Green Financial Technology and Natural Resource Rents for Clean Energy: Pathways Towards Ecological Sustainability in Sub-Saharan Africa

Abstract

Sub-Saharan Africa has the potential to achieve sustainable development through facilitating green transition projects, leveraging the revenue generated from its abundant natural resources. However, the resource curse hypothesis suggests that developing nations often face problems with corruption that hinder economic development in these countries. The present study aims to investigate how environmental sustainability can be advanced in Sub-Saharan Africa using revenue from natural resources in the presence of green financial technology and clean energy. Therefore, data for Sub-Saharan Africa from 2000 to 2023 are employed in the analysis. The analysis of these data is undertaken with the ‘Method of Moments Quantile Regression’ technique, and the ‘Panel Correlated Standard Errors’ is used for robustness checks. The key findings presented in this research depict the importance of natural resource rents in supporting sustainable environments in Sub-Saharan Africa. Therefore, the revenue from natural resources can be used to support green transition projects in developing nations with high natural resource endowments. Moreover, renewable energy and green finance foster a reduction in ecological footprint, hence supporting environmental sustainability. Consequently, technological innovation and financial development do not promote the achievement of environmental sustainability, raising questions about the environmental policies and regulations in Sub-Saharan Africa. To this end, there is a need for policy reforms and corruption control in order to prevent the misallocation and misuse of resources designed to support green transition projects.

1. Introduction

Sub-Saharan Africa (SSA) is experiencing an intensified and pervasive environmental crisis, evidenced by escalating pollution levels and the degradation of natural resources (NRs), leading to severe ecological and human health consequences. The highest net loss in forest area has been experienced in SSA. For instance, from 2010 to 2020, the annual net forest loss was recorded at around 3.9 million hectares, marking a significant increase when compared to the 3.4 million hectares per year recorded between the years 2000 and 2010, as well as the 3.3 million hectares recorded between the years 1990 and 2000 [1]. The FAO (2020) indicates that the high rate of deforestation in SSA is because of cropland expansion, which accounts for over three-quarters of deforestation [1]. Mining activities, too, have increased at the rate of approximately 11,200 hectares per annum when considering the mines established between the years 2009 and 2011, representing a major contributor to deforestation [2]. The loss of forest cover in SSA, as evidenced through these statistics, contributes to climate change, reduces biodiversity, and exacerbates soil erosion. Reference [3] states that salinization, soil erosion, and pollution have led to an approximate 20% increase, that is, 6.6 million km2, in land degradation, and an estimated 39% decline in vertebrate species.

The emissions of carbon dioxide (CO₂) per capita in SSA are relatively low in comparison to global averages, but growth is high, meaning that, if left uncontrolled, SSA nations may become top emitters in the near future [4]. A major cause of the deterioration in the air quality of urban areas in SSA is particulate matter (PM2.5), which causes serious health problems in people. For example, Reference [5] reports that, in 2019, in SSA, air pollution, comprising household air pollution and ambient PM2.5, caused over 900,000 premature deaths. Furthermore, empirical evidence also shows that exposure to heavy metals in environmental bodies leads to low birth weight, impaired semen quality, intrauterine growth retardation, recurrent pregnancy loss, and preeclampsia [6,7,8,9,10,11]. In addition, climate change has caused significant changes in the rainfall patterns and increased temperatures, alongside many other weather changes that have significantly affected agricultural production, and this may lead to an approximately 50% loss in key crops like sorghum, maize, and millet by 2050 [12]. Additionally, the World Bank estimates that SSA and South Asia may face annual contractions in GDP, which are projected to occur at 9.7% and 6.5%, respectively, because of their reliance on NRs [13]. Therefore, considering the alarming rate at which environmental deterioration (ED) is rising in SSA, cutting-edge studies that recommend robust environmental policies are required.

