Taxonomic Evaluation of the Sustainable Energy and Environmental Development in European Union Member States

Abstract

The present paper focuses on the transformation of energy and its connection with one of the areas of sustainable development, namely environmental sustainability. Energy and environmental sustainability are complex and multidimensional processes. Transitioning to more sustainable energy sources, such as renewable energy, has a significant impact on reducing carbon dioxide and other greenhouse gas emissions. Furthermore, improving energy efficiency, a key element of the energy transition, contributes to reducing overall energy demand and is therefore consistent with environmental sustainability goals. A fundamental goal of environmental sustainability is to minimise the carbon footprint. In this way, the fields of energy transition and environmental sustainability mutually reinforce each other. The objective of the present study is to evaluate territorial differentiation and to analyse the interdependence of energy and environmental sustainability in the European Union (EU). The study period covers the years from 2015 to 2022. The TOPSIS method, a multidimensional comparative analysis method, was used in the research procedure. Moreover, the paper undertakes an examination of the existence of a statistically significant relationship between energy and environmental sustainability. The study demonstrates that there is considerable territorial differentiation in both energy and environmental sustainability in the EU. The present study makes a contribution to the growth of existing knowledge in the field by highlighting the importance of energy and environmental sustainability. The results of this study could prove of assistance to policymakers, governments, and legislators.

1. Introduction

Ensuring sustainable socio-economic development is one of the most pressing challenges facing contemporary economies, particularly those of EU member states. The Sustainable Development Goals (SDGs) have received substantial global attention since their adoption in 2015, when 193 United Nations (UN) member states committed to advancing sustainable development worldwide. The concept of sustainable development rests on the premise that economic growth must occur within the ecological limits of the planet. This principle challenges traditional models of economic expansion and underscores the importance of selective development—characterised by the growth of sectors aligned with environmental objectives (e.g., renewable energy) and the phased reduction in those reliant on conventional, resource-intensive technologies. Improving efficiency, ensuring policy coherence, and safeguarding the long-term sufficiency of natural resources, including energy resources, remain essential in this context [1,2,3,4,5,6].

In recent years, the EU has adopted several initiatives aimed at operationalising the principles of sustainable development, particularly in the fields of energy and environmental protection. Following the entry into force of the Lisbon Treaty, the EU has progressively developed an integrated environmental, climate, and energy policy architecture. Today, developments in the energy sector are inseparably linked with environmental policy, making a holistic approach indispensable.

By anchoring its energy policy guidelines in the principle of sustainable development, the EU has moved away from the traditional paradigm that prioritised meeting national energy needs while insufficiently accounting for environmental impacts. This shift has led to a stronger policy focus on: (1) mitigating climate change and addressing major environmental challenges; (2) promoting efficient energy consumption; (3) enhancing the competitiveness of renewable energy sources; and (4) creating a common energy market grounded in shared environmental priorities. Sustainable energy development therefore constitutes a core dimension of environmental progress, as clean energy generation and emissions reduction are essential for maintaining a healthy environment. The simultaneous pursuit of these objectives is fundamental to ensuring safe and stable living conditions for future generations.

From the standpoint of the EU’s economic reconstruction and ecological transformation, energy production, its structural composition, consumption patterns, and the associated externalities—manifested through pollutant emissions and environmental burdens—play a decisive role. Sustainable energy management is accordingly a central element in each of the EU’s strategic priorities for the coming decades [7]. Within the broader framework of sustainable development, rational energy resource management entails the optimal use of energy resources to support balanced economic and social development while addressing environmental imperatives [8]. This includes coordinated supply- and demand-side actions, supported by innovative technologies that reduce environmental pressure and contribute to improving quality of life [2].

This understanding of sustainable development highlights the deep interdependence between energy systems and environmental processes [9]. Methodologically, integrating research on energy systems and environmental protection provides a solid foundation for interdisciplinary development analyses. Energy production, distribution, and consumption are among the principal determinants of environmental quality, shaping pollutant emissions, natural resource depletion, and ecosystem degradation. Conversely, environmental conditions and environmental policies influence the trajectory of technological development in the energy sector, particularly in the transition toward a low-carbon economy. An integrated research perspective is therefore crucial for capturing the complex feedback loops between the energy system and the natural environment. Such an approach enables the identification of long-term impacts of economic and political decisions and supports the formulation of coherent, evidence-based sustainable development strategies. It also broadens the scope for interdisciplinary knowledge exchange and aligns with contemporary scientific trends that emphasise cross-sectoral cooperation in addressing systemic and global challenges.

This paper is situated within this broader research context. Its primary objective is to assess territorial differentiation and to examine the interrelationship between sustainable energy and environmental development across EU member states. To address this aim, the following hypotheses were formulated: (1) energy sustainability and environmental sustainability exhibit territorial differentiation within the EU; and (2) a positive relationship exists between these two dimensions.

The extant literature reveals a shortage of studies that provide a comprehensive assessment of energy and environmental sustainability across EU member states. Although numerous analyses investigate specific aspects of the energy transition—such as the development of renewable energy sources, improvements in energy efficiency, or reductions in greenhouse gas emissions—relatively few integrate these dimensions within a unified analytical framework. In addition, comparative studies offering updated and systematic evaluations across EU countries remain limited. For these reasons, the period 2015–2022 was selected for this analysis. This timeframe reflects the adoption of the 2030 Agenda in 2015 [10,11], while 2022 represents the most recent year for which consistent statistical data are available, as provided by Eurostat.

Many existing studies rely on isolated indicators, thereby overlooking the complex interactions between the energy sector and the natural environment. This hinders holistic assessments of progress towards sustainable development. Moreover, several analyses rely on outdated data and therefore do not capture the effects of recent policy innovations. The absence of integrated, cross-sectional analyses has resulted in fragmented knowledge on sustainability levels across the EU, limiting the ability to draw clear conclusions on the effectiveness of implemented energy and environmental policies.

To address these limitations, the present study employs multivariate comparative analysis (MCA), a robust methodological tool for examining complex socio-economic and environmental phenomena. MCA accommodates a broad set of indicators that represent the multidimensional nature of energy and environmental sustainability and integrates them into synthetic measures. Constructing these measures involves several steps, including the selection of diagnostic variables, their standardisation or normalisation, and the aggregation of values using appropriate mathematical procedures. Synthetic measures allow for a comprehensive comparison of sustainability levels across EU member states, facilitating the identification of leaders and laggards and enabling the examination of both spatial and temporal dynamics. In this context, MCA provides an objective, data-driven assessment of progress towards sustainable development goals in the energy and environmental domains.

The indicators used in this study constitute an original research proposal designed to capture both contemporary conditions of the energy transition and current EU environmental policy priorities. Their selection was informed by a detailed analysis of EU strategic documents and shaped by the availability of harmonised statistical data for all 27 EU member states over the period 2015–2022. This alignment between strategic priorities, theoretical foundations, and empirical evidence strengthens the credibility and policy relevance of the study.

The paper is structured into two main parts. The first outlines the contemporary policy context, presenting the principal EU initiatives aimed at promoting energy and environmental sustainability. The second part describes the research methodology, presents the empirical findings, and situates them within the broader academic debate, offering conclusions and recommendations.

2. Literature Review

2.1. Analysis of Sustainable Energy and Environmental Policies in the European Union

In the contemporary era, it is widely acknowledged that the predominant challenges of the future pertain predominantly to climate-related issues and concerns regarding energy. At present, the EU is one of the world’s leading economies in implementing climate protection measures, which are also aligned with the EU environmental strategy [12,13,14,15,16]. Moreover, the determination of policy pertaining to climate protection is instrumental in the decision-making processes within other domains of EU action, exhibiting a close correlation with energy policy. Achieving climate goals is dependent on effective energy transformation. The promotion of energy savings and the increased utilisation of renewable energy sources are recognised as pivotal factors in the stimulation of job creation, economic growth, and emission reduction [17]. Consequently, the EU’s environmental, climate, and energy policies have evolved into a pivotal instrument for achieving sustainable development [18].

In the EU development strategy entitled “Europe 2020—A Strategy for Smart, Sustainable and Inclusive Growth” [19], a range of actions in the area of environment and energy were included. In the context of the EU’s strategic agenda, the 20/20/20 climate and energy target is of particular significance. This target, which the EU was expected to achieve by the year 2020, is one of the five primary objectives of the strategy. The overarching objective of the initiative was to curtail greenhouse gas emissions by 20% in comparison with 1990 levels. Additionally, the initiative sought to attain a 20% share of renewable energy in the EU’s aggregate energy consumption, whilst concomitantly endeavouring to effect a 20% diminution in energy consumption through the medium of enhanced energy efficiency [18]. The Europe 2020 strategy initiatives encompassed two initiatives pertaining to energy management and the environment. The first, entitled “A resource-efficient Europe” [20], proposed measures to ensure that economic growth is contingent on the utilisation of energy sources and a transition towards a low-emission economy. The realisation of this objective was to be accompanied by the completion of the internal energy market and the utilisation of incentives for sustainable production and consumption, including through green public procurement. The modernisation and integration of energy and transport infrastructure was identified as a key strategy to enhance energy efficiency, with the objective of achieving this being to raise energy efficiency standards in buildings [21]. The second initiative was the “Industrial policy for the globalised era” [22], which had a direct and synergistic impact on energy policy. It assumed actions to improve the business environment, develop a horizontal approach to industrial policy, support sectors transitioning to more efficient use of energy resources, and restructure declining sectors. These actions were intended to result in the establishment of an innovative and sustainable industrial base, thereby enhancing competitiveness [21].

Further action and a more integrated approach to the EU energy system and ambitious climate policy were reflected in the adoption of a new strategy entitled “A Framework Strategy for a Resilient Energy Union with a Forward-Looking Climate Change Policy” [23]. This strategy encompassed five interconnected areas of action, namely: (1) energy security, solidarity, and trust, (2) a fully integrated European energy market, (3) energy efficiency, (4) decarbonization of the economy, and (5) research, innovation, and competitiveness. The strategy involved a shift away from an economy based on conventional energy carriers, their associated technologies, and business models, as well as a transformation of the existing supply side energy system towards a decentralised system with an active demand side by strengthening the role of consumers. The Energy Union strategy is a long-term programme that also addresses energy and climate issues. Its aim is to fundamentally transform the EU economy into a low-emission, energy- and resource-efficient one. Research and innovation are key drivers in modernising the EU economy and in taking a leading role in advanced renewable energy technologies and energy efficiency initiatives. The strategy, which aims to accelerate the development of clean energy innovations, identifies priorities and actions for the development and rapid application of low-emission innovations [15,24].

In the short term, the EU’s climate and energy policy until 2030 assumes a reduction in greenhouse gas emissions whilst simultaneously increasing the share of energy production from renewable sources and improving energy efficiency. The primary objectives of this policy, which was adopted in 2014, included a 40% reduction in greenhouse gas emissions compared to 1990 levels, an increase in the share of renewable energy to 32%, and an enhancement in energy efficiency to 32.5%. In 2020, the target for the reduction in greenhouse gas emissions was revised, with the initial 40% target being elevated to 55% by 2030 [17,25]. Moreover, the EU 2040 target is predicated on the objective of reducing net greenhouse gas emissions by 90% compared to 1990 levels. This is a critical milestone on the road to achieving a climate-neutral economy by 2050 [26]. In the long term, further revision of key EU regulations concerning the energy sector was assumed, which refer to the objectives and tools of the EU energy and climate policy beyond 2030. This assertion is particularly pertinent in the context of decisions concerning the long-term vision of reducing greenhouse gas emissions in the EU by 2050 [27]. The aforementioned alterations signify a transposition of the Paris Agreement, which was established at the UN Conference of Parties (COP-21), into the EU’s policy framework. This agreement acknowledged the necessity to restrict the increase in the Earth’s average temperature to below 2 °C (ideally to 1.5 °C) compared to pre-industrial levels and to minimise global greenhouse gas emissions as expeditiously as possible [28]. The EU’s response to the breakthrough in global cooperation that occurred in 2015 at COP21 was to strengthen climate-related actions [29]. A fundamental component of the greenhouse gas emission reduction system in the EU is the European Union Emissions Trading System (EU ETS), which was established by Directive 2003/87/EC [30]. This directive has undergone multiple revisions with a view to enhancing and expanding the system, as well as improving the cost-effectiveness of emission reductions and low-emission investments. The system was initiated in 2005. The most significant reductions in emissions were observed in sectors covered by the EU ETS, particularly in power plants, thereby validating the efficacy of this cost-effective emission reduction instrument. Between 2005 and 2019, installations subject to the ETS reduced their emissions by approximately 35% [12]. For the period 2021–2030, the EU has set itself a new, higher target of reducing greenhouse gas emissions by 62% compared to 2005 levels [31,32]. Recent experience demonstrates the efficacy of emissions trading as a cost-effective mechanism for reducing emissions. Furthermore, revenues from emissions trading can be utilised to support the transition towards greener production and to stimulate innovation [17].

