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Energy-Growth Nexus in European Union Countries During the Green Transition

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22 October 2024

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Abstract
This study investigates the relationship between economic growth and energy con-sumption—both renewable and non-renewable—in European Union countries during the green transition. Using a panel dataset of 28 EU countries from 1995 to 2021, we employ econometric techniques, including the Westerlund cointegration test, to assess long-term equilibrium relation-ships, accounting for cross-sectional dependence and slope heterogeneity. The results indicate that, while both renewable and non-renewable energy consumption are associated with economic growth, their roles differ. Renewable energy consumption shows a positive but less robust rela-tionship with GDP, with limited evidence of a predictive impact on economic growth. In contrast, non-renewable energy consumption demonstrates a more robust bidirectional causality with GDP, indicating a more intertwined relationship with economic growth during the study period. Our results have significant policy recommendations, indicating that promoting renewable ener-gy sources does not hinder economic growth. Moreover, such promotion has the potential to con-tribute substantially to economic growth in the future. Therefore, in addition to other crucial ben-efits, such as increased energy security, the development of renewable energy sources does not pose a threat to the economy. This is particularly relevant as many EU countries, including Po-land, Romania, Hungary, Bulgaria, Slovakia, and Lithuania, still have underdeveloped renewa-ble energy sectors.
Keywords: 
Subject: Business, Economics and Management  -   Economics

1. Introduction

Introducing the definition of the green transition requires a brief introduction to the plan that initiated transformations in Europe, dating back to 2019. At that time, the European Commission announced the European Green Deal, referring to a plan to (re)build the EU’s sustainable economy by addressing climate and environmental challenges across all policy areas [1]. The main goal of the European Green Deal is to achieve climate neutrality by 2050, making Europe the first climate-neutral continent. The planned actions include investments in clean technologies and a fair transition for all sectors and regions, as well as support for sustainable trade [1]. The implementation of the European Green Deal comes with numerous challenges, such as the transition to renewable energy sources, improving energy efficiency, increasing the share of energy from renewable sources, and reducing dependence on fossil fuels. These goals aim not only to achieve climate neutrality but also to gain a competitive edge in the global market and redefine economic growth, taking into account environmental and social aspects [2,3].
The "green transition," in turn, has a broader scope, as it can be defined as a global process of change that involves not only initiatives undertaken by public institutions but also actions taken by the private sector, civil society, and local governments [4,5] It is a long-term adaptive process aimed at adjusting to changing environmental and economic conditions, as well as minimizing the negative impact of human activity on the natural environment. Through the development of the green transition, planned actions include the expansion of renewable energy, improvement of energy efficiency, biodiversity protection, and the promotion of sustainable practices in agriculture and industry [6] This process continues to evolve, and different countries and communities are introducing unique strategies tailored to their specific conditions and challenges, covering an increasing number of aspects. Green growth emphasizes, above all, the greening and intensification of the economy [7,8]
The shift to a green economy during economic development involves a transition toward a more sustainable and environmentally neutral economic model. Recently, there has been a growing interest in eco-friendly products and services, as well as sustainable business practices, which has prompted companies to adjust to consumer expectations [9]
The comprehensive improvement of green total factor productivity is a fundamental way to promote the green transition in the development process and achieve high-quality economic growth. An important component of the green transition is the energy transition, which covers a wide range of aspects, such as the development of innovative renewable technologies, improving energy efficiency, modernizing energy infrastructure, analyzing energy policy, and promoting ecological awareness and changes in social behavior through education in this field [10,11]
The European Union has set specific targets regarding the reduction of greenhouse gas emissions and the increase in the share of renewable energy in total energy production. These goals impact various economic sectors and require actions aimed at improving energy efficiency and promoting clean technologies. It is widely recognized that the green transition is essential for achieving sustainable development, as it can lead to economic growth while simultaneously ensuring environmental and social well-being [12,13]
The aim of this study is to explore the relationship between economic growth and energy consumption, both renewable and non-renewable, within the European Union. To achieve this, we analyze a panel dataset covering 28 EU countries (with the United Kingdom) over a 27-year period from 1995 to 2021. The theoretical framework is grounded in the neoclassical production function, which incorporates gross fixed capital formation and total labor force as essential determinants of economic growth. We initiated our analysis by testing for cross-sectional dependence among the variables using Pesaran’s CD test, followed by the Pesaran and Yamagata Delta test to examine slope homogeneity. To check for stationarity in our panel data, we applied the Cross-sectional Augmented Dickey-Fuller test and the Cross-sectional Im, Pesaran, and Shin test. We then evaluated cointegration using the Westerlund cointegration test. To accommodate heterogeneous slope coefficients, we employed various econometric techniques like the Mean Group estimator, Augmented Mean Group, and Fully Modified Ordinary Least Squares are used to capture the dynamic relationships within the data. Finally, we conducted the Granger causality test by Dumitrescu and Hurlin to determine whether one series could predict or influence another.
This study contribute to the existing literature on the energy-growth nexus in the context of the European Union during the green transition. The authors provide a long-term perspective on this relationship during a critical period of environmental policy evolution in Europe. The findings from this analysis contribute to the policy discourse on sustainable development, highlighting the nuanced roles of different energy types in promoting long-term economic growth while transitioning toward a more sustainable and environmentally friendly energy system in the European Union. The findings offer important policy implications for EU member states, particularly in terms of supporting the shift to renewable energy while maintaining economic growth.
The paper unfolds as follows: Section 2 reviews key literature on the energy-growth link. Section 3 describes the data and methods. Section 4 presents the results and compares them with previous research. Section 5 wraps up with policy recommendations and future research paths.