To this end, several problems relating to how ED could be alleviated in SSA have been studied, and green finance, institutional quality, and green technology, among other factors, have emerged as the key drivers towards sustainable futures. Reference [14] states that green finance is important in supporting the development of business models that are friendly to the environment. Additionally, Reference [15] suggests that green finance enhances the promotion of funds leading to energy efficiency; hence, social, ecological, and environmental benefits are realized. Reference [16] also supports this by showing that the overall performance of the environment, resulting from green finance and innovation, is positively impacted by financial technology by financial institutions. Green financial products also enable the development of renewable energies that are favorable to the environment [17,18]. However, for green finance and green technology to be efficient in fostering environmental sustainability (ES), there is a need for strong institutions that facilitate the appropriate allocation of green financial resources, channeling them where they are most needed, as well as ensuring they are not misused. The misuse and misallocation of green funds in SSA, where corruption is high, have become major obstacles to the achievement of ES. Therefore, addressing ES in SSA requires multi-faceted approaches, like paying special attention to green financial technology, the accelerated adoption of RE, and improved institutional quality. The green financial technology used in this research is adopted to represent the investment in projects meant to develop green resources like renewable energy (RE) and non-polluting technologies.

In an attempt to address the severe ED challenges ravaging the whole of the SSA region, this research contributes to the literature in three ways. Firstly, this research seeks to show how SSA countries can turn their rich-NR endowments into a blessing through ensuring the revenue generated from selling NRs is employed to support green investments and RE programs, leading to environmental efficiency in this region. Second, this study adds to the literature by explaining how SSA countries can capitalize on the synergies of green finance and RE in fostering sustainable environments and development through raising ecological quality. Last but not least, this research contributes to the literature by showing how corruption tends to impede the effectiveness of financial development (FD) in supporting investments in green transition projects, showing the need for policy reforms and the adoption of cutting-edge technologies that are not polluting the environment. To this end, the empirical evidence presented in the study addresses the following research questions: (1) How can countries in SSA ensure the correct allocation of green funds for RE development and clean technology, in order to transition to a green and sustainable economy? (2) How can the governments in SSA capitalize on the revenue received by their economies from the sale of the NRs that are abundantly available in this region, and allocate such funds to green innovation initiatives that will see the region transitioning to the attainment of carbon-free environments? (3) What policies can be adopted in SSA to ensure a significant reduction in corruption, facilitating improvements in the financial markets and institutions and, hence, the appropriate allocation of green financial resources and improvements in ES? The present research employs the data of SSA countries for the period from 2000 to 2023. Alternatively, the data was analyzed with the ‘Methods of Moments Quantile Regression’ (MMQR) technique, enabling the presentation of robust findings after correcting for ‘cross-sectional dependence’ (CD) and ‘heterogeneity’ in panel data [19].

2. Literature Review and Gaps

SSA countries still experience poverty and are still suffering from rampant environmental problems. Environmental challenges, such as the rise in air pollution and temperatures in the SSA region, worsen health problems, droughts, diseases, and poverty. However, the SSA region is heavily endowed with NRs, and is expected to utilize natural resources rents (NRRs)—the revenue generated from selling NRs—as an immediate remedy to its problems. The literature suggests that NRs could be a blessing to the economic development of a country, as illustrated in the ‘Resource Blessings’ (RB) hypothesis of Rostow (1961) [20]. The RB hypothesis states that an economy can prosper if it uses its NRs to foster economic development programs. Nonetheless, the ‘Resource Curse’ (RC) articulates that even if developing nations have an abundance of NRs and might have a chance of advanced economic development, the existence of high levels of corruption in such nations hinders economic development [21]. To this end, several empirical studies have examined the influence of NRRs on economic development in different regions to understand the support for this theory in each region, and mixed outcomes are presented [22,23,24].

