The Effect of Economic Freedom, Indicators of Financial Sector Development, Income and Education on Renewable Energy Use: An Empirical Analysis of Post-Transition EU Member States

Abstract

Fossil fuels are among the most crucial factors underlying global environment impairment through CO₂ emissions. In addition to this, the globalized world has witnessed significant price volatility, instability and disruptions in the supply of fossil fuels. Therefore, renewable energy transition (RET) has become a mandatory option for countries to tackle these environmental, economic, and energy insecurity problems in energy markets dominated by fossil fuels. But the RET process has remarkably slowed down in recent years due to increasing economic volatility, financial obstacles, geopolitical risks, and bottlenecks in the development of low-carbon technologies. In this regard, this study investigates the effect of market structure proxied by economic freedom and indicators of financial development, together with real GDP per capita and education, on the utilization of renewable energy in post-transition EU member states across the 2000–2021 duration by utilizing causality and cointegration tests. The outcomes of the causality analysis reveal a feedback relationship among renewable energy use, economic freedom, indicators of financial development, and real GDP per capita but a unilateral causality between education and renewable energy use. On the other hand, the outcomes of AMG estimation reveal a positive effect of economic freedom, real GDP per capita, and education on the utilization of renewable energy in some countries but a negative effect of financial institutions’ development on renewable energy use and mixed results on the effect of financial markets’ development regarding renewable energy use. Our results indicate that education is a significant instrument to make progress in renewable energy use via multiple channels, but governments should incentivize the financial system to support the RET process by favorable lending and sustainable finance instruments like green bonds or sustainability-linked loans.

1. Introduction

The environment has experienced significant deterioration since the Industrial Revolution, and recognizing the negative health, economic, and social results of environmental impairment such as global warming, climate change, natural disasters, chronic medical illnesses, famine, poverty, and species loss has made environment protection one of the high-priority issues of the globalizing world. In this sense, the utilization of fossil fuels has been uncovered as the primary driver of environmental impairment [1] and leads nearly 90% of world-wide CO₂ emissions [2], but renewable energy sources such as wind power, solar power, bioenergy, and geothermal with low carbon are limiting the growth in global CO₂ emissions [3]. On the other hand, limited reserves of fossil fuels and disruptions in the supply of fossil fuels mainly resulting from geopolitical conflicts, together with environmental and economic concerns, have motivated countries to transition to the renewable energy and also use demand-side management measures in the energy sector [4].

However, global clean energy investments were about USD 1.8 trillion in 2023, and most of these investments were carried out by advanced economies and China, and less than 15% of these investments were carried out by developing and emerging countries [5]. In this regard, renewable energy transition has been progressing slowly, and the amount of fossil energy consumption will stay substantially unchanged in the near future [6]. The ETI (Energy Transition Index) by the World Economic Forum also verified a slower increase in the last three years, and only 53 of the 120 countries have made a steady improvement in renewable energy transition during the past decade [7]. The growing economic volatility, geopolitical tensions, and technological shifts in recent years have been the main causes underlying the slowdown in renewable energy transition [7].

In the empirical literature, a great number of economic, technological, social, environmental, and institutional factors have been discovered to be considerable factors underlying renewable energy transition [8]. In this research study, the effect of economic freedom, indicators of financial development, real GDP per capita, and education on the utilization of renewable energy is explored given their crucial roles in renewable energy transition.

Economic freedom is defined as the ability of persons to make their own decision without any governmental limitations and intervention or limitations by some groups with privileges granted by governments [9]. Economic freedom can foster renewable energy investments by encouraging innovation, technological progress and entrepreneurial activities resulting from higher flexibility and rewards, owing to the market-oriented legal system and regulations, rational fiscal and monetary policies, and low taxes [10]. However, free markets and market-oriented legal system and regulations can sometimes cause the environmental standards to be ignored, and in turn, short-term profits can take precedence over long-term sustainability in free markets [11]. In this sense, attitudes of persons towards renewable energy use are also significant for the interaction between renewable energy and economic freedom. Therefore, socio-economic factors such as income, education, and environmental awareness are decisive in changing people’s attitudes towards clean energy use. As a consequence, the influence of economic freedom on the utilization of renewable energy may be different depending on the environmental policies in force and the socioeconomic development levels of societies.

The financial system is another driver of renewable energy utilization through many channels such as direct, business and wealth effects [12]: The direct effect arises when economic units easily access financial sources as a result of an efficiently functioning financial system and in turn buy durable products and increase the energy demand. The business effect can also increase the energy demand when the development of the financial sector enables companies to establish new ventures or extend their activities through introducing less costly funds. Last, the wealth effect is induced by households and firms in the developed financial system. However, at the demand side of renewable energy markets, preferences of economic units mainly towards renewable energy use depending on socio-economic factors and environmental regulations are determinative for the interaction between financial indicators and the utilization of renewable energy. At the supply side of renewable energy markets, firms can be fundraised easier through a developed financial system.

Both economic freedom and financial sector development have been proposed as significant determinants of economic growth [13,14]. Therefore, both variables can also impact renewable energy use via economic growth. Moreover, hypotheses of growth, conservation, feedback, and neutrality have been suggested on the relation between energy deployment and economic growth [15]. The growth hypothesis states that there is a unilateral causal nexus between energy use and economic growth, but the conservation hypothesis suggests the exact opposite causal association between economic growth and energy utilization. On the other hand, the feedback hypothesis introduces a bilateral causal nexus between energy use and economic growth. Last, the neutrality hypothesis claims that there is an insignificant interaction between economic growth and energy use.

