Exploring a symmetric nexus between financial development and renewable energy consumption in South Africa

Abstract

Global economic development is closely linked to energy use, yet rising fossil-fuel consumption has intensified carbon dioxide (CO₂) emissions and environmental degradation. This study examines the long-run and short-run relationship between renewable energy consumption and financial development in South Africa over the period 1990–2024 using the Autoregressive Distributed Lag (ARDL) model. The variables included are renewable energy consumption, financial development, economic growth, capital formation, and inflation. Financial development is proxied by credit extension, monetary aggregate (M3), and stock exchange transactions. The results confirm the existence of a long-run equilibrium relationship among the variables. Financial development has a positive and statistically significant effect on renewable energy consumption in both the short run and long run, indicating that deeper and more efficient financial markets improve access to capital for renewable energy projects. Economic growth and capital formation also promote renewable energy expansion, while inflation negatively affects renewable energy consumption by raising investment costs and uncertainty. The study recommends that the government should promote an environmentally friendly economy by expanding green finance instruments, introducing tax incentives and subsidies for clean energy investments, and strengthening regulatory certainty. Public-private partnerships should be encouraged to mobilise private capital for solar, wind, and biomass infrastructure. Maintaining macroeconomic stability and improving financial inclusion are also essential for achieving sustainable growth, energy security, and climate change mitigation in South Africa.

1 Introduction

Climate change and widening global inequality continue to intensify debates on the drivers of sustainable economic growth. There is growing evidence that developing economies bear disproportionate climate-related costs due to weaker adaptive capacity, infrastructure deficits, and constrained fiscal resources, resulting in adverse impacts on productivity, livelihoods, and public health (IPCC, 2023; World Bank, 2024). Within this context, energy remains a central pillar of economic and social development. It underpins industrial production, infrastructure expansion, and improvements in living standards, and is widely recognised as a strategic input into long-term development (Kaygusuz, 2012). However, the historical dominance of fossil fuels such as coal, oil, and gas has entrenched carbon-intensive growth patterns, making energy systems a major contributor to greenhouse gas emissions and environmental degradation.

The shift toward renewable energy has therefore emerged not merely as an environmental imperative but also as a structural economic transformation. Since the early twenty-first century, scientific and policy attention has increasingly focused on renewable energy sources as viable alternatives capable of enhancing energy security, reducing greenhouse gas emissions, and supporting sustainable growth (Kariuki, 2018; Rifkin, 2011). Renewable energy investment is also viewed as a channel for improving air quality, reducing import dependence, stimulating job creation, and promoting macroeconomic resilience (Mukhtarov et al., 2020). Yet despite the expanding role of renewables globally, financing constraints remain among the most binding obstacles to large-scale deployment. Renewable energy projects are typically capital intensive, require long-term funding horizons, and are highly sensitive to borrowing costs, credit conditions, and capital market development. This places financial development at the centre of the renewable energy transition debate.

The South African case illustrates the urgency of this transition. Although renewable energy capacity has expanded in recent years, the electricity system remains heavily dependent on fossil fuels. Recent estimates show that 83% of electricity generation in 2024 was derived from fossil fuels, while only 17% came from low-carbon sources such as solar, wind, hydro, and nuclear. Coal alone accounted for approximately 82% of total electricity generation in 2024 (Ember, 2025). This continued reliance on coal has coincided with persistent reliability challenges linked to ageing generation infrastructure, maintenance backlogs, delayed investment, and grid constraints. Since 2007, recurring electricity shortages and load-shedding have disrupted industrial production and household welfare, exposing structural weaknesses in the coal-dependent electricity system (Khobai et al., 2016). Eskom began implementing electricity rationing in late 2007 due to insufficient generation capacity. Earlier studies had already warned that delays in restructuring the electricity supply industry would result in persistent electricity shortages and declining system efficiency (Eberhard, 2001; Gaunt, 2008; Khobai, 2013). Although some recent operational improvements have been recorded, electricity supply remains vulnerable to plant outages, transmission limitations, and maintenance pressures.

