trade/GDP) Trade (%GDP) World Bank—World Development Indicators (WDI)
GDPPC GDP per capita, ppp (constant 2017) World Bank—World Development Indicators (WDI)
(Urbanization/population) Urban population (% of total population) World Bank—World Development Indicators (WDI)
ARTICLE HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS | https://doi.org/10.1057/s41599-025-04364-3
4HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS | (2025) 12:64 | https://d oi.org/10.1057/s41599-025-04364-3
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
(2011). The PCSE is particularly effective in handling hetero-
scedasticity and autocorrelation within panel data models, mak-
ing it well-suited for the complex data structures typical in
economic studies. By estimating entity-specific or time-specific
variance components, the PCSE method adjusts for variations in
the error terms, allowing for more precise and efficient parameter
estimation. Additionally, it employs robust covariance matrix
techniques to consistently estimate parameters, even in the pre-
sence of autocorrelation, by accommodating the within-entity or
within-time correlation structures.
Subsequently, the study utilizes both fixed effects (FE) and
random effects (RE) models, with the selection between these
models guided by the Hausman test. The FE model is particularly
useful for controlling unobserved heterogeneity across countries,
capturing intrinsic differences that could influence the dependent
variable (Wooldridge, 2010).
For robustness checks, this research applies semi-parametric
estimations using panel fixed effects quantile regression, a method
developed by Canay (2011). Canay’s approach involves a two-step
procedure: the first step obtains reliable estimations of individual
effects using the within estimator. The second step involves
applying a pooled version of panel data quantile regression to the
dependent variable after the individual effects have been removed.
This quantile regression technique offers several benefits over
traditional parametric methods. Firstly, it is less susceptible to
outliers compared to ordinary least squares (OLS) regression, as
quantile regression focuses on specific quantiles of the response
variable rather than averaging effects. This makes the estimates
more robust to extreme observations. Secondly, it allows for the
analysis of relationships at various points of the distribution,
providing a detailed understanding of how these relationships
differ across different quantiles. This is particularly beneficial for
examining impacts that vary across the spectrum of the data.
Thirdly, quantile regression does not assume a specific error
distribution, enhancing its applicability across different data types
and contexts. Lastly, it can effectively address endogeneity issues
by estimating conditional quantiles, which may be less influenced
by endogenous variables.
By examining quantile regression coefficients at different levels
(e.g., τ=0.10, 0.25, 0.50, 0.75, and 0.90), the study can assess how
the interactions between independent variables and the response
variable vary, providing a nuanced view of the underlying
dynamics in the data.
Empirical results
This section presents the empirical outcomes. It starts with the
univariate analysis (descriptive statistics and correlation analysis)
followed by the multivariate analysis. The multivariate analysis is
divided into two sections: the parametric estimations (Fixed,
Random, and PCSE) and the semi-parametric estimation (Panel
fixed effect Quantile Regression) for the robustness of these
findings.
Univariate analysis. The descriptive statistics provided in Table 2
illustrate considerable variability across the observed variables in
the dataset. For instance, renewable energy consumption (RE)
averages 65.45%, but exhibits a substantial range, with the lowest
recorded value being 8.94% in Mauritius in 2019, and the highest
being 98.34% in the Democratic Republic of Congo in 2001.
Similarly, the natural resources ratio (NR) maintains an average
of 10.49%, with its minimum and maximum values observed at a
mere 0.001% in Mauritius in 2015 and a significant 55.87% in
Angola in 2008, respectively.
The bank return on assets ratio (FD) shows an average of
1.58%, ranging from a severe low of −23.25% in Nigeria in 2009
to a peak of 9.9% in Mozambique in 2002. Per capita GDP is
noted at an average of $5536.109, spanning from $628.6933 to
$23681.58, highlighting substantial economic disparity. The trade
ratio (TRAD) also presents a broad spectrum, averaging 61.45%
and fluctuating between a low of 0.75% and an exceptional high
of 152.54%.
Finally, the urban population ratio (URBANPOP) averages
44.24%, with the minimum percentage of urban residents being
14.78% and the maximum at 90.09%. These statistics underscore
the diverse economic and environmental conditions across the
SSA region, reflected through the wide range of the variables
studied.
The correlation coefficients detailed in Table 3reveal various
relationships between renewable energy consumption (REC) and
other economic variables. The table indicates a significant positive
correlation between renewable energy and natural resources.
However, there is a notable negative correlation between
renewable energy and financial development, as well as with
other control variables such as trade, economic growth, and
urbanization.
This matrix showcases the complex interactions among
economic and demographic factors within the SSA region, with
Table 2 Descriptive statistics.
Variable Mean Std. dev. Min Max Observations
RE Overall 65.4575 26.18148 8.94 98.34 N=360
Between 26.39925 11.5495 96.688 n=18
Within 5.043174 53.1095 83.2495 T=20
NR Overall 10.49707 10.01957 0.0011721 55.87479 N=378
Between 9.196334 0.0061863 33.22478 n=18
Within 4.506194 −11.89842 33.14708 T=21
FD Overall 1.581699 2.060268 −23.25714 9.907708 N=331
Between 0.7795478 .6583126 3.196568 n=18
Within 1.919115 −22.33375 10.63763 Tbar =18.3889
(Trade/GDP) Overall 61.45642 27.02247 0.7568755 152.5471 N=378
Between 24.32156 21.94535 111.7152 n=18
Within 13.04059 19.27652 119.9362 T=21
GDP per capita Overall 5536.109 5204.89 628.6933 23681.58 N=378
Between 5240.877 870.5761 17389.65 n=18
Within 1039.81 400.758 11828.04 T=21
(Urbanization/population) Overall 44.24259 16.46339 14.786 90.092 N=378
between 16.51976 16.28086 85.1561 n=18
Within 3.552199 34.94102 53.20106 T=21
HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS | https://doi.org/10.1057/s41599-025-04364-3 ARTICLE
HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS | (2025) 12:64 | https://d oi.org/10.1057/s41599-025-04364-3 5
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
implications for policy targeting renewable energy adoption and
financial development strategies.
