Assessment of Sustainable Energy Development in European Union—Correspondence Analysis

Abstract

The energy transition has now been recognised by European Union (EU) member states as a necessary condition for their long-term development. The process of energy transformation is predicated on the simultaneous implementation of the Sustainable Development Goals, which present a considerable challenge for modern economies and impose significant restrictions on their functioning. The objective of this article is to evaluate the transformation of EU member states in the field of sustainable energy development and to categorise them based on their alignment with Sustainable Development Goal No. 7 of the United Nations Agenda 2030, concerning affordable and clean energy, in 2015 and 2023. The monitoring of the progress of the energy transition, as well as the examination of its temporal trends and spatial characteristics, can provide a fundamental analysis framework for the strategic development of energy policy in the EU at national and regional levels. An important approach for this endeavour is the indicator-based assessment of trends. The correspondence analysis proposed in this study and the hierarchical classification, which was intended to help link categories in a two-dimensional space, have the potential to facilitate a more comprehensive assessment of the degree to which EU member states are developing sustainable energy. The study results confirmed significant heterogeneity among the EU-27 countries in terms of energy sustainability. In both 2015 and 2023, the groups of EU countries that achieved a certain level of energy sustainability were identified. However, it should be noted that the composition of each group changed in 2023 compared to 2015. Moreover, no group of EU countries was without its own particular strengths and weaknesses. The results provide opportunities for their interpretation, both in terms of analysing changes in individual indicators and in terms of the global assessment of sustainable development in individual countries. The adoption of an original research approach, divergent from previous studies, for the new timeframe contributes to the resolution of the research gap in empirical studies concerning the classification of EU member states in terms of SDG7 implementation.

1. Introduction

In recent years, a fundamental priority of the EU has been to reshape the socio-economic model in its member states towards a low-emission economy, ensuring sustainable energy development [1,2]. The consequence of these actions has been the development of a model in Europe in which economic growth is placed on an equal footing with climate neutrality, care for natural resources, and the principles of justice [3]. A significant proposal grounded in reality, aimed at effecting a paradigm shift in the way socio-economic development is conceptualised, was put forward by K. Raworth [4] in her monograph Doughnut Economics. The author challenges the prevailing notion of perpetual economic expansion, proposing an alternative paradigm in which economic activity is constrained by an “ecological ceiling” and a “social base”. However, she highlights the following dilemma: “No country has ever ended human deprivation without a growing economy. And no country has ever ended ecological degradation with one” [4] (p. 245).

Increased social awareness, geopolitical changes, emphasis on problems with access to resources, and the expectation of changes in the economic development paradigm are just some of the factors accelerating sustainable transformation processes [5,6]. The implementation of sustainable development principles is a subject that is currently attracting a great deal of interest from the research community [7,8,9,10,11,12]. It is imperative to acknowledge the pivotal role that transformations within the energy sector play in determining the efficacy of this process [13,14,15]. The most significant manifestation of these changes is a reduction in greenhouse gas emissions, as evidenced by a decreasing carbon footprint. This is primarily achieved through the process of energy transformation, which involves the replacement of fossil fuels with renewable sources of energy. Energy transformation is frequently considered synonymous with green transformation. In addition to the transformation of energy sources, this concept aims to ensure access to stable, sustainable, and modern energy at an affordable price for all, in accordance with Goal 7 of the 2030 Agenda. According to experts in the field, the transformation of the energy sector, with the aim of increasing the share of energy production from renewable sources, along with modernisation measures in the economy that improve energy efficiency, is one of the key processes leading to sustainable development [16,17,18,19,20,21,22,23,24,25]. The European Union is widely recognised as the preeminent global authority in the implementation of sustainable development principles. Energy policy has been a priority area of EU activities for many years, and in recent years, it has been gaining increasing importance [26,27,28,29,30,31,32,33]. The present actions being undertaken are closely related to the European Green Deal [26,34] and are in line with the implementation of the assumptions of the 2030 Agenda (Goal 7: “Clean and affordable energy”), adopted in 2015 by the UN General Assembly [35]. This has resulted in highly ambitious sustainable development assumptions that focus on the transformation of the energy sector and achieving climate neutrality [15]. European energy policy is currently in a state of flux, with the introduction of regulatory mechanisms and incentives to achieve decarbonisation goals being a primary focus. This is in order to ensure energy security in the most cost-effective manner possible, taking into account economic competitiveness and technological development. This includes the generation of energy from renewable sources, increasing energy efficiency, and reducing greenhouse gas emissions [30]. The EU’s actions for rational energy management, which take into account the requirements of environmental protection and combating climate change, are consistent with the principle of sustainable development and also constitute a basis for economic integration processes in Europe.

The quest for sustainable and reliable energy supplies represents a substantial domain of scientific research [36,37,38,39,40,41,42,43]. The principal objective of such studies is to analyse activities and evaluate energy systems with regard to their impact on the economy, society, and the environment. Research on sustainable energy development and its level has been conducted in various countries [44,45,46,47,48,49,50,51,52,53] and groups of countries [54,55,56,57,58,59,60,61,62,63,64,65,66,67,68]. However, a paucity of studies assessing the level of sustainable energy development in the EU in recent years has been observed in the literature. The objective of this article is to evaluate the transformation of EU member states in the field of sustainable energy development and to categorise them based on their alignment with Sustainable Development Goal No. 7 of the United Nations Agenda 2030, concerning affordable and clean energy, in 2015 and 2023. The cognitive objective is to ascertain the factors determining sustainable energy development in EU countries. The application’s objective is twofold: firstly, to ascertain the directions of development of EU activities in pursuit of sustainable energy transformation, and secondly, to formulate recommendations in this regard for economic practice. The impetus for the study was the mounting significance of energy transformation for sustainable development, as well as the necessity for EU countries to transition to a safe, climate-neutral, climate-resilient, and more resource-efficient circular economy. Furthermore, the succession of crises in recent years, most notably Russia’s military incursion into Ukraine and the European energy crisis, has led to an increased emphasis on sustainable energy development among a diverse range of stakeholders [30]. The present article is founded upon a comprehensive review of the extant literature and quantitative research. The study employed the following two key analytical techniques: classical correspondence analysis for ranked data and hierarchical classification using Ward’s method. The EU member states were ranked based on individual features (variables). These ratings were then doubled to obtain anti-feature ranks [69]. The analysis was the basis for identifying the position of EU countries in the co-occurrence space of variables characterising sustainable energy development. Ward’s method enabled the identification of relationships between feature categories in a two-dimensional space. The article adopts an interdisciplinary approach due to the multidimensional nature of the transformation process towards sustainable energy development, providing an original, holistic approach to this issue. The research period covers 2015 and 2023. This is largely due to the adoption of “Transforming our world: the 2030 Agenda for Sustainable Development” in 2015, while the latest statistical data on the issues studied is from 2023. This is a pivotal moment for implementing regulations that support actions towards achieving a sustainable EU economy and mitigate the effects of climate change.

Energy is a pivotal component in the existence of modern civilisations, providing the basis for their functioning and development. The significance of the research topic is attributable to the pivotal role of the energy sector. Energy policy exerts a significant influence on the realisation of strategic interests, with its effectiveness manifesting not only in enhanced energy security, but also in augmented economic competitiveness. The conclusions drawn from the research conducted are of particular importance for those EU countries whose energy sector remains an unresolved issue regarding its future development. The results presented in this article constitute new knowledge that can facilitate a better understanding of the instruments used in energy policy and energy transformation processes by the most competitive economies in Europe, which constitutes the innovative nature of this research.

