The Corrosive Grip: How Corruption Inhibits Green Finance in Enhancing Environmental Sustainability

Abstract

Ecological sustainability is one of the key dimensions of sustainable development in any economy. Developing economies exhibit high-risk levels in terms of political stability and corruption, thereby inhibiting them from successfully adopting techniques for ecological sustainability. A framework that comprises a strong financial system for green financial investment, coupled with correct policy frameworks becomes fundamental in the attainment of sustainable environments. Pervasive corruption in developing nations is a formidable barrier that impedes financial development and undermines green finance initiatives’ efficacy in fostering ecological sustainability. This research takes the data of the Central African nations, which is analyzed with the ‘Methods of Moments Quantile Regression’ technique. The major results presented show that digitalization, renewable energy, and governance support ecological sustainability. Institutional quality and green finance are expected to increase ecological sustainability, but the findings show that in the Central African countries with high corruption they tend to reduce ecological sustainability. The poor institutional quality in the Central African nations, because of high corruption and political instabilities, impedes the efficacy of financial development and green finance in advancing ecological sustainability. The Central African nations can achieve sustainability by fostering digitalization and renewable energy, as well as reducing corruption and political instabilities.

1. Introduction

Africa continues to withstand the worst of environmental degradation (ED) (Adekunle 2021), particularly in Central Africa, where the interaction of socio-economic factors followed by governance and environmental policies creates a multi-faceted crisis. The region is endowed with many natural resources (NRs) and considerable biodiversity, but it faces several serious environmental problems such as deforestation, soil erosion, and pollution (Abernethy et al. 2016). The dynamics of factors including demographic expansion and economic growth, which are characterized by accelerated urbanization, exacerbate the situation on the ground, which directly contributes to the aggravating problem of increased waste and the depletion of resources (Jiang et al. 2021; Cobbinah et al. 2015). In addition, climate change is also related to the aggravation of the situation due to the changing of extremes and shifts within agricultural systems leading to stress on already stressed ecosystems (Dagestani et al. 2022; Asongu et al. 2017). A holistic approach combining technological progress, renewable energies, institutional efficiency, green investment, and good governance is necessary for combating the issue of environmental degradation in Central Africa (Godfrey et al. 2020; Adekunle 2021).

This study is all the more topical as it seeks to examine some of the key variables that affect the environmental sustainability (ES) of Central Africa. The present research employs the Ecological Economic (EE) approach to sustainability of Daly and Townsend (1992) in ascertaining the various factors that influence ES in the Central African nations. Therefore, in line with the EE approach, renewable energy (RE) is taken to represent natural capital, while digitalization and green finance are taken to represent human-made capital. In that regard, the objective of this research is to analyze the effects of digitalization, RE, green finance, and institutional quality on ES. In 2015, under the historic Paris Agreement in France, 195 states agreed to seek harmony between humans and the natural environment to combat climate change and reduce greenhouse gas (GHG) emissions, which makes this study applicable (United Nations 2015; Khoshnava et al. 2019). This research is most relevant and necessary because it has a stake in the issue of sustainable development (SD) for a region, which is generally not included in global environmental discussions (Asongu et al. 2019; Asongu and Odhiambo 2021; Li et al. 2023).

Empirical studies highlight the need to strengthen governance structures to enable broader acceptance of sustainable practices (Diallo and Ouoba 2024). In addition, it is demonstrated that green finance can extend support for RE initiatives as well as technologies, which has a large possibility of reducing carbon emissions (CE) (Brunnschweiler 2010). Further literature has also established that institutional quality has synergistic association in enhancing successful environmental policy factors, whereby better institutional quality leads to better the environment outcome (Lozano 2011; Ioannou and Serafeim 2012). These results suggest the need for an integrated framework that considers the relationships between these variables for ES in Central Africa (Menon and Suresh 2022; Duodu et al. 2021). While past studies have examined the influence of various factors on ES in many regions, the effects of digitalization and green finance in the Central African nations is lacking. Thus, there is need for contemporary research that addresses the sustainability of Central Africa to be performed in order to provide crucial policies for this region.

This research contributes to the literature in threefold; firstly, the EE approach to sustainability is adopted to examine how ES is improved with natural capital. Secondly, this research investigates how ES has advanced with human-made capital (green finance and digitalization). This follows the lack of studies in the literature on how digitalization affects ES. Moreover, the influence of green finance on ES is a key factor to be understood in order to provide vital policies that advances ES in the Central African nations. Third, this research provides robust empirical findings through employing the ‘Method of Moments Quantile Regression’ (MMQR). To this end, this research answers the following critical research questions: What is the impact of digitalization and the introduction of RE sources on the environment in the Central African region? What is the contribution of the institutional quality factor and governance on environmental change? In what ways can green finance be mobilized to strengthen SD efforts in the region? To answer these questions, annual data from Central African countries ranging from 2000 to 2022 is used. This methodological approach allows us to explore in detail the link between key variables and ED.

