The Impact of Financial Development on Renewable Energy Consumption: Evidence from RECAI Countries

Abstract

Many environmental risks, such as global warming and depletion of natural resources, force governments to achieve economic growth and financial development without causing environmental degradation. The dependency of countries’ dependence on fossil fuels also causes energy supply security problems due to the associated risks at regional and global levels. These reasons lead countries to diversify and increase their renewable energy investments. In this context, this study focuses on the most attractive countries in terms of renewable energy investments and analyzes the relationships between renewable energy consumption (REC), carbon dioxide emissions (CO₂), economic growth (EGRO), financial development (FD), and energy dependence (EDP) using the panel regression method. This research uses data from 38 countries between 1991 and 2021 within the scope of the “Renewable Energy Attractiveness Index” (RECAI) created by Ernst & Young. As a result of the heterogeneity and cross-sectional dependency tests, the data were analyzed using the Westerlund cointegration test, the Augmented Mean Group (AMG) estimator, and the Emirmahmutoglu and Kose causality test. The findings from this study show that FD and EGRO have a positive and significant effect on REC, whereas they have a negative and significant relationship with CO₂ emissions. Findings from the causality test show that FD has an impact on both CO₂ and EGRO. In addition, within the scope of this study, a causality was determined between EDP and REC, and a mutual relationship between energy demand and CO₂ was revealed. In light of these findings, governments should increase their investments in renewable energy to ensure sustainable economic growth and energy supply security while minimizing environmental degradation.

1. Introduction

The high environmental costs of carbon-rich energy sources and the economic costs of fossil fuels due to their limited supply encourage countries to increase the diversity and consumption of renewable energy sources. Rising regional and global tensions between countries, such as the Russia–Ukraine conflict in recent years, also significantly affect the supply and consumption costs of fossil fuels. Hence, it is critical that the majority of nations formulate and institute strategic renewable energy policies founded on carbon-neutral sources. They include solar, wind, precipitation, tidal forces, ocean waves, and geothermal energy. Enhancing renewable energy sources is also crucial in mitigating CO₂ emissions, which contribute immensely to environmental degradation.

When we look at the historical rate of increase in CO₂ emissions, we see that emissions were 6 billion tons in the 1950s and reached 20 billion tons in the 1990s. Today, it is known that the world’s CO₂ emissions exceed 35 billion tons every year [1]. It is known that a significant portion of CO₂ emissions originate from production, and that CO₂ emissions in developed countries are higher than in developing or underdeveloped countries. To this end, governments should turn to renewable energy sources in order to reduce CO₂ emissions and achieve sustainable development goals [2,3]. In fact, when Ember’s Global Electricity Outlook 2024 Report is examined, 30% of global electricity is expected to be produced from renewable sources in 2023. Among the most significant effects of renewable energy consumption (REC) is the minimization of CO₂ emissions, alongside curbing the impact that energy consumption has on environmental degradation. It is evident that governments must accelerate their plans for renewable energy sources if they are to realize the COP 28 goals.

An important problem faced by governments and global actors is how to ensure economic growth (EGRO) in a way that creates minimal environmental degradation. It is also known that EGRO is one of the most important causes of CO₂ emissions and thus environmental degradation [4]. Therefore, achieving EGRO with renewable energy sources will contribute to reducing CO₂ emissions. In this context, there are studies showing that the relationship between REC and EGRO varies from country to country [5,6,7,8], as well as studies demonstrating the positive effects of REC on EGRO [9,10,11,12,13,14,15,16,17].

The countries’ external dependency on energy (EDP) is another important parameter that directs governments to diversify and increase renewable energy sources. For example, when Europe’s EDP rate was examined, it was projected to be 58.3% by 2024 [18]. Regional and global threats in energy-supplying countries endanger energy supply security and pose serious risks to production, particularly to EGRO. In addition, increases in input costs in countries’ EDPs also affect their current account deficits. Consuming renewable energy can be an alternative solution to problems related to energy supply. Therefore, examining the relationship between REC and EDP, and presenting policy recommendations, will provide useful information to governments for the actions they will take in this regard. Nonetheless, the existing literature suggests that there is little research in this field. This research adds to the existing body of knowledge by bridging this gap.

Another significant determinant that influences environmental degradation is financial development (FD). While FD enables the creation of funds that are environmentally friendly, such as green finance practices, it can also cause environmental degradation by increasing EGRO through the use of these funds [19]. However, it is also known that FD has significant effects on REC [20]. For example, Shahbaz et al. [21] found in their study that FD increases the demand for renewable energy. Therefore, examining the relationship between REC and FD and presenting the results with empirical findings impacts the decisions of policymakers. When reviewing studies reported in the literature, it is observed that FD has asymmetric effects on REC in the long term [22]. In addition, some studies reveal that FD has positive effects on REC [23,24,25]. In addition, while studies reveal that FD has reducing effects on CO₂ emissions [26], some studies find it has an increasing effect on CO₂ emissions [27]. Therefore, this study aims to fill the gap in the literature by using current data on the relationship between FD and both REC and CO₂ emissions, especially for countries that supply renewable energy.

