The Impact of Financial Development and Economic Growth on Renewable Energy Supply in South Africa

Abstract

Eskom’s power plants in South Africa face frequent breakdowns due to a lack of maintenance and increasing energy demand. The high dependence of South Africa on coal for power generation, which is a resource that significantly contributes to carbon dioxide (CO₂) emissions that impact the environment negatively, could be reduced by considering renewable energy sources. Renewable energy supply, dependent on private sector funding and economic growth, is seen as a solution to energy and environmental problems. The study aimed to examine if financial development and economic growth impact renewable energy supply in South Africa and to discover if co-integration exists between these variables, including the variables defined as the determinants of renewable energy supply, namely: CO₂ emission by coal power generation; secondly, coal electricity supply; thirdly, coal price changes; and lastly, load shedding levels. The research gap identified for the study is twofold. Firstly, there is a lack of research on the relationship between renewable energy supply, financial development, and economic growth, specifically in South Africa. Furthermore, the existing research on these variables in other countries has produced inconclusive results. Secondly, minimal research has been conducted on how economic growth impacts renewable energy supply in emerging markets. Thus, the present study sought to bridge the gap and contribute to the scientific body of knowledge related to the drivers of renewable energy supply. The autoregression distributed lag (ARDL) model was employed to test if economic growth and financial development have a statistically significant impact on renewable energy supply, as well as to test the direction of the relationship, for an observation period from 1990 to 2021. The results proved that financial development and economic growth were reported to have a statistically significant positive impact on renewable energy supply in the long run and the short run. A study on the relationship between financial development, economic growth, and renewable energy supply in South Africa can influence policy reforms and assist the National Energy Regulator of South Africa (NERSA) and the government in developing and implementing renewable energy policies that encourage the deployment of renewable energy infrastructure to increase renewable energy supply, particularly regarding factors associated with addressing challenges in financial development and economic growth.

1. Introduction

There is a major global drive away from non-renewable to renewable energy sources. This is driven by multiple factors, including global regulation that requires countries and companies to reduce the use of non-renewable energy sources [1]. Large economies such as China, the United States of America (USA), and European Union (EU) countries have adopted net zero goals, which necessitate the transition away from fossil fuel to renewable energy sources. This is to address the existential threat of global warming. Additionally, renewable energy technologies are more adaptable and cost effective than coal-based ones. In this light, hydroelectric and wind power energy are now two of the most affordable energy sources globally. For example, globally, the cost of wind power ranges from USD 0.04 to USD 0.06 per kilowatt hour (kWh) [2], whereas fossil fuel ranges from USD 0.05 to USD 0.17 per kWh. In this light, the cost-effective nature of renewable energy sources addresses the need to reduce the rising cost of electricity globally.

Eskom’s power plants in South Africa are struggling due to increasing energy demand, hence the country should consider renewable energy sources to reduce coal dependence for power generation [3] South Africa has the potential for numerous renewable energy sources due to its location and population [4]. However, capital expenditure is a major obstacle to the utilisation of renewable energy sources in South Africa, hindering their broad deployment [5]. Moreover, South Africa’s low electricity rates, due to coal reserves and low feed-in rate, pose a challenge for renewable energy to be financially viable; this includes obtaining renewable energy licences becoming difficult and time consuming due to the involvement of multiple organisations and environmental impact assessment protocols in South Africa [6].

In 2022, the World Bank approved and funded a USD 497 million initiative to replace the Komati coal power station with renewable energy sources and batteries. The funding was split into a subsidy and concessional loan [7], while the European Investment Bank (EIB) and Development Bank of Southern Africa (DBSA) approved funding of EUR 400 million for private sector renewable energy development in South Africa at the Conference of the Parties to the United Nations (UN) Framework Convention on Climate Change (COP) 27 [8]. One of its largest pledges to a single nation was the Climate Investment Funds’ (CIF) investment in South Africa. South Africa’s USD 462 million Clean Technology Fund (CTF) investment strategy was intended to assist in overcoming the substantial initial capital costs, first-mover risks, and other obstacles to public and private investments in renewable energy supply [9].

Despite the funding initiatives mentioned above, funding for renewable energy in South Africa continues to be challenging due to disjointed energy policy guidelines and financial market uncertainty. The implementation of the Renewable Energy Independent Power Producer Procurement Programme has been successful, however, the bidding window interruptions negatively impacted investor views. [10] USD 12 trillion of renewable energy funding is available worldwide from organisations like the Green Climate Fund, however, developed nations receive most of it, making it difficult for developing nations to acquire funding [11].

Local and foreign investors must be enticed to offer funding for the growth of the renewable energy sector. Subsidies, loans, and even government policies can encourage the development of the renewable energy sector [4]. Investors need to offer funding for renewable energy growth through subsidies, loans, and green bonds to bridge the gap between public and private sector funding. Green bonds are seen as less risky than traditional bonds due to lower default rates [12].

Green bonds were introduced in 2007 and the World Bank was the first to offer them in 2008 [13]. They are a financial instrument with lower risk than traditional bonds, with a ten-year average default rate of 5.7% [12]. The requirements are that issuers must have a clause that the proceeds must be used for green investments [14]. To entice private sector funding, South Africa’s government introduced a green finance taxonomy to encourage the issuance of green bonds and reduce costs for private sector funding [11]. However, the South African government must do more than implement a green finance taxonomy. The government should develop green framework legislation to protect green bond issuers and encourage green banks. The government may redirect subsidies and resources to green projects or amend public procurement legislation to accommodate them [15].

The South African government should evaluate and monitor green bond issuance to ensure projects align with climate sustainability policies [16]. Implementing new policies on renewable energy is crucial for funding and addressing problems in the sector [4]. South Africa needs to further balance economic expansion and CO₂ reduction through renewable energy policies and global access to new technologies [16].

The study aims to discover if financial development and economic growth have an impact on renewable energy supply in South Africa. Hence, the research question of the study is: do financial development and economic growth have a statistically significant impact on renewable energy supply in South Africa? This study has three supporting objectives, which are: firstly, to determine whether financial development and economic growth have a statistically significant impact on renewable energy supply in South Africa; secondly, to identify the long-run relationship between renewable energy supply, financial development, and economic growth; and, lastly, to identify the short-run relationship between renewable energy supply, financial development, and economic growth.

This is achieved by implementing an ARDL. Several steps are followed, including unit root tests on all the variables, an ARDL bounds test to test for co-integration, followed by an analysis of the long-run impact of economic growth and financial development on renewable energy supply. Lastly, an error correction model (ECM) is implemented to determine the short-run impact of economic growth and financial development on renewable energy supply. The study intends to contribute to solving the issue of environmental pollution by utilising renewable energy as a source of energy generation. It guides the relationship between renewable energy supply, financial development, and economic growth in South Africa. The financial sector and South Africa’s government should invest in renewable energy for economic growth. The government should provide incentives to encourage funding and adopt policies for growth in the renewable energy sector.

2. Literature Review

Global warming has been identified as an existential threat to society and, therefore, environmental protection has become a central theme in policy making. This, in turn, attracted substantial academic research interest related to green energy and its adoption [17]. A bibliometric study by Qin et al. [18] provides insights into the themes and evolution of research related to green energy and its adoption. Figure 1 shows the thematic evolution and trending topics within green finance research.