Many studies have examined the influence of NRRs on economic growth, with very few recent studies investigating its influence on ES and presenting mixed outcomes [18,25,26,27]. Some empirical evidence, such as that presented in [25], supports the RC theory in selected resource-rich African nations by indicating that ED is worsened through the NRRs in these countries. The findings of [25] show that even though the top ten resource-rich African countries are blessed with abundant NRs, they fail to foster ES with the revenue generated from these. Reference [26] also supports this by showing that NRR worsens CO₂ emissions in resource-rich African nations. This shows that factors such as corruption and instabilities in the political system lead to the misallocation and misuse of NRRs and fail to foster sustainable development programs. Reference [18] specifically addresses this matter in the context of SSA countries and presents evidence that points to the importance of NRRs in lowering the ‘Ecological Footprint’ (EFP). The importance of NRRs in alleviating the deterioration of the ecological system is also supported in the empirical evidence obtained in other regions, such as the BRICS, where they were observed to improve the ‘Load Capacity Factor’ (LCF) [27]. Additionally, more empirical evidence supports the effectiveness of NRRs in advancing sustainable programs that lead to ES in many other regions [28]. Therefore, the uncertainties around the influence of NRRs on ES are a cause of concern among policymakers and call for cutting-edge studies to empirically provide evidence that can be of fundamental help to policymakers and governments. To this end, this study is structured to address and provide clarity on the significance of the revenue generated from NRs through financing green projects and the ‘research and development’ (R&D) on clean technologies.

Green technological innovations (GTIs) could also be of great significance in supporting ES in SSA countries. References [29,30] suggest that GTIs include various innovations that can foster ES, such as energy-efficient programs, pollution abatement techniques, RE development programs, and sustainable agricultural programs, among many other clean innovations. Evidence on the importance of GTIs in supporting ES in the SSA region is supported by various empirical studies showing that RE reduces CO₂ emissions [17,31]. Likewise, energy efficiency programs are praised for significantly saving energy and lowering the CO₂ emissions in the process [32], while sustainable agricultural programs help improve resource efficiency and enhance water and soil management [33]. GTIs are supported by the presence of green finance, allocated to developing nations to facilitate R&D ensuring the development of RE and clean technology [34]. While there is limited empirical evidence on how SSA nations could capitalize on green finance to foster green transition programs, ref. [18] indicates that, with green finance, the SSA region could move towards a green future. Additionally, ref. [14] presents the importance of green finance in supporting the development of business models that are friendly to the environment. At the same time, ref. [15] shows that green finance enhances the promotion of funds promoting energy efficiency, leading to the realization of social, ecological, and environmental benefits. Reference [16] also supports this by showing that the overall environment is positively impacted by green finance and innovation developed using financial technology from financial institutions. Other key studies employed green bonds and observed that CO₂ emissions reduced with the green bond investments [35].

This leads to the importance of FD in supporting GTIs, which will eventually result in the achievement of sustainable futures with free carbon environments. References [36,37] explain the dilemma in SSA resulting from the variations in the FD in the different sub-regions across SSA, hindering financial services that could fundamentally support green finance. Specific studies addressing the direct connection between FD and ES concurred regarding its importance in advancing ES [37,38]. Another key factor to consider that is enhanced with green financial tools for sustainable development is digital technology and various technological innovations that support green projects. References [39,40] show the importance of digital technology in advancing sustainable development. Therefore, the present research contributes to the literature in devising the best policies to help SSA to achieve the ‘Sustainable Development Goals’ (SDGs), specifically targeting SDG13, focused on climate action through pollution abatement policies. To achieve this, the present research investigates the synergies of green technologies, green finance, and FD to ensure that their combined efforts lead to sustainable futures.