2. Literature Review

Advancements in renewable energy research can contribute to sustainable economic growth while simultaneously supporting the green transition. Therefore, the role of technological innovations and supportive policies is crucial in accelerating the achievement of both ecological and economic goals [14].
The relationship between renewable energy consumption and economic growth has gained significant attention in recent years. As the world grapples with climate change and the depletion of fossil fuels, understanding the impact of renewable energy on economic development is crucial for policymakers and researchers [15,16,17,18,19,20]
Most studies have focused on the link between energy consumption and income or the energy-income-emissions nexus. Since economic growth is closely tied to the availability and consumption of energy, which drives economic and industrial activities, increasing attention has also been devoted to exploring the impact of renewable energy consumption on economic growth. These studies examine its effects on aspects such as energy security, technological innovation, investment, and research and development expenditures [21,22,23,24,25]
The utilization of different energy sources, both renewable and non-renewable, constitutes a pivotal factor in fostering economic growth. Analyzing the causal linkages between energy consumption and economic expansion is of paramount importance, as a stable and robustly growing economy can generate the requisite financial resources to support the advancement and efficient deployment of energy systems [26,27,28]
Attention should also be given to the four distinct hypotheses regarding the relationship between energy consumption and economic growth. Shahbaz et al.[29] synthesized the main findings from the literature, highlighting that these four different hypotheses have been confirmed. The first, known as the "non-causality hypothesis" or "neutrality hypothesis," suggests that there is no statistically significant relationship between energy consumption and the production of final goods in the economy. Confirmation of this hypothesis indicates that policies aimed at reducing energy consumption to lower greenhouse gas emissions would not negatively impact domestic production. This relationship has been observed by, among others, Mbanda et al. [30], Zeren et al. [31].
The second hypothesis is the "uni-directional causality from economic growth" or "conservation hypothesis," which asserts that the growth of real GDP affects energy consumption. In this case, decisions to reduce energy consumption would have only a marginal impact on economic dynamics. Examples of such analyses can be found in the works of Sadorsky [32]. The third hypothesis, "uni-directional causality from energy consumption" or the "growth hypothesis," assumes that energy consumption influences economic growth. If there is a positive relationship between these variables, then measures aimed at reducing pollution may negatively impact domestic production. Examples of such analyses can be found in the works of Bhuiyan et al. [33], Apergis and Payne [34], Pao and Fu [35], Dergiades, Martinopoulos, and Tsoulfidis [36], as well as Fuinhas and Marques [37].
The fourth hypothesis is the "bi-directional causality" or "feedback hypothesis," which suggests that energy consumption and economic growth are interdependent. Increased energy consumption leads to higher real GDP, which in turn positively affects further energy consumption in the country. This relationship has been observed by, among others, Fuinhas and Marques [36], Ozturk and Bilgili [37], as well as Apergis et al. [34], Ahmed and Shimada [38]
Recent studies conducted in European Union countries merit particular attention. The research by Tutak and Brodny [39] showed that the increase in renewable energy consumption between 2000 and 2019 had a positive impact on economic growth in European Union countries, with the growth dynamics being stronger in the "old" EU countries (EU-14) than in the "new" ones (EU-13). Countries that increased the share of renewable energy in their energy mix experienced higher economic growth due to lower energy costs, the creation of new jobs, technological innovations, and enhanced energy security. Additionally, the rise in renewable energy consumption contributed to a significant reduction in greenhouse gas emissions and decreased the use of conventional energy sources, which improved environmental quality and reduced dependence on fossil fuel imports. These results confirm a strong, positive correlation between the development of renewable energy sources and economic growth, highlighting the need for further support of policies promoting sustainable development in EU countries.
The bi-directional relationship between economic growth and renewable energy consumption in EU countries was analysed by Radmehr, Henneberry, and Shayanmehr [40]. The research results indicate that renewable energy consumption supports economic growth and that the development of the renewable energy sector in one country can positively affect neighboring countries. Bhattacharya [41] emphasized that higher incomes may lead to increased energy consumption and higher greenhouse gas emissions, particularly in developing countries. Energy policy, investments in renewable energy, and appropriate regulations can significantly reduce emissions and support economic growth.