2. Literature Review

2.1. Renewable Energy and Environmental Sustainability

The adoption of renewables in the energy systems of Central Africa is promising in enhancing ES (Kumba and Olanrewaju 2024; Jinapor et al. 2023). Renewable energy projects in the region are imperative, especially as they replenish energy shortages and reduce the negative effect on the environment by over-reliance on fossil fuels (Adekunle 2021; Leonard 2024). Recent research suggests that renewables are one of the most important factors for economic development in Africa, with the extension of energy and the fight against climate change (Kumba and Olanrewaju 2024; Jinapor et al. 2023). Central Africa is rich in NRs and therefore the region is able to sustain the environment, while shifting towards renewables such as solar, wind, and hydroelectric energy (Kumba and Olanrewaju 2024; Jinapor et al. 2023).

The adoption of renewables is not only becoming an environmental issue, but an economic one too (Adekunle 2021; Asongu and Odhiambo 2021). The aforementioned aspect also creates the need for policies that facilitate the use of investment in renewable technologies to achieve better environmental and energy security objectives (Jinapor et al. 2023; Adekunle 2021). The growing use of renewable technologies makes them more affordable, which strengthens the continuous effort toward other sustainable development goals (SDGs) and makes them more relevant as part of the region’s development context (Jinapor et al. 2023; Adekunle 2021). The RE scalability projects in Central Africa are affected by a host of obstacles that include insufficient infrastructure, inadequate funding, and inconsistent policies (Adekunle 2021). While some of the available literature highlights the productivity and ecological advantages of RE, very few studies focus on region-specific diagnostic issues such as technology implementability and public participation in decentralized power systems. Filling these gaps is necessary to formulate policies that enhance the integration of RE.

The influence of RE resources on ES has been extensively examined in the literature, with many studies supporting the favorable influence on the environment. Nonetheless, the influence of low-carbon energy (LCE) that comprises both RE and nuclear sources is still emerging (Jarijari et al. 2025a). While this nexus is yet to be extensively performed, a few findings in the literature reveal the importance of LCE on ES (Jarijari et al. 2025a). However, because of the unavailability of nuclear energy data in Central Africa, this research fails to examine the nexus of LCE on ES, but still furthers knowledge on how RE can influence ES in Central Africa.

2.2. Institutional Quality and Environmental Sustainability

The concept of institutional quality as the effectiveness, efficiency, and fairness of an organization indicates that the quality of the institution is fundamental for achieving ES in Central Africa (Dagestani et al. 2022; Leonard 2024). Creating and enforcing environmental policies and regulations, as well as ensuring that sustainable practices are promoted across all sectors, require effective institutions (Adekunle 2021; Asongu and Odhiambo 2021). Insufficient and weak institutions hamper the implementation of the strategies that are designed to improve the ES (Dagestani et al. 2022; Leonard 2024).

Furthermore, as noted by several studies, the alignment of institutional quality and SDGs has the potential to increase the ability of local people to sustainably manage environmental resources (Asongu and Odhiambo 2021; Dagestani et al. 2022). Better access to information due to a stronger institutional framework enables stakeholders at all levels to contribute towards the sustainability initiatives (Dagestani et al. 2022; Leonard 2024). Studies that target the specific institutional reforms that need to be put in place for Central Africa’s environmental issues are lacking in the literature (North 2021). It is also the case that the contribution of informal institutions, like the traditional ecological knowledge systems, has not been studied much (Berkes 2017). Addressing these issues may lead to the formulation of more integrated policies.

In Central Africa, sustainable environmental outcomes are achieved as a product of good governance and effective leadership. There is need to establish strong institutional frameworks aimed at promoting environmental policies, community participation, and effective systems of accountability (Adekunle 2021; Asongu and Odhiambo 2021). Such governance policies that allow for the inclusion of environmental issues within policy development enhance natural resource management and sustainability among different sectors (Dagestani et al. 2022).

It has been found that the absence of governance can lead to the destruction of the environment, owing to the fact that the attempt to grow the economy can take priority over environmental considerations (Adekunle 2021; Leonard 2024). The interactions between governance and the environment can be seen in the form of politics or law and the implementation of measures needed to protect the environment, which are critical to promoting sustainable development in the region (Adekunle 2021; Asongu and Odhiambo 2021). For the ecosystems of Central Africa to be healthy in the long term, there requires sustained attempts to ‘govern’ in a manner that is conducive to sustainability (Dagestani et al. 2022; Leonard 2024).

Even with this knowledge, the available literature remains limited on more nuanced questions, such as how Central Africa’s sociopolitical reality affects the functioning of governance mechanisms like anti-corruption and decentralized environmental protection. In addition, the relationship between the quality of governance and local environmental action is underspecified (Agrawal and Gibson 2001) and represents an important gap for further investigation.