The article is divided into two sections. The initial section of the text presents a series of theoretical issues related to key aspects of sustainable energy transformation in the EU. The second part of the thesis characterises the research method used, presents the results of the conducted research, and refers to other studies, drawing conclusions and recommendations.

2. Literature Review

Sustainable Transformation of Energy Sector

The EU is a global leader in the transformation of energy, as evidenced by the significant and wide-ranging reforms it has implemented. The objective of these measures is to ensure that all citizens have access to sources of stable, sustainable, and modern energy at affordable prices [70]. In this particular context, sustainable energy is defined as energy that is produced and utilised, to the greatest extent possible, from renewable energy sources (RESs). This approach is underpinned by the principle of achieving net-zero emissions, with a particular focus on eliminating greenhouse gases (GHGs) from the atmosphere [15]. The EU energy policy, as articulated in the framework strategy for the Energy Union, contributes to the transformation of the European energy sector. This is evident in the reduction in final energy consumption, the increase in the share of RESs and energy efficiency, the reduction in greenhouse gas emissions, and the growing importance of research and innovation [71]. The significance and relevance of the energy transition topic, in conjunction with its multidimensional nature, result in a broad spectrum of definitions [72,73]. Energy transformation is defined as a series of actions designed to achieve a given nation’s climate and energy objectives. This process entails the modification of the economy in such a manner that it becomes more sustainable. It is acknowledged that actions are implemented in all energy markets, including the electricity market, the heat market, and the fuel market. These actions result in alterations in the utilisation of primary energy carriers and qualitative changes within the technical infrastructure. Additionally, they establish conditions for enhanced levels of system efficiency and effectiveness [74]. P.A. O’Connor conceptualises the energy transition process as “a particularly significant set of changes to the patterns of energy use in a society, potentially affecting resources, carriers, converters, and services” [75] (p. 2). The notion of an “energy transition” can be defined as a historical shift in which a predominant energy source or group of sources that once dominated a market are superseded by new, dominant sources. This concept has been articulated by M. Melosi [76]. Whilst the modern definitions are accurate in historical terms, they also encompass a more extensive transformation than simply replacing one fuel source with another. Indeed, they focus on a structural shift in the energy system towards lower-emission RESs. Furthermore, as asserted by R. Fouquet and P. J. G. Pearson [77], the energy transition signifies a paradigm shift from an economic system that is dependent on particular energy sources and technologies to a different system. Similarly, R.F. Hirsh and C.F. Jones [78] characterise the energy transition as the process of transitioning between fuels and associated technologies. This perspective is echoed by C.A. Miller, J. Richter, and J. O’Leary [79], who view the energy transition as alterations in the fuel structure used in energy production and the technologies employed. The subject of energy transformation is intricate and encompasses numerous disciplines and domains that extend well beyond the energy sector. This field of research is interdisciplinary in nature, drawing upon the insights of technical, natural, and social sciences [80,81].

The EU’s evolving energy economy is marked by a transition from conventional energy carriers to RESs, a reduction in energy consumption through enhanced energy efficiency, and the establishment of a long-term competitive, inclusive, low-emission economy that fosters new employment opportunities [82]. Energy transformation is frequently considered synonymous with green transformation. In addition to modifying energy sources, it is also intended to ensure access to stable, sustainable, and modern energy at an affordable price for all, in accordance with Goal 7 of the 2030 Agenda [83].

Energy transformation is a pivotal component in the transition of the economy towards sustainability [58,84,85]. The process is predicated on profound vertical and horizontal economic changes, with due consideration for the natural environment and the principles of sustainable development. Presently, this process is a subject of keen anticipation in energy markets, with the potential to yield multifarious benefits. These include, but are not limited to, energy savings and enhanced generation efficiency. Of particular significance, however, is the ability to reduce CO₂ emissions and augment the share of RESs in the energy mix [74].

The functioning of energy markets, which constitute a significant domain of economic activity, determines not only the competitiveness of individual economic sectors but, above all, energy security. The transition from hydrocarbons to low-emission energy carriers is expected to stimulate technological advancements, including enhanced energy and equipment efficiency [86]. As G. Wojtkowska-Łodej [87] asserts, advancements in technology (in particular, production and supporting technologies) are poised to play a pivotal role in the implementation of a novel economic paradigm predicated on clean energy technologies, ensuring both safety and affordability in energy provision. The propensity to adopt novel solutions may be derived from economic processes, bolstered by sound financial calculations, as well as from policies that may be conservative or oriented towards facilitating a green and equitable transition [88]. Contemporary energy markets are confronted with the necessity of allocating substantial investment funds to the development of novel technologies and technical solutions that facilitate the transition to alternative energy carriers. The increased utilisation of RESs is congruent with the climate policy framework implemented by EU countries, as well as with the introduction of green deal programs [74].

The EU’s energy transformation is the result of the development strategy adopted in 2019, included in the European Green Deal. This strategy assumes the building of a highly competitive, resource-efficient, and zero-emission economy, which is crucial for the long-term development of the EU [89]. The core of the process under discussion is the reduction in greenhouse gas emissions. The objective is a 55% decrease in emissions by 2030 compared to 1990, with the aim of achieving net-zero emissions by 2050. Furthermore, the global temperature increase is to be limited to below 2 °C (ultimately to 1.5 °C) compared to the pre-industrial era. These objectives are part of the Paris Agreement goals [90,91]. These objectives are driven by two primary motivations. Firstly, there is a profound concern for the quality of the natural environment, coupled with a determination to mitigate the adverse effects of human economic activity. Secondly, there is an acknowledgement of the imperative to reduce the risks and damage posed by climate change, which is impacting this activity [92].

It is important to note that global warming is not simply a phenomenon that increases the average air temperature. Indeed, it also has specific economic consequences. Projections indicate that climate change will result in a 75% decline in the income of 40% of the lowest-income countries by 2100 compared to pre-industrial times. Conversely, a 0.01 °C per year reduction in the global temperature increase will limit the loss of global GDP per capita to 1% by 2100 [93] (p. 10). An important voice in the scientific discussion on global warming is W. Nordhaus, who proposed the model approach, which was appreciated by the Nobel Committee when they honoured its author in 2018 with the Bank of Sweden Prize in Economics in memory of Alfred Nobel “for incorporating climate change into long-term macroeconomic analyses.” In 2013, W. Nordhaus [94] (pp. 3–4) wrote the following: “… global warming is a major threat to humans and the natural world. I will use the metaphor that we are entering the Climate Casino. By this, I mean that economic growth is producing unintended but perilous changes in the climate and earth systems. These changes will lead to unforeseeable and probably dangerous consequences. We are rolling the climatic dice, the outcome will produce surprises, and some of them are likely to be perilous. But we have just entered the Climate Casino, and there is time to turn around and walk back out.” As W. Nordhaus [94] asserts, it is challenging to achieve a favourable outcome in the long term when engaging with the climate change issue. Of particular concern are the so-called tipping points, as identified by scientists. These are defined as irreversible changes occurring in various Earth systems after certain thresholds are crossed. This encompasses the accelerated melting of substantial glaciers and ice sheets (for example, in Greenland), substantial alterations in ocean currents (for example, the Gulf Stream), feedback loops between the climate, the state of the biosphere, and the carbon cycle, the intensification of temperature increases in the so-called fast carbon cycles (measured in seasons and years), and increased long-term warming in the slow carbon cycle (measured on a geological timescale). The sheer scale of these processes renders them beyond the capacity of current human capabilities [88]. The Dynamic Integrated Climate-Economy (DICE) model, which integrates neoclassical economics, the carbon cycle, climate science, and estimated impacts, was designed by W. Nordhaus. This model enables the weighing of subjectively predicted costs and subjectively predicted benefits of taking steps to slow climate change. Furthermore, W. Nordhaus developed the Regional Integrated Climate-Economy (RICE) model, a variant of the DICE model. A forerunner to DICE was a linear programming model of energy supply and demand, also developed by W. Nordhaus. It has been established that, in accordance with climate models, an increase in the rate of change in emissions of CO₂ and other GHGs will result in an increase in the accumulation of these gases in the atmosphere. It is estimated that an average annual increase of 1.2% would result in a 3.3-fold increase in emissions over the course of a century. The disparities between model projections of emissions in 2100 are between 1.6 and 5.4 times greater than the disparities between the emission estimates in 2000. These models represent the best efforts of economic and energy experts today, and they indicate that the CO₂ problem is not going to disappear or be magically solved by unrestrained market forces [94] (p. 34). This discrepancy can be attributed to divergent assessments of the future values of the primary determinants of emissions, including the rate of economic growth, changes in population size, technological advances, and energy consumption [94].