For GTIs, green finance, FD, and RE to be effective in supporting ES, there is a need for proper policies, governance, and strong government effectiveness to support them. Reference [18] supports the idea that government intervention plays an important role in green innovation programs. This is because, in the absence of government effectiveness, corruption could disrupt the financial system and financial institutions, leading to inefficiency in the financial markets’ allocation of resources to where they are most needed. This misallocation of resources could arise because of corruption that facilitates the embezzlement of funds that could have been meant for green innovation programs, which then end up in the hands of individuals in pursuit of their personal gains. Empirical evidence shows that government intervention is essential in enforcing business regulations to ensure the FD facilitates the adoption of green technology as well as the optimal use of key resources [41,42]. Reference [43] points to the importance of control of corruption in ensuring that CO₂ emissions are lowered. The decreases in CO₂ emissions are facilitated through various environmental regulations, including financial subsidies on environmentally friendly programs, environmental taxes on polluting companies, and environmental campaigns that bring awareness to the detrimental effects of pollution. Reference [44] also illustrates the importance of transformative policy tools in advancing GTIs for sustainable development. Therefore, institutional quality becomes important, especially in areas of SSA with high corruption alongside rampant political problems, which can hinder various factors regarding NRRs, green finance, FD, and green technologies in supporting ES. This research, therefore, investigates how corruption and political issues in the SSA could pose a challenge to fostering sustainability programs.

3. Research Model, Data, and Methodology

3.1. Research Model

The model employed in this research is based on the RC and RB hypotheses, which seek to explain the effects of NRs on economic development. The RB theory postulates that economic development in nations with an abundance of NRs is supported by the revenue generated from such resources [20]. However, the RC theory opposes the postulations of the RB theory by indicating that, although some developing nations have high NR endowments, they tend to lag behind in terms of economic development due to corruption [21]. This research follows the research framework proposed in recent studies that employed NRRs to explain ES in developing nations [18,26]. Therefore, this research intends to test the RC and RB theories regarding their influence on ES, making it unique among most studies, which tested the RC and the RB theories regarding economic growth. This research also takes digitalization and green finance as explanatory variables in the model, following recent studies that presented these variables as key factors in reducing damage to the environment [18,27,45]. Green finance enables advancements in the R&D on clean energy sources, such as RE, and on clean technologies that do not pollute the environment [34]. Advancements in digital technologies also promote leapfrogging into the use of clean technologies by developing nations; hence, ES is promoted. Additionally, FD and RE are taken as control variables in this research model. FD is praised for support of green transitions through supporting RE development [46], while RE is widely accepted as the key driver to a low-carbon future [17,26]. Therefore, through employing the EFP index to represent the damage to the environment, the research model employed in this research is shown in Equation (1).

$${EFP}_{ti}={\beta }_0+{{\beta }_{1}NRR}_{ti}+{{\beta }_{2}lnGF}_{ti}+{{\beta }_{3}DIG}_{ti}+{{\beta }_{4}RE}_{ti}+{{\beta }_{5}FD}_{ti}+\mu$$

(1)

where EPP is the dependent variable, which stands for the Ecological Footprint. NRR, GF, DIG, RE, and FD are the independent variables and stand for natural resources, rents, green finance, digitalization, renewable energy, and financial development, respectively. ${\beta }_0$ is the constant of the statistical model, ${\beta }_{1-5}$ are the coefficients of the independent variables showing the rate of change in EFP due to a change in the respective independent variable, and $\mu$ is the error white noise term. The superscripts $ti$ indicate that the data used is longitudinal data, where t represents the time frame of the data, that is, from 2000 to 2023, and t the size of the ‘cross-sections’, that is, the forty-three SSA nations. The term $ln$ is the log operator, indicating the conversion of the variable into the log form of base 10. The logarithm in this study is employed to standardize variables that are in the thousands and million in order to make them stationary and hence avoid spurious regressions. Therefore, only green finance is standardized to log form in this analysis, as other variables are in standard form, percent, or ratios.

3.2. Data

Data from the 43 SSA nations is employed in this research, considering the time range from 2000 to 2023. The data was retrieved from various open datasets. For instance, EFP data was retrieved from the ‘Global Footprint Network’ (GFN), while the data of FD and GF were retrieved from the ‘International Monetary Fund’ (IMF) and the ‘Our World in Data’ (OWD) databases. Additionally, the data of NRR and RE, and the dimensions used to calculate digitalization (‘mobile cellular subscription’ (MCS) and ‘number of internet users’ (IU)), were retrieved from the ‘World Bank’ (WB).