Electricity price escalation has compounded these supply challenges. Historically, electricity tariffs remained below inflation between 1988 and 2007, with cumulative tariff increases of 223% compared to inflation of 335% over the same period. However, following the 2008 electricity crisis, tariff adjustments accelerated sharply. Between 2007 and 2019, electricity tariffs increased by approximately 446%, compared to inflation of 98%, while by 2021 cumulative tariff increases since 2007 had reached roughly 520%, meaning electricity prices had more than quadrupled in just over a decade. More recent evidence indicates that the upward trend has persisted, with consumer inflation rising by approximately 125% between 2008 and 2024, whereas electricity tariffs increased by nearly 860% over the same period (NERSA, 2024; Statistics South Africa, 2024). These increases were largely driven by Eskom’s mounting debt burden, infrastructure expansion costs, maintenance backlogs, and repeated applications to the regulator for substantial tariff hikes to finance operational and capital obligations. Consequently, households and firms now face electricity costs that have risen significantly faster than general prices, intensifying production costs, weakening competitiveness, and constraining household purchasing power. Persistent load-shedding, tariff escalation, and financial stress within the electricity sector collectively undermine economic performance and highlight the structural weaknesses of a fossil-fuel-dependent energy system.

While energy conservation policies may improve efficiency and reduce emissions (Ertugrul et al., 2014), in an economy heavily reliant on energy-intensive production, poorly designed conservation measures can suppress growth if alternative energy sources are not adequately developed. For South Africa, therefore, the central challenge is not simply reducing energy consumption but transforming the energy mix toward reliable, affordable, and sustainable renewable sources. This transition requires substantial capital mobilisation, technological upgrading, and institutional coordination, all of which are closely linked to the depth and efficiency of the financial system.

Despite extensive global research on the finance–energy nexus, a critical gap remains in the South African context. Existing studies largely examine aggregate energy consumption rather than renewable energy specifically, or rely on panel data approaches that may obscure country-specific structural characteristics. There is limited time-series evidence that rigorously investigates how financial development interacts with renewable energy consumption within South Africa’s unique institutional, structural, and supply-constrained environment. Given the country’s persistent power shortages, escalating electricity tariffs, and climate commitments, understanding whether financial development facilitates renewable energy expansion or whether renewable energy growth itself stimulates financial deepening becomes essential for policy design.

This study addresses this gap by examining the relationship between financial development and renewable energy consumption in South Africa using annual time-series data for the period 1990–2024. By focusing explicitly on renewable energy rather than aggregate energy use, the analysis aligns the empirical investigation with the structural transition challenge facing the country. The study contributes context-specific evidence on whether financial development can act as a catalyst for renewable energy deployment in an energy-constrained, coal-dependent emerging economy. In doing so, it informs both financial sector reform and energy transition policy, offering insight into how South Africa can simultaneously address electricity supply instability, rising tariffs, and climate mitigation commitments through a coordinated finance energy strategy.

The remainder of this paper is organised as follows: Section 2 discusses the literature review, followed by the explanation of methodological frameworks and data sources in Sections 3, 4 covers the estimations and interpretation of the results. Section 5 concludes the study.

2 Literature review

There is extensive evidence supporting the existence of a causal relationship between financial development and the use of alternative energy from different perspectives. Based on the findings of Sadorsky (2010), Mahalik et al. (2017), and Kassi (2020), financial development can influence energy consumption through several channels. First, improvements in the financial system enhance the mobilization and allocation of funds to the private sector by reducing borrowing costs and facilitating access to credit for profitable investments and energy-intensive technological innovations. As financial institutions expand credit to the private sector, firms are able to increase their assets, physical capital, and production capacity, which in turn stimulates industrial activity and increases the demand for energy through the production of electric machinery, expansion of manufacturing processes, and increased energy usage in workplaces.