2.3. Digitalization and Technological Innovation on Environmental Sustainability

In Central Africa, innovation is vital for achieving ES (Kumba and Olanrewaju 2024). The growing advancements in technology can enhance the efficiency of energy systems and aid in shifting to renewables, as well as improve waste management systems (Jinapor et al. 2023). The adoption of green technologies is crucial for not only mitigating the economic activities footprint, but also improving the quality of the environment (Jinapor et al. 2023; Muazu et al. 2023).

With greater focus on developing technologies, the sophisticated problems that the region faces can be addressed more easily (Kumba and Olanrewaju 2024; Jinapor et al. 2023). Based on the observations of specialists, there is promise that further progress in technology will help lower the costs associated with RE systems, making them more attractive for adoption throughout Central Africa (Kumba and Olanrewaju 2024; Jinapor et al. 2023). It becomes necessary, therefore, to create conditions that promote innovations for supporting environmental sustainability objectives within the area institutions (Adekunle 2021).

The body of literature barely tackles the micro level processes of how digital technology influences a region. The focusing point is far from a region-specific environmental problem (Ockwell and Byrne 2016). On top of this, the public–private cooperation for the uptake of green technologies in Central Africa is also a gap (Altenburg and Pegels 2017).

2.4. Green Finance and Environmental Sustainability

The notion of green finance is understood to be a crucial marker in achieving environmental sustainability in Central Africa (Jinapor et al. 2023; Muazu et al. 2023). Green finance is defined as the important influx of funds that is set aside for environmental benefits as a result of sustainable development projects and initiatives (Zisuh 2021; Mwamidi et al. 2023). This includes investments to support renewable energy, sustainable farming, and infrastructure development (Jinapor et al. 2023; Muazu et al. 2023). The infusion of green finance is capable of improving the ability of nations to undertake critical environmentally friendly projects that are necessary to facilitate sustainable development approaches (Zisuh 2021).

The value of investing in green finance does not only relieve resource constraints and promote new innovation, research, and development activities, along with the dissemination of clean technologies, but also supports environmental quality (Jinapor et al. 2023; Muazu et al. 2023). In addition, Zisuh (2021) and Mwamidi et al. (2023) argue that green financial instruments like green bonds are capable of deepening the investor base which, in turn, increases funding for the sustainability projects within the region. These conflicting goals illustrate why green finance is essential for Central Africa’s economic growth alongside environmental protection (Muazu et al. 2023).

What remains central in this literature is that there are few works reinforcing the arguments of GF effectiveness in Central Africa. Also little is known about the GF gap from the point of view of international financial institutions (Gabor 2021).

3. Methodology and Data

3.1. Model Specification

The present research model is based on the EE approach to sustainability, which points to the need for a non-declining level of natural resources (NRs) in order to achieve sustainable development (Georgescu-Roegen 1970; Boulding 1973; Daly and Townsend 1992). The EE approach is accepted as one of the theories that explains sustainability as it shows that NRs and human-made resources are complements that are needed to work together to ensure the needs of future generations are met (Hussen 2000). Moreover, because the EE approach advocates for the maintenance of a non-declining level of NR, the theory advocates that sustainability is achieved when the following conditions are met. (1) The rate at which the renewable resources are being exploited should be aligned to its regeneration rate; (2) the rate of waste emission should be less than the absorptive capacity of the environment; and (3) the use of nonrenewable resources should be consistent with the rate at which renewable substitutes are developed (Hussen 2000). Therefore, this research takes RE as the major factor affecting ecological sustainability. The importance of RE is supporting ecological sustainability is supported by various recently published works of the studies of Deka et al. (2024) and Deka (2025), among others. Thus, RE in this study represents the natural capital that is needed to be maintained in order to achieve sustainability as postulated in the EE approach. This research also takes green finance as the major indicator influencing ecological sustainability, following the research models of Deka (2024) and Jarijari et al. (2025b). Green finance, as well, is essential in supporting R&D in the development of RE and clean technologies; hence, it is important in supporting ecological sustainability (Deka 2024). Additionally, this research also takes digitalization as one of the core variables that affects ecological sustainability, following the research models of Alsabhan and Anas (2025) and Osinubi et al. (2025). Thus, in this research, digitalization and green finance become the human-made resources that are necessary in fostering ecological sustainability. Thus, this model is specified in such a way that both NRs (that is, the RE) and the human-made resources (digitalization and green finance) are essential in achieving sustainability. Therefore, the postulations of the EE approach that NRs and human-made resources are complements and are both are needed for sustainability are adopted in this research model. Institutional quality and governance are also included as the control variables in the model following Deka (2024) and Jarijari et al. (2025b). To this end, the LCF—a ratio of biocapacity to EFP—is adopted to represent ecological sustainability, following Samour et al. (2023). Therefore, the research model of this study is specified as shown in the statistical representation in Equation (1):

$${LCF}_{ti}={\beta }_0+{{\beta }_{1}RE}_{ti}+{{\beta }_{2}logGF}_{ti}+ {{\beta }_{3}DIG}_{ti}+{{\beta }_{4}GOV}_{ti}+{{\beta }_{5}IQ}_{ti}+ \mu$$