Qin et al. [18] identified four main themes. The first theme relates to technical matters as a contributing factor to green energy adoption. The second theme relates to the determinants of adopter level as a contributing factor to green energy adoption. The third theme relates to the determinants of corporate promotion as a contributing factor to green energy adoption. The fourth theme relates to the determinants of environmental challenges as a contributing factor to green energy adoption. Financial development and economic growth as drivers of green energy adoption have emerged as a key focus point in research [5].

2.1. Determinants of Renewable Energy Supply

Tu et al. [19] undertook a study on 27 EU countries to discover the determinants of renewable energy supply. The study’s results revealed that advanced technology, economic growth, political participation, and geographic location influence renewable energy supply. Another study was performed on EU countries by Dogan et al. [20] and it was discovered that oil prices and economic growth positively impact renewable energy supply, while energy-related and environmental taxes negatively impact it.

Bourcet [21] conducted a study to discover the quantitative determinants of renewable energy supply in developed and developing countries. The study revealed that CO₂ emission, fossil fuel prices, environmental sustainability, and income had minimal influence on renewable energy supply, while renewable energy policy development and implementation, population size and income, and CO₂ and energy security had a negative impact. Meanwhile, a study on developed and developing countries revealed that a 1% increase in economic growth leads to an increase in renewable energy supply [22]. However, the impact was insignificant for oil prices and income [23]. Fatima et al. [24] discovered that high unemployment, economic growth, and government debt positively impact renewable energy supply in developing countries, while CO₂ emission and market competition negatively impact it.

Fatima et al. [24] also discovered that quality governance and renewable energy policies negatively impact renewable energy supply in Pakistan, while power generation, natural resources, public acceptance, power demand, environmental aspects, and high returns drive it. On the other hand, Chen et al. [25] studied democratic institutions and discovered that democratic institutions have a significant influence on renewable energy supply. Economic growth in countries with high levels of democracy decreases the cost of renewable energy. Moreover, Chen et al. [15] revealed that governance, innovation, financial development, and a stable political environment significantly influence renewable energy supply, while a study by Ergun et al. [26] on 21 African countries, including South Africa, revealed that economic growth, democracy, and a high human development index have an insignificant impact on renewable energy supply, however, foreign direct investment was found to have a significant impact.

Da Silva et al. [27], who performed a study on several sub-Saharan African countries, discovered that economic growth is a major driver of renewable energy supply, while population growth negatively affects it. Income and CO₂ emission have an insignificant impact, while oil prices, population growth, and manufacturing expansion have a significant impact.

2.2. Nexus between Renewable Energy Supply and Financial Development

Kim and Park [28] conducted a study on a global scale to investigate how financial development impacts renewable energy supply. It was discovered that developed financial sectors improve renewable energy generation and supply, while underdeveloped financial sectors impede it. The study concluded that developed financial sectors determine renewable energy infrastructure development and, ultimately, renewable energy supply. Similarly, Anton and Nuca [29] concluded that the banking sector, bond market, and capital market positively impact renewable energy supply in 28 EU countries. A 1% increase in the banking sector and bond market resulted in a 0.0284% and 0.0148% increase in renewable energy supply, respectively, while the capital market had an insignificant impact.

Moreover, Mukhtarov et al. [30] have concluded that for every 1% increase in financial development, renewable energy supply increased by 0.21%, which indicates that financial development impacted renewable energy supply positively. Mukhtarov et al. [30] further discovered that renewable energy has low financial risk, resulting in reduced borrowing costs and easy access to funding from the financial sector. Similarly, the study by Le et al. [31] discovered that financial development positively impacts renewable energy supply in developed and developing countries, with higher statistical significance in developed countries.

Pata et al.’s [32] study revealed that financial development plays a significant role in improving renewable energy infrastructure development in the USA. Financial development can positively impact renewable energy supply by decreasing start-up costs, providing cheaper debt financing, increasing borrowing capacity, and attracting foreign investment. Studies by Pata et al. and Oji et al. [32,33] revealed that renewable energy projects in South Africa primarily rely on project finance, which is an affordable alternative to funding substantial infrastructure development. On the other hand, Ji and Zhang [34] concluded that renewable energy supply is constrained by financing, with the financial sector contributing 40% of the adjusted R-squared. Financial institutions are hesitant to issue debt financing for renewable energy infrastructure, therefore projects are predominantly funded by equity in underdeveloped countries.

Chang et al. [35] discovered that every 1% increment in financial development caused a 0.24% increase in the supply of renewable energy, reducing borrowing costs and making funding easier for renewable energy projects. Moreover, a study by Shahbaz et al. [36] on 34 developing countries, including South Africa, discovered that financial development has a long-term effect on renewable energy supply. In a South African context, Lefatsa et al. [37] concluded that financial development positively impacts renewable energy consumption, and that the government should develop policies to encourage the financial sector to fund renewable energy infrastructure development projects. Inconsistent with the results of Lefatsa et al. [37], Odhiambo [38] concluded that financial development did not impact renewable energy consumption in the long run in South Africa.

2.3. Nexus between Renewable Energy Supply and Economic Growth

On a global scale, Kim and Park [28] found that economic growth depended on developing a country’s renewable energy infrastructure. Similarly, Inglesi-Lotz [39] concluded that for every 1% increase in renewable energy supply, economic growth increased by 0.105%. Moreover, Chang et al. [40] discovered that a bidirectional causal relationship between renewable energy supply and economic growth existed.

Vural’s [41] study revealed that economic growth, trade, CO₂ emission, and technological innovation statistically and significantly impacted renewable energy supply in various Latin American countries. Additionally, Ntanos et al. [42] discovered a causal relationship between economic growth and renewable energy supply. At the same time, the study by Alper and Oguz [43] revealed that renewable energy had a statistically significant impact on economic growth.

Kasperowicz et al. [44] discovered a long-run equilibrium relationship between renewable energy supply and economic growth. Furthermore, Kocak and Sarkgunesi [45] discovered that a long-run relationship between renewable energy supply and economic growth existed. In addition, renewable energy supply had a statistically significant impact on economic growth. On the other hand, Singh et al. [46] revealed that a 1% increase in renewable energy supply in developing countries resulted in a 0.07% increase in economic growth, while for developed countries, a 1% increase in renewable energy supply resulted in a 0.05% increase in economic growth.

The study performed by Rahman and Vekayutham [47] revealed that with every 1% increase in renewable energy supply, there was a 0.66% increase in economic growth. However, the study revealed a unidirectional causal relationship between the two variables. On the other hand, a study by Shahbaz et al. [36] revealed that economic growth impacted renewable energy supply negatively and that developing countries are mostly reliant on power for production and industrial activities to grow, which ultimately increases economic growth. Therefore, the direction of impact is from renewable energy supply to economic growth.

Moreover, Wang et al. [5] discovered that in developing countries with high political risk, for every 1% increase in renewable energy supply, there was a consequential increase in economic growth of 0.0204%. In comparison, developing countries with lower political risk had a 0.0892% increase in economic growth for every 1% increase in renewable energy supply. Correspondingly, Luqman et al. [48] revealed that renewable energy supply had a positive impact on economic growth. Moreover, the study by Kahia et al. [49] revealed a long-run equilibrium relationship between renewable energy supply and economic growth, resulting in a positive statistically proven significant elasticity. The results further indicated a bidirectional causality relationship between these two variables.

