Integration of Renewable Energy in Central and Eastern Europe: Policy and Efficiency Analysis

Abstract

The growing environmental challenges and the urgent need for an accelerated energy transition have intensified the European Union’s efforts to expand the use of renewable energy sources and reduce dependence on fossil fuels. This study estimates the impact of selected socioeconomic, political, and technological factors on the share of renewable energy in gross final energy consumption. This issue is of key relevance for EU energy and climate policy, particularly in the context of the ongoing transformation processes in Central and Eastern European (CEE) Member States. The analysis covers eleven CEE countries—Poland, the Czech Republic, Slovakia, Hungary, Romania, Bulgaria, Lithuania, Latvia, Estonia, Croatia, and Slovenia—over the years 2004–2023. Using panel data models in both static (fixed effects) and dynamic specifications, the study identifies the determinants of renewable energy development and captures inertia effects. The results reveal strong links between energy security and renewable energy deployment, where the pursuit of greater self-sufficiency often slows the expansion of renewable energy. Countries with high greenhouse gas emissions also show limited incentives to accelerate renewable energy integration. Based on the findings, this study proposes policy recommendations aimed at enhancing energy efficiency, strengthening energy security, and supporting the long-term sustainable growth of renewable energy in the region.

1. Introduction

Socio-economic development brings both positive and negative consequences. On one hand, it generates benefits for the economy and society as a whole; on the other, it contributes to the irreversible depletion of natural resources, increasingly taking on a global dimension. Energy efficiency is one of the key priorities of European Union policy. To ensure a higher level of energy security and efficiency among member states, the EU issues a range of documents and undertakes numerous initiatives.

The implementation of the EU’s strategy known as the European Green Deal (2019) aims to transform the economy toward sustainable development, with one of its main goals being the reduction in greenhouse gas emissions. A key instrument for achieving these long-term objectives is the “Fit for 55” package, adopted by the European Commission in 2021. Through this package, the EU has committed to reducing greenhouse gas emissions by at least 55% by 2030 compared to 1990 levels, and to achieving climate neutrality by 2050 [1].

The “Fit for 55” package introduced amendments to EU legislation aimed at updating the Energy Efficiency Directive. One of its key targets is to increase the share of renewable energy in the EU’s total energy consumption to 42.5% by 2030, with the potential to raise this figure by an additional 2.5 percentage points—reaching up to 45% [2]. Conventional energy sources remain a major driver of rising carbon dioxide emissions, significantly influencing the trajectory of global climate change. Moreover, their extraction has caused substantial biodiversity loss and contributes to widespread environmental degradation through various forms of pollution [3,4,5,6,7,8]. In the face of these adverse climate-related changes [9], transitioning from conventional to renewable energy sources is becoming essential, and effectively addressing climate change [10] and its consequences requires international cooperation [11].

Regulation (EU) 2020/852 of the European Parliament and of the Council of 18 June 2020 on the establishment of a framework to facilitate sustainable investment, amending Regulation (EU) 2019/2088 [12], created a framework to balance the energy mix. This involves a gradual transition toward energy mixes dominated by renewable energy sources, while allowing the parallel use of nuclear energy and natural gas, provided that strict criteria are met. With socio-economic development, changes in the structure of renewable energy consumption are becoming increasingly evident, characterized by a gradual shift from less efficient energy sources to more efficient ones, leading to increased use of renewable energy [13].

It is also worth noting that the pace and structure of renewable energy integration differ significantly across EU member states, particularly in Central and Eastern Europe. These countries display highly diverse energy mixes, ranging from a strong reliance on solid fossil fuels to substantial shares of nuclear or renewable energy. Against this background, the present study aims to assess the impact of selected socio-economic, political, and technological factors on the share of renewable energy in gross final energy consumption during the period 2004–2023.

Section 1 introduces the issue of integrating renewable energy sources in Central and Eastern European countries. Section 2 provides a literature review, highlighting the complexity and conditions influencing the development and integration of renewable energy. Section 3 describes the research material and the econometric analysis methods used. Section 4 presents the results of the panel analysis conducted for Central and Eastern European countries and includes a discussion of the findings in the context of previous studies and the European Union’s energy and climate policy. Section 5 outlines the key conclusions drawn from the study.

2. Complexity and Determinants of Renewable Energy Development and Integration—A Literature Review

Energy Transition in Central and Eastern European Countries

Research on the integration of renewable energy is influenced by a wide range of factors and requires consideration of numerous determinants, including socio-economic, political, and technological aspects. The literature contains many references to studies on the integration of renewable energy sources in Central and Eastern Europe; however, they do not always cover all countries in the region, which hinders a comprehensive assessment of transformation processes from a holistic perspective. The lack of an integrated approach makes it difficult to fully understand the state of renewable energy integration in Central and Eastern Europe. To address this gap, the present analysis includes all countries in the region.

Opydo, Twardosz and Twardosz [14] examine the integration of renewable energy sources and energy storage systems with the power grid, which, in their view, enables effective management of electricity supply and demand. Based on their research, they conclude that the use of renewable energy sources is necessary for multiple reasons; for instance, the alternative of nuclear power plants with comparable capacity proves to be approximately 10% more expensive under Polish conditions [14].

The impact of nuclear and renewable energy on carbon footprint reduction has been examined by researchers such as Usman and Radulescu [15], as well as Adebayo and Ullah [16]. Nuclear energy is increasingly viewed as a low-emission alternative to fossil fuels, capable of supporting climate transition. However, as noted by Adebayo and Ullah, its impact remains ambiguous and still requires further in-depth research in the context of environmental sustainability [16].

In the energy sector, a key element of the green transition is the shift from a fossil fuel-based system toward renewable energy sources and other clean energy alternatives. A security-oriented energy transition involves not only ensuring stable supplies of raw materials but also intensifying regional cooperation. An essential component of a secure transition is the expansion of intersystem connections and transmission infrastructure for gas, electricity, and oil, which, particularly for landlocked countries, forms the foundation for strengthening energy security. At the same time, the integration of renewable energy sources remains an equally important and parallel priority.

