The Role of Climate-Oriented Funding in Advancing Renewable Energy Transition Across the EU

Abstract

The shift to renewable energy is a key goal for the European Union as it aims for climate neutrality; however, the effectiveness of climate-focused funding instruments varies significantly across member states. This research investigates the influences of mitigation investments, R&D spending, environmental tax revenues, subsidies, GDP growth, and capital formation on renewable energy expansion within the EU-27, placing particular emphasis on the structural differences between Old Member States (OMS) and New Member States (NMS). The study utilizes robust long-run estimation techniques alongside causality analysis over a span of 13 years, from 2010–2023. The findings highlight notable distinctions among the EU-27, OMS, and NMS regions. While the EU-27 and OMS show that funds designated for climate mitigation and R&D are critical drivers of the clean energy transition, in the NMS, environmental taxes, subsidies, innovation, and gross fixed capital formation play vital roles in advancing this transition. Furthermore, economic development shows mixed results in achieving sustainable objectives, underscoring the necessity for climate-oriented funding and initiatives. Therefore, policy measures should focus on mitigation finance and innovation across the EU, while the design of subsidies and environmental tax structures must be tailored to each region to ensure a fair and expedited transition.

1. Introduction

Renewable energy is an important subject in academia due to its status as a clean alternative to fossil fuels. The European Commission aims to boost the share of renewable energy in the EU-27 and lessen dependence on fossil fuels. The Renewable Energy Directive is crucial for the implementation of clean alternative energy sources; currently, the EU is a worldwide leader in renewable energy development and aims for a minimum of 42.5% of energy to come from renewable sources by 2030 [1]. Renewables play a key role in minimizing the EU’s carbon footprint within its energy system. By 2050, renewable sources, in conjunction with energy efficiency measures, could contribute to a 90% reduction in total emissions [2]. While achieving the goal of carbon neutrality is commendable, it entails significant costs that require financial backing. Climate-oriented funds are designated for environmental initiatives and sustainability efforts, aiming to protect the environment and mitigate the adverse effects of climate change. This research seeks to determine the types of climate-focused funds that encourage the advancement of renewable energy sources, along with their support and adaptation. Furthermore, the European Environmental Agency emphasizes the significance of renewable energy adoption in reaching climate neutrality by 2050 [3]. The originality and innovation of this study arise from its examination of the entire EU-27, as well as its breakdown into smaller regions, including New Member States (NMS) and Old Member States (OMS), to explore the unique characteristics exhibited by each member region. As far as the author knows, no other studies have utilized this specific methodology on the data currently sampled. This research is unique in its evaluation of the most impactful climate-related financing strategies for renewable energy transitions, customized to the regional contexts and particular needs of these regions.

The objective of this study is to address the following questions that seek to offer a deeper insight into climate-focused funds and their connection to renewable energy:

• Which climate-oriented financing mechanisms are most effective in accelerating the deployment and expansion of renewable energy sources?
• Do the EU-27, OMS, and NMS display distinct patterns in their use of climate-focused financial instruments, indicating a need for differentiated funding strategies?

The organization of the study is as follows: Section 2 introduces the Literature Review, and Section 3 outlines the Methodology and Materials utilized in the research. Following this, Section 4 reveals the findings of the analysis. Finally, Section 5 and Section 6 conclude the study with a Discussion and Conclusions, respectively.

2. Literature Review

The topic of renewable energy consumption has been a central pillar of the European Union’s climate and energy strategy [4,5,6,7], driven by the last decades’ escalating concerns regarding climate and energy crises and considering the expanding importance of green economic growth. In response to these concerns, the European initiatives point to one of the most ambitious policy frameworks globally, the European Green Deal, focusing on projects that aim to accelerate the transition to a climate-neutral Europe by 2050 [8]. Consequently, a wide body of literature examines renewable energy consumption as a major priority for EU member states. Existing studies analyze the determinants of renewable energy consumption through multiple lenses, and categorize these drivers as macroeconomic drivers (GDP, trade, industrial structure), social and behavioral factors, environmental pressures, and more recently as governance quality [9,10,11,12,13,14]. However, one important pillar and dimension within the economic factors considered in the literature consists of climate-oriented funding instruments, which are emerging as a central lever in accelerating the transition toward renewable energy systems.

The existing literature suggests that funding aimed at addressing climate change is not merely an extra component of energy policy, but an essential driver that influences the actual growth of renewable energy projects. This is because meeting climate targets demands not only advances in technology but also a significant mobilization of financial resources alongside supportive financial structures. In broader terms, climate-focused financing encompasses a range of public, private, and hybrid financial tools specifically aimed at directing funds toward objectives related to climate mitigation, such as the implementation of renewable energy solutions. Public instruments include grants, concessional loans, state guarantees, tax incentives and regulated price-support schemes such as feed-in tariffs or Contracts for Difference [15,16]. Private market-based instruments include green bonds, green loans and specialized infrastructure funds that allocate capital into renewable energy assets [17], while blended or mixed finance mechanisms combine public seed capital with private investment to reduce risk and scale up investment volumes, which now play a key role in major European and multilateral policy frameworks [18,19,20,21]. Among the various dimensions of climate-oriented funding, when it comes to the availability of the datasets of the instruments to quantify the impact, the literature focuses on more governmental-level indicators such as investments in climate change mitigation, expenditures on research and development, environmental tax revenues, as well as on a wide variety of financial instruments.

Although the wider body of research recognizes climate-focused funding as a vital facilitator of the clean energy shift, empirical investigations interpret this idea through a series of separate but interconnected financial aspects.

These dimensions reflect the numerous pathways through which financial resources and funding support renewable energy deployment (by expanding capital availability, fostering technological innovation, reshaping price incentives, and reducing investment risk). Accordingly, we identified four major categories that emerge from existing research: investments in climate change mitigation, expenditures on research and development, environmental tax revenues, and market-based financial instruments. Each category captures a unique mechanism through which climate-oriented funding influences the scale, speed, and feasibility of renewable energy consumption.

The following subsections synthesize empirical evidence across these dimensions, highlighting both common findings and areas of divergence that shape our understanding of financial drivers in the EU’s renewable energy transition.

Additionally, the literature review concludes with a subsection focused on investors’ views about climate-related funding, along with a section that highlights the identified research gap.

2.1. Investments in Climate Change Mitigation

Investments in climate change mitigation refer to financial flows directed toward activities, infrastructure, and technologies that reduce greenhouse gas emissions. This includes both public and private investment in renewable energy generation, energy efficiency and other areas that sustain green energy development. Numerous studies consistently highlight a link between public and private investments in mitigation and the progression of renewable energy usage [22,23,24,25,26,27,28,29,30], employing various analytical methods and sample groups. Considering the overall level of investments, the paper by Qamruzzaman and Karim [22] highlights that public and private partnership investment in energy exert positive and mutually reinforcing effects on renewable energy consumption in Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) nations. According to IPCC [23], sustained investment in mitigation activities is essential for scaling renewable energy and achieving emission-reduction goals. A wide range of literature in the field supports this hypothesis by applying different econometric approaches on diverse datasets. For example, Aquilas and Atemnkeng [24] focus on a dataset from 2002 to 2020 and apply panel regression estimates in the Congo Basin, emphasizing the importance of climate-related development mitigation finance in relation to the growth of renewable energy shares. Wang and Pang [25] focus on renewable energy consumption, climate mitigation technology, environmental tax and economic growth for 38 OECD countries, for the 2000–2020 period, using dynamic panel modeling techniques. The analysis demonstrates that climate-mitigation technologies and environmental taxes introduce threshold dynamics that shape how renewable energy consumption interacts with economic growth. More precisely, the above-mentioned authors argue that as climate-mitigation technology advances, the development of renewable energy makes a significantly positive contribution to economic growth. Moreover, the influence of climate-mitigation technology on the relationship between renewable energy consumption and economic growth is particularly pronounced in high-carbon regions.

Li et al. [26] illustrate this relationship in the Chinese context, showing that higher levels of green investment, captured through expenditures in renewable energy supply, are associated with measurable reductions in CO₂ emissions. Similar patterns emerge across broader international samples. Ang et al. [27], examining OECD and G20 economies, find that mitigation-oriented investment is closely linked to the expansion of renewable energy capacity, particularly where regulatory and institutional frameworks are strong. This connection is echoed in the work of Bashir and Jamaani [28], Fang et al. [29], and Chien et al. [30], who collectively emphasize that climate-related development finance and mitigation technologies shape the trajectory of renewable energy deployment and play an important role in addressing climate degradation.

Taken together, these studies point to a broader insight: investment directed toward mitigation efforts consistently aligns with shifts in energy systems toward cleaner sources, even though the mechanisms and contexts differ across regions.

2.2. Expenditure on Research and Development

While traditional definitions of climate finance emphasize direct capital investment in mitigation projects, a growing body of literature recognizes that public and private expenditure on research and development (R&D) in clean energy technologies constitutes a complementary dimension of climate-oriented finance. Such spending facilitates technological innovation, reduces costs, and enhances the long-term efficiency of climate mitigation investments, laying the base for a more efficient renewable energy transition, with a wide body of literature in the field considering this proxy for technological progress as a prerequisite for decarbonizing the global energy system. Expenditure on R&D in renewable energy and low-carbon technologies represents an indirect yet crucial form of climate-oriented finance.

Research on clean-energy R&D consistently underscores its role as a catalyst for technological progress and long-term decarbonization. Alam et al. [31] show that R&D efforts, particularly when supported by developed financial markets, foster technological innovation that translates into cleaner energy production and greater efficiency Research from developing economies reinforces this trend: Dilanchiev et al. [32], in their analysis of BRICS nations, discover that rises in renewable energy R&D correlate with significant decreases in greenhouse gas emissions, underscoring the lasting capability of research investments to promote sustainable development. This relationship also appears in more nuanced contexts. Furthermore, He et al. [33] demonstrate that public renewable-energy R&D can moderate the environmental pressures linked to natural resource use and urbanization, while Zhang et al. [34] show, using quantile regression, that technological innovation, proxied through R&D spending, is an important channel through which green finance reduces carbon emissions.

In the EU sample, the findings are also consistent. Chmielewski et al. [35] establish connections between R&D expenditure and the advancement of renewable energy for both the short and long term, determining that increased investment in R&D fosters a rise in renewable energy usage among member states. Petre [36] further reinforces this finding by demonstrating that R&D investments strengthen renewable energy uptake within the industrial sector.