EFP, the dependent variable, is an index of six key dimensions (cropland, forestland, carbon footprint, grazing land, built-up area, and fishing grounds) that represent the area that is needed to support human activities, including the area required to dump the waste from such activities [18,47]. The EFP index employed in this research is measured in global hectares (gha) per capita. The NRR represents the total revenue generated from selling the various NRs of a country, expressed as a percentage of GDP. The digitalization index employed in this research is an index developed with the ‘Principal Component Analysis’ (PCA) with STATA. The dimensions used to develop digitalization are MCS per hundred people and IU as a percentage of the total population. Green finance is funds meant to support the green transition and a sustainable future. This research uses the funds meant to support R&D on clean energy and technologies in developing nations, expressed in USD. FD is an index representing the depth and efficiency of the institutions and financial markets. RE represents the clean energy sources that do not deplete through use, like solar and hydro-energy, which does not emit CO₂ in the air. The indicator of RE employed in this research is the total clean energy sources, expressed as a percentage of total energy use.

Table 1. Summary of variables.

Variable	Measurement	Source
Ecological Footprint (EFP)	gha per capita	GFN
Natural Resources Rent (NRR)	Total rents as a % of GDP	WB
Digitalization (DIG)	IU and MCS	WB
Financial Development (FD)	Index of the financial wellbeing of a country	IMF
Green Finance (GF)	Funds in USD	OWD
Renewable energy (RE)	% of total energy	WB

summarizes the sources and measurements of the variables, while

Table 2. Descriptive statistics.

Variable	Obs	Mean	Std. Dev.	Min	Max
EFP	1032	1.4354	0.6991	0.5540	4.0242
NRR	1032	11.5582	11.1264	0.0024	88.5923
DIG	1032	0.0000000009	1	−1.1845	3.1719
GF	1032	59,700,000	257,000,000	0	5,150,000,000
FD	1032	0.1356	0.1049	0.0264	0.6364
RE	1032	66.3337	25.4601	3.68	98.34

summarizes the measures of central tendency and the dispersion of the variables.

3.3. Method

The MMQR method, which is capable of providing heterogeneous results in the different quantiles, was employed to analyze the relationship presented in this study [19]. The MMQR method is supported in many empirical studies that were recently published as one of the best methods of analysis in models that uses panel data [45,48]. Its significance in investigating the asymmetric and/or symmetric relationships in the model makes it the favorite when producing findings that are useful to policymakers. The MMQR method shows how the independent factors influence the dependent factor in different quantiles, and hence allows for policies to be adopted that are related to the different quantiles presented. The statistical representation in Equation (2) is the MMQR version after adding the conditional quantile of the dependent variable ($Q_y(\delta !X^{\prime }it)$ to the research model in Equation (1).

$$Q_y(\delta !X^{\prime }it)={\beta }_0+{{\beta }_{1}NRR}_{ti}+{{\beta }_{2}lnGF}_{ti}+{{\beta }_{3}DIG}_{ti}+{{\beta }_{4}RE}_{ti}+{{\beta }_{5}FD}_{ti}+\mu$$

(2)

The MMQR employed in this research was selected after running various pretests that helped in the selection of the appropriate method of analysis [26]. For example, the significant (sig.) CD results in the model [49,50,51,52,53], as shown in the Table in the Appendix A, point to the importance of employing an MMQR method that overcomes such problems. Moreover, the existence of ‘heterogeneity’ problems, as indicated by the ‘slope heterogeneity’ results in

Table A4. Cointegration results.

	Statistic	p-Value
Kao test
Modified DF	−4.0220 a	0.0000
DF	−6.0291 a	0.0000
ADF	−1.9950 b	0.0230
Unadjusted Modified DF	−10.7053 a	0.0000
Unadjusted DF	8.8773 a	0.0000
Pedroni test
Modified PP	3.4076 a	0.0003
PP	−7.2666 a	0.0000
ADF	−8.3345 a	0.0000

Sig. at 1% (a), 5% (b).