According to Liang, Zhu and Zeng [17], the energy transition should consider not only the structure of energy consumption by source but also energy security, which has recently become a major concern. In their view, “the energy transition can not only reduce energy dependence, improve energy diversification and self-sufficiency, and mitigate environmental damage caused by energy production and consumption, but also serve as an important pathway to ensuring energy security” [17].

A study on renewable energy in the context of global energy transitions, conducted by Hassan, Viktor, Al-Musawi, Ali, Algburi, Alzoubi, Al-Jiboory, Sameen, Salman and Jaszczur, highlights significant disparities in the development of renewable energy technologies across countries, reflecting the complexity of political, economic, and infrastructural conditions [3].

Hoicka, Lowitzsch, Brisbois, Kumar and Ramirez Camargo [18], in the context of research on implementing a just transition toward renewable energy sources, evaluated the implementation of the RED II Directive [19], which aims to promote the use of renewable energy across the European Union between 2021 and 2030. Based on their analysis, they concluded that a universal legislative approach does not adequately address the diverse needs of member states. For example, Central and Eastern European countries should strive to develop individualized legislative solutions that reflect their specific socio-economic conditions and development priorities [18]. As the authors noted, countries in Central and Eastern Europe tend to focus on cluster-based projects led by local governments, which often serve as tools for modernizing outdated energy infrastructure. In contrast, other member states, such as Germany, are characterized by bottom-up cooperative initiatives. The authors also emphasize that the exchange of best practices among member states is essential and highlight its potential benefits. However, they stress that the successful adaptation of such practices largely depends on local conditions and priorities [18].

It is also worth noting that Germany has played a significant role in initiating the global energy transition. The country developed a comprehensive energy transformation strategy, Energiewende, aimed at gradually phasing out fossil fuels and nuclear energy in favor of expanding renewable energy sources [20]. Numerous studies confirm that consumers and other end users increasingly have the opportunity to become more self-sufficient by becoming co-producers of electricity. However, for this to happen, it is essential to develop and adopt an institutional theoretical approach that takes into account the mechanisms for implementing renewable energy sources tailored to local conditions, including the management of shared resources [21,22,23].

Alsayegh, Alhajraf and Albusairi [24] analyzed the key challenges and concerns arising from the integration of renewable energy sources into the power grid. They observed that the development of decentralized renewable energy systems represents a significant alternative to traditional investments in large power plants and extensive transmission infrastructure. The use of local energy generation enables flexible and rapid deployment of installations tailored to current demand [24].

Hansen, Wilson, Fitts, Jansen, Beiter, Steffen, Xu, Guillet, Münster and Kitzing [25] in their research explore the issue of offshore wind energy, which they believe has the potential to play a key role in the transition toward renewable energy sources. They also identified five major challenges facing the financing of offshore wind energy, including [25], including securing stable funding in the early stages of projects, political support to ensure investment viability, development of a skilled workforce, and advancement of innovative technologies such as floating wind farms.

Mamkhezri and Khezri [26] examined the impact of renewable energy and research and development (R&D) expenditures on CO₂ emissions, analyzing data from 54 countries. They found that global investments in R&D have led to an overall reduction in CO₂ emissions, driven by an indirect effect through two channels: renewable energy and economic growth.

Ullah, Luo, Nadeem and Cifuentes-Faura [27] assessed the role of green energy innovation, natural resources, and environmental policy in forecasting green growth and energy transition in the United States.

As highlighted by Jiang, Dong, Qing and Teng [28], the conducted research provides valuable insights into factors such as technology, policy, and socio-economic conditions in shaping energy development pathways. However, as noted by Berglund and Söderholm [29], bottom-up energy models with endogenous learning also have limited capacity to characterize the diffusion of technologies and innovations. Moreover, they do not incorporate a comprehensive approach that would include general equilibrium effects along with intersectoral interactions and endogenous policy responses.

Camacho Ballesta, da Silva Almeida and Rodríguez [30] analyze the main factors determining renewable energy consumption in the European Union and indicate that this process is shaped by a combination of economic, social, political, and technological conditions. Research conducted by da Silva Almeida [31] that the energy transition in European Union countries is a highly heterogeneous process, and its dynamics depend on both economic and social factors. The results confirm that GDP per capita plays a crucial role in shaping the share of renewable energy consumption, yet its impact is heterogeneous—in the EU-15 countries higher income levels are associated with a lower share of renewables, whereas in the new member states the effect is the opposite and positive. This implies that policies supporting the development of renewable energy must be tailored to the level of economic development of individual countries.

Based on the above, and drawing on a literature review and an analysis of original research using a panel model, which enables the examination of dynamic effects, i.e., the tendency of variables to change over time, making it particularly useful for studying the causes and consequences of phenomena and their long-term implications [17], this article aims to estimate the impact of selected socio-economic, political, and technological factors on the share of renewable energy in gross final energy consumption.

In light of the presented research, energy integration at the current stage of transformation encompasses issues related to emission reduction, climate change, economic development, and energy security. The contemporary approach to the development of the renewable energy sector assumes the need to simultaneously consider technological, economic, social, and environmental aspects.

The variation in the level of technological development and the implementation of renewable energy sources across different countries largely stems from differing political, economic, and infrastructural conditions. Therefore, effective and efficient transformation requires not only increasing the share of renewable energy sources but also a systemic approach that integrates climate, social, and technological policy.

In this context, analyzing the influence of socio-economic, political, and technological factors on the development of renewable energy sources allows for a better understanding of the mechanisms of energy transition in the Central and Eastern European region and the identification of strategic directions to accelerate this process.