A seminal EU programming document was the Commission’s 2018 communication entitled “A Clean Planet for all: A European strategic long-term vision for a prosperous, modern, competitive, and climate-neutral economy” [33]. The strategy encompassed a shift away from coal, oil, and gas, as well as a profound transformation of the sectors responsible for the largest CO₂ and other greenhouse gas emissions. In addition, it was recognised that a strong influence on consumer behaviour would be necessary to achieve the assumed goals, and that this involvement would be impossible to achieve without the involvement of consumers. The EU has set itself the target of achieving climate neutrality, utilising economically viable carbon dioxide capture technologies, and compensating for the residual minor emissions. In 2019, the EU’s strategic programme for 2019–2024 was adopted. As indicated by [34], one of the four primary areas identified in the programme was the establishment of a climate-neutral, ecological, fair, and social Europe. A programme of intensive work was planned, with a primary focus on the transition to renewable energy sources and increasing energy efficiency. Other key objectives included improving water and air quality, and promoting sustainable agriculture.

The EU’s fundamental priority has become the reconstruction of the socio-economic model in member states towards a low-emission economy, as reflected in the assumptions of the European Green Deal (EGD) [35]. It is imperative to emphasise that the EGD, which aims to “transform the EU into a fair and prosperous society, with a modern, resource-efficient and competitive economy where there are no net emissions of greenhouse gases in 2050 and where economic growth is decoupled from resource use” [35] (p. 2), constitutes an integral component of the European Commission’s strategy to implement the 2030 Agenda for Sustainable Development [10,11]. The consequence of the undertaken actions is to develop a new economic model in Europe, in which economic growth will be placed on an equal footing with climate neutrality, care for natural resources and the principles of justice [2,36,37]. Achieving the goal of climate neutrality has been broken down into a number of specific objectives. The selection is based on the available knowledge regarding the contribution of individual economic sectors to climate pressure, the potential for emission reduction and greenhouse gas absorption, and the role of research and innovation. However, this reduction alone is insufficient to achieve the climate targets set for 2030 and 2050; it is also necessary to consider the role of other sectors [12]. The process of decarbonising the EU economy is predicated on the electrification of all sectors. At present, in excess of 50% of the EU’s electricity is zero-emission. By the mid-21st century, it is projected that 80% of electricity will be derived from renewable energy sources, with nuclear power plants accounting for approximately 15%. Technologies associated with fuel cells and alternative fuels, energy storage, and CO₂ capture, storage, and utilisation have been identified as particularly attractive in the transition to net zero emissions. It is imperative that energy efficiency becomes a priority. Greater emphasis was placed on achieving climate neutrality in the construction and transport sectors, which were included in the expanded EU ETS 2 system. The transport sector is responsible for approximately 25% of total emissions, while the construction sector generates almost 40% of CO₂ emissions. As is well documented, the decarbonisation of transport is a particularly challenging endeavour. However, recent technological innovations, the introduction of next-generation fuels, and the implementation of organisational solutions have rendered it eminently achievable [12].

In the context of the emergent geopolitical landscape consequent to Russia’s incursion into Ukraine, the EU was compelled to expedite its energy transition [2,9,15,38,39,40]. In 2022, the European Commission adopted the REPowerEU Plan, which aimed to “rapidly reduce (…) dependence on Russian fossil fuels by fast forwarding the clean transition and joining forces to achieve a more resilient energy system and a true Energy Union” [41] (p. 1). This document, in addition to the necessary short-term measures to guarantee supplies, referred to the idea of consolidating projects and creating a “true Energy Union,” the purpose of which would be to support the creation of a system in Europe that is resilient to energy crises. This marks the commencement of a novel approach to energy security within the EU’s energy and environmental policy framework [18,38,42,43].

The presentation of EU energy and environmental policy offers a comprehensive overview of complex, interconnected actions and instruments, encompassing both implemented initiatives and those still in the planning and consultation stages. This policy is multidimensional and interdisciplinary in nature, integrating issues related to energy security, environmental protection, technological innovation, and equitable socio-economic transformation. On the one hand, it is a response to the growing threats posed by climate change, and on the other, it is part of a long-term economic development strategy based on the principles of sustainable development and resource efficiency. Furthermore, this policy has a global and coordinating dimension, as actions undertaken within the EU constitute an important element of implementing international commitments arising from the Paris Agreement (2015) and the UN 2030 Agenda. The EU, by establishing ambitious regulatory frameworks and financial mechanisms [44,45,46], seeks to establish standards for other economies, whilst concurrently supporting transformation processes in member states exhibiting varying degrees of development.

2.2. Sustainable Energy and Environmental Development in the Light of Existing Research

A review of the extant literature indicates that studies on energy and environmental development focus primarily on assessing countries’ energy-climate performance and the implementation of sustainable development policies within international frameworks such as the 2030 Agenda and the European Green Deal.

Table A1. Review of literature on energy and environmental development research in the context of sustainable development.