Different perspectives have emerged in studies on different regions exploring the relationship between renewable energy consumption, economic growth, and their impact on greenhouse gas emissions. Acheampong, Dzator, and Savage (2021) investigated the causal link between renewable energy, economic growth, and carbon dioxide emissions in 45 Sub-Saharan African countries from 1960 to 2017. Utilizing the Generalized Method of Moments - Panel Vector Autoregression (GMM-PVAR) method, their findings revealed a bi-directional causal relationship between economic growth and renewable energy consumption. Similarly, Koengkan, et. al. [42] employed a PVAR model in 12 Latin American countries, confirming that renewable energy consumption promotes economic growth.
In OPEC countries, Keshavarzian and Tabatabaienasab [43] examined the relationship between renewable and non-renewable energy consumption and economic growth over the period 1980–2018. Their findings confirmed the neutrality hypothesis for Angola, Iraq, Nigeria, Venezuela, and Congo. Similarly, Göksu [44] explored the asymmetric effects of fluctuations in non-renewable energy consumption on economic growth in Turkey. This study underscores the complexity of the relationship between different types of energy consumption and economic growth, suggesting that the impact of renewable energy on economic performance may be less clear in certain regions, thereby aligning with Menegaki's [45] neutrality hypothesis.
Table 1 provides an overview of studies examining the relationship between energy consumption (both renewable and non-renewable) and economic growth across various countries and regions. The majority of studies support the “feedback hypototalthesis”, indicating a bidirectional relationship between energy consumption and GDP, as seen in regions such as Sub-Saharan Africa, OECD countries, and BRICS nations. Some studies, like those in Bangladesh and Greece, suggest the “conservation hypothesis”, where GDP impacts energy consumption. Conversely, the “neutrality hypothesis” was confirmed in Europe, showing no significant link between renewable energy and GDP. Additionally, findings in OECD countries support the “growth hypothesis”, where energy consumption drives economic growth.
The differing results across studies in Table 1 examining the relationship between energy consumption and economic growth can be attributed to several factors, including variations in regional and economic contexts, the types of energy consumed (renewable vs. non-renewable), and the time periods studied. Methodological differences, such as the use of different econometric techniques, also play a role in yielding varying outcomes. Moreover, the relationship between energy consumption, including renewable energy, and economic growth is complex and multifaceted. It is shaped by factors such as the level of economic development, energy policy and the share of renewable energy in the overall energy mix.
Recent studies offer significant insights into this dynamic, suggesting that while renewable energy consumption generally fosters economic growth, its impact can vary depending on regional and economic contexts. It is essential for policymakers to consider these intricacies when formulating strategies that aim to advance both sustainable energy use and economic development.

3. Materials and Methods

3.1. Materials and Model Specification

To investigate the relationship between economic growth, gross fixed capital formation, total labor force and energy consumption (both renewable and non-renewable) in the European Union, we utilize a panel dataset covering 28 countries (with the United Kingdom) over 27 years, spanning the period from 1995 to 2021. The corresponding variables are gross domestic product measured in a million constant 2015 US dollars ( G D P i t ), capturing the economic output, gross fixed capital formation measured in million constant 2015 US dollars ( G F C F i t ), reflecting the investments in fixed assets, total labor force ( L F i t ), representing the available workforce, renewable energy consumption quantified in quadrillion British thermal units per 1000 ( R E C i t ), and non-renewable energy consumption measured in quadrillion Btu per 1000 ( N R E C i t ), including energy derived from coal, natural gas, petroleum, and other liquids. The data are compiled from Worldbank database. Table 2 reports descriptive statistics of all variables, which provide a robust overview of the data distribution. These statistics underscore the significant variability in economic and energy-related measures across the EU, highlighting member states' diverse energy portfolios and economic scales.
To provide valuable insights into how these variables interact within the context of the EU’s evolving green transition, energy policies and economic frameworks, the study employs the following log-linear regression model [62]:
ln ⁡ G D P i t = β 0 + β 1 ln ⁡ G F C F i t + β 2 ln ⁡ L F i t + β 3 ln ⁡ R E C i t + β 4 ln ⁡ N R E C i t + ε i t
where ln ⁡ G D P i t represents the natural logarithm of gross domestic products, ln ⁡ G F C F i t the natural logarithm of gross fixed capital formation, ln ⁡ L F i t represents the natural logarithm of the labor force, and ln ⁡ R E C i t is the natural logarithm of renewable energy consumption.