Energy is recognised as one of the most significant drivers of development and is a key source of competitiveness for services and manufactured goods. The changes taking place as part of the energy transformation are intended to reduce energy poverty, bridge the gap between regions and areas with varying degrees of urbanisation, and ultimately contribute to social development [74]. It is imperative to emphasise that the effective execution of energy transformation necessitates, above all, comprehensive monitoring of the progress of its implementation [64,95,96,97,98,99,100,101,102,103,104,105,106].

3. Materials and Research Methods

The study employs correspondence analysis, a multivariate statistical method that examines the co-occurrence of qualitative variable categories. This method is primarily employed in the following two instances: firstly, when dealing with two nominal variables of different categories (simple correspondence analysis), and secondly, when dealing with more than two nominal variables of different categories (multiple correspondence analysis). Correspondence analysis is a method that can be utilised to ascertain the co-occurrence of variables and objects. It is evident that the potential applications of correspondence analysis extend to the analysis of small samples. In such cases, a complex marker matrix can be utilised as the foundation for calculations, or alternatively, the observation doubling procedure can be employed [107]. The present article conducts classic correspondence analysis for a small sample using the observation doubling procedure [69].

The research was conducted based on indicators used by Eurostat to monitor the EU’s progress in implementing the 2030 Agenda. An analysis and assessment of the sustainable energy development of the 27 EU Member States in 2015 and 2023 was conducted. The evaluation of the situation in the domain of sustainable energy development was grounded in indicators that appraise progress in achieving Sustainable Development Goal 7 (SDG 7) “Affordable and clean energy” (

Table 1. Values of diagnostic variables in EU countries (source: own study based on Eurostat data).

	2015							2023
	E1	E2	E3	E4	E5	E6	E7	E1	E2	E3	E4	E5	E6	E7
AT	3.66	3.00	767	9.12	33.50	60.38	2.6	3.21	2.65	721	10.93	40.84	61.05	3.9
BE	4.05	3.06	730	6.28	8.06	84.23	5.2	3.57	2.66	591	7.84	14.74	76.10	6.0
BG	2.57	1.36	314	5.06	18.26	36.45	39.2	2.57	1.49	317	2.94	22.55	39.72	20.7
HR	1.92	1.59	585	8.33	28.97	48.79	9.9	2.21	1.84	577	6.68	28.05	55.72	6.2
CY	2.63	1.94	376	7.65	9.90	97.32	28.3	2.63	1.95	351	9.48	20.21	92.20	16.9
CZ	3.74	2.21	642	6.15	15.07	32.09	5.0	3.27	2.08	591	5.23	18.59	41.68	6.1
DK	2.96	2.49	782	11.08	30.47	13.08	3.6	2.58	2.25	698	18.81	44.40	38.87	6.9
EE	3.62	2.13	652	5.47	28.99	11.18	2.0	3.00	1.90	683	4.64	40.95	3.47	4.1
FI	5.69	4.32	904	5.05	39.23	47.90	1.7	5.61	3.98	982	6.10	50.75	29.57	2.6
FR	3.67	2.18	601	7.47	14.80	45.89	5.5	3.06	1.90	531	10.23	22.28	44.87	12.1
DE	3.62	2.54	673	8.89	14.90	62.13	4.1	2.85	2.25	635	11.84	21.56	66.38	8.2
GR	2.16	1.53	412	8.00	15.69	71.05	29.2	1.91	1.51	363	8.94	25.27	75.60	19.2
HU	2.38	1.74	609	7.53	14.49	53.88	9.6	2.31	1.75	560	5.69	17.12	62.06	7.2
IE	2.96	2.41	605	16.89	9.08	88.65	9.0	2.65	2.26	479	26.16	15.25	77.90	7.2
IT	2.48	1.91	540	10.27	17.52	77.03	17.0	2.29	1.84	468	11.80	19.59	74.81	9.5
LV	2.16	1.92	559	7.40	37.54	51.18	14.5	2.27	2.08	574	5.66	43.22	32.73	6.6
LT	1.99	1.67	467	8.31	25.75	75.45	31.1	2.21	1.85	533	6.02	31.93	68.04	20.0
LU	7.27	7.00	894	10.57	4.99	95.96	0.9	5.48	5.27	686	15.08	14.35	90.62	2.1
MT	1.69	1.31	180	5.34	5.12	97.30	14.1	1.65	1.30	206	4.52	15.08	97.55	6.8
NL	3.78	2.78	563	7.02	5.71	48.37	2.9	3.01	2.29	431	10.46	17.42	70.45	7.1
PL	2.37	1.62	501	7.67	11.88	29.85	7.5	2.54	1.90	542	5.69	16.56	48.02	4.7
PT	2.09	1.54	265	9.10	30.51	76.30	23.8	1.96	1.63	285	8.98	35.16	66.87	20.8
RO	1.55	1.10	372	9.65	24.78	16.69	13.1	1.57	1.22	396	6.35	25.76	27.86	12.5
SK	2.91	1.76	474	6.98	12.88	58.01	5.8	2.81	1.68	447	5.48	16.99	57.73	8.1
SI	3.08	2.29	568	7.08	22.88	49.26	5.6	2.79	2.11	490	7.72	25.07	49.27	3.6
ES	2.55	1.73	329	9.02	16.22	72.74	10.6	2.28	1.67	287	9.94	24.85	68.42	20.8
SE	4.47	3.18	756	7.03	52.22	30.07	1.2	3.93	2.87	669	10.07	66.39	26.39	5.9

AT—Austria. BE—Belgium. BG—Bulgaria. HR—Croatia. CY—Cyprus. CZ—Czechia. DK—Denmark. EE—Estonia. FI—Finland. FR—France. DE—Germany. GR—Greece. HU—Hungary. IE—Ireland. IT—Italy. LV—Latvia. LT—Lithuania. LU—Luxembourg. MT—Malta. NL—Netherlands. PL—Poland. PT—Portugal. RO—Romania. SK—Slovakia. SI—Slovenia. ES—Spain. SE—Sweden.

