This study applies an integrated theoretical framework grounded in environmental economics, financial development, and sustainability theory to analyze how stock market development and renewable energy consumption affect CO₂ emissions in nine African countries and assess whether domestic credit to the private sector moderates the association. The Environmental Kuznets Curve (EKC) is central to this framework, proposing that pollution typically rises during the initial stages of growth as production and energy use intensify, but can fall once higher income levels make cleaner technologies and tougher environmental regulation more feasible [7]. Therefore, in Africa, stock market development should lead to an increase in CO₂ emissions because the increased amount of capital available through SMC is backed by financial institutions that are providing loans for financing polluting industries. It may also create an opportunity to provide green finance as stock market development approaches and exceeds the EKC peak [8].

This perspective aligns with the Sustainable Development Goals, which emphasize pursuing economic progress alongside environmental protection, particularly through SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action) [9]. Renewable energy would be an effective positive pathway to disassociate growth from emissions, with evidence demonstrating that countries’ markets that attract investment in solar, wind, and other clean tech can lower carbon emissions during economic growth [10,11]. However, the shift to a green economy cannot be achieved without adequate financing; without it, participation from the financing sector will be limited, sporadic, and prone to reverting to fossil fuel reliance, as financing borrowers and the underlying economics of distribution lock low-income populations into higher-carbon-emission energy sources.

Domestic credit to the private sector is a key moderating factor in this study. According to [12,13], reduced financing expenditures and increased capital availability through domestic credit provision lead to lower funding levels, thus mitigating the environmental pressures that can accompany rapid market expansion. [14], in their study, they focus on credit markets within carbon-intensive business operations, which lead to increased environmental pollution. They demonstrate how the expansion of financial markets creates negative effects when lenders lack green lending standards.

Financial inclusion will also shape this expected outcome by democratizing access to financial services. Financial inclusion will provide both increased liquidity to a market and greater opportunity for lower-income households and small businesses to invest in clean energy products like distributed solar that will provide spillover benefits to the environment [15,16]. Conversely, if the priority of increased inclusion is to maximize volume rather than sustainability, the intention of providing even minimal subsidies towards sustainable outcomes, new banked consumers may increase their consumption of fossil-fuel-based goods and temporarily raise emissions while waiting for the longer-term effects of inclusive green finance to establish themselves, then this may occur.

Finally, corporate governance and corporate social responsibility (CSR) frameworks help contextualize these relationships. It is often observed that firms that disclose performance and engage in CSR activities enjoy lower capital costs and higher investor confidence. Firms that disclose performance and firms that engage in CSR activities encourage green investment [17,18,19]. When disclosure standards and CSR practices are weak, market actors may overlook environmental externalities, even as financial markets expand.

In summary, this integrated framework highlights both positive and negative dynamics: stock market growth and financial development can increase CO₂ emissions, but they can move towards sustainable trajectories if combined with targeted domestic credit, investments in renewable energy, financial inclusion, and effective environmental regulation. Policymakers in African states must therefore align financial regulations, credit policies, and market oversight with sustainability objectives to fully harness the potential of capital markets to improve the environment.

This study focuses on three hypotheses:

The stock market in Africa is in many ways a significant factor in CO₂ emissions, both positively through greater investment in green technology, and negatively through the lack of stringent regulations resulting in environmental abuse. On the plus side, developed financial markets can create pathways for increased capital flow toward renewable energy and low-carbon projects through tools such as green bonds and sustainability-linked securities, thus decreasing the carbon intensity of investment portfolios [13,20]. Stock exchanges can further enable companies to access R&D funding for clean-technology innovations that will allow them to implement sustainable production processes, and ultimately reduce their emissions [14,21]. Additionally, combining stock market financing and green financing strategies helps align investors' interests with sustainable goals over the long term. It creates a framework in which sustainable businesses can thrive [22,23].

On the other hand, the increase in CO₂ emissions may result from an emphasis on economic growth over environmental sustainability, driven by a desire for and need for financial growth, rather than placing equal emphasis on environmental sustainability [24,25,26]. This pattern is often framed through the Environmental Kuznets Curve, which posits that emissions tend to rise as economies grow up to a particular income threshold, after which environmental conditions may improve [27]. In sub-Saharan Africa, where many stock markets are still developing, the environmental effects of stock-market development may be shaped by institutional quality and governance. Inadequate frameworks for allocating funds can divert structural capital to carbon-heavy industries and adversely affect climate-related objectives [28,29]. Hence, while the African stock markets offer great potential for financing sustainable development, the environmental upside still depends on sound regulation and some focus on environmental risk and green finance.

The link between renewable energy use and CO₂ emissions is increasingly important in Africa, given rising energy demand, ongoing socio-economic change, and mounting environmental pressures. The dependence on fossil fuels in Sub-Saharan Africa, which contributes to rising CO₂ emissions, acts as an impediment to climate change goals. Accordingly, integrating renewable energy technologies into national energy strategies in Sub-Saharan Africa is essential for advancing sustainable development [R30]. Previous studies have shown a connection between increased renewable energy use and decreased CO₂ emissions. [R31] use panel cointegration techniques to show this link in six North African countries. They find that emissions decrease as renewable energy use increases. [R32] extend the Environmental Kuznets Curve (EKC) model to Sub-Saharan Africa, examining differences in renewable energy uptake across the economies, and find that increases in renewable energy are associated with levels of economic growth, causing emissions to fall.