Education is also an indispensable factor in the development and utilization of renewable energy. On the one hand, the development of renewable energy technologies and ultra-high-voltage (UHV) transmission projects requires a higher level of human capital resulting from higher education levels [16,17]. On the other hand, persons with lower education levels are usually reluctant to use renewable energy due to lower environmental awareness and a lower propensity to accept new technologies [18]. Consequentially, a reciprocal interplay between renewable energy use and education is theoretically expected.

Based on the above considerations, this article aims to explore answers to the following research questions:

Does economic freedom increase or decrease renewable energy use?

Do financial institutions and markets increase or decrease renewable energy use?

Does real GDP per capita increase or decrease renewable energy use?

Does education increase or decrease renewable energy use?

The effect of economic freedom, indicators of financial development, real GDP per capita, and education on renewable energy use is explored in a sample of post-transition EU (European Union) member states because the transition and EU membership processes have caused these countries to experience significant improvements in economic freedom, financial sector development, real GDP per capita, and education, as presented in Table 1.

This article seeks to make a contribution to the associated literature from three perspectives. In the empirical literature, Amoah et al. [15], Marinescu and Fucec [23], Abeka et al. [24], Özçelik and Önder [25], Bétila [26], Uzar [27], and Aydin et al. [28] have already examined the different indicators of economic freedom regarding renewable energy use in the panels of different countries, but this study will be one of the first papers investigating the relationship between economic freedom and renewable energy use in transition countries that have achieved remarkable progress in economic freedom. Secondly, the nexus between different financial indicators related to banks, stock markets, and the overall financial system and renewable energy use has been widely studied in the related empirical literature, but this study will be one of the first papers separately analyzing the interaction between the development of financial institutions and financial markets and renewable energy use in conjunction with Sun et al. [29]. Last, the empirical literature on the interaction between economic freedom, financial indicators, and the utilization of renewable energy has remained inconclusive. Therefore, conducting more studies on this research topic should be useful to clarify the interplay among economic freedom, financial indicators, and the utilization of renewable energy.

In the remainder of this study, the existing literature on the nexus between economic freedom, indicators of financial development, real GDP per capita, education, and renewable energy use is reviewed, and the main findings of these studies are summarized in the literature review. The hypotheses of this study based on the associated literature are also presented at the end of literature review. Then, the variables employed in the empirical analysis and methodological approach including the second-generation cointegration test and causality test are explained in the Data and Methodological Approach Section. The pre-tests of cross-sectional dependence (CD) and heterogeneity, the unit root test, cointegration and causality tests are performed, and their results are presented and evaluated in the Results and Discussion Section. This research paper is finalized with conclusions and policy suggestions.

2. Literature Review

In the associated empirical literature, the relationship between economic freedom indicators and renewable energy has remained inconclusive as a result of few empirical studies, presented below, being carried out. In this regard, Amoah et al. [15], Abeka et al. [23], Marinescu and Fucec [24], Özçelik and Önder [25], and Bétila [26] performed regression analysis to investigate the relationship between indicators of economic freedom and renewable energy use for different panels of countries, but they reached mixed results. On the other hand, Uzar [27] and Aydin et al. [28], respectively, utilized AMG (augmented mean group), CCEMG (common correlated effects mean group) and RCCE (regularized common correlated effect) estimators, and these studies also revealed different results in a similar vein.

Amoah et al. [15] studied the effect of economic freedom and economic wellbeing on the utilization of renewable energy in 32 African countries across the 1996–2017 duration by means of dynamic ordinary least squares and discovered that improvements in indicators of business and trade freedom increased the use of renewable energy, while tax burden and property rights decreased the utilization of renewable energy. Abeka et al. [23] also studied the influence of economic freedom and its main dimensions on renewable energy production in 41 African states by means of the dynamic regression approach and unraveled a positive relation between economic freedom and the utilization of renewable energy. In addition, they unveiled that government size and trade freedom had a positive effect on renewable energy, while legal system and property rights had a negative effect on the production of renewable energy.

Marinescu and Fucec [24] studied the interaction between economic freedom and renewable energy in Germany, Greece, Romania, Switzerland, and Ukraine between 1995 and 2011 by means of regression and revealed that economic freedom had a positive effect on investments in renewable energy in Germany and Greece, and economic freedom also had a positive influence on the production of renewable energy investments in Romania, Switzerland, and Ukraine.

Özçelik and Önder [25] conducted an exploration on the factors underlying the utilization of renewable energy in 146 countries between 2010 and 2019 via the regression approach and identified that monetary and labor freedom positively affected the use of renewable energy, but indicators of financial and investment freedom negatively influenced the utilization of renewable energy. Bétila [26] explored the interplay between CO₂ emissions and economic freedom in a panel of 138 states through regression and causality approaches and disclosed a bidirectional causal relation between the utilization of renewable energy and economic freedom.