3.2. Methods

We began our panel data analyzis by checking for cross-sectional dependence among the variables with Pesaran’s CD test [63] which revealed significant correlations across counties. The growing body of panel data research has focused heavily on the issue of cross-sectional dependence in macro panel data. This kind of correlation may have emerged due to the global financial crisis beginning in 2007 or from economic integration within Europe, possibly driven by local spillover effects. The Pesaran CD test uses the correlation coefficients between the time series of each panel member. For instance, in a dataset with N = 28 countries, this would involve calculating the 28 × 27 correlations between country i and all other countries, where i ranges from 1 to N − 1 . Denoting these estimated correlation coefficients between the time series of countries i and j as ρ i j * , the Pesaran CD statistic is then calculated as follows:
C D = 2 ( N N − 1 ) × ∑ i = 0 N − 1 ∑ j = i + 1 N T i j ρ i j
where T i j represents the number of observations used to compute the correlation coefficient. Under the null hypothesis of cross-sectional independence, the statistics mentioned are normally distributed if T i j > 3 and N is sufficiently large. The test is robust to nonstationarity (since any spurious effects would be averaged out), parameter heterogeneity, or structural breaks, and has been demonstrated to perform well even with small sample sizes.
Currently, the European Union consists of 27 diverse countries (with the United Kingdom also included in our sample), which makes it essential to test the relationship between dependent and independent variables to ensure consistency across all panels in our dataset. This testing is vital in panel data analysis as it helps in selecting the appropriate econometric model. We utilized the Pesaran and Yamagata [64] Delta test ( ∆ -test) to assess slope homogeneity, which examine whether the slope coefficients are identical across different cross-sectional countries. This test is widely used to evaluate slope homogeneity in panel data models, testing the null hypothesis that the slope coefficients are homogeneous against the alternative hypothesis that they are heterogeneous.
To assess stationarity in our panel data, we employed the Cross-sectional Augmented Dickey-Fuller (CADF) test, proposed by Pesaran [65], and Cross-sectional Im, Pesaran, and Shin (CIPS) test, also introduced by Pesaran [66]. Both tests are econometric methods used to detect unit roots in panel data, especially when cross-sectional dependence is present. The CADF test performs a t-test for unit roots in heterogeneous panels with cross-sectional dependence. To account for cross-dependence, it enhances standard Dickey-Fuller (or Augmented Dickey-Fuller) regressions by including cross-sectional averages of lagged levels and first differences of individual series, resulting in the CADF statistics. Besides, the CIPS test extends the IPS test by incorporating cross-sectional averages to better handle cross-sectional dependence.
Next, we assessed cointegration to determine if the variables share a stable long-term relationship. We employed the Westerlund cointegration test [67], which provides two VR test statistics to test the null hypothesis of no cointegration. Additionall, we applied Westerlund’s [68]- error-correction-based panel cointegration tests, which include the Ga,Gt,Pa, and Pt tests.
The G a and G t tests check if at least one panel member shows cointegration ( H 0 :   α i = 0   v s .   H 1 :   α i < 0 for at least one i ). The P a and P t tests assess cointegration for the entire panel ( H 0 :   α i = 0   v s .   H 1 :   α i < 0 for all i ). These tests handle heterogeneous models and varying series lengths, with bootstrap iterations (1000) used for robustness if cross-sectional units are correlated.
We applied various econometric techniques, which allow for heterogeneous slope coefficients across group members and are also concerned with correlation across panel members: Mean Group (MG) [69], Augmented Mean Group (AMG) (Bond and Eberhardt, [70]; Eberhardt and Teal, [71], Common Correlated Effects Mean Group (CCEMG) (Pesaran, [72]), Fully Modified Ordinary Least Squares (FMOLS) (Philips and Hansen [73], Dynamic Ordinary Least Squares (DOLS) (Stock and Watso [74]), and Canonical Cointegrating Regression (CCR) (Park, [75]).
In the final stage, we assessed causality, specifically whether one time series can predict or drive changes in another, using the Granger non-causality test developed by Dumitrescu and Hurling (2012). This approach extends the Granger causality test to panel data, enabling the examination of causality across multiple cross-sectional units (such as countries) while considering potential heterogeneity among these units. The test’s null hypothesis asserts that no causality exists for any of the cross-sectional units. To account for cross-sectional dependence, we employed a bootstrap procedure to calculate p-values and critical values, as recommended by Dumitrescu and Hurlin [76].