[54], demonstrate the significance of using the MMQR technique to take care of ‘heterogeneity’ and present robust outcomes. Furthermore, the signifier ‘cointegration’, depicted in the results of Pedroni and Kao in Table A4, points to why the MMQR method is used, as it provides ‘long-run’ (LR) results. Therefore, the MMQR method ensures the LR connection in the model is analyzed, and key policies and policy implications are provided. Additionally, variables that have mixed ‘integration orders’ can be analyzed with the MMQR technique; thus, with the unit root (UR) results of the ‘cross-sectional Im-Pesaran-Shin’ (CIPS) [55] in

Table A2. CADF results of UR.

	Level
	Statistic	p-Value
EFP	−2.370 a	0.000
NRR	−2.677 a	0.000
TI	−2.529 a	0.000
lnGF	−4.097 a	0.000
FD	−2.851 a	0.000
RE	−2.119 a	0.008

Sig. at 1% (a).

showing the existence of I(0) and (1) variables, the MMQR method was selected. The ‘Panel Correlated Standard Errors’ (PCSE) tool was also employed to ensure the MMQR outcomes are robust, following recent studies like [56,57]. Most importantly, the model is suitable for analysis because there are no ‘multi-collinearity’ issues in the independent variables, as shown in

Table A3. VIF results.

Variable	VIF	1/VIF
RE	1.62	0.6172
FD	1.62	0.6179
TI	1.54	0.6505
lnGF	1.25	0.7971
NRR	1.05	0.9502
Mean VIF	1.42

, according to the ‘Variance Inflation Factor’ (VIF) test.

4. Findings and Discussion

The findings of this study are presented in

Table 3. MMQR and PCSE results.

	Coefficient	Std. Err	p-Value	Coefficient	Std. Err	p-Value
Quantiles	0.1			0.25
NRR	0.0010	0.0017	0.564	−0.0021	0.0015	0.165
TI	−0.0056	0.0350	0.871	0.0257	0.0297	0.388
lnGF	−0.0050	0.0040	0.219	−0.0100 a	0.0034	0.004
FD	−0.5376	0.4393	0.221	−0.2422	0.3727	0.516
RE	−0.0079 a	0.0013	0.000	−0.0080 a	0.0011	0.000
Quantiles	0.5			0.75
NRR	−0.0061 a	0.0015	0.000	−0.0126 a	0.0021	0.000
TI	0.0658 b	0.0290	0.024	0.1311 a	0.0416	0.002
lnGF	−0.0164 a	0.0034	0.000	−0.0269 a	0.0049	0.000
FD	0.1353	0.3634	0.710	0.7488	0.5203	0.150
RE	−0.0081 a	0.0010	0.000	−0.0083 a	0.0015	0.000
Quantiles	0.9			PCSE
NRR	−0.0202 a	0.0035	0.000	−0.0084 a	0.0010	0.000
TI	0.2059 a	0.0657	0.002	0.0891 a	0.0299	0.003
lnGF	−0.0388 a	0.0078	0.000	−0.0202 a	0.0038	0.000
FD	1.4523 c	0.8182	0.076	0.3543 c	0.1847	0.055
RE	−0.0085 a	0.0023	0.000	−0.0082 a	0.0006	0.000

Sig. at 1% (a), 5% (b), 10% (c).

, according to the MMQR method that overcomes CD problems.

