Human Capital and the Sustainable Energy Transition: A Socio-Economic Perspective

Abstract

This article addresses the role of human capital in socio-economic development processes during Europe’s energy transition. The main empirical objectives are firstly to diagnose the overall level of human capital in the energy transition economy based on the original synthetic measure, HCIe, and secondly to analyse and assess the variation in its spatial distribution across the European socio-economic landscape, which serves as a foundation for developing a targeted policy typology directly linked to the identified cluster profiles and their specific weaknesses. The general research question is: what is the level and degree of variation in the internal structure of human capital across the European socio-economic landscape? What actions should individual European countries take to support the development of human capital in the context of the energy transition? The research concept adopted also raises additional questions. Firstly, how can the importance of human capital be captured in an economy undergoing an energy transition? Secondly, are there appropriate indicators for measuring this based on the adopted research approach? European countries were selected as the subjects of the study. In the empirical section, taxonomic methods were employed to develop a proprietary synthetic measure of human capital in a transforming energy economy (HCIe), which was then used for the hierarchical classification of entities. The internal structure of human capital was explored using multi-criteria cluster analysis with the k-means algorithm. This approach resulted in a non-hierarchical classification of entities (typologisation). The main data sources used to construct the synthetic measures were international databases: IRENA, OECD, EUROSTAT, and the World Bank. Analysis of the HCIe measure and the clustering of European countries revealed that the key risk factor for transformation is the absence of integrated human capital within individual groups of countries. This highlights the urgent need for targeted investment in health and the development of systemic and green competencies.

1. Introduction

In an era of rapid technological advancement and mounting global climate challenges, the transition to a low-emission economy is imperative. A key factor in the success of this process is the redefinition of human capital to encompass specialised knowledge and skills, as well as a society’s willingness and ability to accept and implement innovative changes in the new model of a transformative energy economy. While it may seem that the approach to human resources has remained largely unchanged for decades, the accelerating pace of change and intensifying global competition require swift action to foster the development of human capital. This is particularly important for the European socio-economic space [1], where deficits such as the lack of basic green skills in key sectors are common place. According to Cedefop analyses (2021) and IEA reports (2022), there is a shortage of qualified renewable energy and heat pump installers, energy efficiency technicians, and building renovation specialists [2,3]. This shortage directly hinders the achievement of transition goals, as these technologies, although available, cannot be implemented quickly and on a large scale. This qualitative and systemic shortage reveals a lack of engineers and managers who are experts in energy, digitalisation (AI and big data), and cybersecurity. The ability to integrate distributed renewable energy sources and manage grid complexity is directly limited by the shortage of personnel with this unique set of interdisciplinary competencies.

The third area of the deficit relates to the lack of behavioural and social competencies, which manifests in two ways. Firstly, social resistance to key renewable energy investments (e.g., wind turbines) is often the result of decision-makers’ inability to build trust and communicate with local communities. Secondly, in regions dependent on fossil fuels, the process of retraining workers encounters motivational and social barriers as well as technical ones. Therefore, the human capital required for an energy-transforming economy must include the ability to manage change and accept energy policies, which is crucial to overcoming the bottleneck in the permitting process [4]. Strongly advocating the implementation of sustainable development principles while simultaneously striving to maintain a balance between the three priorities of environment, society and economy will not deliver the expected results unless key resources are properly defined and methods for measuring them are developed that reflect changing operating conditions.

The issue of human capital has been a popular research topic for years, seeking to answer questions about the factors that cause disparities in socio-economic development levels and the varying dynamics of development processes worldwide [5,6,7]. Although current research provides extensive knowledge in this area, ongoing climate change necessitates a shift from a conventional energy source-based model to a low-emission model. Therefore, it is essential to revise the current approach to human resources so that they can play a pivotal role in the development of this new green economy model.

A review of the literature on the role of human capital in an economy undergoing energy transformation reveals a significant research gap. Although numerous studies address the issue of a low-emission economy, attempts to connect human capital to this new economic model are relatively rare. Given the current state of knowledge, researchers of socio-economic development processes should be interested not only in redefining human capital, but also in understanding its essence in the new economic model. The main problem is the operationalisation of human capital, i.e., selecting appropriate empirical variables to express its essence in the new economic model. Therefore, identifying the optimal measure that would enable the universal diagnosis and assessment of this resource at local, regional and international levels, and which would be appropriate for a new economic model based on green energy, is crucial.

It is estimated that the interpretive value of human capital indicators used in the past has become somewhat outdated. This is because they were based on empirical variables that adequately expressed the essence of this resource in relation to the economic model adopted (e.g., a knowledge-based economy). However, in the current conditions of ongoing energy transformation, these indicators may be inaccurate. Therefore, the current approach must be revised and new empirical variables must be identified that adequately capture the essence of human capital in an economy undergoing energy transformation.

This article contributes to the ongoing discussion in this area and fills a gap in the existing literature. The general research problem is formulated as a question about how to express the essence of human capital in an economy undergoing energy transformation and whether there are any appropriate metrics for measuring it based on this research approach. The lack of similar research and key knowledge about the role of human capital, particularly with regard to measurement methods in an economy undergoing energy transformation, necessitates revising the approach to expressing its essence in a new economic model, thereby justifying research in this area. In this sense, the article takes an innovative approach, albeit with certain limitations.

The main empirical objectives are firstly to diagnose the overall level of human capital in the energy transition economy based on the original synthetic measure, HCIe, and secondly to analyse and assess the variation in its spatial distribution across the European socio-economic landscape, which serves as a foundation for developing a targeted policy typology directly linked to the identified cluster profiles and their specific weaknesses. The article also analyses and assesses variations in the spatial distribution of this resource across the European socio-economic landscape, exploring its internal structure in terms of innovation and creativity, health, the labour market, education and quality of life. The main sources of data used to construct the synthetic measures were international databases: IRENA, OECD, EUROSTAT and the World Bank. These databases provide a classical approach to the human capital index, which was the subject of comparative analyses. 26 European countries were selected to measure and explore the internal structure of human capital within the socio-economic landscape. The main selection criteria were the availability, completeness and continuity of the data used to construct the synthetic measures.

In light of the observed changes in international competition, this article addresses a difficult yet important and timely issue. It contributes to further scholarly discussion on the role of key factors in socio-economic development—specifically, human capital—in the context of energy transformation in Europe and globally. The article attempts to indicate the direction of the further evolution of economic thought, with a particular focus on practical methods for diagnosing this resource and examining the social potential required to foster local, regional and global competitiveness within an economy undergoing energy transformation. It is important to emphasise that the article proposes an innovative approach that combines the classic concept of human capital with a new approach stemming from the inevitable and ongoing process of socio-economic development in an era of energy transformation. This addresses the existing need to expand knowledge in the area of defining, diagnosing and measuring human capital in a new era.