Second, the development of modern financial instruments such as mobile banking, digital payments, electronic cards, and other online financial services improves the efficiency of financial transactions and enhances financial inclusion. These advancements encourage households and businesses to increase spending on energy-consuming goods and services, including vehicles, residential properties, and household appliances such as air conditioners, refrigerators, and washing machines, thereby contributing to higher energy consumption. Third, financial sector development reflects broader economic prosperity and strengthens consumer and business confidence, which improves the overall investment climate. The expansion of stock markets, bond markets, and other financial institutions improves access to external financing for firms and facilitates capital accumulation. As a result, financial development stimulates economic activity, which subsequently increases energy demand.

Empirical research examining the relationship between financial development and renewable energy consumption has produced mixed results across different countries, time periods, and econometric methodologies. Mukhtarov et al. (2020) investigated the relationship between renewable energy consumption and financial development in Azerbaijan over the period 1993–2015 using the Autoregressive Distributed Lag (ARDL) model while controlling for economic growth and energy prices. Their findings revealed that both economic growth and financial development exert a positive and statistically significant effect on renewable energy consumption. Similarly, Dimnwobi et al. (2022) analysed the relationship between financial development and renewable energy consumption in Nigeria for the period 1981–2019 using the ARDL approach and confirmed that financial development plays a crucial role in promoting renewable energy consumption.

Zhe et al. (2021) examined the relationship between renewable energy consumption, financial development, and economic growth in Turkey between 1990 and 2015 using a Vector Autoregressive (VAR) model. Their results indicated that while renewable energy consumption significantly promotes financial development, neither renewable energy consumption nor financial development has a statistically significant impact on economic growth. In Burakov and Freidin (2017) employed the Granger causality test and the Vector Error Correction Model (VECM) to examine the relationship between financial development, economic growth, and renewable energy consumption from 1990 to 2014. The results indicated no causal relationship between renewable energy consumption and financial development; however, a bidirectional causal relationship was found between economic growth and financial development.

Similarly, Ari and Cergibozan (2017) analysed the relationship between economic growth, renewable energy consumption, financial development, trade openness, and capital for Turkey over the period 1960–2011. Using the ARDL model and VECM Granger causality analysis, the study confirmed the existence of a long-run relationship among the variables and identified a unidirectional causality running from renewable energy consumption, capital formation, trade openness, and financial development to economic growth.

Eren et al. (2019) examined the impact of financial development and economic growth on renewable energy consumption in India for the period 1971–2015. Maki (2012) cointegration test and Dynamic Ordinary Least Squares (DOLS), the study found a long-run equilibrium relationship among renewable energy consumption, financial development, and economic growth. The results further revealed that both economic growth and financial development positively influence renewable energy consumption, while the VECM Granger causality results suggested a long-run unidirectional causality running from financial development to renewable energy consumption and economic growth.

Razmi and Salari (2020) investigated the relationship between stock market development, economic growth, and two forms of renewable energy consumption in Iran between 1990 and 2014 using the ARDL bounds testing approach. Their findings indicated that stock market development positively affects renewable energy consumption in the long run, while economic growth significantly influences renewable energy consumption in both the short and long run. In China, Wang et al. (2021) explored the relationship between renewable energy consumption, financial development, and economic growth using the ARDL-PMG model for the period 1997–2017. The study found that financial development negatively affects renewable energy consumption at the national level, whereas economic growth has a positive influence. Granger causality results further revealed a unidirectional causality from financial development to renewable energy consumption. Wu and Broadstock (2015) examined the impact of institutional quality, financial development, and economic growth on renewable energy consumption across 22 emerging economies between 1990 and 2010 using the dynamic system Generalized Method of Moments (GMM) estimator. Their results demonstrated that institutional development and financial development positively influence renewable energy consumption, suggesting that coordinated institutional and financial reforms are necessary to support renewable energy expansion.