Authors	Paper Title	The Paper Aims	Research Methods	Variables	Sample/Data	Main Research Results
Kryk, B. (2019) [76]	Ensuring Sustainable Energy as A Sign of Environmental Responsibility and Social Justice in European Union Members	The aim of the paper was to measure and assess the degree of providing sustainable energy in European Union (EU) countries in the context of social and environmental responsibility and social justice, as well as in the context of implementing the 7th goal of Agenda 2030.	Cluster analysis (Ward method and k-means)	In the study, available statistical data on 8 indicators for SDG 7, reported by Eurostat and established by the UN: primary energy consumption [million tons of oil equivalent], final energy consumption [million tonnes of oil equivalent], final energy consumption in households per capita, energy productivity [Euro per kilogram of oil equivalent], share of renewable energy in gross final energy consumption by sector, energy dependence by-product, population unable to keep home adequately warm by poverty status, greenhouse gas emissions intensity of energy consumption	Data by Eurostat and established by the UN	The variables were used, covering the years 2010 and 2016. The study enabled the grouping of EU countries by the degree of provision of sustainable energy and, thus, the determining of their environmental responsibility and social justice in this area.
Quintana-Rojo, C.; Callejas-Albiñana, F.-E.; Tarancón, M.-Á.; Martínez-Rodríguez, I. (2020) [84]	Econometric Studies on the Development of Renewable Energy Sources to Support the European Union 2020–2030 Climate and Energy Framework: A Critical Appraisal	A critical assessment of scientific achievements concerning the use of econometric analyses in research on the development of renewable energy sources (RES), with particular emphasis on their role in achieving the EU’s strategic climate and energy targets for 2020–2030.	A systematic review and bibliometric analysis of 153 scientific articles from 2002–2019. Classification of publications by document type, field of science, country of authors and econometric methods (including panel models, regression, cost–benefit analysis).	Frequency and areas of research on RES development. Types of econometric methods used (panel models, regressions, cost analysis). Economic, political, technical and social factors influencing RES development. Key words in research (e.g., “energy policy”, “investment”, “wind power”, “CO2 emissions”). Geographical areas of analysis (EU, USA, China, developing countries).	153 scientific publications from 2002–2019. Data source: Scopus, Google Scholar, Mendeley, ResearchGate. Scope: global, with an emphasis on EU and OECD countries.	Econometric research on RES focuses mainly on wind and solar energy. The most frequently analysed policy instruments are feed-in tariffs (FIT) and renewable portfolio standards (RPS). The literature confirms the importance of RES for reducing CO2 emissions, increasing energy security and economic innovation. There is a research gap concerning: the impact of RES on retail energy prices; biomass as an energy source; the of regional support policies; and public acceptance of RES.
Pakere, I.; Prodanuks, T.; Kamenders, A.; Veidenbergs, I.; Holler, S.; Villere, A.; Blumberga, D. (2021) [73]	Ranking EU Climate and Energy Policies	Development and application of a methodology for assessing and comparing climate and energy policies in EU countries and creation of an integrated Energy and Climate Policy Index (ECPI) enabling the ranking of countries in terms of energy efficiency and climate sustainability.	Quantitative analysis; the correlation method, Weitendorf’s linear normalisation and the construction of a composite ECPI based on 9 energy and environmental efficiency indicators were used.	Analysis indicators: share of RES in final energy consumption, GHG emissions relative to GDP, energy intensity, primary energy efficiency; industrial efficiency, energy consumption in households, space heating efficiency, transport emissions, specific energy consumption in transport.	Data from Eurostat and the Odyssee-Mure database for 27 EU countries for the period 2010–2017.	GDP growth between 2010 and 2017 did not lead to an increase in GHG emissions. Strong correlation between GDP per capita and transport emissions and industrial efficiency. Best performers: Sweden, Denmark, Austria, Ireland, Latvia, Finland, Lithuania. Worst performers: Bulgaria, Poland, Hungary, Czech Republic, Cyprus, France. Countries with high GDP and effective policies show lower emission intensity. The integrated ECPI indicator can serve as a tool for monitoring the implementation of EU policies. It is recommended that climate strategies be adapted to national specificities.
Rosen, M.A. (2021) [85]	Energy Sustainability with a Focus on Environmental Perspectives	Presentation and analysis of the concept of energy sustainability in an environmental context and identification of methods and technologies conducive to its achievement. The author seeks to define sustainable energy and identify its key dimensions (ecological, economic and social).	Theoretical and review article—analysis of literature on the subject, data from the IEA and the World Bank (1960–2018).	change in energy consumption and GDP per capita in G7 countries (1960–2015); types of energy resources (renewable and non-renewable); key sustainability factors: environmental impact, efficiency, energy carriers, social acceptance, intergenerational justice.	Data from the World Bank Group (2021) and the International Energy Agency (2020–2021); global analysis, with a particular focus on G7 countries.	In G7 countries, there has been a decoupling of GDP growth and energy consumption. Key conditions for sustainability: low environmental impact, energy efficiency, renewable resources, affordability and public acceptance. Renewable energy technologies (photovoltaic, wind, geothermal) and energy storage significantly support sustainable development. Required integration of energy, social and environmental policies. Promotion of efficiency, storage and clean energy technologies. Need to increase public acceptance and intergenerational justice in energy transition processes.
Tutak, M.; Brodny, J.; Bindzár, P. (2021) [16]	Assessing the Level of Energy and Climate Sustainability in the European Union Countries in the Context of the European Green Deal Strategy and Agenda 2030	Assessment of the level of energy and climate sustainability in EU countries in 2009–2018 in the context of the implementation of the European Green Deal Strategy and Agenda 2030. The study also aimed to identify groups of countries with similar levels of sustainability and to assess changes over time.	Quantitative analysis: the hybrid COPRAS (Complex Proportional Assessment) Entropy method from the group of multi-criteria decision-making (MCDM) methods was used.	Fourteen indicators in four dimensions: energy: primary and final energy consumption, energy productivity, share of renewable energy sources, import dependency; climate: GHG emissions per capita, GHG and CO2 emission intensity, emissions from cars; social: energy poverty (percentage of the population unable to keep their homes warm); economic: GDP per capita, electricity prices for households and industrial consumers.	27 EU countries, 2009–2018, data from Eurostat.	Highest level of sustainability: Sweden, Denmark, France, Austria. Lowest level: Bulgaria, Cyprus, Poland. Most EU countries saw an improvement in indicators, but at different rates of progress. Most progress: Spain, Lithuania; largest decline: Malta, Luxembourg. Four classes of countries with similar levels of development were identified. Countries with low levels were characterised by high energy consumption, low productivity and high emissions. The need to differentiate EU energy policies according to country groups was identified. Strengthening energy efficiency and investment in renewable energy sources was recommended. Strengthening renewable energy sources, energy efficiency and GHG emission reductions. Promoting a just energy transition and reducing energy poverty.
Kryk, B.; Guzowska, M.K. (2021) [77]	Implementation of Climate/Energy Targets of the Europe 2020 Strategy by the EU Member States	The aim of the paper was to evaluate the implementation of the climate/energy targets of the Europe 2020 Strategy by the EU Member States in 2010 and 2019 and to compare the results achieved by them.	For the purposes of the article, taxonomic research using the zero-unitarisation method has been used, which allows for a synthetic assessment of EU countries according to the level of implementation of the climate/energy target package. The Europe 2020 strategy (3 × 20) and an analysis of changes in achievements for each of the three groups of goals in the package.	Europe 2020 Index; EU climate/energy; indicators.	Data by European Commission	The analysis was based on normalisation with a constant reference point for the entire period of the analysis (the years 2010 and 2019), which allows for dynamic analysis and enables comparison of the values of the synthetic index for the analysed years.
Bai, X.; Wang, K.-T.; Tran, T.K.; Sadiq, M.; Trung, L.M.; Khudoykulov, K. (2022) [86]	Measuring China’s green economic recovery and energy environment sustainability: Econometric analysis of sustainable development goals	An assessment of the extent to which China’s economy is developing sustainably after the COVID-19 pandemic, particularly in the context of a ‘green economic recovery’ and environmental stability. The authors analyse the relationships between green energy financing, environmental regulations, technological innovation and economic growth, referring to the implementation of the Sustainable Development Goals (SDG 7 and SDG 13).	econometric (quantitative) analysis; consistency tests (LLC) and generalised method of moments (GMM); panel models (dynamic and threshold) analysing non-linear relationships between variables.	lnGPI: green process innovation; GFs: green finance (government subsidies); EP: energy prices; ER: environmental regulation; GDP, GDP2: economic development (with a quadratic non-linear effect); Trade, lnSize, lnENT, GER: control variables (trade, company size, intellectual resources, research expenditure).	Panel data for 30 provinces of China (excluding Tibet, Hong Kong, Macao and Taiwan) from 2009 to 2017; Sources: China Statistical Yearbook, Environmental Statistics, Technology Publications.	Environmental regulations have a U-shaped effect: initially, they inhibit innovation, but at higher subsidy levels, they stimulate the development of green technologies. There is a threshold for the effectiveness of government subsidies: when the subsidy level exceeds 0.13, their impact becomes positive. Green finance and investment in research and development (R&D) significantly support the implementation of green processes and innovation. Economic growth promotes environmental innovation only after a certain level of development (“critical mass”) has been reached. It has been confirmed that larger companies and regions with higher economic activity perform better in terms of green innovation. The state should increase and diversify its financial support for green innovation. More stringent environmental regulations are needed to overcome the initial slowdown in innovation. Authorities should support R&D, develop financing for low-carbon technologies and promote renewable energy sources.
Gökgöz, F.; Yalçın, E. (2022) [87]	Sustainability of G20 Countries within Environmental and Energy Perspectives	Assessment of the energy and environmental performance of G20 countries in the context of their contribution to sustainable development and the achievement of climate goals.	Quantitative analysis using integrated multi-criteria decision-making (MCDM) methods: CRITIC (determination of indicator weights), VIKOR and CoCoSo (comparison of performance and ranking of countries in terms of sustainable development).	CO2 emissions (thousand tonnes); energy consumption per capita (toe/person); energy intensity (toe/GDP); share of renewable energy (RES, % of total energy); forest area (km2)	OECD data for 19 G20 countries (excluding the EU), 2012–2018.	Brazil achieved the highest level of energy and environmental sustainability. India and the USA also performed well (high share of RES). Countries with low energy intensity and a high share of RES rank higher in the ranking. Saudi Arabia and Canada performed worst due to high energy consumption and low share of RES.
Ollier, L.; Metz, F.; Nuñez-Jimenez, A.; Späth, L.; Lilliestam, J. (2022) [14]	The European 2030 climate and energy package: do domestic strategy adaptations precede EU policy change?	Determining whether changes in the national climate and energy strategies of EU Member States preceded EU policy reforms, particularly the transition from the 2020 package to the 2030 package, which places greater emphasis on the internal market and technological neutrality.	Qualitative and quantitative analysis: an innovative social network analysis method was developed to examine the relationships between the objectives, instruments and implementation logic of national policies; content analysis of policy documents from six countries.	policy objectives: security of supply, cost reduction, climate protection; objects: internal market, technological neutrality, energy efficiency; instrumental logic: market forces vs. state forces	6 EU countries (Germany, France, Spain, Italy, United Kingdom, Sweden); data from national energy and climate strategies from two periods: TP1 (2007–2009) and TP2 (2017–2019).	Changes in national strategies preceded changes in EU policy: countries gradually redefined energy security, linking it to the development of the internal market. The importance of market forces and technological neutrality grew. The biggest changes concerned the energy strategies of Germany and Italy. The energy market became increasingly important as a tool for security of supply and integration of renewable energy sources. National strategic changes were crucial to the evolution of EU energy and climate policy. The growing role of the internal market promotes the integration of RES and security of supply. The changes indicate a shift from subsidy policies to market mechanisms and emissions trading. Recommendation: further research on the dynamics of the “argumentative logic” in the process of Europeanisation of energy policy.
Fulzele, R.; Fulzele, V.; Dharwal, M. (2022) [88]	Mapping the impact of the COVID-19 crisis on the progress of Sustainable Development Goals (SDGs)—a focus on global environment and energy efficiencies	Identification and quantitative determination of the impact of the COVID-19 pandemic on progress in achieving environmental and energy sustainable development goals (SDG 7, SDG 9, SDG 13).	Quantitative analysis using the AHP (Analytic Hierarchy Process) method; assessment of the positive and negative effects of the pandemic on SDGs 7, 9, 13 based on expert opinions (expert method, 4-point Likert scale).	SDG 7: energy availability, energy prices, renewable energy use, energy efficiency; SDG 9: innovation, industrial development, infrastructure investment, CO2 emissions; SDG 13: climate change, fossil fuel investment, adaptation measures.	Qualitative and quantitative data obtained from surveys of experts (scientists, decision-makers, energy industry practitioners); data sources: UN, UNDESA, IEA, World Bank (2015–2021).	The need to invest in green technologies, increase R&D spending and implement economic recovery plans in line with sustainable development was highlighted. Post-pandemic strategies need to be integrated with climate action.
Siksnelyte-Butkiene, I.; Karpavicius, T.; Streimikiene, D.; Balezentis, T. (2022) [15]	The Achievements of Climate Change and Energy Policy in the European Union	Assessment of EU countries’ achievements in implementing the Europe 2020 strategy objectives for climate and energy policy, including reducing greenhouse gas emissions, increasing the share of renewable energy sources and improving energy efficiency.	Quantitative analysis: an innovative multi-criteria decision-making method (MCDM)—Kernel-based Comprehensive Assessment (KerCA). This method allows for ranking of EU countries based on their progress towards the strategy’s objectives. The results were also compared with the SAW (Simple Additive Weighting) and TOPSIS methods to verify the consistency of the ranking.	greenhouse gas emissions in non-ETS sectors (GHG Emissions in ESD sectors, Mtoe CO2e); final energy consumption (Final Energy Consumption, Mtoe); primary energy consumption (Primary Energy Consumption, Mtoe); share of energy from renewable sources in final energy consumption (%).	All EU Member States (27). Statistical data from Eurostat (2022). Analysis period: 2010–2020, in relation to the Europe 2020 strategy targets.	The EU achieved all of its climate and energy targets; however, 14 Member States failed to meet at least one of the objectives. The greatest difficulties concerned energy efficiency, with 9 countries not reaching the target. The best-performing countries were Greece, Croatia, Italy, Portugal and Romania, while the weakest performers were Malta, Belgium, Ireland, France and Poland. The targets related to the development of renewable energy sources were achieved most successfully. The results can serve as a tool for monitoring the implementation of climate and energy strategies. Further research is needed on the relationship between economic development and the achievement of energy targets. The study also highlights the need for the exchange of good practices among EU Member States.
Brodny, J.; Tutak, M. (2023) [72]	Assessing the Energy and Climate Sustainability of European Union Member States: An MCDM-Based Approach	Development and application of a methodology for assessing the level of energy and climate sustainability of the EU-27 countries in 2010, 2015 and 2020. The aim was to rank EU countries in terms of the implementation of the energy and climate objectives of the Europe 2020 strategy and the UN 2030 Agenda (goals 7 and 13).	Quantitative analysis using five MCDM (multi-criteria decision-making) methods: CODAS, EDAS, TOPSIS, VIKOR, WASPAS. A proprietary methodology was used to integrate the results of these methods in order to obtain an unambiguous final assessment score.	17 indicators from the Eurostat database concerning, among others: primary and final energy consumption, energy efficiency, the share of RES in final energy consumption, transport, heating, greenhouse gas emissions, energy poverty, energy productivity, energy imports.	Statistical data from Eurostat for 27 EU countries for 2010, 2015 and 2020.	Significant differences in the level of energy and climate sustainability in the EU have been identified. Sweden and Denmark are the leaders, maintaining a stable, high level of sustainable development. Central and Eastern European countries (e.g., Bulgaria, Poland, Romania) achieve lower results. An improvement in RES and energy efficiency indicators was observed across the EU between 2010 and 2020. It is recommended to continue the development of renewable energy sources, improve energy efficiency and reduce energy poverty. The need for further research using multi-criteria models and long-term data has been identified.
Ahmed, N.; Areche, F.O.; Araujo, V.G.S.; Ober, J. (2024) [4]	Synergistic evaluation of energy security and environmental sustainability in BRICS geopolitical entities: An integrated index framework	Development and application of an integrated energy security and environmental sustainability index (ESESI) for BRICS countries to assess their progress in sustainable energy and environmental development.	Quantitative analysis using Multi-Criteria Decision Analysis (MCDA) and Multiplicative Data Envelopment Analysis (MDEA). The indicators were combined using the Weighted Product Method (WPM).	11 indicators in two categories: energy security: per capita energy consumption, energy dependency, diversification of energy sources (Shannon Index), GDP per capita, share of renewable energy sources, electrification rate; environmental sustainability: energy intensity, CO2 emissions per capita, emissions from energy production, CO2 intensity, forest area index.	Data from BP Statistical Review, IEA, World Bank, Global Carbon Project, national statistical offices. Period: 2000–2020. Sample: 5 BRICS countries (Brazil, Russia, India, China, South Africa).	Highest sustainability level: Brazil, thanks to a high share of RES. Lowest score: South Africa—heavy dependence on coal; Russia and China: progress in efficiency and RES, but still high emissions; India—slow pace of improvement, unequal access to energy; ESESI proved stable and reliable in sensitivity analysis.
Šević, A.; Nerantzidis, M.; Tampakoudis, I.; Tzeremes, P. (2024) [89]	Sustainability indices nexus: Green economy, ESG, environment and clean energy	Examining the interrelationships, spillover effects and dynamic interdependence between four key sustainability indices: Green Economy Index (GE), ESG Index, Global Environment Index (GENV) and Global Clean Energy Index (CENE).	Quantitative analysis: Quantile Vector Autoregression (QVAR) model; analysis of dynamic interdependence and spillover effects between indices. Analysis for three quantiles (5%, 50%, 95%) and dynamic modelling over time were included.	GE Index: green economy index; ESG Index: environmental, social and corporate governance index; GENV Index: global environmental index; CENE Index: clean energy index.	Daily data from the Refinitiv database, period 08.2014–04.2023 (2078 observations), covering the period before COVID-19, during the pandemic and after it.	GE and ESG are the main sources of information (net contributors) in the system of interrelationships; GENV and CENE act as recipients of information (net receivers); during the COVID-19 pandemic, an increase in the interdependence between indices was observed; the GE Index has the strongest impact on other indices, especially in the 5% and 95% quantiles.
Terzić, L. (2024) [3]	An investigation of the interlinkages between green growth dimensions, the energy trilemma, and sustainable development goals: Evidence from G7 and E7 economies	An investigation of the interlinkages between green growth dimensions, the energy trilemma, and sustainable development goals (SDGs) in G7 and E7 economies and an assessment of the effectiveness of green energy transition measures.	Quantitative analysis using statistical assessment tools and Spearman’s correlation coefficients. Based on the endogenous growth model and green economy theory.	ESRU—Efficient and Sustainable Resource Use Index; NCP—Natural Capital Protection Index; GEO—Green Economic Opportunities Index; SI—Social Inclusion Index; ETI—Energy Trilemma Index; GDP (PPP) per capita; SDG Index.	Data for 14 countries (G7 and E7); period: 2022–2023; sources: Global Green Growth Institute (GGGI), World Energy Council (WEC), Sustainable Development Report (UN).	Very strong positive correlations between all variables: particularly between ESRU and NCP (r = 0.877), GEO (r = 0.943), SI (r = 0.938) and SDG (r = 0.899). The results indicate that the efficient and sustainable use of resources and the balance of the energy trilemma (security, equity, environmental sustainability) are key to achieving the SDGs. G7 countries perform better than E7 countries. All dimensions of green growth and the energy trilemma are interdependent and have a strong impact on progress towards the SDGs.
Jędrzejczak-Gas, J.; Wyrwa, J.; Barska, A. (2024) [80]	Sustainable Energy Development and Sustainable Economic Development in EU Countries	The aim was to assess sustainable energy development and sustainable economic development in EU Member States and to identify the correlation between the two in the EU	The Hellwig method was used, synthetic measures were applied, and Spearman’s rank correlation coefficients were calculated between the synthetic measures of sustainable energy development and those of sustainable economic development	Sustainable energy development (7 indicators): primary energy consumption, final energy consumption, final energy consumption in households per capita, energy efficiency, share of renewable energy in gross final energy consumption by sector (%), energy import dependency by product (%), population unable to maintain adequate temperature in their homes due to poverty status (%). Economic sustainability (11 indicators): Real GDP per capita, young people who are neither in employment nor in education, employment rate, long-term unemployment rate, fatal accidents at work, at-risk-of-poverty rate among working people, gross domestic expenditure on research and development (% of GDP), patent applications filed with the European Patent Office, share of buses and trains in domestic passenger transport, level of tertiary education, availability of high-speed internet	Eurostat data	The analysis covers the period 2014–2021. The research conducted revealed significant differences in the level of sustainable energy development and sustainable economic development among EU countries. A correlation was established between synthetic measures of sustainable energy development and sustainable economic development in 2014–2021.
Brodny, J.; Tutak, M. (2025) [79]	Decade of Progress: A multidimensional measurement and assessment of energy sustainability in EU-27 nations	Assessment of the level and effectiveness of sustainable energy development in EU-27 countries in 2013–2022. The aim was to examine progress in energy security, economic, environmental and social aspects.	Quantitative analysis based on multi-criteria decision-making (MCDM) methods: COPRAS (Complex Proportional Assessment), CRITIC and Entropy (weighting), AHP (hierarchical dimensioning), indicators of change dynamics 2013–2022.	19 indicators in 4 dimensions: energy: primary energy consumption, import dependency, HHI diversification index, share of low-carbon sources; economic: GDP per capita, productivity and energy intensity, energy prices; environmental: GHG emissions per capita, share of RES, forest cover; social: disposable income, energy poverty, energy prices for households, premature deaths due to PM2.5.	27 EU countries (EU-27); data from Eurostat and OECD databases; period: 2013–2022.	A two-level Sustainable Energy Development Index (SEDI) was developed, showing that Sweden, Finland, Romania and France achieved the highest sustainability levels, while Malta, Cyprus, Luxembourg and Ireland scored the lowest. Eastern European countries remain reliant on fossil fuels, and significant economic and environmental disparities persist, underscoring the need to reinforce EU energy policy and support lower-performing states. The methodology can also be used to monitor progress toward SDG 7 and SDG 13.
Dugo, V.: Gálvez-Ruiz, D.; Díaz-Cuevas, P. (2025) [90]	The sustainable energy development dilemma in European countries: a time-series cluster analysis	A study classifying European countries in terms of their sustainable energy development, analysing economic, energy and environmental evolution between 2004 and 2018. The study aimed to identify similarities and differences between EU countries in order to support the creation of policies tailored to the national context.	Quantitative analysis based on two-stage cluster analysis using Dynamic Time Warping (DTW) and Ward’s method. The data was analysed in three dimensions: economic structure, energy policy and energy consumption.	13 variables grouped into three dimensions: socio-economic structure (GDP per capita, HICP, employment, public debt, household consumption); energy policy (GHG emissions, energy productivity, share of renewable energy sources); energy consumption (primary energy, final energy, household energy consumption).	30 European countries (EU + Iceland, Norway, United Kingdom); data from Eurostat and the IMF, period 2004–2018	Nine clusters of countries with diverse sustainable energy development profiles were identified. Nordic countries and Austria: strong economic structures, high energy consumption and advanced RES policies. Central and Eastern European countries: low GDP, low energy productivity and high emissions. The phenomenon of a “multi-speed Europe” in the context of energy was confirmed. Increased energy efficiency and reduced emissions should complement the development of renewable energy sources. The potential of using cluster analysis ( ) as a tool for EU and regional policy has been identified.
Jędrzejczak-Gas, J.; Wyrwa, J.; Barska, A. (2025) [82]	Assessment of Sustainable Energy Development in the European Union—Correspondence Analysis	The aim of the paper was to assess the transformation of EU member states in the field of sustainable energy development and to categorise them based on their compliance with Sustainable Development Goal No. 7 of the United Nations 2030 Agenda, concerning affordable and clean energy, in 2015 and 2023.	The correspondence Analysis, Ward’s method	7 indicators: primary energy consumption, final energy consumption, final energy consumption in households per capita, energy productivity, share of renewable energy in gross final energy consumption by sector (%), energy import dependency by products (%), population unable to keep home adequately warm by poverty status (%).	Eurostat data	The authors diagnosed the situation of EU countries in terms of sustainable energy development, dividing them into four groups. They showed the changes that took place in 2023 compared to 2015.