).

• Primary energy consumption (tonnes of oil equivalent per capita)—E1,
• Final energy consumption (tonnes of oil equivalent per capita)—E2,
• Final energy consumption in households per capita (kilogram of oil equivalent)—E3,
• Energy productivity (PPS per kilogram of oil equivalent)—E4,
• Share of renewable energy in gross final energy consumption by sector (%)—E5,
• Energy import dependency by products (%)—E6,
• Population unable to keep home adequately warm by poverty status (%)—E7.

The research procedure included the following stages:

1. Determining the nature of the variables.

Stimulants: E4. E5. Destimulants: E1. E2. E3. E6. E7.

2. Assigning ranks (+) and anti-ranks (−) to the values of the studied variables.

For stimulants, the highest rank was assigned to the highest variable value, and the lowest rank was assigned to the lowest value. For destimulants, the ranking process was reversed. Anti-ranks were determined based on the complete opposite of the original ranks. If the same values occurred for the studied variable, the associated ranks and anti-ranks were used, calculated as the arithmetic mean of the ranks and anti-ranks that corresponded to these values in order [69] (p.110).

3. Conducting classical correspondence analysis for the rank and anti-rank matrices.

4. Conducting hierarchical classification using Ward’s method [108].

Based on the coordinate values of the EU country and energy sustainability categories, a hierarchical classification was performed using Ward’s method. Since the classification was intended to support the association of categories in two-dimensional space, the coordinates from the first and second axes for each variant of both characteristics were considered variables.

5. Formulating a diagnosis of the situation of EU countries in terms of energy sustainability based on a graphical presentation of the results of the correspondence analysis and Ward’s method.

Calculations and graphical presentation of the results were performed using Statistica 13.3.

4. Research Results and Discussion

The analysis of the obtained empirical material will begin with a discussion of individual indicators in a one-dimensional space using a dynamic approach, indicating changes between 2015 and 2023 (Table 1). The first indicator analysed, Primary Energy Consumption (E1), refers to the total amount of energy consumed within a specific area (such as a country) from all sources. This includes energy used by the energy sector itself, losses during energy transformation and distribution, and the energy used by end consumers. Following the Second World War, energy consumption grew rapidly due to population growth and global economic growth. Regrettably, this has had a detrimental effect on the environment, contributed to climate change, and exacerbated the increasingly severe effects of global warming. Primary energy consumption is a key indicator of economic development. It is, therefore, essential for a country to consume increasing amounts of primary energy in order to develop. It is possible to mitigate the negative effects of economic development resulting from rising energy consumption by shifting from fossil fuels to more sustainable energy sources. It is vital that efforts are made to increase the share of RESs in the overall energy mix. This is to ensure that the increased demand for primary energy, driven by economic development, does not lead to increased consumption of non-renewable energy sources [109]. In 2023, primary energy consumption in the EU reached 1211 million tonnes of oil equivalent (Mtoe). This represents a 3.9% decline compared to 2022 and is the lowest level since data collection began. In 2023, only five EU countries (Hungary, Lithuania, Latvia, Poland, and Romania) saw an increase in this indicator compared to 2015, but only Hungary did not record an increase in the share of renewable energy in gross final energy consumption by sector (%). Although primary energy consumption remains high compared to the 2030 targets (the EU’s primary energy consumption should amount to no more than 992.5 Mtoe), the observed trend suggests that these goals can be achieved with continued determination and the implementation of innovative renewable energy solutions. The next indicator to be analysed is final energy consumption in tons of oil equivalent per capita (E2). This is an important tool for analysing and comparing energy consumption levels across different regions worldwide, enabling the assessment of energy consumption structures and the planning of energy policies. In 2023, final energy consumption in the EU totalled 894 Mtoe, representing a 3.0% decrease compared to the previous year. This value represents a mere 0.3% increase compared to the record low levels recorded in 2020 [110]. According to the latest data, the current EU energy consumption stands at 17.2 percentage points higher than the 2030 final energy target of 763 Mtoe. Luxembourg and the Netherlands experienced the most significant reductions, with Luxembourg reducing by one-quarter (27.42%) and the Netherlands by 15.46%. This commitment reflects a growing dedication among EU countries to enhancing energy productivity and pursuing sustainable development. In nine EU countries, there was an increase in final energy consumption in tonnes of oil equivalent per capita in 2023 compared to 2015, with Croatia experiencing a particularly high 22% reduction (Table 1). The EU aims to achieve a 42.5% share of renewable energy in final energy consumption by 2030, with the potential to raise this target to 45%. The assessment of final energy consumption in households per capita (kilogram of oil equivalent) (E3) indicates a 3% decline in 2023 compared to 2015 across EU countries, indicating that households are becoming increasingly energy efficient. In 2023, Finland, Austria, and Denmark had the highest final energy consumption in households per capita. Households account for approximately 25% of final energy consumption, though this varies significantly across EU countries. In 2023, the highest share was recorded in Croatia (37%), and the lowest in Luxembourg (13%). In EU households, most final energy consumption was covered by natural gas (33.5%) and electricity (24.6%). In 2023, renewable sources accounted for 24.6% of the energy consumed in the EU. Energy productivity (PPS per kilogram of oil equivalent) (E4) plays a key role in decarbonisation, competitiveness, and resource optimisation. Achieving these goals requires the mobilisation of public and private funds, as well as the development of models to support its financing. Energy productivity is defined as the most efficient and cost-effective approach to enhancing energy security, environmental sustainability, and economic viability. European Union (EU) countries are encountering challenges in achieving their 2030 energy efficiency targets. Despite a decline in energy consumption in 2023, levels still exceed target limits by more than 17%. It is imperative to prioritise investment and public awareness in optimising energy, financial, and technological resources. The most favourable energy productivity index, measured by purchasing power parity (PPS) per kilogram of oil equivalent, was observed in Ireland (29.25), Romania (18.53), and Denmark (17.30) in 2023. The least favourable index was observed in Finland (6.66), Malta (7.17), and Bulgaria (9.07). An analysis of available statistical data indicates that disparities between individual EU countries are clearly increasing. The coefficient of variation was 25.4% in 2015 and increased to 34.4% in 2023. An important indicator for assessing the energy transition path is the share of energy from renewable sources in gross final energy consumption (%) (E5). In this regard, excluding Hungary, all EU countries experienced an increase in 2023 compared to 2015. The largest shares in 2023 were recorded in Sweden (66.39%), Finland (50.75%), and Denmark (44.40%), and the smallest in Luxembourg (14.36%), Belgium (14.74%), and Malta (15.07%) (Table 1). The EU’s 2030 goal is to increase the share of renewable energy in its overall energy consumption to 42.5%. The next change discussed is energy dependency (E6), which provides information on the share of imported energy in a given country’s total energy consumption, expressed as a percentage. In essence, it highlights the degree to which a nation relies on imported energy resources to satisfy its energy needs. As the indicator rises, so does the company’s reliance on foreign supplies. In 2023, the energy dependence rate of the 27 EU countries was 57.18%, indicating that over half of the EU’s energy needs were met through imports of energy resources. Europe was largely dependent on Russia for imports of natural gas, crude oil, and certain raw materials. However, in the context of the war in Ukraine, the EU took steps to reduce this dependence, including diversifying its energy supplies and introducing trade sanctions. In 2023, the EU successfully reduced its energy dependence on Russia, which is no longer its main supplier of energy products. The drop was most significant for solid fossil fuel imports, for which Russia’s share in total extra-EU imports decreased from 23.1% in 2022 to just 1.0% in 2023. Similarly, the share of imports of petroleum products from Russia fell from 21.1% to 3.6% [111]. Malta was the most dependent on energy imports in 2023—97.55%—while Estonia was the least dependent—3.47%. Denmark had a rate of 38.86%, which is below the EU average. However, in this country, this rate more than doubled in 2023 compared to 2015, and this was the greatest increase in energy dependence among the 27 EU countries between 2015 and 2023 (an increase of 25.78 percentage points from 13.09% to 38.86%). During the same period, the average energy dependence rate in the EU remained at a similar level (56.72% in 2015, 57.18% in 2023). The level of energy poverty among EU households can be assessed based on the inability to adequately heat their homes due to poverty (E7). The share of the population unable to adequately heat their homes in Europe decreased over the period under review (2015—11.22%, 2023—9.47%). However, in some countries, such as Spain, Lithuania, Portugal, and Bulgaria, a large proportion of the population still experiences this problem, approximately one in five residents. The most favourable situation is in Luxembourg, where only 2.1% of the population experiences energy poverty.

