Evolution of Regional Innovation Strategies Towards the Transition to Green Energy in Europe 2014–2027

Abstract

This paper explores the factors influencing regions’ commitment to the EU’s green energy transition during the 2021–2027 period, with a particular focus on the impact of prior commitments and fossil fuel dependence. Using multimodel regression analysis along with a null classification approach with large language models, we assess how regions’ existing green energy initiatives, their dependence on fossil fuels, and specific energy targets shape their progress towards the EU Green Deal goals. The results confirm path dependency in regional energy policies, where regions with prior investments in decarbonization and energy infrastructure show greater commitment in the current period. Fossil-fuel-dependent regions, on the other hand, face structural barriers slowing their transition to green energy. In addition, the study highlights the selective prioritization of decarbonization and energy efficiency goals, while goals such as consumer empowerment and offshore wind energy remain undervalued. The findings underscore the need for a more comprehensive, sustainable approach to energy transition, particularly in regions with significant dependence on fossil fuels. The paper concludes with a discussion of policy implications for achieving a holistic and equitable energy transition across all EU regions.

1. Introduction

In recent years, governments and international organizations have begun to give a major priority to shifting energy systems from fossil fuels to renewable sources. Leading this change has been the European Union (EU), which is implementing the European Green Deal. Its ambitious goal is to achieve climate neutrality by 2050 [1]. To achieve these goals, cooperation from all levels of government is necessary. Regions, in particular, play a key role in this process.

The green transition is not just a matter of technology or money; it is also a political challenge. First and foremost, for the EU to achieve its climate goals, regions must align their strategies with the provisions of the Green Deal. Regional policies must enable effective changes in the economy, politics, and infrastructure. This means incorporating renewable energy into development priorities, improving energy efficiency, and reducing dependence on fossil fuels [2].

One of the key documents underpinning this transformation is the Research and Innovation Strategies for Smart Specialization (RIS3). These strategies help regions identify their strengths and focus on innovation to increase competitiveness, while supporting EU goals, such as the transition to green energy [3,4]. This study examines regions as geopolitical and administrative units that have developed and are implementing RIS3. These can be subregions (NUTS 3), regions (NUTS 2), macroregions (NUTS 1), or countries (NUTS 0).

RIS3 are central to the EU’s vision of sustainable development through innovation [5]. However, we need to ask to what extent these strategies address green energy priorities, especially in the 2021–2027 programming period [6]. It is important to see whether regions that previously focused on green energy are still moving in that direction, and how regions that depend on fossil fuels are changing their strategies to achieve the EU’s green energy goals [7].

This study aims to fill this research gap. We wish to explore how green priorities are addressed in RIS3, and how regions are aligning their policies with the European Green Deal [8]. We propose two main hypotheses. The first tests whether previous commitments to green energy continue into the current programming period. The second tests the challenges for regions dependent on fossil fuels and examines whether this dependence is slowing the transition to renewable energy.

Through our research, we aim to understand how regional policies fit into the EU’s green energy goals and how past commitments and economic dependencies shape future energy transition efforts. Our results show that regions with a history of strong commitments to green initiatives, especially decarbonization, are the most committed to these efforts in 2021–2027. This confirms our first hypothesis of path dependency in regional policies. On the other hand, regions dependent on fossil fuels exhibit slower progress due to the structural challenges associated with the transition to green energy. In addition, we found that RIS3 tended to focus on immediate goals, such as decarbonization and energy efficiency, while areas such as consumer empowerment and offshore wind were less present in the strategies. This raises concerns about the effectiveness of the energy transition as a whole.

The remainder of this paper is divided into four main chapters. The second chapter reviews the existing literature on green energy topics and the EU’s Green Deal energy goals. The next chapter presents our methodology, explaining the methods and techniques used in our research. Finally, we present and discuss our results in relation to the existing literature and research objectives. We conclude with recommendations for regional, national, and European institutions to improve the efficiency of the energy transition.

2. Literature Review

2.1. Green Energy Goals and the European Green Deal

Green energy is mainly derived from renewable sources, such as solar, wind, hydropower, bioenergy, geothermal energy, and ocean energy. It is, therefore, a key solution for reducing greenhouse gas emissions and promoting environmental sustainability [9,10]. These sources naturally complement each other and are, therefore, key to long-term strategies to reduce dependence on fossil fuels [11]. With green energy, communities can continue to thrive, while creating more energy-secure, more sustainable, and less carbon-intensive ecosystems.

The European Union has, therefore, recognized the strategic importance of green energy, particularly in the context of implementing the European Green Deal. The EU wishes to make Europe carbon neutral by 2050 through a series of initiatives aimed at decarbonizing industry, increasing energy efficiency and promoting sustainable economic growth. To this end, it has developed seven specific green energy goals that are in line with the European Green Deal, which we highlight below:

• Interconnected energy systems and integrated grids. Renewable energy, like solar and wind, requires integration into existing power grids. Efficiently interconnected systems ensure smooth energy distribution across regions and borders, maximizing green energy use [12,13].
• Innovative technologies and modern infrastructure. Investing in new technologies and infrastructure enhances energy sustainability and resilience, scaling up green energy and supporting green economic growth [14,15].
• Energy efficiency and eco-design. Reducing energy consumption, especially in buildings and industry, is a key Green Deal goal. Measures like green roofs and sustainable designs lower demand and promote eco-friendly urban areas [16,17,18,19,20].
• Decarbonization and smart sectors integration. A central Green Deal goal, decarbonization integrates renewable energy across sectors, like transport and industry. The EU promotes renewable energy in electric vehicle infrastructure and seeks to minimize reliance on fossil fuels [21,22,23,24].
• Consumer empowerment and energy poverty. Addressing energy poverty and empowering consumers are essential to a fair energy transition. Efforts include lowering energy costs, promoting green energy choices, and giving consumers more control [13,24,25].
• EU energy standards and technologies globally. The EU aims to set high standards in renewable energy, exporting green technologies and expertise worldwide, establishing itself as a leader in sustainable energy [14,15,22].
• Offshore wind energy potential. Offshore wind energy offers significant growth potential. Expanding offshore wind capacity diversifies the EU’s renewable energy sources and enhances grid integration [9,10,21].

By aligning with these goals, the EU has established a roadmap for the green energy transition, allowing Europe to lead the global shift to renewable energy. These initiatives seek to increase energy efficiency, encourage innovation, and reduce emissions; however, their success depends on regional and European coordination. This will be explored in the following sections.

2.2. Energy Transition Challenges of Incorporating Green Energy Priorities in European Policies

The transition to green energy is necessitated by pressing global issues, such as climate change and deepening economic and social inequality. It aims to reorient socio-economic systems toward ecologically sustainable development, minimize the environmental impact of economies and societies, address resource scarcity, and ensure the stability of energy supplies. Hence, this transformation goes beyond the energy space. Indeed, it affects entire societies.

As the World Economic Forum’s Energy Transformation Index (ETI) shows, transformation is not a simple matter. The index measures progress across 46 indicators. The 2024 report, however, outlines changes over the past 10 years in 120 countries and concludes that, as of 2021, many aspects of the progress previously observed have stalled. Technological disruption, geopolitical tensions, and economic instability are among the factors contributing to this slowdown. As a result, the transformation is now proceeding at a much slower pace than at the end of the last decade. It should be mentioned here that the ETI illustrates national trends—but most of the changes are occurring at the regional level, as a result of the implementation of smart specializations and local initiatives [26].