(Appendix A) presents a compilation and discussion of selected scientific publications dedicated to the issues of energy and environmental development, analysing the implementation of global and regional sustainable development strategies. The review encompasses works published between 2019 and 2025 that examine the relationships among the structure of energy consumption, energy efficiency, the share of renewable energy sources, and the level of greenhouse gas emissions, as well as other environmental indicators. The collected studies adopt both a macroeconomic perspective (international comparisons—EU, G7, G20, BRICS, E7) and a political-institutional approach focusing on the evolution of European climate and energy strategies. The authors employ a variety of methodological approaches, including quantitative modelling techniques (econometrics, multicriteria decision-making (MCDM), panel models) and qualitative methods (case studies, content analysis of strategic documents). The literature synthesis presented in Table A1 (Appendix A) identifies key research directions in sustainable energy and demonstrates their relevance to the implementation of EU climate and environmental policy.

The findings of the reviewed studies reveal considerable variation in the level of sustainable energy and environmental development across European countries. Scandinavian and Western European states, including Sweden, Denmark, Germany, and Austria, have consistently exhibited leadership in sustainable energy development. These efforts have resulted in high levels of energy efficiency, dynamic expansion of renewable energy sources, and effective reductions in greenhouse gas emissions. In contrast, Central and Eastern European countries, such as Poland, Bulgaria, and Romania, continue to display lower energy efficiency and a substantial dependence on fossil fuels. In these regions, the pace of the energy transition is slower, and the energy generation structure remains largely dominated by conventional sources. As demonstrated in the literature, the development of renewable energy sources and improvements in energy efficiency constitute key drivers of greenhouse gas emission reduction, while simultaneously strengthening economic competitiveness and energy resilience at the national level. It is essential to acknowledge the critical importance of integrating climate and economic policy to achieve long-term climate neutrality.

Researchers also identify significant gaps in the existing body of work. These include the insufficient consideration of social and qualitative factors, such as the just transition and public acceptance of renewable energy development. The lack of long-term analyses addressing the effects of the war in Ukraine and the post-2022 energy crisis on the direction and pace of Europe’s energy transition is also highlighted. Furthermore, the literature reveals a marked scarcity of interregional comparisons that include both EU member states and developing countries, particularly in Africa and South Asia.

In conclusion, the extant literature unequivocally demonstrates that the energy transition is multidimensional in nature and necessitates the simultaneous consideration of economic, environmental, and social perspectives. It is recommended that future research focus on the following areas: first, the expansion of studies examining causal relationships between energy and environmental indicators; second, the evaluation of the effectiveness of public policies; and third, the development of long-term energy system scenarios in the context of achieving climate neutrality by 2050.

3. Materials and Research Methods

The TOPSIS method, a multidimensional comparative analysis method, was utilised to evaluate energy and environmental sustainability in the EU [47,48]. This approach was utilised to estimate synthetic measures that served as the foundation for evaluating multidimensional phenomena, including energy and environmental sustainability. The synthetic measures also formed the basis for linearly ranking EU countries based on the phenomena under study. Moreover, the synthetic measures and the linear ranking results were utilised as a foundation for the examination of the relationship between energy and environmental sustainability.

The TOPSIS method is one of many methods of multi-criteria decision analysis (other methods include SAW [49], AHP [50], SMART [51], ELECTRE [52]). With so many methods available, the selection of the appropriate multi-criteria method becomes a multi-criteria problem in itself, and many works are devoted to this issue [53,54,55]. Triantaphyllou in [54] emphasises its importance, but also notes the difficulty in finding the answer to the question: “Which method is the best for a given problem?” The scope of applicability of the TOPSIS method is very wide and includes, among others, issues related to supply chain management and logistics [56,57], engineering and production systems [58], business management and marketing [59], health care [60], sustainable development [61]. A comprehensive literature review on the applications of the TOPSIS method in various areas is included in [62,63]. The TOPSIS method was used due to its advantages, i.e., simplicity, comprehensibility, and the guarantee of easy interpretation of results [59,64,65]. TOPSIS is characterised by high computational transparency and ease of result interpretation—the ranking of countries is obtained based on their distance from the ideal (most favourable) and anti-ideal (least favourable) solutions, which allows for an unambiguous interpretation of each country’s position. The literature also indicates certain limitations of this method [34,49] and problems associated with its application [63,66], which resulted in modifications of the classical TOPSIS algorithm. The limitations of the TOPSIS method include primarily the sensitivity of results to the method of variable normalisation and the subjectivity in determining criterion weights. To minimise these limitations, no weighting factors were assigned to diagnostic variables and a uniform normalisation scheme was used, ensuring comparability of results across countries. The TOPSIS method was chosen due to its appropriateness to the nature of the study, interpretative simplicity, computational transparency, and the ability to integrate various types of indicators into a single assessment model.

The study employed a multi-stage procedure. The initial stage of the research process entailed the substantive and formal selection of variables. The primary criteria for variable selection were their significance for the phenomena under study, as well as the scope of available statistical data. The source data was obtained from the Eurostat database [67].

Table 1. Potential diagnostic variables (source: own study based on Eurostat date).

Symbol	Indicators
Diagnostic variables—energy sustainability
X1	Primary energy consumption (tonnes of oil equivalent per capita)	SDG 07.10
X2	Final energy consumption (tonnes of oil equivalent per capita)	SDG 07.11
X3	Final energy consumption in households per capita (kilogram of oil equivalent)	SDG 07.20
X4	Energy productivity (purchasing power standard per kilogram of oil equivalen	SDG 07.30
X5	Share of renewable energy in gross final energy consumption (%)	SDG 07.40
X6	Energy import dependency (% of imports in total energy consumption)	SDG 07.50
X7	Population unable to keep home adequately warm by poverty status (%)	SDG 07.60
Diagnostic variables—environmental sustainability
Y1	Net greenhouse gas emissions (tonnes per capita)	SDG 13.10
Y2	Premature deaths due to exposure to fine particulate matter, PM2.5 (number of premature deaths, per 100,000 people)	SDG 11.52
Y3	Air emission intensity from industry (grams per euro, chain linked volumes, 2010)	SDG 09.70
Y4	Average CO2 emissions per km from new passenger cars (grams per kilometre)	SDG 13.31
Y5	Area under organic farming (% of total utilised agricultural area)	SDG 02.40
Y6	Ammonia emissions from agriculture (kg per hectare)	SDG 02.60
Y7	Water exploitation index, plus, WEI+ (%)	SDG 06.60
Y8	Circular material use rate, CMR (% of total material use)	SDG 12.41
Y9	Consumption footprint (per inhabitant)	SDG 12.31
Y10	Climate-related economic losses (current prices, euro per capita)	SDG 13.40
Y11	Share of environmental taxes in total tax revenues (% of total revenues from taxes and social contributions, excluding imputed social contributions)	SDG 17.50

presents the sets of variables that were deemed suitable for further research.

Given that many authors question the validity of weighting procedures for variables related to spatial data, we decided not to assign weighting coefficients to diagnostic variables. This solution is supported by, among other things, the fact that variables that were not selected would be assigned zero weights in advance [68,69].

Subsequently, a preliminary analysis and assessment of environmental sustainability in relation to energy sustainability was conducted, utilising the diagnostic variables presented in Table 1 as a foundation.

The subsequent stage involved the calculation of synthetic measures of energy and environmental sustainability. The TOPSIS method was utilised for this purpose. Consequently, a statistical selection of the variables presented in Table 1 was conducted, according to which the variables should be characterised by hight variability and should be weakly correlated with each other. It was assumed that variables should demonstrate adequate variability, as poorly differentiated variables are of little analytical value. The coefficient of variation was used to measure the variability of variables. It was assumed that those for which the coefficient value was less than 20% would be eliminated from the set of diagnostic variables. After conducting appropriate calculations, it was determined that variable Y4 should be eliminated from further research (Appendix B,

Table A2. Coefficients of variation in potential diagnostic variables characterising sustainable energy development in 2015–2022 (source: own study).

	X1	X2	X3	X4	X5	X6	X7
2015	40.7%	50.8%	33.7%	28.6%	58.0%	44.6%	93.0%
2016	40.5%	49.5%	34.7%	29.1%	57.5%	43.2%	98.0%
2017	38.7%	48.4%	34.5%	31.5%	57.1%	43.2%	101.0%
2018	39.5%	48.7%	33.5%	31.9%	54.1%	41.0%	100.3%
2019	38.9%	48.1%	32.1%	31.2%	53.1%	37.0%	95.9%
2020	37.3%	45.8%	30.5%	35.2%	47.1%	36.4%	91.4%
2021	37.2%	45.0%	31.8%	36.3%	49.9%	40.8%	93.0%
2022	33.5%	39.7%	30.9%	38.1%	49.6%	36.8%	72.5%

and

Table A3. Coefficients of variation in potential diagnostic variables characterising environmental sustainability in 2015–2022 (source: own study).