Uzar [27] analyzed the relationship between the freedom of expression and utilization of renewable energy in selected high-income economies for the 1990–2022 term by means of AMG and CCEMG estimators and disclosed a positive association between expression freedom and the utilization of renewable energy. On the other hand, Aydin et al. [28] explored the long-term effect of economic freedom, environmental policy, material productivity, and economic globalization on the utilization of renewable energy in 22 OECD members across the 1995–2020 duration by the Westerlund and Edgerton cointegration approach and their estimations by the RCCE estimator revealed a positive influence of economic freedom on the utilization of renewable energy only in South Korea and a negative influence in Italy, Ireland, and the United States. Moreover, the estimations by the AMG estimator disclosed that economic freedom had a positive influence on the utilization of renewable energy in the Netherlands, the Republic of Korea and Turkiye but a negative influence in Australia, Japan, and Mexico.

Based on the theoretical thoughts and empirical results in the associated literature, the first hypothesis of the study is defined as follows:

H1. 

Economic freedom has a significant effect on the utilization of renewable energy.

The association between various indicators of financial sector development and the utilization of renewable energy has been widely explored, and their results are summarized in Table 2. The empirical papers on the relation between financial indicators and the utilization of renewable energy have usually employed regression-based analyses and cointegration analyses, but their results have also remained inconclusive. On the one hand, Wu and Broadstock [30], Çetin and Bekirtaş [31], Khan et al. [32], Kutan et al. [33], Anton and Nucu [34], Mukhtarov et al. [35], Prempeh [36], Liu et al. [37], Athari [38], and Musakwa et al. [39] identified a positive influence of proxies of financial sector development on the utilization of renewable energy. On the other hand, Assi et al. [40], Saadaoui and Chtourou [41], and Van Nguyen [42] disclosed a negative influence of financial indicators on renewable energy use. Furthermore, Sun et al. [29], Lahiani et al. [43], and Vatamanu and Zugravu [44] have mixed results. Last, Burakov and Freidin [45], Saadaoui [46], Dong and Akhtar [47], Saadaoui and Chtourou [41], and Prempeh et al. [48] also researched the causal association between proxies of financial sector development and the utilization of renewable energy, and they discovered mixed causal results between the two variables.

Based on the theoretical thoughts and empirical results in the associated literature, the second and third hypotheses of the study is defined as follows:

H2. 

The development of financial institutions has a significant effect on the utilization of re-newable energy.

H3. 

The development of financial markets has a significant effect on the utilization of renewable energy.

The empirical studies on the nexus between real GDP per capita and renewable energy use have revealed mixed findings. In this regard, Bekun and Alola [49], da Silva et al. [50], Gholami et al. [51], and Ackah and Kizys [52] revealed a positive effect of real GDP per capita on renewable energy use. On the other hand, Akarsu and Korucu Gümüşoğlu [53] and Ergun and Rivas [54] found that real GDP per capita had a negative effect on renewable energy use in lower income levels, but the effect of income on renewable energy use became positive after a threshold income level. Furthermore, empirical studies on the causal interplay between income and renewable energy use have also revealed the consequences supporting hypotheses of feedback, growth, conservative, and neutrality [8].

Based on the theoretical thoughts and empirical results in the associated literature, the fourth hypothesis of the study is defined as follows:

H4. 

Real GDP per capita has a significant effect on the utilization of renewable energy.

Last, the majority of researchers exploring the nexus between education and renewable energy use have uncovered a positive effect of various educational indicators on renewable energy use for different panels of countries. In this context, Sart et al. [16], Gholami et al. [51], and Esenyel İçen [55] concur, but Wang et al. [56] revealed a negative long-run effect of education on renewable energy use in Next-11 countries.

Based on the theoretical thoughts and empirical results in the associated literature, the fifth hypothesis of the study is defined as follows:

H5. 

Education has a significant effect on the utilization of renewable energy.

In sum, all hypotheses of the study presented in Figure 1 are specified as based on the related theoretical and empirical literature research.

3. Data and Methodological Approach

This research studies the connection between economic freedom, indicators of financial sector development, real GDP per capita, and education and the utilization of renewable energy in new EU members across the 2000–2021 period. In the econometric analyses, renewable energy utilization (RNW) is represented by renewable energy use as a percent of the overall final energy use and is acquired from World Bank [57]. On the other hand, the country-level economic freedom index is mainly measured by the Fraser Institute and the Heritage Foundation using five and ten indicators, respectively. Both indexes have been widely used by researchers in the associated literature, and there also exists a high correlation between these two economic freedom indexes [58]. However, Murphy [58] discerns a better performance of the Fraser index compared to the Heritage index when these indexes are used in regressions by themselves and when all institutional quality indicators are together used as independent variables. Therefore, economic freedom (ECNFR) is proxied by the economic freedom index of the Fraser Institute [19] and reflects the combination of five main components including government size, the legal system and property rights, the consistency of monetary institutions and policies, the freedom of international trade, and regulations.

Financial development is proxied by two indicators including the development of financial institutions (FININST) and financial markets (FINMARK) of IMF [20]. These two indicators are calculated by simultaneously regarding the depth, access, and efficiency of the financial sector differently from other proxies of financial sector development such as private credit to GDP and stock market capitalization [20]. Furthermore, control variables of real GDP per capita (RGDP) based on constant 2015 USD and education (EDU) proxied by mean schooling years are, respectively, obtained from World Bank [21] and the UNDP (United Nations Development Programme) [22].