Hamit-Haggar [50] discovered that for every 10% increase in renewable energy supply, a 0.91% to 1.19% increase was experienced in economic growth. Bhattacharya et al. [51] showed with the data retrieved for the top 38 identified countries that supplied renewable energy, including South Africa, that renewable energy supply had a significant favourable impact on economic growth for 57% of the countries that formed part of the sample population. However, South Africa was one of the countries in the sample population where the econometrics model could not demonstrate if renewable energy supply significantly influenced economic growth. On the other hand, Sasana and Ghozali [52], who studied the five BRICS countries, including South Africa, discovered that the supply of renewable energy had a negative relationship with economic growth, while Banday and Aneja’s [53] results on BRICS countries revealed that there is a bidirectional causality between the variables for Brazil and China and a unidirectional relationship between renewable energy supply and economic growth for Russia and South Africa.

2.4. Nexus between the Three Variables

Zhe et al. [54] performed a study to discover if renewable energy positively impacted financial development and economic growth. The results revealed that a significant relationship between renewable energy and economic growth did not exist. In contrast, renewable energy and economic growth appeared to have a positive impact on financial development. Furthermore, the results revealed that financial development influenced renewable energy [55]. Additionally, Zhe et al. [54] discovered that renewable energy supply, financial development, and economic growth had a long-run equilibrium relationship and a bidirectional causal relationship. The variables altered their disequilibrium at a rate of 22.98% each year.

On the other hand, Guan et al. [56] discovered that there was a negative long-run relationship between renewable energy and financial development in western China while there was a positive short-run relationship for all of China, with unidirectional causality between financial development and economic growth. Moreover, Mukhtarov et al. [57] revealed that financial development and economic growth had a positive and statistically significant impact on renewable energy supply. The results indicated that a 1% increase in financial development and economic growth resulted in a rise of 0.16% and 0.60% in renewable energy supply, respectively.

2.5. Barriers to Renewable Energy Supply

The study by Manu et al. [58] examined the hindrances to renewable energy supply and found that the absence of practical policies for deploying renewable energy and their effective implementation posed significant obstacles. This conclusion aligns with the findings of Byrnes et al. [59], who emphasised that energy policies predominantly favoured established technologies due to their perceived lower risk and investment requirements, while renewable energy infrastructures, being relatively new, faced more significant challenges. Consequently, these energy policies acted as barriers to adopting renewable energy technologies.

Furthermore, South Africa’s governmental support for renewable energy has been limited [60], necessitating the development of non-punitive policies to attract local and foreign private investments [15]. Extensive tax incentives and government-sponsored initiatives should be considered to stimulate renewable energy supply, especially since public sector financing is constrained in the renewable energy sector. In conclusion, amending policies to support new technologies in renewable energy infrastructure development is essential for encouraging and expediting private sector investments in renewable energy supply.

2.6. Conclusion

Lefatsa et al. [37] concluded that various methodologies employed by researchers across different periods in both developed and developing countries, including South Africa, have yielded mixed findings regarding the relationship between renewable energy, financial development, and economic growth. Upon examining the literature, it became apparent that the conclusions reached by Lefatsa et al. [37] are consistent with the findings of this study as the literature reviewed revealed that the nexus between renewable energy supply, financial development, and economic growth is inconclusive due to the findings being inconsistent, highlighting a gap that the current study aims to address.

The research gap identified in the study is twofold. Firstly, there is a lack of research on the relationship between renewable energy supply, financial development, and economic growth, specifically in South Africa. Furthermore, the existing research on these variables in other countries has produced inconclusive results. Secondly, minimal research has been conducted on how economic growth impacts renewable energy supply. Thus, the present study sought to bridge the gap by analysing and concluding the relationship between financial development, economic growth, and renewable energy supply in South Africa. Furthermore, it improves the currently limited research on how economic growth impacts renewable energy supply, where economic growth is defined as the explanatory variable in the study.

3. Research Methodology

A methodological framework is a set of steps and principles used by researchers to complete a study and achieve a specific goal [61]. The null hypothesis of the study is defined as there being no significant relationship between the variables defined for this study, and co-integration does not exist, while the alternative hypothesis is defined as there being a significant relationship between the variables and co-integration being present [37].

The study adopted a quantitative methodology involving secondary data to investigate the impact of financial development and economic growth on renewable energy supply in South Africa. The study aims to achieve the research objectives of the study and to determine the validity of the null hypothesis.

3.1. Data Description

The study used annual time series data from 1990 to 2021 for all the variables reported in the econometric model to understand the relationship between renewable energy supply, financial development, and economic growth in South Africa. Secondary data were obtained from various sources to perform the study, including the World Bank, Oxford Economics, and the Council for Scientific and Industrial Research (CSIR).

Secondary data were used to ensure data accuracy and circumvent many of the ethical problems that could arise while collecting primary data and the associated sampling procedures.

Confidentiality and anonymity issues are minimal when using publicly accessible secondary data. The World Bank, Oxford Economics, and CSIR are public databases, therefore the possibility of data protection issues is eliminated. Furthermore, the data do not include specific individuals or names of people but are at an aggregated country level.

The data collected were classified into the following variables: renewable energy supply, financial development, economic growth, load shedding levels, CO₂ emission by coal power generation, coal price changes, and coal electricity supply.

3.1.1. Dependent Variable

Renewable energy supply is a dependant variable, represented as electricity generated from renewable sources as a percentage of total electricity generation, with the unit of measurement being a percentage [62].

The employment of the proxy for renewable energy supply was consistent with similar studies performed, where the dependent variable, being renewable energy supply or renewable energy consumption, was measured as a percentage of total electricity. These studies include the study by Anton and Nuca [29] on the effect of financial development on renewable energy deployment in EU countries, that of Shahbaz et al. [39] on 34 developing countries, including South Africa, to investigate financial development’s influence on renewable energy supply, that of Mukhtarov et al. [30] on the impact of financial development on the deployment of renewable energy in Turkey, that of Ji and Zhang [34] on how much financial development contributes to the growth of renewable energy supply in China, and lastly, the study performed by Inglesi-Lotz [39] on the impact of renewable energy supply on economic growth in various Organisation for Economic Cooperation and Development (OECD) countries, including Colombia, Poland, and Portugal.

3.1.2. Explanatory Variables

Financial development is represented as domestic credit provided by the financial sector as a percentage of gross domestic product (GDP), with the unit of measurement being a percentage. The employment of this proxy was consistent with similar studies performed by Anton and Nuca [29] on the effect of financial development on renewable energy deployment in EU countries, that of Lefatsa et al. [37] on the nexus between financial development and renewable energy consumption in South Africa, and Wang et al.’s [5] study examining the relationship of renewable energy supply with economic growth and financial development in China. The financial sector provides domestic credit on a gross basis to various sectors, including deposit money banks, monetary authorities, and other financial businesses. The credit provided as a percentage of GDP is based on the banking sector’s depth and financial sector development in terms of size [62]. It fully accounts for financial market borrowing by governments to fund infrastructure for economic development in emerging nations. [63]

Economic growth is represented as the seasonally adjusted real GDP growth, with the unit of measurement being a percentage. The aggregated absolute value contributed by all entrepreneurs who belong to a country was applied to determine the GDP, considering the taxes and subsidies applicable [63]. The employment of this proxy was consistent with similar studies performed by Kim and Park [28] on how economic growth impacts renewable energy supply and Rahman and Vekayutham [47] on the nexus between renewable energy and economic growth.