(1)

where LCF is the endogenous variable representing the Load Capacity Factor. RE, GF, and DIG are the exogenous factors representing renewable energy, green finance, and digitalization. These factors also represent the NRs and the human-made resources that are needed as complementary factors for sustainability. GOV and IQ are the control variables, representing governance and institutional quality. The parameter ${\beta }_0$ is the model’s constant term, and ${\beta }_{1–5}$ are the coefficients of the explanatory factors. $log$ is the logarithm of GF at base 10, $\mu$ is the white noise error, while the superscript $i$ represents the ‘cross-sections’, that is, the total eleven Central African Nations, and superscript $t$ is the timeframe of the data, that is, 2000 to 2022.

3.2. Data

This research collects the data of the Load Capacity Factor from the Global Footprint Network (GFN) and green finance from the Our World in Data (OWD), RE, digitalization, governance, and institutional quality from the World Bank (WB).

The Load Capacity Factor is the ratio of biocapacity (BC) to EFP and shows that an increase in biocapacity relative to EFP signals improved ecological sustainability. Biocapacity represents nature supply, while EFP represents nature’s demand. Thus, increased nature supply with respect to nature’s demand maintains the replicability of the environment (Samour et al. 2023). RE is the total amount of energy sources that are clean because they do not emit carbon, like hydro, solar, and waves, among others. RE is measured as a percent of the total amount of energy used. The Digitalization Index used in this research is adopted from recent studies of Tan et al. (2023) and Osinubi et al. (2025). It is calculated with the ‘Principal Component Analysis’ (PCA) from the dimensions of ‘fixed broadband subscriptions’ (FBS), ‘mobile cellular subscription’ (MCS), and ‘internet users’ (IU), see the component loadings in

Table A1. Digitalization Index developed with PCA.

Principal components/correlation Rotation: (unrotated = principal)
Component	Eigenvalue	Difference	Proportion	Cumulative
Comp1	2.3105	1.84002	0.7702	0.7702
Comp2	0.4705	0.251524	0.1568	0.9270
Comp3	0.2189	.	0.0730	1.0000
Principal components (eigenvectors)
Variable	Comp1	Comp2	Comp3	Unexplained
MCS	0.5508	0.7629	0.3385	0
FBS	0.5694	−0.6400	0.5160	0
IU	0.6103	−0.0915	−0.7869	0
Principal components/correlation Rotation: orthogonal varimax (Kaiser off)
Component	Variance	Difference	Proportion	Cumulative
Comp1	1.00002	0.00001	0.3333	0.3333
Comp2	1	0.00002	0.3333	0.6667
Comp3	0.9999	.	0.3333	1.0000
Rotated components
Variable	Comp1	Comp2	Comp3	Unexplained
MCS	1.0000	−0.0000	−0.0000	0
FBS	0.0000	1.0000	−0.0000	0
IU	0.0000	0.0000	1.0000	0
Component rotation matrix
	Comp1	Comp2	Comp3
Comp1	0.5509	0.5694	0.6102
Comp2	0.7629	−0.6400	−0.0915
Scoring coefficients for orthogonal varimax rotation
Variable	Comp1	Comp2	Comp3
MCS	1.0000	−0.0000	−0.0000
FBS	0.0000	1.0000	−0.0000

in the Appendix A. Where IU is measured as the number of people using internet as a percent of the total population, MCS and FBS are measured as the number of people with subscriptions per 100 people. Green finance represents the money given to developing nations in order for them to support R&D on RE and clean technology, measured as a total amount in U.S dollars. Institutional quality is an index calculated with the PCA method with the dimensions of ‘government effectiveness’ (GE) and ‘control of corruption’ (CC), see the component loadings in

Table A2. Institutional Quality Index developed with PCA.

Principal components/correlation Rotation: (unrotated = principal)
Component	Eigenvalue	Difference	Proportion	Cumulative
Comp1	1.7979	1.5958	0.8989	0.8989
Comp2	0.2021	.	0.1011	1.0000
Principal components (eigenvectors)
Variable	Comp1	Comp2	Unexplained
CC	0.7071	0.7071	0
GE	0.7071	−0.7071	0
Principal components/correlation Rotation: orthogonal varimax (Kaiser off)
Component	Variance	Difference	Proportion	Cumulative
Comp1	1	0.0000	0.5000	0.5000
Comp2	1	.	0.5000	1.0000
Rotated components
Variable	Comp1	Comp2	Unexplained
CC	0.0000	1.0000	0
GE	1.0000	−0.0000	0
Component rotation matrix
	Comp1	Comp2
Comp1	0.7071	0.7071
Comp2	−0.7071	0.7071
Scoring coefficients for orthogonal varimax rotation
Variable	Comp1	Comp2
CC	0.0000
GE	1.0000	−0.0000

in the Appendix A. The CC and GE indicators are ranks given from −2.5 to 2.5. Governance measures the confidence of agents on the rule of law, measured as a percentile rank from 0 to 100.