	Y1	Y2	Y3	Y4	Y5	Y6	Y7	Y8	Y9	Y10	Y11
2015	33.2%	62.7%	182.9%	8.1%	68.0%	87.3%	186.1%	73.3%	93.4%	219.3%	25.9%
2016	34.0%	66.7%	191.5%	6.8%	66.9%	87.6%	202.1%	75.3%	95.2%	226.1%	25.2%
2017	33.1%	74.6%	181.9%	6.6%	68.4%	84.9%	180.1%	70.3%	96.9%	137.8%	25.1%
2018	33.3%	62.3%	193.3%	6.8%	68.1%	86.8%	192.7%	70.0%	97.7%	138.0%	24.6%
2019	31.2%	63.8%	207.7%	7.2%	66.4%	87.6%	190.4%	66.7%	97.7%	148.6%	24.8%
2020	28.5%	73.8%	238.0%	8.5%	64.5%	97.2%	192.7%	67.6%	93.0%	112.4%	25.7%
2021	28.4%	71.2%	240.1%	12.4%	63.2%	97.5%	201.9%	67.9%	95.2%	237.6%	26.4%
2022	26.3%	64.4%	237.2%	15.1%	62.1%	96.6%	177.2%	65.0%	98.6%	137.2%	45.1%

). In addition to variability, an important criterion for variable selection is their correlation. It is assumed that two highly correlated variables (correlation coefficient greater than 0.7) convey similar information. Therefore, it is recommended to eliminate one of them. For this purpose, an analysis of the Pearson correlation coefficient matrix was performed. The analysis indicated a high correlation between the following pairs of variables: X1 and X2, X1 and X3, X2 and X3, and Y2 and Y9 (Appendix B,

Table A4. Matrices of correlation coefficients between potential diagnostic variables characterising sustainable energy development in 2015–2022 (source: own study).

2015
	X1	X2	X3	X4	X5	X6	X7
X1	1	0.944	0.778	0.056	0.030	0.032	0.574
X2	0.944	1	0.766	0.115	0.003	0.197	0.522
X3	0.778	0.766	1	0.120	0.296	0.223	0.675
X4	0.056	0.115	0.120	1	0.109	0.255	0.062
X5	0.030	0.003	0.296	0.109	1	0.496	0.096
X6	0.032	0.197	0.223	0.255	0.496	1	0.282
X7	0.574	0.522	0.675	0.062	0.096	0.282	1
2016
	X1	X2	X3	X4	X5	X6	X7
X1	1	0.931	0.791	0.096	0.058	0.020	0.523
X2	0.931	1	0.757	0.104	0.005	0.178	0.481
X3	0.791	0.757	1	0.063	0.318	0.292	0.607
X4	0.096	0.104	0.063	1	0.107	0.181	0.027
X5	0.058	0.005	0.318	0.107	1	0.487	0.049
X6	0.020	0.178	0.292	0.181	0.487	1	0.232
X7	0.523	0.481	0.607	0.027	0.049	0.232	1
2017
	X1	X2	X3	X4	X5	X6	X7
X1	1	0.935	0.776	0.091	0.071	0.044	0.482
X2	0.935	1	0.747	0.094	0.020	0.141	0.445
X3	0.776	0.747	1	0.049	0.364	0.339	0.562
X4	0.091	0.094	0.049	1	0.081	0.106	0.073
X5	0.071	0.020	0.364	0.081	1	0.540	0.048
X6	0.044	0.141	0.339	0.106	0.540	1	0.233
X7	0.482	0.445	0.562	0.073	0.048	0.233	1
2018
	X1	X2	X3	X4	X5	X6	X7
X1	1	0.926	0.747	0.130	0.119	0.049	0.476
X2	0.926	1	0.706	0.058	0.043	0.186	0.439
X3	0.747	0.706	1	0.049	0.393	0.329	0.595
X4	0.130	0.058	0.049	1	0.083	0.124	0.070
X5	0.119	0.043	0.393	0.083	1	0.546	0.069
X6	0.049	0.186	0.329	0.124	0.546	1	0.234
X7	0.476	0.439	0.595	0.070	0.069	0.234	1
2019
	X1	X2	X3	X4	X5	X6	X7
X1	1	0.950	0.713	0.121	0.058	0.001	0.458
X2	0.950	1	0.660	0.028	0.008	0.172	0.427
X3	0.713	0.660	1	0.011	0.399	0.350	0.614
X4	0.121	0.028	0.011	1	0.097	0.112	0.075
X5	0.058	0.008	0.399	0.097	1	0.590	0.078
X6	0.001	0.172	0.350	0.112	0.590	1	0.221
X7	0.458	0.427	0.614	0.075	0.078	0.221	1
2020
	X1	X2	X3	X4	X5	X6	X7
X1	1	0.955	0.768	0.063	0.132	0.042	0.442
X2	0.955	1	0.743	0.081	0.113	0.108	0.434
X3	0.768	0.743	1	0.109	0.317	0.316	0.630
X4	0.063	0.081	0.109	1	0.098	0.146	0.093
X5	0.132	0.113	0.317	0.098	1	0.506	0.083
X6	0.042	0.108	0.316	0.146	0.506	1	0.265
X7	0.442	0.434	0.630	0.093	0.083	0.265	1
2021
	X1	X2	X3	X4	X5	X6	X7
X1	1	0.952	0.729	0.106	0.122	0.089	0.495
X2	0.952	1	0.688	0.041	0.101	0.073	0.477
X3	0.729	0.688	1	0.015	0.392	0.414	0.685
X4	0.106	0.041	0.015	1	0.141	0.233	0.069
X5	0.122	0.101	0.392	0.141	1	0.584	0.158
X6	0.089	0.073	0.414	0.233	0.584	1	0.324
X7	0.495	0.477	0.685	0.069	0.158	0.324	1
2022
	X1	X2	X3	X4	X5	X6	X7
X1	1	0.930	0.720	−0.127	0.201	−0.130	−0.552
X2	0.930	1	0.681	0.063	0.164	0.077	−0.562
X3	0.720	0.681	1	−0.028	0.435	−0.392	−0.706
X4	−0.127	0.063	−0.028	1	−0.151	0.214	0.015
X5	0.201	0.164	0.435	−0.151	1	−0.587	−0.184
X6	−0.130	0.077	−0.392	0.214	−0.587	1	0.194
X7	−0.552	−0.562	−0.706	0.015	−0.184	0.194	1

and

Table A5. Matrices of correlation coefficients between potential diagnostic variables characterising environmental sustainability in 2015–2022 (source: own study).

2015
	Y1	Y2	Y3	Y5	Y6	Y7	Y8	Y9	Y10	Y11
Y1	1	−0.253	−0.214	−0.024	0.071	−0.130	0.265	0.451	−0.144	−0.182
Y2	−0.253	1	−0.002	−0.281	−0.211	0.077	−0.213	−0.771	0.575	0.393
Y3	−0.214	−0.002	1	−0.013	−0.242	0.084	−0.281	−0.229	0.020	0.122
Y5	−0.024	−0.281	−0.013	1	−0.387	−0.296	0.076	−0.007	−0.332	−0.169
Y6	0.071	−0.211	−0.242	−0.387	1	0.442	0.235	0.469	−0.207	0.047
Y7	−0.130	0.077	0.084	−0.296	0.442	1	−0.256	0.066	0.041	0.196
Y8	0.265	−0.213	−0.281	0.076	0.235	−0.256	1	0.317	−0.241	−0.223
Y9	0.451	−0.771	−0.229	−0.007	0.469	0.066	0.317	1	−0.353	−0.242
Y10	−0.144	0.575	0.020	−0.332	−0.207	0.041	−0.241	−0.353	1	0.230
Y11	−0.182	0.393	0.122	−0.169	0.047	0.196	−0.223	−0.242	0.230	1
2016
	Y1	Y2	Y3	Y5	Y6	Y7	Y8	Y9	Y10	Y11
Y1	1	−0.267	−0.214	0.024	0.037	−0.125	0.203	0.441	0.305	−0.158
Y2	−0.267	1	−0.009	−0.287	−0.249	−0.004	−0.190	−0.790	−0.099	0.415
Y3	−0.214	−0.009	1	−0.022	−0.238	−0.019	−0.263	−0.209	0.003	0.184
Y5	0.024	−0.287	−0.022	1	−0.384	−0.288	0.131	0.026	−0.198	−0.107
Y6	0.037	−0.249	−0.238	−0.384	1	0.604	0.198	0.494	0.361	−0.019
Y7	−0.125	−0.004	−0.019	−0.288	0.604	1	−0.240	0.146	0.710	0.190
Y8	0.203	−0.190	−0.263	0.131	0.198	−0.240	1	0.285	0.048	−0.164
Y9	0.441	−0.790	−0.209	0.026	0.494	0.146	0.285	1	0.349	−0.267
Y10	0.305	−0.099	0.003	−0.198	0.361	0.710	0.048	0.349	1	0.018
Y11	−0.158	0.415	0.184	−0.107	−0.019	0.190	−0.164	−0.267	0.018	1
2017
	Y1	Y2	Y3	Y5	Y6	Y7	Y8	Y9	Y10	Y11
Y1	1	−0.208	−0.130	0.051	0.024	−0.131	0.243	0.370	−0.286	−0.104
Y2	−0.208	1	−0.027	−0.288	−0.230	0.076	−0.241	−0.745	−0.133	0.393
Y3	−0.130	−0.027	1	−0.014	−0.249	0.067	−0.288	−0.168	0.421	0.177
Y5	0.051	−0.288	−0.014	1	−0.386	−0.291	0.142	0.002	0.326	−0.075
Y6	0.024	−0.230	−0.249	−0.386	1	0.501	0.275	0.527	−0.255	−0.014
Y7	−0.131	0.076	0.067	−0.291	0.501	1	−0.234	0.137	−0.162	0.181
Y8	0.243	−0.241	−0.288	0.142	0.275	−0.234	1	0.325	0.014	−0.195
Y9	0.370	−0.745	−0.168	0.002	0.527	0.137	0.325	1	−0.043	−0.219
Y10	−0.286	−0.133	0.421	0.326	−0.255	−0.162	0.014	−0.043	1	0.116
Y11	−0.104	0.393	0.177	−0.075	−0.014	0.181	−0.195	−0.219	0.116	1
2018
	Y1	Y2	Y3	Y5	Y6	Y7	Y8	Y9	Y10	Y11
Y1	1	−0.243	−0.168	0.042	0.012	−0.072	0.247	0.385	−0.214	−0.165
Y2	−0.243	1	−0.063	−0.282	−0.246	−0.009	−0.243	−0.814	0.065	0.361
Y3	−0.168	−0.063	1	−0.077	−0.231	0.024	−0.288	−0.117	−0.023	0.154
Y5	0.042	−0.282	−0.077	1	−0.392	−0.267	0.150	0.003	0.210	−0.078
Y6	0.012	−0.246	−0.231	−0.392	1	0.444	0.299	0.511	−0.158	−0.010
Y7	−0.072	−0.009	0.024	−0.267	0.444	1	−0.204	0.159	−0.150	0.189
Y8	0.247	−0.243	−0.288	0.150	0.299	−0.204	1	0.302	0.259	−0.183
Y9	0.385	−0.814	−0.117	0.003	0.511	0.159	0.302	1	−0.004	−0.207
Y10	−0.214	0.065	−0.023	0.210	−0.158	−0.150	0.259	−0.004	1	0.046
Y11	−0.165	0.361	0.154	−0.078	−0.010	0.189	−0.183	−0.207	0.046	1
2019
	Y1	Y2	Y3	Y5	Y6	Y7	Y8	Y9	Y10	Y11
Y1	1	−0.220	−0.220	−0.066	0.057	−0.092	0.180	0.385	0.392	−0.224
Y2	−0.220	1	−0.036	−0.344	−0.179	0.124	−0.256	−0.755	−0.305	0.447
Y3	−0.220	−0.036	1	−0.060	−0.210	0.050	−0.299	−0.075	0.005	0.149
Y5	−0.066	−0.344	−0.060	1	−0.404	−0.297	0.138	0.019	−0.039	−0.059
Y6	0.057	−0.179	−0.210	−0.404	1	0.480	0.383	0.492	0.212	−0.033
Y7	−0.092	0.124	0.050	−0.297	0.480	1	−0.170	0.138	−0.099	0.105
Y8	0.180	−0.256	−0.299	0.138	0.383	−0.170	1	0.289	0.220	−0.072
Y9	0.385	−0.755	−0.075	0.019	0.492	0.138	0.289	1	0.384	−0.308
Y10	0.392	−0.305	0.005	−0.039	0.212	−0.099	0.220	0.384	1	−0.406
Y11	−0.224	0.447	0.149	−0.059	−0.033	0.105	−0.072	−0.308	−0.406	1
2020
	Y1	Y2	Y3	Y5	Y6	Y7	Y8	Y9	Y10	Y11
Y1	1	−0.181	−0.235	−0.138	0.004	−0.108	0.102	0.429	0.092	−0.280
Y2	−0.181	1	−0.036	−0.331	−0.207	0.110	−0.231	−0.779	−0.084	0.512
Y3	−0.235	−0.036	1	−0.056	−0.174	0.027	−0.274	−0.087	−0.117	0.101
Y5	−0.138	−0.331	−0.056	1	−0.404	−0.315	0.135	0.016	−0.006	−0.135
Y6	0.004	−0.207	−0.174	−0.404	1	0.520	0.418	0.417	−0.105	−0.036
Y7	−0.108	0.110	0.027	−0.315	0.520	1	−0.108	0.026	−0.069	0.112
Y8	0.102	−0.231	−0.274	0.135	0.418	−0.108	1	0.280	0.197	−0.086
Y9	0.429	−0.779	−0.087	0.016	0.417	0.026	0.280	1	0.088	−0.380
Y10	0.092	−0.084	−0.117	−0.006	−0.105	−0.069	0.197	0.088	1	−0.082
Y11	−0.280	0.512	0.101	−0.135	−0.036	0.112	−0.086	−0.380	−0.082	1
2021
	Y1	Y2	Y3	Y5	Y6	Y7	Y8	Y9	Y10	Y11
Y1	1	−0.110	−0.250	−0.182	−0.025	−0.087	0.063	0.373	0.315	−0.223
Y2	−0.110	1	−0.046	−0.356	−0.218	0.075	−0.326	−0.787	−0.193	0.620
Y3	−0.250	−0.046	1	0.226	−0.164	0.056	−0.285	−0.084	−0.114	0.108
Y5	−0.182	−0.356	0.226	1	−0.432	−0.223	0.083	−0.005	−0.098	−0.140
Y6	−0.025	−0.218	−0.164	−0.432	1	0.481	0.458	0.440	0.130	−0.142
Y7	−0.087	0.075	0.056	−0.223	0.481	1	−0.049	0.100	−0.104	0.096
Y8	0.063	−0.326	−0.285	0.083	0.458	−0.049	1	0.330	0.359	−0.121
Y9	0.373	−0.787	−0.084	−0.005	0.440	0.100	0.330	1	0.234	−0.522
Y10	0.315	−0.193	−0.114	−0.098	0.130	−0.104	0.359	0.234	1	−0.283
Y11	−0.223	0.620	0.108	−0.140	−0.142	0.096	−0.121	−0.522	−0.283	1
2022
	Y1	Y2	Y3	Y5	Y6	Y7	Y8	Y9	Y10	Y11
Y1	1	−0.079	−0.236	−0.142	−0.040	−0.094	0.064	0.261	−0.298	−0.075
Y2	−0.079	1	0.010	−0.266	−0.181	0.192	−0.357	−0.726	0.191	0.709
Y3	−0.236	0.010	1	0.227	−0.164	0.074	−0.289	−0.034	0.092	0.043
Y5	−0.142	−0.266	0.227	1	−0.451	−0.236	0.107	−0.019	0.040	−0.177
Y6	−0.040	−0.181	−0.164	−0.451	1	0.537	0.502	0.446	−0.143	−0.205
Y7	−0.094	0.192	0.074	−0.236	0.537	1	0.069	0.138	−0.038	0.061
Y8	0.064	−0.357	−0.289	0.107	0.502	0.069	1	0.412	0.132	−0.292
Y9	0.261	−0.726	−0.034	−0.019	0.446	0.138	0.412	1	−0.099	−0.611
Y10	−0.298	0.191	0.092	0.040	−0.143	−0.038	0.132	−0.099	1	−0.207
Y11	−0.075	0.709	0.043	−0.177	−0.205	0.061	−0.292	−0.611	−0.207	1