2. Human Capital in a Renewable Energy Economy: Directions for Development and Challenges

2.1. Theoretical Framework for Research on Human Capital in the Energy Transition

This study is based on an integrated theoretical framework that goes beyond the traditional macroeconomic approach to human capital, intentionally combining four key perspectives. This interdisciplinary synthesis is essential in order to adequately reflect the complexity of human capital in an economy undergoing an energy transition, and to justify the multidimensional construction of a synthetic measure.

2.1.1. Human Capital Theory (HCT)

This study is based on the classic Human Capital Theory formulated by Becker and Schultz [6,7]. According to this theory, investments in education, vocational training and health constitute intangible capital, increasing the productivity and innovation of individuals and entire economies. In the context of current European programmes, such as the Green Deal, the assumptions of this theory must be adapted to the specific nature of the green economy. Energy transformation requires investment in so-called ‘green skills’, which encompass not only narrow technical skills (e.g., PV panel installation), but also a broad spectrum of competencies (e.g., circular system design, energy auditing and data management [8,9]. Therefore, the developed HCIe human capital measure is assumed to reflect not only formal education, but also the overall efficiency and potential of the labour market, including health indicators. These indicators are fundamental to determining the effectiveness and sustainability of human capital.

2.1.2. The Theory of Technological Innovation Systems (TIS)

As energy transition is primarily a technological change, the conceptual framework of this study draws on the TIS theory, as formalised by Hekkert et al. [10]. According to this framework, innovation is the result of dynamic interactions within a system rather than a single invention. The functioning of a TIS depends on seven key functions, such as knowledge generation, networking and search management. Human capital plays a pivotal role in this system [11]. Deficits in human capital in European countries are strongly associated with the inefficiency of TIS functions, including weakened networking. This is evidenced by the lack of qualified specialists, which hinders collaboration between engineers, companies, and research centres. This results in a shortage of guidance skills for searches, meaning there are ultimately insufficient experts capable of strategically identifying the most promising technological paths (e.g., hydrogen vs. batteries). In this context, the HCI measure must consider system and network competencies, which are critical to the effectiveness of TIS.

2.1.3. Socio-Technical Transition Theory (Multi-Level Perspective, MLP)

From a socio-economic perspective, Socio-Technical Transition Theory (MLP) appears to be pivotal. It views transformation as a complex, long-term process in which new technologies (niches) must overcome established technical and institutional structures (regimes) amid broad changes (landscapes) [12,13]. The MLP argues that human capital must be considered in terms of social embeddedness and agency. Transformation will not be successful without social acceptance and the administration’s ability to manage systemic change [14]. According to MLP, deficiencies in HCIe, such as a lack of communication and trust-building skills, are a key cause of bottlenecks as they lead to social resistance.

2.1.4. Value-Belief-Norm (VBN) Theory

The socioeconomic and behavioural aspects are reinforced by the VBN theory. According to VBN, pro-environmental decisions and behaviours (e.g., purchasing an electric car or supporting a local wind farm) are preceded by a specific sequence: values shape beliefs, which lead to norms and ultimately actions [15,16]. In the context of transformation, the HCIe deficit encompasses not only competencies, but also behavioural readiness and policy acceptance. Incorporating the VBN perspective into the study justifies why HCIe indicators must indirectly reflect social and institutional capacity for adaptation and changing attitudes, which are fundamental to the success of the entire European Green Deal.

This article proposes the integration of the above theoretical framework, in which human capital is treated not solely as an individual resource, but as a systemic variable crucial to the energy transition. This capital acts as an enabling mechanism that firstly provides the necessary skills and knowledge for developing and diffusing innovations (TIS); secondly enables the institutional adaptation required for transitioning between regimes (MLP); and thirdly encompasses behavioural readiness (VBN), i.e., the attitudes and social norms necessary for social acceptance and demand for green solutions. This deliberate integration of the four perspectives forms the basis of the multidimensional, contextual construction of the HCIe index.

2.2. Conceptual Rationale for the HCIe Measure: Integrating the HCT, TIS, MLP and VBN Frameworks

There are many human capital measures in the literature, such as the World Bank’s HCI, as well as indicators describing so-called green human capital. However, none of these measures are designed to capture the context of the energy transition comprehensively by integrating innovation and behavioural aspects simultaneously. In this sense, the proposed synthetic HCIe measure is advantageous due to its multidimensionality and focus on the energy transition (see

Table 1. Comparison of the HCIe Index with Alternative Approaches.

Example Indicator:	Major Conceptual Limitation (Problem)	Theoretical Bridge (HCT-MLP-TIS-VBN)	New/Unique Function HCIe
HCI according to the World Bank methodology:	Lacks sectoral context. Focuses on health and general education.	None. Does not provide pro-transformation indicators.	Useful only at the macro level, inadequate for sectoral policy. Does not allow for clustering countries according to specific sectoral and social weaknesses.
Green Skills Indicators:	Focuses on the supply side, primarily skills. Omits demand and social acceptance.	Limited (affects HCT), but ignores VBN and TIS.	Does not capture socio-technical dimensions of transformation (e.g., lack of social acceptance of VBN) that hinder innovation (TIS).
Sustainable Development Indicators (SDG 7, 13):	Insufficient aggregation (measures are scattered and do not constitute Human Capital).	No. Lack of a coherent theory of integration within Human Capital.	It does not allow for a holistic assessment and clustering of countries’ transformation readiness at the human resources level.
HCIe (proprietary measure)	None or minimal (Contextual and Multidimensional).	Yes. Through the deliberate integration of VBN, TIS, and MLP indicators.	Systemic Index. Aggregates innovation (TIS) and behavioural (VBN) factors into a single human resources indicator, which is unique in the Green Human Capital literature, which is fundamental for formulating targeted recommendations.

Source: own elaboration.

for a comparison).

As illustrated in Table 1, existing human capital measures, including the World Bank’s HCI, are inadequate for capturing the systemic and contextual nature of the energy transition. In contrast, the HCIe addresses a fundamental theoretical and contextual shortcoming. While the classic HCI measures productive potential based on health and education, the HCIe is a purpose-built indicator measuring the adaptive capacity of a socio-economic system to the transition. It achieves this by integrating classic human capital theory (HCT) with TIS, MLP and VBN theories into a single indicator. This integration enables an empirical assessment of not only the availability of skills (HCT), but also the systemic mechanisms (TIS/MLP) and level of acceptance (VBN) required for a successful sociotechnical transition. The approach used to construct the HCIe measure is an important step towards a holistic diagnosis of human resources in light of the complex energy transition process requirements, at both the national and regional levels. At this point, it seems appropriate to refer to the institutions that specifically study Green Skills. While not without its flaws, the HCIe measure avoids the limitations of measures developed by institutions such as CEDEFOP, OECD and EUROSTAT. While these are important for analysing the labour market, they primarily focus on technical and professional supply. In contrast, HCIe broadens this perspective, integrating the VBN aspect with the systemic TIS/MLP measures. This allows for a holistic diagnosis of countries’ preparedness for transformation, considering both existing skills and the societal willingness to utilise them (see

Table 2. Institutions using ‘green skills’ versus HCIe measures.