4. Results and Discussion

4.1. Cross-Sectional Dependence and Slope Heterogeneity

In a panel data analysis, controlling cross-sectional dependence plays a crucial role. The presence of cross-sectional dependence can lead to significant issues in choosing econometric models and interpreting results. To detect cross-sectional dependence, we employ Pesaran’s CD test. According to the results shown in the Table 3, all variables exhibit significant cross-sectional dependence, indicating that the units are not independent.
As we proceed, Table 4 presents the results of the slope homogeneity test, another critical aspect of panel data analysis. According to the results of the Delta test (Δ-test) the slope coefficients are not homogeneous across the units in the panel. This implies that the relationship between the dependent variable ( G D P i t ) and independent variables ( G F C F i t , L F i t , R E C i t ) varies across the different countries in the panel.
Considering the presence of cross-sectional dependency and slope heterogeneity indicated by the tests above, we assess the stationarity of the variables, or whether they contain a unit root, using the Cross-sectional Augmented Dickey-Fuller (CADF) and Cross-sectional Im, Pesaran, and Shin (CIPS) unit root tests. According to the results in Table 5, all variables become stationary after first differencing. ln ⁡ R E C i t and ln ⁡ N R E C i t show some evidence of stationarity even at the level, with stronger evidence after the first differencing.

4.2. Cointegration Tests

We employ the Westerlund cointegration test to examine the long-term relationship between the variables, considering cross-sectional dependency and slope heterogeneity. The results, shown in Table 6, indicate a p-value of 0.0587, which is slightly above the conventional significance level of 0.05. Given its proximity to 0.05, one could argue that there is weak evidence against the null hypothesis. At a 10% significance level, we could reject the null hypothesis and infer the presence of cointegration.
To enhance the robustness of our findings, we perform additional tests. Table 7 presents the results of four panel cointegration tests proposed by Westerlund [68]. The robust p-values are all reported as 0.000, providing strong evidence against the null hypothesis. This suggests that, even when accounting for potential cross-sectional dependence or other factors that could impact the test’s validity, cointegration is likely present.
Thus, the Westerlund cointegration test in variance ratio and error-correction-based forms indicates significant long-run relationships among the variables, reinforcing the interconnected dynamics between energy consumption and economic growth. Our results differ from the findings of Afonso et al. [77] and Papież et al. [78], who found no cointegration between economic growth and renewable and non-renewable energy consumption in 28 and 26 countries (excluding Malta and Cyprus) the member states of the European Union, respectively. These differences are attributable to several factors, including methodological advancements, broader data coverage, and the inclusion of more recent policy impacts. Both studies may not fully account for the effects of more recent EU energy policies and the acceleration of renewable energy adoption, which became more prominent after 2015, particularly with the launch of the European Green Deal. Our dataset, which spans from 1995 to 2021, includes this critical period. It allows us to capture the evolving relationship between energy consumption (especially renewables) and economic growth, reflecting ongoing transitions that may have been less prominent in earlier periods.

4.3. Long-Run Coefficients

We calculate the long-run coefficients using six different econometric estimation techniques, selecting each method according to its specific underlying assumptions. Table 8 presents the results. ln ⁡ G F C F i t consistently exhibits a positive and significant impact across all estimation methods, underscoring the role of gross fixed capital formation as a robust predictor of ln ⁡ G D P i t . Similarly, ln ⁡ L F i t demonstrates a consistently positive and significant association with ln ⁡ G D P i t , highlighting the importance of labor force size as a key determinant, in line with classical economic theory. The findings for ln ⁡ R E C i t generally indicate a positive relationship with ln ⁡ G D P i t , although the significance and magnitude of the effect vary across different tests. Notably, the AMG and CCEMG tests provide comparatively weaker evidence.
As for ln ⁡ N R E C i t , the results are mixed. While the MG test suggests that ln ⁡ N R E C i t is not significant, other models indicate a positive and significant relationship, though the effect size is generally smaller compared to the other variables.
C o n s