Together, these studies point to a broader conclusion: across diverse economic settings, R&D expenditures contribute to cleaner energy systems by driving technological innovation and enabling sustained reductions in emissions.

2.3. Environmental Tax Revenues

While direct investment and innovation funding stimulate the supply side of the clean energy transition, environmental tax revenues represent a fiscal mechanism aimed at influencing the demand side by internalizing environmental externalities. When well-designed, environmental taxes create economic incentives for renewable energy adoption by making fossil-based energy more expensive and clean alternatives more competitive.

The impact of environmental taxes on influencing renewable energy use appears in the literature as complex and reliant on specific contexts. Gerni et al. [37], examining newly industrialized countries, suggest that such taxes function as an automatic stabilizer, helping to steer energy systems toward cleaner sources. Yet not all studies find immediate effects. Fang et al. [38] report that while environmental taxes may initially suppress renewable energy consumption, likely due to short-term adjustment costs, while their long-run influence becomes supportive of a cleaner energy mix. Broader global evidence aligns with this pattern. Nchofoung et al. [39], drawing on a sample of 49 countries, show that higher environmental tax intensity is generally associated with increased renewable energy consumption, though the magnitude of this relationship differs between developed and developing economies. Research focusing specifically on the EU further confirms the presence of heterogeneous effects. Degirmenci and Yavuz [40] demonstrate that both environmental taxes and R&D expenditures influence renewable energy consumption unevenly across member states, reflecting institutional and structural differences. Complementing these findings, Savranlar et al. [41] show that rising environmental tax revenues are linked not only to improvements in CO₂ dynamics but also to sustained increases in renewable energy consumption over the long term, as evidenced through panel VAR analysis.

Collectively, these studies suggest that while environmental taxes can encourage a shift toward renewable energy, their effectiveness varies across countries and evolves over time.

2.4. Financial Instruments

The growing body of literature emphasizes the importance of the maturity of the financial sector and financial instruments in supporting the transition to renewable energy. Insights from the study conducted by Apeaning and Labaran [42] emphasize that developing nations need to enhance their financial sectors in order to effectively utilize Climate Mitigation Innovation, leading to substantial decreases in emissions and an increase in renewable energy use. Within the European context, Batog and Pluskota [43] demonstrate a similar logic, showing that financial instruments, defined under EU operational programs as market-based tools such as equity, quasi-equity, loans, and guarantees, have become a central component of the region’s energy transition. These tools are leveraged support from the EU budget intended to meet specific policy targets by engaging financial markets, and they can be strategically utilized alongside traditional grants or other financial support measures [44].

This connection between financial sector strength and renewable energy deployment is further reinforced by broader findings in the field. Cochran et al. [45] argue that well-functioning financial markets are foundational to scaling clean energy projects, as they reduce financing barriers and improve institutions’ ability to manage long-term investment flows. Prempeh [46] adds evidence from Ghana, showing that financial development, captured through indicators of overall market depth, accessibility, and efficiency, supports higher levels of renewable energy consumption.

Considering these studies, the expansion of renewable energy systems depends not only on targeted policy incentives but also on the underlying strength of financial institutions and the strategic deployment of financial instruments capable of mobilizing capital.

2.5. Investors’ Perception

Investor perceptions play a critical role in shaping renewable energy deployment. Risk assessment, expected returns, and policy credibility influence the scale and speed of capital flows into clean energy. Polzin et al. [16] review 96 empirical studies that focus on the impact of RE support policies on investor decision metrics, proving that to effectively foster renewable energy deployment, policymakers should design instruments that are credible and predictable and that actively work to both reduce investment risk and increase investment return for private finance. Also focusing on the risk dimension from an investor’s perspective, Egli [47] focuses on the dynamics of investment risk for renewable energy technologies in Germany, Italy and the United Kingdom. The paper shows that overall investment risk for solar photovoltaics and onshore wind technologies have declined in all three countries over time, mainly due to increasing technology reliability at a lower cost and implementation of credible and stable policies. However, the same author ends the research paper with recommendations for policymakers, emphasizing the importance of mitigating the dominant risks (curtailment and price risk of generated electricity) to accelerate the transition to a Paris-agreement compatible energy system. The role of investor behavior is further explored by Mazzucato and Semieniuk [48], who investigate how different types of public and private actors allocate capital across a wide range of renewable technologies. Their findings underscore that understanding how various financial actors perceive and approach clean energy investments is crucial, as these behavioral patterns directly shape both the pace and direction of the energy transition.

Across recent literature, a consistent theme emerges: climate-oriented financial instruments exert a powerful and measurable influence on renewable energy deployment, primarily by lowering the cost of capital and de-risking long-term investments. Egli [47] and Egli et al. [49] illustrate that the weighted average cost of capital significantly influences the global deployment of renewable energy, and that policy measures aimed at reducing financing expenses have a greater impact on the adoption of renewable energy technologies than subsidies on operational costs. Moreover, Egli et al. [49] assert that affordable financing is vital for moving towards a low-carbon energy system. Similarly, Steffen [50] emphasizes the significance of financing conditions in connection with the implementation of renewable energy, maintaining the same focus on capital costs.

2.6. Research Gaps

Examining the current literature, while there has been significant advancement in comprehending the impact of climate-focused funding on renewable energy implementation, notable gaps continue to exist. Most studies examine individual funding dimensions, such as mitigation investments, R&D expenditures, environmental taxes, or financial instruments, without integrating these channels into a comprehensive analytical framework. Furthermore, existing literature evaluates climate financing mechanisms and their impact on renewable energy uptake, but few studies compare the effectiveness of these instruments across EU-27. Limited attention has been paid to the heterogeneity within the EU, particularly the differences between EU-27, Old Member States (OMS), and New Member States (NMS), which may lead to varying effectiveness of policy instruments due to structural, economic, and institutional disparities. Collectively, these gaps indicate a need for a more integrated, regionally nuanced, and methodologically consistent examination of climate finance and renewable energy consumption.

This research addresses these gaps identified in the literature by adopting a multi-dimensional approach that considers mitigation investment, R&D expenditures, environmental taxes within a unified analytical framework. By comparing the impacts of these funding mechanisms across EU-27, OMS, and NMS, the study explicitly accounts for regional heterogeneity in economic structures, institutional quality, and financial maturity.

All selected variables related to the financial dimension are based on existing empirical research and theoretical frameworks, which serve as the foundation for our hypotheses. Specifically, higher levels of mitigation investment are expected to support renewable energy deployment by providing the necessary capital for project expansion. Increased R&D expenditures are hypothesized to stimulate renewable energy consumption through technological innovation and efficiency gains, while greater environmental tax revenues are anticipated to incentivize adoption of clean energy technologies by internalizing the cost of carbon. Furthermore, the study allows a systematic evaluation of key financial variables in boosting renewable energy use in the defined samples. This research not only contributes to a theoretical understanding of climate financing mechanisms, but it also offers policymakers with concrete insights for optimizing the design and targeting of renewable energy support instruments in various EU scenarios.

3. Materials and Methods

This study analyzes data from all 27 European Union (EU) member states over a ten-year period, from 2013 to 2023. The data were obtained from Eurostat, a recognized and reliable source. The dataset is complete, with no missing variables, although most recent years were excluded due to limitations in data availability and completeness. To assess the impact of various funds intended to mitigate and adapt to negative externalities during the renewable energy transition, the data was organized into three groups. The first group, the EU-27 dataset, includes all 27 EU member states. The second group, the Old Member States (OMS), comprises Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxembourg, Netherlands, Portugal, Spain, and Sweden. The third group, the New Member States (NMS), consists of Bulgaria, Croatia, Cyprus, Czechia, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Romania, Slovakia, and Slovenia.

The objective of this study is to determine which funding sources dedicated to climate change adaptation and mitigation most effectively facilitate the renewable energy transition, while taking into consideration the particularities of different regions.

This study employs three long-term estimation techniques: Dynamic Ordinary Least Squares (DOLS), Fully Modified Ordinary Least Squares (FMOLS), and Canonical Cointegration Regression (CCR). The analysis also applies the Method of Moments Quantile Regression (MMQR), Quantile Regression (QREG), and the Dumitrescu-Hurlin test. The DOLS method was introduced by Stock and Watson [51], FMOLS by Phillips and Hansen [52], and CCR by Park [53]. CCR serves as a robustness check for DOLS and FMOLS estimators [54]. DOLS is effective for small samples because it corrects and reduces bias by incorporating lagged and lead values [55]. Furthermore, DOLS addresses potential biases such as endogeneity and serial correlation, providing reliable results regardless of sample size [56]. The DOLS long-term estimation technique demonstrates superiority over FMOLS due to its ability to eliminate correlation. FMOLS, a regression method utilizing test residuals, yields robust results for cointegrated variables [57]. The FMOLS long-term estimator corrects endogeneity, autoregression, and sampling bias errors [58]. Additionally, this methodology accounts for serial correlation, resulting in reliable estimates [56]. After applying DOLS, FMOLS, and CCR estimators, the analysis utilizes the moments of quantile regression (MMQREG) developed by Machado and Santos Silva [59] to examine how regressors affect the conditional distribution, making it appropriate for panel data analysis. MMQREG serves as an additional instrument for quantile regression that considers the presence of endogeneity among the variables. Furthermore, the technique includes fixed effects and accounts for heterogeneous relationships between the regressor and the regressant [60]. MMQREG is particularly useful to be used with techniques such as DOLS, FMOLS and CCR based on its superior competencies of efficiently analyzing data distribution compared to linear data estimation methods [61] allowing the tracing of heterogenous and distributional variations across different quantiles [62] capturing nonlinear and asymmetric responses [63]. Employing quantile regression (QREG) in conjunction with MMQREG demonstrates whether the findings remain stable under varying assumptions. MMQREG serves as a complement to traditional QREG, although it is applied in different contexts, such as models with endogenous explanatory variables and individual effects [59]. The quantile function offers a clear perspective on the influence of the explanatory variable and the distribution’s shape [64], while also delivering robust insights into the heterogeneous relationship between the dependent and independent variables [65]. The DOLS, FMOLS, and CCR methods focus on examining the long-term relationships between variables, whereas MMQREG and QREG highlight distributional and heterogeneous relationships to offer a comprehensive understanding of the relationships’ directionality. As a final measure, the Granger causality test has been implemented. To evaluate causality, the Dumitrescu & Hurlin [66] test has been utilized. This test was designed to determine the existence of causality within a panel data framework. A key benefit of applying this test is that it delivers reliable results even for unbalanced panels, accounts for cross-sectional dependencies, and offers robust findings irrespective of the sample size [67].