Table 1. Summary of the variables.

Variable	Source	Measurements
Load Capacity Factor (LCF)	GFN	Ratio of BC to EFP gha per capita
Renewable Energy (RE)	WB	% of total energy used
Governance (GOV)	WB	Percentile rank, with 0 corresponding to lowest rank and 100 to highest rank to which agents have confidence in and abide by the rules of society, and, in particular, the quality of contract enforcement, property rights, the police, and the courts, as well as the likelihood of crime and violence
Institutional Quality (IQ)	WB	Index calculated with CC and GE, measured as ranks from −2.5 to 2.5, with the PCA
Digitalization (DIG)	WB	Index of three dimensions of FBS, MCS and IU calculated by employing the PCA
Green finance (GF)	OWD	International financial support on R&D for clean technologies and RE (total in US dollars)

summarizes the measurements and sources of the indicators employed, while the descriptive statistics of the variables are presented in

Table 2. Results of descriptive statistics.

Variable	Obs	Mean	Std. Dev.	Min	Max
BC	253	5.2175	6.8614	0.2434	32.5134
EFP	253	1.2681	0.5151	0.5540	2.7678
RE	253	71.5477	25.1596	3.68	98.34
FBS	253	0.1968	0.4764	0	3.3622
IU	252	11.3749	15.4089	0.0059	73.4717
MCS	253	40.8840	36.2202	0	149.1076
GOV	253	16.2333	14.7512	0.4694	69.7202
GE	253	−1.0953	0.4281	−1.8794	0.2740
CC	253	−0.9960	0.5635	−1.6454	0.7763
GF	253	29,800,000	127,000,000	0	1,280,000,000

.

3.3. Method

The method employed in the analysis of this research is the MMQR that gives heterogeneous findings (Machado and Silva 2019). The provision of heterogeneous findings by the MMQR method is essential in this study considering the heterogeneous conditions of the Central African countries. Most importantly the MMQR method provide findings in the different quantiles (0.1 to 0.9) making it possible to examine the symmetric or asymmetric effects of the exogenous variables on the endogenous variables (Jarijari et al. 2025b). To this end, the MMQR specification is illustrated as shown in Equation (2) following Lv et al. (2024) and Deka (2025):

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

(2)

where $Q_y(\delta !X^{\prime }it)$ is the conditional quantile representation of the endogenous variable—the LCF in this study.

The MMQR technique is also employed following the considerations of the preliminary test results presented in the

Table A3. CD and heterogeneity test.

	Statistic	p-Value		Statistic	p-Value
	Pesaran (2004)
LCF	18.85 ***	0.000	Friedman (1937)	28.768 ***	0.0014
RE	5.52 ***	0.000	Pesaran (2015)	0.653	0.5138
GOV	−2.05 **	0.040	Frees (2004, 1995)	2.508 ***
IQ	0.36	0.719
DIG	33.28 ***	0.000	Heterogeneity
logGF	4.33 ***	0.000	Δ	8.416 ***	0.000
			Δ adj.	10.432 ***	0.000

Note: *** is significant at 1%; ** is significant at 5%.

,

Table A4. Results of CADF UR test.

	Statistic	p-Value	Statistic	p-Value
	Level		1st Difference
LCF	−2.454 **	0.010
RE	−2.496 ***	0.006
GOV	−1.748	0.530	−3.778 ***	0.000
IQ	−1.391	0.902	−3.120 ***	0.000
DIG	−2.912 ***	0.002
logGF	−3.042 ***	0.000

Note: *** is significant at 1%; ** is significant at 5%.

,

Table A5. VIF results.

Variable	VIF	1/VIF
GOV	3.94	0.2540
IQ	3.69	0.2710
DIG	1.47	0.6807
logGF	1.13	0.8814
RE	1.11	0.8982
Mean VIF	2.13

and

Table A6. Cointegration results.

	Statistic	p-Value
	Kao
Modified DF	−2.5103 ***	0.0060
DF	−4.4697 ***	0.0000
ADF	−3.5163 ***	0.0002
Unadjusted modified DF	−4.6067 ***	0.0000
Unadjusted DF	−5.2818 ***	0.0000
	Pedroni
Modified PP	2.5342 ***	0.0056
PP	−2.9802 ***	0.0014
ADF	−3.9276 ***	0.0000

Note: *** is significant at 1%.