This study includes recent EU members consisting of Bulgaria, Croatia, Czechia, Estonia, Hungaria, Latvia, Lithuania, Poland, Romania, Slovakia, and Slovenia. The rationale behind the determination of the study sample is that these countries have experienced a transition process and have made notable progress in economic freedom and the development of financial institutions and financial markets. The presence of economic freedom and renewable energy use data obliged us to perform econometric analyses during the 2000–2021 period. In the empirical analysis presented in Figure 2, summary statistics of the series are firstly reported, and the pretests of CD and heterogeneity are employed to decide which unit root, causality, and cointegration tests we employ. As a result, the Pesaran [59] CIPS (cross-sectional augmented Im–Pesaran–Shin) unit root test, Juodis, Karavias, and Sarafidis (JKS) [60] causality test, and Westerlund and Edgerton [61] LM bootstrap cointegration test are preferred to increase the reliability of the findings resulting from the identification of CD and heterogeneity among the variables.

The mean values of RNW, ECNFR, FININST, FINMARK, RGDP, and EDU are, respectively, 19.327% of the overall final energy use, 7.409 out of 10, 0.497 out of 1, 0.181 out of 1, USD 12,829.92, and 11.889 years, as presented in Table 3. However, the standard deviation values of each series demonstrate that RNW and RGDP display noteworthy variation during the 2000–2021 period, but ECNFR, FININST, and FINMARK demonstrate moderate variation during the same period. Any outliers are not identified for each series, but there are considerable differences in terms of renewable energy use and real GDP per capita among post-transition EU member states, as seen in Table 1 and Table 3.

This article performs unit root, cointegration and causality tests sensitive to the subsistence of CD and heterogeneity to investigate the connection between economic freedom, indicators of financial sector development, real GDP per capita, and education and the utilization of renewable energy. In this context, the causality test investigates whether one variable is helpful in forecasting another variable [62], while the cointegration test analyzes the series that exhibit a co-movement in the long run. In other words, two variables with stochastic series are cointegrated if their linear combination eliminates the trends [63]. Therefore, the use of causality and cointegration enables us to investigate the short-term and long-term relationship among the variables differently from the regression-based analyses. Also, the causality test allows us to make a two-way analyses unlike the other econometric techniques.

The causal association amongst RNW, ECNFR, FININST, FINMARK, RGDP, and EDU is examined by means of the JKS [60] causality test to see whether there exists feedback or a one-way interplay among these variables. Additionally, the JKS causality test has some advantages in comparison of the conventional Granger causality test. The test takes into account heterogeneity and is robust under the subsistence of CD [60]. The JKS causality test benefits from the half-panel jackknife (HPJ) method by Dhaene and Jochmans [64] to reduce Nickell bias and in turn improves the robustness of test outcomes.

The LM bootstrap cointegration test of Westerlund and Edgerton [61] is employed to study the long-term connection between RNW, ECNFR, FININST, FINMARK, RGDP, and EDU because, on the one hand, this test takes into account CD, and on the other hand, it generates robust outcomes for small samples.

In the last stage of the study, the long-term effect of economic freedom, indicators of financial sector development, real GDP per capita, and education on the utilization of renewable energy is specified by use of the AMG (augmented mean group) estimator of Eberhardt and Bond [65] because this estimator addresses the issues of endogeneity, CD, heterogeneity, and non-stationarity [66]. The AMG estimator adopts the model in Equation (1) and takes into account CD by adding a common dynamic effect produced from the period dummy coefficients of a pooled regression in the first differences to the group-specific regressions [65].

(1)$y_{it}={\beta }_i^{\prime }{x}_{it}+u_{it} u_{it}={\alpha }_i+{\lambda }_i^{\prime }{f}_t+{\epsilon }_{it}$

(2)$x_{mit}={\pi }_{mi}+{\delta }_{mi}^{\prime }{g}_{mt}+{\rho }_{1mi}{f}_{1mt}+\cdots +{\rho }_{nmi}{f}_{nmt}+v_{mit}$

where $x_{it}$ is a vector of observable covaritates and unobservables $u_{it}$ are established as a combination of ${\alpha }_i$ (group-specific effects), $f_t$ (common factors), and ${\lambda }_i^{\prime }$ (group-specific factor loadings).

The AMG estimation process is realized through three stages [65]. In the first stage, a standard ordinary least squares regression is performed in the first difference with T-1 period dummies in first differences $∆D_t$, and the estimated coefficients are relabeled as ${\widehat{\mu }}_t^{.}$. Then, ${\widehat{\mu }}_t^{.}$ is included in each of the N group-specific regressions, which also consist of linear trend terms to account for omitted idiosyncratic processes that evolve in a linear fashion over time [65]. The inclusion of ${\widehat{\mu }}_t^{.}$ in the second stage allows for their heterogeneous impact on $y_{it}$ to be noted and accounts for the unobserved common factors [63].

(3)$AMG Stage 1: ∆y_{it}=b^{\prime }∆x_{it}+{\displaystyle \sum }_{t=2}^T{c}_t∆D_t+e_{it} \to \widehat{c_t}\equiv {\widehat{\mu }}_t^{.}$

(4)$AMG Stage 2 \&3: y_{it}={\alpha }_i+b_i^{\prime }∆x_{it}+c_{i}t+d_i{\widehat{\mu }}_t^{.}+e_{it} \to {\widehat{b}}_{AMG}=N^{-1}{\displaystyle \sum }_i{\widehat{b}}_i$

4. Results and Discussion

Econometric tests of the unit root, cointegration and causality can be differed based on whether they take into account CD and heterogeneity. Therefore, the availability of CD and heterogeneity is firstly researched by different versions of LM (Lagrange multiplier) and delta tilde tests. In line with this objective, LM_adj., LM CD, and LM tests [67,68,69] are run, and the null hypothesis of CD independence is abnegated by taking notice of their outcomes introduced in Table 3. Then, delta tilde tests [70] are run, and the null hypothesis of homogeneity is similarly rejected in the light of the tests’ outcomes shown in Table 4. To conclude, there exists CD and heterogeneity amongst RNW, ECNFR, FININST, FINMARK, RGDP, and EDU, and econometric tests of the unit root, cointegration, and causality taking notice of CD and heterogeneity should be opted for.