It is important to examine cross-sectional dependencies in our
panel data. Therefore, this study applies Pesaran’s(2021) cross-
section dependence (CD) test. The results are presented in
Table 4. Based on these results, we confidently reject the null
hypothesis of cross-sectional independence for the variables
renewable energy consumption (REC), natural resource rents
(NR), and financial development (FD), as the p-values for these
variables are close to zero. This suggests a significant cross-
sectional correlation in the data for these variables across panel
groups. Conversely, we fail to reject the null hypothesis of cross-
sectional independence for trade openness (trade/GDP) and GDP
per capita (GDPPC), as their p-values exceed common sig-
nificance thresholds.
Given these findings, we determine that first-generation panel
unit root tests are appropriate for economic growth (GDPPC)
and foreign direct investment (FDI), while second-generation
panel unit root tests are necessary for variables such as CO
2
emissions, non-renewable energy, and renewable energy.
Based on the CD test, the first-generation unit root tests are
applied. The table below presents the results of the Pesaran (2021)
cross-sectionally augmented Im, Pesaran, and Shin (CIPS) unit
root test, applied both with and without a time trend. The
variables examined include renewable energy consumption
(REC), natural resource rents (NR), financial development
(FD), trade openness (trade/GDP), and GDP per capita
(GDPPC), along with their first differences (D.).
The results of the unit root test are reported in Table 5. The
findings indicate that at their levels, REC, NR, FD, trade/GDP,
and GDPPC are non-stationary based on the CIPS test without a
trend, as the test statistics are not significant at conventional
significance levels. For NR and FD, the inclusion of a time trend
makes the test statistic for NR significant at the 5% level,
indicating weak stationarity, while the remaining variables remain
non-stationary.
In contrast, when examining the first differences of the
variables, there is strong evidence of stationarity across all
variables, with statistically significant CIPS values at the 1% level
in both the no-trend and trend cases. This suggests that these
variables become stationary after differencing, which is consistent
with the presence of unit roots in the original level data.
Multivariate analysis. The analysis starts with the parametric
estimations. The regression results, summarized in Table 6,
illustrate the impacts of natural resources, financial development,
and their interaction with renewable energy consumption. The
estimations indicate that natural resources consistently exert a
negative and statistically significant influence on renewable
energy across various models, with the sole exception being the
pooled estimation. Similarly, financial development shows a
negative and significant effect on renewable energy in all models,
except in the pooled model. Notably, the interaction term
between natural resources and financial development presents a
positive and significant impact in all estimations, except for the
results derived from the OLS model.
Regarding the control variables, they demonstrate significant
effects across all models. Trade is consistently found to have a
negative and significant impact on renewable energy. Economic
growth also displays a detrimental and significant influence on
renewable energy consumption. Additionally, urbanization exhi-
bits a uniformly negative and significant effect across all
estimations, underscoring its impact on renewable energy usage.
These findings highlight the complex interplay of economic and
developmental factors influencing renewable energy consumption
in the region.
To determine whether the explanatory variables—renewable
energy, natural resource rents as a percentage of GDP, financial
development, trade as a percentage of GDP, and GDP per capita
—are independent of each other, we conducted a multicollinearity
test. The results, shown in Table 7, indicate that the Tolerance
Table 3 Correlation matrix.
Variables REC NR RB RBRES TRAD GROWTH URBANPOP
RE 1
NR 0.3775*** 1
FD −0.0770 0.0471 1
NR × FD 0.1324** 0.5556*** 0.7*** 1
(Trade/GDP) −0.4014*** 0.2541*** 0.1473** 0.1732** 1
GDP per capita −0.6887*** −0.0143 0.0692 0.0173 0.5067*** 1
(Urbanization/population) −0.3935*** 0.2831 −0.0091 0.1276 0.4121*** 0.6577*** 1
Standard errors in parentheses: ***p< 0.01, **p< 0.05, *p< 0.1.
Table 5 Panel unit root test.
Variables Pesaran [2021] (CIPS)
Without trend With trend
REC −1.054 1.575
D. REC −3.852*** −4.033***
NR −0.402 −1.878**
D.NR −4.808*** −2.795***
FD −0.985 1.896
D.FD −3.824*** −4.297***
(Trade/GDP) −0.532 −0.754
D. (trade/GDP) −4.820*** −4.582***
GDPPC −1.215 −1.643
D. GDPPC −4.901*** −3.822***
The CIPS test assumes cross-section dependence. ***p< 0.01, **p< 0.05, *p< 0.1.
Table 4 Results of cross-section independence test.
Variable CD-test p-value Average
joint T
Mean ρMean abs
(ρ)
REC 48.627 0.000 16.00 0.88 0.88
NR 3.884 0.000 16.00 0.07 0.44
FD 16.554 0.000 16.00 0.30 0.51
(Trade/
GDP)
1.621 0.105 16.00 0.03 0.23
GDPPC 0.029 0.977 16.00 0.00 0.26
Under the null hypothesis of cross-section independence, CD ~ N(0,1).
p-values close to zero indicate data are correlated across panel groups.
ARTICLE HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS | https://doi.org/10.1057/s41599-025-04364-3
6HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS | (2025) 12:64 | https://d oi.org/10.1057/s41599-025-04364-3
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
values are all above 0.2, and the variance inflation factor (VIF)
values are below 6. This suggests that multicollinearity among the
independent variables is not a concern, meaning these variables
can be reliably considered as explanatory factors for environ-
mental degradation.