The analysis focuses on the EU-27, OMS, and NMS regions to gain a clearer understanding of how climate-oriented funding impacts the clean energy transition and its adoption. By differentiating between the OMS and NMS from the EU-27, the research considers how varying levels of economic development, fiscal policies, infrastructure, and regional characteristics influence the shift to green energy. According to the most recent data, the EU-27 currently utilizes 24.5% renewable energy and has set a target of 42.5% for 2030 [68]. The long-term goal of the EU is to establish Europe as “the world’s first climate-neutral continent by 2050” [69]. The variables presented in

Table 1. Variables employed in the model.

Dependent Variable	Abbreviation	Definition	Unit	Source
Renewable energy share	RE	Share of renewable energy in total final consumption [71]	% Of total energy consumption	Eurostat 2010–2023
Independent variables
Investments in climate change mitigation by NACE Rev. 2 activity	CM	Investment aimed to reduce greenhouse gases [72]	% GDP	Eurostat 2010–2023
Gross domestic expenditure on research and development (R&D)	GERD	Investment in innovation [73]	%GDP	Eurostat 2010–2023
Environmental tax revenues	ENVT	Revenue from total environmental taxes [74]	% GDP	Eurostat 2010–2023
Subsidies	SE	Total Subsidies allocated by the public sector [75]	% GDP	Eurostat 2010–2023
GDP growth	GDP	Change in gross domestic product [76]	% Change	World Bank 2010–2023
Gross fixed capital formation (investments)	GFCF	Total purchases and investment in fixed assets and infrastructure [77]	%GDP	Eurostat 2010–2023

Source: processed by the authors.

aim to gather insights related to various climate-oriented funds, the different levels of economic development, and progress in clean energy transition to identify which type of climate-oriented funding is most effective in promoting green transitions according to the unique characteristics of each region. It is essential to note that the variables have undergone standardization. Standardization adjusts the mean to 0 and the standard deviation to 1. The standardization process was carried out after obtaining the descriptive statistics. This method has been applied to render the data comparable and enhance the reliability of the findings. By standardizing the data, the shape of the bell curve remains unchanged, as opposed to using different methods [70].

RE_it= ϑ₀ + ϑ₁CM_it + ϑ₂GERD_it + ϑ₃ENVT_it + ϑ₄SE_it + ϑ₅GDP_it + ϑ₆GFCF_it + ε_it

(1)

The chosen dependent variable is the percentage of renewable energy, representing the transition towards clean energy sources. As the significance of sustainability rises, renewable energy becomes crucial as it helps to reduce pollution and lower greenhouse gas emissions [78]. Currently, renewable energy remains a key area of interest for researchers and is essential for achieving the EU’s neutrality objectives, as well as attaining carbon neutrality [79]. Investments aimed at addressing climate change are linked to sustainability and are classified as climate-focused funds because it contributes to diminishing the adverse impacts of climate change, including greenhouse gas emissions, and encourage eco-friendly practices [80,81,82]. Such investments are vital for facilitating the shift towards renewable energy and fostering conditions aimed at achieving sustainable development. This variable is important for providing insights into how effectively the market aligns with climate mitigation initiatives. Gross expenditure on research and development has been utilized for its potential to spur innovation. Various studies have demonstrated and emphasized the relationships between gross R&D spending and the adoption of clean energy, alongside the fulfillment of sustainability goals [83,84,85,86]. Consequently, evaluating its impact based on various regions can offer further insights into whether it should be prioritized as pivotal in the green energy transition. Environmental taxes have been extensively implemented as a financial tool designed to promote environmental protection and reduce pollution. Moreover, environmental taxes represent a significant portion of government revenue, which can be redirected to fund sustainable projects. Although environmental taxes have been discussed in several studies [87,88,89], findings about their effectiveness in facilitating the transition to clean energy show mixed results. Public subsidies are included in the analysis as an explanatory factor, providing financial aid from the public sector to encourage and alleviate expenses for individuals. Their effectiveness has been reviewed multiple times [90,91,92]. General subsidies act as government support that influences market dynamics and provides incentives for fostering desirable behaviors. Additionally, the application of public subsidies allows for an assessment of the impact of government incentives on the progress of renewable energy. Including subsidies as explanatory variables enables an additional systematic analysis on the effect of government incentives. Gross fixed capital formation is essential for funding infrastructure and fixed assets in nations. It effectively reflects long-term compared investments in tangible assets such as technological equipment, infrastructure, and energy systems. The transition to renewable energy necessitates substantial capital investment in grid enhancement, storage solutions, and supporting infrastructure. Using it as an explanatory variable sheds light on whether capital accumulation is aimed at promoting clean energy and infrastructure investment or is facilitating carbon-intensive systems. GDP growth serves as a measure of economic advancement to explore the connection between economic development and sustainable growth. The inclusion of GDP in this study aims to illustrate the broader macroeconomic environment, especially since a rising GDP may augment the financial resources available for developing renewable energy infrastructure, research and development, industrial efforts, and more. Therefore, it is crucial to evaluate whether economic development is fundamentally sustainable.

4. Results

To get an understanding of the different particularities and differences in the EU-27 regions, OMS and NMS within the renewable energy transition context,

Table 2. Summary statistics in the EU-27 sample.

Variables	Obs.	Mean	Std. Dev.	Min	Max
RE	297	22.4196	12.0698	3.494	66.393
CM	297	0.6620	0.4482	0.06	2.6
GERD	297	1.6546	0.8856	0.38	3.6
ENVT	297	2.6477	0.7876	0.85	5.6
SE	297	1.6616	1.0330	0.3	5.6
GDP	297	2.1565	3.7631	−11.4	23.5
GFCF	297	21.3013	4.1669	11	53.2

Source: processed by the authors.

,

Table 3. Summary statistics in the OMS sample.

Variables	Obs.	Mean	Std. Dev.	Min	Max
RE	154	23.5618	14.2823	3.494	66.393
CM	154	0.5798	0.4479	0.09	2.6
GERD	154	2.1720	0.8420	0.82	3.6
ENVT	154	2.5739	0.8305	0.85	5.6
SE	154	1.8168	1.0453	0.4	5.6
GDP	154	1.3448	3.8757	−11.4	23.5
GFCF	154	21.0058	4.8760	11	53.2

Source: processed by the authors.

and

Table 4. Summary statistics in the NMS sample.

Variables	Obs.	Mean	Std. Dev.	Min	Max
RE	143	21.1895	8.9970	3.76	43.72
CM	143	0.7506	0.4328	0.06	2.05
GERD	143	1.0974	0.5177	0.38	2.59
ENVT	143	2.7272	0.7332	1.42	4.76
SE	143	1.4944	0.9964	0.3	5.3
GDP	143	3.0307	3.4424	−7.5	13.4
GFCF	143	21.6195	3.2216	12.8	31.2

Source: processed by the authors.

provide the results of the summary statistics for the respective regions. All the tables contain data such as mean, standard deviation and minimum and maximum values of the regions.

Table 2 presents data from the 27 EU regions, comprising 296 observations. Among the variables, the renewable energy share has the highest average, followed by gross fixed capital formation (GFCF). The results indicate that member states are making significant progress in clean energy adoption. However, the high standard deviation suggests considerable disparity in adoption levels among member states. Disparities in investment in infrastructure and fixed assets are also present, but these are less pronounced than those observed for clean energy adoption. The variables with the lowest averages are investments in climate change mitigation and gross domestic expenditure on research and development (R&D). Table 3 and Table 4 display the summary of the descriptive statistics in the OMS and NMS regions, which exhibit similar patterns to EU-27. In the OMS region renewable energy and GFGF display the same behavior as EU-27, emphasizing the consistency in renewable energy adoption as well as investment in the members’ infrastructure and fixed assets. The standard deviation of the two variables also emphasizes the discrepancy withing the variables between the member countries. Investment in climate change mitigation as well as economic development are the variables that have the lowest mean, pointing out that very few countries focus on reducing the externalities of climate change, and there is a high variability between the economic development of the member states. Overall, data suggests that the member states display heterogeneity withing the member states. In the NMS region renewable energy share and GFCF exhibit the same behavior as in the EU-27 as well as OMS. The variables that have the lowest mean are investment in climate change mitigation as well as gross expenditure in R&D, indicating low levels of investment in R&D with moderate variability within the member states.

4.1. Correlation Matrix

Table A1. Correlation Matrix for the EU-27 sample.

	RE	CM	GERD	ENVT	SE	GDP	GFCF
RE	1.0000
CM	0.4818 ***	1.0000
GERD	0.4009 ***	0.1833 ***	1.0000
ENVT	0.0722	0.0530	−0.0192	1.0000
SE	−0.0361	0.0170	0.3242	0.0875	1.0000
GDP	−0.1040 *	−0.0046	−0.2556 ***	−0.0038	−0.1655 ***	1.0000
GFCF	0.2292 ***	0.2446 ***	0.2313 ***	−0.3963 ***	0.0609	0.0661	1.0000

*** p < 0.01, * p < 0.1; source: processed by the authors.

,

Table A2. Correlation Matrix for the OMS sample.

	RE	CM	GERD	ENVT	SE	GDP	GFC
RE	1.0000
CM	0.4923 ***	1.0000
GERD	0.5710 ***	0.6441 ***	1.0000
ENVT	−0.0320	0.1296 *	0.0331	1.0000
SE	−0.0588	0.2855 ***	0.3502 ***	0.2201 ***	1.0000
GDP	−0.1054 *	−0.0943	−0.1657 **	−0.0589	−0.1129	1.0000
GFCF	0.2066 ***	0.1994 ***	0.3709 ***	−0.4458 ***	0.0738	0.1189	1.0000

*** p < 0.01, ** p < 0.05, * p < 0.1; source: processed by the authors.

and

Table A3. Correlation Matrix for the NMS sample.