On this basis, it can be concluded that the existing research gap stems from the fact that previous studies on renewable energy in the region are often fragmented and focus on selected countries or individual aspects of the energy transition. There is still a lack of comprehensive analyses covering all countries in the region that would capture the complex interrelations between socio-economic, political, and technological factors and the development of renewable energy sources. The dynamic effects and their impact on energy security also appear to be insufficiently explored. The dynamic nature of these processes remains insufficiently explored, particularly the role of inertia, investment delays, and the cumulative character of renewable energy development. Despite progress in research, there is a lack of analyses employing panel models with a dynamic component, which would allow for the estimation not only of static relationships but also of the short- and long-term effects of the energy transition in Central and Eastern European (CEE) countries. Consequently, a clear research gap exists regarding a comprehensive assessment of the impact of socioeconomic, political, and technological factors on the development of renewable energy sources in the CEE region, taking into account institutional specificities and the dynamics of change. This study addresses this gap by applying both static and dynamic panel models to eleven countries in the region over two decades, enabling the identification of inertia processes, determinants of renewable energy sources development, and linkages with energy security.

Taking into account the identified limitations of previous studies and the existing research gap, the following research questions have been formulated:

(1)

What socioeconomic, political, and technological factors influence the pace of renewable energy development in Central and Eastern European countries, and how do they differ across the region?

(2)

How do inertia processes, investment delays, and the cumulative nature of previous actions affect the short-term and long-term outcomes of the energy transition in CEE countries?

(3)

What is the role of EU legal instruments and support mechanisms in fostering a sustainable energy transition in CEE countries?

The formulated research questions serve as a starting point for further analysis and interpretation of the results.

3. Data and Methods

In the European Union, energy is generated from a variety of sources, including solid fuels, natural gas, crude oil, nuclear energy, and renewable energy sources such as hydropower, wind, and solar energy.

Table 1. Share of main energy sources in electricity production in Central and Eastern European countries in 2023 [32].

Country	Renewables and Biofuels	Solid Fossil Fuels	Natural Gas	Nuclear Heat	Oil and Petroleum Products (Excluding Biofuel Portion)	Share of Renewable Energy
Bulgaria	2,643,949	3,491,047	8396	4,218,004	0	25.5
Croatia	2,571,587	0	610,922	0	584,699	68.3
Czech Republic	5,354,136	9,711,743	133,168	7,571,872	70,985	23.5
Estonia	1,908,812	0	0	0000	0	100.0
Hungary	3,454,103	663,883	1,237,984	4015	1,131,752	32.9
Latvia	3,246,821	0	0	0	0	100.0
Lithuania	1,996,602	0	0	0	23,559	98.8
Poland	13,863,105	35,351,741	3,157,595	0	848,946	26.1
Romania	5,994,164	2,245,552	7,652,169	2,867,000	2,907,683	27.7
Slovakia	2,084,256	179,089	41,853	473,254	2028	29.6
Slovenia	115,213	70,097	3828	1,317,601	163	36.3

presents a breakdown of the energy balance structure of Central and Eastern European countries [32].

According to the data presented in Table 1, considering the share of renewable energy sources in the energy mix of Central and Eastern European countries, a significant variation between nations can be observed. The highest share of renewables was recorded in Estonia, Latvia, and Lithuania, where renewable sources account for nearly 100% of energy consumption, which may be due to limited access to fossil fuels. In contrast, countries such as the Czech Republic, Poland, and Bulgaria show a relatively low share of renewables. In their energy balance structure, Poland, the Czech Republic, and Bulgaria have a high reliance on fossil fuels.

Latvia, Estonia, Lithuania, Poland, and Croatia show no use of nuclear energy sources. Meanwhile, the Czech Republic, Slovakia, Bulgaria, and Slovenia have a significant share of nuclear energy, which may indicate a slower but potentially more secure energy transition, allowing for fuel and energy structure balancing without dependence on fossil fuel imports.

Central and Eastern European countries such as Bulgaria, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia, and Slovenia were selected for the study due to their similar economic structures, historical backgrounds, and ongoing economic and political integration. The choice of these countries was also driven by the aim of developing recommendations for the integration of renewable energy sources, especially since they share commitments to achieving climate goals under the European Green Deal and the Fit for 55 package. Coordinated action can help increase the share of renewable energy sources and reduce CO₂ emissions.

Nevertheless, it is evident that the region still faces a long road toward energy transition and numerous challenges related to renewable energy sources integration. Although the energy balance structures of Central and Eastern European countries vary, they are subject to similar integration processes concerning renewable energy sources due to their comparable transformation potential.

The analysis was based on statistical data from sources such as Eurostat [33] and World Bank [34]. Calculations were performed using the STATA 17.0 software [35]. The data used for the analysis included: Energy efficiency, Share of energy from renewable sources, HICP, GDP per capita (PPP, USD), CO₂ emissions per capita, Gross domestic expenditure on R&D by sector, and Energy self-reliance.

In the first stage, a panel data analysis in a static framework was conducted, estimating fixed effects (FE) models. The application of this method allows for the consideration of individual heterogeneity across countries and the identification of the average impact of selected factors at the level of the entire panel [13]. To verify the model assumptions, the Hausman test, the Breusch–Pagan LM test (Appendix A). The static models made it possible to identify the structural conditions of renewable energy integration.

In the second stage, a dynamic panel analysis was applied, which enables the inclusion of inertia in energy processes and temporal dependencies by introducing a lagged dependent variable. Dynamic models are widely used in energy and climate policy research, as they allow for distinguishing short-term adjustments from long-term effects [13]. The article employed the Arellano–Bond estimator (GMM), which controls for endogeneity, autocorrelation, and the problem of variables permanently correlated with unobserved effects. Diagnostic tests were also conducted, such as the Arellano–Bond AR(1)/AR(2) and the Sargan/Hansen test (Appendix B) for overidentification, in order to confirm the validity of the specification. Dynamic models are broadly applied to assess how changes in the dependent variable affect other related variables in the system, thereby identifying causal relationships among variables [36].