Institution	Tools/Publications	Why Are Their Measures Narrower than HCIe?
CEDEFOP European Centre for the Development of Vocational Training	Publications, reports, and databases on the demand and supply of green skills in the EU, e.g., enterprise surveys on skills gaps.	Focus: Vocational: Focus on education and training (VET) systems, measuring specific gaps in technical skills. They omit VBN (social attitudes) and systemic (TIS/MLP) aspects.
OECD Organisation for Economic Cooperation and Development	Reports on competences and the ecological transition. They often use PIAAC (Programme for the International Assessment of Adult Competencies) surveys to measure skills.	Focus: Macroeconomic: Their analyses are broad, but their measures often focus on general digital and technical skills, which may or may not be “green.” They do not create a synthetic index linking VBN and HCIe.
EUROSTAT Statistical Office of the European Union	Green Employment Statistics and Education and Training Statistics.	Focus: Statistical: They provide raw, valid data (indicators) that measure only the supply or employment in green sectors. They do not create a theory or a synthetic systemic indicator (e.g., VBN/TIS).
ILO International Labour Organization	Global reports on Just Transition and the demand for green jobs and skills.	Focus: Equity: Their primary purpose is to measure the impact of the transition on the labour market. Green Skills measures are a supporting tool, not an end in themselves, and do not provide a comprehensive approach to VBN.

Source: own elaboration.

).

By contrast, the 2030 Agenda indicators (e.g., SDG 7 and SDG 13) are not the typical measures used to express or measure human capital. Various institutions use them to form a reporting and monitoring framework for many global, regional and national organisations. SDG 7 focuses on access to affordable, reliable, sustainable and modern energy (

Table 3. SDG 7: Clean and Affordable Energy.

Monitoring Institution	Role in the Application of SDG 7 Indicators
International Energy Agency (IEA)	The main reporting entity on renewable energy investments and energy efficiency. Provides data on indicators 7.2 (renewable energy share) and 7.3 (efficiency)
World Bank	Monitors progress in access to electricity (indicator 7.1) in developing countries.
International Renewable Energy Agency (IRENA)	Collects data on renewable energy capacity and its integration into energy systems.

Source: own elaboration.

), while SDG 13 focuses on tackling climate change and its effects (

Table 4. SDG 13: Climate Action.

Monitoring Institution	Role in the Application of SDG 13 Indicators
United Nations Framework Convention on Climate Change (UNFCCC)	Oversees the implementation of climate commitments and the reporting of emission reductions (indicators 13.2 and 13.3).
United Nations Development Programme (UNDP)	Monitors indicators related to the integration of climate policy into national development strategies.
National and Regional Organizations (e.g., Central Statistical Office, EUROSTAT)	Collect national data used to monitor indicators such as greenhouse gas emissions (indicators 13.2).

Source: own elaboration.

). A key advantage of the HCIe measure over the SDG measures is that the latter are distributed and descriptive, whereas the former is synthetic and analytical. SDG measures are collected, but not aggregated into a coherent measure of human capital, making them less suitable for clustering analysis, such as that undertaken in this article.

In summary, unlike generic measures of human capital that focus on basic human resources (e.g., the World Bank’s HCI), HCIe is a contextual measure designed to capture the holistic requirements of the energy transition process. Integrating the VBN and TIS/MLP dimensions within human capital theory provides an analytical bridge between individual capabilities (in the classical theory’s approach) and the dynamics of systemic change (MLP), a feat unattainable using single-dimensional indicators of so-called “green skills.” The advantage of HCIe over other such measures lies in its analytical function, which directly addresses the need to formulate and implement appropriately targeted policy support instruments. By aggregating diverse but complementary dimensions, HCIe not only classifies countries as good or bad in transition (as a simple ranking would do), but also identifies a unique profile of weak links within each cluster (e.g., the diagnosis of ‘strong TIS and weak VBN’ for Cluster B). This level of analytical typology is crucial for formulating targeted policy recommendations, which is impossible when using general measures that could erroneously suggest a uniform intervention for all countries. In this context, the HCIe indicator should be assessed as a decision-making tool, not simply as a statistical one.

2.3. The Role of Human Capital in Socio-Economic Development in Light of Current Strategic Documents in Europe Undergoing Energy Transformation

There is a general consensus that the socio-economic development of European countries can only be realised by future generations if there is coherence between the three components of development: the natural environment, society, and the economy. This coherence is guaranteed by sustainable development, a concept that strikes a balance between economically viable actions and those that are ecologically safe and socially acceptable [17].

The current goals of this strategy are defined by the UN’s Agenda 2030 programme [18], which, together with the Paris Agreement [19], aims to achieve long-term effects such as reducing social exclusion and poverty, social inequalities, and improving healthcare and the state of the natural environment. It also aims to increase the role of human and social capital in development. However, the Draghi report emphasises that Europe’s long-term prosperity and competitiveness must be based on an ambitious investment plan, calling for additional annual investments of EUR 800 billion to accelerate the green and digital transformation [20].

Energy transformation involves switching from fossil fuels to renewable energy sources and improving energy efficiency. This directly affects all three aspects of activity. It is a process that goes beyond ideas and states of mind. It is a real challenge that requires specialist knowledge and social awareness. High-quality human capital plays a key role in this process, i.e., individuals who actively, consciously and preferably voluntarily participate in the changes. Unfortunately, the issue of voluntariness is often controversial, resulting from low levels of education, a lack of motivation, and negative attitudes towards change. However, the biggest barrier seems to be the economic factor. Individual choices, such as a household’s decision to purchase a wood stove or heating technology based on renewable energy sources, are often influenced by financial constraints. What is beneficial for the climate and the environment is unfortunately not always accessible to individuals. This raises questions about the rationality of individual decisions, particularly in situations of material deprivation.

The problem of priorities—social, economic and environmental—has already been highlighted in many earlier works in the context of implementing the principles of sustainable development in European Union countries [21]. Just as the level of sustainability achieved was the result of the different political priorities of individual countries, we should expect to observe differences in the currently energy-transforming European economies. Some countries, despite complying with environmental restrictions and meeting basic human needs, do not and will not guarantee social justice. Conversely, others are able to meet basic human needs in addition to fulfilling environmental recommendations and ensuring social balance. For these countries, the priority is to eliminate extreme poverty and economic deprivation. Therefore, to achieve full sustainable development, policies and institutions that support economic growth and eliminate these inequalities are key.