	RE	CM	GERD	ENVT	SE	GDP	GFCF
RE	1.0000
CM	0.5783 ***	1.0000
GERD	0.0434	−0.0636	1.0000
ENVT	0.3076 ***	−0.0838	0.0871	1.0000
SE	−0.0437	−0.2293 ***	0.2039 ***	−0.0433	1.0000
GDP	−0.0476	0.0066	−0.1388 *	0.0183	−0.1646 **	1.0000
GFCF	0.3204 ***	0.3071 ***	0.2895 ***	−0.3452 ***	0.0760	−0.0723	1.0000

*** p < 0.01, ** p < 0.05, * p < 0.1; source: processed by the authors.

(From the Appendix A) present the correlation matrices for the EU-27 region, the OMS region, and the NMS region, respectively.

The correlation matrix for the EU-27 region shows limited significant relationships among the variables. There is a significant positive correlation between renewable energy and funding designated for climate change mitigation as well as gross domestic expenditure on R&D. This indicates that efforts to mitigate the adverse effects of climate change, along with a focus on innovation, can enhance the transition to clean energy within the EU region. Additionally, investment in infrastructure and fixed assets within the member states also plays a role in advancing renewable energy initiatives. In EU-27, funding aimed at reducing climate change risks is positively correlated with innovation and investments in infrastructure and fixed assets, highlighting a connection between these factors. The correlation matrix reveals a significant positive relationship between investment in innovation and gross fixed capital formation, suggesting that countries that allocate more funds to innovation also tend to invest greater amounts in infrastructure and fixed assets. Conversely, the correlation matrix indicates a negative correlation between GDP and renewable energy, gross expenditure on R&D, and subsidies, suggesting that economic growth and development do not necessarily lead to favorable social, environmental, and economic outcomes. Additionally, a negative relationship is observed between environmental taxes and investment in gross fixed capital formation, indicating that countries with higher environmental taxes tend to invest less in infrastructure and fixed assets.

In the OMS region, the correlation matrix (Table A2) exhibits similar trends as in the EU-27 region. In the OMS area, there is a significant and positive association between renewable energy funds aimed at climate mitigation, investments focused on innovation, and investments in infrastructure and fixed assets. Additionally, the OMS region demonstrates a stronger positive effect between funds dedicated to addressing climate change’s negative externalities and public innovation as well as gross fixed capital. In comparison to the EU-27 region, a robust positive correlation exists between climate mitigation funds and subsidies, indicating that member states allocating more resources to climate mitigation also tend to offer higher financial support in the form of subsidies. A weak significant correlation can be observed between climate change mitigation funds and environmental taxes. Furthermore, in the OMS region, alongside the correlation between climate mitigation funds and subsidies, a similar pattern is seen with gross expenditure on R&D and subsidies, as well as with environmental taxes and subsidies. This suggests that member states prioritizing public R&D and imposing higher environmental taxes are also providing greater subsidies to their citizens. Conversely, there is a negative and significant correlation between environmental taxes and gross fixed capital formation (GFCF), indicating that in older member states, countries with higher environmental taxes invest less in infrastructure and fixed assets. Additionally, a moderate negative relationship exists between economic development and investment in R&D, along with a weak correlation between economic development and renewable energy, highlighting that in the OMS region, economic progress does not lead to sustainable development.

In the NMS region, the correlation matrix (Table A3), shows somewhat different findings when compared to EU-27 and OMS. In the NMS region, there is a significant and positive correlation between renewable energy and funds aimed at climate change mitigation, environmental taxes, and investments in infrastructure and fixed assets of the countries. The analysis indicates that in NMS, member states with a higher rate of clean energy adoption also tend to have higher environmental taxes and maintain elevated levels of fixed capital formation. Conversely, member states that possess larger funds allocated for climate change mitigation tend to provide fewer subsidies to the population, despite an increase in GFCF. Additionally, there is a positive and pronounced relationship between increased investment in R&D and a greater amount of funds accessible to the population in the form of subsidies, along with more investment in infrastructure and fixed assets. However, countries with elevated levels of environmental taxes generally experience a decline in infrastructure investment. Furthermore, economic development has a negative association with investment in R&D and the availability of subsidies.

4.2. VIF

Table A4. VIF analysis for EU-27, OMS and NMS samples.

	EU-27		OMS		NMS
Variable	VIF	1/VIF	VIF	1/VIF	VIF	1/VIF
CM	1.12	0.8964	1.75	0.5713	1.21	0.8262
GERD	1.28	0.7841	2.10	0.4770	1.22	0.8194
ENVT	1.24	0.8035	1.41	0.7067	1.19	0.8374
SE	1.14	0.8743	1.21	0.8259	1.13	0.8820
GDP	1.10	0.9076	1.08	0.9284	1.04	0.9596
GFCF	1.38	0.7250	1.60	0.6246	1.46	0.6850
Mean VIF	1.21		1.52		1.21

Source: processed by the authors.

(from Appendix A) presents the findings from the Variance Inflation Factor (VIF) test. VIF is utilized to examine the existence of multicollinearity among the regions. Variables exceeding values between 5–10 indicate multicollinearity, making the values insignificant [93,94]. Not identifying and addressing multicollinearity through variable removal can result in misleading outcomes and conclusions [95]. The results for the EU-27, OMS, and NMS show values of 1.21, 1.52, and 1.21, respectively. The OMS regions present the highest value at 2.1 among all three datasets. These findings underscore the absence of multicollinearity within the regions, indicating that there are no problems related to identical relationships among the regions.

4.3. Slope Homogenity

Table A5. Slope Homogeneity for EU-27, OMS and NMS samples.

	EU-27		OMS		NMS
	Pesaran, Yamagata [96]		Pesaran, Yamagata [96]		Pesaran, Yamagata [96]
Delta	3.552	0.000	2.154	0.031	1.950	0.051
Delta adj	6.801	0.000	4.124	0.000	3.734	0.000
	Blomquist, Westerlund [97]		Blomquist, Westerlund [97]		Blomquist, Westerlund [97]
Delta	4.550	0.000	2.264	0.024	0.548	0.584
Delta adj	8.712	0.000	4.336	0.000	1.049	0.294

Source: processed by the authors.

in the Appendix A presents the outcomes of slope homogeneity using the Pesaran & Yamagata [96] and Blomquist and Westerlund [97] tests. The findings for the EU-27 and OMS regions reject the null hypothesis of slope homogeneity, suggesting that the influence of the explanatory variable on the outcome differs across countries. The evidence concerning slope homogeneity in the NMS region provides mixed results. While the results from Bloomquist and Westerlund [97] indicate that the countries may be homogeneous, Pesaran & Yamagata [96] support the presence of heterogeneity. Consequently, the analysis suggests that heterogeneous estimators are suitable for the panels.

4.4. Panel-Stationary Test

Table A6. Unit root tests for EU-27.

	EU-27
	IPS unit root test		Fisher-type unit-root test
	Level	Difference	Level	Difference
RE	−0.291	−3.1074 ***	−3.8497	17.3031 ***
CM	−1.9910 ***	−3.7899 ***	2.5563 ***	30.0063 ***
GERD	−1.4451	−3.0001 ***	0.6329	16.2082 ***
ENVT	−0.6084	−3.1923 ***	−1.9682	19.3646 ***
SE	−1.8058 **	−3.1834 ***	0.6677	19.7389 ***
GDP	−3.7883 ***	−4.7730 ***	25.9740 ***	46.1170 ***
GFCF	−1.2341	−2.8706 ***	−1.0687	14.1656 ***

*** p < 0.01, ** p < 0.05 source: processed by the authors.

and

Table A7. Unit root tests for OMS and NMS samples.

	OMS				NMS
	IPS unit root test		Fisher-type unit-root test		IPS unit root test		Fisher-type unit-root test
	Level	Difference	Level	Difference	Level	Difference	Level	Difference
RE	7.3295	−5.6648 ***	−3.0011	17.8686 ***	3.5647	−3.2958 ***	−2.4335	6.3932 ***
CM	−2.6900 ***	−6.8254 ***	−2.0782 ***	23.4612 ***	−1.7788 **	−4.6645 ***	1.5274 **	18.8969 ***
GERD	0.5832	−5.2817 ***	−0.8236	14.8396 ***	−0.3325	−3.6336 ***	1.7668 **	7.9588 ***
ENVT	5.4744	−5.6284 ***	−1.761	15.0587 ***	2.864	−3.9737 ***	−1.0089	12.2802 ***
SE	−2.0090 ***	−5.4452 ***	0.6605	11.6042 ***	−1.2282	−4.0219 ***	0.2768	16.4045 ***
GDP	−7.7808 ***	−8.5420 ***	19.0386 ***	32.4397 ***	−5.3080 ***	−5.9830 ***	17.6755 ***	32.7974 ***
GFCF	1.4799	−5.0928 ***	−0.5091	9.8565 ***	1.0128	−3.8103 ***	−1.0199	10.1862 ***

*** p < 0.01, ** p < 0.05; source: processed by the authors.

(Appendix A) showcase the panel stationary analysis utilizing the first-generation unit root tests Im-Pesaran-Shin (IPS) and the Fisher type test. The IPS method is preferred in this analysis due to its straightforward approach [98]. This test is especially beneficial when the panel’s variables predominantly lack stationarity. Furthermore, the existence of cross-sectional dependency diminishes the robustness of the outcomes [99]. The Fisher type test has also demonstrated effectiveness owing to its ability to account for cross-sectional dependence and its non-requirement for a balanced panel. When combined with IPS, it offers additional assurance of robustness [100]. At the level, both the IPS and Fisher tests indicate that only a few variables across all regions are stationary, primarily those related to funding for climate change mitigation, subsidies, and gross expenditure on research and development. However, at the differencing level, all variables exhibit significance suggesting that all variables become stationarity, regardless of the region.

4.5. Cross-Dependence

Table A8. Cross-dependence for EU-27, OMS and NMS samples.

	EU-27				OMS				NMS
	CD-test	p-value	corr	Abs(corr)	CD-test	p-value	corr	Abs(corr)	CD-test	p-value	corr	Abs(corr)
RE	45.20	0.000	0.727	0.730	28.30	0.000	0.894	0.894	16.33	0.000	0.558	0.569
CM	8.09	0.000	0.130	0.396	2.88	0.000	0.091	0.394	7.69	0.000	0.263	0.409
GERD	14.29	0.000	0.230	0.454	5.49	0.000	0.173	0.492	9.05	0.000	0.309	0.426
ENVT	26.63	0.000	0.429	0.663	18.32	0.000	0.579	0.721	8.09	0.000	0.276	0.589
SE	33.39	0.000	0.537	0.597	19.23	0.000	0.608	0.656	14.15	0.000	0.483	0.544
GDP	45.22	0.000	0.728	0.728	22.67	0.000	0.717	0.717	22.07	0.000	0.753	0.753
GFCF	16.51	0.000	0.266	0.536	15.87	0.000	0.502	0.653	3.44	0.001	0.117	0.434

Source: processed by the authors.