Firstly, it is observed that EFP, in SSA, is reduced with GF. The MMQR technique’s findings depict that the effect of GF on EFP is significant in the 0.25 to 0.9 quantiles. While the findings of the MMQR in the first quantile are insignificant (insig), results were negative, implying detrimental effects on EFP. The research findings in Table 3 depict that increasing the GF by 1% has the effect of reducing EFP by a magnitude of 0.01%, 0.0164%, 0.0269%, and 0.0388% in the 0.25 to 0.9 quantiles. The significance of GF in advancing ES in SSA is supported by the PCSE results, which indicate that a 1% increase in GF leads to a 0.02% decrease in the EFP. These findings depict the importance of GF in ensuring the attainment of ES in SSA countries. The findings show that GF is fundamental in the transition toward sustainable futures, as indicated by the strong negative significance of GF in the upper quantiles. Therefore, the impact of GF in reducing EFP becomes stronger in areas with high ED, indicating that GF is fundamental in supporting long-term green transition projects. The findings presented in this study are supported by various empirical evidence presented in the literature in previous empirical studies. For instance, refs. [18,27] show the importance of GF in achieving sustainable environments in the future in SSA and BRICS countries, respectively. To this end, SSA countries should ensure the correct use of GF for the purposes of supporting R&D in clean technologies as well as RE.

On top of the significance of GF in advancing ES in SSA, this study also depicts the importance of RE in lowering EFP in this region. The MMQR findings presented in Table 3 depict the presence of symmetric negative effects of RE on EFP across all the quantiles. Specifically, the findings depict that increasing RE by one unit decreases EFP by 0.0079, 0.008, 0.0081, 0.0083, and 0.0085 units in the lower to upper quantiles. The presence of symmetric negative effects of RE on EFP in the findings presented in this study shows the importance of RE in supporting ES in both low- and high-ED areas. Therefore, the symmetric negative effects of RE on EFP presented in this study imply the significance of RE in advancing ES in SSA that have serious environmental problems, as well as in those SSA countries that have high levels of ES. The PCSE findings support these results by showing that increases in the use of RE can lead to a reduction in EFP by 0.008 units. Therefore, RE becomes the most crucial factor in alleviating ED, irrespective of the level of damage in the country. The significance of RE in advancing ES is supported by many empirical studies employed in the SSA region and in other areas of the world, such as the EU [17,31,45]. This suggests that SSA countries should hasten the transition to clean energy sources and shun the use of polluting sources of energy such as fossil fuels. Such transitions can only be achieved if appropriate funding is allocated toward R&D on clean fuels.

Of great importance in this study is the influence of NRRs on EFP, as the results show a negative link between NRRs and ES in the developing countries that have an abundance of NRs and high corruption levels. This research indicates that NRRs only tend to support ES in areas of high ED and not in areas where ED is less acute, as depicted by the sig effect of NRRs in the upper quantiles and insignificant effects in the lower quantiles. The MMQR results show that increasing NRs by one unit leads to a reduction in the EFP by 0.006, 0.0126, and 0.02 units in the 0.5 to 0.9 quantiles. Therefore, NRRs have a significantly asymmetric influence on the EFP in SSA. The asymmetric effects of NRRs on EFP imply that SSA countries that have serious environmental problems tend to benefit from the use of NRRs to curb ED. This is supported by the PCSE results showing that increases in NRRs potentially reduce EFP by 0.008 units. These findings depict that NRRs are not a curse in the context of ES, as they support it in the LR. The findings of this study support the postulations of the RB hypothesis, which indicates that nations with abundant NRs can develop their economies through the use of the revenue generated from such resources (Rostow, 1961) [20]. Recent empirical studies that support the importance of NRRs in advancing ES include the studies of [18,27,28] in the SSA and BRICS nations, respectively. However, ref. [26] found that in the top selected resource-abundant African nations, NRRs may be a curse. Despite some studies pointing to the possibility of the presence of an RC, the findings of this study and of many other previous empirical studies indicate that SSA countries could benefit from improving ES through the use of the revenue from their NRs. However, employing NRRs to support ES requires proper policies and the presence of strong institutions that can implement such environmental policies and ensure the green funds are not misused.