From a macroeconomic perspective, energy transformation is a fundamental, long-term process of shifting from a fossil fuel-based energy system to a low-emission system based mainly on renewable energy sources (RES). This process involves real decisions and actions. The aim is to guarantee higher energy efficiency and security for societies and future generations than before. At the level of the European Union, a new binding target has been set to increase energy efficiency by 11.7% by 2030. This means that EU Member States will have to save an average of 1% per year by 2030. They will also have to prioritise improving energy efficiency for those affected by energy poverty [22,23].

This second issue is extremely important from the perspective of the issues addressed in this article. However, it seems that relatively little in the global discussion and in the strategic goals of states addresses the issue of the individual (human) and decisions at the individual level. Economic theory, including the theory of human capital, has provided scientific evidence many years ago that it is this resource that determines economic success and success at the local, regional and global level [6,24,25,26,27]. Deficiencies of this endogenous resource mean that even currently, certain areas and regions in highly developed countries, referred to as problematic or peripheral, experience serious development problems. They have serious problems in creating the so-called critical mass and initiating development processes [28,29,30,31,32,33]. Social potential plays a key role in these processes, particularly at the local, rural level. The pillars of social potential are social capital and human capital. Regardless of the approach adopted, it is worth noting that properly diagnosing and effectively using internal potential can stimulate development in areas with deficits, including less developed areas [34,35] less industrialised or revitalised areas (e.g., post-industrial areas [36,37] rural areas [38,39,40,41] and peripheral areas. In this socio-economic revolution, it is expected that this resource will play a key role in initiating development processes at local and national levels. Therefore, it is worthwhile delving deeper into this research to find answers to the question of what resources an economy undergoing energy transformation needs. It is also crucial to redefine and operationalise the concept in light of the new challenges posed by the new economic model. This will enable us to determine which metrics to use to measure the level of this resource while also enabling international comparative analyses. This will provide decision-makers with the knowledge they need to plan and implement development programmes that support human capital development.

In response to concerns about the future energy security of future generations of Europeans, the European Commission has adopted a package of legislative proposals to adapt EU climate, energy, transport and tax policies. The aim is to reduce net greenhouse gas emissions by at least 55% by 2030 compared to 1990 levels. The European Green Deal is a comprehensive strategy aimed at transforming the European Union into a modern, resource-efficient and competitive economy. It is a key document defining the strategy for action in the area of energy transformation for Europe [42].

It is worth noting that, compared to previous strategic plans for reforming European Union society and economy (e.g., the Europe 2020 Strategy), energy transformation was included alongside competitiveness and growth as one of the main objectives (cf. [43,44]). The European Commission’s activities cover a wide range of sectors, including energy.

Table 5. European Commission initiatives in the area of energy transformation and security.

Typ of Programme	Programme Characteristics	Goals and Activities.
Fit for 55 package	This set of legislative proposals aims to align existing EU climate, energy and transport legislation with the goal of reducing greenhouse gas emissions by at least 55% by 2030 compared to 1990 levels. Energy is key to achieving this target.	1. Revision of the Renewable Energy Directive (RED III): The binding target for the share of renewable energy sources in the EU’s final energy consumption is increased to 42.5% by 2030, with the aim of reaching 45%. 2. Revision of the Energy Efficiency Directive (EED): Setting more ambitious binding targets for improving energy efficiency at the EU and national levels. 3. Revising the Energy Taxation Directive: Aligning the taxation of energy products and electricity with climate objectives. 4. Carbon Border Adjustment Mechanism (CBAM): Preventing carbon leakage by imposing a levy on imports of certain goods from countries with less ambitious climate policies. 5. Reform of the Emissions Trading System (EU ETS): Extending and strengthening the EU ETS to include new sectors.
REPower EU	Developed in response to the Russian invasion of Ukraine, the plan aims to make the EU independent of Russian fossil fuels as soon as possible. It also supports accelerating the energy transition.	The main objectives of REPowerEU in the energy context are: 1. Saving energy: Promoting measures to reduce energy consumption. 2. Diversifying energy supplies: Searching for alternative energy sources and suppliers. 3. Accelerating the deployment of renewable energy sources. Increasing the pace at which renewable energy sources are developed and integrated.
European Green Deal	It is a comprehensive strategy designed to transform the EU into a modern, resource-efficient and competitive economy. Its main objective is to achieve net-zero greenhouse gas emissions by 2050. It encompasses a variety of actions across different sectors, including energy.	The main objectives in the energy sector are: 1. Increasing the share of renewable energy sources (RES). 2. Improving energy efficiency. 3. Ensuring secure and affordable access to energy. 4. Supporting innovation and the development of clean technologies. 5. A just transition, taking into account regions and sectors dependent on fossil fuels.

Source: own study based on: [42,45,46,47].

provides detailed data on current initiatives and programmes.

The energy transformation process itself is shrouded in doubt and controversy for social and economic reasons. Many groups have raised questions about its purpose, as well as about the level of public awareness and acceptance, the state of public knowledge and education, and social readiness in the material sense, which seems to be rarely emphasised in discussions. The latter issue is rarely discussed, but it is important to emphasise that human potential, including the contribution of local actors and civil society (e.g., the European Union), is crucial throughout the transformation process. A thorough and accurate understanding of the complex relationships between the scientific, economic, political and social levels is essential for shaping effective political and economic strategies that will enable sustainable development and a high standard of living in a new, low-emission future [48].

The author’s previous work has linked human capital with energy, introducing the concept of ‘homo energeticus’ [49]. The research explains that energy levels change throughout life, as does the level of human capital (‘appreciation’ and ‘depreciation’), with consequences for individuals’ activity, as well as for individual regions and even entire economies from a macroeconomic perspective [50]. This thesis is reflected in the latest data published by the World Bank, which estimates human capital levels using its own methodology. It transpires that, despite being considered highly developed, European countries exhibit significant variation in terms of this resource (see Figure 1) and the wealth of their inhabitants (see Figure 2).

2.4. Directions and Challenges in the Development of Human Capital in the Energy-Transforming Economy

The renewable energy sector is a promising one worldwide, offering great potential for job creation. Investing in energy efficiency saves energy, reduces consumer bills and creates green jobs. According to the IRENA report [51], the renewable energy sector recorded a record increase in employment in 2023, reaching 16.2 million jobs worldwide compared to 13.7 million in 2022. This indicates a strong growth trend that is likely to continue in 2025. Figure 3 and Table 6 present detailed data on the current level of employment. Furthermore, solar photovoltaics remained the largest employer in 2023, accounting for 7.2 million jobs. Significant numbers of jobs were also recorded in the liquid biofuels, hydropower, and wind energy sectors. The agency reports that 2024 saw a record increase in the installed capacity of renewable energy sources, which directly translates into job creation [51]. Notably, China remains the leader in terms of the number of jobs in the renewable energy sector, followed by the European Union, Brazil, the United States, and India. However, according to a Eurobarometer survey conducted in autumn 2023, 62% of small and medium-sized energy sector enterprises had difficulty recruiting employees with the right skills [52,53].