(Appendix A) displays the outcomes of the Pesaran [101] test, highlighting the presence of cross-sectional dependence among the EU-27, OMS, and NMS regions, along with their pairwise correlations and absolute values. The results of the analysis are consistent throughout all regions. Each region presents insignificant p-values, allowing for the rejection of the null hypothesis, indicating an interconnectedness among the different regions. While all areas encounter spillover effects, these are especially prominent in OMS. The renewable energy sector and economic growth reveal aligned trends in their renewable energy characteristics and economic advancement, whereas climate mitigation and total R&D spending display more varied patterns. In contrast to the NMS, both OMS and EU-27 show more significant spillover effects.

4.6. Cointegration Test

Table A9. Panel Cointegration for EU-27, OMS and NMS samples.

	EU-27		OMS		NMS
Kao Test	Statistic	p-value	Statistic	p-value	Statistic	p-value
MDFt	3.1965	0.0007	2.6920	0.0036	0.7156	0.2371
DFt	3.6496	0.0001	3.0572	0.0011	0.1290	0.4487
ADFt	5.2202	0.0000	4.4270	0.0000	1.5797	0.0571
UMDFt	−1.3089	0.0953	−1.1970	0.1157	−2.1982	0.0140
ADFt	−0.8454	0.1989	−1.0618	0.1442	−1.9580	0.0251
Pedroni Test
MPPt	8.5836	0.0000	6.2365	0.0000	5.9258	0.0000
PPt	−12.1892	0.0000	−10.5907	0.0000	−6.6661	0.0000
ADFt	−6.1919	0.0000	−5.7155	0.0000	−2.9923	0.0014
Westerlund Test
Variance ratio	4.2336	0.0000	1.4561	0.0727	4.5903	0.0000

Source: processed by the authors.

(Appendix A) presents the findings of the cointegration tests conducted using the methodologies of Kao [102], Pedroni [103], and Westerlund [104]. These tests are particularly useful for evaluating the presence of a long-term relationship among the variables in the EU-27, NMS, and OMS contexts. The results for all three regions show mixed outcomes. The Kao [102] test indicates a stable long-run relationship among the variables for both the EU-27 and the OMS region. Conversely, the NMS sample reveals weaker evidence of cointegration, as demonstrated by p-values exceeding 0.05, suggesting a diminished long-term relationship in this region. The results from the Pedroni [103] test show insignificant p-values for all regions, leading to the conclusion that there is strong evidence of cointegration. According to the Westerlund [104] test results, a clear long-term relationship is observed in the EU-27 and NMS regions; however, the marginal significance in the OMS challenges the notion of cointegration in that area. Overall, the findings highlight that within the EU-27 and OMS regions, there exists an interconnection among the variables of the member states, whereas in the NMS region, the presence of cointegration is significantly weaker. The outcomes of the cointegration tests once more underline the structural differences among the three samples.

4.7. DOLS, FMOLS, CCR

To evaluate the existence of a long-term relationship among the variables, the DOLS, FMOLS, and CCR approaches have been utilized. The findings from this methodology for the EU-27, OMS, and NMS regions are shown in

Table 5. DOLS, FMOLS and CCR analysis on EU-27, OMS and NMS samples.

	EU-27			OMS			NMS
	(1)	(2)	(3)	(1)	(2)	(3)	(1)	(2)	(3)
VARIABLES	DOLS	FMOLS	CCR	DOLS	FMOLS	CCR	DOLS	FMOLS	CCR
CM	0.6182 * (0.3564)	0.6016 *** (0.0662)	0.6139 *** (0.1403)	0.5127 (0.3697)	0.5327 *** (0.0759)	0.6775 *** (0.1665)	−0.1267 ** (0.0547)	−0.1512 *** (0.0060)	0.3702 *** (0.0165)
GERD	0.8122 *** (0.2846)	0.7592 *** (0.0607)	0.6896 *** (0.0921)	0.1340 (0.3363)	0.2740 *** (0.0956)	0.3213 (0.2160)	0.1355 *** (0.0457)	0.1057 *** (0.0046)	0.2443 *** (0.0108)
ENVT	−0.2951 (1.5372)	−0.3759 (0.2874)	−0.5109 (0.5589)	0.8029 *** (0.2619)	0.7654 *** (0.0634)	0.6594 *** (0.0881)	0.0549 (0.1440)	0.1318 *** (0.0144)	0.2971 *** (0.0341)
SE	−0.3331 (0.2545)	−0.3077 *** (0.0404)	−0.3417 *** (0.0821)	−0.0087 (0.1083)	−0.0368 (0.0299)	−0.0462 (0.0837)	0.1486 ** (0.0656)	0.2047 *** (0.0065)	0.6628 *** (0.0160)
GDP	−0.1123 (0.1676)	−0.1266 *** (0.0265)	−0.2042 *** (0.0678)	−0.2875 (1.5486)	−0.2325 (0.3269)	0.1155 (0.6361)	0.0282 (0.0243)	0.0596 *** (0.0024)	0.8460 *** (0.0137)
GFCF	0.7737 (0.6543)	0.8414 *** (0.1034)	0.8457 *** (0.2284)	0.0116 (0.0486)	0.0002 (0.0133)	−0.0001 (0.0287)	0.3063 (0.2389)	0.4177 *** (0.0242)	0.8315 *** (0.0583)
R-squared	0.9218	0.8192	0.8017	0.9147	0.8447	0.8222	0.9477	0.1076	0.0690

Standard errors in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1; source: processed by the authors.

.

In the EU-27 sample, the long-term analysis displays that the key drivers of green energy adoption are investments targeting climate change mitigation as well as gross expenditure in R&D. The DOLS, FMOLS and CCR techniques indicate that 1 percentage point change in the amount of funds aimed to mitigate the climate change negative externalities facilitate green energy adoption by 0.6182 percentage point, 0.6016 percentage points, respectively, 0.6139 percentage points, confirming that mitigating the effects of climate change speed up the renewable energy transition. Gross expenditure in R&D also provides robust results across estimators exhibiting that 1 percentage point change increases the adoption of renewable energy sources by 0.8122 percentage point, 0.7592 percentage point, respectively, 0.6896 percentage point increase. The DOLS, FMOLS and CCR analysis within the EU-27 region indicate that the previous stated variables are key drivers in facilitating renewable energy transition. On the other hand, environmental taxes do not facilitate clean energy adoption. Subsidies and economic development have a negative long-term relationship with renewable energy supporting the conclusion that economic development as well as government support measures inhibit the adoption of clean energy. GFCF displays a significant and positive long-term relationship, showing that the investment in infrastructure and fixed assets of member states facilitate renewable energy transition.

The OMS region exhibits different particularities compared to the EU-27 region. In the OMS region environmental taxes are the only variable that exhibits a positive impact on the clean energy transition across all 3 models, FMOLS, DOLS and CCR. An Increase of 1 percentage point in environmental taxes facilitates the adoption of clean energy by 0.8029, 0.7654, respectively, 0.6594 percentage points. Investment in climate change mitigation also displays a positive influence on the adoption of renewable energy deployment in the FMOLS and CCR model. Investment in innovation only displays a significant and positive long-run relationship only in the FMOLS model, displaying a weaker impact compared to the one in the EU-27 region. Moreover, subsidies, economic development as well as GFCF do not have any significant long-term relationship with the adoption of clean energy.

The NMS region exhibits different particularities compared to EU-27 and OMS region. In the NMS region, investments in the mitigation of climate change externalities inhibit the renewable energy transition. On the other hand, investment in innovation has a positive and robust long-term relationship with renewable energy share adoption. Moreover, 1 percentage points change in R&D increases the clean energy transition by 0.1555, 0.1057, and 0.2445 percentage points, respectively. Another key driver of clean energy transition are subsidies. A change of 1 percentage points in subsidies increases the energy transition by 0.1486, 0.2047, respectively, 0.6628 percentage points. Other variables that facilitate renewable energy development are environmental taxes, economic development as well as investment in infrastructure and fixed assets.

While the outcomes are consistent across the three estimation methods, it is evident that the R-squared values are considerably lower for the FMOLS and CCR in the NMS region, which diminishes the explanatory power of the variables. Although there has been extensive debate regarding R²’s effectiveness in assessing goodness of fit, with some authors supporting it while others are critical [105], the low values observed in the NMS region may indicate the structural differences present there [106]. Conversely, the higher R² values observed in the EU 27 and OMS suggest that the long-run equilibrium analysis is more precise, whereas the long-run equilibrium in the NMS region is less robust due to variations in economic development, maturity, as well as political, institutional, and innovative disparities.

4.8. MMQREG

Table 6. MMQR analysis for the EU-27 sample.

	EU-27
	Quantile 0.25	Quantile 0.50	Quantile 0.75	Quantile 0.90
CM	0.3003 *** (0.0616)	0.3800 *** (0.0600)	0.4719 *** (0.0716)	0.5477 *** (0.0883)
GERD	0.2016 *** (0.0638)	0.3426 *** (0.0636)	0.5051 *** (0.0746)	0.6391 *** (0.0903)
ENVT	0.1169 *** (0.0499)	0.1198 *** (0.0480)	0.1232 ** (0.0577)	0.1260 * (0.0720)
SE	−0.2012 *** (0.0542)	−0.1838 *** (0.0521)	−0.1638 *** (0.0627)	−0.1474 ** (0.0781)
GDP	−0.0746 (0.0453)	−0.0512 (0.0437)	−0.0242 (0.0525)	−0.0020 (0.0653)
GFCF	0.0905 * (0.0507)	0.1137 *** (0.0488)	0.1405 *** (0.0587)	0.1626 *** (0.0731)
_cons	−0.5581 *** (0.0553)	−0.0363 ** (0.0728)	0.5654 ** (0.0705)	1.0615 *** (0.0681)

Standard errors in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1; source: processed by the authors.

and

Table 7. MMQR analysis for the OMS and NMS samples.