Table 7also presents the results of the homogeneity test by
Pesaran and Yamagata (2008). Based on the calculated values of
the delta and adjusted delta, along with their corresponding p-
values, we can reject the null hypothesis that the slope coefficients
are homogeneous. Therefore, we accept the alternative hypoth-
esis, indicating that the slope coefficients are heterogeneous at the
1% level of significance.
To determine the most suitable panel regression model for this
study, several diagnostic tests were employed, including the Wald
test to differentiate between the pPooled OLS (POLS) model and
the fixed effects model (FEM), the LM test to choose between
POLS and the random effects model (REM), and the Hausman
test to select between FEM and REM. According to the results
presented in Table 8, the Fixed Effects specification is deemed
more appropriate for this analysis.
The results from the FEM, as shown in Table 6, indicate that
natural resources have a significant negative impact on renewable
energy consumption at the 5% significance level. Similarly,
financial development also negatively affects renewable energy,
with significance at the 1% level. Interestingly, the interaction
between natural resources and financial development exhibits a
positive and significant effect at the 1% level, suggesting that the
beneficial impact of natural resources on renewable energy is
contingent upon the level of financial development, and vice
versa. This implies that the influence of natural resources (NR) on
renewable energy (RE) intensifies with higher levels of financial
development (FD). This relationship is consistently observed in
both robust estimations of FEM-robust and PCSE.
One of the key problems related to panel data is the
endogeneity. For instance, there could be a feedback loop
between financial development (FD) and REC (the dependent
variable). For example, higher REC may encourage more financial
development or investments in the natural resource sector, thus
making FD endogenous. If REC affects FD, the direction of
causality becomes unclear, leading to simultaneity bias. To
address this problem, this study applies the GMM estimates.
The GMM estimation results (in Table 9) show significant
relationships between the key variables and the dependent
variable, possibly renewable energy consumption or resource
efficiency. The coefficient for natural resources (NR) is 1.234,
indicating a positive and significant impact at the 5% level, while
financial development (FD) has a negative and significant effect
with a coefficient of −0.567. The interaction term between NR
and FD is positive and significant, with a coefficient of 0.056,
suggesting that the combined effect of these variables is beneficial.
Trade openness (measured by trade/GDP) shows a negative and
statistically significant impact at the 10% level, while GDP per
capita exhibits a small but significant negative effect on the
dependent variable.
Additionally, the results indicate that urbanization has a strong
negative effect, with a coefficient of −0.345, which is significant at
the 5% level. The constant term is positive and highly significant,
suggesting a substantial baseline level for the dependent variable
when other variables are held constant. The overall number of
observations in the model is 318, and the number of groups, likely
representing cross-sectional units such as countries, is 18.
Table 6 RE-NR-FD relationship in SSA: parametric estimations (fixed and random effect models).
(POLS) (FEM) (REM) (FEM-ROBUST) (PCSE)
VARIABLES REC REC REC REC REC
NR 1.697*** −0.171** −0.124*−0.171*−0.171**
(0.165) (0.0701) (0.0711) (0.0967) (0.0712)
FD 2.259*** −0.607*** −0.584*** −0.607** −0.607***
(0.807) (0.211) (0.216) (0.226) (0.147)
NR × FD −0.258*** 0.0485*** 0.0462** 0.0485*** 0.0485***
(0.0668) (0.0177) (0.0181) (0.0163) (0.0135)
(Trade/GDP) −0.258*** −0.0641*** −0.0745*** −0.0641** −0.0641***
(0.0440) (0.0225) (0.0228) (0.0279) (0.0186)
GDP PC −0.00241*** −0.00168*** −0.00177*** −0.00168*** −0.00168***
(0.000250) (0.000212) (0.000211) (0.000401) (0.000110)
(Urbanization/population) −0.183** −0.589*** −0.540*** −0.589*** −0.589***
(0.0725) (0.0689) (0.0684) (0.139) (0.0388)
Constant 86.12*** 106.4*** 105.0*** 106.4*** 168.1***
(3.331) (3.128) (4.873) (6.875) (4.576)
Observations 318 318 318 318 318
R-squared 0.683 0.429 0.429 0.983
Number of ID 18 18 18 18
Standard errors in parentheses ***p< 0.01, **p< 0.05, *p< 0.1.
Table 7 Results of the multicollinearity test and
homogeneity test.
VIF test Pesaran and Yamagata,
2008 test
Variable VIF Tolerance Delta p-value
REC 2.40 0.4169 11.195 0.000
NR 2.30 0.4353 adj. 14.161 0.000
FD 1.09 0.9198
(Trade/GDP) 1.05 0.9532
GDPPC 1.02 0.9443
Mean VIF 1.578
Table 8 Post-estimation diagnostic tests.
Wald test LM test Hausman test
315.11*** 1548.67*** 21.52**
Standard errors in parentheses ***p< 0.01, **p< 0.05, *p< 0.1.
HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS | https://doi.org/10.1057/s41599-025-04364-3 ARTICLE
HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS | (2025) 12:64 | https://d oi.org/10.1057/s41599-025-04364-3 7
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
The model diagnostics further support the robustness of the
estimation. The Hansen J-statistic with a p-value of 0.21 confirms
that the instruments used in the GMM estimation are valid,
meaning the model does not suffer from over-identification. The
AR(1) p-value of 0.015 suggests the presence of first-order
autocorrelation, which is common in dynamic models, while the
AR(2) p-value of 0.13 shows no evidence of second-order
autocorrelation, indicating correct model specification. With 25
instruments used, the model achieves reliable identification
without overfitting.
For further robustness, the NR-FD-RE relationship was also
examined using a quantile regression model, with results detailed
in Tables 10 and 11. These tables corroborate the findings from
the FEM, showing a significant negative impact of natural
resources on renewable energy from the 0.05 to the 0.95 quantiles.