The interconnection between REC, EGRO, CO₂ emissions, EDP, and FD is analyzed in the current research for nations with the most potential for renewable energy investment. The countries whose data are used in this study were determined based on the “Renewable Energy Country Attractiveness Index” (RECAI), created by Ernst & Young in 2003. Examining the link between REC, EGRO, CO₂ emissions, EDP, and FD in RECAI countries is important for revealing the renewable energy investment effectiveness.

When the literature is examined, the connection between REC and various variables has been investigated for different countries and country groups. There are only four different studies on RECAI countries [28,29,30,31]. The majority of literary records analyze the relationship between energy consumption and the economy, focusing on countries that use renewable energy sources. This causes the special role of producing countries in renewable energy use to be overlooked. Therefore, it is important to focus on the energy–economy–environment link using panel data from countries that produce the most renewable energy. For this reason, this study used data from RECAI countries. However, despite the importance of these countries in terms of renewable energy investments, studies examining the energy–economy–environment link are limited in the literature. This study is the first to examine this link by focusing on financial development and energy dependency in RECAI countries. Thus, it also fills the literature gap regarding countries producing renewable energy.

This study makes several important contributions, focusing on the link between renewable energy, economic, and environmental factors, particularly in countries that are attractive for renewable energy investments. First, this study differs from the general literature in its country selection. The RECAI index, which takes into account energy-specific, macroeconomic, and technological factors, is used in the country selection. This demonstrates that RECAI focuses not only on energy but also on economic stability in country selection. Second, this study advances our understanding of the impact of economic and energy factors on environmental degradation in the context of RECAI countries, which is of the utmost importance for the energy–economy–environment nexus. Thirdly, this study contributes to the existing literature by examining the relationship between renewable energy consumption, economic development, and environmental degradation in RECAI countries, focusing on financial development and energy dependency variables. Thus, this study contributes new data sets and variables to the renewable energy–economy–environment nexus.

Within the scope of this study, the relationship between REC, CO₂ emissions, EGRO, FD, and EDP was investigated using data from 38 RECAI countries for the period 1991–2021. The appropriate panel unit root tests, cointegration tests, and causality tests were chosen based on the results of heterogeneity and cross-section dependence tests. After the stationarity test, analysis results were obtained using the Westerlund [32] cointegration test, the Augmented Mean Group (AMG) estimator, and the Emirmahmutoglu and Kose [33] causality test.

This study is organized into five main sections. The Section 1 examines the theoretical background of the research and emphasizes the importance of the variables used. The Section 2 provides an overview of the renewable energy–economy–environment hypothesis, focusing on the empirical literature explaining the dynamics of renewable energy and the economy. The Section 3 presents the data set and explains the research methodology employed in this study. The empirical findings of the research are reported and compared with those of other studies in the Section 4. The Section 5 presents conclusions and provides policy suggestions.

2. Review of the Literature

In the empirical analysis conducted within the scope of this study, the relationships between REC, EGRO, FD, CO₂ emissions, and EDP were examined. In this section, previous studies examining the relationship between these variables are presented, and the relevant literature is reviewed.

2.1. REC and EGRO

In this context, various studies available in the literature have been carried out to analyze the relationship between REC and economic indicators. As in this study, a significant number of these studies attempt to analyze the relationship between REC and EGRO [5,6,7,8,9,10,11,12,13,14,15,16,17,34,35,36,37,38,39,40,41,42,43]. While some of these studies reveal that the relationship between REC and EGRO varies from country to country [5,6,7,8], others emphasize that the effect of REC on EGRO is positive [9,10,11,12,13,14,15,16,17]. Although the number is limited, some studies in the literature provide evidence that EGRO has negative effects on REC [44]. Some recent studies reveal that EGRO encourages REC [42,43].

During the process of reviewing the literature, researchers discovered that the nexus between EGRO and renewable energy is not consistent across all groups of countries. For example, Ivanowski et al. [37] show that REC does not have a significant effect on EGRO in OECD countries, while in non-OECD countries they show a positive effect of REC on EGRO. Muhammad et al. [39] provide evidence that EGRO increases environmental degradation. Therefore, in addition to the positive effect of REC on EGRO, a model in which renewable energy sources are not used increases environmental degradation. This situation appears to be an issue that policymakers should pay close attention to when shaping EGRO models. It is especially important for developed countries to establish long-term and balanced relationships between renewable energy and EGRO [45]. Therefore, especially for countries with a renewable energy supply, examining the relationship between REC and EGRO and providing evidence to policymakers on the current situation has not lost its importance. For this reason, it is important to empirically reveal the relationships between REC and EGRO of the countries that are important in renewable energy supply, which are examined within the scope of this study. In this regard, the following is assumed:

H1. 

Economic growth helps to increase renewable energy consumption.