4.4. Causality Analysis

To test causality in panel dataset, we employ the Dumitrescu and Hurlin [76] Granger causality test since it takes into heterogeneous causality relationships specific to individual units (countries). Table 9 provides the related results. For the relationship between renewable energy consumption ( R E C i t ) and gross domestic product ( G D P i t ), the p-values (0.1044 and 0.0929) exceed the conventional significance level of 0.05. This indicates insufficient evidence to reject the null hypothesis, suggesting that R E C i t does not Granger-cause G D P i t in this context.
The analysis reveals bidirectional Granger causality between G D P i t and both gross fixed capital formation G F C F i t and labor force L F i t , indicating a mutual predictive relationship between these variables. Furthermore, G D P i t is found to Granger-cause both renewable and non-renewable energy consumption, while non-renewable energy consumption also Granger-causes G D P i t . However, no such Granger-causal relationship is observed between renewable energy consumption and G D P i t , indicating a lack of predictive power in this direction.
However, the literature presents a range of mixed findings. Marques et al. [14] reveal a bidirectional Granger causality between renewable energy consumption and GDP in France. Apergis and Payne [34] focused on 20 OECD countries over a specific period from 1985 to 2005, finding bidirectional causality between renewable energy consumption and economic growth. Furuoka [79] examined the causal links in newly industrialized countries, finding different energy-growth relationships across countries due to varying levels of renewable energy adoption. In countries like South Africa and Mexico, negative shocks in renewable energy were found to influence GDP positively. Farhani and Shahbaz [80], focusing on MENA countries, found a unidirectional causality from renewable energy to economic growth.
These differences can be attributed to variations in geographical focus, time periods, and methodologies. While Apergis and Payne [34] identified bidirectional causality in OECD countries, and Farhani and Shahbaz [80] found unidirectional causality in MENA countries, both regions and periods differ significantly from our study in the European Union, which focuses on more recent years with more robust renewable energy policies. Furthermore, methodological differences may be an additional reason.

5. Conclusions and Policy Recommendations

This study examines the relationship between economic growth, gross fixed capital formation, total labor force, and energy consumption in the European Union, utilizing a panel dataset of 28 countries (including the United Kingdom) from 1995 to 2021. The results from the Westerlund cointegration test, supported by additional robustness checks, suggest a long-term equilibrium relationship between variables, accounting for cross-sectional dependency and slope heterogeneity.
The relationship between renewable energy consumption and GDP is generally positive, indicating that increased use of renewable energy is associated with economic growth in the long run. However, the significance and strength of this relationship vary across different econometric models. Notably, the AMG and CCEMG tests provide comparatively weaker evidence of a significant impact. Furthermore, the Dumitrescu and Hurlin [76] Granger causality test finds no significant Granger-causal relationship between renewable energy consumption and GDP. This suggests that while renewable energy consumption is positively linked to economic growth, it does not have a strong predictive influence on GDP within the study period.
The results for non-renewable energy consumption are mixed. While the MG model indicates that non-renewable energy consumption is not significant, other models suggest a positive and significant relationship with GDP, though the effect size is generally smaller compared to other variables like capital formation and labor force. The Granger causality analysis reveals a bidirectional relationship between non-renewable energy consumption and GDP, indicating that not only does GDP influence non-renewable energy use, but non-renewable energy consumption also has a predictive impact on GDP.
In summary, while both renewable and non-renewable energy consumption are associated with economic growth, their roles differ. Renewable energy consumption shows a positive but less robust relationship with GDP, with limited evidence of a predictive impact on economic growth. In contrast, non-renewable energy consumption demonstrates a stronger bidirectional causality with GDP, indicating a more intertwined relationship with economic growth during the study period. These findings highlight the ongoing challenge of transitioning to a more sustainable energy system while maintaining economic growth.
Our results have significant policy recommendations, indicating that promoting renewable energy sources does not hinder economic growth. Moreover, such promotion has the potential to substantially contribute to economic growth in the future. Therefore, in addition to other crucial benefits, such as increased energy security, the development of renewable energy sources does not pose a threat to the economy. This is particularly relevant as many EU countries, including Poland, Romania, Hungary, Bulgaria, Slovakia, and Lithuania, still have underdeveloped renewable energy sectors. This study contributes meaningfully to the ongoing debate on renewable energy promotion within the European Union. It provides a clear signal to policymakers that supporting this development is essential, given the low risk of negative impacts on economic growth and the high likelihood of achieving significant benefits.
Supporting efforts to enhance energy efficiency is also critical from an economic standpoint. Investment in energy-efficient technologies not only reduces reliance on non-renewable energy sources but also lowers operational costs across various sectors. Improved energy efficiency can lead to substantial reductions in greenhouse gas emissions, potentially decreasing future expenditures related to climate regulations and fostering long-term cost savings.
Despite the current lack of direct evidence linking renewable energy development to immediate economic growth, it remains a strategic investment for long-term economic stability. Increased financial support and streamlined regulatory processes are necessary to expedite the transition to modern energy sources, thereby reducing dependence on volatile fossil fuel markets and contributing to more stable and predictable energy costs. At the same time, in EU countries that still rely heavily on fossil fuels, it is essential to raise public awareness about the benefits of energy transition. Enhanced societal awareness can drive legislative and market changes, facilitate the diversification of energy sources, and mitigate risks associated with global price fluctuations in raw materials. In the long run, this approach will strengthen energy security and minimize potential negative economic impacts.
Finally, expanding energy integration within the European Union through increased regional cooperation and interconnected energy grids is crucial. Such integration will optimize energy resource utilization, bolster energy security and market stability, and reduce the costs associated with adapting economies to the evolving dynamics of global energy markets.