In the subsequent phase of the study, correspondence analysis was conducted for ranked data and hierarchical classification utilising Ward’s method.

Classical correspondence analysis was performed for rank and anti-rank matrices (

Table A1. Rank and anti-rank matrix for the studied variables in 2015 (source: own study).

	E1(+)	E1(−)	E2(+)	E2(−)	E3(+)	E3(−)	E4(+)	E4(−)	E5(+)	E5(−)	E6(+)	E6(−)	E7(+)	E7(−)
AT	8	20	5	23	4	24	22	6	24	4	12	16	23	5
BE	4	24	4	24	6	22	6	22	4	24	5	23	18	10
BG	16	12	25	3	25	3	2	26	16	12	21	7	1	27
HR	25	3	22	6	13	15	18	10	20	8	17	11	11	17
CY	15	13	14	14	22	6	14	14	6	22	1	27	4	24
CZ	6	22	11	17	9	19	5	23	12	16	22	6	19	9
DK	12	16	8	20	3	25	26	2	22	6	26	2	21	7
EE	9.5	19	13	15	8	20	4	24	21	7	27	1	24	4
FI	2	26	2	26	1	27	1	27	26	2	19	9	25	3
FR	7	21	12	16	12	16	12	16	10	18	20	8	17	11
DE	9.5	18	7	21	7	21	19	9	11	17	11	17	20	8
GR	21.5	7	24	4	21	7	16	12	13	15	10	18	3	25
HU	19	9	18	10	10	18	13	15	9	19	14	14	12	16
IE	13	15	9	19	11	17	27	1	5	23	4	24	13	15
IT	18	10	16	12	17	11	24	4	15	13	6	22	6	22
LV	21.5	6	15	13	16	12	11	17	25	3	15	13	7	21
LT	24	4	20	8	20	8	17	11	19	9	8	20	2	26
LU	1	27	1	27	2	26	25	3	1	27	3	25	27	1
MT	26	2	26	2	27	1	3	25	2	26	2	26	8	20
NL	5	23	6	22	15	13	8	20	3	25	18	10	22	6
PL	20	8	21	7	18	10	15	13	7	21	24	4	14	14
PT	23	5	23	5	26	2	21	7	23	5	7	21	5	23
RO	27	1	27	1	23	5	23	5	18	10	25	3	9	19
SK	14	14	17	11	19	9	7	21	8	20	13	15	15	13
SI	11	17	10	18	14	14	10	18	17	11	16	12	16	12
ES	17	11	19	9	24	4	20	8	14	14	9	19	10	18
SE	3	25	3	25	5	23	9	19	27	1	23	5	26	2

and

Table A2. Rank and anti-rank matrix for the studied variables in 2023 (source: own study).

	E1(+)	E1(−)	E2(+)	E2(−)	E3(+)	E3(−)	E4(+)	E4(−)	E5(+)	E5(−)	E6(+)	E6(−)	E7(+)	E7(−)
AT	6	22	5	23	2	26	22	6	22	6	14	14	24	4
BE	4	24	4	24	8.5	19.5	14	14	2	26	5	23	20	8
BG	16	12	25	3	24	4	1	27	14	14	21	7	3	25
HR	22.5	5.5	18.5	9.5	10	18	12	16	19	9	16	12	18	10
CY	14	14	13	15	23	5	17	11	11	17	2	26	6	22
CZ	5	23	11.5	16.5	8.5	19.5	4	24	9	19	20	8	19	9
DK	15	13	8.5	20.5	3	25	26	2	25	3	22	6	15	13
EE	9	19	15	13	5	23	3	25	23	5	27	1	23	5
FI	1	27	2	26	1	27	10	18	26	2	24	4	26	2
FR	7	21	15	13	15	13	20	8	13	15	19	9	8	20
DE	10	18	8.5	20.5	7	21	24	4	12	16	12	16	10	18
GR	25	3	24	4	22	6	15	13	17	11	6	22	5	23
HU	18	10	20	8	12	16	7.5	20.5	7	21	13	15	12.5	15.5
IE	13	15	7	21	17	11	27	1	4	24	4	24	12.5	15.5
IT	19	9	18.5	9.5	18	10	23	5	10	18	7	21	9	19
LV	21	7	11.5	16.5	11	17	6	22	24	4	23	5	17	11
LT	22.5	5.5	17	11	14	14	9	19	20	8	10	18	4	24
LU	2	26	1	27	4	24	25	3	1	27	3	25	27	1
MT	26	2	26	2	27	1	2	26	3	25	1	27	16	12
NL	8	20	6	22	20	8	21	7	8	20	8	20	14	14
PL	17	11	15	13	13	15	7.5	20.5	5	23	18	10	22	6
PT	24	4	23	5	26	2	16	12	21	7	11	17	1.5	26.5
RO	27	1	27	1	21	7	11	17	18	10	25	3	7	21
SK	11	17	21	7	19	9	5	23	6	22	15	13	11	17
SI	12	16	10	18	16	12	13	15	16	12	17	11	25	3
ES	20	8	22	6	25	3	18	10	15	13	9	19	1.5	26.5
SE	3	25	3	25	6	22	19	9	27	1	26	2	21	7

. Appendix A). In order to check whether there was a relationship between the analysed variables (features), a correlation analysis was performed. The chi-square independence test was used. The calculations showed that in 2015, chi-square = 1637.8, and in 2023 = 1638.32. The critical chi-square value (p = 0.01) for 338 degrees of freedom was 401.4. This means that the hypothesis of feature independence should be rejected, concluding that sustainable energy development depends on the EU country. Based on the chi-square statistic, the strength of the relationship between the variables was determined using Pearson’s contingency coefficient C. The calculations showed that in both 2015 and 2023, Pearson’s C was 0.49, which means that the relationship between the characteristics is moderate.

The presentation of the results began with determining the dimension of the actual projection space (K) according to the following formula:

K = min (r − 1; c − 1)

where:

r—number of variants of the first variable,

c—number of variants of the second variable.