in the Appendix section. Firstly, the existence of ‘cross-sectional dependence’ (CD) in each variables (Pesaran 2004) and in the model as a whole (Friedman 1937; Frees 2004, 1995; Pesaran 2015), as shown in Table A3, indicates the importance of using the MMQR method. Second, the existence of ‘heterogeneity’ problems in the model, as shown in the results of Table A3, supports the use of the MMQR method (Pesaran and Yamagata 2008). This is so because the MMQR is the ‘second-generation’ (SG) method that ensures robust findings are presented regardless of the CD issues in the data and ‘heterogeneity’ in the model (Deka and Efe-Onakpojeruo 2024). Third, the existence of mixed ‘integration orders’, according to the ‘Cross-sectional Augmented Dickey–Fuller’ (CADF) in Table A4 (Pesaran 2007), depicts the importance of using the MMQR method that accepts variables with mixed ‘integration orders’. Fourth, the significant (sig) cointegration results of Kao and Pedroni in Table A5 requires a method that presents long run (LR) findings, such as the MMQR technique. Lastly, the absence of ‘multi-collinearity’, as shown by the VIF results in Table A6, makes it feasible for the present research model to be analyzed, since no strong relations exists among the exogenous variables (Shuayb et al. 2024).

The robustness of the findings presented is achieved by employing the ‘Panel Correlated Standard Errors’ (PCSE) and ‘Driscoll–Kraay’ (DK) (Beck and Katz 1995; Driscoll and Kraay 1998). Recent empirical studies have used these methods in checking the robustness of the MMQR results (Deka 2025), while other studies have relied on this methods for the whole analysis because of their ability to correct the within-panel correlations (Bailey and Katz 2011; Deka et al. 2025). Figure 1 illustrates the steps followed in analyzing the relationship specified in this research.

4. Results and Discussion

The crucial findings of this study are presented in

Table 3. MMQR findings.

	Coef.	Std. Err.	p-Value	Coef.	Std. Err.	p-Value
Quantile	0.1			0.25
RE	0.0227 ***	0.0078	0.004	0.0241 ***	0.0072	0.001
logGF	−0.0962 ***	0.0253	0.000	−0.1086 ***	0.0234	0.000
DIG	1.3637 ***	0.2019	0.000	1.2380 ***	0.1867	0.000
GOV	0.0219	0.0274	0.423	0.0414	0.0253	0.102
IQ	−0.8253 **	0.3260	0.011	−1.0683 ***	0.3017	0.000
Quantile	0.5			0.75
RE	0.0269 ***	0.0080	0.001	0.0269 ***	0.0080	0.001
logGF	−0.1322 ***	0.0263	0.000	−0.1322 ***	0.0263	0.000
DIG	0.9981 ***	0.2118	0.000	0.9981 ***	0.2118	0.000
GOV	0.0786 ***	0.0290	0.007	0.0786 ***	0.0290	0.007
IQ	−1.5321 ***	0.3454	0.000	−1.5321 ***	0.3454	0.000
Quantile	0.9
RE	0.0385 *	0.0233	0.099
logGF	−0.2326 ***	0.0765	0.002
DIG	−0.0230	0.6145	0.970
GOV	0.2370 ***	0.0840	0.005
IQ	−3.5060 ***	1.0001	0.000

Note: *** is significant at 1%; ** is significant at 5%; * is significant at 10%.

, according to the MMQR method, and in

Table 4. PCSE and DK findings.

	Coef.	Std. Err.	p-Value	Coef.	Std. Err.	p-Value
	PCSE			FGLS
RE	0.0289 ***	0.0054	0.000	0.0289 ***	0.0087	0.003
logGF	−0.14948 ***	0.0345	0.000	−0.1494 ***	0.0181	0.000
DIG	0.8225 **	0.3855	0.033	0.8225 *	0.4194	0.063
GOV	0.1058 ***	0.0220	0.000	0.1058 **	0.0460	0.031
IQ	−1.8715 ***	0.3337	0.000	−1.8715 ***	0.5344	0.002

Note: *** is significant at 1%; ** is significant at 5%; * is significant at 10%.

, according to the PCSE and DK methods. Key findings reveal that ES is promoted with RE, digitalization, and governance, while green finance and institutional quality are counterproductive.

According to the results in Table 3, digitalization has a significant and positive influence on LCF, revealing its importance in supporting ES in the Central African region ($\beta$ is between 1.3637 and 0.998 in the 0.1 to 0.75 quantiles, respectively; p-value < 0.000). In the 0.9 quantile, the influence of digitalization on ES becomes insignificant. These findings reveal that digitalization presents asymmetric influence on the ES of the Central African economies. Countries with low levels of ES can significantly benefit from digital technology in advancing sustainable environments, while countries with high levels of ES do not. This implies that regions with high ED problems can capitalize on digital technology in reducing this problem. Regions with low ED problems have no urgent need, as their level of ES is high; hence, the influence of digital technology in these regions is minimal. In ensuring the robustness of the MMQR findings, the PCSE and DK tools support the importance of digital technology in supporting ES, showing that a positive relationship with the LCF exists ($\beta$ = 0.8225; p-value < 0.01 and 0.1, respectively). These findings are supported in the various empirical outcomes presented in studies of Raihan and Tuspekova (2022); Bekun (2024); Raihan et al. (2022); Ahmad et al. (2023); and Obobisa et al. (2022). Therefore, with the harmony between the findings of this study and those of past empirical studies, digital technology can be taken as a very useful tool in advancing ES. The Central African nations could benefit in working towards improving ES with improved digitalization and technological innovations, and may dedicate a great deal of funds in supporting the development of clean technologies.