Author Contributions

conceptualization, B.J., A.K.T., A.G., K.G.; methodology, B.J., A.K.T., A.G., K.G.; formal analysis, B.J., A.K.T., A.G., K.G., B.T.; investigation, B.J., A.K.T., A.G., K.G., B.T.; data curation, A.G., K.G.; writing—original draft preparation, B.J., A.K.T., A.G., K.G., B.T.; writing—review and editing, B.J., A.K.T.; visualization, A.G., K.G., B.T.; supervision, B.J., A.K.T.; project administration, B.J.; funding acquisition, B.J. All authors contributed equally to the paper and have read and agreed to the published version of the manuscript.

Funding

The APC was funded by the John Paul II Catholic University of Lublin.

Data Availability Statement

The data (variables) can be found at: World Bank Open data, https://data.worldbank.org, Date of Access: 01.06.2024; EIA, U.S. nuclear industry, https://www.eia.gov/energyexplained/nuclear/us-nuclear-industry.php, Date of Access: 01.06.2024.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Table 1. Overview of the main studies in the area.
Table 1. Overview of the main studies in the area.
Authors Research sample / period Causality results
Al-Mulali [46] 30 major nuclear energy
consuming countries
(1990–2010)		 FEC <=> GDP
GDP => FEC
NEC => GDP
Nasreen and Anwar [47] 15 Asian countries
(2000–2010)		 GDP <=> EC
GDP => EC
Marques and Fuinhas [48] 24 European countries
(1990-2007)		 REC <=> GDP
Dedeoğlu and Kaya [49] OECD countries
(1990–2011)		 EC <=> GDP
Ciarreta and Zarraga [50] 12 European countries
(1970-2007)		 EL => GDP
Mozumder and Marathe [51] Bangladesh
(1980–2008)		 GDP => ELC
Al-Mulali and Che Sab [52] Sub Saharan African countries
(1971–2009)		 EC <=> GDP
Islam et al. [53] Malaysia
(1960–2007)		 EC <=> GDP
(Short and long run)
Menegaki [45] Europe
(1997-2007)		 GDP ≠ RE
Oh and Lee [54] South Korea
(1970–1999)		 GDP <=> EC
Fallahi [55] USA
(1960–2005)		 GDP <=> EC
Tsani [56] Greece
(1960–2006)		 GDP => EC
(negatively in high income)
Belke et al. [57] OECD countries
(1981–2007)		 EC => GDP
Shahbaz et al. [58] Portugal
(1971-2002)		 ELC <=> GDP
Shahbaz et al. [59] BRICS
(1970-2015)		 EC <=> GDP
Pirloge and Cicea [60] Spain, Romania, European Union
(1990-2010)		 EC => GDP
Armeanu et al. [61] European Union
(2003-2014)		 GDP => REC
Notes: => indicates unidirectional relationship, <=> indicates bidirectional relationship, ≠ indicates no causal relationship, FEC – final energy consumption, NEC – nuclear energy consumption, EC – energy consumption, C O 2 – carbon dioxide emissions, RE – renewable energy, ELC – electricity consumption, OC – oil consumption, NGC – natural gas consumption, CC – coal consumption.
Table 2. Descriptive statistics.
Table 2. Descriptive statistics.
Variable Mean Std. Dev. Min. Max.
G D P i t 540752.5 839899.2 4659.17 3597317
G F C F i t 109928 166826.3 0 752844.8
L F i t 8542400 1.09e+07 147038 4.44e+07
R E C i t 233.61 347.09 -168.21 2187.1
N R E C i t 2077.49 2824.95 33.58 13014.55
Note: number of obs. 756.
Table 3. Pesaran’s cross-sectional dependence test results.
Table 3. Pesaran’s cross-sectional dependence test results.