The large-scale renewable energy skills partnership, launched in 2023, will help to deploy the renewable energy sources needed for the clean energy transition on a massive scale [55]. According to the 2024 IRENA Report, the renewable energy sector in the EU is growing rapidly. In 2020, the sector employed around 1.3 million people, rising to 1.8 million three years later [54]. However, to reach the 2030 target, it is estimated that 3.5 million jobs will be needed by 2030 [51,54]. Meanwhile, the EU construction sector employs almost 25 million people in around 5.3 million companies. Small and medium-sized enterprises (SMEs) in particular benefit from the increased renovation market, as they account for 99% of EU construction companies and 90% of employment in the sector [56]. Experts in the social aspects of climate change and just transition emphasise that necessary but worrying actions, such as closing coal mines in fossil fuel-based regions, can cause conflict in local communities. For this reason, it is believed that the transition should be carried out carefully, taking into account the social dimension of the problem. A key argument is that companies producing renewable energy could mitigate the effects of the energy and climate crises while creating around 500,000 new jobs by 2025. However, significant investment in human capital is required to improve employee qualifications. The European Commission argues that projects related to the energy transition (e.g., solar panels) could be financed by the Just Transition Fund, which would support European regions that rely on fossil fuels and high-emission industries in their green transition. One example of such a region is the Hornonitriansky region in Slovakia, which focuses mainly on producing black circles. Projects supported by the Just Transition Fund will receive investment, typically in small and medium-sized enterprises, as well as in research and innovation, renewable energy, emission reduction, the circular economy, and worker retraining.

Table 6. Employment characteristics in the renewable energy sector by technology worldwide in 2022.

Technologies (Main)	Characteristics—Some Facts
Photovoltaics	Global solar PV employment reached 4.9 million in 2022, up from approximately 4.3 million the previous year. Four of the ten leading countries are in Asia, two are in the Americas, and three are in Europe. Together, the top ten countries accounted for almost 4.1 million jobs, representing 85% of the global total. Asian countries accounted for 73% of the world’s PV jobs, reflecting the region’s continued dominance in manufacturing and installations. The remaining jobs were in the Americas (11.5%), Europe (11%, with EU member states accounting for 10.6%), and the rest of the world (4.8%). PV employment in Europe was estimated at 540,000, 517,000 of which were in EU Member States. Ten countries account for 85% of all jobs in the photovoltaic sector worldwide. China clearly dominates in terms of employment, accounting for around 56% of all jobs worldwide (approximately 2.7 million). Second place goes to India, the USA and Brazil. The third group comprises Japan, Vietnam and Poland. Germany, Spain and Australia make up the top 10.
Wind	In 2022, global employment in the onshore and offshore wind sector remained stable at 1.4 million jobs. However, employment in the wind sector was concentrated in a relatively small number of countries. China accounted for 48% of the global total alone, followed by Asia (55%), Europe (29%), the Americas (16%), and Africa and Oceania (0.7%). Together, the ten largest countries employed 1.23 million people (88%). Of these, four were in Europe (Germany, the UK, France and Spain), four were in Asia (China and India) and two were in the Americas (the USA and Brazil). The recent phenomenon of rising input costs has prompted OEMs to increase their efforts to outsource some of their component production to low-wage countries. This will change the geographic structure of the industry.
Hydropower	More than a third of all people employed directly in the sector worldwide worked in China (35.3%), while India and Brazil had significant shares of 18.8% and 7.8%, respectively. Other countries with significant shares include Vietnam and Pakistan. Although smaller than the leaders, Vietnam (5.1%) and Pakistan (4.2%) also have significant shares in global hydropower employment. The United States and Colombia have similar shares: The United States (2.7%) and Colombia (2.3%) have relatively similar shares, which are much smaller than those of the leading countries. Russia (1.9%), Ethiopia (1.8%), Turkey (1.6%) and Canada (1.4%) have even smaller shares. Together, other countries account for 17.3% of global direct hydropower employment. This demonstrates that, while the leading countries have a significant presence, many other countries also contribute substantially to employment in the sector.
Liquid Biofuels	Worldwide biofuel employment reached 2.5 million in 2022, primarily in feedstock operations. There was a high concentration of employment, with the top ten countries accounting for 94% of global employment in the liquid biofuel sector. In cross-regional terms: Latin America accounted for 42% of all biofuel jobs worldwide and Asia (principally Southeast Asia) accounted for 37%. The more mechanised agricultural sectors of North America and Europe represent smaller employment shares (15% and 6%, respectively). Brazil clearly dominates the liquid biofuel sector in terms of employment, with around 0.8 million jobs. This represents 34% of all jobs in this sector worldwide. Indonesia ranks second with around 0.6 million jobs and the United States ranks third with around 0.35 million. Colombia and Thailand also have significant shares, with around 0.18 and 0.09 million jobs, respectively. The remaining countries—Malaysia, China, the Philippines, India and Poland—have a relatively small share of global employment in the liquid biofuels sector, with fewer than 0.1 million jobs each.

Source: own elaboration based on: [57].

Table 6. Employment characteristics in the renewable energy sector by technology worldwide in 2022.