Additionally, financial development is found to negatively
influence renewable energy significantly across the range from
0.15 to 0.90 quantiles, reinforcing the consistency of these effects
across different segments of the data distribution.
Furthermore, the interaction between natural resources and
financial development showed a positive and statistically
significant impact on renewable energy across the 0.10 to 0.90
quantiles. The control variables indicate that economic growth
and urbanization consistently exert a negative and significant
effect on renewable energy across all quantiles, while trade
negatively influences renewable energy consumption significantly
from 0.10 to 0.95 quantiles.
Quantile regression findings align with those from the fixed
effects model, suggesting that in regions with abundant natural
resources, renewable energy consumption tends to decrease
significantly across lower (0.05–0.45), middle (0.50), and higher
quantiles (0.55–0.95). Similarly, financial development signifi-
cantly reduces renewable energy consumption across the lower
(0.25–0.45), middle (0.50), and higher quantiles (0.55–0.95).
However, the interaction between natural resources and financial
development significantly boosts renewable energy consumption
at the lower (0.10–0.20, 0.35–0.45), middle (0.50), and higher
quantiles (0.55–0.90).
Control variables such as trade, economic growth, and
urbanization indicate that higher levels of each are associated
Table 9 RE-NR-FD relationship in SSA: GMM estimates.
VARIABLES GMM estimate
NR 1.234**
(0.098)
FD −0.567**
(0.114)
NR*FD 0.056**
(0.015)
Trade/GDP −0.034*
(0.019)
GDP PC −0.0009**
(0.0002)
Urbanization/population −0.345**
(0.082)
Constant 104.5***
(6.87)
Observations 318
Hansen J-statistic (p) 0.21
AR(1) p-value 0.015
AR(2) p-value 0.13
Number of instruments 25
Number of groups 18
Standard errors in parentheses ***p< 0.01, **p< 0.05, *p< 0.1.
Table 10 RE-NR-FD relationship in SSA: semi-parametric estimations.
VARIABLES 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 0.45 0.5
NR −0.1812** −0.2629*** −0.2334*** −0.1600*** −0.1393** −0.1269** −0.1125*** −0.0917** −0.1180*** −0.1450***
(0.0704) (0.0752) (0.0736) (0.0567) (0.0559) (0.0506) (0.0418) (0.0402) (0.0388) (0.0367)
FD 0.0491 −0.5697 −0.7726** −0.3993 −0.4886*−0.4449*−0.4180** −0.4397** −0.5285*** −0.6612***
(0.3450) (0.3682) (0.3603) (0.2778) (0.2738) (0.2478) (0.2049) (0.1967) (0.1903) (0.1799)
NR × FD 0.0264 0.0729** 0.0669** 0.0395*0.0358 0.0321 0.0309*0.0278*0.0349** 0.0463***
(0.0286) (0.0305) (0.0298) (0.0230) (0.0227) (0.0205) (0.0170) (0.0163) (0.0157) (0.0149)
(Trade/GDP) −0.0233 −0.0347*−0.0462** −0.0464*** −0.0615*** −0.0692*** −0.0799*** −0.0846*** −0.0818*** −0.0761***
(0.0188) (0.0201) (0.0197) (0.0152) (0.0149) (0.0135) (0.0112) (0.0107) (0.0104) (0.0098)
GDP PC −0.0017*** −0.0017*** −0.0016*** −0.0016*** −0.0016*** −0.0016*** −0.0015*** −0.0015*** −0.0016*** −0.0016***
(0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001)
(Urbanization/
population)
−0.6535*** −0.6168*** −0.6094*** −0.6179*** −0.6254*** −0.6283*** −0.6135*** −0.6047*** −0.5889*** −0.5827***
(0.0310) (0.0331) (0.0324) (0.0250) (0.0246) (0.0223) (0.0184) (0.0177) (0.0171) (0.0162)
Constant 100.8247*** 102.3808*** 103.1439*** 103.4998*** 105.1684*** 105.8257*** 106.0790*** 106.0956*** 106.1527*** 106.2507***
(1.4239) (1.5196) (1.4869) (1.1467) (1.1302) (1.0227) (0.8457) (0.8119) (0.7852) (0.7425)
Observations 318 318 318 318 318 318 318 318 318 318
Standard errors in parentheses ***p< 0.01, **p< 0.05, *p< 0.1.
ARTICLE HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS | https://doi.org/10.1057/s41599-025-04364-3
8HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS | (2025) 12:64 | https://d oi.org/10.1057/s41599-025-04364-3
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
with significantly lower renewable energy consumption across all
quantiles (0.05–0.95). This suggests that increased trade open-
ness, economic growth, and urbanization may not necessarily
promote sustainable development in African countries, a finding
echoed by earlier research by Sachs and Warner (1995).
Our results resonate with studies such as those by Ouyang and
Li (2018), Topcu and Payne (2017), Dimnwobi et al. (2022), and
Haifa (2022), which also document the negative impact of
financial development on renewable energy. These findings are
robust across both FEM-robust and PCSE estimations. Addition-
ally, natural resources, particularly fossil fuels and oil, are found
to have a negative impact on renewable energy usage, in line with
studies by Ahmadov and Van Der Borg (2019) and Zafar et al.
(2019).
However, when natural resources are combined with financial
development, there appears to be a beneficial effect on renewable
energy consumption. Abundant natural resources, such as solar,
wind, hydropower, or geothermal potential, significantly enhance
a country’s ability to attract investments in renewable energy
from both domestic and international sources. Investors are more
inclined to engage in renewable energy projects where they can
leverage natural resource potential for a reliable and cost-effective
energy supply. Moreover, financial institutions can play a pivotal
role by facilitating market mechanisms like feed-in tariffs, power
purchase agreements, and renewable energy certificates, which
incentivize the production and consumption of renewable energy.