2.2. REC and CO₂ Emissions

There are a number of studies in the literature that investigate the link between CO₂ emissions, which is one of the significant sources of environmental degradation, and REC. A number of studies identified a positive correlation between CO₂ emissions and REC [46,47,48,49]. In their investigation of the nexus between REC and CO₂ emissions based on the development stages of nations, Nguyen and Kakinaka [50] also established a positive correlation for poor nations and a negative correlation for wealthy nations. Some studies also identified negative and significant relationships between REC and CO₂ emissions in developed countries [51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66]. There are also studies showing that REC reduces CO₂ emissions in countries with high renewable energy production, such as Pakistan [67]. In addition, when the literature is generally evaluated, the effects of REC on CO₂ emissions vary among different countries and country groups [68]. In another study, Chen et al. [69] found that REC can have positive effects on CO₂ emissions when it exceeds a certain level, while Mahapatra and Jena [70] found that positive shocks on REC reduce CO₂ emissions, whereas negative shocks increase CO₂ emissions. In this regard, the following is assumed:

H2. 

Renewable energy consumption helps reduce CO₂ emissions.

2.3. REC, FD, and CO₂ Emissions

Another important parameter for policymakers is the relationship between REC, environmental degradation, and FD. Some of the studies examining the relationship between FD and environmental degradation have found that FD can cause environmental degradation by increasing CO₂ emissions [27,71,72]. Charfeddine and Kahia [73] provide evidence in their study for the MENA region that the effects of financial development (FD) on CO₂ emissions are limited. In a different study, Batool et al. [74] discovered evidence that indicates financial development has a positive long-run impact on CO₂ emissions in selected developing nations of East and South Asia. The connection between FD and CO₂ emissions varies for nations and groups of nations.

In the literature, studies consider the direct effects of FD on REC, as well as the effects on CO₂ emissions. In a study conducted in this context, Wang et al. [75] found that FD may have negative effects on REC in the long term, while they observed a positive relationship in the short term. Similarly, there are studies indicating that FD encourages REC [76,77,78,79]. In addition, there are studies in the literature that identify asymmetric and cross-relationships between FD and REC [80,81]. In this regard, the following is assumed:

H3. 

Financial development helps to increase renewable energy consumption.

2.4. REC and EDP

Another critical factor that countries should pay attention to in order to increase their level of FD and achieve sustainable economic growth is their EDP. A review of the literature shows that, although the problem is relevant, few studies exist focusing on the link between nations REC and EDP. However, countries’ dependencies on foreign energy are seen as a factor that causes crises and growth in current account deficits in economic activities like production and consumption, especially in periods of intensified regional and global tensions. In this context, the study by [82] addressed the Russia–Ukraine conflict for EU countries and found that the conflict created an energy security problem in Europe. The results of the study emphasize that alternative solutions such as renewable energy should be implemented to ensure energy security in Europe. Another study addressing the relationship between EDP and energy consumption was conducted by Bastida-Molina et al. [83]. In the study, the Mediterranean region countries were examined by being divided into two groups. One of these groups is the northern side, which has sufficient energy supply but is dependent on foreign energy, while the other group is the MENA countries, which have an energy supply deficit. Both are dependent on foreign energy. The study results show that increasing renewable energy resources at different levels is necessary for both groups of countries.

The impact of external dependency on energy is significant for the energy security of importing countries, but there are few studies on this topic from the perspective of the REC, FD, and EGRO. This study fills the gap in the literature by addressing REC and EDP in terms of countries that are attractive for renewable energy investments. In this regard, the following is assumed:

H4. 

Renewable energy consumption helps reduce energy dependency.

In this context, the research focuses on the countries that are most attractive in terms of REC. To select these countries, the Renewable Energy Attractiveness Index (RECAI) created by Ernst & Young in 2003 is used. When the literature is examined, only four studies have been conducted based on RECAI countries [28,29,30,31]. The focus of the studies, taken as a whole, is on the relationships between REC, EGRO, and CO₂ emissions. This study, unlike other studies, focuses on REC, EGRO, and CO₂ emission variables, as well as FD and EDP. This study contributes to the literature by presenting findings that provide guidance to policymakers and investors. In this context, the following questions are addressed in this study:

• Does financial development impact renewable energy consumption in RECAI countries?
• Can renewable energy consumption help countries reduce their energy dependency?
• Can renewable energy consumption help reduce CO₂ emissions?
• Does economic growth encourage renewable energy consumption?

3. Data and Methodology

In this section of this study, data on the variables used in the empirical analysis, as well as the methodology employed to analyze the data, will be presented. Annual data for 38 RECAI countries between 1991–2021 were used in the study. In this context, the description of the data used in the analysis and the databases from which these data were obtained are presented in

Table 1. Information about data.

Variables	Description	Abbreviation	Source
Renewable Energy Consumption	Indicates the proportion of energy from renewable sources in total energy consumption.	REC	World Bank (https://data.worldbank.org/indicator/EG.FEC.RNEW.ZS, accessed on 7 May 2025)
Financial Development	It is a composite index reflecting the depth, accessibility, and efficiency of the financial system, and it includes financial market and institution indicators.	FD	IMF (https://legacydata.imf.org/?sk=f8032e80-b36c-43b1-ac26-493c5b1cd33b, accessed on 7 May 2025)
Carbon Dioxide Emissions	The per capita emissions or total amount of carbon dioxide emissions show the environmental impact of economic activities.	CO2	World Bank (https://data.worldbank.org/indicator/EN.GHG.CO2.PC.CE.AR5, accessed on 7 May 2025)
Economic Growth	The annual growth rate in gross domestic product is the main indicator that measures the performance of a country’s economy.	EGRO	World Bank (https://data.worldbank.org/indicator/NY.GDP.MKTP.KD.ZG, accessed on 7 May 2025)
Energy Dependence	The ratio of net energy imports to total energy supply indicates the country’s degree of energy external dependence	EDP	World Bank (https://data.worldbank.org/indicator/EG.IMP.CONS.ZS, accessed on 7 May 2025)

.