However, using renewable energy to reduce carbon emissions is not guaranteed to have this effect. The development of financial systems in a country can act as a key moderator of how renewable energy use translates into lower CO₂ emissions. [R33] contend that, in the absence of strong financial systems, the influx of capital through green technology investments will fall short of its true potential to reap the rewards of these new technologies, limiting their positive impact on the environment. [11] Low levels of financial inclusion are preventing access to capital by renewable projects in Sub-Saharan African economies. Similarly, [R34] emphasizes the need for domestic credit to promote financial development for renewable energy projects. [20] highlight that the country’s institutional financial structure variable in Ghana increased the benefits of using renewables and producing lower overall emissions.

Beyond finance, governance and policy frameworks, which play a pivotal role, good governance, effective environmental policies, and dedicated incentives are instrumental in facilitating the uptake of cleaner practices and low-carbon technologies [R35,R36]. The studies conducted by [R37,R38] highlighted the important role of effective governance and access to environmentally friendly technology on greenhouse gas emissions through energy consumption. [R39] identified several obstacles to the development of renewable energy systems, including difficulty in securing funding and challenges with implementing policies related to renewable energy technologies. There is a clear need to converge solutions across a range of levels as existing renewable energy capacity increases, integrating clean technology, financial development, and institutional reform.

In sum, the increase of renewable energy will result in increased reductions in CO₂ emissions while contributing to sustainable development in Africa’s economy. The extent to which renewable energy can contribute to sustainable development in Africa will depend on the existence of functioning financial systems that are functioning effectively: financial Inclusion, the inclusion of private companies, and governance. Because of this, the creation of clean energy systems must consider all of the above as elements that will enhance coordination among the financial systems of both public and private energy markets and the public policy frameworks that have the power to reduce CO₂ emissions within African economies.

By promoting clean investments and enabling high-carbon growth, stock market development influences CO₂ emissions. Domestic credit plays a significant role in these dynamics. The expansion of stock markets allows them to mobilize and allocate funds to renewable energy projects and other low-carbon activities through instruments such as green bonds and sustainability-linked securities, thereby contributing to emissions reductions [13,R40]. A more developed capital market can also provide firms with capital for research & development to adopt clean technologies and other eco-innovations that lower CO₂ emissions [14,21]. However, without strong environmental regulations, growth of financial activity may spur industrial activity in polluting industries with emissions increasing in line with the Environmental Kuznets Curve until reaching that GDP threshold [24,R41].

Domestic credit to the private sector modulates these pathways: well-allocated credit can reinforce the green-finance channel, enhancing firms’ capacity to invest in sustainable ventures, while credit growth directed toward traditional industries may magnify carbon-intensive output [25]. In sub-Saharan Africa, where capital markets and credit markets are comparatively new in development, institutional quality and governance are important to determining outcomes; whether stock market and credit growth contribute to environmental sustainability or preventively lock economies in high emission pathways [R42]. Overall, it is important to get stock market development and domestic credit that underpins it in step with regulations to harness positive green investment pathways and simultaneously manage negative emissions pathways.

This research aims to explore the effects of stock market development and renewable energy on CO₂ emissions in Africa, including the moderating effect of domestic credit across nine African countries with stock markets, using the available data on the core variables required for the empirical analysis.

The study’s empirical model draws on earlier studies, with key guidance taken from [R43,R44].

Where ln⁡CO2,it is carbon emission, SMC_it is stock market development, REN_it is renewable energy, X_it is a set of control variables (economic growth, industrialization, education, and trade openness).

All variables are converted to natural logarithms for more precise estimation [R45]. The baseline empirical specification is expressed as:

With i and t representing country and year, respectively, ln⁡CO2,it is used to denote carbon emission. The two core explanatory variables are ln⁡SMCit, which is stock market development, and ln⁡RENit, which is renewable energy. ln⁡EGit, ln⁡INDit, ln⁡EDUit, and ln⁡TOit are used as control variables. θi captures country-fixed effects, δ_t captures time-fixed effects, and ε_it denotes an unobserved error term.

The study will further check the moderating effect of domestic credit (DC) on carbon emission. To examine moderation, the model is expanded to include an interaction term as follows:

The study employs panel data for nine African countries (Table 1) over the period 2000-2024 and uses eight variables, as defined in Table 2.

Before estimation, the study applies Pesaran [46] cross-sectional dependence test to assess whether shocks are correlated across countries and to guide the choice of appropriate panel estimation methods.

Where M is the number of countries, L is the number of time periods, and r_ij denotes the sample correlation between the residuals of countries i and j.

At conventional significance levels, if we reject the null hypothesis H₀: r_ij = 0 ∀ i ≠ j, we have identified cross-sectional dependence and should proceed to use second-generation panel techniques.