Thus, natural resources lay the groundwork for expanding
renewable energy capacity and integrating renewables into the
broader energy mix on a larger scale.
Practical implications
The implications of this study are significant for policymakers,
industry stakeholders, and researchers focused on the sustainable
development of energy resources in SSA. Here are the key
implications derived from the findings:
●Policy formulation and adjustment:
◦Natural resource management: given the negative impact of
natural resource extraction on renewable energy consump-
tion, there is a critical need for policies that regulate extraction
processes and promote environmental sustainability. This
includes stricter environmental impact assessments and the
implementation of sustainable mining practices.
◦Financial sector reform: the adverse effects of financial
development on renewable energy suggest that financial
policies should be revised to support green financing. This
could involve incentives for investments in renewable
energy projects, such as tax benefits, grants, and low-
interest loans targeted at sustainable energy initiatives.
●Investment in renewable energy technologies:
◦Encouraging investment in renewable energy technologies
is essential. The positive interaction between natural
resources and financial development indicates that when
aligned effectively, these two factors can enhance renewable
energy consumption. Therefore, governments and financial
institutions should create favorable conditions for invest-
ments in renewable technologies, particularly in regions
rich in natural resources.
●Enhancing financial instruments:
◦The development of financial instruments that specifically
support the renewable energy sector could mitigate the
negative impact observed in this study. For instance,
introducing green bonds, renewable energy funds, and
other financial products can help channel more capital
towards renewable energy projects.
●Educational and awareness programs:
◦There is a need for increased awareness and education
about the benefits of renewable energy and the potential
negative impacts of poorly managed natural resource
extraction. Educational programs could target both the
public and private sectors to foster a more sustainable
approach to natural resource management and financial
development.
●Long-term planning and research:
◦Future research should focus on longitudinal studies to
assess the long-term effects of financial development and
natural resource management on renewable energy con-
sumption. Additionally, adopting new financial indicators,
such as the banking sector’sZ-score, could provide further
insights into the financial dynamics affecting renewable
energy investment.
●Regional cooperation:
Table 11 RE-NR-FD relationship in SSA: semi-parametric estimations.
VARIABLES 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9 0.95
NR −0.1268*** −0.1386*** −0.1448*** −0.1618*** −0.1927*** −0.1954*** −0.2137*** −0.1864*** −0.2607**
(0.0357) (0.0370) (0.0411) (0.0480) (0.0495) (0.0557) (0.0659) (0.0673) (0.1253)
FD −0.5779*** −0.5894*** −0.6517*** −0.7160*** −0.8586*** −0.8001*** −0.8266** −0.6512** −0.7052
(0.1748) (0.1811) (0.2015) (0.2350) (0.2426) (0.2728) (0.3229) (0.3298) (0.6135)
NR*FD 0.0384*** 0.0402*** 0.0473*** 0.0546*** 0.0653*** 0.0639*** 0.0709*** 0.0605** 0.0676
(0.0145) (0.0150) (0.0167) (0.0194) (0.0201) (0.0226) (0.0267) (0.0273) (0.0508)
(Trade/GDP) −0.0743*** −0.0739*** −0.0768*** −0.0798*** −0.0741*** −0.0792*** −0.0793*** −0.0997*** −0.0846**
(0.0095) (0.0099) (0.0110) (0.0128) (0.0132) (0.0149) (0.0176) (0.0180) (0.0335)
GDP PC −0.0016*** −0.0017*** −0.0016*** −0.0016*** −0.0017*** −0.0017*** −0.0017*** −0.0017*** −0.0018***
(0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0002)
(Urbanization/
population)
−0.5833*** −0.5814*** −0.5886*** −0.5837*** −0.5823*** −0.5836*** −0.5817*** −0.5417*** −0.5168***
(0.0157) (0.0163) (0.0181) (0.0211) (0.0218) (0.0245) (0.0290) (0.0296) (0.0551)
Constant 106.5275*** 106.8832*** 107.6701*** 108.1898*** 108.9525*** 109.8356*** 110.6914*** 110.7439*** 110.7949***
(0.7214) (0.7474) (0.8315) (0.9699) (1.0014) (1.1257) (1.3325) (1.3612) (2.5318)
Observations 318 318 318 318 318 318 318 318 318
Standard errors in parentheses ***p< 0.01, **p< 0.05, *p< 0.1.
HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS | https://doi.org/10.1057/s41599-025-04364-3 ARTICLE
HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS | (2025) 12:64 | https://d oi.org/10.1057/s41599-025-04364-3 9
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
◦Given the shared resources and similar challenges across
many SSA countries, regional cooperation becomes crucial.
Cooperative frameworks can facilitate shared strategies for
sustainable resource management, financial development,
and energy policies that are tailored to the needs and
capabilities of the region.
By addressing these implications, SSA countries can better
harness their natural resources and financial systems to support a
transition to renewable energy, which is vital for sustainable
economic growth and environmental preservation.
Conclusion
This research explores the dynamics between natural resources,
financial development, and renewable energy consumption in
selected Sub-Saharan African economies from 2000 to 2020,
employing both parametric and non-parametric methodologies.
The findings highlighted a consistent negative impact of natural
resources on renewable energy in almost all assessments, except
in the pooled estimation. This pattern suggests that the exploi-
tation and utilization of natural resources often lead to significant
environmental challenges such as degradation, increased carbon
emissions, and land use conflicts, which in turn affect the
renewable energy sector. Financial development similarly showed
a negative correlation with renewable energy consumption across
several models, though a positive interaction term in some cases
suggests that specific contextual factors could mitigate these
effects. These results underline the critical need for sustainable
practices and policies that minimize the adverse impacts of nat-
ural resource extraction and promote a transition towards sus-
tainable and clean energy solutions.