As indicated above, shifting away from fossil fuels to renewable energy sources is essential to achieving climate goals. However, this comes with challenges, the key ones being the lack of adequate energy infrastructure and high costs, as well as the need to ensure the equitable distribution of energy resources. In order for the transition to be successful, it is necessary not only to rapidly adopt renewable energy but also to increase energy efficiency and technological development throughout the energy sector [27,28]. One of the biggest challenges is the decarbonization of energy systems, which account for 75% of the EU’s greenhouse gas emissions. The European Green Deal lays out a roadmap to address these issues (see Figure 1).

The need to integrate green energy priorities into European policy stems from the sheer scale of change required. The transition will not succeed without clear, robust policies that promote the development of renewable energy and create a framework to support the integration of these new energy sources into the broader energy system. The EU has realized this by formulating specific goals to guide the transition, focusing on four key areas, as follows:

• Development and use of cleaner energy sources. The EU focuses on renewables, such as offshore wind and solar, as alternatives to fossil fuels. These technologies are central to decarbonization but require overcoming technical challenges, economic costs, and existing fossil fuel dependencies [30].
• Integration of energy systems across the EU. Connecting national energy systems across the EU helps balance supply and demand, allowing for the efficient use of renewables. This cross-border integration aims to cut energy losses and improve the reliability of renewable energy supply [12].
• Developing an interconnected energy infrastructure. The EU is investing in projects like the EU Energy Corridors, aiming to build a continent-wide grid that transports renewable energy efficiently. Modernizing outdated infrastructure and adding new connections are essential for smooth, cost-effective energy transitions [30].
• Review of energy efficiency and renewable energy legislation. To meet urgent climate goals, the EU is updating its 2030 targets, emphasizing energy efficiency in buildings and industry, while setting more ambitious renewable targets [30].

Urban centers and regions play a key role, especially in the face of pressures related to urbanization, climate change, and resource management. They can reduce emissions and build climate resilience by building green infrastructure, implementing sustainable urban planning, and closed-loop practices [31].

Industry also needs to transform, with green technologies, efficient processes and circular supply chains, reducing the environmental impact of production. This shift supports a low-carbon economy but requires investment in innovation and supportive regulations to succeed [32,33]. Achieving the energy transition requires setting green priorities not only at the EU level but also at the regional level. This will be the subject of Section 2.3.

2.3. European Smart Specialisation Strategies as a Regional Tool for Implementing Green Energy Priorities

The European Green Deal emphasizes that achieving climate neutrality and sustainability requires sustained efforts at all levels of governance. Therefore, EU policy provides the framework, while regional engagement is key to actual implementation. It is up to the regions to translate EU goals into local initiatives, adapting them to their needs and capabilities. Without adaptation at the regional level, the ambitious goals of the Green Deal may not be achieved.

A key tool for this adaptation is the Research and Innovation Strategies for Smart Specialization (RIS3), introduced by the European Commission in 2013. RIS3 supports regional development by helping regions identify their unique strengths and invest in competitive, sustainable economic sectors [34]. Building on these strengths, RIS3 aligns regional innovation efforts with EU priorities, particularly the Green Deal climate goals.

RIS3 are essential for linking regional innovation to the EU’s 2050 climate neutrality goal enshrined in the Green Deal. The energy transition requires regional solutions that maximize green energy potential, prevent policy fragmentation, and promote cross-border cooperation. These strategies allow regions to complement each other’s strengths, contributing to a unified European approach to climate change [35].

Regions can align their RIS3 with green energy priorities in different ways. Their efforts include renewable technologies, energy efficiency, and closed-loop economy practices. By focusing on key technologies, regions can specialize where they have an advantage, benefiting local economies and supporting EU-wide decarbonization efforts [36,37]. Regions that align RIS3 with the goals of the Green Deal are better positioned to play a leading role in areas such as renewable energy production and storage.

RIS3 also provide a pathway to diversify energy sources and reduce dependence on fossil fuels for regions that rely on these fuels. Here, the challenges are even greater. What is needed here is not only massive investment in the infrastructure necessary for future renewables but also changing attitudes and building new green skills. Success in such regions depends on how well they adapt their development paths, while dealing with challenges from entrenched fossil fuel interests.

To explore how effectively these strategies integrate into the green energy transition, we pose two hypotheses that explore the evolutionary relationship between regional commitment to green energy goals and broader structural factors influencing progress, including, in particular, dependence on fossil fuels:

Hypothesis 1: 

Regions that engaged more with green energy transition goals during the 2014–2020 period are more likely to increase their engagement with these goals in the 2021–2027 period.

This hypothesis suggests that regions with a history of investment in green energy are likely to continue or even expand their efforts in the next programming period. Investment in renewable energy infrastructure, policy development, and stakeholder networks create a self-perpetuating cycle of progress. These regions are often better prepared to take advantage of the existing dynamics of change thanks to their developed institutional system. Therefore, it can be believed that their past experience will increase the likelihood of maintaining and even intensifying the transition to green energy in 2021–2027.

Hypothesis 2: 

Regions with higher dependence on fossil fuels will show slower progress or lower engagement with green energy transition goals in the 2021–2027 period compared to the 2014–2020 period.

In contrast, regions with a high dependence on fossil fuels are likely to face greater challenges in maintaining or increasing their commitment to green energy goals. Economic and political pressures from the fossil fuel sector often lead to opposition to legislation aimed at accelerating the transition. These areas may also lack the knowledge and resources needed to rapidly implement green technologies. Concerns about job losses in traditional energy sectors are also common. Even with EU incentives, this can hinder the transition process.

3. Materials and Methods

3.1. Empirical Strategy

Investigating the evolution of the level of integration of green energy objectives over two successive programming periods requires a multipronged approach. The methodology can be summarized in four key steps: (1) the identification of key phrases describing each of the seven green energy goals defined by the European Green Deal (see Section 2.1); (2) the assessment of smart specialization priorities to determine the extent to which smart specialization priorities in RIS3 align with green energy goals in both periods; (3) the comparative analysis of two periods to identify the regions most and least aligned with these goals, as well as those with significant policy changes towards green energy transition; (4) the analysis of the impact of engagement with green energy goals in the period 2014–2020 and the actual reduction in fossil fuels in energy supply on engagement with these goals in the next period 2021–2027.

3.2. Identification of Key Phrases

The first step involved identifying the most frequent and informative key phrases (candidate labels) for the seven objectives of the European Green Deal [30]. This was carried out through an extensive review of sources, including the European Commission’s official website and publications related to the European Green Deal [30]; reports and policy documents from reputable organizations, such as the International Energy Agency [38] and the European Environment Agency [39]; and academic literature and industry publications on the latest developments in energy, sustainability, and innovation.

Candidate labels were refined through an iterative process in which we reviewed and adjusted terms based on their relevance, specificity, and potential to capture the essence of each objective. This process included (1) identifying overlaps or redundancies between labels; (2) ensuring a balanced representation of different aspects within each objective; (3) considering the context of RIS3 and the European Green Deal; and (4) finally, having created an initial set of candidate labels, we consulted with energy experts and came up with a final set.