The findings of this analysis also show that RE is important for ES in the Central African economies. The findings of the MMQR presented in Table 3 show the existence of a significant positive relationship between RE and LCF ($\beta$ = 0.0227 to 0.0269 in the 0.1 to 0.75 quantiles; p-value < 0.1). Nonetheless, the significant influence of RE on the LCF weakens in the upper-quantile, while the coefficient value increases ($\beta$ = 0.0385; p-value < 0.1). The MMQR findings presented in this analysis show that RE fosters the attainable, sustainable environments in all quantiles, showing that Central African economies with serious ED and those with improved ES can all benefit from RE use in supporting the low-carbon goals. The PCSE and DK results in Table 4 support the results of the MMQR method and show that RE has a positive and significant influence on the LCF ($\beta$ = 0.0289; p-value < 0.01). The past literature (Khan et al. 2020; Qudrat-Ullah and Nevo 2021; Gyimah et al. 2023; Tariq and Hassan 2023; Riti et al. 2022; Salahuddin et al. 2020) supports the importance of RE in advancing ES. This calls for the Central African nations to support RE development in order to reduce carbon emission and move toward low-carbon economies. RE development can be improved through adopting various mechanisms, such as investment in the R&D projects intended to develop new RE sources (OWD 2025). Many other mechanisms, like employing the financial resources of the economy and the national income for RE development, can be employed (Mukhtarov et al. 2022).

We also present in this study that governance plays a pivotal role in enhancing ES, as evidenced by its significant positive influence on LCF. For instance, the MMQR findings show that governance presents a significant positive influence on the LCF ($\beta$ = 0.0786 to 0.237; p-value < 0.01), though this positive relationship is insignificant in the 0.1 and 0.25 quantiles. It is shown in these findings that governance has an asymmetric relationship with ES in Central Africa, with an insignificant relationship in the lower-quantiles and a significant relationship in the upper-quantiles. This is also evidenced by the impact of the coefficient, which is low in the lower-quantiles and high in the upper-quantiles. The positive influence of governance on LCF is also supported by the findings of the PCSE and DK methods ($\beta$ = 0.1058; p-value < 0.01 and 0.05, respectively). Many empirical findings presented in the literature support the findings of the present research, stressing the importance of governance in sustainable development (Lei et al. 2023; Traoré et al. 2024; Safdar et al. 2022; Adekunle 2021; Abid et al. 2021). For the Central African nations, governance implies improvements in the rule of law, which translates into the respect of environmental regulations meant to foster sustainable futures in the region. Nations that respect, enforce, and follow laws that are set by the government can easily achieve SDGS. This calls for Central Africa to ensure significant improvements in the rule of law of their nations to ensure continued advancements in ES in this region.

Moreover, the findings presented in Table 3 also show that green finance exhibits a consistently negative and statistically significant impact on LCF across all quantiles ($\beta$ = −0.096 to −0.2336; p-value < 0.01). The findings show that green finance presents a significant symmetric relationship with the LCF. These results imply that the goal of green finance in encouraging sustainable development is being undermined in the Central African nations. Green finance mechanisms and policy tools developed to support green projects in this region are being mismanaged. The PCSE and DK methods’ findings in Table 4 also support the results of the MMQR technique by showing that green finance and LCF are positively linked ($\beta$ = −0.1494; p-value < 0.01). Studies like Jarijari et al. (2025b) have shown that green finance is fundamental in supporting ES in Sub-Saharan Africa. Nabbanja and Deka (2025) in their empirical studies of the SSA show that green finance is fundamental in supporting sustainable development. However, the findings of this analysis are supported by the findings of the study of Ibrahim et al. (2025) that show that the influence of green finance in supporting ES is impeded by corruption. Therefore, high corruption in Central Africa impedes green financial mechanisms, misdirecting green funds. Central Africa reports many cases of corruption, for instance, in Cameroon, the National Anti-Corruption Commission (CONAC) reported that the state lost billions from corruption (CONAC 2025). Other examples of corruption took place in the Democratic Republic of Congo (DRC), where public funds are reported to have been embezzled by ministers (PPLAAF 2025), and in the Central Africa Republic, where income from NRs is reported to have been diverted for private interests (Transparency International 2024). The Central African nations should revisit their financial policies and ensure that the funds meant to support the green transition are used for this purpose.