The actual point projection space has dimension K = 13. The generally accepted solution in correspondence analysis is to project the points onto a space of lower dimension. To determine the best projection space, the method of the degree of explanation of the total inertia by the principal inertias was used. The singular values (γ_k) and eigenvalues (λ_k) are presented in

Table 2. Singular values and eigenvalues with the degree of explanation of the total inertia (source: own study).

k	Singular Values (γk)		Eigenvalues (λk)		Percentage of Eigenvalue in Total Inertia		Cumulative Percentage Contribution of Eigenvalues to the Total Inertia
	2015	2023	2015	2023	2015	2023	2015	2023
1	0.398418	0.382717	0.158737	0.146472	51.29098	47.33053	51.2910	47.3305
2	0.259080	0.284988	0.067123	0.081218	21.68859	26.24456	72.9796	73.5751
3	0.219100	0.212963	0.048005	0.045353	15.51121	14.65534	88.4908	88.2304
4	0.148242	0.130021	0.021976	0.016906	7.10080	5.46280	95.5916	93.6932
5	0.084555	0.095646	0.007150	0.009148	2.31018	2.95609	97.9018	96.6493
6	0.069997	0.085449	0.004900	0.007302	1.58314	2.35942	99.4849	99.0087
7	0.039878	0.055331	0.001590	0.003062	0.51384	0.98929	99.9987	99.9980
8	0.001975	0.002479	0.000004	0.000006	0.00126	0.00199	100.0000	100.0000
9	0.000000	0.000000	0.000000	0.000000	0.00000	0.00000	100.0000	100.0000
10	0.000000	0.000000	0.000000	0.000000	0.00000	0.00000	100.0000	100.0000
11	0.000000	0.000000	0.000000	0.000000	0.00000	0.00000	100.0000	100.0000
12	0.000000	0.000000	0.000000	0.000000	0.00000	0.00000	100.0000	100.0000
13	0.000000	0.000000	0.000000	0.000000	0.00000	0.00000	100.0000	100.0000
Total inertia (λ)			0.309480	0.309467

. The cumulative contribution of these to the total inertia (λ) is then used to assess the extent to which the eigenvalues of the lower-dimensional space explain the total inertia. The presented data demonstrate that the degree of explanation of inertia in the two-dimensional space is 72.98% in 2015 and 73.57% in 2023, indicating that this dimension of space is adequate to reflect the connections between the variants of variables from the actual projection space. Moreover, the calculations performed indicate that the full explanation of the total inertia is achieved for K = 8.

The diagnosis of the situation of EU countries in terms of sustainable energy development was formulated based on a graphical presentation of the results of the correspondence analysis (Appendix A.

Table A3. Row coordinates and contribution to inertia (2015) (source: own study).

	Coordinates		Mass	Quality	Relative Inertia	Interia Dimension 1	Cos2 Dimension 1	Interia Dimension 2	Cos2 Dimension 2
	Dimension 1	Dimension 2
AT	−0.4398	−0.0111	0.0370	0.5700	0.0406	0.0451	0.5696	0.0001	0.0004
BE	−0.3897	0.3704	0.0370	0.7554	0.0458	0.0354	0.3968	0.0757	0.3586
BG	0.4357	−0.3100	0.0370	0.6410	0.0534	0.0443	0.4255	0.0530	0.2155
HR	0.2665	−0.2119	0.0370	0.6211	0.0223	0.0166	0.3805	0.0248	0.2406
CY	0.3180	0.3779	0.0370	0.8408	0.0347	0.0236	0.3486	0.0788	0.4922
CZ	−0.3280	−0.1338	0.0370	0.6329	0.0237	0.0251	0.5427	0.0099	0.0902
DK	−0.3816	−0.2368	0.0370	0.4924	0.0490	0.0340	0.3555	0.0309	0.1369
EE	−0.3727	−0.4116	0.0371	0.8900	0.0416	0.0325	0.4010	0.0937	0.4891
FI	−0.7004	−0.2862	0.0370	0.8575	0.0799	0.1145	0.7349	0.0452	0.1226
FR	−0.2097	−0.0212	0.0370	0.4998	0.0106	0.0103	0.4947	0.0002	0.0051
DE	−0.2960	0.1938	0.0369	0.8760	0.0171	0.0204	0.6132	0.0207	0.2629
GR	0.4897	0.0294	0.0371	0.9659	0.0299	0.0561	0.9624	0.0005	0.0035
HU	0.0978	0.0402	0.0370	0.1765	0.0076	0.0022	0.1509	0.0009	0.0255
IE	−0.0254	0.4626	0.0370	0.7631	0.0337	0.0002	0.0023	0.1181	0.7608
IT	0.2792	0.1796	0.0370	0.5859	0.0225	0.0182	0.4144	0.0178	0.1715
LV	0.2117	−0.2599	0.0369	0.6257	0.0214	0.0104	0.2495	0.0372	0.3762
LT	0.4768	−0.0175	0.0370	0.8092	0.0337	0.0530	0.8082	0.0002	0.0011
LU	−0.5914	0.6168	0.0370	0.9434	0.0926	0.0816	0.4519	0.2099	0.4915
MT	0.6137	0.2432	0.0370	0.6627	0.0787	0.0879	0.5728	0.0326	0.0899
NL	−0.3342	0.1446	0.0370	0.4751	0.0334	0.0261	0.4002	0.0115	0.0749
PL	0.1942	−0.1174	0.0370	0.2814	0.0219	0.0088	0.2061	0.0076	0.0753
PT	0.5496	−0.0537	0.0370	0.7392	0.0494	0.0705	0.7322	0.0016	0.0070
RO	0.4973	−0.3240	0.0370	0.7300	0.0577	0.0577	0.5125	0.0579	0.2176
SK	0.0901	0.0549	0.0370	0.1263	0.0106	0.0019	0.0921	0.0017	0.0342
SI	−0.1402	−0.0853	0.0370	0.6371	0.0051	0.0046	0.4651	0.0040	0.1719
ES	0.3250	0.0981	0.0370	0.7494	0.0184	0.0246	0.6868	0.0053	0.0625
SE	−0.6364	−0.3304	0.0370	0.9507	0.0647	0.0945	0.7488	0.0602	0.2018

,

Table A4. Column coordinates and contribution to inertia (2015) (source: own study).

	Coordinates		Mass	Quality	Relative Inertia	Interia Dimension 1	Cos2 Dimension 1	Interia Dimension 2	Cos2 Dimension 2
	Dimension 1	Dimension 2
E1(+)	0.5153	−0.0935	0.0714	0.8868	0.0714	0.1195	0.8585	0.0093	0.0283
E1(−)	−0.5148	0.0921	0.0714	0.8836	0.0714	0.1192	0.8562	0.0090	0.0274
E2(+)	0.5200	−0.1364	0.0714	0.9344	0.0714	0.1217	0.8743	0.0198	0.0601
E2(−)	−0.5201	0.1366	0.0714	0.9337	0.0715	0.1217	0.8735	0.0199	0.0602
E3(+)	0.5146	−0.0020	0.0714	0.8559	0.0714	0.1192	0.8558	0.0000	0.0000
E3(−)	−0.5147	0.0022	0.0714	0.8557	0.0715	0.1192	0.8557	0.0000	0.0000
E4(+)	0.0799	0.1800	0.0714	0.1253	0.0715	0.0029	0.0206	0.0345	0.1046
E4(−)	−0.0800	−0.1798	0.0714	0.1252	0.0714	0.0029	0.0207	0.0344	0.1045
E5(+)	−0.0510	−0.4358	0.0714	0.6220	0.0715	0.0012	0.0084	0.2021	0.6135
E5(−)	0.0509	0.4361	0.0714	0.6227	0.0714	0.0012	0.0084	0.2023	0.6143
E6(+)	−0.1644	−0.4690	0.0714	0.7986	0.0714	0.0122	0.0874	0.2341	0.7112
E6(−)	0.1644	0.4692	0.0714	0.7985	0.0715	0.0122	0.0873	0.2343	0.7112
E7(+)	−0.5240	−0.0116	0.0714	0.8877	0.0714	0.1235	0.8872	0.0001	0.0004
E7(−)	0.5239	0.0118	0.0714	0.8874	0.0714	0.1235	0.8869	0.0001	0.0005

,

Table A5. Row coordinates and contribution to inertia (2023) (source: own study).