The candidate labels identified for each green energy objective are provided in

Table 1. Candidate labels relating to green energy objectives of the European Green Deal.

Objective	Candidate Labels
1. Interconnected energy systems and integrated grids	Automated demand response, cross-border grid integration, demand response, distributed energy resources, energy data interoperability, energy management systems, energy storage, energy system integration, grid flexibility, grid interconnection, grid resilience, integrated energy services, interoperable energy systems, microgrids, peer-to-peer energy sharing, power-to-X, real-time grid monitoring, smart grid analytics, smart grid technology, smart grids, smart metering infrastructure, virtual power plants
2. Innovative technologies and modern infrastructure	5G for energy, AI for sustainability, advanced energy materials, automated energy systems, blockchain in energy, clean technology, cybersecurity in energy infrastructure, digital transformation, digital twins in energy, edge computing in energy, energy innovation clusters, green data centers, green innovation, hybrid renewable systems, IoT in energy, precision energy technologies, quantum computing in energy, renewable energy technologies, smart cities, smart energy management, smart grids for urban planning, sustainable infrastructure
3. Energy efficiency and eco-design	Building retrofitting, circular economy, dynamic energy pricing, eco-design, energy audits, energy benchmarking, energy efficiency, energy-efficient appliances, energy-efficient industrial processes, green buildings, green retrofitting, high-performance building materials, intelligent energy control systems, LED lighting, life cycle assessment, net-zero buildings, passive house design, smart thermostats, sustainable architecture, sustainable production, sustainable urban mobility, zero-emission buildings
4. Decarbonization and smart sector integration	Bioenergy, carbon capture and storage, carbon-neutral fuels, carbon offset projects, carbon pricing mechanisms, climate-neutral technologies, climate resilience, decarbonization, decarbonized electricity systems, electrification, energy transition, green hydrogen, hydrogen fuel cells, industrial decarbonization, integrated resource planning, low-carbon heating, negative emissions technologies, power-to-gas, renewable gas, sector coupling, smart sector coupling, sustainable transport, synthetic fuels
5. Consumer empowerment and energy poverty	Affordable energy solutions, community-driven renewable projects, community energy resilience, community solar, consumer-centric energy services, consumer engagement, digital energy platforms for consumers, energy access, energy bill reduction, energy co-operatives, energy communities, energy democracy, energy independence, energy justice, energy literacy, energy poverty alleviation, inclusive energy policies, local energy markets, peer-to-peer energy trading, prosumers, smart metering, social energy initiatives, vulnerable consumers protection
6. EU energy standards and technologies globally	Clean energy diplomacy, cross-border energy projects, energy efficiency standards, energy standardization, energy transition knowledge transfer, global climate action, global energy cooperation, global energy policy alignment, global renewable energy benchmarks, harmonized energy regulations, international carbon markets, international climate agreements, international clean energy alliances, international energy financing, international energy innovation hubs, international partnerships, renewable energy certification, sustainable energy solutions export, technology export, transnational energy projects
7. Offshore wind energy potential	Blue economy, coastal ecosystem preservation, coastal energy development, deep-water wind turbines, environmental impact assessments for offshore projects, floating wind turbines, integrated marine energy systems, marine biodiversity protection, marine energy, marine spatial planning, offshore energy financing models, offshore energy storage, offshore grid infrastructure, offshore hydrogen production, offshore renewable energy, offshore wind, offshore wind farm maintenance, subsea power cables, sustainable marine practices, tidal energy, wave energy, wind farm optimization

. This detailed mapping ensures comprehensive coverage of key areas within each objective, facilitating accurate analysis and comparison.

3.3. Extracting Relevant Topics from the RIS3 Priority Descriptions

To extract relevant topics from the RIS3 priority descriptions, a pretrained language model was employed using the Hugging Face “transformers” library. The model utilized was the “facebook/bart-large-mnli”, which is well-suited for zero-shot classification tasks.

Zero-shot classification is a machine learning technique that allows models to classify data into categories they have not been explicitly trained on. In the context of this analysis, zero-shot classification is used to extract relevant topics from the RIS3 descriptions without having seen any labeled examples of those topics during training.

The key idea behind zero-shot classification is to leverage semantic embeddings of the classes (in this case, the candidate labels) to determine which label best matches the input text. The model compares the embedding of the input text to the embeddings of the candidate labels and assigns the label whose embedding is closest according to a similarity measure. Each description was processed to extract topics and assign appropriate labels, which were returned in a structured format containing a sequence of text, labels, and corresponding scores.

Zero-shot classification is particularly well-suited for this analysis for several reasons: (1) We did not have labeled data, and obtaining labeled examples for all possible topics that could be present in the RIS3 descriptions would be extremely challenging and time-consuming. Zero-shot classification allows the model to classify the descriptions without any labeled training data. (2) With zero-shot classification, the set of candidate labels (topics) can be easily defined and modified based on the objectives of the analysis. This flexibility is crucial when exploring the alignment of regional priorities with the European Green Deal. (3) By using a pretrained BART language model, the zero-shot classifier can leverage the general knowledge and semantic understanding acquired during pretraining to classify the RIS3 descriptions.

3.4. Statistical and Comparative Analysis

After the topics were extracted from the RIS3 priority descriptions, the following procedure was implemented: (1) For each region, the scores associated with the extracted labels were aggregated to produce a weighted count for each objective. In particular, a threshold was established to filter out less relevant labels. Labels with scores below the defined threshold (0.01) were excluded from the analysis. (2) The weighted counts were then organized and summarized the prioritization of each objective by region, allowing for comparative analysis across different regions. (3) The differences between the two periods in terms of how each region fits into each of the seven objectives were calculated, conditional on them having prepared and sent strategies for both periods to the JRC. (4) The final results were visualized using maps for both periods to illustrate the extent to which each region prioritized the various objectives of the European Green Deal and how this prioritization evolved over time.

3.5. Regression Analysis

Regression analysis was employed to test both hypotheses, evaluating the relationship between regions’ past engagement in green energy objectives and their subsequent alignment with these objectives, while also accounting for the influence of fossil fuel dependence at the country level. We compared the full model with a null model containing only regional and objective effects, as well as models with main predictors and models without the interaction term to test the robustness of the results. The regression equation for the full model is as follows:

$$C_{2021-{2027}_{r,i}}=C_{2014-2020_{r,i}}{\beta }_1\ast Fossi{l}_c{\beta }_2+C_r\gamma +{\Delta }_r\delta +Z_i\zeta +\epsilon ,$$

(1)

where dependent variable $C_{2021-{2027}_{r,i}}$ represents the engagement level in green energy goal $i$ within the RIS3 priorities by region $r$ for the period 2021–2027. Among the main predictors, $C_{2014-2020_{r,i}}$ measures the engagement level in green energy goal $i$ for region $r$ during the previous programming period (2014–2020). The variable $Fossi{l}_c$ representing the share of fossil fuels in gross available energy for country $c$ is interacted with $C_{2014-2020_{r,i}}$ to reflect the influence of fossil fuel dependency on changes in engagement levels. Additionally, we include a vector of control variables for region $r$ before the first period began (2013), including quality of government (QoG) following [40,41,42]; agglomeration measured by population density; economic performance, measured by GDP per capita and unemployment rates; and technological capabilities, measured by the number of patent applications per million inhabitants and the share of people with higher education (following [43,44]). We also introduce fixed effects ${\Delta }_r$ for region $r$, controlling for unobservable region-specific characteristics, and fixed effects $Z_i$ for green objective $i$, controlling for unobservable characteristics specific to each green objective. Finally, the residual term $\epsilon$ captures any unaccounted variation. All the variables were initially transformed using ordered quantile (ORQ) normalization and then scaled by subtracting the column means and dividing the centered columns by their standard deviations.