	OMS				NMS
	Quantile 0.25	Quantile 0.50	Quantile 0.75	Quantile 0.90	Quantile 0.25	Quantile 0.50	Quantile 0.75	Quantile 0.90
CM	0.2653 *** (0.1006)	0.2483 * (0.0912)	0.2316 *** (0.1037)	0.2208 ** (0.1206)	0.5569 *** (0.0651)	0.5314 ** (0.0601)	0.5007 *** (0.0704)	0.4754 *** (0.0887)
GERD	0.3606 *** (0.0996)	0.5219 * (0.0930)	0.6809 *** (0.1036)	0.7837 *** (0.1182)	−0.0567 (0.0643)	−0.0819 (0.0593)	−0.1125 * (0.0695)	−0.1376 * (0.0875)
ENVT	−0.0530 (0.0723)	−0.0252 ** (0.0656)	0.0021 (0.0745)	0.0198 (0.0866)	0.5049 *** (0.0610)	0.4835 *** (0.0563)	0.4576 *** (0.0660)	0.4363 *** (0.0830)
SE	−0.3350 *** (0.0854)	−0.3095 * (0.0775)	−0.2843 *** (0.0880)	−0.2680 *** (0.1024)	0.1020 * (0.0597)	0.0862 * (0.0551)	0.0671 (0.0646)	0.0514 (0.0811)
GDP	−0.0593 (0.0612)	−0.0294 ** (0.0556)	0.0002 (0.0631)	0.0193 (0.0733)	−0.0259 (0.0617)	−0.0325 (0.0569)	−0.0404 (0.0667)	−0.0469 (0.0836)
GFCF	0.0155 (0.0458)	−0.0216 ** (0.0417)	−0.0581 (0.0472)	−0.0818 (0.0548)	0.2217 *** (0.0816)	0.3269 *** (0.0747)	0.4543 *** (0.0876)	0.5589 *** (0.1158)
_cons	−0.5862 *** (0.0733)	−0.0063 * (0.0940)	0.5654 *** (0.0841)	0.9350 *** (0.0779)	−0.4195 *** (0.0800)	−0.0462 (0.0704)	0.4054 *** (0.0801)	0.7766 *** (0.1482)

Standard errors in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1; source: processed by the authors.

exhibit the dynamic relationship between the variables by employing a MMQR analysis in order to capture the effect of variables across different quantiles (Q 0.25, Q 0.50, Q 0.75, Q 0.90). MMQREG is a method proposed by Machado & Silva [59] being useful in methods where the explanatory variables exhibit endogeneity as well as individual effects.

Table 6 indicates that in the EU-27 region, the main factors driving the transition to clean energy are investments in climate change mitigation and research and development (R&D). Innovation boosts the adoption of renewable energy by nearly 217% when comparing the 20th quantile to the 90th quantile, suggesting that countries that prioritize R&D experience a quicker green transition. Additionally, nations that dedicate funds to climate change mitigation show an increase of 81% in green energy adoption. The findings highlight that to expedite the green transition; significant emphasis must be placed on innovation and climate finance to achieve strong and sustainable outcomes. While environmental fiscal policy generally supports clean energy adoption, its beneficial effects diminish at higher levels. Subsidies tend to hinder clean energy adoption, with countries that provide substantial subsidies showing a decline in clean energy utilization. The analysis reveals that economic development does not significantly influence the green transition, nor is it effective in decreasing reliance on fossil fuel energy sources. Investment in the infrastructure and fixed assets of all EU member states supports the use of green energy sources, demonstrating a positive impact across higher quantiles.

In the OMS and NMS areas, increasing financial resources aimed at reducing climate change risks fosters the adoption of clean energy. The analysis reveals that while the effect is positive in the OMS, its impact varies by quantile and diminishes at higher quantiles. Conversely, in the NMS region, the effect of investments in climate change mitigation on the energy transition remains stable, though it also decreases at higher quantiles. Funding for innovation has a significant influence on the adoption of clean energy, although in the NMS region, its effect is weak at higher quantiles concerning the green energy transition. Environmental taxes show varying effects across the two regions. In the OMS, they moderately hinder clean energy adoption, while in the NMS, they promote the use of renewable energy. However, the influence of environmental taxes on clean energy adoption remains steady, with a reduction in effect at higher quantiles. Subsidies act as a constraint on renewable energy transition, and their impact diminishes at higher quantiles, whereas in the NMS, they weakly support the green transition at quantiles 0.25 and 0.50, as indicated in Table 7. Economic development contributes to both sustainable development and the adoption of clean energy in both regions; furthermore, in the NMS at quantile 0.50, it exhibits a moderate constraining effect, akin to the behavior of environmental factors. In both the NMS and OMS regions, funding directed at climate change risk mitigation and environmental fiscal policies are identified as key elements driving the clean energy transition.

4.9. QREG

Table A10. QREG analysis for the EU-27 sample.

	EU−27
	Quantile 0.25	Quantile 0.50	Quantile 0.75	Quantile 0.90
CM	0.3674 *** (0.0476)	0.3664 *** (0.0683)	0.5563 *** (0.0764)	0.5631 *** (0.0729)
GERD	0.1129 *** (0.0508)	0.1002 (0.0730)	0.4908 *** (0.0817)	0.6257 *** (0.0780)
ENVT	0.1257 *** (0.0502)	0.1773 *** (0.0722)	0.1395 * (0.0807)	0.0129 (0.0770)
SE	−0.0736 * (0.0481)	−0.0393 (0.0692)	−0.0713 (0.0774)	−0.0777 (0.0738)
GDP	−0.0476 (0.0473)	−0.0799 (0.0679)	−0.0059 (0.0759)	0.0268 (0.0725)
GFCF	0.0453 (0.0529)	0.1290 * (0.0760)	0.1033 (0.0849)	0.0894 (0.0811)
_cons	−0.5614 *** (0.0449)	−0.1287 ** (0.0646)	0.5614 *** (0.0722)	1.0921 *** (0.0689)

Standard errors in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1; source: processed by the authors.

and

Table A11. QREG analysis for OMS and NMS samples.

			OMS			NMS
	Quantile 0.25	Quantile 0.50	Quantile 0.75	Quantile 0.90	Quantile 0.25	Quantile 0.50	Quantile 0.75	Quantile 0.90
CM	0.3967 *** (0.0985)	0.3209 *** (0.1127)	0.2780 ** (0.1301)	0.5360 *** (0.1314)	0.6556 *** (0.0869)	0.5688 *** (0.0756)	0.4697 *** (0.0886)	0.3558 *** (0.1420)
GERD	0.1585 (0.1078)	0.4246 *** (0.1233)	0.7349 *** (0.1424)	0.5825 *** (0.1438)	−0.0291 (0.0873)	−0.0950 (0.0760)	−0.1676 ** (0.0890)	−0.1341 (0.1426)
ENVT	−0.1251 (0.0886)	0.0833 (0.1013)	−0.0805 (0.1170)	−0.0321 (0.1181)	0.4932 *** (0.0864)	0.4612 *** (0.0751)	0.4423 *** (0.0880)	0.4141 *** (0.1411)
SE	−0.2091 *** (0.0819)	−0.4037 *** (0.0937)	−0.3073 *** (0.1082)	−0.1694 (0.1093)	0.1655 ** (0.0841)	0.1322 ** (0.0732)	0.1445 * (0.0858)	0.1065 (0.1374)
GDP	−0.0351 (0.0773)	−0.0585 (0.0884)	−0.0125 (0.1021)	0.0581 (0.1031)	−0.0119 (0.0807)	0.0200 (0.0702)	0.0732 (0.0822)	−0.0296 (0.1318)
GFCF	−0.0038 (0.0942)	−0.0391 (0.1078)	−0.1010 (0.1244)	−0.0913 (0.1257)	0.1045 (0.0955)	0.2610 *** (0.0831)	0.4997 *** (0.0973)	0.7137 *** (0.1560)
_cons	−0.6174 *** (0.0742)	−0.0237 (0.0849)	0.5764 *** (0.0980)	1.0271 *** (0.0990)	−0.4151 *** (0.0787)	−0.0521 (0.0685)	0.4073 *** (0.0803)	0.8353 *** (0.1286)

Standard errors in parentheses; *** p < 0.01, ** p < 0.05, * p < 0.1; source: processed by the authors.

from Appendix A showcase the results from the quantile regression analysis for the EU-27, which includes both Old Member States (OMS) and New Member States (NMS). The findings for the EU-27 sample reveal that the shift towards green energy is facilitated by funding designated for tackling climate change externalities. As investments aimed at climate mitigation rise, the adoption of clean energy sources accelerates and expands. A comparable trend is noted with gross expenditures on research and development, where public funding for innovation significantly influences the transition to renewable energy, particularly at the upper quantiles. Environmental taxes also encourage the uptake of clean energy, though their positive effects are more significant at lower levels of taxation. At higher levels of environmental taxes, the effect on renewable energy adoption is not statistically significant. The details in Table A11, which presents the quantile regression results for the OMS and NMS regions, highlight the distinct characteristics between these areas. In the OMS region, funds directed toward climate mitigation positively impact the renewable energy transition, although their effect diminishes between the 0.25 and 0.75 quantiles and significantly rises at the highest quantiles. Conversely, the trend in the NMS region is similar, but it shows a decline across all quantiles, despite having a greater impact. Investment in innovation reveals a notable positive influence on clean energy adoption in the OMS, yet it shows no effect in the NMS area. While environmental taxes do not affect green energy transition in the OMS region, in the NMS region they consistently demonstrate a positive and highly significant impact. Subsidies display contrasting effects; in the OMS region, they inhibit progress from the 0.25 to 0.75 quantiles, whereas in the NMS region, they have a moderate positive influence in the same quantile range. Investment in infrastructure and fixed assets in member states also shows varied effects by region. While there is no impact on clean energy adaptation and transition in the OMS region, in the NMS region, it fosters clean energy adoption, particularly at higher quantiles.

4.10. Panel Causality

To develop efficient policy recommendations, it is imperative to use the panel-causality methodology to complement the long-term relationship estimator’s analysis. The panel-causality analysis, employed through the Dumitrescu-Hurlin test, is used to assess the presence of Granger causality within the panel and provide evidence regarding the directional influence of the variables within the 3 regions, EU-27, OMS, and NMS.

Analyzing

Table A12. Causality analysis for EU27, OMS and NMS samples.