Coal electricity supply is represented as electricity generated from coal and coal products as a percentage of total electricity generation, with the unit of measurement being a percentage. The generation of power through coal is performed through a process of combustion, whereby steam is created, which in turn is utilised to generate electricity (Department of Mineral Resources and Energy) [64].

Coal price change is represented as a percentage change in coal price, with the unit of measurement being a percentage. Coal prices are affected by market fluctuations in costs for acquiring and transporting coal to power plants [64].

Load shedding levels are identified as an explanatory variable in the study, with the unit of measurement being gigawatts per hour (GWh) of the electricity demand deficit in South Africa [65]. South Africa experiences frequent electrical supply outages due to insufficient power generation, resulting in load shedding. This crisis has been ongoing since 2007 [66].

CO₂ emission by coal power generation is represented as the percentage of CO₂ emission by coal power generation, with the unit of measurement being a percentage. CO₂ is created through coal combustion and interaction with oxygen in coal-fired power plants, with the amount of CO₂ molecules being 3.67 times greater than that of carbon molecules [62]. The employment of this proxy was consistent with similar studies performed by Vural [41] on the impact of pollution on renewable energy supply and Bourcet [21] on the determinants of renewable energy.

3.2. Empirical Model

The study examined the impact of financial development and economic growth on renewable energy supply from a South African perspective. To test the hypothesis, the study applied econometric techniques to examine the time series [55]. An ARDL model was employed for the study (Figure 2).

The ARDL model is suitable for uncovering co-integration relationships in small samples, with limited data points and different levels of integration, and avoids choosing an optimal number of variables [68]. Several studies in line with this study have employed the ARDL model.

Table 1. Examples of sustainability studies that implemented ARDL methodology.

Author/s	Title	Year
Odhiambo [38]	Energy consumption, prices and economic growth in three SSA countries: A comparative study	2010
Alper and Oguz [43]	The role of renewable energy consumption in economic growth: Evidence from asymmetric causality	2016
da Silva, Cerqueira, and Ogbe [27]	Determinants of renewable energy growth in Sub-Saharan Africa: Evidence from panel ARDL	2018
Luqman, Ahmad, and Bakhsh [48]	Nuclear energy, renewable energy and economic growth in Pakistan: Evidence from non-linear autoregressive distributed lag model	2019
Alam and Murad [69]	The impacts of economic growth, trade openness and technological progress on renewable energy use in organization for economic co-operation and development countries	2020
Mukhtarov, Humbatova, Hajiyev, and Aliyev [57]	The Financial Development-Renewable Energy Consumption Nexus in the Case of Azerbaijan	2020
Adebayo, Awosusi, Bekun, and Altuntas [70]	Coal energy consumption beat renewable energy consumption in South Africa: Developing policy framework for sustainable development	2021
Lefatsa, Sibanda, and Garidzirai [37]	The Relationship between Financial Development and Energy Consumption in South Africa	2021
Wang, Zhang, and Zhang [5]	The relationship of renewable energy consumption to financial development and economic growth in China	2021
Mukhtarov, Yuksel, and Dincer [30]	The impact of financial development on renewable energy consumption: Evidence from Turkey	2022
Chang, Qian, Azer, and Dilanchiev [35]	Nexus between financial development and renewable energy: Empirical evidence from nonlinear autoregression distributed lag	2022

Source: Authors’ construction.

shows examples of sustainability studies over time that implemented ARDL methodology.

For example, Lefatsa et al.’s [37] study examined the relationship between financial development and renewable energy consumption in South Africa and applied the ARDL model to achieve its aims and objectives. Similarly, Wang et al.’s [5] study on the relationship between renewable energy, financial development, and economic growth in China used an ARDL model to achieve the study’s aim and objectives. Furthermore, Odhiambo [38] studied the relationship between financial development, economic growth, and renewable energy consumption in Congo, South Africa, and Kenya. The study employed the ARDL model. Thus, the ARDL model has frequently been employed in studies similar to the current study, which justified its use.

Several crucial steps influenced the methodology of the study, including the unit root test to examine if the variables employed for the study are stationary and integrated in the same order, followed by the determination of the optimal lag term for the study that is considered in the ARDL regression estimate to discover possible delays the variables might possess in impacting renewable energy supply. The ARDL bounds test to discover if co-integration exists between the variables employed for the study follows the optimal lag term determination.

The null hypothesis for the ARDL bounds test is that no long-run relationship exists between the variables.

The alternative hypothesis is that a long-run relationship does exist between the variables.

After the ARDL bounds test, the long-run relationships between renewable energy supply and the explanatory variables are determined, followed by various diagnostic tests performed on the final long-run model that is interpreted in the study. The diagnostic tests include a multicollinearity test to ensure that the explanatory variables are not highly correlated. In addition, an autocorrelation Breusch–Godfrey serial correlation LM test is performed to ensure that the residuals in the model do not possess serial correlation.

A heteroscedasticity Breusch–Pagan–Godfrey test is also conducted to ensure the model is free from heteroscedasticity. The fourth diagnostic test is the normality test to ensure that all series in the model are normally distributed. The last diagnostic test is the parameter stability test, interpreting the CUSUM and CUSUMSQ to confirm the lack of any coefficient unsteadiness within the model. After the diagnostic tests, the short-run relationships between renewable energy supply and the explanatory variables are examined by employing the ECM. Each section of the research methodology describes the steps followed to determine whether financial development and economic growth have a statistically significant impact on renewable energy supply in South Africa and if long-run and short-run relationships exist within the variables.

To establish stationarity, the unit root test employing the Phillips–Perron (PP) and augmented Dickey–Fuller (ADF) tests was conducted as carried out by De Wet and Botha [71]. The study, therefore, adopted both these unit root testing techniques to examine the stationarity of the variables employed in the study.

RES_t = β₀ + ∑_i^k₌₁β_1iFD_t−1 + ∑_i^k₌₁β_2iGDP_t−1 + ∑_i^k₌₁β_3iCO2_t−1 + ∑_i^k₌₁β_4iCES_t−1 + ∑_i^k₌₁β_5iCP_t−1 + ∑_i^k₌₁β_6iLS_t−1 + α₁FD_t−1 + α₂GDPC_t−1 + α₃CO2_t−1 + α₄CES_t−1 + α₅CP_t−1 + α₆LS_t−1 + D_t−1 + ε_t

(1)

Equation (1): ARDL Model

In the equation, k represents the lag length [72], RES represents renewable energy supply [62], β₀ represents the coefficients of the explanatory variables, β₁…β₆ represents the variable long-run parameters, FD represents financial development explanatory variable [62,63], GDP represents economic growth explanatory variable [62], CO₂ represents the CO₂ emission by coal power generation explanatory variable [62], CES represents the coal electricity supply explanatory variable [62], CP represents the coal price changes explanatory variable [62], LS represents the load shedding explanatory variable [63,64], ɛ_it represents the residual term [73], I represents the country [73], and t represents time [71]. Terms α₁ to α₆ are the estimated coefficients of the model related to each respective explanatory variable. Lastly, D_t−1 represents the one-period lagged distributed lag term.