First, models with fixed effects for both regions and green objectives were compared against those with random effects, allowing for the control of unobservable characteristics specific to regions and objectives. Regions were also nested within countries, but since the fossil fuel variable operates at the country level, the country effects became redundant. This is because country-specific variation is already accounted for by the fossil fuel dependency variable, making separate country effects unnecessary. The models were compared using ANOVA to evaluate model fit, determining whether fixed or random effects were more appropriate. For both regions and green objectives, fixed effects models yielded better results, showing a statistically significant improvement over the random effects models.

Second, the normality of residuals was examined using the Kolmogorov–Smirnov and other tests, which indicated deviations from normality. To address this, a Box–Cox transformation was applied to the raw data (before normalization and scaling), significantly improving the distribution of residuals. After the transformation, the Kolmogorov–Smirnov (K–S) test indicated no significant deviations from normality. The K–S test was relied upon due to its sensitivity to deviations from the expected distribution, particularly in large samples, and its ability to assess normality without requiring strict parametric assumptions. The results of the K–S test suggested that the residuals were adequately normal, allowing for further analysis to proceed confidently.

Despite the improvements in normality, heteroskedasticity remained present in the model, as confirmed by the Breusch–Pagan test. To address this, robust standard errors were calculated to adjust for the remaining heteroskedasticity, ensuring that the inferences remained valid even in the presence of nonconstant variance in the residuals. This adjustment allowed for the production of more reliable standard errors and improved the robustness of the regression estimates.

3.6. Data Sources

The data were sourced from the Joint Research Centre (JRC) of the European Commission in Seville, which compiles the smart specialization strategies of European regions for both the 2014–2020 and 2021–2027 programming periods. The JRC also identifies and describes the priorities within each document, which serves as the foundation for our analysis. Each document may include a different number of priorities, depending on the number of smart specializations. The dataset for 2014–2020 contains 245 documents (27 national, 29 NUTS1, 130 NUTS2, and 59 NUTS3), encompassing a total of 1432 priorities. For 2021–2027, the dataset includes 192 documents (27 national, 37 NUTS1, 88 NUTS2, and 40 NUTS3), totaling 1027 priorities. Additional data come from the Eurostat database, with the exception of the quality of government (QoG) index, which is derived from [41,42].

4. Results

The results of the analysis offer valuable insight into the evolving commitment of European regions to green energy goals over two programming periods: 2014–2020 and 2021–2027. By analyzing the key priorities identified in RIS3, we seek to highlight changes in focus and commitment to green energy goals. The descriptive statistics provide an overview of changes in regional priorities, allowing us to better understand how the energy transition is progressing across Europe. The sections below first provide general statistical trends and then a breakdown of the progress made in each country and region in each of the key areas of energy transition.

4.1. Descriptive Statistics and Visualisation of the Engagement in Green Energy Goals in 2014–2020 and 2021–2027

The descriptive statistics summarize the shifts in focus for each objective across the two periods (

Table 2. Descriptive statistics of RIS3 commitment level to green energy goals.

Goal	Period	Mean	SD	Median	Min	Max
1. Interconnected energy systems and integrated grids	2014–2020	0.38	0.23	0.36	0.04	1.19
	2021–2027	0.37	0.22	0.33	0.02	1.32
2. Innovative technologies and modern infrastructure	2014–2020	0.66	0.40	0.59	0.03	2.62
	2021–2027	0.73	0.36	0.68	0.08	1.86
3. Energy efficiency and eco-design	2014–2020	0.53	0.32	0.46	0.02	2.10
	2021–2027	0.60	0.33	0.55	0.04	1.69
4. Decarbonization and smart sector integration	2014–2020	1.04	0.55	0.94	0.11	3.06
	2021–2027	1.15	0.58	1.08	0.22	3.69
5. Consumer empowerment and energy poverty	2014–2020	0.31	0.16	0.29	0.04	1.07
	2021–2027	0.26	0.15	0.23	0.01	0.84
6. EU energy standards and technologies globally	2014–2020	0.09	0.07	0.07	0.00	0.35
	2021–2027	0.08	0.09	0.06	0.00	0.71
7. Offshore wind energy potential	2014–2020	0.11	0.17	0.04	0.00	0.88
	2021–2027	0.10	0.16	0.02	0.00	0.67

). The changes are characterized using measures, such as mean values, standard deviation, and median, which provide a comprehensive overview of the regional contributions to green energy priorities. Outliers are also noted to capture any regions that have deviated significantly from the general trend.

When comparing data on energy priorities between 2014–2020 and 2021–2027 (Figure 2), there is a clear increase in the regions’ commitment to decarbonization and smart sector integration, signaling a strengthened commitment to the green energy transition. This upward trend is also reflected in the growing emphasis on energy efficiency and eco-design, which are becoming more prominent in regional strategies. However, there is a noticeable decline in investment and focus on innovative technologies and modern infrastructure, suggesting potential barriers or changes in regional priorities.

In addition, the data reveal a decline in interest in consumer empowerment and efforts to combat energy poverty, indicating areas where future policies may require further support. The analysis highlights discrepancies in the way regions are prioritizing different green energy goals, with decarbonization, smart sector integration, and energy efficiency receiving significantly more attention than other areas. This imbalance points to the need for a more holistic approach to ensure that all aspects of the energy transition are adequately addressed.

A detailed breakdown of the results by country is provided in Figure 3, Figure 4, Figure 5, Figure 6, Figure 7, Figure 8 and Figure 9, offering further insights into how each region is adapting its policies to align with the green energy transition goals.

In the area of interconnected energy systems and integrated grids (Figure 3), several regions show negative trends, suggesting a decline in focus on this goal over the 2021–2027 period. Notable examples include Etelä-Karjala in Finland (−0.92), Ireland (−0.77), Champagne-Ardenne in France (−0.64), and Bremen in Germany (−0.59), where contributions to interconnected energy systems have declined markedly.

On the other hand, some regions show stable performance or slight improvement, remaining in the “mild change” zone with minimal fluctuations around zero. Examples include Västmanlands län, Sweden (0.00), Podlaskie, Poland (0.00), Gotlands län, Sweden (+0.07), and Eastern Macedonia and Thrace, Greece (+0.12). These regions reflect a more consistent approach to this goal, with little difference between the two periods.