However, unlike what is generally believed, TI fosters ES; this study shows that it does not support ES in SSA countries. The MMQR findings show that TI has a tendency to raise EFP in the upper quantiles, with insignificant outcomes in the lower quantiles. Specifically, the MMQR findings depict that a rise in the TI by one unit raises the EFP by 0.06, 0 13, and 0.206 units in the 0.5 to 0.9 quantiles. The PCSE method also supports these findings by showing that EFP is significantly increased with TI, by 0.089 units. The empirical evidence for this that is also present in the research field, as many studies have looked at the importance of TI in ensuring the attainment of ES [26,45], does not support these findings. Although various empirical studies have shown that TI is fundamental in achieving sustainable futures through green transitions, other recent studies have shown that SSA countries do not significantly benefit from TI in terms of ES [18]. The presence of such empirical evidence aligns with the findings of this study, especially in the context of SSA countries, showing the need for policy reforms in SSA in order to benefits of TI in achieving ES. The use of polluting technologies in developing African countries is one of the major reasons behind the negative effects of technology on sustainability. This suggests the need for African countries to engage in leapfrogging, skipping old technologies, and adopting clean and new sophisticated technologies used by developed countries.

In addition to that, this research also shows that FD is not essential in advancing ES in SSA. The findings presented by the MMQR show that FD is insignificant in fostering ES in this region. The findings observed are insignificant in the 0.1 to 0.75 quantiles and become significant at 10% in the 0.9 quantiles. In the 0.9 quantiles, where FD becomes significant at 10%, it is observed that increasing FD by 1 unit causes the EFP to increase by 1.5 units. Therefore, FD has weak detrimental effects on ES in SSA. The PCSE method also supports this accession by showing that raising FD by 1 unit leads to an increase in the EFP by 0.354 units at a 10% significance level. Though the literature on the impact of FD on ES is limited, few studies have indicated the significance of FD in ensuring ES is met [37,38]. The insignificant and weak detrimental effects of FD shown in this study are explained by the high corruption level in SSA countries, which hinders the correct use of finances in green financial projects.

5. Conclusions

This research is essential considering the high level of ES in developing SSA countries. It provides policies that could be adopted in advancing sustainable futures through green transitions in SSA. The major novelty of the study is in showing that developing countries with abundant NRs, such as SSA countries, could benefit from using the revenue from NRs to finance green projects that support ES. To achieve this, the present study employs data from 43 SSA countries for the period ranging from 2000 to 2023 and analyzes it with the contemporary MMQR method to ensure robust findings are obtained. With the use of the MMQR method in this study, reliable results are obtained with no hindrances from CD and ‘heterogeneity’ problems. Most importantly, this research also ensures that the findings presented are reliable, thereby facilitating the adoption of the correct policies. This is achieved by using PCSE method to verify the robustness of the MMQR findings. Major findings of this study indicate the significance of GF, RE, and NRRs in advancing ES through lowering EFP in SSA. These findings indicate that the revenue from NRs is essential in ensuring the achievement of sustainable futures, thus supporting the RB hypothesis. Therefore, the RC is found not to hold in this context, as the revenue from NRs raises ES. Consequently, TI and FD do not support ES in this region. Despite the widely agreed importance of TI in supporting ES, the present findings show that it is detrimental to sustainability. FD is also observed to have an insignificant influence on sustainability. Such major deviations from what other empirical studies have postulated indicate the need for policy reforms in SSA to ensure that clean technologies are adopted and financial resources are allocated towards supporting ES. This research is limited to SSA, and other major factors, such as institutional quality, which could help reduce the corruption level that hinders FD in supporting ES, are not included. However, these limitations occurred because the present study seeks to address whether developing countries, such as SSA countries, can advance ES with the use of the revenue from NRs. Therefore, peripheral variables in the model used to directly answer the research questions of this study were excluded for simplicity. To this end, this study calls for future studies to investigate the influence of corruption in hindering FD in advancing ES in developing countries. Major policies presented in this study call for SSA countries to capitalize on the revenue generated from the sale of NRs in order to support sustainability, as well as the correct use of GF in the development of clean technologies and the adoption of clean energy sources that are fundamental in achieving a green transition.