	Coordinates		Mass	Quality	Relative Inertia	Interia Dimension 1	Cos2 Dimension 1	Interia Dimension 2	Cos2 Dimension 2
	Dimension 1	Dimension 2
AT	−0.5666	0.0136	0.0370	0.8537	0.0450	0.0812	0.8532	0.0001	0.0005
BE	−0.3246	0.4191	0.0370	0.7863	0.0428	0.0266	0.2948	0.0801	0.4914
BG	0.4660	−0.3060	0.0370	0.7552	0.0492	0.0549	0.5276	0.0427	0.2276
HR	0.0495	−0.2133	0.0370	0.4167	0.0138	0.0006	0.0213	0.0207	0.3954
CY	0.2630	0.3286	0.0370	0.7889	0.0269	0.0175	0.3080	0.0492	0.4809
CZ	−0.2519	−0.1109	0.0370	0.3425	0.0265	0.0160	0.2869	0.0056	0.0556
DK	−0.3567	−0.1718	0.0372	0.4454	0.0423	0.0323	0.3616	0.0135	0.0838
EE	−0.3246	−0.5083	0.0370	0.8923	0.0488	0.0266	0.2585	0.1178	0.6338
FI	−0.7176	−0.3236	0.0370	0.9606	0.0772	0.1302	0.7983	0.0477	0.1623
FR	−0.0379	0.0289	0.0370	0.0209	0.0130	0.0004	0.0132	0.0004	0.0077
DE	−0.2052	0.2117	0.0372	0.5362	0.0195	0.0107	0.2596	0.0205	0.2765
GR	0.5193	0.0290	0.0370	0.8422	0.0384	0.0682	0.8395	0.0004	0.0026
HU	0.1797	−0.0106	0.0370	0.2945	0.0132	0.0082	0.2934	0.0001	0.0010
IE	−0.0247	0.5315	0.0370	0.9038	0.0375	0.0002	0.0019	0.1288	0.9019
IT	0.2468	0.2532	0.0370	0.7373	0.0203	0.0154	0.3593	0.0292	0.3780
LV	−0.0630	−0.4273	0.0370	0.8045	0.0277	0.0010	0.0171	0.0832	0.7874
LT	0.2942	−0.1279	0.0370	0.5460	0.0225	0.0219	0.4592	0.0075	0.0868
LU	−0.5726	0.6031	0.0370	0.9565	0.0865	0.0829	0.4534	0.1658	0.5031
MT	0.6213	0.1712	0.0370	0.6360	0.0781	0.0976	0.5911	0.0134	0.0449
NL	−0.0847	0.3690	0.0370	0.7673	0.0224	0.0018	0.0384	0.0621	0.7289
PL	−0.0218	−0.0356	0.0370	0.0111	0.0187	0.0001	0.0031	0.0006	0.0081
PT	0.5469	−0.0811	0.0370	0.7711	0.0474	0.0756	0.7544	0.0030	0.0166
RO	0.4646	−0.3931	0.0370	0.8719	0.0508	0.0546	0.5081	0.0704	0.3638
SK	0.2218	−0.0105	0.0370	0.2837	0.0208	0.0124	0.2830	0.0001	0.0006
SI	−0.2022	−0.0619	0.0370	0.3867	0.0138	0.0103	0.3536	0.0017	0.0331
ES	0.4892	0.0876	0.0370	0.8071	0.0366	0.0605	0.7820	0.0035	0.0251
SE	−0.6049	−0.2648	0.0370	0.8643	0.0604	0.0925	0.7252	0.0320	0.1390

and

Table A6. Column coordinates and contribution to inertia (2023) (source: own study).

	Coordinates		Mass	Quality	Relative Inertia	Interia Dimension 1	Cos2 Dimension 1	Interia Dimension 2	Cos2 Dimension 2
	Dimension 1	Dimension 2
E1(+)	0.4733	−0.1216	0.0714	0.7714	0.0714	0.1092	0.7236	0.0130	0.0478
E1(−)	−0.4728	0.1216	0.0714	0.7700	0.0714	0.1090	0.7223	0.0130	0.0478
E2(+)	0.5079	−0.1672	0.0714	0.9247	0.0713	0.1258	0.8343	0.0246	0.0904
E2(−)	−0.5086	0.1666	0.0718	0.9245	0.0719	0.1267	0.8349	0.0245	0.0896
E3(+)	0.5071	0.0916	0.0714	0.8570	0.0715	0.1254	0.8299	0.0074	0.0271
E3(−)	−0.5066	−0.0917	0.0714	0.8579	0.0713	0.1251	0.8307	0.0074	0.0272
E4(+)	−0.1696	0.3217	0.0714	0.4285	0.0712	0.0140	0.0932	0.0910	0.3353
E4(−)	0.1702	−0.3218	0.0714	0.4276	0.0715	0.0141	0.0935	0.0910	0.3341
E5(+)	−0.1157	−0.4231	0.0714	0.6222	0.0714	0.0065	0.0433	0.1574	0.5789
E5(−)	0.1163	0.4231	0.0714	0.6215	0.0715	0.0066	0.0437	0.1573	0.5778
E6(+)	−0.1929	−0.4846	0.0714	0.8791	0.0714	0.0181	0.1203	0.2064	0.7589
E6(−)	0.1935	0.4845	0.0714	0.8788	0.0715	0.0182	0.1208	0.2064	0.7579
E7(+)	−0.4540	−0.0178	0.0714	0.6670	0.0714	0.1005	0.6659	0.0003	0.0010
E7(−)	0.5239	0.0118	0.0714	0.8874	0.0714	0.1235	0.8869	0.0001	0.0005

) and hierarchical classification performed using Ward’s method. The classification was based on the coordinate values of the EU country and energy sustainability feature categories. The variables were the coordinates from the first and second axes for each variant of both features. The Euclidean distance was used as the distance measurement method in cluster analysis. To isolate groups of objects as similar as possible, binary tree pruning was performed. Among the numerous available procedures for selecting the number of clusters, this study employed the Mojena criterion and agglomeration graph analysis. The Mojena rule (d_i+1 > 1.1394; for k = 3.5) and agglomeration graph analysis (Appendix A. Figure A1) indicated that four classes should be identified. The results of the grouping of EU countries in 2015 and 2023 are presented in Figure 1. The red line denotes the point at which the process of merging classes was halted. In both 2015 and 2023, four classes were defined.