Further evidence is provided by Kutan et al. (2018), who analysed the effects of foreign direct investment and stock market development on renewable energy consumption in Brazil, China, India, and South Africa from 1990 to 2012. Using several panel econometric techniques, the study found that both foreign direct investment and stock market development positively influence renewable energy consumption. Qamruzzaman and Jianguo (2020) examined the nonlinear relationship between financial development, trade openness, capital flows, and renewable energy consumption for a panel of countries covering the period 1990–2017 using the panel Non-linear ARDL approach. The results revealed the presence of long-run asymmetric relationships among the variables and indicated that financial development, trade openness, and capital flows drive renewable energy consumption in the long run.

Hassine and Harrathi (2017) analysed the relationship between renewable energy consumption, economic growth, trade, and financial development in Gulf Cooperation Council countries for the period 1980–2012 and found no causal relationship between renewable energy consumption and private sector credit, although exports and financial development significantly influenced renewable energy use. Similarly, Alsaleh and Abdul-Rahim (2019) evaluated the impact of financial development on bioenergy consumption in eight European Union countries between 1990 and 2013 using the panel ARDL model. Their results confirmed that financial development significantly increases bioenergy consumption. Anton and Nucu (2020) also found that financial development significantly promotes renewable energy consumption in 28 European Union countries, while Khan et al. (2020) demonstrated using panel quantile regression that financial development positively influences renewable energy consumption and reduces carbon emissions across different quantiles. In addition, Kim and Park (2016) found that countries with more developed financial markets experience faster growth in renewable energy technologies because such projects rely heavily on external financing.

More recent studies continue to emphasize the importance of financial systems in facilitating renewable energy investments. Moyo (2023) analysed the relationship between financial development and renewable energy consumption in Sub-Saharan Africa and found that financial sector expansion significantly promotes renewable energy adoption by improving access to capital and supporting investment in clean energy technologies. Similarly, Mensah and Kaseke (2023) showed that financial deepening enhances renewable energy deployment in African economies by facilitating investment flows and technological innovation within the energy sector. Alsagr (2023) examined the asymmetric effects of financial efficiency on renewable energy investment and found that improvements in financial efficiency significantly stimulate renewable energy investment, although the magnitude of the effect differs across positive and negative shocks.

Prempeh et al. (2024) investigated the impact of financial development on renewable energy consumption in 38 Sub-Saharan African countries using panel econometric techniques and found that financial development significantly promotes renewable energy consumption by improving investment capacity and supporting the transition toward cleaner energy sources. Similarly, Alshagri et al. (2024) analysed the relationship between financial development and renewable energy consumption in developed economies and reported that well-functioning financial markets significantly facilitate renewable energy deployment through improved access to financing for green energy projects. However, Saadaoui and Chtourou (2024) showed that the relationship between financial development and renewable energy consumption may vary across countries depending on the structure of the financial system, with some financial systems still favouring investments in fossil-fuel-based energy sectors.

More recent empirical evidence further strengthens the finance-renewable energy nexus. Demirtas et al. (2025) examined the relationship between financial development and renewable energy consumption in the United Kingdom using wavelet and Fourier-based econometric methods and found that financial development significantly stimulates renewable energy consumption in both the short and long run. Tambari et al. (2025) analysed the relationship between financial development and renewable energy generation across oil-exporting and oil-importing economies using the Method of Moments Quantile Regression and reported that financial development significantly promotes renewable energy generation, particularly in oil-importing countries. Similarly, Abubakar et al. (2025) demonstrated that financial development and institutional quality play an important role in supporting renewable energy consumption in Sub-Saharan Africa. In addition, Asongu et al. (2025) found that financial inclusion enhances renewable energy adoption by improving access to financial services and facilitating investments in green energy technologies. These recent studies collectively highlight the critical role of financial development in accelerating renewable energy consumption and supporting the global transition toward sustainable energy systems.