). Therefore, X1, X2, and Y9 were removed from the set of variables. The remaining variables were qualified for the next stage of constructing the synthetic index. The nature of the variables was then determined. Following comprehensive analysis and substantive assessment, it was determined that variables X4, X5, Y1, Y7, and Y11 were stimulants, while variables X3, X6, X7, Y2, Y3, Y4, Y5, Y8, and Y10 were destimulants.

The subsequent stage of the research was to standardise the variables.

A quotient transformation was applied [47,48]:

$$z_{ij}={\displaystyle \frac{x_{ij}}{\sqrt{{\sum }_{i=1}^n{x}_{ij}^2}}}$$

(1)

where:

• $n$—number of variables,
• $x_{ij}$—j-th diagnostic variable of i-th country.

Subsequently, the coordinates of the pattern and anti-pattern were determined, and the distances of the EU countries from the pattern and anti-pattern were calculated according to the following formulas:

–

Pattern coordinates:

$${\mathrm{z}}_{0\mathrm{j}}^{+}={\mathrm{m}\mathrm{a}\mathrm{x}}_i\left\{z_{ij}\right\}—\mathrm{for} \mathrm{stimulant} \mathrm{variables}$$

(2)

$${\mathrm{z}}_{0\mathrm{j}}^{+}={\mathrm{m}\mathrm{i}\mathrm{n}}_i\left\{z_{ij}\right\}-\mathrm{for} \mathrm{destimulant} \mathrm{variables}$$

(3)

–

Anti-pattern coordinates:

$$z_{0j}^{-}={\mathrm{m}\mathrm{i}\mathrm{n}}_i\left\{z_{ij}\right\}-\mathrm{for} \mathrm{stimulant} \mathrm{variables}$$

(4)

$$z_{0j}^{-}={\mathrm{m}\mathrm{a}\mathrm{x}}_i\left\{z_{ij}\right\}-\mathrm{for} \mathrm{destimulant} \mathrm{variables}$$

(5)

–

Distance of EU countries from the pattern:

$$d_{i0}^{+}=\sqrt{\sum _{j=1}^m{\left(z_{ij}-z_{0j}^{+}\right)}^2}$$

(6)

–

Distance of EU countries from the anti-pattern:

$$d_{i0}^{-}=\sqrt{\sum _{j=1}^m{\left(z_{ij}-z_{0j}^{-}\right)}^2}$$

(7)

The subsequent phase of the research entailed the calculation of synthetic measures of energy and environmental sustainability (S_i) and the generation of rankings of EU countries.

$$S_i={\displaystyle \frac{d_{i0}^{-}}{d_{i0}^{+}+d_{i0}^{-}}}$$

(8)

where:

• in general $S_i\in \left[0;1\right]$,
• ${\mathrm{m}\mathrm{a}\mathrm{x}}_i\left\{S_i\right\}-\mathrm{b}\mathrm{e}\mathrm{s}\mathrm{t} \mathrm{i}\mathrm{t}\mathrm{e}\mathrm{m}$,
• ${\mathrm{m}\mathrm{i}\mathrm{n}}_i\left\{S_i\right\}-\mathrm{w}\mathrm{o}\mathrm{r}\mathrm{s}\mathrm{t} \mathrm{i}\mathrm{t}\mathrm{e}\mathrm{m}$.

The final stage of the analysis entailed an examination of the relationship between energy sustainability and environmental sustainability in EU countries during the period under analysis. The variables employed in the examination of the relationship were synthetic measures calculated for individual EU countries between 2015 and 2022. Given the nature of the relationship analysis, which involves variables with a panel data structure, the Fama-MacBeth method was employed [70]. The method under discussion involves the estimation of parameters for each year (in this case, Pearson’s linear correlation coefficient values) based on cross-sectional data. Student’s t test is then used to test the significance of the mean parameter values for the entire analysed period. In their 2002 study, E. F. Fama and K. R. French [71] proposed an increase in the critical value of the t statistic during the inference process, with the aim of accounting for autocorrelation over time. This increase was proposed to be 2.5 times the original value.

4. Research Results and Discussion

However, analysis of individual variables selected to assess environmental or energy sustainability does not provide a complete picture of the situation in individual EU countries. Therefore, the authors decided to use complex statistical measures. This studies indicate significant geographic differences in energy and environmental sustainability levels across the EU. The mean value of the energy sustainability measure ($S_{X_i}$) between 2015 and 2022 was approximately 0.4960. The highest levels of $S_{X_i}$ were observed in countries such as Sweden (0.6691), Denmark (0.6525), Estonia (0.6045), and Latvia (0.5929). The lowest levels were observed in countries such as Cyprus (0.3064), Lithuania (0.3144), Bulgaria (0.3261), and Greece (0.3419). The values of $S_{X_i}$ in countries with the highest values were found to exceed twice the levels observed in countries with the lowest values (Figure 1).

The measures outlined above demonstrate a high level of diversification in the field of sustainable energy development among EU member states. Furthermore, during the period under review, the EU experienced unfavourable changes in its energy sustainability performance. The majority of EU countries (24) recorded a lower synthetic measure in 2022 compared to 2015. In the years immediately following 2015, all EU countries recorded positive changes in energy sustainability (an increase in the synthetic measure). However, this positive trend was brought to a halt, and in the final years of the period under review, almost all EU countries recorded negative changes (a decrease in the synthetic measure) (

Table 2. The value of the $S_{X_i}$ measure in EU countries in 2015–2022 (source: authors’ computation).

	EU Countries	2015	2016	2017	2018	2019	2020	2021	2022	2015–2022
1.	Belgium	0.4570	0.4867	0.4776	0.4649	0.4765	0.4601	0.4294	0.3944	0.4558
2.	Bulgaria	0.3209	0.3285	0.3299	0.3315	0.3361	0.3142	0.3198	0.3278	0.3261
3.	Czechia	0.5396	0.5658	0.5588	0.5531	0.5466	0.5291	0.4892	0.4557	0.5297
4.	Denmark	0.6639	0.6885	0.7036	0.6832	0.6698	0.6188	0.6069	0.5853	0.6525
5.	Germany	0.5324	0.5511	0.5530	0.5528	0.5439	0.4834	0.4829	0.4139	0.5142
6.	Estonia	0.6243	0.6296	0.6230	0.6199	0.6238	0.5802	0.5962	0.5388	0.6045
7.	Ireland	0.5064	0.5556	0.5831	0.5750	0.5724	0.5915	0.5494	0.5248	0.5573
8.	Greece	0.3136	0.3277	0.3532	0.3660	0.3955	0.3561	0.3243	0.2984	0.3419
9.	Spain	0.5139	0.5433	0.5484	0.5207	0.5314	0.4761	0.3884	0.3487	0.4839
10.	France	0.5368	0.5608	0.5571	0.5491	0.5332	0.5130	0.4756	0.3941	0.5150
11.	Croatia	0.5800	0.6018	0.5923	0.5787	0.5773	0.5550	0.5124	0.4590	0.5571
12.	Italy	0.4407	0.4694	0.4680	0.4571	0.4767	0.4876	0.4360	0.4070	0.4553
13.	Cyprus	0.2791	0.3316	0.3371	0.3365	0.3185	0.2827	0.2850	0.2809	0.3064
14.	Latvia	0.5739	0.6258	0.6232	0.6214	0.6088	0.5927	0.5697	0.5277	0.5929
15.	Lithuania	0.3357	0.3544	0.3433	0.3050	0.3001	0.2860	0.2684	0.3225	0.3144
16.	Luxembourg	0.4813	0.4935	0.4938	0.4941	0.4873	0.4769	0.4531	0.4497	0.4787
17.	Hungary	0.4946	0.5124	0.5118	0.5107	0.4983	0.4956	0.4452	0.4245	0.4866
18.	Malta	0.4154	0.4851	0.4809	0.4668	0.4588	0.4413	0.4057	0.3976	0.4440
19.	The Netherlands	0.5136	0.5372	0.5350	0.5243	0.5139	0.5091	0.4775	0.4190	0.5037
20.	Austria	0.6194	0.6287	0.6166	0.6171	0.5970	0.5938	0.5491	0.4905	0.5890
21.	Poland	0.5372	0.5536	0.5454	0.5399	0.5454	0.5310	0.4965	0.4521	0.5251
22.	Portugal	0.451	0.4937	0.4847	0.4692	0.4578	0.4459	0.4219	0.4011	0.4549
23.	Romania	0.6056	0.6201	0.6251	0.6195	0.6090	0.5608	0.5115	0.4470	0.5748
24.	Slovenia	0.5712	0.5902	0.5856	0.5791	0.5849	0.5681	0.5374	0.5015	0.5648
25.	Slovakia	0.5320	0.5480	0.5370	0.5255	0.4888	0.4912	0.4515	0.4006	0.4968
26.	Finland	0.5992	0.6079	0.5948	0.5821	0.5818	0.5637	0.5291	0.5063	0.5706
27.	Sweden	0.6943	0.6980	0.6920	0.6780	0.6807	0.6536	0.6429	0.6130	0.6691
MIN		0.2791	0.3277	0.3299	0.3050	0.3001	0.2827	0.2684	0.2809	0.3064
MAX		0.6943	0.6980	0.7036	0.6832	0.6807	0.6536	0.6429	0.6130	0.6691
${\overline{x}}_j$		0.5020	0.5266	0.5255	0.5170	0.5128	0.4925	0.4620	0.4296	0.4960
$V_j$		21.2%	19.4%	19.1%	19.4%	19.1%	19.7%	20.6%	19.3%	19.4%

). It is evident that this situation was influenced by two major global events: the emergence of the Coronavirus pandemic and the Russian invasion of Ukraine in early 2022.

The second area of research focused on environmental sustainability. The study demonstrates that there is also territorial differentiation in the level of environmental sustainability in the EU. The mean value of the environmental sustainability measure ($S_{Y_i}$) from 2015 to 2022 was 0.7131. The highest level of $S_{Y_i}$ was observed in countries such as Estonia (0.8335), Sweden (0.8026), Austria (0.7975), and Finland (0.7669). The lowest levels were observed in countries such as Cyprus (0.5003), Portugal (0.5411), Malta (0.5818), and Romania (0.6472). The $S_{Y_i}$ measures in countries exhibiting the highest values were found to be approximately 1.5 times higher than in countries demonstrating the lowest values (Figure 2).