In this study, a panel data set was created and analyzed using the panel regression method. A panel tracks a sample of individuals at several points in time, thus generating many observations for each sample unit [84]. Therefore, the empirical analysis is based on the model given in Equation (1) and is analyzed using panel data from 38 RECAI countries.

$${REC}_{it}={\beta }_0+{\beta }_1{FD}_{it}+{\beta }_2{{CO}_2}_{it}+{\beta }_3{EGRO}_{it}+{\beta }_4{EDP}_{it}+{\epsilon }_{i,t}$$

(1)

The econometric tests and the visual representation of the analysis processes are shown in Figure 1.

Before proceeding to panel data analysis, various preliminary tests should be applied. The choice of cointegration tests and estimation procedures is based on whether the panel units are heterogeneous or homogeneous. Tests for homogeneity are thus required to be conducted prior to selecting appropriate methods [85].

The homogeneity test developed by Swamy [86] is suitable for panel structures where the time dimension is large and the panel dimension is small. Pesaran and Yamagata [87] developed a standardized version of the Swamy test to overcome this constraint. Therefore, this study first investigates homogeneity using the delta test of Pesaran and Yamagata [87,88]. The hypotheses and mathematical model of this test are shown in Equations (2) and (3).

$$H_0:{\beta }_i=\beta$$

$$H_1:{\beta }_i\ne \beta$$

$$∆=\sqrt{N}\left({\displaystyle \frac{N^{-1}\widehat{S}-k}{\sqrt{2k}}}\right)$$

(2)

$${∆}_{adj}=\sqrt{N}\left({\displaystyle \frac{N^{-1}\widehat{S}-E\left({\widehat{z}}_{iT}\right)}{\sqrt{Var\left({\widehat{z}}_{iT}\right)}}}\right)$$

(3)

In Equation (2), $∆$ is the Swamy homogeneity test statistic, $N$ is the number of cross-sections, $k$ is the number of independent variables, and $\widehat{S}$ is the Swamy test statistic. In Equation (3), ${∆}_{adj}$ is the adjusted homogeneity test statistic, ${\widehat{z}}_{iT}$ is the normalized deviation of the estimated coefficient for each cross-section, $E\left({\widehat{z}}_{iT}\right)$ is the expected value of this statistic, and $Var\left({\widehat{z}}_{iT}\right)$ is the variance of this statistic.

Since the homogeneity or heterogeneity of the variables changes the choice of unit root and cointegration tests, the interdependence of the horizontal cross-sections forming the panel is examined using various horizontal cross-section dependence tests. To test horizontal cross-section dependence, the CD test developed by Pesaran [89] can be applied to a large number of dynamic panel models that differ in terms of stability and homogeneity [90]. Therefore, since the cross-sectional dimension is 38 and the time dimension is 31, the CD test developed by Pesaran [89] is used in this study. The mathematical representation of the CD test is given in Equation (4).

$$CD=\sqrt{{\displaystyle \frac{2T}{N(N-1)}}}{\sum }_{i=1}^{N-1}{\sum }_{j=i+1}^N{\widehat{\rho }}_{ij}^2$$

(4)

The hypotheses for the CD test can be expressed as follows:

${\mathrm{H}}_0$: There is no horizontal cross-section dependence.

${\mathrm{H}}_1$: There is horizontal cross-section dependence.

After testing the model for cross-sectional dependence, it is necessary to perform a unit root test to determine the stationarity of the panel variables. It has been observed that traditional unit root tests do not yield satisfactory results when faced with heterogeneity and/or cross-sectional dependence (CSD) in the data. To mitigate the negative effects of this limitation, Pesaran [91] developed the CIPS test, which falls under the category of second-generation unit root tests. Therefore, this study examines the stationarity of the series using the CIPS test, which falls under the category of second-generation unit root tests that also take into account the presence of inter-unit correlation and heterogeneity. This test provides more robust results since it can control both heterogeneity and cross-sectional dependence (CSD) [92].

$$CIPS\left(N,T\right)=N^{-1}{\sum }_{i=1}^N{t}_i\left(N,T\right)$$

(5)

Equation (5) presents the mathematical expression of the CIPS unit root test, where $N$ is the number of countries in the panel, $T$ is the time dimension, and $t_i\left(N,T\right)$ is the CADF test statistic.