Following the subsistence of CD and heterogeneity, discerning the presence of the unit root in the series of RNW, ECNFR, FININST, FINMARK, RGDP, and EDU is also required to perform the appropriate cointegration and causality tests. Therefore, the panel CIPS unit root test of Pesaran [67] is run and its outcomes are introduced in Table 5. The test outcomes demonstrate to us that RNW, ECNFR, FININST, FINMARK, RGDP, and EDU contain a unit root, but these variables become stationary when the unit root test is performed with their first-differenced values.

The long-run association between RNW, ECNFR, FININST, FINMARK, RGDP, and EDU is investigated by means of the LM bootstrap cointegration test, and its outcomes are introduced in Table 6. Bootstrap p values are taken as references to decide the cointegration interplay amongst RNW, ECNFR, FININST, FINMARK, RGDP, and EDU under the condition of CD. Otherwise, asymptotic p-values are utilized to decide the presence of cointegration relationship among the variables. In this study, bootstrap p values are taken as references, and bootstrap p values at both the constant and constant+ trend are unraveled to be greater than 5%, and thus, significant cointegration among RNW, ECNFR, FININST, FINMARK, RGDP, and EDU is concluded. The selection of the cointegration test sensitive to CD presence justifies the implementation of CD tests. On the contrary, we may have revealed insignificant cointegration among these variables if a traditional cointegration test had been run.

The long-run influence of economic freedom, indicators of financial sector development, real GDP per capita, and education on renewable energy use is specified by means of the AMG estimator, and the cointegration coefficients are revealed in Table 7. The results reveal that economic freedom has a positive effect on renewable energy use in Bulgaria, Czechia, and Lithuania. On the other hand, the development of financial institutions has a negative effect on renewable energy use across the overall panel and also in Croatia, Hungary, Latvia, Lithuania, Poland, and Slovakia but positive only in Romania. However, the development of financial markets has a positive influence on the utilization of renewable energy in Estonia and Hungary and a negative effect only in Bulgaria and Slovakia. Real GDP per capita has a positive effect on renewable energy use across the overall panel and in Estonia and Hungary. Last, education has a positive impact on renewable energy use in Czechia, Estonia, Lithuania, and Slovakia.

The interaction between economic freedom and renewable energy can be varied depending upon the environmental policies in force, environmental awareness of individuals and businesses, and current economic development levels of the countries. Our results reveal that the overall economic freedom index has a significant positive effect on renewable energy use only in Bulgaria, Czechia, and Lithuania. In the associated empirical literature, researchers have analyzed the effect of the overall economic freedom index and the main components of economic freedom on renewable energy use. Abeka et al. [23] and Aydin et al. [28], respectively, revealed a positive effect of economic freedom on renewable energy use in African countries and South Korea. Other studies by Amoah et al. [15], Abeka et al. [23], Marinescu and Fucec [24], Özçelik and Önder [25], Bétila [26], Uzar [27] and Aydin et al. [28] have revealed mixed results on the nexus between components of economic freedom and renewable energy use for the panels of countries from different regions of the world. Our results partially support the validity of the first hypothesis of this study based on the related theoretical and empirical literature.

Financing is one of the crucial factors at both supply and demand sides of renewable energy markets. Therefore, financial sector development can foster renewable energy use in case a financial system introduces favorable lending and sustainable finance instruments to renewable energy markets. However, environmental policies and socio-economic factors are also important for the interaction between financial development and renewable energy use. As a consequence, different results on the relationship between indicators of financial development and renewable energy use may seem possible. Our results reveal that the development of financial institutions has a negative effect on renewable energy use across the overall panel and also in Croatia, Hungary, Latvia, Lithuania, Poland, and Slovakia and a positive effect only in Romania. In addition, the development of financial markets has a positive influence on the utilization of renewable energy in Estonia and Hungary and a negative influence only in Bulgaria and Slovakia. In the related empirical literature, researchers have employed different indicators of financial development such as domestic credit provided by the financial sector, outstanding international private debt securities, the stock market turnover ratio, stock market capitalization, M2, and IMF’s financial development index. In this context, Prempeh [36] and Liu et al. [37], respectively, disclosed a positive relationship between IMF’s financial development index and renewable energy use for Ghana and G7 economies, while Saadaoui and Chtourou [41] revealed a negative relationship between IMF’s financial development index and renewable energy use for Tunisia. On the other hand, Sun et al. [29] found a positive effect of financial institution and market indexes on renewable energy use for developed countries but an insignificant effect for developing countries. The negative effect of financial institutions and financial markets on renewable energy use is probably a result of the targeted favorable lending and sustainable finance instruments together with insufficient environmental policies. Our results partially support the validity of the second and third hypotheses of this study based on the related theoretical and empirical literature.