Variable CD-Test P-Value Corr Abs (Corr)
ln ⁡ G D P i t 88.99 0.000 0.881 0.881
ln ⁡ G F C F i t 63.88 0.000 0.632 0.685
ln ⁡ L F i t 28.59 0.000 0.283 0.742
ln ⁡ R E C i t 57.79 0.000 0.572 0.576
Note: under the null hypothesis of cross-section independence CD ~ N(0,1).
Table 4. Results of slope heterogeneity test.
Table 4. Results of slope heterogeneity test.
Statistics P-Value
Delta 19.527 0.000
Delta Adj. 22.688 0.000
Note: the null hypothesis is a slope coefficients are homogenous.
Table 5. CADF and CIPS unit root tests results.
Table 5. CADF and CIPS unit root tests results.
Variable CADF CIPS
Level First Diff. Level First Diff.
ln ⁡ G D P i t -2.106** -2.702*** -2.020 -3.640***
ln ⁡ G F C F i t -1.944 -3.728*** -1.314 -4.263***
ln ⁡ L F i t -1.875 -2.795*** -1.925 -3.998***
ln ⁡ R E C i t -3.075*** -4.514*** -2.893*** -5.308***
ln ⁡ N R E C i t -2.153** -3.819*** -2.269** -5.160***
Note: *, **, *** denote statistical significance at the 10%, 5% and 1% levels, respectively.
Table 6. Westerlund cointegration test results.
Table 6. Westerlund cointegration test results.
Statistic P-Value
Variance Ratio -1.5655 0.0587
Notes: the null hypothesis is that there is no cointegration; cross-sectional means removed.
Table 7. Westerlund error-correction-based panel cointegration tests results.
Table 7. Westerlund error-correction-based panel cointegration tests results.
Statistic Value Z-Value Robust P-Value
G t -1.045 4.807 0.000
G a -1.334 6.358 0.000
P t -1.650 5.450 0.000
P a -0.424 4.118 0.000
Notes: the number of bootstrap iterations 1000. The null hypothesis is that there is no cointegration.
Table 8. Long-run coefficients.
Table 8. Long-run coefficients.
Variable MG AMG CCEMG FMOLS DOLS CCR
ln ⁡ G F C F i t 0.364*** 0.166*** 0.137*** 0.37
(117.68)***		 0.35
(64.00)***		 0.37
(105.49)***
ln ⁡ L F i t 0.886*** 0.293*** 0.241** 0.85
(64.25)***		 1.08
(23.32)***		 0.88
(47.56)***
ln ⁡ R E C i t 0.077*** 0.024* 0.018 0.07
(40.83)***		 0.16
(36.16)***		 0.07
(32.96)***
ln ⁡ N R E C i t 0.033 0.192*** 0.189*** 0.01
(10.93)***		 0.08
(15.55)***		 0.00
(9.21)***
C o n s -5.920 4.277*** -10.759
Notes: *, **, *** denote statistical significance at the 10%, 5% and 1% levels, respectively.
Table 9. Dumitrescu and Hurlin Granger causality test results.
Table 9. Dumitrescu and Hurlin Granger causality test results.
W-bar Z-bar P-value Z-bar tilde P-value
G F C F i t => G D P i t 1.8934 3.3428 0.0008 2.5340 0.0113
L F i t => G D P i t 2.0868 4.0664 0.0000 3.1480 0.0016
R E C i t => G D P i t 0.5660 -1.6240 0.1044 -1.6804 0.0929
N R E C i t => G D P i t 2.1943 4.4686 0.0000 3.4893 0.0005
G D P i t => G F C F i t 3.1655 8.1026 0.0000 6.5728 0.0000
G D P i t => L F i t 4.5093 13.1305 0.0000 10.8390 0.0000
G D P i t => R E C i t 4.7143 13.8975 0.0000 11.4898 0.0000
G D P i t => N R E C i t 4.3367 12.4849 0.0000 10.2912 0.0000
Note: the null hypothesis is that there is no Granger causality from one variable to another across all individuals in the panel.
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