Despite the growing body of empirical literature examining the relationship between financial development and renewable energy consumption, several important gaps remain. First, many existing studies focus primarily on panel analyses across multiple countries, which often mask country-specific structural characteristics and institutional differences that may influence the finance-renewable energy nexus. While studies such as Prempeh et al. (2024), Moyo (2023), and Abubakar et al. (2025) provide valuable cross-country evidence, their findings may not fully capture the dynamics within individual economies. Second, although recent research has increasingly explored nonlinear and asymmetric relationships between financial development and renewable energy consumption (Alsagr, 2023; Tambari et al., 2025), limited attention has been given to country-specific time-series analyses that examine how financial sector developments influence renewable energy consumption within the context of emerging economies facing energy supply constraints. Third, in the case of South Africa, most existing studies focus on the broader energy-growth nexus or environmental sustainability, with relatively few studies examining the specific interaction between financial development and renewable energy consumption. Given South Africa’s heavy dependence on fossil fuels, persistent electricity shortages, and the growing need to transition toward sustainable energy sources, understanding the role of financial development in facilitating renewable energy consumption is particularly important. Therefore, the current study contributes to the literature by providing country-specific time-series evidence on the relationship between financial development and renewable energy consumption in South Africa. By applying the Autoregressive Distributed Lag (ARDL) modelling framework, the study examines both the short-run and long-run dynamics among financial development, renewable energy consumption, economic growth, capital formation, and inflation. In doing so, the study provides new empirical insights into the finance-energy nexus within the South African context and contributes to the policy debate on how financial sector development can support the transition toward a more sustainable and resilient energy system.

3 Methodology

3.1 Model specification

The study’s main objective is to determine the nexus between renewable energy consumption and financial development. Drawing from this main objective and from the previous literature (Mahalik and Mallick, 2014; Burakov and Freidin, 2017; Mukhtarov et al., 2020), the study specifies a partial model depending on the relevance of the variables. The following model (Equation 1) expresses the link between renewable energy consumption, financial development, Gross Domestic Product, Inflation, and gross fixed capital formation.where; LREt is the natural log of renewable energy consumption (measured as percentage of total final energy consumption, LFDt is the natural log of financial development (measured by three proxies–Monetary aggregate, stock exchange transactions and credit extension), LGDPt denotes the natural log of gross domestic product per capita (measured as real gross domestic product using constant prices of 2010), LCPIt is the natural log of consumer price index, LKt is the natural log of capital formation (measured as Gross fixed capital formation) and ηt is the error term. For the purpose of this study, the equation is expressed in log forms.

3.2 Data collection

The study employs annual time-series data spanning the period from 1990 to 2024. The dataset is compiled from multiple reputable secondary sources. Renewable energy consumption, which serves as the dependent variable, is obtained from the International Energy Agency (IEA) and is measured as renewable energy consumption as a percentage of total final energy consumption. Data on financial development (FD) and gross domestic product (GDP) are sourced from the South African Reserve Bank (SARB) database, while data on capital formation are obtained from the World Development Indicators (WDI) of the World Bank. Financial development is proxied using three indicators, namely, the monetary aggregate (M3), stock exchange transactions, and credit extension. Economic growth is measured using real GDP per capita (2010 constant USD), while capital formation is represented by gross fixed capital formation. Inflation data are sourced from the South African Reserve Bank and are proxied by the consumer price index (CPI).

3.3 Unit root

Prior to testing for the presence of a long-run cointegration relationship, it is necessary to examine the time-series properties of the variables to determine whether they are stationary or non-stationary. In this study, the stationarity of the variables is assessed at both levels and first differences using three commonly applied unit root tests: the Augmented Dickey-Fuller (ADF) test (Dickey and Fuller, 1979), the Phillips-Perron (PP) test (Phillips and Perron, 1988), and the Dickey-Fuller Generalized Least Squares (DF-GLS) test (Elliott et al., 1996). Employing these complementary tests strengthens the robustness of the unit root analysis, as each procedure relies on different estimation techniques and assumptions, thereby providing more reliable evidence on the integration properties of the variables.