Such a combination of static and dynamic methods provides a complementary assessment of the phenomenon: FE models identify structural differences between countries, while GMM enables the analysis of accumulation processes, inertia, and responses to socio-economic and political changes. The applied approach allows for a more precise evaluation of the determinants of renewable energy development in Central and Eastern Europe, while simultaneously accounting for regional specificities and the dynamics of transformation.

The general form of the panel equation model is as follows [37]:

Renewablesit = α+ + β₁⋅EnergyEfficiency_it + β₂⋅HICP_it + β₃⋅GDP_pcit + β₄⋅CO_2it + β₅⋅R&D_it + β₆⋅SelfReliance_it + μ_i

(1)

where

• Renewablesit—share of energy from renewable sources in country i in year t (dependent variable)
• HICP—energy prices: classification of individual consumption by purpose (COICOP): Energy. The data in the COICOP ‘Energy’ category refer to energy prices, but in a broad sense
• EnergyEfficiency_it—energy efficiency
• InfRate_it—harmonized index of consumer prices
• GDPp_cit—GDP per capita in USD (PPP)
• CO_2it—CO₂ emissions per capita
• R&D_it—gross domestic expenditure on R&D
• SelfRelianceit—energy self-reliance
• μ_i—country fixed effects
• λ_t—time effects
• ε_it—random error term.

In the case of a dynamic panel model, the formula takes the following form [37]:

y_it = γy_i,t−1 + βX_it + α_i + εit

(2)

where

• γ—coefficient for the lagged dependent variable
• yi,_t−1—lagged dependent variable
• α_i—individual effect (country-specific)
• ε_it—random error term
• β—vector of coefficients for explanatory variables.

The research process aimed to identify relationships between variables, specifically the impact of one variable on others.

Table 2. Results of the OLS linear regression.

Source	SS	df	MS	Number of Obs = 198 F(6, 191) = 51.08
Model	25.1585617	6	4.19309361	Prob> F = 0.0000
Residual	15.6794583	191	0.082091405		R-squared = 0.6161
					Adj R-squared = 0.6040
Total	40.83802	197	0.207299594		Root MSE = 0.28652
ln_Renewables	Coefficient	Std. err.	t	p > |t|	[95% conf. interval]
ln_EnergyEfficiency	−0.332246	0.0268477	−12.38	0.000	−0.3852021	−0.2792899
ln_HICP	0.3686208	0.1225927	3.01	0.003	0.1268114	0.6104301
ln_GDPpc	1.026048	0.1487076	6.90	0.000	0.7327275	1.319368
ln_CO2	−0.2785247	0.0631633	−4.41	0.000	−0.4031119	−0.1539375
ln_RnD	−0.3278758	0.055084	−5.95	0.000	−0.4365269	−0.2192246
ln_SelfReliance	0.2193137	0.0549986	3.99	0.000	0.110831	0.3277964
_cons	−8.980713	1.40008	−6.41	0.000	−11.74232	−6.219108
.vif
Variable			VIF	1/VIF
ln_GDPpc			2.30	0.434358
ln_RnD			1.67	0.597654
ln_EnergyEfficiency			1.39	0.716855
ln_HICP			1.39	0.717495
ln_SelfReliance			1.38	0.723640
ln_CO2			1.38	0.724538
Mean VIF			1.59

presents the results of the OLS linear regression. In addition, the variance inflation factors (VIF) were reported for the following variables: ln_GDPpc, ln_RnD, ln_EnergyEfficiency, ln_HICP, ln_SelfReliance, and ln_CO₂, together with the mean VIF. Based on the VIF test, the obtained values range from 1.38 to 2.30, with a mean VIF of 1.59. All indicators are well below the threshold of 5 commonly accepted in the literature, above which potential collinearity may be considered, and far below the critical value of 10. This indicates that the explanatory variables do not exhibit significant multicollinearity and that the model is not affected by variance inflation. Therefore, the estimated parameters can be interpreted as stable and reliable. It is worth noting at this point that the energy transition is a process strongly conditioned by regional and institutional factors [38,39].

The OLS regression assessment indicates that the model is statistically significant (F = 51.08, p < 0.001), and the coefficient of determination R² = 0.616 suggests a moderate fit. Energy efficiency, inflation, and R&D expenditures have a positive impact on the share of renewable energy sources, whereas GDP per capita, CO₂ emissions, and the energy self-reliance index exert a negative effect. All variables are statistically significant. Since the OLS model ignores country heterogeneity, an evaluation of the appropriate specification (FE/RE) was conducted. In this context, OLS serves as a reference point rather than the final model. Subsequently, to verify the validity of the specification between OLS, FE, and RE, the Breusch–Pagan and Hausman tests were performed.

4. Research Results and Discussion

4.1. Research Results

The data used in the study were logarithmized, which allowed for the standardization of variables and improved the stability of the estimated models [40]. At the same time, the coefficients obtained in the estimations are interpreted as elasticities. The first step involved selecting the appropriate model specification. The Hausman test analysis (Appendix A) was conducted to determine whether the fixed effects (FE) or random effects (RE) model was more suitable for the panel analysis. In order to select the appropriate specification of the panel model and to assess the reliability of the estimators, several diagnostic tests were conducted. The Breusch–Pagan (LM) test indicated that differences between countries are statistically significant (p < 0.001), which confirms the validity of applying a panel model instead of a simple pooled OLS. Subsequently, the Hausman test rejected the null hypothesis of no correlation between country effects and explanatory variables (χ² (6) = 33.49, p < 0.001), indicating that the random-effects model would be biased and that the preferred specification is the fixed-effects (FE) model. Therefore, in the further analysis, robust standard errors adjusted for country clusters were applied, which ensures reliable inference. Taken together, the conducted tests clearly demonstrate that the panel data require an FE model that accounts for differences between countries, while simultaneously employing robust standard errors to guarantee the validity of statistical inference. It is worth emphasizing that the occurrence of a non–positive definite variance–covariance matrix of the differences between the FE and RE estimators is a phenomenon well documented in the econometric literature [41,42].