The null hypothesis is that a statistically significant long-run relationship between renewable energy supply, economic growth, and financial development does not exist.

The alternative hypothesis is that there is a statistically significant long-run relationship between renewable energy supply, economic growth, and financial development.

Additionally, related to the ECM model, the null hypothesis is that a statistically significant short-run relationship between renewable energy supply, economic growth, and financial development does not exist.

The alternative hypothesis is that there is a statistically significant short-run relationship between renewable energy supply, economic growth, and financial development.

The co-integration test, applying the bounds test, uses two sets of values based on the F-statistic test within the model [74]. The null hypothesis, indicating the absence of co-integration, is rejected in the case where the derived F-statistic value is determined to be larger than the upper-bound critical value, while the alternative hypothesis indicating the presence of co-integration is rejected when the derived F-statistic value is determined to be smaller than the upper-bound critical value [37].

Suppose the F-statistic value is below the upper-bound critical value at a 5% significance level. In that case, the null hypothesis will be accepted, meaning that co-integration does not exist within the variables of a study. However, the null hypothesis will be rejected if the F-statistic value is above the upper-bound critical value at a 5% significance level, meaning co-integration exists within a study’s variables [67,75].

The variables used in the study were standard and publicly available variables that are accessible for multiple countries and from various data sources. The model used for the study is a standard ARDL model. Therefore, if similar variables for other countries are employed with an ARDL model, this study can be replicated for those countries.

4. Results

Section 4 presents the quantitative research results achieved by applying the model construct detailed in Section 3. The data obtained and the model construct were employed to achieve the study’s aim and address the three objectives of the study discussed in Section 1.

4.1. Results of Unit Root Testing

Table 2. Augmented Dickey–Fuller unit root test results.

Variables	Model Construct	Order of Integration	Probability Value
Renewable energy supply	Trend and intercept	I(0)	0.9995
	Trend and intercept	I(1)	0.0020 *
Financial development	Trend and intercept	I(0)	0.1293
	Trend and intercept	I(1)	0.0000 *
Economic growth	Trend and intercept	I(0)	0.0251 *
Coal price changes	Trend and intercept	I(0)	0.0000 *
Load shedding	Trend and intercept	I(0)	0.9278
	Trend and intercept	I(1)	0.0002 *
Coal electricity supply	Trend and intercept	I(0)	0.9823
	Trend and intercept	I(1)	0.0000 *
CO2 emission by coal power generation	Trend and intercept	I(0)	0.5789
	Trend and intercept	I(1)	0.0000 *

* Null hypothesis rejected at a 95%+ confidence level. Source: Authors’ construction.

presents the unit root test results obtained by employing the ADF technique to examine the stationarity of the variables employed in the study.

Table 3. Phillips–Perron unit root testing results.

Variables	Model Construct	Order of Integration	Probability Value
Renewable energy supply	Trend and intercept	I(0)	1.0000
	Trend and intercept	I(1)	0.0021 *
Financial development	Trend and intercept	I(0)	0.1878
	Trend and intercept	I(1)	0.0000 *
Economic growth	Trend and intercept	I(0)	0.0274 *
Coal price changes	Trend and intercept	I(0)	0.0000 *
Load shedding	Trend and intercept	I(0)	0.9721
	Trend and intercept	I(1)	0.0000 *
Coal electricity supply	Trend and intercept	I(0)	0.9957
	Trend and intercept	I(1)	0.0000 *
CO2 emission as a result of coal power generation	Trend and intercept	I(0)	0.0000 *

* Null hypothesis rejected at a 95%+ confidence level. Source: Authors’ construction.

presents the unit root test results obtained by employing the PP technique to examine the stationarity of the variables employed in the study.

The results presented in the tables above indicate that the series do not exhibit the same level of integration, i.e., some of the series are integrated at I(0) and some at I(1). Renewable energy supply, financial development, load shedding levels, and coal electricity supply are integrated at I(1) in both ADF and PP, which implies that the null hypothesis of unit roots existing in the series can be accepted at I(0) as the p-values are above 0.05. However, the null hypothesis is rejected at I(1) as the p-values are below 0.05. Economic growth and coal price changes are integrated at I(0) in both ADF and PP; therefore, the null hypothesis is rejected at I(0) as the p-values for the series are below 0.05.

CO₂ emission by coal power generation is stationary at I(1) when applying the ADF test and stationary at I(0) when applying the PP test. The ADRL model can be run with variables with different levels of integration [68]. Therefore, the study continues to test co-integration regardless of the differences in the order of integration mentioned above.

4.2. Optimal Lag Term

To determine the optimal number of lag terms for the model construct for the study, the AIC in EViews was employed. The results from the AIC are presented in

Table 4. Optimal lag term determined by information criterion.

Lag	LogL	LR	FPE	AIC	SC	HQ
0	−389.390	NA	1758.670	27.337	27.667	27.441
1	−302.369	126.031	141.550	24.715	27.355 *	25.542
2	−230.637	69.258 *	57.788 *	23.147 *	28.098	24.698 *

* Indicates lag order selected by the criterion: LR: Sequential modified LR test statistic (each test at 5% level). FPE: Final prediction error. AIC: Akaike information criterion. SC: Schwarz information criterion. HQ: Hannan–Quinn information criterion. Source: Authors’ construction.

.

The results shown in Table 4 indicate that the model’s optimal lag length is two. Therefore, the results are consistent with those of Alper and Oguz’s [43] study on the nexus between renewable energy and economic growth and Lefatsa et al.’s [37] study on the nexus between financial development, economic growth, and renewable energy consumption in South Africa.

4.3. ARDL Bounds Test for Co-integration

Table 5. ARDL test for integration.

Test Statistic	Value	Significance	I(0)	I(1)
F-statistic	6.831	10%	1.99	2.94
k	6	5%	2.27	3.28
		2.5%	2.55	3.61
		1%	2.88	3.99

presents the results of the ARDL bounds test. The results reveal that the F-statistic value of 6.831 is larger than the I(1) bounds critical value of 3.28 at a 5% significance level, which indicates that co-integration between the variables exists. Therefore, a long-run relationship exists within the variables, and the null hypothesis is rejected.

Based on the above results proving co-integration exists between the variables, the ARDL regression is estimated to discover the long-run relationship and the ECM is employed to discover the short-run relationship.

4.4. Final Model Interpretation—Long-Run Relationship

Table 6. Outputs of final model.