3.4 Co-integration test

3.4.1 ARDL model

The study uses the ARDL limits testing approach to co-integration developed by Pesaran et al. (2001) to evaluate the link between financial development and renewable energy usage in South Africa. This approach is applied since this method offers many advantages over other traditional techniques such as Engle and Granger’s (1987), and Juselius (1990) technique. For example, these two traditional co-integration techniques estimate long-run relationships in the context of a system of the equations, whereas on the other hand the ARDL technique utilizes only a single decreased form of equation (Pesaran and Shin, 1999).

Moreover, the ARDL method does not entail pre-testing variables. This means that the test analyses the long-run equilibrium relationship between variables, regardless of whether the underlying repressor’s are entirely I (0), I (1) or moderately integrated (Pesaran et al., 2001; Bahmani-Oskooee and Ng, 2002). Thus, the ARDL estimation evades the complication of non-stationary time series data. This attribute alone, given the features of the recurrent elements of the data, made the other standard co-integration methods inappropriate. The use of existing unit root tests to detect the co-integration order was very uncertain (Pradhan et al., 2014). The ARDL technique eradicates the need to utilize the huge number of requirements in other standard co-integration tests. These incorporate decisions concerning the addition of a number of variables (Both explained and explanatory), the treatment of deterministic components, the choice of log lengths, etc. (Duasa, 2007).

The empirical results attained in tests such as co-integration are highly sensitive to the chosen technique and the various alternative options available in the estimation method (Pesaran and Shin, 1999). With the ARDL, different variables can have distinct optimal lags, which cannot be conceived by other standard co-integration tests. An adequate number of lags to record the data-generating process in a general-to-specific modelling framework (Laurenceson and Chai, 2003). Most importantly, the ARDL provides better results for small sample data (Haug, 2002).

In recent studies, this paradigm has grown in popularity. In its most basic form, the ARDL model entails estimating the conditional error correction models listed below in Equations 2–6:where denotes the natural logarithm of renewable energy consumption, represents the natural logarithm of financial development, refers to the natural logarithm of gross domestic product, denotes the natural logarithm of inflation, and represents the natural logarithm of capital formation. The time period and the first difference operator are denoted by T and Δ, respectively. The residuals (ε1t, ε2t, ε3t, ε4t, ε5t) are considered to be regularly distributed and white noise.

By setting the coefficients of one period lagged levels of the independent variables to zero, an F-test (Wald test) is performed to establish the presence of a long run connection between the variables. The null hypothesis of no co-integration among the variables is H₀: α_RE = α_FD = α _GDP = α_CPI=α_K = 0 tested against the alternative hypothesis H₁: α_RE ≠ α_FD ≠ α_GDP ≠ α_CPI ≠ α_K ≠ 0. In order to accept or reject the null hypothesis, the value of the F-test is compared to critical value bounds. Lower critical bound values are computed assuming that all variables in the regression equation are I (0), whereas higher critical bound values are computed utilising the assumption that all variables in the regression equation are I (1). As a result, the critical value bounds for all classifications of repressors into solely I (0), purely I (1), or mutually co-integrated are provided by the two sets of critical values I (1).

As a result, the H0 is rejected if the predicted F-statistics exceed the upper critical bound value, and the findings show that co-integration is the best option. On the other hand, H₀ cannot be rejected if the F-statistics falls below the lower critical bound value. Finally, if the F-statistics fall between the two boundaries, the co-integration test is rendered ineffective.