	EU−27		OMS		NMS
	W−stat	Z−stat	W−stat	Z−stat	W−stat	Z−stat
RE = CM	1.6811	2.5026 **	1.1561	−0.3259	2.2465	1.0901
CM = RE	1.9603	3.5284 ***	0.9567	−0.5924	3.0411	2.1133 **
RE = GERD	1.8979	3.2990 ***	2.2015	1.0710	0.2201	0.8258
GERD = RE	2.0746	3.9483 ***	1.7586	0.0786 **	1.3838	−0.0208
RE = ENVT	5.9643	18.2399 ***	7.0439	7.5420 ***	4.8016	4.3803 ***
ENVT = RE	5.1376	15.2023 ***	2.5500	1.5368	7.9241	8.4011***
RE = SE	2.1421	4.1962 ***	2.2724	1.1658	2.0017	0.7748
SE = RE	1.6869	2.5240 **	1.5801	0.2406	1.8020	0.5177
RE = GDP	1.4562	1.6761 *	1.6322	0.3103	1.2666	−0.1718
GDP = RE	1.1640	0.6025	0.7868	−0.8195	1.5702	0.2192
RE = GFCF	3.5038	3.9041 ***	3.0286	2.1763 **	4.0156	3.3680 ***
GFCF = RE	3.6228	9.6367 ***	5.0347	4.8571 ***	2.1022	0.9042

*** p < 0.01, ** p < 0.05, * p < 0.1; source: processed by the authors.

from Appendix A, in the EU-27 region, there is a moderate and significant bidirectional causality between renewable energy share and investments targeting mitigating the negative externalities of climate change, gross domestic expenditure in innovation, environmental taxes, as well as GFCF. The results point out that although the different financial sources aimed at environmental sustainability and protection accelerate the adoption of renewable energy, the adoption and expansion of clean alternative sources in itself facilitate the innovation, capital accumulation, and development of sustainable environmental policies, emphasizing the necessity of an integrated policy strategy for achieving sustainable economic development. Renewable energy has a significant and positive impact on subsidies expenses, while subsidies expenses display a moderate support to the green energy transition. Moreover, in the EU-27, although renewable energy has a weak impact on economic development, economic development does not facilitate the adoption of clean energy sources, highlighting the necessity of developing targeted policy interventions.

In the OMS regions the only variables that exhibit a bidirectional influence are renewable energy share and GFCF. Although renewable energy has a moderate contribution to the investment in infrastructure and fixed assets of member states, the reverse relationship highlights those higher levels of GFCF, accelerating the expansion and adoption of clean energy sources. The variables that present a unidirectional strong influence are renewable energy and environmental taxes. There is a moderate relationship between investment in R&D and the adoption of clean energy sources. Within the OMS region, it is noticeable that green transition is closely related to investment in capital assets rather than policy measures, funds targeting mitigating the negative externalities of climate change, as well as economic development.

The NMS region exhibits a bidirectional relationship between environmental taxes and the share of renewable energy, highlighting the critical role that fiscal policy plays in the growth of clean energy adoption and the ways in which renewable energy influences the formulation and execution of fiscal policies aimed at environmental sustainability. Furthermore, although the clean energy transition is not linked to the upgrading of infrastructure and fixed assets, renewable energy has a favorable impact on investments in these areas inside the member states. The investments aimed at energy transition and climate change mitigation show a moderate link. The existence of subsidies intended to mitigate the negative externalities of climate change has a moderate impact on clean energy, although it has no effect on the transition to renewable energy.

5. Discussion

This research aimed at examining how financial and economic dimensions relate to the proportion of employed renewable energy consumption at national levels within the levels of renewable energy consumption, analyzing the overall EU framework, alongside comparative assessments on the two main clusters of the EU: old and new member state clusters.

Considering the results section presented above, the main findings of the study highlight the importance of climate change mitigation investments in relation with increased consumption of renewable energy, in the European Union sample. For the whole EU-27 sample, the climate change mitigation investments are significant at 1%, 5% and 10% significance levels, in all estimation techniques presented above (DOLS, FMOLS, CCR, MMQR and QREG). The results are in line with a significant body of literature in the field. The study by Ang et al. [27] argues that policies that set the frame for climate mitigation investment significantly increase investment and innovation in renewable energy technologies, accelerating the renewable energy transition. On the same note, the econometric study by Vergil et al. [107] highlights that investment in renewable energy exerts a causal, positive effect on countries’ climate-change performance, confirming that mitigation-oriented public spending strengthens national capacity to reduce emissions through greater renewable deployment. Similarly, the paper by Nichifor et al. [108] using panel data and spatial Durbin models demonstrates that investments in renewable energy, coupled with expenditures on environmental mitigation, substantially decrease CO₂ emissions in Eastern Europe, illustrating that mitigation strategies driven by investment yield tangible environmental benefits. Saether [109], following the same note, proves that technological innovation support-policies and deployment-support policies correlate negatively with CO₂ emission intensity, facilitating a shift from fossil fuels toward renewable electricity sources. The paper by Alharbi et al. [110] proved through the analysis of 44 countries for 2007–2020 period that green finance fosters renewable energy, while innovation serves as a critical mechanism through which green finance influences renewable energy development. Similarly, Shi and Shi [111], focusing on Renewable Energy Technology Innovation in China for the 2012–2020 period show that green financial development significantly promotes renewable energy innovation and that technological innovation in renewable energy can play a decisive role in advancing the transition toward a low-carbon energy consumption structure. Authors Wang and Pang [25], analyzing 38 OECD countries with the help of dynamic panel modeling techniques validates that climate-mitigation technologies and environmental taxes introduce threshold dynamics that shape how renewable energy consumption interacts with economic growth.

Considering the subsamples of the EU structure, not many studies are focusing on this subdivision when analyzing the determinants of renewable energy consumption from financing and economic indicators. The OMS sample, in the case of climate change mitigation investments, is similar to the EU-27 full sample with climate change mitigation investments positively and significantly impacting renewable energy deployment, while for the NMS sample the DOLS, FMOLS and CCR results show that mitigation investments have a negative influence on renewable energy consumption. Similar observation is made in the paper by Wałachowska and Ignasiak-Szulc [112], who emphasize that NMS’s structural obstacles and differences from the OMS sample in terms of governance, development and social factors, can cause investments or mitigation spending to have limited or negative short-term association with renewable consumption. Simionescu et al. [113] also argue that socioeconomic and structural constraints, such as energy poverty, make the translation of investments into higher renewable consumption less effective when analyzing the Central and Eastern European countries.

Nichifor et al. [108], focusing on Eastern European states, find that effectiveness of renewable investments in some cases might not translate into higher renewable consumption, unless accompanied by high levels of governance, policy consistency and infrastructure adaptability. The heterogeneity of EU groupings in terms of determinants of RE consumption is also validated by Torok [114], when analyzing the economic drivers of renewable energy consumption in the EU. The positive link between climate change mitigation investments is present; however, the effect is different across the defined subsets. The differences in terms of renewable energy sources are also debated in the paper of Bak et al. [115].

Differences in the NMS structure in comparison with the overall sample or the OMS sample are also present in the case of the subsidy’s variable, for the DOLS, FMOLS and CCR estimators. While for the EU-27 and for the OMS sample the effect of subsidies on renewable energy is negative, for the NMS sample an increase in the level of subsidies is translated into increased levels of renewables deployment. This is also valid for the MMQR estimators, while for the QREG estimators the results are inconsistent. The results regarding the impact of subsidies on renewable energy are consistent with the findings of Torok [114], who argues that strengthening regulatory instruments (such as subsidies) are extremely important in the transition to a green energy system, especially in the Central and Eastern European Member States. Nguyen and Ponomarenko [116] also sustain the results regarding the subsidies dimensions, arguing that subsidies are important for accelerating and promoting green energy transition, especially in developing countries. Moreover, the study by Nicolini and Tavoni [90] also highlights that the effectiveness of subsidies varies by the country’s characteristics when analyzing the EU sample. In old member states renewable energy markets are considered already mature and future developments may be constrained by policies that overlap [117,118,119], subsidies losing their effects. In contrast, in the less developed countries such as the NMS, subsidies still have room to drive investments in renewable energy capacities.

Differences across subsamples are also found for the economic growth impact on RE dependent variable. While for the EU-27 and OMS (although it is not statistical significant) increased economic growth measured through GDP variable has negative impacts on renewable energy for the DOLS, FMOLS and CCR scenarios, consistent with the results of Ergun et al. [120] and Humbatova et al. [121], in the NMS the effects are positive and significant for the FMOLS and CCR models, in line with the results of Tu et al. [12], Przychodzen and Przychodzen [122], Polcyn et al. [123] and Papiez et al. [13]. This can be explained through the structural economic differences between the samples, the New Member States are often still in the process of modernizing their energy systems, meaning that economic growth provides the necessary financial resources and incentives to expand renewable energy capacity. However, when considering quantile estimators, economic growth appears neither statistically significant nor consistent in the direction of its effect across most quantiles in the samples. These mixed results suggest that the impact of GDP on renewable energy consumption is heterogeneous and not uniform across different levels of renewable energy use, highlighting the limited role of economic growth in driving renewable energy adoption.

Gross domestic expenditure on research and development proved a positive and significant influence on all samples in the FMOLS regression, but consistent in size and in sign of the influence across all applied estimators, in line with the results of Alam et al. [31], Petre [36] and Chmielewski et al. [35], with the exception of MMQR and QREG quantiles for the NMS sample. In the NMS countries, which generally have smaller or less mature renewable energy markets, higher R&D spending may not immediately translate into increased consumption due to infrastructural, technological, or policy constraints. However, as authors Gajdzik et al. [124] point out, higher investment in research and development, together with strengthened support for innovators, will play a crucial role in advancing renewable energy technology and promoting sustainable growth throughout the European Union. These mixed results in terms of sample are sustained by the papers of Rahmane et al. [125], who find that R&D positively affects renewable energy, but the significance or strength varies across quantiles when studying the OECD countries from 1990 to 2021. Analyzing 17 European Union countries, Degirmenci and Yavuz [40] also find mixed results for the impact of R&D expenditures on renewable energy consumption across EU states. Churchill and all [126], also focusing on an OECD dataset, conclude that R&D show mixed effects on renewable energy consumption.