Technologies (Main)	Characteristics—Some Facts
Photovoltaics	Global solar PV employment reached 4.9 million in 2022, up from approximately 4.3 million the previous year. Four of the ten leading countries are in Asia, two are in the Americas, and three are in Europe. Together, the top ten countries accounted for almost 4.1 million jobs, representing 85% of the global total. Asian countries accounted for 73% of the world’s PV jobs, reflecting the region’s continued dominance in manufacturing and installations. The remaining jobs were in the Americas (11.5%), Europe (11%, with EU member states accounting for 10.6%), and the rest of the world (4.8%). PV employment in Europe was estimated at 540,000, 517,000 of which were in EU Member States. Ten countries account for 85% of all jobs in the photovoltaic sector worldwide. China clearly dominates in terms of employment, accounting for around 56% of all jobs worldwide (approximately 2.7 million). Second place goes to India, the USA and Brazil. The third group comprises Japan, Vietnam and Poland. Germany, Spain and Australia make up the top 10.
Wind	In 2022, global employment in the onshore and offshore wind sector remained stable at 1.4 million jobs. However, employment in the wind sector was concentrated in a relatively small number of countries. China accounted for 48% of the global total alone, followed by Asia (55%), Europe (29%), the Americas (16%), and Africa and Oceania (0.7%). Together, the ten largest countries employed 1.23 million people (88%). Of these, four were in Europe (Germany, the UK, France and Spain), four were in Asia (China and India) and two were in the Americas (the USA and Brazil). The recent phenomenon of rising input costs has prompted OEMs to increase their efforts to outsource some of their component production to low-wage countries. This will change the geographic structure of the industry.
Hydropower	More than a third of all people employed directly in the sector worldwide worked in China (35.3%), while India and Brazil had significant shares of 18.8% and 7.8%, respectively. Other countries with significant shares include Vietnam and Pakistan. Although smaller than the leaders, Vietnam (5.1%) and Pakistan (4.2%) also have significant shares in global hydropower employment. The United States and Colombia have similar shares: The United States (2.7%) and Colombia (2.3%) have relatively similar shares, which are much smaller than those of the leading countries. Russia (1.9%), Ethiopia (1.8%), Turkey (1.6%) and Canada (1.4%) have even smaller shares. Together, other countries account for 17.3% of global direct hydropower employment. This demonstrates that, while the leading countries have a significant presence, many other countries also contribute substantially to employment in the sector.
Liquid Biofuels	Worldwide biofuel employment reached 2.5 million in 2022, primarily in feedstock operations. There was a high concentration of employment, with the top ten countries accounting for 94% of global employment in the liquid biofuel sector. In cross-regional terms: Latin America accounted for 42% of all biofuel jobs worldwide and Asia (principally Southeast Asia) accounted for 37%. The more mechanised agricultural sectors of North America and Europe represent smaller employment shares (15% and 6%, respectively). Brazil clearly dominates the liquid biofuel sector in terms of employment, with around 0.8 million jobs. This represents 34% of all jobs in this sector worldwide. Indonesia ranks second with around 0.6 million jobs and the United States ranks third with around 0.35 million. Colombia and Thailand also have significant shares, with around 0.18 and 0.09 million jobs, respectively. The remaining countries—Malaysia, China, the Philippines, India and Poland—have a relatively small share of global employment in the liquid biofuels sector, with fewer than 0.1 million jobs each.

Source: own elaboration based on: [57].

Turning to the challenges that new socio-economic conditions pose to human capital, the first area is innovation and the development of modern technologies. Energy transformation relies on implementing new technologies in renewable energy sources (RES), energy storage, smart grids, energy efficiency and carbon capture. These innovative solutions require a high level of human capital, including educated engineers, scientists, researchers and programmers, to develop, improve and implement them. The second area of challenges relates to the growing demand for a qualified workforce. These qualifications mainly enable the construction, installation, operation and maintenance of new energy systems based on RES. All of these projects require the employees implementing them to have specialist knowledge and skills. The market is expected to provide experts in areas such as RES, energy storage, smart grids, energy efficiency, hydrogen technologies and carbon capture and storage (CCS), as well as digitalisation and the automation of energy systems. The energy transition is creating new jobs in sectors such as wind energy, photovoltaics and electromobility, as well as in areas related to the energy efficiency of buildings and industrial processes. A highly skilled workforce is essential to meet these new requirements. However, the energy transition will necessitate changes to the employment structure, particularly in sectors related to fossil fuels. Reskilling and upskilling programmes supported by appropriate investment in human capital will be essential for the fair and effective implementation of the transition. A high level of human capital, characterised by the ability to learn and adapt, will make it easier for employees to retrain and acquire the new skills required in the developing renewable energy sectors (RES).

Another important area is entrepreneurship and new business models, specifically the need to recruit managers who can understand and implement them. The energy transition is creating new business opportunities in areas such as renewable energy, energy services, smart grid technologies, and sustainable transport. Developing these new businesses and models will require entrepreneurs with competencies such as project management, communication, collaboration, problem solving, adaptability, entrepreneurship and innovation. These are key to coordinating complex transformation initiatives and engaging various stakeholders.

It should not be forgotten that a significant challenge in the process of energy transformation is obtaining broad public support and understanding of the benefits of switching to clean energy. A higher level of education and ecological awareness in society contributes to greater acceptance of changes in the energy system and supports the implementation of climate policies. School and even pre-school education (from a young age) should play an important role in this process. Properly prepared human capital will encourage greater citizen involvement in saving energy and using sustainable solutions. The success of the energy transformation process will also be evident in the effectiveness with which public policies are managed and implemented. A high level of human capital in the public sector is paramount, particularly at local level. Managing the energy transformation process requires competent, well-educated public administration specialists who can develop and implement coherent, effective energy and climate policies. At the local (rural) level, these individuals are often the primary point of contact for citizens and the key to transferring knowledge to society. This is a key condition for the transformation to proceed smoothly.

In summary, the complexity of the energy transition makes it necessary to adopt a new framework for expressing the essence of human capital. In this context, ‘Human Capital for an Economy in Energy Transition’ can be defined as the knowledge, technical skills (‘Green Skills’), behavioural attitudes and systemic adaptability required to achieve the goals of a sustainable energy transition. However, this human capital is distinguished from traditional technical green skills (e.g., the ability to install renewable energy panels) by the inclusion of aspects such as:

• Systemic competencies: These encompass an understanding of technological, political and regulatory interconnections. They are a fundamental dimension of the Theory of Innovation Systems (TIS) because new technologies (e.g., hydrogen and energy storage) do not develop in isolation. They require skilled individuals (such as engineers, scientists and managers) who can manage knowledge flows, create networks and overcome institutional barriers [10,58]. Therefore, in a transforming economy, the role of human capital is to support (or hinder) the development of any technological innovation system.
• Behavioural competencies: These represent the ability to manage change and the ethics of sustainable development. Energy transformation requires changes in social behaviour, as well as the attitudes of consumers and decision-makers. Technical knowledge alone is insufficient. In a transforming economy, human capital equips people with the ability to understand the environmental consequences of their actions and to incorporate pro-ecological norms, which, according to the Value-Belief-Norm Theory (VBT), leads to the acceptance or opposition of energy policies [59]. This dimension appears to be essential for implementing the assumptions of an energy-transformation economy.

3. Material and Methods

3.1. Research Objective and Research Area

The main empirical objectives are firstly to diagnose the overall level of human capital in the energy transition economy based on the original synthetic measure, HCIe, and secondly to analyse and assess the variation in its spatial distribution across the European socio-economic landscape, which serves as a foundation for developing a targeted policy typology directly linked to the identified cluster profiles and their specific weaknesses.

The general research question is: what is the level and extent of variation in the internal structure of human capital in the European socio-economic space? What actions should individual European countries take to support the development of human capital in the context of energy transformation?

The adopted research concept also poses two further questions: First, how can the essence of human capital be expressed in an energy-transforming economy? Second, are there appropriate measures for measuring it based on this research approach?

The empirical analysis focuses on the overall level of the human capital index (HCIe) in energy-transforming economies in European countries. Based on this index, a hierarchical classification of European countries has been developed. A detailed structural analysis of human capital was also conducted, including indicators—partial measures expressing the level of human capital for each of the five structural components: Innovation and Creativity (INN), Labour Market (LM), Health (HLH), Education (EDU) and Social Quality (LQ).