Income proxied by real GDP per capita is one of the dominant factors for renewable energy use. Our results indicate that real GDP per capita has a positive effect on renewable energy use across the overall panel and in Estonia and Hungary. The majority of the empirical studies including Bekun and Alola [49], da Silva et al. [50], Gholami et al. [51], and Ackah and Kizys [52] revealed a positive effect of real GDP per capita on renewable energy use. However, some studies like karsu and Korucu Gümüşoğlu [53] and Ergun and Rivas [54] suggested that real GDP per capita had a negative effect on renewable energy use in lower income levels, but the effect of income on renewable energy use became positive after a threshold income level. Our results partially support the validity of the fourth hypothesis of this study based on the related theoretical and empirical literature.

Last, education is also an indispensable factor for the development and utilization of renewable energy and fosters renewable energy through multiple channels. Our results demonstrate that education has a positive impact on renewable energy use in Czechia, Estonia, Lithuania, and Slovakia. In a similar vein, Sart et al. [16], Gholami et al. [51], and Esenyel İçen [54] also revealed a positive effect of education on renewable energy use. Therefore, our results support the validity of the last hypothesis of this study based on the related theoretical and empirical literature.

The causal relationship between economic freedom, indicators of financial sector development, real GDP per capita, and education and renewable energy use is investigated by the JKS non-causality test, and its outcomes revealed in Table 8 demonstrate a bidirectional causality between economic freedom, indicators of financial development, real GDP per capita, and renewable energy use but a unilateral causal relationship between education and renewable energy use. In other words, economic freedom, indicators of financial sector development, real GDP per capita, and the utilization of renewable energy have a feedback interaction, but education has a significant effect on renewable energy use.

In the associated empirical literature, only Bétila [26] examined the causal relationship between economic freedom and the utilization of renewable energy and found a two-way causal nexus between economic freedom and the utilization of renewable energy in line with our outcomes. On the other hand, Saadaoui and Chtourou [41], Burakov and Freidin [45], Saadaoui [46], Dong and Akhtar [47], and Prempeh et al. [48] examined the causal association between indicators of financial sector development and the utilization of renewable energy. Conversely, Saadaoui and Chtourou [41] and Prempeh et al. [48] disclosed a bilateral causal nexus between financial sector development and the utilization of renewable energy similar to our results. Saadaoui [46] identified a significant causality between financial sector development and the utilization of renewable energy, Dong and Akhtar [47] discovered a significant causal association between the utilization of renewable energy and financial sector development, but Burakov and Freidin [45] unveiled an insignificant causality between the two series. On the other hand, the results of Sart et al. [8], Dilanchiev et al. [71], and Kahia et al. [72] support the feedback interplay between real GDP per capita and renewable energy use in post-transition EU member states. Last, our unilateral causality between education and renewable energy use is found to be compatible with the results of Sart et al. [16] and Inglesi-Lotz and Morales [73].

5. Conclusions, Limitations, Policy Recommendations, and Future Research Directions

Renewable energy sources are a crucial alternative to struggling with the environmental problems resulting from fossil fuels and also decrease the negative effects of disruptions and instability in the supply of fossil fuels. However, while especially advanced countries have made noteworthy progress in the production and consumption of renewable energy, developing and emerging countries have made limited progress in the utilization of renewable energy. In this empirical study, the influence of economic freedom, the development of financial institutions and markets, real GDP per capita, and education on the utilization of renewable energy in post-transition EU states were investigated to see the influence of the transition process on renewable energy.

The limitations of this study are as follows:

The study sample includes post-transition EU member states.

The study period is limited to the 2000–2021 duration due to presence of economic freedom and renewable energy use data.

The study investigates the relationship between overall economic freedom and renewable energy use. Therefore, any assessments about the components of economic freedom and renewable energy use are not possible.

Other external variables, except for economic freedom, indicators of financial development, real GDP per capita, and education, that can impact renewable energy use are disregarded in the empirical analysis.

The results of the JKS causality test reveal a feedback relationship between renewable energy use, economic freedom, the development of financial institutions and financial markets and real GDP per capita but unilateral causality between education and renewable energy use. In other words, renewable energy, financial indicators, real GDP per capita, and economic freedom influence each other, but education has a significant effect on renewable energy use. Additionally, outcomes of the AMG estimation reveal that economic freedom has a positive effect on renewable energy use in Bulgaria, Czechia, and Lithuania. On the other hand, the development of financial institutions has a negative effect on renewable energy use across the overall panel and also in Croatia, Hungary, Latvia, Lithuania, Poland, and Slovakia and a positive effect only in Romania. In addition, the development of financial markets has a positive influence on the utilization of renewable energy in Estonia and Hungary but a negative influence in Bulgaria and Slovakia. Real GDP per capita has a positive effect on renewable energy use across the overall panel and in Estonia and Hungary. Last, education has a positive impact on renewable energy use in Czechia, Estonia, Lithuania, and Slovakia.

Based on our results and the findings of the related literature are as follows.

Market-oriented institutions and regulations positively impact renewable energy use through multiple channels such as innovation, technological progress, and entrepreneurship in cases where a market-oriented structure is supported by market-oriented environmental instruments such as marketable permits, pollution charges, and well-defined property rights.

Targeted favorable lending and sustainable finance instruments for renewable energy markets and environmental policies directing businesses and individuals to use renewable energy are required to obtain a positive effect of financial sector development on renewable energy use.

Education is a very crucial factor in the supply and demand of renewable energy. Therefore, educational programs should be designed to increase environmental awareness and improve human capital for renewable energy production, especially for developing and emerging economies considering the high share of developed countries in global renewable energy production and consumption.