After the cross-sectional dependence test, the study proceeds with stationarity to retain regression results that are valid, reliable, and meaningful, to avoid spurious correlations, and establish correct model specification. If cross-sectional dependence is confirmed, the study employs second-generation panel unit-root tests to assess stationarity and minimize the likelihood of spurious regression outcomes [47]. The study used the Breitung test for testing the variables for stationarity because it yields a lambda statistic robust to possible cross-sectional dependence, making it a necessary approach in the case of CSD.

To address potential cross-sectional dependence (CSD), the study applies both first- and second-generation panel unit-root tests:

The individual t-statistic from (2), denoted Si(L,M), is averaged to form the GIPS statistic:

Rejection of stationarity for the series implies they are integrated of order one.

Given non-stationarity and significant CSD, the study employs Westerlund [49] second-generation panel cointegration tests to test long-run equilibrium relationships that are valid in the presence of CSD. If we reject the null of no cointegration, this confirms that we are estimating a stable long committee.

After the pre-estimation tests, the study employed the feasible generalized least squares (FGLS) methodology of [47,50]. The Parks-Kmenta FGLS approach is well-suited to panels that exhibit individual effects, heteroskedasticity, serial correlation, and cross-sectional dependence [47,50,51], as is characteristic of the dataset in the study. The study also employed the panel-corrected standard error (PCSE) technique of [52] as a robustness test, as it is an alternative method for handling short and wide panels, which the study is conducting. The PCSE approach retains the OLS point estimates. Still, it adjusts the standard errors to better reflect heteroskedasticity and contemporaneous correlation, helping to mitigate the tendency of FGLS to understate standard errors in finite samples [52].

Table 3 displays the summary statistics of the dataset, providing information on how the data is distributed and varies across various metrics. The mean values offer insights into the average values of indicators like CO₂ emissions at around 3.4974, while stock market development and renewable energy used averaged 3.4382 and 3.0708, respectively. On the other hand, the standard deviation values indicate variations within the dataset with variables showing a notably lower dispersion around their mean, pointing to small disparities among the observations. To directly compare the variables measured on multiple scales, we have also calculated the Coefficient of Variation (CV = SD/Mean); these results again indicate that CO₂ has relatively greater variability than EG and IND, with a CV of 0.4516 versus 0.0751 and 0.0589, respectively. The minimum and maximum stats highlight the data's range. Moreover, all the variable correlation coefficients are less than 0.75; in which 0.8 as the threshold used in the literature; This indicates that the model is unlikely to suffer from serious multicollinearity among the explanatory variables [53]. The variance inflation factors reported in Table 4 provide additional reassurance: the maximum VIF is 3.85, and the mean VIF is 3.26, both well below the commonly cited threshold of 10, indicating that multicollinearity is not a major concern in the estimated models.

As shown in Table 5, the Pesaran [46] CD test indicates significant cross-sectional dependence (p < 0.01). Unit-root results from Breitung and Pesaran [48] CIPS imply that the variables become stationary after first differencing, and the Westerlund [49] panel tests support a long-run relationship.

Table 6 presents the results from Equations (2) and (3) with columns (1), (2), and (3) as estimates based on the FGLS estimation technique, while columns (4), (5), and (6) are used to demonstrate thorough robustness for estimating models based on the PCSE method. Here we limit to the discussion in regard to our three main informative variables: stock-market capitalization (SMC), renewable energy usage, domestic credit to private sector (DC), and their interaction (SMC*DC).

Across both specifications, SMC is positively and statistically significantly associated with CO₂ emissions. It is observed from baseline (column 1) that, a 1 percent increase in SMC is followed by 0.0957 percent increase in CO₂ emissions. With inclusion of DC in column 2, coefficient of SMC is slightly reduced to 0.0856 and still significant at 5% suggesting that part of stock-market effect operates through the wide credit channel. When the SMC*DC interaction term is included in column (3), the SMC coefficient is increased to 0.119. Importantly, the interaction SMC*DC coefficient itself is large and highly significant, indicating that a one-point increase in DC contributes 0.252% to the marginal effect of SMC on CO₂ emissions.

In addition, the positive and significant domestic credit (DC) coefficient in columns (2) and (3) shows that deeper credit markets independently lead to higher emissions, possibly promoting further industrial and energy-intensive activity. Moreover, the significance and positive coefficient of the SMC*DC interaction are evident, indicating that the carbon-intensifying effect of stock-market growth is even stronger in more advanced credit markets.

Across all specifications, REN has a statistically significant negative association with CO₂ emissions. Demonstrating that promoting investment in renewable energy goes a long way in mitigating environmental degradation. All models were additionally controlled for economic growth (EG), industrial share (IND), education (EDU), and trade openness (TO). Estimates of the PCSE in columns (4)-(6) have the same sign and similar statistical significance for SMC, DC and SMC*DC confirming that the core findings are robust with respect to both, heteroskedasticity and contemporaneous correlation among the disturbances in the panel.

In conclusion, stock-market development in Africa leads to increases in CO₂ emissions and, independently and interactively, magnifies this effect. Hence, the demand for green finance frameworks to influence equity and credit expansion towards lower-carbon investments in African capital markets becomes paramount.