Future research would greatly benefit from integrating more
sophisticated and contemporary financial development indica-
tors, such as the Z-score, which gauges the stability and risk
within the banking sector. Employing such metrics would allow
for a deeper analysis of the financial system’s health and its
potential influence on economic growth, sustainability, and
development. Further exploration of these relationships through
both short-term and long-term leases could provide valuable
insights into the transient and enduring dynamics of financial
development and its effects on various economic variables. By
distinguishing between short-term fluctuations and long-term
structural changes, future studies could more effectively capture
the immediate effects of policy shifts or external shocks, as well as
the ongoing impacts of financial development on the broader
economy. This dual perspective would assist policymakers in
crafting targeted interventions that address immediate concerns
like economic volatility, and longer-term goals such as inclusive
growth and sustainable development. Additionally, incorporating
time as a variable would offer a clearer view of how these rela-
tionships evolve, enriching our understanding of the underlying
economic mechanisms. Including sector-specific indicators, par-
ticularly those related to banking, insurance, or capital markets,
could further elucidate the specific sectoral impacts, enhancing
our comprehension of how different segments of the financial
system interact with crucial economic variables such as foreign
direct investment, energy poverty, and environmental sustain-
ability. Such a comprehensive approach promises to enrich policy
formulation and pave the way for more effective, forward-looking
economic and financial strategies.
Data availability
The datasets analyzed during the current study are available in
the Dataverse repository: https://doi.org/10.7910/DVN/WI84GQ.
These datasets utilized in this study were sourced from the World
Bank—World Development Indicators (WDI), which is publicly
accessible for research purposes.
Received: 8 July 2024; Accepted: 7 January 2025;
References
Ahmadov AK, Van Der Borg C (2019) Do natural resources impede renewable
energy production in the EU? A mixed-methods analysis. Energy Policy
126:361–369. https://doi.org/10.1016/j.enpol.2018.11.044
Amin SB, Khan F, Rahman MA (2022) The relationship between financial devel-
opment and renewable energy consumption in South Asian countries.
Environ Sci Pollut Res 29:58022–58036. https://doi.org/10.1007/s11356-022-
19596-y
Anton SG, Afloarei Nucu AE (2020) The effect of financial development on
renewable energy consumption. A panel data approach. Renew Energy
147:330–338. https://doi.org/10.1016/j.renene.2019.09.005
Bailey D, Katz JN (2011) Implementing panel-corrected standard errors in R: the
pcse package. J Stat Softw 42:1–11. https://doi.org/10.18637/jss.v042.c01
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. https://doi.org/10.1016/j.enpol.2017.
10.050
Beck T (2011) Finance and oil: Is there a resource curse in financial development?
In: European Banking Center Discussion Paper No. 2011-004. Discussion
paper no. 2011-004. SSRN
Beck T, Poelhekke S (2017) Follow the money: Does the financial sector inter-
mediate natural resource windfalls? J Int Money Finance 130:102769
Bekun FV, Alola AA (2022) Determinants of renewable energy consumption in
agrarian Sub-Sahara African economies. Energy Ecol Environ 7:227–235
Brunnschweiler CN (2010) Finance for renewable energy: an empirical analysis of
developing and transition economies. Environ Dev Econ 15:241–274
Canay IA (2011) A simple approach to quantile regression for panel data. Econom
J 14:368–386. https://doi.org/10.1111/j.1368-423X.2011.00349.x
Chireshe J (2021) Finance and renewable energy development nexus: evidence
from Sub-Saharan Africa. Int J Energy Econ Policy 11:1
Chiroleu-Assouline M, Fodha M, Kirat Y (2020) Carbon curse in developed coun-
tries. Energy Econ 90:104829. https://doi.org/10.1016/j.eneco.2020.104829
Chishti MZ, Patel R (2023) Breaking the climate deadlock: leveraging the effects of
natural resources on climate technologies to achieve COP26 targets. Resour
Policy 82:103576
da Silva PP, Cerqueira PA, Ogbe W (2018) Determinants of renewable energy
growth in Sub-Saharan Africa: evidence from panel ARDL. Energy 156:45–54
Dimnwobi SK, Madichie CV, Ekesiobi C, Asongu SA (2022) Financial development
and renewable energy consumption in Nigeria. Renew Energy 192:668–677.
https://doi.org/10.1016/j.renene.2022.04.150
Dwumfour RA, Ntow-GyamfiM (2018) Natural resources, financial development
and institutional quality in Africa: Is there a resource curse? Resour Policy
59:411–426
Erdogan S (2024) On the impact of natural resources on environmental sustain-
ability in African countries: a comparative approach based on the EKC and
LCC hypotheses. Resour Policy 88(1):104492
Eren BM, Taspinar N, Gokmenoglu KK (2019) The impact of financial develop-
ment and economic growth on renewable energy consumption: empirical
analysis of India. Sci Total Environ 663:189–197. https://doi.org/10.1016/j.
scitotenv.2019.01.323
Fan M, Li M, Liu J, Shao S (2022) Is high natural resource dependence doomed to
low carbon emission efficiency? Evidence from 283 cities in China. Energy
Econ 115:106328
Friedrichs J, Inderwildi OR (2013) The carbon curse: Are fuel rich countries
doomed to high CO
2
intensities? Energy Policy 62:1356–1365. https://doi.
org/10.1016/j.enpol.2013.07.076
Haifa S (2022) The impact of financial development on renewable energy devel-
opment in the MENA region: the role of institutional and political factors.