The data presented (Figure 2) herein indicate a high level of variation in environmental sustainability across the EU, although this variation is less pronounced than that observed in energy sustainability (Figure 1). Moreover, the period under review (2015–2022) demonstrates neither a positive nor a negative trend. In all EU countries, synthetic measures exhibit fluctuations, increasing and then decreasing (or vice versa) in subsequent years, without any discernible trend (

Table 3. The value of the $S_{Y_i}$ measure in EU countries in 2015–2022 (source: authors’ computation).

	EU Countries	2015	2016	2017	2018	2019	2020	2021	2022	2015–2022
1.	Belgium	0.7571	0.7643	0.7509	0.7112	0.7720	0.7441	0.6000	0.7362	0.7295
2.	Bulgaria	0.5623	0.6658	0.6433	0.5980	0.6723	0.6706	0.6909	0.6784	0.6477
3.	Czechia	0.7561	0.7557	0.7391	0.7244	0.7865	0.7792	0.7859	0.7530	0.7600
4.	Denmark	0.7599	0.7636	0.7539	0.7266	0.7820	0.7784	0.7956	0.7650	0.7656
5.	Germany	0.7441	0.7428	0.7337	0.5949	0.7343	0.7790	0.7186	0.7249	0.7215
6.	Estonia	0.8018	0.8003	0.7846	0.7966	0.8676	0.8733	0.8928	0.8509	0.8335
7.	Ireland	0.6873	0.7093	0.6830	0.6724	0.7158	0.6914	0.7328	0.7017	0.6992
8.	Greece	0.7007	0.6945	0.6444	0.6400	0.7156	0.6212	0.7521	0.7387	0.6884
9.	Spain	0.7483	0.7607	0.7390	0.6946	0.7274	0.7309	0.7847	0.6730	0.7323
10.	France	0.7843	0.7872	0.7586	0.7326	0.7694	0.7256	0.8122	0.7455	0.7644
11.	Croatia	0.6976	0.7147	0.7011	0.6144	0.7372	0.7201	0.7540	0.7160	0.7069
12.	Italy	0.7712	0.7995	0.7017	0.6682	0.7760	0.7594	0.8275	0.6507	0.7443
13.	Cyprus	0.5268	0.4122	0.5047	0.4167	0.5321	0.5371	0.5535	0.5192	0.5003
14.	Latvia	0.7461	0.7541	0.6556	0.6945	0.7633	0.7744	0.7760	0.7474	0.7389
15.	Lithuania	0.7377	0.7421	0.6780	0.7022	0.7497	0.7526	0.7623	0.7368	0.7327
16.	Luxembourg	0.7106	0.6599	0.7032	0.6732	0.5957	0.7078	0.7106	0.7235	0.6855
17.	Hungary	0.7027	0.7178	0.6997	0.6762	0.7322	0.7247	0.7411	0.6447	0.7049
18.	Malta	0.5794	0.5793	0.5633	0.5292	0.5972	0.5924	0.6307	0.5831	0.5818
19.	The Netherlands	0.7523	0.7369	0.7372	0.7065	0.7594	0.7462	0.7848	0.7424	0.7457
20.	Austria	0.8144	0.8163	0.7667	0.7562	0.8018	0.8076	0.8256	0.7914	0.7975
21.	Poland	0.7247	0.7222	0.7006	0.6518	0.7188	0.7111	0.7223	0.7034	0.7069
22.	Portugal	0.5357	0.5529	0.4642	0.6381	0.5266	0.5309	0.5760	0.5046	0.5411
23.	Romania	0.5432	0.6759	0.6401	0.6150	0.6770	0.6512	0.7007	0.6744	0.6472
24.	Slovenia	0.7580	0.7591	0.7269	0.7406	0.7383	0.7725	0.7879	0.7362	0.7524
25.	Slovakia	0.7469	0.7500	0.7328	0.6990	0.7774	0.7840	0.7909	0.7763	0.7572
26.	Finland	0.7620	0.7559	0.7525	0.7434	0.7834	0.7747	0.7942	0.7694	0.7669
27.	Sweden	0.7975	0.7957	0.7914	0.7794	0.8109	0.8105	0.8298	0.8052	0.8026
MIN		0.5268	0.4122	0.4642	0.4167	0.5266	0.5309	0.5535	0.5046	0.5003
MAX		0.8144	0.8163	0.7914	0.7966	0.8676	0.8733	0.8928	0.8509	0.8335
${\overline{x}}_j$		0.7114	0.7181	0.6944	0.6739	0.7267	0.7241	0.7457	0.7108	0.7131
$V_j$		11.8%	12.0%	12.2%	11.5%	11.9%	11.2%	11.2%	10.9%	11.1%

). This situation may be indicative of an absence of effective EU policy in the area of environmental sustainability.

The creation of rankings of EU member states was based on the values of the $S_{X_i}$ and $S_{Y_i}$ measures, with the objective of evaluating the states’ levels of energy and environmental sustainability.

Figure 3 presents a ranking of EU countries based on the average value of the synthetic measure of energy sustainability for the years 2015–2022. During the period under review, the highest positions in the ranking and the highest level of energy sustainability were achieved by Sweden, Denmark, Estonia, Latvia, and Austria. Sweden’s dominant position is, among other things, a consequence of having the highest share of energy from renewable sources in gross final energy consumption compared to other EU countries. Sweden is also distinguished by a low share of the population unable to adequately heat their homes due to poverty. The last positions in the ranking and the lowest level of energy sustainability were achieved by Cyprus, Lithuania, Bulgaria, Greece, and Malta. The lowest average value of the energy sustainability measure for Cyprus results from a very high energy dependency rate and a significant share of the population unable to adequately heat their homes due to poverty (Figure 3).

Table 4. Ranking of EU countries by $S_{X_i}$ in 2015–2022 (source: authors’ computation).

	EU Countries	2015	2016	2017	2018	2019	2020	2021	2022	2015–2022
1.	Belgium	21	21	22	22	21	21	20	21	20
2.	Bulgaria	25	26	27	26	25	25	25	24	25
3.	Czechia	10	10	11	11	11	12	12	10	11
4.	Denmark	2	2	1	1	2	2	2	2	2
5.	Germany	13	14	13	12	13	18	13	16	14
6.	Estonia	3	3	5	4	3	6	3	3	3
7.	Ireland	17	12	10	10	10	5	5	5	9
8.	Greece	26	27	24	24	24	24	24	26	24
9.	Spain	15	16	14	17	15	20	23	23	18
10.	France	12	11	12	13	14	13	15	22	13
11.	Croatia	7	8	8	9	9	10	9	9	10
12.	Italy	22	23	23	23	20	17	19	17	21
13.	Cyprus	27	25	26	25	26	27	26	27	27
14.	Latvia	8	5	4	3	5	4	4	4	4
15.	Lithuania	24	24	25	27	27	26	27	25	26
16.	Luxembourg	19	20	19	19	19	19	16	12	19
17.	Hungary	18	18	18	18	17	15	18	14	17
18.	Malta	23	22	21	21	22	23	22	20	23
19.	The Netherlands	16	17	17	16	16	14	14	15	15
20.	Austria	4	4	6	6	6	3	6	8	5
21.	Poland	11	13	15	14	12	11	11	11	12
22.	Portugal	20	19	20	20	23	22	21	18	22
23.	Romania	5	6	3	5	4	9	10	13	6
24.	Slovenia	9	9	9	8	7	7	7	7	8
25.	Slovakia	14	15	16	15	18	16	17	19	16
26.	Finland	6	7	7	7	8	8	8	6	7
27.	Sweden	1	1	2	2	1	1	1	1	1

presents the detailed position of EU countries in the energy sustainability ranking in the individual years of the period under review. The data presented in this table shows that in each year of the period under review, most EU countries held similar positions in the energy sustainability ranking. For the majority of countries, the change in position compared to the previous year was minimal, with only a one-to-two-place shift observed. Ireland witnessed a substantial enhancement in its ranking (from 17th position in 2015 to 5th position in 2020–2022). This is primarily a consequence of significant improvements in energy efficiency, a reduction in final energy consumption in households per capita, and a decrease in the proportion of the population unable to adequately heat their homes due to poverty. Furthermore, Latvia, Luxembourg and Italy experienced an increase of at least four places. In Latvia, this was achieved by increasing energy efficiency and reducing the proportion of the population unable to heat their homes adequately due to poverty. The most significant declines in ranking were observed in Spain, Romania, and Austria. In the case of Spain, worrying changes can be observed in two indicators: a decrease in energy efficiency and an increase in energy poverty. Germany has demonstrated a marked fluctuation in its position in comparison with the previous year—in 2020, its position deteriorated by five places, and then underwent an improvement of five places in 2021. This was primarily due to changes in the percentage of the population unable to adequately heat their homes due to poverty. Poland experienced a decline in its position during the 2015–2017 period (a drop of four places), followed by a systematic improvement, and in 2020–2022, it returned to its 11th place position, which it had held in 2015 (Table 4).

Figure 4 presents the ranking of EU countries based on the average value of the synthetic measure of environmental sustainability for the years 2015–2022. During the period under review, Estonia, Sweden, Austria, Finland, and Denmark achieved the highest positions in the ranking and the highest level of environmental sustainability. Estonia’s high ranking is the result of a low rate of premature deaths caused by exposure to particulate matter, one of the highest shares of green space, and a significant rate of material circulation. Estonia’s success stems from a consistent, long-term climate strategy. Since regaining its independence, the country has reduced its carbon footprint by 60% while developing one of Europe’s most digitised and efficient economies. Cyprus, Portugal, Malta, Romania, and Bulgaria achieved the lowest positions in the ranking and the lowest level of environmental sustainability. Water resources are a major problem in Cyprus. In the context of sustainable environmental development, it is essential to take into account the Water Exploitation Index (WEI+), as it serves as an indicator of water scarcity. This index provides information on the level of pressure exerted by human activity on the natural water resources of a given territory. The Water Exploitation Index (WEI+) is an indicator that quantifies total water consumption as a percentage of renewable freshwater resources available in a given territory and period. Values above 20% are generally considered a sign of water scarcity, while values equal to or greater than 40% indicate severe water scarcity, meaning that freshwater consumption is unsustainable. In 2022, the WEI+ index in the EU reached 5.8%, the highest since 2015. This represents an increase of 1.16 percentage points compared to 2015 statistics. Cyprus (71%), Malta (34.1%), and Romania (21%) experience the most severe water scarcity, indicating unsustainable freshwater consumption. Portugal’s distant position in the ranking is significantly determined by the unfavourable industrial air emissions intensity index.

The detailed position of EU countries in the environmental sustainability ranking in the subsequent years of the period under review is presented in

Table 5. Ranking of EU countries by $S_Y$ in 2015–2022 (source: authors’ computation).

	EU Countries	2015	2016	2017	2018	2019	2020	2021	2022	2015–2022
1.	Belgium	9	6	7	9	9	14	25	14	15
2.	Bulgaria	24	23	23	24	23	22	23	20	23
3.	Czechia	10	11	8	8	4	5	10	7	7
4.	Denmark	7	7	5	7	6	7	6	6	5
5.	Germany	15	14	11	25	16	6	20	15	16
6.	Estonia	2	2	2	1	1	1	1	1	1
7.	Ireland	22	20	19	17	20	21	18	19	20
8.	Greece	20	21	22	20	21	24	16	11	21
9.	Spain	12	8	9	13	18	15	12	22	14
10.	France	4	5	4	6	10	16	5	9	6
11.	Croatia	21	19	16	23	15	18	15	17	17
12.	Italy	5	3	15	18	8	11	3	23	11
13.	Cyprus	27	27	26	27	26	26	27	26	27
14.	Latvia	14	12	21	14	11	9	13	8	12
15.	Lithuania	16	15	20	11	13	12	14	12	13
16.	Luxembourg	18	24	14	16	25	20	21	16	22
17.	Hungary	19	18	18	15	17	17	17	24	19
18.	Malta	23	25	25	26	24	25	24	25	25
19.	The Netherlands	11	16	10	10	12	13	11	10	10
20.	Austria	1	1	3	3	3	3	4	3	3
21.	Poland	17	17	17	19	19	19	19	18	18
22.	Portugal	26	26	27	21	27	27	26	27	26
23.	Romania	25	22	24	22	22	23	22	21	24
24.	Slovenia	8	9	13	5	14	10	9	13	9
25.	Slovakia	13	13	12	12	7	4	8	4	8
26.	Finland	6	10	6	4	5	8	7	5	4
27.	Sweden	3	4	1	2	2	2	2	2	2

. The data presented in this table show that in the years 2015–2022 many countries recorded significant changes in their position. A divergent scenario is observable in the environmental sustainability rankings. Between 2015 and 2022, a considerable number of countries underwent substantial alterations in their respective rankings. The period 2020–2022 was characterised by particularly high levels of volatility, which may be attributable to the impact of the pandemic of Coronavirus (SARS-CoV-2) and the subsequent disorder and uncertainty it engendered. Between 2020 and 2022, almost half of the EU countries witnessed a substantial decline and subsequent rise in their rankings (or, conversely, an increase and then a decrease). For instance, Italy experienced an initial rise in its ranking by eight places, followed by a subsequent decline of up to 20 places. Analogous circumstances have been documented in a number of European countries, including Belgium, Germany, Greece, Spain, France, Latvia, Luxembourg, Hungary, the Czech Republic and Slovakia. The countries that have been assigned the highest (Estonia, Sweden and Austria) and lowest (Cyprus, Portugal and Malta) rankings have demonstrated a degree of stability in their respective positions (Table 5).