In this stage, after the unit root tests, the Westerlund [32] cointegration test, which takes into account the horizontal cross-section dependence, is applied to detect a cointegration relationship between the series. The Westerlund [32] cointegration test consists of four basic test statistics, namely group statistics $G_a$ and $G_t$ and panel statistics $P_a$ and $P_t$. The panel cointegration test developed by Westerlund [32] shows strong performance in small samples and allows for the flexibility to take cross-sectional dependence into account. When cross-sectional dependence is detected, the bootstrap distribution is used, and when no dependence is detected, the classical asymptotic normal distribution is used [93]. The Westerlund [32] cointegration test is based on the error correction model (ECM) shown in Equation (6):

$$\Delta y_{it}={\alpha }_i+{\delta }_{i}t+{\varphi }_i\left(y_{i,t-1}-{\beta }_i^{\prime }{x}_{i,t-1}\right)+\sum _{j=1}^p{\gamma }_{ij}\Delta y_{i,t-j}+\sum _{j=0}^q{\theta }_{ij}^{\prime }\Delta x_{i,t-j}+{\epsilon }_{it}$$

(6)

where $y_{it}$ is the dependent variable, $x_{it}$ is the independent variable, ${\varphi }_i$ is the error correction coefficient, ${\beta }_i$ is the long-run relationship coefficients, $\mathrm{\Delta }$ is the first difference operator, ${\alpha }_i$ is the constant term, ${\delta }_{i}t$ is the time trend, and ${\epsilon }_{it}$ is the error term. The four different test statistics proposed by Westerlund [32] are shown in Equations (7)–(10).

$$G_t={\displaystyle \frac{1}{N}}\sum _{İ=1}^N{\displaystyle \frac{{\widehat{\varphi }}_i}{{\widehat{\sigma }}_{{\varphi }_i}}}$$

(7)

$$G_a=\sum _{i=1}^N{T}_i.{\widehat{\varphi }}_i^2$$

(8)

$$P_t={\displaystyle \frac{\widehat{\varphi }}{{\widehat{\sigma }}_{\widehat{\varphi }}}}$$

(9)

$$P_a={\displaystyle \frac{N.{\widehat{\varphi }}^2}{{\widehat{\sigma }}_{\widehat{\varphi }}^2}}$$

(10)

where ${\widehat{\varphi }}_i$ is the error correction coefficient for each unit, ${\widehat{\sigma }}_{{\varphi }_i}$ is the standard error of this coefficient, and $T_i$ is the time dimension of each unit.

After testing the cointegration relationship between the series, the Augmented Mean Group (AMG) estimator, developed by [94], which takes into account heterogeneity and cross-sectional dependence, is used to determine the long-run relationship. The AMG estimator provides robust estimates in the presence of cross-sectional dependence (CSD) and also allows for country-specific heterogeneity and non-stationarity [95]. The equation for the AMG estimator is given in Equation (11) [96]:

$$\mathrm{\Delta }{Y}_{i,t}={\theta }_{1,i}+{\theta }_{2,i}\mathrm{\Delta }E{V}_{i,t}+{\theta }_{3,i}UC{F}_i+\sum _{t=2}^T{\theta }_{4,t}{TD}_t+{\epsilon }_{i,t}$$

(11)

where $Y_{i,t}$ is the independent variable, $E{V}_{i,t}$ is all other independent variables, $UC{F}_i$ is unobservable common factors, $TD$ is the time dummy variable, and ${\epsilon }_{i,t}$ is the error term.

After determining the long-run coefficients, the causality relationship between the variables is investigated using the Emirmahmutoglu and Kose [33] causality test, which does not require preliminary tests such as the series containing unit roots and horizontal cross-section dependence. To apply the Emirmahmutoglu and Kose [33] causality test, a system defined by two equations, shown in Equations (12) and (13), can be established.

$$y_{n,t}=c_{1,i}+{\sum }_{j=1}^{k_n+{dmax}_n}{\delta }_{1,nj}{y}_{n,t-j}+{\sum }_{j=1}^{k_n+{dmax}_n}{y}_{1,nj}{x}_{n,t-j}+{\epsilon }_{1n,t}$$

(12)

$$x_{n,t}=c_{2,i}+{\sum }_{j=1}^{k_n+{dmax}_n}{\delta }_{2,nj}{y}_{n,t-j}+{\sum }_{j=1}^{k_n+{dmax}_n}{y}_{2,nj}{x}_{n,t-j}+{\epsilon }_{2n,t}$$

(13)

where $y$ is the dependent variable, $x$ is the independent variable, $n$ is the horizontal cross-section, $t$ is the time dimension, $dmax$ is the maximum order of integration for each $n$, and $k_n$ is the lag length for both estimated models. Emirmahmutoglu and Kose [33] have a number of advantages, such as taking causality and cross-sectional dependence into account, being usable even when the series are not stationary at the same level, and providing both individual and panel-level estimates [97].

4. Findings and Discussion

In this part of this study, the empirical findings on REC, CO₂, FD, EGRO, and EDP variables are compared with those of previous studies using the econometric methods mentioned in the methodology.

Table 2. Cross-section dependence for variables.

Variables	CD-Test	p-Value	Result
REC	15.69	0.000 ***	Dependency exists
FD	95.79	0.000 ***	Dependency exists
CO2	15.74	0.000 ***	Dependency exists
EGRO	64.39	0.000 ***	Dependency exists
EDP	2.13	0.033 **	Dependency exists

Note: ** indicates 0.05, and *** 0.01 significance levels.

and

Table 3. Cross-sectional dependence test results.