After establishing a long-term relationship between the variables, the following stage is to look into the long-term and short-term relationships between the variables of interest. The following equation is constructed using the ARDL technique to investigate the long-term link between variables (Equation 7):

3.4.2 Error correction model (ECM)

The study estimates the error correction model, which is constructed as follows, in order to further explore the short run dynamics from the ARDL model and confirm the existence of co-integration established in the ARDL model.:

If the ECM coefficient in the equation is negative and significant (Equation 8), there is a long-term relationship between the variables. This also refers to the rate at which the equilibrium is restored.

4 Findings of the study

4.1 Unit root tests

Table 1 presents the results for the Augmented Dickey Fuller (ADF), Phillips-Perron and Dickey-Fuller Generalised Least Squares (DF-GLS). The results obtained from ADF and PP tests show that the some variables are stationary at the levels while others are stationary at first difference at 1% and 5% level of significance. The DF-GLS test was also conducted to support that the difference of the series are stationary. According to Table 1, the DG-GLS confirmed that some variables are stationary at levels while others are stationary at first difference. This implies that the variables are integrated at both I (0) and I (1). Since the variables were found to be stationary either in level or first difference, the next step is to determine whether there is a long run relationship among the variables. The ARDL cointegration test is used because the mixed unit root findings allow for the integration of variables with diverse ordering.

4.2 Co-integration

This section investigates co-integration between the variables, whether there is a long-run correlation between the variables. The ARDL model is utilised. It is pertinent since the series was found to be I (0) or I (1). It is essential to first find the maximum lag length. The findings are presented in Table 2 using the LR, FPE, AIC, SC and HQ. According to Sajid et al. (2019) the Akaike Information Criteria (AIC) has been shown to be the most effective in selecting the number of lags. This has led to the choice of the AIC to select the number lags. The AIC identified the maximum order of lags as 2 in the ARDL model, which is also supported by FPE and HQ.

The results of the ARDL bounds test are illustrated in Table 3. The findings illustrated the calculated F-statistics of 5.46 (model 1), 5.00 (model 2) and 4.82 (model 3), which are greater than the critical value of the upper bound of 1% level of significance. The indicates the rejection of the null hypothesis of no long run relationship among the variables. This means that there is an existence of a long run relationship between renewable energy consumption, financial development, capital formation, economic growth, and inflation in South Africa. These results are consistent to the findings of Zhe et al. (2021) for Turkey and Wu and Broadstock (2015) for 22 emerging markets countries. The next step after establishing the existence of a long run relationship among the variables is to determine the long-run and short run estimates.

Table 4 presents the results for the estimation of the long run results for the three models. The results indicate that the credit extension, monetary aggregate (M3) and stock exchange transportation as measures of financial development, have a positive impact on renewable energy consumption at 5%, 1% and 10% level of significance, respectively. The results confirm the findings of Kutan et al. (2018) who showed that financial development has a positive effect on renewable energy consumption in Brazil, China, India, and South Africa. Financial development affects renewable energy consumption positively by providing means for companies especially ESKOM and other energy generating companies to raise funds for investment purposes (Levine and Zeros, 1998). The results further show that inflation has a negative and significant effect on renewable energy consumption at 5% level of significance (Model 1). Specifically, a 1% increase in inflation will lead to 0.01% fall in renewable energy consumption.

Model 1 further posited that economic growth has a positive and significant effect on renewable energy consumption and the results are such that a 1% increase in economic growth will lead a 0.11% increase in renewable energy consumption. These findings confirm the results of Azeakpono and Lloyd (2020) for Nigeria and Khobai and Le Roux (2018) for South Africa. The results are similar to the findings of Model 2 illustrates that capital formation has a positive and significant effect on renewable energy consumption at 5% level of significance. The results are such that a 1% increase in capital formation will result in renewable energy consumption increasing by 0.122%.