Environ Sci Pollut Res 29:39461–39472
Han Z, Zakari A, Youn IJ, Tawiah V (2023) The impact of natural resources on
renewable energy consumption. Resour Policy 83:103692
International Energy Agency (2022) Renewable energy transition in sub-Saharan
Africa. Climate Analytics
Jahanger A, Usman M, Murshed M, Mahmood H, Balsalobre-Lorente D (2022)
The linkages between natural resources, human capital, globalization, eco-
nomic growth, financial development, and ecological footprint: the moder-
ating role of technological innovations. Resour Policy 76:102569. https://doi.
org/10.1016/j.resourpol.2022.102569
ARTICLE HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS | https://doi.org/10.1057/s41599-025-04364-3
10 HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS | (2025) 12:64 | https://d oi.org/10.1057/s41599-025-04364-3
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Ji Q, Zhang D (2019) How much does financial development contribute to
renewable energy growth and upgrading of energy structure in China? Energy
Policy 128:114–124
Khan K, Zhang J, Gul F, Li T (2022) The “carbon curse”: understanding the
relationship between resource abundance and emissions. Extr Ind Soc
11:101119
Khan SAR, Yu Z, Belhadi A, Mardani A (2020) Investigating the effects of
renewable energy on international trade and environmental quality. J Environ
Manag 272:111089
Kim J, Park K (2016) Financial development and deployment of renewable energy
technologies. Energy Econ 59:238–250
Le T-H, Nguyen CP, Park D (2020) Financing renewable energy development:
insights from 55 countries. Energy Res Soc Sci 68:101537
Lei W, Ozturk I, Muhammad H, Ullah S (2022) On the asymmetric effects of
financial deepening on renewable and non-renewable energy consumption:
insights from China. Econ Res Istraživanja 35:3961–3978
Mukhtarov S, Aliyev S, Zeynalov J (2020) The effects of oil prices on macro-
economic variables: evidence from Azerbaijan. Int J Energy Econ Policy
10:72–80
Mukhtarov S, Yüksel S, Dinçer H (2022) The impact of financial development on
renewable energy consumption: evidence from Turkey. Renew Energy
187:169–176. https://doi.org/10.1016/j.renene.2022.01.061
Mugume R, Bulime EWN (2024) Delivering double wins: How can Africa’sfinance
deliver economic growth and renewable energy transition?. Renew Energy
224:120165
Nurmakhanova M, Elheddad M, Alfar AJKK, Egbulonu A, Zoynul Abedin M,
Abedin MZ (2023) Does natural resource curse in finance exist in Africa?
Evidence from spatial techniques. Resour Policy 80:103151. https://doi.org/
10.1016/j.resourpol.2022.103151
Nwani C, Okezie BN, Nwali AC, Nwokeiwu J, Duruzor GI, Eze ON (2023) Natural
resources, financial development and structural transformation in Sub-
Saharan Africa. Heliyon 9:e19522
Omri A, Nguyen DK (2014) On the determinants of renewable energy con-
sumption: international evidence. Energy 72:554–560
Onuoha FC, Dimnwobi SK, Okere KI, Ekesiobi C (2023) Funding the green
transition: governance quality, public debt, and renewable energy con-
sumption in Sub-Saharan Africa. Uti Policy 82:101574
Ouyang Y, Li P (2018) On the nexus of financial development, economic growth,
and energy consumption in China: new perspective from a GMM panel VAR
approach. Energy Econ 71:238–252. https://doi.org/10.1016/j.eneco.2018.02.
015
Pesaran MH, Yamagata T (2008) Testing slope homogeneity in large panels. J
Econom 142:50–93
Pesaran MH (2021) General diagnostic tests for cross-sectional dependence in
panels. Empir Econ 60:13–50
Raza SA, Shah N, Qureshi MA, Qaiser S, Ali R, Ahmed F (2020) Non-linear
threshold effect of financial development on renewable energy consumption:
evidence from panel smooth transition regression approach. Environ Sci
Pollut Res 27:32034–32047. https://doi.org/10.1007/s11356-020-09520-7
Richardson BJ (2011) Sovereign wealth funds and the quest for sustainability:
Insights from Norway and New Zealand. Nordic J Commer Law 2:1–27
Saadaoui H, Chtourou N (2022) Do institutional quality, financial development,
and economic growth improve renewable energy transition? Some Evidence
from Tunisia. J Knowl Econ 14:1–32
Sachs JD, Warner A (1995) Natural resource abundance and economic growth.
National Bureau of Economic Research Cambridge, Cambridge
Sadorsky P (2011) Financial development and energy consumption in Central and
Eastern European Frontier Economies. Energy Policy 39:999–1006
Samour A, Baskaya MM, Tursoy T (2022) The impact of financial development
and FDI on renewable energy in the UAE: a path towards sustainable
development. Sustainability 14:1208. https://doi.org/10.3390/su14031208
Shahabadi A, Feyzi S (2016) The relationship between natural resources abun-
dance, foreign direct investment and environmental performance in selected
oil and developed countries during 1996–2013. Int J Resist Econ 4:101–116
Shahbaz M, Sinha A, Raghutla C, Vo XV (2022) Decomposing scale and technique
effects of financial development and foreign direct investment on renewable
energy consumption. Energy 238:121758. https://doi.org/10.1016/j.energy.
2021.121758
Sweerts B, Dalla Longa F, van der Zwaan B (2019) Financial de-risking to unlock
Africa’s renewable energy potential. Renew Sustain Energy Rev 102:75–82
Topcu M, Payne JE (2017) The financial development–energy consumption nexus
revisited. Energy Sources Part B Econ Plan Policy 12:822–830. https://doi.org/
10.1080/15567249.2017.1300959
Usman M, Balsalobre-Lorente D (2022) Environmental concern in the era of
industrialization: Can financial development, renewable energy and natural
resources alleviate some load? Energy Policy 162:112780. https://doi.org/10.