Test	Statistic	p-Value	Result
LM	1433	0.000 ***	Dependency exists
LM adj *	48.57	0.000 ***	Dependency exists
LM CD *	2.51	0.012 **	Dependency exists

Note: ** 0.05 indicates, and *** 0.01 significance levels.

present the findings on cross-sectional dependence, concerning units and models.

Table 2 presents the results of horizontal cross-section dependence between the units in the panel using the Pesaran CD test. In this context, cross-sectional dependence is found at the 1% significance level for the REC, FD, CO₂, and EGRO variables, and at the 5% significance level for EDP variable. Table 3 shows the test results for the horizontal cross-section dependence of the model. The test results for horizontal cross-section dependence analysis include LM, LM adj, and LM CD test statistics, as well as probability values. According to the probability values given, p < 0.05 means that “Ho: There is no horizontal cross-section dependence” is rejected. Thus, it is determined that the model exhibits horizontal cross-section dependence at a 5% significance level. These results show that there is horizontal cross-section dependence in the model established within the scope of the research, both on the basis of the variables and other influencing factors.

The results of the homogeneity delta test developed by Pesaran and Yamagata [87] following the horizontal cross-section dependence test are shown in

Table 4. Test of heterogeneity.

Test	Statistics	p-Value	Result
$\widetilde{∆}$	39.887	0.000 ***	Slopes are heterogeneous
${\widetilde{∆}}_{adj}$	44.416	0.000 ***	Slopes are heterogeneous

Note: *** indicates 0.01 significance levels.

. According to the results obtained from the delta test, the null hypothesis that “slope coefficients are homogeneous” is statistically rejected, as p < 0.05. This result indicates that there is no homogeneity among the variables used in the research; that is, the relationships differ between units.

Following the assessment that the slope coefficients are not homogeneous and that there is horizontal cross-sectional dependence between variables and the model, it was realized that employing second-generation unit root tests is appropriate, considering the availability of correlations between units, along with heterogeneity. In this regard, the study employed the CADF and CIPS tests, which are types of second-generation unit root tests. The results of CADF and CIPS tests are presented in

Table 5. CADF and CIPS unit root test results.

Stationary
Variables	CADF			CIPS
	Level	First Difference	Stationary at Level	Level	First Difference	Stationary at Level
REC	−0.911	−3.758 ***	I(I)	−0.949	−5.039 ***	I(I)
FD	−1.984	−3.886 ***	I(I)	−2.319 ***		I(0)
CO2	−0.943	−3.548 ***	I(I)	−1.172	−4.844 ***	I(I)
EGRO	−3.077 ***		I(0)	−3.796 ***		I(0)
EDP	−1.623	−3.746 ***	I(I)	−1.740	−5.038 ***	I(I)
Stationary Trending
REC	−2.280	−3.771 ***	I(I)	−2.277	−5.088 ***	I(I)
FD	−2.367	−3.807 ***	I(I)	−2.942 ***		I(0)
CO2	−2.178	−3.646 ***	I(I)	−2.233	−4.979 ***	I(I)
EGRO	−3.081		I(0)	−3.845 ***		I(0)
EDP	−2.328	−3.928 ***	I(I)	−2.490	−5.241 ***	I(I)

Note: *** indicates 0.01 significance levels.

, both with and without trend.

According to the unit root test results in Table 5, the EGRO variable is stationary at the level value in both CADF and CIPS unit root tests. While the FD variable is stationary at first difference according to CADF test results, it is stationary at level according to CIPS results. REC, ${\mathrm{C}\mathrm{O}}_2$, and EDP variables are found to be stationary at first difference in both CADF and CIPS unit root tests.

After analyzing the stationarity levels of the series, the Westerlund [32] test, which is appropriate for cases where there is cross-sectional dependence, is applied to determine the cointegration relationship. Since the slope coefficients are heterogeneous in our study, $G_a$ and $G_t$ test statistics are used to determine whether there is a cointegration relationship. The results of the Westerlund cointegration test are presented in

Table 6. Westerlund panel cointegration tests.

Statistic	Value	z-Value	p-Value	Robust p-Value
$G_t$	−3.551	−7.135	0.000 ***	0.030 **
$G_a$	−15.510	−2.055	0.000 ***	0.010 **
$P_t$	−29.535	−15.039	0.000 ***	0.740
$P_a$	−19.652	−8.412	0.000 ***	0.410

Note: ** 0.05, and *** 0.01 indicates significance levels.

.

According to the Westerlund [32] panel cointegration test results in Table 6, according to the robust probability values of $G_t$ and $G_a$ statistics, the null hypothesis of “No Cointegration” is statistically rejected at 5% significance level, since p < 0.05. According to this result, there is a cointegration relationship between the variables. The cointegration test results indicate that renewable energy consumption, financial development, economic growth, and carbon emissions share a long-run equilibrium. The result implies that structural interactions between the variables can have long-term implications for the energy transition process. For instance, Germany’s “Energiewende” policy not only subsidized investment in renewables but also created financial instruments like green banks and green bonds to mobilize finance for investment. Due to such financial structural changes, renewable energy utilization in Germany has risen consistently in the long run [98]. Similarly, it has been empirically established that the development of the Turkish financial system and renewable energy use are in favor of each other and that their nexus has a long-run equilibrium framework [77]. Having specified a cointegration relation, the Augmented Mean Group (AMG) estimator is applied to estimate the coefficient of the long-run relation among the variables. The estimator is applicable under the conditions of heterogeneous slope parameters and horizontal cross-section dependence.