A divergent scenario is observable in the environmental sustainability rankings. Between 2015 and 2022, a considerable number of countries underwent substantial alterations in their respective rankings. The period 2020–2022 was characterised by particularly high levels of volatility, which may be attributable to the impact of the pandemic of Coronavirus (SARS-CoV-2) and the subsequent disorder and uncertainty it engendered. Between 2020 and 2022, almost half of the EU countries witnessed a substantial decline and subsequent rise in their rankings (or, conversely, an increase and then a decrease). For instance, Italy experienced an initial rise in its ranking by eight places, followed by a subsequent decline of up to 20 places. Analogous circumstances have been documented in a number of European countries, including Belgium, Germany, Greece, Spain, France, Latvia, Luxembourg, Hungary, the Czech Republic and Slovakia. The countries that have been assigned the highest (Estonia, Sweden and Austria) and lowest (Cyprus, Portugal and Malta) rankings have demonstrated a degree of stability in their respective positions.

In order to verify the aforementioned hypothesis, the relationship between synthetic measures of energy sustainability ($S_{X_i}$) and synthetic measures of environmental sustainability ($S_{Y_i})$ was measured. The Fama-MacBeth method, a tool for assessing relationships appropriate to the panel nature of the data, was utilised. In accordance with the criterion proposed by Fama and French, it can be concluded that the relationship between $S_{X_i}$ and $S_{Y_i}$ is significant (At a significance level of 0.05 for seven degrees of freedom, the value of the t statistic is 2.3639. Due to autocorrelation, the critical value of the t statistic is 2.3639 2.5 = 5.9097). The results of the calculations are presented in

Table 6. Student’s t test of the significance of the average Pearson linear correlation coefficient in 2015–2022 (source: authors’ computation).

	2015	2016	2017	2018	2019	2020	2021	2022
correlation coefficient (r) between $S_{X_i}$ and $S_{Y_i}$ in individual years	0.5341	0.5504	0.5520	0.5575	0.5390	0.5859	0.5405	0.5368
average r t-statistic (7)	0.5495 92.2833

.

5. Discussion

The interpretation of the study’s findings concerning the level of sustainable energy and environmental development in EU member states requires consideration of the regulatory framework within which national systems operate. EU energy and environmental policy is largely shaped by Community law, which defines the objectives, obligations, and mechanisms for implementing the principles of sustainable development.

The empirical results presented in the article are consistent with the objectives and assumptions of several key EU legal acts and strategic documents that provide the foundation for a sustainable energy and climate transition. The diagnostic indicators employed in the study—such as greenhouse gas emissions, the share of renewable energy in final energy consumption, energy efficiency, the share of organic farming, the use of recycled materials, and economic losses related to climate change—are closely aligned with the goals set out in EU legislation. They reflect the priorities of EU public policies on energy and climate transition, which aim not only to reduce emissions and improve efficiency but also to ensure a just economic transformation and enhance citizens’ quality of life. For this reason, EU legislation and strategic frameworks should be regarded as a key interpretative context for the study’s findings.

The level of sustainable energy and environmental development in individual member states is largely determined by the degree of implementation and effectiveness of EU regulations, as well as by each country’s specific socio-economic conditions (such as the level of economic development, living standards, climatic circumstances, and historical factors, including reliance on fossil-fuel-based energy systems or the timing of EU accession). The structure of national production systems also plays a significant role; for example, countries with a substantial agricultural sector tend to face greater difficulties in achieving environmental sustainability. Agriculture remains one of the most resource-intensive sectors—requiring extensive land, water, and energy use—and is the EU’s second-largest source of greenhouse gas emissions, accounting for approximately 11% of the total.

It should additionally be noted that the energy systems of most member states are still predominantly fossil-fuel based, although the share of renewable energy has been steadily increasing. The energy sector accounts for roughly 80% of total greenhouse gas emissions in the EU. Nevertheless, the study indicates a gradual convergence in net greenhouse gas emissions (expressed per capita) between EU countries. The findings suggest that member states which consistently implement adopted strategies and directives achieve higher values of the synthetic indicators, a conclusion corroborated by the empirical evidence. Conversely, countries with less advanced energy transitions—such as Bulgaria, Greece, and Cyprus—exhibit lower levels of energy and environmental sustainability.

The transition process generates substantial costs, particularly in coal-dependent regions such as Poland, the Czech Republic, and Bulgaria. Excessively rapid decarbonization without appropriate compensatory mechanisms may lead to social marginalisation and increased resistance to climate policies. The principle of a just transition should therefore constitute an integral element of European energy and climate policy. Its objective is to ensure a balance between economic efficiency and social cohesion, thereby making the transformation process inclusive and responsive to the needs of all regions.

Current EU energy and environmental policy represents a complex, multidimensional system of measures designed to achieve climate neutrality, strengthen energy security, and support sustainable economic growth in the context of global environmental and geopolitical challenges. The analysis conducted in the study confirms substantial spatial disparities in the level of energy and environmental sustainability across EU member states. The highest indicator values were recorded in Northern and Western European countries—particularly Sweden, Denmark, Austria, and Finland—characterised by high technological advancement, well-developed energy infrastructure, and long-standing pro-environmental policies. In contrast, Southern and Eastern European countries such as Bulgaria, Cyprus, Malta, Greece, and Poland exhibit a considerably slower pace of modernization and decarbonization. This phenomenon, commonly referred to in the literature as the “energy development gap” [16], constitutes a major challenge for the coherence of EU climate policy. The findings of J. Brodny and M. Tutak [72], I. Siksnelyte-Butkiene et al. [15], and I. Pakere et al. [73] clearly indicate that the energy transition in the EU remains asymmetrical.

Northern and Western European countries have been more successful in integrating climate objectives with economic development, whereas Central and Eastern European countries face financial, structural, and social barriers that limit the pace of transformation. A comparative analysis of strategic documents and empirical data shows that despite a high degree of institutional and normative integration, the outcomes of EU policy implementation vary significantly, driven by differences in economic potential and national energy structures. Consequently, it is necessary to adapt energy and environmental policies to specific national conditions.

The results presented contribute to the broader debate on the need to design regionally differentiated climate policies and on the monitoring of progress in implementing the 2030 Agenda. They highlight the necessity of developing dedicated environmental and energy policies for groups of countries with similar levels of development. Effective energy policy should balance security, equity, and environmental sustainability. Emerging recommendations include promoting green investments, technologies, and innovations in countries with lower levels of energy and environmental sustainability to reduce disparities between them and the frontrunners, as well as further diversification of energy sources and strengthening the role of renewable energy.

6. Conclusions

The relevant literature includes studies that focus either on EU energy policy and the determinants of energy development in EU member states [74], or on EU environmental policy [75]. Many previous analyses have examined various aspects of EU energy and environmental policy using single indicators [76,77,78]. In contrast, the research presented in this paper is original in its use of a set of indicators to measure the degree of advancement in energy and environmental sustainability—indicators that have not been employed in earlier studies. Another advantage of the present study is its assessment of the interdependence between sustainable energy policy and sustainable environmental policy. The synthetic measures developed in the article enable an objective evaluation of these multidimensional phenomena.

This paper provides the most up-to-date research results, whereas existing studies on energy and environmental policy usually include data only up to 2022 [79]. Therefore, the findings presented herein expand the current state of knowledge and may contribute to monitoring progress in the implementation of sustainable development objectives within EU energy and environmental policy.

A notable strength of this study lies in its originality, particularly in its approach to environmental development. The authors do not consider environmental development solely from the perspective of climate-policy objectives, which constitutes an innovative contribution to the literature.

The authors’ research demonstrates that levels of energy sustainability and environmental sustainability vary across EU member states and that these dimensions are positively correlated. Moreover, the analysis indicates that in the most recent years of the study period, EU countries experienced adverse changes in energy sustainability, reflected in declining values of the synthetic measures. These unfavourable developments are clearly related to the disruptions caused by the COVID-19 pandemic and the subsequent invasion of Ukraine by the Russian Federation. The findings highlight the need for adjustments in the EU’s energy sustainability policy.

Energy is essential for work, security (including energy security), mitigating the adverse impacts of climate change, food production, and fostering prosperity and economic development [80]. The lack of a well-defined transformation strategy may lead to heightened risks related to system balancing and security maintenance. The ongoing transformation is driven both by societal expectations—manifested in the shift from fossil fuels toward renewable and low-emission energy sources—and by rising energy production costs. Consequently, energy transformation aims to align societal expectations (environmental and climate protection) with a strategy for economic growth and industrial development grounded in innovative technologies. Energy transition is therefore crucial for achieving three key objectives: ensuring security of supply, maintaining competitiveness, and supporting global environmental sustainability. It is also important to recognise the role of energy efficiency as a major catalyst for entrepreneurship and innovation.

A sustainable energy policy should ensure an adequate level of energy services for economic actors while complying with environmental requirements.

The authors’ analyses further indicate that, between 2015 and 2022, no clear positive or negative trends in environmental sustainability emerged: synthetic measures increased in some years and declined in others. This pattern may point to the absence of an effective EU policy in the area of sustainable environmental development.

The implemented energy policy should focus on [80,81]:

• achieving a uniform level of greenhouse gas emissions;
• reducing energy consumption through efficiency and sufficiency strategies;
• creating conditions for the development of renewable energy sources, which can at least partially replace fossil fuels while being environmentally safe and economically beneficial;
• ensuring more efficient and less harmful energy production, transmission, and distribution.

When discussing the research results, it is necessary to acknowledge the inherent limitations of the study. This study uses only comprehensive energy and environmental sustainability indicators published by Eurostat for the designated study period. This document does not address or adequately discuss all issues related to energy and environmental sustainability in EU countries; however, these have been the subject of previous publications by the authors [80,82,83].

It should also be emphasised that the selection of variables for individual countries is challenging and was primarily determined by the availability and completeness of statistical data in the Eurostat database. In their research, the authors employed the TOPSIS method, one of the multidimensional comparative analysis techniques, which enables a synthetic assessment of complex phenomena. However, the results obtained are highly sensitive to the choice and number of diagnostic variables, as well as to the methods of standardisation and aggregation applied. It is also worth noting that the period under review includes years marked by significant economic and political disruptions, such as the COVID-19 pandemic and the energy crisis triggered by Russia’s aggression against Ukraine. These events may have considerably influenced the analysed indicators, constituting an additional potential source of distortion in the results. Despite these limitations, the findings offer a valuable contribution to the analysis of sustainable energy and environmental development in the EU, providing a foundation for further research with a broader temporal and thematic scope.

Taking into account the identified limitations of the study, it is reasonable for future research to focus on deepening and broadening existing analyses in several key areas. First, it appears necessary to extend the scope of the investigation to encompass the social and economic dimensions of sustainable development. Such an approach would allow for a more comprehensive assessment of the progress made by EU member states in achieving the objectives of Agenda 2030, as well as for the identification of synergies and potential trade-offs among the social, economic, and environmental pillars. Second, it is advisable for future studies to employ alternative multi-criteria methods, including variable-weighting techniques such as the Analytic Hierarchy Process (AHP), Entropy, or CRITIC. These methods would enable a more accurate representation of the relative importance of individual diagnostic indicators and enhance the robustness of the resulting composite measures. Third, it is recommended to extend the temporal scope of the analyses and to incorporate dynamic approaches. Panel analyses that account for the time lags inherent in public policies and energy transition processes would facilitate a more complete understanding of the causal relationships between changes in energy and climate policies and subsequent environmental outcomes. Fourth, interregional comparisons, including analyses of countries outside the EU, should be undertaken. Such comparisons would make it possible to identify global patterns and determinants of sustainable development policy effectiveness, while also providing insights into the EU’s position relative to other world regions in the context of the transition toward a low-carbon economy. Fifth, future research could benefit from the inclusion of qualitative components, such as case studies and comparative analyses of specific national policies and strategies. Combining quantitative and qualitative methods would provide a deeper understanding of the institutional, political, and technological contexts in which energy and environmental transformation processes take place.

In summary, the proposed directions for further research could significantly contribute to advancing knowledge on the interdependencies between energy and environmental development and sustainable development policies within the EU. The incorporation of additional research perspectives will support the formulation of more precise recommendations for policymakers and the refinement of tools used to monitor progress in implementing sustainable development goals.