The final result of the research was the identification of similar groups of countries and their characteristics, as well as proposed scenarios dedicated to supporting the energy transition process for individual groups. Furthermore, based on the collected empirical material, an attempt was made to describe scenarios that would support the development of human capital in the different groups of countries, with the aim of optimising energy transition activities.

3.2. Research Sample: Criteria for Selecting Objects for the Study

The empirical analysis focused on European countries. The initial conceptual framework for this article had aimed to cover all European countries. However, this was not possible due to several limitations. The main selection criteria were data availability, completeness and continuity. The first limitation arose from the fact that the planned model data came from three different international databases: IRENA, EUROSTAT, OECD and the World Bank. However, it transpired that these data were not comparable due to the different countries for which these databases publish data. The second criterion for selecting the subjects was data completeness and continuity. Some countries did not meet this criterion (Montenegro, Liechtenstein, Monaco, Serbia, Kosovo, Albania, the United Kingdom, Bosnia and Herzegovina, North Macedonia, Switzerland, Malta, and Cyprus). Ultimately, an analysis of the availability of data on empirical variables verified the original assumption and limited the study sample to 26 countries, eliminating those for which data were either unavailable or incomplete. The final study group comprised 24 EU member states and two non-EFTA countries: Norway and Iceland. It is important to note that twelve countries were excluded from the analysis due to a critical lack of data on key indicators, particularly those relating to the VBN (Behavioural Capital) and MLP (Institutional Governance) dimensions. It is fully recognised that this exclusion may affect the representativeness of regional findings, and therefore, great caution should be exercised when generalising the results of the study to the entire population.

3.3. The Definition of Human Capital in an Energy-Transforming Economy

The approach to constructing the human capital index used in this article directly refers to the classical approach to defining human capital and the basic components of its structure based on the methodological approach of Domański [60] and Klonowska-Matynia [61]. According to this approach, human capital is defined as the knowledge, skills, competencies, experience and health that individuals possess, which contribute to their productivity and economic growth. Investments in human capital, such as education and training, are considered a means of increasing the capacity of individuals and groups to generate economic value. An extension of this approach is “green” human capital, which considers the knowledge, skills, competencies, attitudes and commitment of individuals in relation to environmental protection and sustainable development. This includes the ability to innovate and implement green technologies, as well as the ecological awareness and skills necessary for functioning in a sustainable economy. In the context of energy transformation, human capital refers to the knowledge, skills, competencies, experience and health of individuals that are necessary for the effective design, implementation, management and maintenance of new, sustainable energy systems. This includes technical specialists, as well as people with non-technical skills who support the process.

3.4. The Operationalisation of the Concept of Human Capital in the Context of an Energy-Transforming Economy:Selection of Diagnostic Variables for the Model

Below, we propose a synthetic measure that expresses the overall level of human capital in an energy-intensive economy (HCIe). We also propose partial synthetic measures that express the level of this resource in a given component of its structure. We used the developed tool to diagnose and explore human capital in relation to the European socio-economic space.

Following the construction of the author’s human capital measure [61] certain modifications were made to include features that express the essence of human capital in the classical approach while adapting it to an economy undergoing energy transformation. Five classic components of the HCIe indicator’s structure were adopted in its construction, as follows: Innovation and Creativity (INN), Labour Market (LM), Health (HLH), Education (EDU) and Social Quality (LQ). The set of diagnostic features was also modified to align with the adopted research concept. Twenty-four empirical variables (diagnostic variables) were introduced to the model. A detailed list is presented in

Table 7. Structure and empirical variables used to construct the human capital index in the energy transformation economy (HCIe).

Diagnostic Variable	Characteristics	Indicator Description	Time Range Aggregation Level	Destimulant (d)/Stimulant (s)/Nominant (n)	Data Source
Structure Component: Innovation and Creativity [INN]
X1	Renewable Energy Patents Evolution	Number of patents per 100 tys. Inhabitans	Number of patents average from 2018–2023 Number of inhabitants average from 2022–2024	s	IRENA EUROSTAT
X2	Research and development expenditure [R&D)	% of GDP	average for 2021–2023, annual	s	EUROSTAT
Structure Component: Labour Market [LM]
X3	Labour productivity of market production in the renewable energy sector	Full-time equivalent [FTE)	Annual 2022	s	IRENA EUROSTAT
X4	Employment in the environmental goods and services sector	Full-time equivalent [FTE)	average for 2021–2023, annual	s	EUROSTAT
X5	Job creation in the environmental economy. Protection of ambient air and climate	Full-time equivalent [FTE)	dynamics 2022/2021, annual	s	EUROSTAT
X6	Job creation in the environmental economy. Waste management	Full-time equivalent [FTE)	dynamics 2022/2021, annual	s	EUROSTAT
X7	Job creation in the environmental economy. Management of energy resources	Full-time equivalent [FTE)	dynamics 2022/2021, annual	s	EUROSTAT
Structure Component: Health [HLH]
X8	Cause of death: Neoplasms, Malignant neoplasms, Malignant neoplasms of trachea, bronchus, lung	Deaths per 100,000 inhabitants	Annual, average for 2019–2021	d	OECD
X9	Cause of death: Diseases of the circulatory system	Deaths per 100,000 inhabitants	Annual average for 2019–2021	d	OECD
X10	Cause of death: Asthma	Deaths per 100,000 inhabitants	Annual average for 2019–2021	d	OECD
X11	Cause of death: Chronic obstructive pulmonary diseases	Deaths per 100,000 inhabitants	Annual average for 2019–2021	d	OECD
X12	Cause of death: Certain infectious and parasitic diseases, Tuberculosis	Deaths per 100,000 inhabitants	Annual average for 2019–2021	d	OECD
X13	Cause of death: Diseases of the respiratory system, Pneumonia	Deaths per 100,000 inhabitants	Annual average for 2019–2021	d	OECD
X14	Healthy life years in absolute value at birth	Healthy life years in absolute value at birth	Annual average for 2020–2022	d	EUROSTAT
X15	Life expectancy	Less than 1 year	Annual average for 2021–2023	d	EUROSTAT
Structure Component: Education [EDU]
X16	Early leavers from education and training	From 18 to 24 years [%]	Annual average 2021–2023	d	EUROSTAT
X17	Employment rates by sex, age and educational attainment level [%]	From 18 to 24 years [%]. Less than primary, primary and lower secondary education (levels 0–2)	Annual average for 2021–2023	s	EUROSTAT
Structure Component: Quality of Life [LQ]
X18	Inability to keep home adequately warm	[%]	Annual average for 2022–2024	d	EUROSTAT
X19	Persons at risk of poverty or social exclusion	[%]	Annual average for 2021–2023	d	EUROSTAT
X20	Long term unemployment	From 15 to 74 years [%]	Annual average for 2021–2024	d	EUROSTAT
X21	Share of energy from renewable sources for heating and cooling	[%]	Annual average for 2021–2023	s	EUROSTAT
X22	Overall share of energy from renewable source	% of gross final energy consumption	Annual average for 2021–2023	s	EUROSTAT
X23	Share of energy from renewable sources in gross electricity consumption	[%]	Annual average for 2021–2023	s	EUROSTAT
X24	Share of energy from renewable sources in transport	[%]	Annual average for 2021–2023	s	EUROSTAT

Source: own elaboration.