Last, future studies should focus on the effect of each component of economic freedom on renewable energy use for a larger number of countries.

Author Contributions

Conceptualization, G.S., Y.B. and M.D.; methodology, G.S., Y.B. and M.D.; data curation, M.D. and G.S.; econometric analysis, Y.B., G.S. and M.D.; writing—original draft preparation, G.S., Y.B. and M.D.; writing—review and editing, G.S., Y.B. and M.D. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The series of economic freedom, indices of financial institutions and financial markets, and renewable energy use are, respectively, acquired from open access databases of the Fraser Institute, IMF, and World Bank.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:

AMG	Augmented mean group
ARDL	Autoregressive distributed lag bound test
CD	Cross-section dependence
CIPS	Cross-sectionally augmented Im–Pesaran–Shin unit root test
ECNFR	Economic freedom
EU	European Union
FININST	Financial institutions
FINMARK	Financial markets
FMOLS	Fully modified ordinary least square
GDP	Gross domestic product
HPJ	Half-panel jackknife
IMF	International Monetary Fund
JKS	Juodis, Karavias, and Sarafidis
LM	Lagrange multiplier
NARDL	Non-linear autoregressive distributed lag bound test
OECD	Organization for Economic Co-Operation and Development
RCCE	Regularized common correlated effect
RNW	Renewable energy use
UHV	Ultra-high voltage
USD	United States dollar
VECM	Vector error correction model

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Hypotheses of the study. Source: Authors’ own elaboration.

Figure 2. Methodological approach of the study. Source: Authors’ own elaboration.

Table 1

Economic freedom, financial development, real GDP per capita, and education in post-transition EU member states (2000, 2021).

Countries	Years	Economic Freedom Index	Index of Financial Institutions	Index of Financial Markets	Real GDP per Capita (Constant 2015 USD)	Education (Mean Years of Schooling)
Bulgaria	2000	5.53	0.4297	0.0289	3721.303	9.740815
	2021	7.47	0.6733	0.0692	8682.882	11.41318
Croatia	2000	5.92	0.3884	0.2109	8879.965	9.620869
	2021	7.21	0.6810	0.2370	15,394.47	12.32872
Czechia	2000	7.11	0.3861	0.3408	12,543.94	10.98871
	2021	7.78	0.5186	0.1027	20,373.88	12.91605
Estonia	2000	7.82	0.4254	0.0331	10,006.77	12.17572
	2021	7.91	0.4439	0.0510	21,351.39	13.54843
Hungary	2000	6.92	0.3944	0.4019	8967.696	10.17091
	2021	7.31	0.5266	0.3840	15,521.38	12.24959
Latvia	2000	7.48	0.3155	0.0196	6336.383	11.47166
	2021	7.68	0.3819	0.0340	16,070.87	13.3333
Lithuania	2000	7.12	0.2704	0.0291	6384.309	10.95179
	2021	7.92	0.3584	0.0270	18,619.7	13.4981
Poland	2000	6.52	0.3134	0.4458	7401.577	11.24677
	2021	7.07	0.4825	0.3623	16,368.62	13.16475
Romania	2000	5.38	0.1392	0.0667	4569.309	9.480434
	2021	7.62	0.4685	0.0713	11,607.98	11.3602
Slovakia	2000	6.85	0.3428	0.0820	8997.212	9.948845
	2021	7.49	0.5250	0.0248	18,821.12	13.0072
Slovenia	2000	6.64	0.4710	0.3353	16,146.12	10.50042
	2021	7.21	0.5441	0.0907	24,659.25	12.88016

Source: Fraser Institute [19], IMF (International Monetary Fund) [20], World Bank [21] and UNDP (United Nations Development Programme) [22].

Table 2

The empirical literature on the relationship between financial indicators and renewable energy.

Study	Sample; Duration	Methodological Approaches	Results (Financial Indicator; Effect of Financial Proxy on Utilization of Renewable Energy)
Wu and Broadstock [30]	22 emerging markets; 1990–2010	Regression	Financial development; positive
Çetin and Bekirtaş [31]	G7 states; 1991–2011	Westerlund cointegration test	Stock market capitalization; positive
Khan et al. [32]	192 countries; 1980–2018	Quantile regression	Domestic credit to private sector; positive
Kutan et al. [33]	Brazil, China, India, and South Africa; 1990–2012	FMOLS	Stock market capitalization; positive
Anton and Nucu [34]	28 EU members; 1990–2015	Regression	Domestic credit provided by the financial sector, outstanding international private debt securities, and stock market turnover ratio; positive
Mukhtarov et al. [35]	Turkiye; 1980–2019	ARDL and VECM	Total values of stocks traded; positive
Prempeh [36]	Ghana; 1990–2019	Cointegration	IMF’s financial development index; positive
Liu et al. [37]	G7 countries; 2000–2020	Cointegration	IMF’s financial development index; positive
Athari [38]	BRICS countries, 2000–2021	Regression	Stock market capitalization; positive
Musakwa et al. [39]	Kenya; 1990–2020	ARDL approach	Financial development; positive
Assi et al. [40]	ASEAN +3 countries; 1998–2018	ARDL approach and Dumitrescu–Hurlin causality test	Financial development index; negative
Saadaoui and Chtourou [41]	Tunisia; 1984–2017	NARDL approach and causality test	IMF’s financial development index; negative
Van Nguyen [42]	Vietnam and ASEAN members; 1970–2022	Regression	M2 and monetary sector credit to the private sector; negative
Lahiani et al. [43]	United States of America; 1975–2019	NARDL approach	Overall and stock-based financial development indicators; asymmetric effect
Sun et al. [29]	103 countries; 1991–2014	Cointegration and regression	IMF’s index of financial institutions; positive for the panelIMF’s index of financial markets; insignificant for both the panel and developing countriesIMF’s indices of financial institutions and markets; positive for the developed countries
Vatamanu and Zugravu [44]	27 EU members; 2000–2020	Regression	Domestic credit to private sector and outstanding international private debt securities; positiveTotal value of stocks traded; insignificant
Burakov and Freidin [45]	Russia; 1990–2014	VECM	Financial development; insignificant causality
Saadaoui [46]	MENA countries; 1990–2018	Westerlund cointegration test and ARDL approach	IMF’s financial development index; a causality between index of financial development and utilization of renewable energy use and insignificant
Dong and Akhtar [47]	China; 1990–2018	ARDL approach	Financial development; a causality from investments of renewable energy to financial sector development
Saadaoui and Chtourou [41]	Tunisia; 1984–2017	NARDL approach and causality test	IMF’s financial development index; a bilateral causality
Prempeh et al. [48]	Sub-Saharan African countries; 2002–2019	Regression	IMF’s financial development index; a bilateral causality and negative