The panel analysis with fixed effects conducted for 11 Central and Eastern European countries revealed many interesting relationships (

Table 3. Results of the fixed-effects (within) regression model.

Fixed-effects (within) regression				Number of obs =		198
Group variable: Country				Number of group =		11
R-squared:				Obs per group:
	within =	0.7637			min =	18
	between =	0.3885			avg =	18.0
	overall =	0.3960			max =	18
				F(6,10) =		40.69
Corr(u_i,Xb) = −0.8483				Prob > F =		0.0000
(Std. Err. adjusted for 11 clusters in Country)
ln_Renewables	Coefficient	Robust Std. err.	t	p > |t|	[95% conf. interval]
ln_EnergyEfficiency	−0.7750314	0.1947473	−3.98	0.003	−1.208955	−0.3411074
ln_HICP	0.2275872	0.1306986	1.86	0.012	−0.0636274	0.5188017
ln_GDPpc	0.602855	0.1843765	3.27	0.008	0.1920386	1.013671
ln_CO2	−0.0621252	0.2141041	−0.29	0.123	−0.5391789	0.4149284
ln_RnD	0.4570917	0.1044925	4.37	0.001	0.224268	0.6899154
ln_SelfReliance	−0.028192	0.1281471	0.22	0.830	−0.2573375	0.3137216
_cons	−2.562686	1.597417	−1.60	0.140	−6.121954	0.9965811
sigma_u	0.66355817
sigma_e	0.11617787
rho	0.97025761	(fraction of variance due to u_i)

).

The panel analysis was conducted using a fixed-effects (FE) model with 11 countries over the period 2004–2023, applying standard errors robust to heteroskedasticity and autocorrelation within countries (vce(cluster Country)). This model allows for controlling unobserved, time-invariant characteristics of individual states, such as energy policy, geographical conditions, or climate, thereby ensuring that the estimated coefficients reflect the impact of variables within countries. The model demonstrated good fit of the data to the explanatory variables, with the within-group R² equal to 0.76, indicating that the variables explain a substantial share of the variability in the share of renewable energy within countries. The correlation coefficient between unit effects and predictors amounted to −0.85, which confirms significant heterogeneity among countries and justifies the use of the FE model.

The coefficient analysis shows that the most important factor influencing the share of renewable energy is energy efficiency. The negative and statistically significant coefficient (β = −0.775; p = 0.003) suggests that improvements in energy efficiency lead to a decrease in the share of renewable energy sources, probably due to reduced overall energy demand, which limits the need to develop new renewable capacities in the short term. This phenomenon is particularly evident in economies where modernization of infrastructure and reductions in energy intensity are rapid and intensive; in such cases, energy consumption declines, and the share of renewable energy sources may fall despite increased production from renewable sources. This effect may reflect demand substitution, as greater energy efficiency reduces total demand, thereby lowering the relative weight of renewable energy sources.

An increase in energy prices, measured by the HICP index, shows a positive impact on the share of RES (β = 0.228; p = 0.012), which is consistent with economic theory indicating that rising costs of conventional energy encourage the search for alternative energy sources. Other significant determinants of the share of RES are macroeconomic factors: GDP per capita and R&D expenditures. A higher level of economic development translates into a greater share of renewable energy (β = 0.603; p = 0.008), which can be linked to greater investment capacity and social pressure to adopt clean technologies. Similarly, R&D expenditures show a strong and statistically significant positive effect (β = 0.457; p = 0.001), underscoring the importance of innovation and technological progress in the development of the renewable energy sector. This relationship is not only statistically robust but also consistent with endogenous growth theory, which highlights the fundamental role of innovation in reducing costs and increasing the efficiency of renewable energy technologies.

Other variables, CO₂, and energy self-reliance, were not statistically significant at the 5% level, although the directions of their effects are consistent with theoretical expectations. The fraction of variance explained by differences between countries exceeded 97% (ρ = 0.97), highlighting the dominant role of unit-specific effects in the variability of the share of renewables. Taken together, the results confirm that in the analyzed countries, economic development and investment in innovation foster an increase in the share of renewable energy sources, whereas improvements in energy efficiency may lead to a reduction in the share of renewables.

At this point, it is worth noting that energy integration requires not only EU policy but also internal economic and technological reforms, so that countries can effectively increase the share of renewable energy sources and reduce energy intensity [43,44].

Next analysis conducted was a dynamic panel analysis (

Table 4. Dynamic panel-data estimation, two-step system GMM.

Group variable: Country				Number of obs =		198
Time variable: Year				Number of group =		11
Wald χ2 (7) = 49,313.37				Obs per group:
Number of instruments = 41					min =	18
					avg =	18.0
Prob > χ2 = 0.000					max =	18
ln_Renewables	Coefficient	Corrected Std. err.	z	p > |z|	[95% conf. interval]
ln_Renewables L1.	0.3991247	0.1831398	2.18	0.029	0.0401774	0.7580721
ln_EnergyEfficiency	−0.1893586	0.0730164	−2.59	0.010	−0.3324681	−0.0462491
ln_HICP	−0.0605947	0.101061	−0.60	0.549	−0.2586707	0.1374813
ln_GDPpc	0.4960784	0.3303295	1.60	0.033	−0.1513555	1.143512
ln_CO2	−0.4432579	0.2396165	−1.95	0.044	−0.9128977	0.0263819
ln_RnD	0.1666661	0.1532132	1.69	0.057	−0.1336262	0.4669584
ln_SelfReliance	0.1231429	0.426459	0.29	0.773	−0.7127014	0.9589872
_cons	−2.310415	3,676,017	−0.63	0.530	−9.515275	4.894446

). Due to the variability of the examined parameters over time, as well as the presence of inertia-related factors, a dynamic assessment was also performed.

The estimation results of the two-way dynamic panel model (system GMM—Table 4) allow for a clear identification of the factors shaping the share of renewable energy in European countries during the analyzed period. The application of the system GMM was justified by the presence of endogeneity related to the lagged dependent variable, the interdependencies among economic variables, and the relatively small number of units (N = 11), which is typical for analyses based on country-level panels.