The short run estimates are presented in Table 5. The coefficient of the error term is negative and significant at 1% level for all the three models thus confirming the existence of a long run relationship between the variables and also suggest that the system will ultimately return to equilibrium in the long-run. Therefore, disequilibrium will be corrected in the long run by means of short run adjustments, with a coefficient of 0.271, 0.231 and 0.090 for model one, model two and model three, respectively, which shows the speed of adjustment towards equilibrium. Financial development (all the three proxies) has a positive and significant effect on renewable while inflation remain negatively and significantly related to renewable energy consumption in the short run. These results are consistent with findings of Mukhtarov et al. (2020) for Azerbaijan, Capital formation has a positive and significant effect on renewable energy consumption for model two and three only while economic growth significantly impact renewable energy consumption for model two only.

4.3 Short-run diagnostics

To ascertain the strength of the specified regression, diagnostics tests for autocorrelation, heteroscedasticity and normality of the residuals are carried out and the findings are illustrated in Table 6. The results show that there is no serial correction, no presence of heteroscedasticity and that the residuals are normally distributed. This is on account that the probability of all tests are greater than 5% level of significance. Therefore, the study fails to reject the null hypothesis and concludes that all the models pass the diagnostic tests.

4.4 Stability

To test for the stability of the coefficients the cumulative sum of recursive residuals (CUSUM) and the cumulative sum of recursive residuals squares (CUSUMsq) are performed in all the models. The CUSUM and CUSUMsq graphs are illustrated in Figure 1. The plots fall within the 5% confidence interval for all the models, which indicates that we fail to reject the null hypothesis at 5% significance level and conclude that the model is stable.

5 Conclusion

This study examined the relationship between financial development and renewable energy consumption in South Africa using annual time-series data for the period 1990–2024. The Autoregressive Distributed Lag (ARDL) bounds testing approach was employed to analyse both the short-run and long-run dynamics between renewable energy consumption, financial development, economic growth, capital formation, and inflation. The results revealed the existence of a long-run equilibrium relationship among the variables. Financial development was found to have a positive and statistically significant effect on renewable energy consumption in both the short and long run, indicating that improvements in the financial sector facilitate investment in renewable energy projects. The findings further showed that economic growth and capital formation positively influence renewable energy consumption by supporting the expansion of renewable energy infrastructure. In contrast, inflation was found to have a negative and significant impact on renewable energy consumption, suggesting that macroeconomic instability may discourage renewable energy investments. The causality results also revealed bidirectional relationships between renewable energy consumption and financial development, as well as between renewable energy consumption and economic growth. Overall, the results highlight the important role of financial sector development in promoting renewable energy expansion and supporting sustainable economic growth in South Africa.

This study makes an important contribution to the literature by providing updated country-specific evidence on the relationship between financial development and renewable energy consumption in South Africa using recent data extending to 2024 and multiple proxies of financial development. The findings offer new insights into how the financial sector can support the transition to a cleaner and more sustainable energy system in an emerging economy characterised by electricity shortages and heavy dependence on coal.

Policymakers should strengthen financial sector mechanisms that support investment in renewable energy projects by improving access to credit, expanding green financing instruments, and encouraging private sector participation. Financial institutions and regulators can further promote renewable energy development through instruments such as green bonds, climate finance initiatives, and concessional lending facilities tailored to clean energy investments. The government should also create an enabling policy environment by implementing investor-friendly policies, including tax incentives, subsidies, and supportive regulatory frameworks. Strengthening public-private partnerships and promoting the participation of independent power producers can enhance electricity generation capacity and reduce pressure on the national utility. Maintaining macroeconomic stability, particularly controlling inflation, is also essential to encourage long-term investments in renewable energy.

These policy measures are particularly important for advancing South Africa’s Just Energy Transition by mobilising capital toward low-carbon technologies, supporting inclusive economic growth, and reducing the social and economic risks associated with the shift away from coal. They also align with South Africa’s green finance strategy and reinforce the country’s commitments under the Paris Agreement and other international climate frameworks. Finally, South Africa should diversify its energy mix by expanding renewable energy sources such as solar, wind, and biomass in order to enhance energy security and support climate change mitigation efforts.

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