Meanwhile, several regions reported significant progress in improving their interconnected energy systems, suggesting successful energy policies or infrastructure upgrades. Emilia-Romagna in Italy (+1.56), Puglia in Italy (+1.27), Poland as a whole (+0.90), and Sweden as a whole (+0.66) stand out for their significant growth in energy interconnection.

A geographic pattern is emerging, with southern regions of Europe, particularly Italy and Greece, showing the greatest growth, while northern and western regions, such as parts of Germany and Ireland, are experiencing declines or stagnation. Overall, countries such as Italy, Finland, and Romania are showing mixed results, with some regions showing significant improvements and others facing setbacks, indicating varying levels of success in implementing interconnected energy systems in these regions.

A detailed analysis of the innovative technologies and modern infrastructure goal shows that many regions have experienced significant declines in this area (Figure 4). Key examples include western Greece (−2.53), Saxony-Anhalt in Germany (−1.89), Blekinge län in Sweden (−1.29), Cyprus (−1.72), and Bremen in Germany (−1.07). These declines may reflect challenges in maintaining or developing technological infrastructure, likely due to economic, political, or structural factors.

On the other hand, some regions show relatively stable performance, showing only slight positive or negative fluctuations. For example, Västmanlands län, Sweden (+0.05), Kalmar län, Sweden (−0.27), Algarve, Portugal (−0.49), and Sardinia, Italy (−0.44) show steady infrastructure investment, showing neither significant improvement nor significant decline.

Encouragingly, several regions reported moderate-to-significant positive changes, signaling progress in the implementation of innovative technologies. Notable examples include Puglia in Italy (+0.73), Pohjanmaa in Finland (+0.31), South Holland in the Netherlands (+0.12), and Piedmont in Italy (+0.08). This progress can be attributed to regional development programs, increased funding for innovation, or strategic initiatives to modernize infrastructure.

Despite some overall declines, several regions maintain relatively high levels of technological infrastructure, including Lower Austria, Austria (+1.63), Sweden (nationwide) (+1.39), and Wallonia, Belgium (+1.21). These areas likely benefit from strong political support, favorable economic conditions, or active technology sectors.

It is worth noting that many regions in Western Europe, particularly parts of Germany and Sweden, are showing a significant decline in innovative technology and infrastructure. In contrast, regions of Southern Europe, such as various areas of Italy, are showing improvement or stability, indicating divergent regional dynamics in technology adoption and infrastructure maintenance. Significant differences are evident across countries; Germany, for example, shows both marked negative changes (e.g., Saxony-Anhalt) and areas of stability (e.g., Baden-Württemberg), reflecting the uneven distribution of innovation initiatives across the country.

Figure 5 shows that several regions have seen significant improvements in energy efficiency and eco-design, indicating the successful implementation of energy-saving measures and environmentally friendly practices. Notable examples include the Czech Republic (+1.21), Attica in Greece (+1.22), Emilia-Romagna in Italy (+0.53), Cantabria in Spain (+0.84), and Västerbottens län in Sweden (+1.03). These regions likely benefit from targeted policies, technological innovation, and increased investment in sustainable practices.

On the other hand, several regions saw declines, suggesting challenges in maintaining or increasing energy efficiency and eco-design initiatives. Regions such as Portugal (−1.12), Central Macedonia, Greece (−0.91), Galicia, Spain (−0.77), Western Greece (−0.80), and Northern Netherlands (−0.07) may be facing economic constraints, inadequate infrastructure, or policy implementation failures.

In terms of stability, many regions show relatively stable results with minimal changes, indicating consistent but unspectacular energy efficiency management. Examples include Saxony in Germany (+0.06), the Małopolska province in Poland (+0.36), the Podkarpackie province in Poland (+0.06), the Loire region in France (+0.71), and Poitou-Charentes in France (+0.65). This stability may reflect effective maintenance of existing infrastructure, albeit with a lack of significant progress.

Regions in Northern and Central Europe, particularly Sweden and Germany, tend to show stable or improving performance, highlighting the impact of strong policy support and technology adoption. In contrast, Southern and Eastern European regions show mixed results; some have improved due to EU funding and local initiatives, while others lag behind. In general, regions with proactive energy policies, access to financing, and strong local governance, such as the Czech Republic and Sweden, show better performance in energy efficiency and eco-design.

The analysis of decarbonization and smart sector integration goal, highlighting changes between 2014 and 2021, reveals several notable trends in the data collected (Figure 6). Some regions show significant improvements, reflecting the successful implementation of decarbonization and smart sector integration strategies. For example, Emilia-Romagna in Italy (+2.61), Puglia in Italy (+2.42), Valle d’Aosta in Italy (+1.55), Poitou-Charentes in France (+1.76), and Castilla-La Mancha in Spain (+1.39) are examples. These positive developments indicate a strong policy framework, significant investment in green technologies, and the successful integration of smart sectors.

On the other hand, several regions have seen declines, suggesting challenges in maintaining or accelerating decarbonization efforts. Regions such as Western Greece (−2.09), Brandenburg in Germany (−1.94), Portugal (−1.65), Central Macedonia in Greece (−1.36), and Greece as a whole (−1.57) illustrate these setbacks. The declines may be due to economic challenges, policy gaps, or interruptions in the uptake of green technologies.

Moreover, some regions show stability with minimal changes, indicating consistent but not outstanding management of decarbonization initiatives. For example, Kalmar län in Sweden (−0.01), Päijät-Häme in Finland (+0.02), Hamburg in Germany (−0.59), and Lithuania (−0.03) represent such cases. The stability observed in these areas may be due to ongoing but limited investment in green infrastructure and technology.

Southern European regions, particularly Italy and Spain, have shown significant improvement, likely due to targeted EU funding and national green strategies. Eastern Europe, including Poland and Romania, shows mixed results, with some regions making significant progress, while others are struggling with declines. Western and Northern Europe show variability; for example, Île-de-France in France is showing good performance, while the Wallonia region in Belgium reflects significant declines.

Overall, our analysis shows that regions with strong political support, good economic conditions, and access to technological innovation tend to perform better in terms of decarbonization and smart sector integration. In addition, regions that invest more in renewable energy, electric mobility, and digital infrastructure are likely to experience positive changes, underscoring the importance of financial support in achieving green goals.

Figure 7 reveals a general decline in consumer standing in most regions, accompanied by an increase in energy poverty. This trend underscores a widespread issue related to energy access and affordability. A significant decline in consumer empowerment is observed in regions such as Etelä-Karjala in Finland (−0.63), Valencia in Spain (−1.01), and Pohjois-Savo in Finland (−0.66), indicating deteriorating conditions for consumers in these areas.

In addition, some regions show minimal or no improvement, suggesting stagnation rather than significant progress. For example, Kujawsko-Pomorskie in Poland (−0.01), Tuscany in Italy (−0.01), and Lappi in Finland (−0.01) show that, while conditions have not deteriorated drastically, there has been little or no effort to empower consumers.

On the other hand, several regions showed positive changes, reflecting effective policies or targeted interventions to combat energy poverty. Notable examples include the Pays de Loire region in France (+0.08), Madrid in Spain (+0.10), and Etelä-Pohjanmaa in Finland (+0.38). These regions could benefit from special consumer protection measures or subsidies aimed at alleviating energy poverty.