Variable	Coefficient	Std. Error	Probability
Financial development	0.034	0.008	0.000 *
Financial development (−2)	−0.012	0.004	0.010 *
Economic growth (−2)	12.576	5.249	0.029 *
Coal electricity supply	−0.831	0.071	0.000 *
Load shedding levels	0.001	0.000	0.003 *
R-Squared	0.944
Adjusted R-squared	0.935
Prob (F-statistic)	0.000

* Statistically significant at a 95%+ confidence level. Source: Authors’ construction.

represents the final model with only the statistically significant explanatory variables with p-values below 0.05. There were several iterations of the model until only significant variables remained. The model depicted below was tested for robustness by applying various model diagnostic tests. The diagnostic test results revealed that the model is free from multicollinearity, autocorrelation, and heteroscedasticity. In addition, it is normally distributed and demonstrates a lack of coefficient unsteadiness.

The null hypothesis of insignificance is tested by means of using the p-value of each beta coefficient and a 95% confidence level is used to reject the null hypothesis. Thus, if the p-value is below 0.05 the null hypothesis of insignificance is rejected. The results presented above reveal that the p-value for financial development in the current period is 0.000, which is below 0.05, resulting in the null hypothesis being rejected. In other words, financial development in the current period significantly impacts renewable energy supply. An increase in financial development in the current period results in a 0.034 percentage increase in renewable energy supply, keeping all else constant. This indicates that financial development in the current period has a positive and significant impact on renewable energy supply in the long run.

The results for financial development two periods back reveal that the p-value is 0.010, which is below 0.05, therefore resulting in the null hypothesis being rejected. In other words, financial development two periods back significantly impacted renewable energy supply. However, unlike the results for financial development in the current period, financial development two periods back had a negative coefficient of −0.012, suggesting that it has a negative impact on renewable energy supply. Therefore, an increase in financial development two periods back results in a 0.012 percentage decline in renewable energy supply, keeping all else constant. This indicates that financial development two periods back had a negative and significant impact on renewable energy supply in the long run.

The p-value for economic growth two periods back is 0.029, which is below 0.05, therefore the null hypothesis is rejected. In other words, economic growth two periods back significantly impacted renewable energy supply. An increase in the production of economic goods and services in South Africa results in a 12.576 percentage increase in renewable energy supply, keeping all else constant. This indicates that economic growth two periods back had a positive and significant impact on renewable energy supply in the long run.

Similar to the results for financial development two periods back, the p-value for coal electricity supply is 0.000, which is below 0.05, resulting in the null hypothesis, that coal electricity supply does not significantly impact renewable energy supply, being rejected. An increase in the coal electricity supply results in a decline in renewable energy supply by a 0.831 percentage change, keeping all else constant. This indicates that coal electricity supply negatively and significantly impacts renewable energy supply in the long run.

The p-value for load shedding levels is 0.003, which is below 0.05, resulting in the null hypothesis that load shedding levels do not significantly impact renewable energy supply being rejected. A GWh increase in the demand deficit of coal electricity supply represented as load shedding in the current period results in a 0.001 percentage change in renewable energy supply, keeping all else constant. This indicates that load shedding levels in the current period positively and significantly impact renewable energy supply in the long run.

The adjusted R-squared of the model is reported at a coefficient of 0.935, which indicates that the explanatory variables in this model explain renewable energy supply at a 93.5% magnitude. These results indicate that the explanatory variables in the model strongly explain renewable energy supply, as only 6.5% is not explained by these variables.

4.5. Diagnostic Tests

The study performs various diagnostic tests on the model that only include statistically significant variables to assess its robustness. The diagnostic tests performed are multicollinearity, autocorrelation, heteroscedasticity, normality, and model parameter stability tests.

4.5.1. Multicollinearity Test

The assumption is that the model for the study has no multicollinearity. Therefore, this diagnostic test intends to confirm this assumption. Whether the correlation is positive or negative, a correlation coefficient of 0.9 or more is considered a high correlation [37]. Presented in

Table 7. Multicollinearity test results.

	Financial Development	Economic Growth	CO2 Emission by Coal Power Generation	Coal Electricity Supply	Coal Price Changes	Load Shedding Levels
Financial development	1.000	0.341	0.562	0.010	0.110	0.047
Economic growth	0.341	1.000	0.206	0.239	0.353	−0.219
CO2 emission by coal power generation	0.562	0.206	1.000	0.276	0.176	−0.068
Coal electricity supply	0.010	0.239	0.276	1.000	−0.101	−0.724
Coal price changes	0.110	0.354	0.179	−0.101	1.000	0.155
Load shedding levels	0.047	−0.219	−0.068	−0.724	0.155	1.000

are the multicollinearity test results that reveal that none of the independent variables employed for this study are highly correlated as they all have a coefficient lower than 0.9.

4.5.2. Autocorrelation Test

Table 8. Breusch–Godfrey serial correlation LM test results.

Breusch–Godfrey Serial Correlation LM Test
Null Hypothesis: No Serial Correlation Up to 2 Lags
F-statistic	0.473	Prob. F (2,8)	0.640

presents the results of the Breusch–Godfrey serial correlation LM test performed to determine whether a serial correlation exists within the model:

Employing the Breusch–Godfrey LM test, the null hypothesis that the model has no serial correlation is tested. The serial correlation test results reflect that the chi-square’s probability value is greater than 0.05, as it is 0.205. This suggests that the null hypothesis can be accepted, and it can be concluded that the residuals in the model do not possess serial correlation.

4.5.3. Heteroscedasticity Test

Table 9. Heteroscedasticity Test: Breusch–Pagan–Godfrey serial correlation LM Test.

Heteroscedasticity Test: Breusch–Pagan–Godfrey Serial Correlation LM Test
Null Hypothesis: Homoscedasticity
F-statistic	2.338	Prob. F (13,16)	0.055

presents the results of the heteroskedasticity test, performed to determine whether heteroskedasticity exists within the model:

Employing the Breusch–Pagan–Godfrey test, the results of the heteroscedasticity test reflect that the probability value of the chi-square is greater than 0.05, as it is 0.104. This indicates that the null hypothesis that the model is free from heteroscedasticity can be accepted. Therefore, it can be confirmed that the residuals in the model have homoscedasticity.

4.5.4. Normality Test

Table 10. Normality test results.

Series: Residuals
Jarque–Bera	0.433
Probability	0.805

presents the results of the test performed to determine whether the series in the model are normally distributed or not:

The results reveal that the probability value of the Jarque–Bera test is greater than 0.05, as it is 0.805. This indicates that the null hypothesis that the model is normally distributed can be accepted. Therefore, it can be concluded that all series in the model are normally distributed.

4.5.5. Model Parameter Stability Test

Figure 3 below presents the results of the CUSUM and CUSUMSQ tests performed to determine the parameter stability of the model:

The CUSUM and CUSUMSQ statistic distribution is within the 5% significance intervals, crucial ranges for parameter stability. This demonstrates the lack of any coefficient unsteadiness, attesting to the robustness of every coefficient in the model.

4.6. Short-Run Relationship

Table 11. Short-run relationship results.