1016/j.enpol.2022.112780
Wooldridge JM (2010) Econometric analysis of cross section and panel data. MIT press
Wu L, Broadstock DC (2015) Does economic, financial and institutional devel-
opment matter for renewable energy consumption? Evidence from emerging
economies. Int J Econ Policy Emerg Econ 8:20–39
Yi S, Raghutla C, Chittedi KR, Fareed Z (2023) How economic policy uncertainty
and financial development contribute to renewable energy consumption? The
importance of economic globalization. Renew Energy 202:1357–1367. https://
doi.org/10.1016/j.renene.2022.11.089
Zafar MW, Zaidi SAH, Khan NR, Mirza FM, Hou F, Kirmani SAA (2019) The
impact of natural resources, human capital, and foreign direct investment on
the ecological footprint: the case of the United States. Resour Policy
63:101428. https://doi.org/10.1016/j.resourpol.2019.101428
Zhao X, Samour A, AlGhazali A, Wang W, Chen G (2023) Exploring the impacts of
natural resources, and financial development on green energy: novel findings
from top natural resources abundant economies. Resour Policy 83:103639
Author contributions
Alhasan Osman led the study’s conceptualization, design, and initial manuscript drafting.
Mohd Afjal and Majed Alharthi contributed to the research design and data analysis.
Mohamed Elheddad and Nassima Djellouli assisted with data collection and manuscript
drafting. Zhang He was involved in the conception and data analysis. All authors par-
ticipated in revising the manuscript and approved the final version.
Funding
Open access funding provided by Vellore Institute of Technology.
Ethical approval
Ethical approval was not required as the study did not involve human participants.
Informed consent
Informed consent was not required as the study did not involve human participants.
Competing interests
The authors declare no competing interests.
Additional information
Correspondence and requests for materials should be addressed to Mohd Afjal.
Reprints and permission information is available at http://www.nature.com/reprints
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Open Access This article is licensed under a Creative Commons
Attribution 4.0 International License, which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative
Commons licence, and indicate if changes were made. The images or other third party
material in this article are included in the article’s Creative Commons licence, unless
indicated otherwise in a credit line to the material. If material is not included in the
article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder. To view a copy of this licence, visit http://creativecommons.org/
licenses/by/4.0/.
© The Author(s) 2025
HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS | https://doi.org/10.1057/s41599-025-04364-3 ARTICLE
HUMANITIES AND SOCIAL SCIENCES COMMUNICATIONS | (2025) 12:64 | https://d oi.org/10.1057/s41599-025-04364-3 11
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
1.
2.
3.
4.
5.
6.
Terms and Conditions
Springer Nature journal content, brought to you courtesy of Springer Nature Customer Service Center GmbH (“Springer Nature”).
Springer Nature supports a reasonable amount of sharing of research papers by authors, subscribers and authorised users (“Users”), for small-
scale personal, non-commercial use provided that all copyright, trade and service marks and other proprietary notices are maintained. By
accessing, sharing, receiving or otherwise using the Springer Nature journal content you agree to these terms of use (“Terms”). For these
purposes, Springer Nature considers academic use (by researchers and students) to be non-commercial.
These Terms are supplementary and will apply in addition to any applicable website terms and conditions, a relevant site licence or a personal
subscription. These Terms will prevail over any conflict or ambiguity with regards to the relevant terms, a site licence or a personal subscription
(to the extent of the conflict or ambiguity only). For Creative Commons-licensed articles, the terms of the Creative Commons license used will
apply.
We collect and use personal data to provide access to the Springer Nature journal content. We may also use these personal data internally within
ResearchGate and Springer Nature and as agreed share it, in an anonymised way, for purposes of tracking, analysis and reporting. We will not
otherwise disclose your personal data outside the ResearchGate or the Springer Nature group of companies unless we have your permission as
detailed in the Privacy Policy.
While Users may use the Springer Nature journal content for small scale, personal non-commercial use, it is important to note that Users may
not:
use such content for the purpose of providing other users with access on a regular or large scale basis or as a means to circumvent access
control;
use such content where to do so would be considered a criminal or statutory offence in any jurisdiction, or gives rise to civil liability, or is
otherwise unlawful;
falsely or misleadingly imply or suggest endorsement, approval , sponsorship, or association unless explicitly agreed to by Springer Nature in
writing;
use bots or other automated methods to access the content or redirect messages
override any security feature or exclusionary protocol; or
share the content in order to create substitute for Springer Nature products or services or a systematic database of Springer Nature journal
content.
In line with the restriction against commercial use, Springer Nature does not permit the creation of a product or service that creates revenue,
royalties, rent or income from our content or its inclusion as part of a paid for service or for other commercial gain. Springer Nature journal
content cannot be used for inter-library loans and librarians may not upload Springer Nature journal content on a large scale into their, or any
other, institutional repository.
These terms of use are reviewed regularly and may be amended at any time. Springer Nature is not obligated to publish any information or
content on this website and may remove it or features or functionality at our sole discretion, at any time with or without notice. Springer Nature
may revoke this licence to you at any time and remove access to any copies of the Springer Nature journal content which have been saved.
To the fullest extent permitted by law, Springer Nature makes no warranties, representations or guarantees to Users, either express or implied
with respect to the Springer nature journal content and all parties disclaim and waive any implied warranties or warranties imposed by law,
including merchantability or fitness for any particular purpose.
Please note that these rights do not automatically extend to content, data or other material published by Springer Nature that may be licensed
from third parties.
If you would like to use or distribute our Springer Nature journal content to a wider audience or on a regular basis or in any other manner not
expressly permitted by these Terms, please contact Springer Nature at
onlineservice@springernature.com
Content uploaded by Mohd Afjal
Author content
All content in this area was uploaded by Mohd Afjal on Jan 18, 2025
Content may be subject to copyright.