Environmental tax revenues manifest significant effects on renewable energy consumption for the OMS and NMS samples in the case of the long run estimators, and also for the overall EU-27 sample in the case of the quantile regressions. The NMS subsample is the only one for which in all scenarios and regressions considered, environmental taxes boost renewable energy consumption, reflecting the greater price sensitivity of these economies. The positive impact of environmental taxes on sustainable energy use is also validated by Owusu Atuahene et al. [89] when analyzing a panel dataset consisting of 88 countries for the 1996–2021 period. The authors also further argue that the relationship between environmental taxes and the transition to sustainable energy is the most pronounced in high, lower-middle, and low-income countries, while in the upper middle-income countries, environmental taxes appear to exert a statistical insignificant influence on sustainable energy consumption. Similarly, the paper by Savranlar et al. [41] proves that environmental taxation boosts environmental quality in the EU sample, through the reduction in CO₂ emissions. The positive association between environmental taxes and renewable energy consumption were also discussed in the studies of Gerni et al. [37], Nchofoung et al. [39], Kukharets et al. [127]. However, the literature also documents contrasting evidence. For example, the paper by Leitão [128] reports findings that contradict the results outlined above. Employing a GMM methodology, the author highlights the positive effect of environmental taxes on carbon dioxide emissions. Similarly, Bashir et al. [81] demonstrate a negative relationship between environmental taxes and renewable energy consumption in the OECD countries. These discrepancies may be explained by differences in methodology, country composition, economic structure, and institutional quality, highlighting that the effectiveness of environmental taxes is context-dependent.

The heterogeneous effects observed between OMS and NMS may be explained by differences in economic and institutional contexts. In NMS, higher price sensitivity and greater responsiveness to fiscal incentives enhance the effectiveness of environmental taxes, while in OMS, mature energy markets, existing renewable policies, and institutional stability may moderate the impact [129,130]. These findings suggest that EU-wide environmental tax policies may need to be complemented by region-specific measures to maximize renewable energy adoption across subsamples, in line with the remarks of Maier et al. [131].

Investments, portrayed in this study through gross fixed capital formation variable, manifest a positive and significant influence on renewable energy consumption in the EU-27 sample as well as in the NMS sample in the case of long-term estimators, the NMS sample being the sample that reports consistent results for all estimators, including the quantile regression scenarios. This pattern reflects the structural characteristics of NMS economies, where less mature energy infrastructures and ongoing modernization processes allow capital investments to translate more directly into renewable energy expansion, in comparison with the OMS sample. The obtained regression results are consistent with the results presented in the paper by Qamruzzaman [132], who proved using CS-ARDL and NARDL estimations techniques that in the long-run and short-run, capital formation is beneficial for renewable energy transition. Similar observations are also concluded by the authors Meng et al. [133] when analyzing G7 countries, highlighting that capital formation can reduce carbon dioxide emissions and improve environmental quality by investments in cleaner technologies. However, on the other hand, the results obtained diverge from those reported by the authors Mujtaba et al. [134], who argue that economic growth and gross capital formation have contributed to a deterioration of environmental quality in the OECD region, as well as from those of Wang and Xu [135], who prove the positive association between increased levels of capital formation and ecological footprint in the E7 economies. Furthermore, with regard to this specific independent variable, our review of the relevant literature indicates that the relationship between capital formation and the use of renewable energy remains insufficiently explored. While several studies have examined the broader determinants of renewable energy consumption, such as economic growth, technological innovation, or other elements of energy policies and regulations, the direct role of gross capital formation as a driver of renewable energy adoption has received limited attention.

6. Conclusions

This study investigates the impact of climate-focused funds on the transition to renewable energy within the EU-27, OMS, and NMS regions. The research analyzes panel data gathered over a 13-year period, spanning from 2010 to 2023. Furthermore, the study utilizes long-term relationship estimation techniques such as DOLS, FMOLS, and CCR to examine the connections between investments in climate mitigation, overall expenditure on R&D, environmental taxes, subsidies, economic growth represented by GDP and gross fixed capital formation, and the shift to renewable energy indicated by the proportion of renewable energy. The analysis also looks into the differing effects of the distribution of these variables using MMQR and QREG methodologies. To gain a clearer understanding of how these variables influence one another, the Granger causality test developed by Dumitrescu and Hurlin [66] is employed to assess the direction and significance of the relationships. While much of the existing literature treats the EU-27 member states as a unified entity, the OMS and NMS regions exhibit unique characteristics, thus any policy suggestions need to be customized based on the behaviors presented in each area. Although the EU-27 displays trends akin to those in the OMS region, the NMS region reveals a significant contrast. The research highlights that there is an interconnectedness among all regions, suggesting that changes in one area can lead to secondary impacts in others. Furthermore, an important observation is that economic growth, regardless of the region in question, is not sustainable and fails to support the conservation of natural resources or consumer mindsets without resource depletion. In addition, economic progress does not foster the adoption of clean energy options nor does it decrease dependence on fossil fuels. Moreover, the behavior of environmental taxes varies by region; in the OMS, they do not influence the adoption of clean energy, while in the NMS, they promote it, though it should be noted that environmental taxes only have a facilitating impact at lower quantiles. Investment in public R&D, along with funding for climate change mitigation, plays a key role in supporting renewable energy initiatives. Additionally, investment in the infrastructure and fixed assets of member states shows varied effects based on the regions, with some instances demonstrating a facilitating effect, but it should not be deemed essential.

When it comes to policy implications, it is important to note that while the EU-27 and OMS exhibit comparable traits and trends, NMS stands out significantly from them. As a result, it can be inferred that the recommendations suitable for the EU-27 and OMS may not be as pertinent for NMS, necessitating customized strategies. Thus, this study highlights that the effectiveness of climate-oriented fiscal measures varies across the EU, with NMS exhibiting higher responsiveness to factors such as environmental taxes and subsidies and OMS showing more moderated effects for these dimensions.

In NMS, tailored subsidy schemes can complement fiscal incentives by addressing the specific financial, structural and behavioral barriers faced by households and businesses [136]. Such schemes could focus on reducing upfront investment costs, supporting energy-efficient technologies, and providing income-based or consumption-based support to ensure equitable access to renewable energy solutions by eliminating administrative bariers [124,136,137]. Furthermore, as Štreimikienė et al. [138] point out, financial support combined with programs that raise awareness of the benefits of renewable energy can accelerate adoption while addressing the higher price sensitivity observed in these economies.

In OMS, where mature energy markets and established institutional frameworks moderate the impact of environmental taxes, policy design should prioritize predictability and alignment with existing renewable energy policies [139]. Periodic adjustments based on market conditions and the observed effectiveness of fiscal instruments can further ensure that the tax framework continues to support the transition to clean energy effectively [87]. Across both NMS and OMS, the study underscores the importance of context-sensitive strategies that integrate subsidies, environmental taxes, and investment support, ensuring that financial resources not only reduce greenhouse gas emissions but also foster sustainable energy transitions tailored to the economic, institutional, and social characteristics of each EU region. Specifically, all areas should work towards channeling financial resources into mitigating the negative effects of climate change, such as reducing greenhouse gas emissions and lessening reliance on polluting energy sources. These efforts will create advantageous and positive spillover effects for the adoption of clean energy. Additionally, an increased allocation of public funds focused on promoting innovation can expedite and enhance the transition to green energy. The results highlight the interconnectedness between regions and member states, suggesting that actions taken in one area can yield positive spillover effects for others. Furthermore, fiscal policies related to environmental taxes should be tailored to each region, even though their constructive impact on clean energy adoption is only observed in the NMS region, not in OMS and EU-27; however, across all regions, high levels of environmental taxes adversely affect the energy transition. Policies aimed at fostering economic development in each country must acknowledge that such growth does not influence sustainability, clean energy adoption, or the mitigation of climate change’s negative externalities. Therefore, when outlining policies, it is essential to consider multiple perspectives to attain sustainability. The study points out several limitations, especially in terms of data availability. Although the dataset is extensive and the panel is well-structured, information from the year 2024 has been excluded due to a lack of adequate data. Including a broader time range in the dataset could enhance the dependability of the findings. Another limitation relates to the methodology used; it relies on long-term relationship assessment techniques, such as a cointegration-based model, which are not well-suited for capturing the impacts of short-term fluctuations or policy shifts, including economic downturns, the repercussions of the COVID-19 pandemic, the Russia-Ukraine conflict, and the effect of The Paris Agreement on the clean energy transition is not explored. These factors could offer an alternative perspective on the specific challenges encountered by the EU-27, OMS, and NMS. Additionally, a limitation arises from the decision to group the data into the OMS with NMS sub-samples in the context of EU-27. By categorizing countries based on their climate characteristics, energy structures, and renewable energy sources, unique patterns may have emerged that demonstrate how clean energy adoption responds under comparable environmental circumstances. Categorizing the member states based on their accession year to the EU-27 might underestimate the efficacy of policy recommendations. Furthermore, the research employs aggregate data for both OMS and NMS member states, which overlooks the unique characteristics and impacts of each region. Another constraint is the lack of consideration for the social aspect of the clean energy transition. While there are climate-focused funds provided by both the public and private sectors to encourage the uptake of green energy and reduce reliance on fossil fuels, some individuals pursue household adoption of clean energy independently. The household adoption is often driven not by financial support or incentives from public or private entities but rather by environmental awareness or a desire to lower energy costs. Consequently, the social factor plays a role in the clean energy transition, which is not captured in this study. Furthermore, the study ignores aspects such as energy efficiency and the composition of the energy mix. It also neglects to consider the adoption of innovative green technologies and the effects of electric vehicle advancements. Additionally, it overlooks market trends in energy adoption and consumer perspectives on clean energy products, along with the environmental ramifications linked to clean energy solutions. The speed of the renewable energy transition can either be boosted or impeded based on the energy mix and reliance specific to each region. Moreover, the research fails to address the constraints and implications of storing renewable energy in batteries as a result of reaching grid capacity.

Future studies could expand on this analysis by categorizing the EU-27 nations according to shared economic situations or environmental traits, enabling a more effective assessment of how targeted climate funding influences the adoption of clean energy in relation to the unique weather conditions of each area. Additionally, upcoming research could explore the green energy transition at a more granular level by examining household adoption of green energy systems, taking into account individual values or the cost-saving and sustainability benefits that green energy offers. At a broader level, upcoming research can utilize the methodology of this study to make comparisons across various continents, examining what actions each one can take to attain sustainable economic growth and utilize renewable energy sources. By implementing such analyses, future studies can offer specific recommendations aimed at promoting a global solution for advancing the transition to clean energy.