Southern Europe, in particular, faces significant challenges related to energy poverty, as evidenced by regions such as western Greece and Crete, where structural issues, such as lower income levels and higher energy costs, dominate. Eastern Europe shows mixed results, with some regions experiencing marginal improvements, while most face declines, underscoring the economic disparities within the EU.

Western and Northern Europe, while generally more stable, still faces significant declines, particularly in some regions of Germany and Scandinavia. This situation underscores the widespread effects of the recent economic and energy crisis.

In general, regions with better economic performance and robust energy policies—such as subsidies and energy efficiency programs—are experiencing smaller declines or small improvements in consumer positions. In contrast, regions struggling economically or lacking comprehensive energy policies are more likely to show significant negative changes.

The widespread decline in consumer standing and increase in energy poverty underscore the urgent need for targeted interventions, including subsidies, energy efficiency improvements, and consumer education. Interregional initiatives can prove beneficial by fostering the exchange of best practices from regions that have successfully dealt with these challenges despite prevailing negative trends.

An analysis of the EU energy standards and technologies target reveals a general improvement in the adoption of these standards. Most regions showed positive changes in alignment with EU energy standards and technologies, reflecting continued efforts to integrate more sustainable energy solutions. Significant progress was recorded in regions such as Valle d’Aosta in Italy (+1.17), Lorraine in France (+1.24), and Puglia in Italy (+1.31), indicating solid adoption and integration of EU standards.

However, several regions, including Portugal (+0.00) and western Greece (+0.00), show stagnant growth in the integration of EU standards, suggesting potential barriers, such as economic constraints or a lack of investment in energy technologies. Some regions, such as Etelä-Karjala in Finland (−0.04) and Blekinge län in Sweden (−0.05), even show slight declines, highlighting the challenges of maintaining progress.

On the other hand, regions such as Champagne-Ardenne in France (+0.73) and Île-de-France in France (+0.40) are showing significant improvements, likely due to supportive policies and significant investments in energy infrastructure. Central European regions, including Brandenburg in Germany (+0.37) and Thuringia in Germany (+0.31), also show good performance, indicating successful energy transition strategies.

Overall, the Western and Northern European regions are showing positive improvement, reflecting strong policy support and higher levels of investment in sustainable energy technologies. While Southern Europe shows some progress, growth is less pronounced, likely due to slower economic growth and structural challenges associated with the transition to modern energy standards. Eastern Europe shows mixed results, with some regions, such as Slovakia (+0.28), making significant progress, while others lag behind, indicating varying levels of commitment and ability to implement EU energy standards.

The data suggest a correlation between economic strength, supportive energy policies, and the level of adoption of EU standards. Regions with better economic conditions and proactive energy policies tend to improve more. Continued investment in sustainable energy technologies, along with a supportive regulatory environment, will be key to sustaining and accelerating progress, especially in regions currently lagging behind. Promoting knowledge sharing and fostering interregional cooperation can help underperforming areas learn from successful regions, thereby increasing overall compliance with EU energy standards.

There are significant differences in offshore wind energy potential across European regions, with Northern European countries, especially coastal areas, showing much higher potential compared to inland or southern regions (see Figure 9). Countries such as Denmark, Ireland, and Norway show remarkable offshore wind potential, with regions such as Sogn og Fjordane in Norway (+0.77) and North Jutland in Denmark (+0.31) standing out.

Several regions have seen significant increases in offshore wind potential, reflecting ongoing investment in offshore wind technology and supportive policies. Significant improvements are evident in Alsace in France (+0.38), the Algarve in Portugal (+0.40), and Satakunta in Finland (+0.24), while regions such as southeastern Romania (+0.19) and the Lisbon metropolitan area in Portugal (+0.42) show emerging offshore wind markets, likely spurred by recent infrastructure development.

On the other hand, many regions show declining or stagnant potential, indicating challenges, such as insufficient investment, regulatory barriers, or geographic constraints. For example, Galicia in Spain (−0.53), Blekinge län in Sweden (−0.70), and Brittany in France (−0.12) show a significant decline in offshore wind potential. Coastal regions, including Schleswig-Holstein in Germany (−0.23), further illustrate how regulatory or market dynamics can negatively affect offshore wind development.

The North Sea and Baltic Sea regions continue to lead the way in terms of offshore wind potential, with Ireland (+0.02) and the coastal areas of Norway remaining in the lead, supported by favorable geographic conditions and favorable policies. Countries such as Denmark and Germany, which have made significant policy commitments and investments in wind infrastructure, consistently show high or growing potential. On the other hand, regions with limited policy support or infrastructure challenges, particularly in southern Europe and inland areas, show minimal or no growth in offshore wind capacity.

The data suggest that targeted investments, especially in emerging markets, such as Portugal and Eastern Europe, can significantly increase offshore wind power capacity. In addition, cross-border cooperation, particularly in the North Sea region, can optimize the use of offshore wind power and, thus, contribute to sustainable energy goals across Europe.

In summary, for the 2021–2027 period, Emilia-Romagna (Italy) leads in decarbonization and smart sector integration with a score of 3.69, highlighting its commitment to energy transition. The Valle d’Aosta region (Italy) excels in energy efficiency and eco-design, with a score of 1.86, showing significant progress in sustainability initiatives. Poland stands out in terms of innovative technologies and modern infrastructure, earning a score of 0.84, indicating a strong emphasis on technological modernization.

The Lorraine region (France) leads the EU in terms of global energy standards with a score of 1.32, reinforcing Europe’s importance in the international energy policy arena. In terms of empowering consumers and fighting energy poverty, the Kujawsko-Pomorskie region (Poland) ranks highest with a score of 0.71. Once again, Emilia-Romagna (Italy) excels in integrated energy systems, scoring 1.69 and demonstrating a comprehensive approach to energy transition.

For the transition period from 2014–2021 to 2021–2027, Emilia-Romagna (Italy) also leads the way in decarbonization and smart sector integration with a score of 2.61, indicating significant progress in this critical area. In energy efficiency and eco-design, Valle d’Aosta (Italy) ranks first with a score of 1.55, showing its commitment to developing sustainable technological solutions.

The Puglia region (Italy) has emerged as a leader in both innovative technologies and modern infrastructure, with a score of 0.73, and in global EU energy standards, with a score of 1.31, highlighting its strong position in implementing advanced technological projects and adhering to international standards.

In the area of consumer empowerment and combating energy poverty, the Etelä-Pohjanmaa region (Finland) ranks highest with a change score of 0.38, demonstrating its commitment to improving the energy situation of its residents. Emilia-Romagna (Italy) once again leads the way in integrated energy systems and networks, with a change score of 1.56, highlighting its efforts to integrate the energy grid. Finally, the Lazio region (Italy) achieved the highest change score of 0.50 in offshore wind energy potential, reflecting its progress in renewable energy sources, particularly in the offshore wind sector.

4.2. Regression Results

We tested four models to explore the factors influencing engagement in green energy goals during the 2021–2027 period and to evaluate both hypotheses (

Table 3. Linear regression analysis of engagement in green energy transition goals (2021–2027) based on prior engagement, fossil fuel dependency, and their interaction.