ECM Regression
Variable	Coefficient	Std. Error	Probability
Renewable energy supply (−2)	0.152	0.171	0.398
Financial development (−1)	0.027	0.006	0.002 *
Financial development (−2)	0.013	0.004	0.006 *
Economic growth (−1)	−6.185	4.816	0.201
Economic growth (−2)	10.999	4.705	0.044 *
CO2 emission by coal power generation (−1)	−0.044	0.083	0.610
CO2 emission by coal power generation (−2)	−0.149	0.0972	0.159
Coal electricity supply (−1)	−0.625	0.112	0.000 *
Coal electricity supply (−2)	−0.167	0.134	0.245
Coal price changes (−1)	0.322	0.191	0.126
Coal price changes (−2)	−0.818	0.230	0.006 *
Load shedding levels (−1)	0.001	0.000	0.005 *
Load shedding levels (−2)	−0.000	0.000	0.169
CointEq (−1) *	−0.760	0.135	0.000 *
R-squared	0.826
Adjusted R-squared	0.684

* Statistically significant at a 95%+ confidence level. Source: Authors’ construction.

presents the results of the short-run relationship test.

The null hypothesis is that the variables in the model do not explain and impact renewable energy supply in the short run. The null hypothesis is rejected if the p-value is below 0.05, which indicates that the variable explains and has an impact on renewable energy supply in the short run. However, if the p-value is above 0.05, the null hypothesis is accepted that the explanatory variable does not explain and impact renewable energy supply in the short run. The ECM results reveal that none of the variables employed for the study in their current period impact renewable energy supply in the short run.

Renewable energy supply two periods back has a p-value of 0.398. The p-value is above 0.05. Therefore, the null hypothesis is accepted, and it can be concluded that this variable does not have a statistically significant impact on renewable energy supply in the short run. These results are consistent with those revealed in the final model of the long-run relationship, where renewable energy supply was insignificant one and two periods back.

One and two periods back, financial development has a p-value of 0.002 and 0.006, respectively. The p-values are below 0.05. Therefore, the null hypothesis is rejected, and it can be concluded that these variables have a statistically significant impact on renewable energy supply in the short run. An increase in financial development one and two periods back results in an increase in renewable energy supply by 0.027 and 0.013 percentage points, respectively, keeping all else constant. The results reveal that financial development both one and two periods back positively and significantly impacts renewable energy supply in the short run. In contrast, financial development two periods back negatively and significantly impacts renewable energy supply in the long run, while financial development one period back is insignificant.

Economic growth one and two periods back has a p-value of 0.201 and 0.044, respectively. The p-value for economic growth one period back is above 0.05. Therefore, the null hypothesis is accepted. However, economic growth two periods back has a p-value below 0.05, which indicates that the null hypothesis can be rejected. It can be concluded that economic growth one period back does not have a statistically significant impact on renewable energy supply in the short run. However, economic growth two periods back has a positive and statistically significant impact on renewable energy supply. An increase in the production of economic goods and services in South Africa results in a 10.999 percentage increase in renewable energy supply in the short run, keeping all else constant. These results are consistent with those revealed in the final model of the long-run relationship, where economic growth two periods back positively and significantly impacts renewable energy supply.

CO₂ emission by coal power generation one and two periods back has p-values of 0.610 and 0.159, respectively. The p-values are above 0.05, therefore the null hypothesis is accepted, and it can be concluded that these variables do not have a statistically significant impact on renewable energy supply in the short run. These results are consistent with those revealed in the final model of the long-run relationship, where CO₂ emissions by coal power generation in the current period and both one and two periods back are insignificant.

One and two periods back, coal electricity supply has p-values of 0.000 and 0.245, respectively. The p-value for coal electricity supply one period back is below 0.05. Therefore, the null hypothesis is rejected. However, the coal electricity supply has a p-value above 0.05 two periods back, meaning the null hypothesis can be accepted. It can be concluded that the coal electricity supply one period back has a negative and statistically significant impact on renewable energy supply in the short run. However, the coal electricity supply two periods back has no statistically significant impact on renewable energy supply in the short run. An increase in coal electricity supply one period back results in a −0.625 percentage decline in renewable energy supply in the short run, keeping all else constant. These results are inconsistent with those revealed in the final model of the long-run relationship, where coal electricity supply one and two periods back is insignificant.

Coal price changes one and two periods back have a p-value of 0.126 and 0.006, respectively. The p-value for coal price changes one period back is above 0.05. Therefore, the null hypothesis is accepted. However, coal price changes two periods back have a p-value below 0.05, meaning the null hypothesis can be rejected. It can be concluded that coal price changes one period back do not significantly impact renewable energy supply in the short run. However, coal price changes two periods back have a negative and statistically significant impact on renewable energy supply in the short run. An increase in coal price changes two periods back results in a 0.818 percentage decline in renewable energy supply in the short run, keeping all else constant. These results are inconsistent with those revealed in the final model of the long-run relationship, where coal price change variables in the current period and one and two periods back are insignificant.

Load shedding levels one and two periods back have p-values of 0.005 and 0.169, respectively. The p-value for load shedding levels one period back is below 0.05. Therefore, the null hypothesis is rejected. However, load shedding levels two periods back have a p-value above 0.05, meaning the null hypothesis can be accepted. It can be concluded that load shedding levels one period back have a positive and statistically significant impact on renewable energy supply in the short run. However, load shedding levels two periods back have no statistically significant impact on renewable energy supply in the short run. A GWh increase in the demand deficit of coal electricity supply represented as load shedding one period back results in an increase in renewable energy supply by 0.001 percentage points in the short run, keeping all else constant. These results are inconsistent with those revealed in the final model of the long-run relationship, where load shedding levels one and two periods back are insignificant.

The error correction term is −0.760, with a p-value of 0.000. The error correction term is significant as it has a p-value below 0.05. The negative coefficient of the error correction term indicates that should there be a shock in the model, there is an adjustment back to equilibrium. Therefore, it can be concluded that the model possesses significant adjustments, given short-run deviations due to the negative and significant error correction term. The adjusted R-squared of the model is reported at a coefficient of 0.684, indicating that the explanatory variables in this model explain renewable energy supply at a 68.4% magnitude. The adjusted R-squared in the ECM has decreased compared to the long-run relationship results.

5. Discussion

South Africa relies heavily on fossil fuel products for energy production, and having an unstable electricity supply, renewable energy has gained significant attention and has become a crucial topic that requires examination and research for potential solutions to increase its supply [4]. The hypothesis is that a well-established financial sector draws adequate investments that stimulate renewable energy supply and economic expansion [3]. Studies contend that financial development is a major influence on renewable energy supply and that when economic growth improves in a country, so does the renewable energy supply [37].

The results from this study revealed that financial development was reported to have a positive and significant impact on renewable energy supply in the long run for the current period and a negative and statistically significant impact two periods back, whilst for the short-run relationship, financial development was reported to have a positive and significant impact on renewable energy supply one and two periods back. The results that indicate that financial development has a positive and significant impact on renewable energy supply are consistent with the results of the study conducted by Anton and Nuca [29], who found that financial development had a positive and significant impact on renewable energy supply. The results revealed that an increase in financial development in the banking sector resulted in a 0.0284% increase in renewable energy supply, while a 1% increase in financial development in the bond market sector resulted in a 0.0148% increase in renewable energy supply. Similarly, the study by Chang et al. [35] revealed that financial development had a positive and significant impact on renewable energy supply, with an increase in financial development causing a 24% increase in renewable energy supply in the long run.