In this way, the initiation of these processes generates a lasting expansion of renewable energy sources. This inertia effect reflects the cumulative nature of investment processes and the durability of past policy actions. Even when countries experience significant short-term fluctuations in renewable energy investments, the lagged dependent variable captures the structural continuity of renewable growth, emphasizing the distinction between short-term dynamics and long-term structural factors.

The estimation results of the two-way dynamic panel model (system GMM—Table 4) provide important insights into the factors determining the development of renewable energy in European countries. The analysis shows that the share of renewable energy depends on the economic structure of the countries, macroeconomic conditions, technological progress, and long-established patterns of energy system functioning. A key finding is the statistically significant and positive coefficient of the lagged dependent variable, which confirms the presence of strong path dependence in the development of renewable energy sources. This means that countries which previously increased their share of renewables are more likely to continue this trend. This result is consistent with the literature emphasizing the cumulative nature of renewable energy investments, the growth of technological capabilities, and the development of institutions supporting renewable energy.

One of the strongest and most stable factors is energy efficiency. Its positive impact on the share of renewable energy sources indicates that a more efficient energy economy reduces technical and economic barriers to the integration of renewables. This finding supports the concept of complementarity between energy efficiency and renewable energy sources, particularly in countries modernizing their industrial and building sectors. The macroeconomic variable of energy prices (HICP) shows a significant and negative impact on renewable energy sources development. This implies that unfavorable macroeconomic conditions reduce the willingness of economies to invest in capital-intensive projects such as renewable installations. This result is consistent with economic literature, which points out that energy prices raises financing costs, reduces the real value of subsidies, and increases investment uncertainty, factors especially detrimental to sectors requiring stable, long-term commitments.

Economic development (GDP per capita) also plays an important role. Its positive effect means that wealthier countries have greater investment capacity and more advanced technological infrastructure, facilitating the adoption of renewable technologies. This result aligns with international studies showing that rising prosperity supports the energy transition, and that higher-income societies place greater emphasis on environmental protection.

The negative impact of CO₂ emissions suggests that economies with more emission-intensive energy structures face greater challenges in the dynamic development of renewable energy sources. This phenomenon is often linked to technological lock-in-persistent dependence on fossil fuels, high costs of abandoning existing infrastructure, and pressure from traditional energy sectors. This finding confirms observations in the literature that countries heavily dependent on fossil fuels require stronger policy interventions to accelerate the shift away from conventional energy.

R&D expenditures have a positive, though moderate, effect on the development of renewable energy sources. This result is consistent with innovation theory, which suggests that R&D effects materialize with a delay, depend on institutional quality, and require complementary public policy instruments. Differences between countries in the efficiency of R&D spending may explain the lower statistical significance of this factor.

The impact of energy self-reliance is negative, though statistically weaker. This suggests that countries with high energy independence, often achieved through conventional resources, do not face strong pressure to develop Renewable Energy Sources. Conversely, countries dependent on energy imports are more likely to perceive Renewable Energy Sources as a strategic tool for strengthening energy security. This finding is consistent with broader observations that political motivations behind the development of Renewable Energy Sources vary across countries depending on their energy resource structures and institutional frameworks. Additionally, diagnostic tests confirm the validity of the estimation. The absence of second-order autocorrelation, the high p-value in the Hansen test, and the correctness of instrument subsets (Difference-in-Hansen) indicate proper model specification and adequacy of the instrument set (Appendix B). The high stability of coefficients in alternative analyses further confirms the robustness of the obtained results.

It is worth emphasizing at this point the importance of applying diagnostic tests (Arellano–Bond, Sargan, Hansen and Difference-in-Hansen tests) as a key tool for assessing the correctness of model specification [45,46,47]. The comparison with the dynamic panel GMM model reveals additional aspects. The GMM specification accounts for the lagged effect, i.e., the influence of the previous level of RES on its subsequent development, and eliminates the risk of endogeneity among certain variables. As a result, it provides a picture of both short-term and long-term determinants of the energy transition. It appears that energy efficiency, energy prices, and GDP per capita remain key factors in both models, whereas the impact of R&D in the GMM specification is less clear, indicating its long-term nature and the need for policy support to accelerate the materialization of innovation effects.

The correctness of the specification was assessed using standard diagnostic tests, namely the Arellano–Bond, Sargan, and Hansen tests (Appendix B). The model exhibits significant first-order autocorrelation in the differenced residuals (AR(1): z = −2.02, p = 0.043), which is an expected outcome in GMM estimation and does not pose a diagnostic problem. The key condition of estimation validity, namely the absence of second-order autocorrelation, was satisfied (AR(2): z = 1.83, p = 0.067). This result confirms that the lagged instruments used are consistent with the assumptions of the estimator and are not correlated with the transformed error term. Based on the Sargan test, we cannot reject the null hypothesis, indicating that the instruments used in the model are valid and consistent with the assumption of exogeneity (Appendix B).

The results of the more robust Hansen test confirm the same interpretation. This demonstrates that the model is reliable in light of the diagnostic tests and that the estimation results are accurate. In addition, the Difference-in-Hansen tests confirmed the exogeneity of both the instruments for the lagged dependent variable and the variables treated as exogenous. Taken together, the diagnostics indicate that the dynamic model is correctly identified, and the instruments employed allow for a credible assessment of the impact of both short-term changes within countries and structural differences across countries.

4.2. Discussion

In light of the conducted research, as well as previous findings by other authors such as Hassan, Viktor, Al-Musawi, Ali, Algburi, Alzoubi, Al-Jiboory, Sameen, Salman and Jaszczur [3], a global trend toward renewable energy sources can be observed. On one hand, this trend reveals existing inequalities stemming from diverse geopolitical, technological, and economic conditions. On the other, it highlights the need to adapt policies, investments, and cooperation to accelerate this global shift.