Predictors	Model (1)	Model (2)	Model (3)	Model (4)
Engagement level in green energy transition goal during the 2014–2020 period		0.232 *** (0.031)	0.232 *** (0.031)	0.608 *** (0.139)
Share of fossil fuels in gross available energy for country in 2013		0.066 (0.035)	0.035 (0.026)	0.072 * (0.029)
Interaction between engagement level in green energy transition goal and the share of fossil fuels in gross available energy				−0.084 ** (0.030)
Quality of government index in 2013			0.032 (0.017)	0.031 (0.017)
Population density in 2013			0.008 * (0.004)	0.007 (0.004)
GDP per capita in 2013			0.000 (0.021)	−0.001 (0.021)
Unemployment rate in 2013			0.232 ** (0.084)	0.214 * (0.084)
Patent applications per million inhabitants in 2013			−0.017 * (0.008)	−0.016 (0.008)
Share of people with higher education in 2013			0.010 * (0.005)	0.009 (0.005)
Goal 1: Interconnected energy systems and integrated grids	−0.024 *** (0.001)	−0.019 *** (0.001)	−0.019 *** (0.001)	−0.019 *** (0.001)
Goal 2: Innovative technologies and moderninfrastructure	−0.011 *** (0.001)	−0.008 *** (0.001)	−0.008 *** (0.001)	−0.008 *** (0.001)
Goal 3: Energy efficiency and eco-design	−0.015 *** (0.001)	−0.012 *** (0.001)	−0.012 *** (0.001)	−0.011 *** (0.001)
Goal 4: Decarbonization and smart sector integration	Ref.	Ref.	Ref.	Ref.
Goal 5: Consumer empowerment and energypoverty	−0.029 *** (0.001)	−0.023 *** (0.001)	−0.023 *** (0.001)	−0.023 *** (0.001)
Goal 6: EU energy standards and technologies globally	−0.039 *** (0.001)	−0.031 *** (0.001)	−0.031 *** (0.001)	−0.030 *** (0.001)
Goal 7: Offshore wind energy potential	−0.039 *** (0.001)	−0.031 *** (0.001)	−0.031 *** (0.001)	−0.030 *** (0.001)
Regional dummies	Yes	Yes	Yes	Yes
(Intercept)	0.461 *** (0.003)	0.074 (0.149)	−0.275 (0.235)	−0.383 (0.237)
Observations	1190	1190	1190	1190
R2/R2 adjusted	0.801/0.767	0.811/0.779	0.811/0.779	0.813/0.780
Deviance	0.065	0.062	0.062	0.061
AIC	−7949.897	−8010.097	−8010.097	−8017.188
log-likelihood	4151.948	4183.048	4183.048	4187.594

Note: * p < 0.05; ** p < 0.01; *** p < 0.001. All variables were Box–Cox transformed. Robust standard errors are in parentheses.

). Model (1), the null model, includes only regional and objective effects. This model accounts for the majority of the variance, as indicated by its high adjusted R² of 0.767. This suggests that much of the variation in engagement levels can be attributed to the fixed characteristics of regions and objectives.

In the regression analysis, Goal 4 (“Decarbonization and smart sector integration”) is used as the reference category, as it exhibited the highest engagement levels in earlier analyses (see Figure 6). This goal has emerged as the most significant priority for regions, reflecting strong alignment with decarbonization efforts across Europe. The regression table indicates that all other goals show significantly lower engagement levels compared to Goal 4, with negative coefficients across the board.

For instance, Goals 1 (“Interconnected energy systems and integrated grids”) and 5 (“Consumer empowerment and energy poverty”) exhibit the most substantial negative coefficients (−0.019 and –0.023, respectively), suggesting that regions place considerably less emphasis on these objectives. Similarly, Goals 6 (“EU energy standards and technologies globally”) and 7 (“Offshore wind energy potential”) also show lower engagement levels, indicating that these areas are not prioritized, despite their importance in the broader green energy transition.

Interestingly, Goals 2 (“Innovative technologies and modern infrastructure”) and 3 (“Energy efficiency and eco-design”) display relatively smaller negative coefficients (−0.008 and –0.011, respectively). This implies that, while these goals are not as prominent as Goal 4, they still receive more attention compared to other objectives. This pattern suggests that regions are selectively focusing on certain goals, particularly those related to decarbonization, energy efficiency, and infrastructure, while potentially neglecting the broader spectrum of green energy objectives.

This selective focus may indicate that regions prioritize immediate and tangible goals, such as decarbonization, over longer-term or more complex objectives, like offshore wind energy or global energy standards. Accessibility to the shore might be a factor in the latter case. Nonetheless, this narrow prioritization raises concerns about the holistic approach needed for a comprehensive green energy transition, as regions may overlook the broader set of goals necessary for a sustainable and integrated energy future.

The main predictors, particularly engagement levels in green energy goals during the 2014–2020 period, provide valuable insights, reinforcing Hypothesis 1 (regions that were more engaged in green objectives in the past are likely to maintain or increase their commitment in the subsequent period). The coefficient for prior engagement in Model (2) is positive and highly significant (0.232, p < 0.001), highlighting the persistence of regional efforts in green energy objectives. This suggests that early adopters of green energy policies tend to stay on this trajectory, reinforcing their commitment in the 2021–2027 period. It is noteworthy that even after controlling variables, such as GDP per capita and population size, are included in Model (3), the coefficient on early commitment remains stable, highlighting the robustness of the relationship.

Model (4) introduces further complexity by showing that the share of fossil fuels in a country’s energy mix has a significant and negative impact on commitment to green energy goals, thus confirming Hypothesis 2. In particular, the coefficient of fossil fuel share is negative, indicating that regions in countries with greater reliance on fossil fuels are less likely to commit to green energy transition goals. More importantly, the interaction coefficient between prior commitment and fossil fuel share is also significant and negative (−0.084, p < 0.01). This suggests that reliance on fossil fuels not only directly hinders commitment to green energy goals but also undermines the positive impact of prior commitment. In other words, even regions that were previously committed to green energy goals may find that their progress is undermined if their country remains heavily dependent on fossil fuels.

To interpret the interaction term more clearly, it indicates that the effect of past engagement is moderated by fossil fuel dependency. Regions with lower fossil fuel dependency are better able to build on their past green energy efforts, while regions with higher fossil fuel dependency struggle to maintain or increase their green energy commitments, even if they had been active in earlier periods. This highlights a key barrier to green energy transitions; regions within countries that still have a significant fossil fuel infrastructure are less likely to sustain momentum in green energy transitions, as the economic and political weight of fossil fuel industries may inhibit progress.

Figure 10 visually illustrates the interaction effect by showing the impact of past engagement (2014–2020) on future contributions to green energy goals (2021–2027), conditioned by the share of fossil fuels in the energy mix in 2013. The red line represents regions with a lower share of fossil fuels (value = −1.74), which exhibit a steep upward trend in their commitment to green energy goals in the 2021–2027 period. This suggests that regions with minimal fossil fuel reliance in 2013 were able to significantly enhance their engagement with green energy priorities over time. The steepness of the red trendline reinforces the idea that low fossil fuel dependency acts as a catalyst, enabling regions to amplify their commitment to green energy objectives.