Economic growth was reported to have a positive and significant impact on renewable energy supply two periods back in the long and short runs. These results indicated that the increase in South Africa’s production of economic goods and services increased the renewable energy supply. These results are consistent with the studies by Sardorky [76], Kim and Park [28] and da Silva et al. [27], who discovered that where there was economic growth in a country, renewable energy supply was positively impacted as it increased due to the growth in the economy.

On the other hand, coal price changes were reported to be insignificant in the long run. However, in the short run, they had a negative and significant impact on renewable energy supply two periods back. These results were consistent with the results of a study performed by Dogan et al. [20], where commodity prices on resources that were used for power generation were considered determinants of renewable energy supply. They negatively and significantly impacted the renewable energy supply over a short-term period.

Similarly, coal electricity supply was reported to have a negative and significant impact on renewable energy supply in the long run during the current period and in the short run one period back. Consistent with the study performed by Dogan et al. [20], coal electricity supply is one of the determinants of renewable energy supply. However, these variables possess an inverse relationship due to the discovery that countries that use less coal for power generation are more aggressive in developing renewable energy infrastructure. South Africa, as reported in 2022, is generating 92% of its power supply from coal-powered stations [62]; therefore, the transition to renewable energy supply is evidently at a slow-moving pace [77].

Load shedding is reported to have had a positive and significant impact on renewable energy supply in the long run during the current period and in the short run, one period back. The results from the study are consistent with Silva and Assis’s [78] discovery that load shedding caused by the numerous factors affecting coal power stations, for example, deteriorating infrastructure that experiences frequent breakdowns, encouraged an increase in the supply of renewable energy. Renewable energy supply is considered a mitigation factor for load shedding, i.e., as the coal-powered electricity supply deficit increases, renewable energy supply is expected to increase to bridge the deficit.

The autoregression distribution term, renewable energy supply, was reported as insignificant in the model of the study for both the long-run and the short-run relationships. Similarly, CO₂ emission by coal power generation was reported as insignificant in the study for both the long-term and the short-term relationships, which was consistent with the findings by Nyiwul [79], which revealed that CO₂ emission by coal power generation did not influence renewable energy supply.

Manu et al. [58] conducted a study and discovered that the absence of practical renewable energy deployment policies and their efficient implementation was the major impediment to renewable energy supply. This is consistent with the study performed by Byrnes et al. [59], who concluded that energy policies supported mature technologies as it is believed that they were less risky and were a low investment risk, while on the other hand, renewable energy infrastructures were new technologies. These energy policies therefore become an obvious barrier to the deployment of renewable energy. Therefore, the government of South Africa needs to develop policies that are not punitive toward renewable energy supply to encourage private local and foreign funding for renewable energy supply in South Africa [15], as financial development has been proven to have a positive and significant impact on renewable energy supply in various literature [30], including the results of this study.

A policy amendment to support the new technologies concerning renewable energy infrastructure development is imperative to encourage and expedite the private sector to invest in renewable energy supply, since the public sector is restricted in financing the renewable energy sector. As part of the development of renewable energy policies, substantial tax incentives or other government-sponsored initiatives must be implemented to promote renewable energy supply [60].

An empirical understanding of financial development, economic growth, and renewable energy supply is crucial for policy reforms in South Africa’s renewable energy sector and NERSA [69]. The South African government should provide tax incentives, remove regulatory barriers to encourage renewable energy projects [58], and recognise the role of the financial sector in promoting innovation and improving renewable energy supply [37].

Moreover, South Africa needs to promote financial development policies, institutional structures, and technological advancements for renewable energy supply through innovative financing policies and easing financing processes [37], including reducing costs of financing renewable energy projects [58]. Economic growth positively impacts renewable energy supply, making fiscal restraint necessary to prevent system leakages in relation to capital or income divergence that could hinder economic growth [5]. Reforms, such as offering affordable financing options with lower interest rates, can indirectly support the growth of renewable energy supply [5].

Financial development and economic growth are variables identified to have the main impact on renewable energy supply as the aim of the study is to examine these particular variables. The main reason for the inclusion of other explanatory variables in the model of this study was to increase the robustness of the model and to contribute to the reported adjusted R-squared.

6. Conclusions

The study endeavoured to advance stakeholders’ understanding of how financial development and economic growth impact renewable energy supply in South Africa; this, in turn, would enhance renewable energy policymakers’ decision making regarding the development and implementation of adequate renewable energy policies and would ultimately aid in the improvement of renewable energy supply. All the hypotheses in this study were tested at a 95% confidence level.

The results revealed that the determined F-statistic value of 6.831 was greater than the I(1) bound critical value of 3.28 at a 95% confidence level. The results indicated that co-integration between the variables existed. Therefore, a long-run relationship existed between the variables and the null hypothesis that co-integration does not exist was rejected. Furthermore, the ECM revealed a statistically significant error correction term of −0.760, indicating that the model possesses significant adjustments, given short-run deviations.

Following the discovery that co-integration existed within the variables, the final reported model revealed that financial development was reported to have a positive impact on renewable energy supply in the long run and the short run. Similarly, economic growth was reported to have a positive impact on renewable energy supply in the long run and the short run.

When considering the determinants of renewable energy supply variables, load shedding positively impacts renewable energy supply in the long run, while coal price changes negatively impact it in the short run, with coal electricity supply negatively impacting renewable energy supply in the long run and the short run. CO₂ emission by coal power generation and the autoregression distribution term—renewable energy supply—both were reported as insignificant.

The aim of the study, which was to examine and obtain an understanding of how financial development and economic growth impact renewable energy supply in South Africa, has been achieved, as the results revealed that financial development and economic growth have a positive and significant impact on renewable energy supply in the long run and short run. Moreover, the twofold research gap identified has been bridged, as the study firstly contributes to the limited research performed on the impact of financial development and economic growth on renewable energy supply, specifically in South Africa, and the results provide conclusive evidence that financial development and economic growth positively and significant impact renewable energy supply. Secondly, the study contributes to the minimal research conducted on how economic growth impacts renewable energy supply.

The study provides valuable insight to NERSA and the South African regulatory body responsible for energy sector policies in formulating and implementing renewable energy policies that promote the deployment of renewable energy infrastructure. This is crucial for enhancing renewable energy supply, especially in addressing financial development and economic growth challenges.

The study suggests a roadmap for future investigations in the field, offering opportunities to deepen our understanding of the factors influencing renewable energy supply and inform more effective policy initiatives. Firstly, future research in this domain could expand the focus and scope of investigations to delve deeper into the intricate relationship between financial development, economic growth, and renewable energy supply. Secondly, the financial sector in South Africa could be analysed in terms of banking, bond market, and capital market, using relevant proxies. This could provide insights into sub-financial sectors that influence renewable energy supply. Thirdly, research could be expanded beyond South Africa to explore the impact of financial development and economic growth on renewable energy supply in other African countries. Lastly, researchers should explore alternative econometric models and conduct experiments to identify the most effective model for analysing the impact of financial development on renewable energy supply.

Data limitations were encountered during the study, particularly regarding data availability for 2022 and 2023, for most variables. As a result, the decision was made to restrict the observation period to 2021. Additionally, the study faced constraints in obtaining quarterly and monthly secondary data for the selected variables. Consequently, the research was conducted using readily available annual data.