The results presented in Table 3 also show that institutional quality exhibits a negative and statistically significant impact on LCF across all quantiles ($\beta$ = −0.8253 to −3.506 in the 0.1 to 0.9 quantiles, respectively; p-value < 0.01). These findings reveal that institutional quality exhibits a symmetric relationship with LCF. Institutional quality is not effective in fostering sustainable futures in Central Africa. The results of the PCSE and DK are consistent with the MMQR findings, supporting that institutional quality is negatively related with the LCF ($\beta$ = −1.8715; p-value < 0.01). With the institutional quality developed from GE and the CC in this study, this reveals that these economies are not efficient in controlling corruption in this region, resulting in the embezzlement of green funds and other developmental funds in this region. Robust policies and policy frameworks that enhance sustainability in this region are called for.

5. Conclusions

Environmental sustainability in Central Africa presents a complex challenge shaped by the interplay of RE adoption, governance structures, financial systems, institutional frameworks, digitalization, and green finance mechanisms. The goal of the research is to analyze the effects of the aforementioned dynamics on ecology as measured by LCF using data from 2000 to 2022. The analysis of the research is undertaken by employing the MMQR method that captures CD and ‘heterogeneity’ in the model as well as proving LR outcomes. The study found out that consuming clean energy resources significantly improves the quality of the environment. Moreover, digitalization and governance are also key in supporting ES in the Central African nations. Surprisingly, green finance and institutional quality are counterintuitive in supporting ES. Policies differ depending on specific scenarios, which means they can have either negative or positive outcomes. In this case, the investment in not only capital but also RE development is necessary. Investment plans must include long-term governance reforms regarding active participatory approaches to develop systematic accountability of sustainable policies. Constructing sustainable financial systems with supportive green bonds and yield instruments make financially incentivized green investments possible. Furthermore, overarching controls must ensure that green finance instruments and objectives are aligned with environmental goals, while public–private consortia as well as local R&D entities can stimulate clean technological change. Central Africa’s journey towards ES calls for a comprehensive method that balances development with conservation. The region is endowed with many NRs, which can be tapped to build resilience, but it requires a sustained effort in transformative leadership, creative investment, and policy inclusiveness. With climate change worsening, the need for collaborative and evidence-based action is more crucial than ever. This research contributes to that deficit by outlining the drivers of change and the challenges that will need to be addressed to enable a sustainable approach for Central Africa and the wider region.

Policy Recommendations

The recommendations that are presented below can be used in order to advance environmental sustainability in Central Africa. By employing these policies, Central Africa will work towards attaining a clean environment for the future generation.

Accelerating the adoption of RE is essential. Private sector involvement in the RE projects can be enhanced through tax breaks, grants, and other forms of subsidization, and, as such, expansion of energy availability through off-grid renewable energies in rural and underdeveloped regions should be prioritized.

In addition, political reforms are necessary, in particular the establishment of a prohibition on the taking of bribes in order to fulfill international standards of governance in the management of natural resources. These practices must be undertaken through the provision of appropriate training of government employees and other policymakers. Furthermore, participatory governance mechanisms should be introduced, allowing local communities to be actively involved in environmental decision-making processes.

Promoting green finance through the establishment of green bonds and other sustainable financing instruments can support eco-friendly initiatives. Pollution and overuse of resources accompanying industrialization processes should be controlled through strict environmental regulations. The balance of financial activities should be shifted towards investment in low-impact industries so that deleterious economic expansion is not endured.

Policies must be developed that are compatible with international environmental treaties, including the Paris Agreement on climate change, particularly arguing that institutional arrangements in governance must undergo drastic changes to enhance their effectiveness. Institutions should be empowered to monitor and enforce environmental regulations rigorously, while performance-based metrics should be introduced to evaluate their contributions to sustainability goals. Strengthening institutional capacity will ensure that Central African countries are better equipped to tackle environmental challenges.

It is important to improve environmental outcomes through technological innovations. Governments ought to commit resources and create green technologies that are suitable to their countries. Creating innovation and technology hubs for startups and entrepreneurs who are developing solutions for environmental problems will facilitate green development. Furthermore, the establishment of public–private partnerships can fast track the deployment and scaling of innovative technologies.

Efforts to enhance the impact of green finance should be intensified. Financial institutions must be trained in the principles of green finance to improve the allocation of resources toward sustainable initiatives. Monitoring and evaluation frameworks should be developed to track the environmental outcomes of green financial instruments. There is tremendous potential for green financing in the region provided the barriers of high transaction costs and limited awareness are addressed.

The strengthening of environmental laws and institutions should be pursued through regional and international efforts. The African Union and other regional business organizations can promote such partnerships among Central African countries. Global and regional partnerships should give support, such as technical expertise and financial support for sustainability initiatives. Knowledge-sharing platforms should be created to facilitate the exchange of best practices and lessons learned between countries within and beyond the region.

Giving power and knowledge to citizens is integral in achieving environmental conservation; thus, there is a need for public awareness campaigns to be properly structured to educate citizens on the impact caring for the environment has. Training should be facilitated in order to provide communities with the skillset to practice sustainable methods. With realizing the need to be mindful of the environment, there is a need for empowering civil society organizations, as such means would guarantee accountability from the governments and industries regarding their environmental activities.