However, it is necessary to test whether the variables used in our study, FD and EGRO, have an endogeneity problem against REC. In our study, the Durbin–Wu–Hausman test was performed to determine whether the FD variable, the primary independent variable, exhibits endogeneity in terms of the REC variable, which is the dependent variable. Since the alternative hypothesis of the test was rejected (χ² = 0.735, p = 0.391), it was concluded that the FD variable is exogenous. Similarly, it was concluded that the EGRO variable also satisfies the exogeneity condition (χ² = 0.903, p = 0.342). The results derived from the application of the AMG estimator are presented in

Table 7. Augmented mean group (AMG) estimator.

REC	Coefficient	Std. Error	Z	p > |Z|	Result
FD	4.7014	2.2630	2.08	0.038 **	Statistically Significant and Positive
CO2	−4.9878	1.2974	−3.84	0.000 ***	Statistically Significant and Negative
EGRO	0.0399	0.0172	2.32	0.020 **	Statistically Significant and Positive
EDP	0.0265	0.0260	1.02	0.309	Insignificant
Wald chi2 = 25.56		Prob > chi2 = 0.000

Note: ** 0.05 and *** 0.01 indicates significance levels.

.

Based on the findings established by the AMG Estimator in Table 7, FD has a positive and significant impact on REC. Based on this finding, FD has a positive influence on REC. To this end, based on the RECAI index, which reflects the most attractive countries for renewable energy investment, it can be concluded that FD fosters REC. These results are in agreement with those of earlier research [76,77,78,79]. It can thus be concluded that policies promoting the use of renewable energy are utilized more efficiently by countries that are financially advanced.

The other important result of the analysis indicates that CO₂ emissions have a statistically significant negative impact on REC. This study concludes that higher REC is linked to a reduction in CO₂ emissions. The results obtained from the current research are in line with findings in previous studies [51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66]. Therefore, in addition to the basic view that increased REC reduces CO₂ emissions, there is also evidence that REC should be encouraged in order to reduce CO₂ emissions.

In the empirical analysis conducted within the scope of the research, it was also found that the EGRO variable has a positive and significant effect on REC. These findings reveal that EGRO has positive effects on REC. The research findings indicate that they align with previous studies [42,43]. The results of the research indicate that expanding economies also promote investment in renewable energy. In particular, it was found that the EDP variable has no statistically significant impact on REC. These findings can be interpreted to mean that the theoretically anticipated negative correlation is not applicable in certain nations due to structural, institutional, and political obstacles. Sovacool [99] notes that energy markets are typically structured to promote the production and consumption of fossil fuels and that market distortions, such as subsidies and institutional privileges, hinder the implementation of renewable energy technologies. This example indicates that even in energy-importing countries, infrastructure for fossil fuels and short-term energy security concerns can limit the shift to renewable energy and thus hinder the development of a substantial relationship with sustainable practices. It is, however, contended that a study on the causal relationships between the variables should be conducted to facilitate a less biased analysis.

The findings in

Table 8. Emirmahmutoglu and Kose panel causality test results.

Variables	Fisher Test Stat.	p-Value	Result
FD→CO2	92.532	0.096 *	Causality exists
CO2→FD	52.814	0.980	No causality
FD→REC	79.224	0.378	No causality
REC→FD	74.279	0.534	No causality
FD→EGRO	119.562	0.001 ***	Causality exists
EGRO→FD	59.277	0.922	No causality
FD→EDP	87.759	0.168	No causality
EDP→FD	59.247	0.922	No causality
REC→CO2	114.313	0.003 ***	Causality exists
CO2→REC	116.106	0.002 ***	Causality exists
REC→EGRO	98.101	0.045 **	Causality exists
EGRO→REC	136.052	0.000 ***	Causality exists
REC→EDP	81.085	0.324	No causality
EDP→REC	132.404	0.000 ***	Causality exists
EDP→CO2	114.776	0.003 ***	Causality exists
CO2→EDP	156.446	0.000 ***	Causality exists
EDP→EGRO	93.123	0.089 *	Causality exists
EGRO→EDP	117.667	0.002 ***	Causality exists
CO2→EGRO	106.506	0.012 **	Causality exists
EGRO→CO2	100.423	0.032 **	Causality exists

Note: * indicates 0.10, ** 0.05, and *** 0.01 significance levels.

of Emirmahmutoglu and Kose’s [33] causality test indicate that there is one-way causality from the FD variable to the CO₂ variable at the 10% significance level. The result could be interpreted to indicate that increases in FD boost economic activity, which consequently results in a rise in CO₂ emissions through channels such as production and transport. That said, one-way causality from FD to EGRO at the 1% significance level means that financial development has a significant role in supporting EGRO.