The analysis of the role of renewable energy in global energy transition processes, conducted by Hassan, Viktor, Al-Musawi, Ali, Algburi, Alzoubi, Al-Jiboory, Sameen, Salman and Jaszczur [3], demonstrated significant variation in the pace of implementation and the level of technological advancement among individual countries. According to the transformation scenario, renewable energy sources could meet up to 66% of global primary energy demand by 2050, which stands in stark contrast to the mere 24% projected in the reference scenario (

Table 5. The Role of Renewable Energy Sources in the Global Energy Transition Processes of Selected Countries/Regions [3].

Region/Country	Share of RES/Trend
Global (optimistic scenario) vs. reference scenario	Up to 66% of primary energy demand from renewable energy sources by 2050, in contrast to 24% in the reference scenario.
European Union	High level of RES integration; Denmark and Germany are leaders in wind energy and overall renewable energy share
China and India	>30% annual growth in the solar and wind sectors
United States, Canada, Brazil	Varying levels of RES integration, with each country contributing differently
Middle East, Africa	Gradual diversification of the energy portfolio. Africa shows potential, but transformation is limited by infrastructure challenges

).

Unlike many global studies, our analysis focuses specifically on Central and Eastern European countries, highlighting unique regional determinants of renewable energy development that are often overlooked in broader research.

This research also correlates with studies conducted by Hoicka, Lowitzsch, Brisbois, Kumar and Ramirez Camargo [18], who observed that the effectiveness of implementing renewable energy integration solutions depends on regional needs and priorities. As they emphasize, the exchange of best practices among member states is essential. However, each country or region, such as Central and Eastern Europe, requires an individual approach. In this context, it is crucial to refine the provisions of the RED II Directive [19] to allow for flexible implementation of business models tailored to regional conditions. Our findings extend this perspective by showing that in CEE countries, inertia effects and cumulative investment processes play a dominant role, which distinguishes this region from others analyzed in previous studies.

Alsayegh, Alhajraf and Albusairi [24] point out that promoting local energy models based on renewable energy sources should be a priority in energy transition strategies—not only for economic benefits but, perhaps more importantly, in the pursuit of sustainable development and climate neutrality.

The analysis conducted by the researchers highlights the need to accelerate the development of renewable energy technologies, especially in the areas of energy storage and conversion efficiency [24,48,49]. At the same time, they note that when developed countries invest in sources other than renewables, the pressure to develop and integrate renewable energy sources at the system level diminishes [50]. Electricity storage offers a well-established approach to improving the reliability and utilization of the power grid, as it enables shifting energy supply over time and aligning it with actual demand when it is needed [51].

The analysis aligns with the broader trend of accelerating renewable energy development. It also reveals that investments in alternative energy sources delay the growth and integration of renewables at the systemic level.

An analysis using threshold panel regression based on data from 120 countries over the past 20 years, conducted by Li, Wang and Wang [52], indicates that on one hand, the development of renewable energy sources can support both economic growth and environmental improvement. On the other hand, ongoing urbanization causes the initially weakening negative impact of renewable energy on the ecological footprint to later intensify, while the positive coefficient for economic growth maintains an upward trend [52]. As the authors emphasize, non-renewable energy has a more evident positive effect on economic growth but increases the ecological footprint. The results obtained are consistent with the findings of Li, Wang and Wang [52], and the observed relationships align with the present study.

Mamkhezri and Khezri [26], using a bidirectional panel analysis of fixed effects across time and space, confirm that investments in research and development (R&D) and the development of renewable energy sources (RES) have a significant impact on reducing CO₂ emissions globally. Their findings also support the Environmental Kuznets Curve (EKC) hypothesis, indicating a diminishing effect of economic growth on CO₂ emissions. The research shows that urbanization and international trade worsen environmental quality, while earlier epidemics preceding COVID-19 had no significant impact on emission levels. In contrast to their global conclusions, our study reveals that in CEE countries the impact of R&D tends to fade over time, underscoring the importance of institutional and market-driven factors rather than technology alone.

Similar conclusions were drawn by researchers Dam, Işık and Ongan [53], who confirmed the EKC hypothesis for OECD countries, showing that economic growth is significant both in the short and long term, but its environmental impact is greater in the short term than in the long term. Accordingly, these results correlate with the present study, indicating that renewable energy sources contribute to CO₂ reduction, and their impact is driven by investments and policy, ensuring sustained and cumulative development. Our results corroborate the EKC hypothesis but add an original dimension by demonstrating that energy security concerns can slow renewable energy integration, a factor rarely emphasized in prior literature.

This study also demonstrated the system’s resilience to socio-economic disruptions, which may be interpreted as a degree of resistance to earlier epidemics without significant effects on CO₂ emissions. Some differences are noticeable in the long-term influence of R&D. According to the conducted research, the development of renewable energy sources in Central and Eastern European countries is sustained and cumulative, based on prior investments, infrastructure, and policy. However, as the studies suggest, this factor tends to fade over time.

In contrast, the research by Mamkhezri and Khezri [26] identifies R&D as a dominant and lasting factor, without reference to a time horizon. Nevertheless, this durability and dominant role may suggest that the fading effect does not apply to all factors, such as technology. Therefore, a significant difference is observed in this aspect compared to previously analyzed studies.

The relationships observed in this study are consistent with earlier analyses conducted by Mohd Alsaleh, Abdul-Rahim and Abdulwakil [54], who applied a fixed effects (FE) model in their research. Their findings showed that the growth of the bioenergy industry in Central and Eastern European countries can be effectively enhanced through the effectiveness of state policy, the quality of legal regulations, and social engagement and pressure from citizens. Their regression analysis using various statistical models confirmed that the quality of governance has a statistically significant impact on bioenergy development, even after accounting for endogeneity risks and using lagged macroeconomic data. The results indicate that improving government efficiency may be a key factor in supporting the energy transition and the development of renewable energy sources in the EU-15, EU-13, and the entire EU-28 region [54].