In contrast, the blue line represents regions with a maximal share of fossil fuels (value = 2.95), showing a largely flat trend. These regions exhibit minimal, if any, increase in their commitment to green energy goals between 2021 and 2027, despite their prior engagement. The wide confidence interval around the blue trendline indicates variability in the relationship, suggesting that other factors, such as regulatory changes or economic conditions, may also play a role. However, the overall flat trajectory for regions with high fossil fuel dependency underscores the difficulty in transitioning to green energy for regions entrenched in fossil fuel infrastructure.

5. Discussion

This study provides critical insights into the green energy transition of European regions during the 2021–2027 period, reinforcing key hypotheses about the role of past engagement and fossil fuel dependency. The findings align with Hypothesis 1, which posited that regions previously engaged in green energy initiatives are more likely to continue or increase their focus on green energy goals. Notably, regions such as Emilia-Romagna and Valle d’Aosta in Italy demonstrated considerable growth in sectors like decarbonization and energy efficiency, validating the theory of path dependence in regional energy policy. Path dependence suggests that initial investments in green infrastructure and policy frameworks create a self-reinforcing cycle, promoting sustained progress in subsequent periods. The leadership of Emilia-Romagna in integrating smart energy sectors and fostering interconnected energy systems exemplifies how prior engagement fosters continued advancement, thereby highlighting the enduring effects of early commitments to the green energy transition.

Conversely, Hypothesis 2, which anticipated slower progress in regions dependent on fossil fuels, is also substantiated by the regression analysis. The significant negative interaction between prior engagement and fossil fuel dependency points to the structural challenges faced by regions with a high share of fossil fuel consumption, such as parts of Germany and Finland. These regions experience slower transitions, burdened by economic reliance on fossil fuel industries and the political complexities of phasing out such energy sources. The findings suggest that, while all regions are making strides towards achieving green energy targets, those more dependent on fossil fuels encounter barriers that decelerate their transition efforts. This is consistent with the notion that fossil fuel dependency creates inertia, limiting the ability to rapidly pivot towards green energy.

A key contribution of this study is to examine the interaction between prior commitment to green energy initiatives and dependence on fossil fuels. A negative and significant interaction term (−0.084, p < 0.01) reveals that fossil fuel dependence reduces the positive impact of prior commitment on future green energy commitments. While prior commitment is a strong predictor of future commitment, regions that remain heavily dependent on fossil fuels face systemic challenges that undermine the amplifying effect of early initiatives. This is particularly important, because it underscores the fact that the earlier momentum of green energy activities is not sufficient to overcome entrenched dependence on fossil fuels.

The shifts in regional energy objectives between the 2014–2020 and 2021–2027 programming periods also reveal important trends. There has been a marked increase in the prioritization of decarbonization, energy efficiency, and eco-design, demonstrating that regions are aligning their strategies with the EU’s overarching climate neutrality goals. However, other critical areas, like consumer empowerment and offshore wind energy, have seen less emphasis or even declines in prioritization. This selective focus on more immediate and tangible objectives, such as reducing greenhouse gas emissions, may impede the broader transition to a sustainable energy system.

Regions such as Attica (Greece) and Lazio (Italy) exemplify these challenges, with relatively flat growth in offshore wind energy potential. Geographical constraints, infrastructural challenges, and regulatory hurdles may explain this underutilization of renewable resources. While the prioritization of decarbonization and energy efficiency is critical, neglecting sectors like offshore wind and consumer engagement could hinder the long-term sustainability of the energy transition, as a more holistic approach is needed to balance technical progress with public support and diversified renewable energy sources.

The results of this study underscore the importance of early involvement in driving future green energy commitments. The theory of self-reinforcing energy policy is confirmed, as regions that actively participated in green energy initiatives between 2014 and 2020 are now leading the way between 2021 and 2027. This phenomenon is consistent with the research of Schwanitz [45], who highlights the role of citizen-led energy initiatives in achieving the Sustainable Development Goals, suggesting that such initiatives can catalyze further regional commitment to green energy practices. Similarly, Kozar and Sulich [46] argue that the green transformation of the energy sector is a direct result of prior investments in green energy initiatives, further reinforcing the path dependency observed in this study.

Regions lagging behind in green energy adoption can learn a lot from early adopters. Many effective strategies can be adapted to save the mistakes of active regions. There is, thus, a need to identify practices that are best suited to regional economic and social contexts. Existing studies suggested that effective collaboration with established green energy “leaders” enables knowledge transfer and capacity building, so that later adopters can make this transition more effective. This can especially empower regions that have limited green energy infrastructure [47]. Flexible measures, such as regional green energy training programs and pilot projects supported by grants and regulatory frameworks, have also been shown to have an impact on “empowering” lagging regions [48]. Such solutions implemented in RIS3 will likely accelerate the adoption of green energy and bring closer the ambitions of the European Green Deal.

Also, the significant interaction between fossil fuel dependency and prior engagement poses a formidable challenge. As Chelminski et al. [49] and Oliveira and Andrade [50] point out, the political economy of green energy transitions is fraught with conflicting interests, particularly in regions with heavy reliance on fossil fuels. These regions face the dual burden of maintaining economic stability, while striving to meet ambitious green energy goals. The negative interaction effect reveals that even regions with a history of green energy initiatives may struggle to sustain their progress if fossil fuel industries continue to dominate their energy landscape.

6. Conclusions

The study emphasizes the importance of continued commitment to green energy transition goals, while highlighting the challenges of fossil fuel dependence and selective targeting of regional policies. Regression analysis of the four models showed that fixed regional characteristics and specific targets account for much of the variation in green energy commitment during the 2021–2027 period. This means that regions’ existing infrastructure, economic structures, and policy frameworks play a key role in shaping their green energy priorities.

One key finding is that regions overwhelmingly prioritize decarbonization and smart sector integration. This focus underscores the alignment of many regions with the EU’s overarching climate goals, reinforcing the hypothesis that early investment in green energy initiatives fosters sustained commitment. However, other critical targets—such as consumer empowerment, energy poverty, and offshore wind—receive much less attention. This selective focus on decarbonization, energy efficiency, and infrastructure may hinder a holistic transition to a sustainable energy system, as important but less immediate goals are neglected.

Lower levels of commitment to targets such as interconnected energy systems and offshore wind reflect a troubling gap in the broader green energy transition. These areas, while critical to the long-term success of the EU’s green energy strategy, are given secondary attention, potentially due to regional constraints or short-term political and economic incentives. This imbalance could slow progress toward fully integrated and sustainable energy systems.

Considering these findings, the study suggests that a more comprehensive and balanced approach to green energy priorities is needed. Regions need to diversify beyond immediate decarbonization efforts to address broader goals, such as energy poverty and offshore wind, which are essential for a truly integrated energy transition. In addition, regions with a high dependence on fossil fuels will require targeted support—such as economic incentives and technological innovation—to overcome structural barriers and accelerate the transition.

Future research should further explore the socio-political and economic factors that drive these regional differences in green energy commitment, with an emphasis on under-researched targets. Comparative research with regions outside the EU could provide valuable insights for designing more effective policies. Addressing these gaps will be critical to ensuring that all regions contribute to the EU’s green energy transition goals and that no region is left behind.