Source: Authors’ own elaboration based on literature research.

Table 3

Summary of statistics.

Variables	Mean Value	Std. Dev.	Minimum	Maximum
RNW	19.327	9.555	3.7	44
ECNFR	7.409	0.506	5.38	8.17
FININST	0.497	0.109	0.139	0.699
FINMARK	0.181	0.159	0.018	0.633
RGDP	12,829.92	4526.484	3721.303	24,659.25
EDU	11.889	1.015	9.480	13.656

Source: Authors’ own elaboration based on summary of statistics.

Table 4

Outcomes of CD and slope homogeneity test.

CD Tests		Slope Homogeneity Tests
Test	Test Statistic	Test	Test Statistic
LMadj.	10.44 ***	Delta	9.941
LM CD	4.829 ***	Bias-Adj. Delta	11.657
LM	115.8 ***

Source: Authors’ own elaboration based on CD and heterogeneity tests. *** significant at 1%.

Table 5

CIPS test’s outcomes.

Variables	Level		First-Differenced Values
	Constant	Constant + Trend	Constant	Constant + Trend
RNW	0.511	1.379	−1.994 **	−3.076 ***
ECNFR	0.621	0.210	−3.311 ***	−2.187 **
FININST	0.448	1.965	−1.911 **	−2.411 ***
FINMARK	0.159	0.518	−2.809 ***	−4.620 ***
RGDP	−0.368	2.492	−1.965 **	−2.370 ***
EDU	0.199	−0.582	−3.963 ***	−2.645 ***

Source: Authors’ own elaboration based on CIPS test. *** and ** are significant at 1% and 5%, respectively.

Table 6

Outcomes of LM bootstrap cointegration test.

Constant			Constant + Trend
LM Statistic	Bootstrap p-Value	Asymptotic p-Value	Test Statistic	Bootstrap p-Value	Asymptotic p-Value
9.056	0.314	0.000	7.412	0.432	0.000

Source: Authors’ own elaboration based on cointegration test. Note: Bootstrap p-values are created via 10,000 simulations, while asymptotic p-values are obtained from normal distribution.

Table 7

Cointegration coefficients by AMG estimator.

Countries	ECNFR	FININST	FINMARK	RGDP	EDU
Bulgaria	0.140 **	−0.522	−0.786 ***	0.447	0.221
Croatia	0.104	−0.658 *	−0.217	0.099	−0.108
Czechia	0.282 ***	−0.837	−0.037	0.320	0.144 *
Estonia	−0.214	−0.037	4.188 ***	0.341 *	0.501 **
Hungary	0.138	−2.561 **	1.566 **	0.790 **	0.213
Latvia	−0.014	−0.662 ***	0.613	0.075	0.124
Lithuania	0.182 ***	−1.148 ***	−0.545	0.104	0.275 **
Poland	−0.103	−1.012 ***	−0.449	0.159	0.106
Romania	0.045	0.431 **	−1.227	0.528	0.243
Slovakia	−0.020	−3.017 *	−4.338 **	0.122	0.394 *
Slovenia	−0.055	0.032	0.122	0.248	−0.028
Panel	0.451	−0.908 ***	−0.100	0.203 **	0.098

Source: Authors’ own elaboration based on AMG estimation. ***, **, * show the levels of significance at 1%, 5%, and 10%, respectively.

Table 8

JKS non-causality test’s outcomes.

Hypothesis	HPJ Statistic
ECNFR ⇏ RNW	31.7998 ***
RNW ⇏ ECNFR	4.2063 **
FININST ⇏ RNW	104.4569 ***
RNW ⇏ FININST	168.7214 ***
FINMARK ⇏ RNW	11.9332 ***
RNW ⇏ FINMARK	12.7507 ***
RGDP ⇏ RNW	98.2370 ***
RNW ⇏ RGDP	7.9629 **
EDU ⇏ RNW	77.7098 ***
RNW ⇏ EDU	1.7001

Source: Authors’ own elaboration based on causality test. *** and ** are significant at 1% and 5%, respectively.

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