A strong bidirectional relationship exists across the variable REC and CO₂ emissions at the level of significance of 1%. The reciprocal relationship points to the importance of the interdependence of the environment’s sustainability and energy policy measures. This finding is consistent with previous studies [67]. This highlights the urgency of improving the development of renewable energy technologies and shaping policies geared toward curbing CO₂ emissions. The establishment of a bidirectional causal relationship identifies the influence of REC on the level of CO₂ and the impact of the level of CO₂ on REC concurrently. The findings are in agreement with those obtained from the estimation using the AMG estimator, as presented in Table 7. Therefore, this study encourages policymakers to implement increased investments in renewable energy as a measure for curbing CO₂ emissions.

Likewise, a causal relationship was established from REC to EGRO at a 5% significance level and from EGRO to REC at a 1% significance level. This discovery, thus, implies that, as much as REC leads to EGRO through the minimization of energy expenses, increasing economies must also invest in renewable energy in order to minimize the impact of EGRO on the environment by limiting CO₂ emissions. The findings from this study are also supported by previous studies [6,9,35,62,76]. Additionally, a causality relationship from EDP to REC is established at the 1% level of statistical significance. In this case, the results from RECAI countries indicate that policymakers need to invest more in renewable energy so that reliance on non-renewable energy sources can be reduced. This is also supported by the bidirectional causality relationship between EDP and CO₂ at 1% level of significance. Thus, when this result is balanced with the causality relation between EDP and REC, it can be stated that policymakers’ growing investment in renewable energy to minimize their reliance on overseas energy can be fruitful in both guaranteeing energy supply security as well as managing CO₂ emissions.

In addition, bidirectional causality was found to run from EDP to EGRO at the 10% level of significance and from EGRO to EDP at the 1% level of significance. The finding indicates that EDP affects EGRO since EGRO affects EDP by increasing the demand for energy. Finally, there is a bidirectional causality between CO₂ and EGRO at the 5% level of significance. This result implies that the speed of EGRO is a driver of certain factors, while CO₂ emissions simultaneously impact EGRO. Thus, this study’s findings imply that governmental bodies should further invest in renewable energy to stimulate sustainable economic growth and ensure energy supply security while preventing environmental degradation.

5. Conclusions

The globe is experiencing numerous environmental issues, including global warming and the degradation of natural resources. The threats are likely to have a negative impact on human life and health, in addition to undermining economic stability, development aspirations, and political conditions. In energy-dependent countries, energy security is a major problem. This study contributes to the literature by investigating the relationship between REC, CO₂ emissions, EGRO, FD, and EDP and discussing the findings. This relationship is particularly addressed in the context of countries that are attractive for renewable energy investments.

In this study, coefficient estimates obtained using the AMG estimator revealed that FD and EGRO have a positive and significant effect on REC. On the other hand, a negative and statistically significant relationship is observed between CO₂ emissions and REC. Additionally, the relationship between EDP and REC is not statistically significant. This may indicate that external dependence is not the sole determining factor in renewable energy consumption or that this relationship may manifest differently depending on country-specific factors such as economic priorities and technological capacity. Considering the findings from the causality test in this study, FD is the cause of both CO₂ emissions and EGRO. The findings from this study can be interpreted as indicating that an increase in FD levels may also increase production and, in parallel, lead to increased consumption and investment. This situation increases EGRO on the one hand while causing an increase in EDP on the other. In this context, to ensure that FD and EGRO do not cause environmental degradation, governments should also implement sustainable energy and financial policies that encourage environmentally friendly investments. In this regard, the use of environmentally friendly financial products and tools such as green bonds, credit policies based on environmental criteria, and carbon markets should be encouraged. Similarly, subsidizing a certain portion of investments by the state or providing tax incentives to encourage the private sector to invest in renewable energy will pave the way for an increase in such investments. Within the scope of preventive restrictive measures, environmental burdens can be reduced within sustainability through tools such as new carbon taxes or sectoral carbon limits. To ensure energy supply security, long-term policies should be developed to accelerate the transition away from fossil fuels and toward domestic renewable sources.

According to the findings, renewable energy supports economic growth, while a growing economy can also support renewable energy investments. In addition, this study determined that EDP is the cause of REC and revealed a reciprocal causality between EDP and CO₂. This situation shows that energy needs require both a shift toward renewable sources for security and the development of regulations that will reduce carbon emissions. The reciprocal causality between EDP and EGRO indicates that EGRO triggers EDP by increasing energy demand, and, in turn, EDP triggers EGRO. Furthermore, the analysis reveals a two-way causality between CO₂ and EGRO: EGRO triggers CO₂ by increasing environmental pressures, while increasing environmental degradation can also have limiting effects on EGRO. When taken together, the findings suggest that governments need to increase investments in renewable energy to minimize environmental degradation while ensuring sustainable economic growth and energy supply security.

Our research also has certain limitations. The sample is restricted to RECAI nations, and possible structural differences between countries in certain variables are deemed limitations of our research. To improve future research, policymakers should increase the sample size, use sector-level information, incorporate other microenvironmental factors, such as ESG, and introduce different insights using dynamic causality analyses.