Due to the ranks and anti-ranks assigned to indicators related to energy sustainability and based on the results of correspondence analysis and classification performed using Ward’s method (Figure 2), it should be noted that in 2015, the sustainable development of Hungary, Slovakia, Poland, France, and Slovenia was assessed as average. The strengths of Romania, Portugal, Lithuania, and Greece (group A) include low primary (E1(+)) and final energy consumption (E2(+)), as well as low final energy consumption in households (E3(+)). A weakness, however, is the high percentage of the population unable to adequately heat their homes due to poverty (E7(−)). The remaining countries in Group A, namely Bulgaria, Croatia, Latvia, and Poland, also achieve high or average values for these indicators. A situation exactly opposite to that of countries in Group A was observed for countries such as Austria, Belgium, Denmark, Luxembourg, and the Netherlands (Group C). The strength of these countries is the low percentage of the population unable to adequately heat their homes due to poverty, but the weaknesses are the high primary and final energy consumption and high final energy consumption in households.

Group B includes countries characterized by a high share of renewable energy in gross final energy consumption (with the exceptions of the Czech Republic and France) and a low energy dependency index. The weakness of the countries classified in Group B—with the exception of Denmark—is their low level of energy efficiency. The exact opposite situation to that observed in Group B was observed for countries belonging to Group C, although it should be noted that this group included countries characterized by low energy efficiency (Malta and Slovakia).

Analysing Figure 3, it should be noted that in 2023, there were some changes in the composition of individual groups compared to 2015. Furthermore, in 2023, the number of countries whose situation in terms of energy sustainability should be considered average also increased. The largest number of countries joined Group B, which became the largest, but also the most heterogeneous group. Poland, Slovakia, and Hungary, characterised by a low share of energy from renewable sources in gross final energy consumption, joined Group B. Furthermore, as in 2015, this group includes countries such as Denmark and France, which are characterised by low energy efficiency. Significant changes also occurred in Groups C and D. Almost all countries (except Austria) moved from Group D to Group C. The general characteristics of the individual groups in 2015 and 2023 are presented in

Table 3. Diagnosis of the situation in the area of sustainable energy development in the EU in 2015 and 2023 (source: own study).

Group	EU Countries		Characteristics of the Group
	2015	2023
Group A	Greece, Portugal, Bulgaria, Romania, Poland, Croatia, Latvia, Lithuania	Greece, Portugal, Bulgaria, Romania, Malta, Spain	The group’s strengths are generally low primary and final energy consumption, as well as low final energy consumption in households. A salient weakness of this demographic is the high percentage of the population who are unable to adequately heat their homes due to impoverished circumstances.
Group B	Estonia, Denmark, Czechia, Slovenia, France, Sweden, Finland	Poland, Croatia, Latvia, Lithuania Estonia, Denmark, Czechia, Slovenia, France Slovakia, Hungary	The strengths of most countries in this group include a high share of renewable energy in gross final energy consumption and a low energy dependency rate (expressed as the percentage of imported energy in total energy consumption). A salient vulnerability is evident in the group’s energy efficiency levels, with the exception of Denmark and France in 2023.
Group C	Malta, Spain, Ireland, Italy, Cyprus, Slovakia, Hungary	Ireland, Italy, Cyprus, Netherlands, Germany, Belgium, Luxembourg	The preponderance of countries in this group is characterised by a notable degree of energy efficiency (with the exceptions of Malta and Slovakia). A key vulnerability is frequently characterised by a low proportion of energy derived from renewable sources within gross final energy consumption, coupled with a high degree of energy dependency.
Group D	Netherlands, Germany, Belgium, Luxembourg Austria	Sweden, Finland, Austria	The group’s deficiencies are characterised by elevated levels of primary and final energy consumption, along with a pronounced tendency towards high final energy consumption within the domestic sector. A notable strength of the group is the relatively low percentage of the population that is unable to adequately heat their homes due to financial constraints.

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The EU’s energy transition is the process of switching from fossil fuels to RESs, aimed at reducing greenhouse gas emissions and improving energy security. Currently, EU countries are striving to achieve climate goals, such as reducing emissions by at least 55% by 2030 (Fit for 55 package) and achieving climate neutrality by 2050. Studies assessing the level of development of the EU-27 in 2015 and 2023 indicate that the European energy transition is progressing rapidly. However, the dynamics of change vary across countries. In 2023, there were significant changes in individual groups compared to 2015 (Table 3). It should be noted, however, that the failure of EU countries to meet the standards resulting from EU directives for specific indicators is often a consequence of their high economic development, high standard of living of the population, specific climatic situation, and historical conditions (dominant energy models based on stone energy sources and moment of accession to the EU).

5. Conclusions

Due to the environmental policy adopted by the EU, member states are obligated to reduce their greenhouse gas emissions. They are reducing emissions largely through energy transition, i.e., switching to sustainable energy sources, aimed at reducing greenhouse gas emissions and increasing energy independence. Key elements include the development of RESs, improving energy efficiency, and modernising grid infrastructure, i.e., expanding interconnections and integrating energy markets, which aims to ensure supply stability and reduce costs, as well as transitioning to RESs. However, the situation in individual countries varies. This results from the following:

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The domestic and international situation of a given country;

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The level of strategic energy resources within the country;

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The origin of raw material supply sources (domestic, foreign);

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The degree of diversification of raw material supply sources;

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The capacity and quantity of stored reserves;

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The country’s energy mix;

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The level of development of renewable energy sources and their share in the energy mix;

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The quality of the energy system and transmission infrastructure.

So, it is important to assess the progress of energy transition in individual EU countries [112]. Energy security is a strategic issue for every country. The generation and transmission of electricity are essential for the efficient functioning of all sectors of the economy, as well as individual households. However, non-renewable fuel resources are finite, so conventional energy may be insufficient. Furthermore, these resources are harmful to the environment because they pollute the air, water, and soil [113]. Therefore, a transformation towards the use of alternative RESs and increased energy efficiency is necessary. However, the energy transition in EU member states towards sustainable development is a complex process. An assessment of the grouping results indicates that in each group of countries, there are indicators whose values do not meet the standards adopted in EU directives or deviate from the EU average. Changes taking place in the energy sector are gradually contributing to the diversification of energy sources. Important directions of change include increasing the share of renewable energy, improving energy efficiency, and striving for a just transition, especially for coal-producing regions. A progress assessment shows that while the EU has made significant efforts, intensified action is necessary to achieve ambitious climate and energy goals. It is essential to develop market-based support systems for investments in renewable energy and energy e ficiency and supporting the transformation towards a circular and resource-efficient economy [114].

The material and research findings presented in this article constitute a new approach to evaluating the transformation of EU member states in the field of sustainable energy development. It contributes to the discussion on assessing the achievements of individual EU countries in the energy transition to support a modern, dynamically developing, and innovative zero-emission economy. Previous studies have not considered all indicators or have used different methods [16,54,55,115]. Individual countries face various difficulties in simultaneously implementing energy transition processes and facing the challenges of maintaining sustainable economic development. There are countries where the energy transition is too costly from an economic perspective, where a significant percentage of households are condemned to energy poverty, and where social opposition to the green transition is slowly growing. Consequently, EU policy should consider the challenges that some countries may encounter in implementing the necessary environmental criteria and acknowledge the necessity of incorporating energy justice into transition processes. The transition should stem from, or be linked to, bottom-up initiatives at the local level. This approach is the most logical for the long-term implementation of the EU energy strategy [61].