The panel causality test results conducted by Dam, Işık and Ongan [53], revealed a bidirectional causal relationship between renewable energy use, demographic dynamics, and the inverse load factor. Additionally, the analysis identified a unidirectional causal link between institutional quality and the inverse load factor, emphasizing the importance of effective policy for the sustainable use of environmental resources. This aligns with previous findings that the use of renewable energy sources plays a crucial role in improving environmental quality.

The results obtained by Liang, Zhu and Zeng [17] confirm the trends described in the literature and thus contribute to the body of research on sustainable development challenges. As the researchers noted, “traditional energy companies focus mainly on oil, natural gas, coal, and water. The extraction and use of the first three resources pose a significant challenge to sustainable development”.

It is worth noting here that the originality of our research lies in identifying the cumulative and inertia-driven nature of renewable energy development in Central and Eastern European countries, as well as in highlighting the trade-off between energy security and renewable integration-aspects that have not been sufficiently addressed in earlier studies. In light of the above, the necessity emerges to create long-term incentives and flexible energy transition strategies tailored to regional conditions.

This study is subject to several limitations that warrant consideration. First, the scope of the analysis is confined to Central and Eastern European countries, which may restrict the generalizability of the findings to other geographic contexts. Second, the reliance on available macroeconomic data introduces potential measurement constraints. Consequently, the findings should be interpreted with caution and complemented by more granular, country-specific data in future research.

5. Conclusions

The conducted research revealed significant differences in the phenomena under consideration across the group of analyzed countries. Based on this, it is possible to distinguish stimulants and deterrents in the development process of renewable energy sources in Central and Eastern European countries. The changes occurring over the years show strong inertia and, consequently, stability in subsequent periods. Thus, initiated investment processes and impulses in the form of economic and institutional effects ensure the continuity of ongoing transformations. These changes are not highly sensitive to the socio-economic factors considered. The development of renewable energy sources in these countries is durable and cumulative, built upon previous investments, infrastructure, and policy. However, this factor tends to fade over a longer time horizon. The effects of implemented actions unfold over time, which simultaneously serves as a recommendation for policy strategies that create long-term incentives. The conducted analysis demonstrates that the development of renewable energy sources is a multidimensional phenomenon, determined by economic and structural factors, as well as macroeconomic conditions and technological-innovative capacities. The results highlight the necessity of creating coherent and long-term policies to support the energy transition, taking into account both technological and economic aspects.

Market-driven changes are a significant factor. Rising energy prices (and undoubtedly inflation rates) encourage increased investment in renewable energy sources. These factors help reduce costs and improve the competitiveness of economic entities or lower living expenses for households. They are important motivators in decision-making within the energy market. Energy prices exert a dual effect: in the short term, they constrain capital-intensive investments, while in the long term, they increase the economic attractiveness of renewable energy sources. This result underscores the necessity of a stable and predictable pricing and financial policy. The crucial role of the level of economic development has also been confirmed. An increase in GDP per capita fosters a higher share of renewable energy, as it enables the financing of new technologies, enhances the capacity to absorb innovations, and strengthens societal expectations regarding environmental policies. A similar, stable relationship was found for R&D expenditures, which reinforce technological potential and facilitate the implementation of modern renewable energy installations. The negative impact of CO₂ emissions observed in the dynamic model indicates a persistent technological lock-in, characteristic of fossil-fuel-based economies. Countries with emission-intensive energy systems require more decisive interventions and political support to overcome structural barriers and accelerate the transition.

In light of the above considerations, it becomes evident that both the energy transition and energy security are redefining the future of the energy landscape in Central and Eastern Europe. The green transition is a process focused on combating climate change. Therefore, investments in energy storage networks and renewable technologies are of key importance. Strengthening energy efficiency is essential, as it constitutes one of the main drivers of renewable energy development. Investments in infrastructure modernization, energy-saving technologies, and efficiency standards should form the foundation of the energy transition. The reduction of CO₂ emissions must go hand in hand with the expansion of renewables. Public policy should encompass both the phasing out of high-emission technologies and the development of infrastructure enabling the integration of clean energy sources.

It is also worth noting that, in line with the commitments made under the European Green Deal, all countries aim to achieve climate neutrality. However, this process will progress at different speeds in each country. The pace of these changes depends on the structure of the energy balance in Central and Eastern European countries. As previously mentioned, although this structure varies, it is subject to similar integration processes related to renewable energy sources due to a comparable transformation potential. Nevertheless, a high share of nuclear energy or fossil fuels may slow down the pace of renewable energy sources integration.

To achieve EU objectives, legal instruments such as regulations, directives, recommendations, and public support mechanisms are used to stimulate the development of renewable energy sources. Based on the above analysis, there is a clear need to incorporate regional conditions into EU support mechanisms, as well as to establish flexible legal frameworks that allow adaptation to changing socio-economic realities.

From this, several policy recommendations can be drawn. First and foremost, the development of renewable energy sources should be balanced with maintaining essential production from conventional sources in countries where energy security requires a stable production surplus. This calls for flexible energy transition strategies that enable the gradual integration of renewable energy sources without risking energy supply disruptions or increased system instability.

Furthermore, legal and financial mechanisms introduced at the EU level should be flexible and take into account the specific needs of individual Central and Eastern European countries, including their energy balance structure, transformation potential, and development priorities.

More broadly, it should be emphasized that successful energy transition and integration require the active involvement of public institutions, support from local governments, and the exchange of best practices between countries, with consideration for local capabilities and constraints.

Future research should place greater emphasis on institutional and regulatory factors, which may play a crucial role in the long-term dynamics of the energy transition. It is also worthwhile to expand heterogeneous analyses that take into account the different structures of the energy balance in individual Central and Eastern European countries, as well as to apply dynamic models that allow for the identification of short- and long-term effects of renewable energy policies. Further studies may also focus on the role of energy storage technologies and on the responses of households and firms to changes in energy prices. In addition, it is advisable to extend analyses to the regional and sectoral level in order to better capture the local conditions of transformation processes.