. It should be noted that the original set of data considered for inclusion in the model was much wider. Firstly, variables related to education published by the OECD were considered particularly useful and valuable. However, due to a lack of data for all countries and continuity issues, the final set of variables was limited to 24. All variables were analysed in the statistical procedure.

Table 7 provides a list of all diagnostic variables, along with their descriptions, sources, and time ranges. The data sources used to construct the HCIe measure and the individual partial measures were the EUROSTAT, OECD and IRENA databases. The IRENA database publishes a number of indicators relating to energy, with a particular focus on renewable energy. Furthermore, the subsequent empirical section of the article uses data on the World Bank’s Human Capital Index (HCI) and GDP from the EUROSTAT database to facilitate international comparisons.

The adopted set of diagnostic variables can contribute to discussions about their specificity in terms of causality and the effect they have on the level of human capital achieved. When studying complex categories such as human capital [62], social capital or socioeconomic development [63], the distinction between input and output, and therefore between cause and effect, becomes analytically blurred.

This issue is not only relevant to the classical theory of human capital [6,64]), but also forms a fundamental assumption of the Theory of Cumulative Causality in regional development. According to Myrdal’s [65] approach, an initial advantage (high human capital) creates feedback loops whereby systemic outputs (e.g., a high level of TIS or VBN stability) become new inputs that enhance human capital. These phenomena are therefore endogenously linked. In this model, therefore, outcome and context indicators are treated not as simple effects, but as proxies for the functional effectiveness of human capital in the transformation system. This is fully consistent with the systems approach (TIS, MLP) and the issue of cumulative regional development.

A synthetic assessment of the correlations reveals that the Pearson correlation coefficients clearly indicate a strong positive correlation between investment in research and development, environmental sector development and renewable energy sources, and job creation. Furthermore, an increased share of renewable energy in final consumption and electricity production is negatively correlated with mortality from cancer, circulatory system diseases and respiratory diseases. This suggests that the transition to cleaner energy supports the economy and labour market and brings tangible health benefits to society by reducing air pollution. Indicators of ‘R&D expenditure’ show positive correlations with ‘job creation in the environmental economy’ and ‘job creation in the renewable energy sector’, suggesting that investment in research and development, as well as an increase in the number of researchers, contributes to job creation in green economy sectors. This is consistent with the expectation that innovations drive the development of new sectors. Mortality rates due to lifestyle diseases (cancer, circulatory and respiratory diseases) are strongly correlated with each other and negatively correlated with life expectancy. ‘Life expectancy at birth’ shows strong negative correlations with these mortality rates. Logically, the more people who die from these diseases, the shorter the average life expectancy. The same mortality rates also show negative correlations with the share of renewable energy and energy efficiency. This suggests that an increase in the share of renewable energy and improvements in energy efficiency tend to lead to a decrease in mortality due to these diseases. In general, the correlation coefficients are in line with expectations.

3.5. Construction of a Synthetic Measure of the General Level of Human Capital in the Energy Transformation Sector [HCIe], as Well as Partial Measures: Description of the Method

The [HCIe] measure was constructed based on the methodology of the original human capital [HCI] measure, which was developed for diagnosing rural areas in Poland [61]. The universality of this measure enabled it to be adapted to the research concept adopted in this article. The study employed the taxonomic method of patternless hierarchy and classification of multi-feature objects, which is well-suited to the study of complex phenomena such as human capital [61,66,67,68]. The essence of human capital in each component of its structure was expressed by selecting diagnostic features (x₁, …, x_n) to create a matrix in the form X= [x(_ij)] [68] as follow:

$$X=\left[{\mathrm{x}}_{\mathrm{i}\mathrm{j}}\right]=\left[\begin{array}{c}\begin{array}{cccc}{\mathrm{x}}_{11}& {\mathrm{x}}_{12}& \dots & {\mathrm{x}}_{1\mathrm{n}}\\ {\mathrm{x}}_{21}& {\mathrm{x}}_{22}& \dots & {\mathrm{x}}_{2\mathrm{n}}\\ \dots & \dots & \dots & \dots \\ {\mathrm{x}}_{\mathrm{r}1}& {\mathrm{x}}_{\mathrm{r}2}& \dots & {\mathrm{x}}_{\mathrm{r}\mathrm{n}}\end{array}\end{array}\right]\left(\begin{array}{c}i=1,\dots ,r\\ j=1,\dots ,n\end{array}\right),$$

(1)

where

• i—object (country);
• j—diagnostic variable.

Each object was characterised by a vector of diagnostic variables in the following form:

$$x_i=\left[x_{i1},x_{i2},x_{i3},x_{i4},\dots {,x}_{in}\right](i=1,\dots ,r)$$

(2)

After initial statistical verification in terms of correlations and the coefficient of variation (V > 0.1), the empirical variables (so-called diagnostic features) were subjected to normalisation using the zero unitarisation method (MUZ) according to the formula [66].

$$Zij={\displaystyle \frac{Xij-min\left\{xij\right\}}{\mathit{max}\left\{xij\right\}-\mathit{min}\left\{xij\right\}}}$$

(3)

where

• i—index of the calculated partial indicator, takes values from 1 to n [n number of partial indicators).
• j—index of a given country. takes values from 1 to 26 [number of countries).
• xij—specific value of i-th factor achieved by j-th country in a given year.
• min{xij}—minimum value of i-th factor. achieved by countries in a given year.
• max{xij}—maximum value of i-th factor. achieved by countries in a given year.

After adding up the previously normalised values, the values of the destimulants were transformed by multiplying them by −1. Having at hand the matrices of the optimal variable values normalized in any way, determined in the first step of the two-stage information capacity method, in the next step, the partial variables were aggregated according to the formula:

$$q_i=\sum _{j=1}^n{z}_{ij}i=1,\dots ,r$$

(4)

As a result of dividing the value of q_i by the number of diagnostic variables n, synthetic variables Q_i were obtained in the i-th object, expressing the assessment of each of the examined objects (countries) in terms of the general level of human capital in the area of the energy transformation of the HCIe economy, contained in the range [0;1]. A characteristic feature of the obtained synthetic measure is the ordering of the complex phenomenon using a single value, allowing for comparative analyses to be carried out in such a way that the interpretation of the obtained hierarchy is facilitated without changing the order of objects [68]. These units were then grouped into classes based on similar levels of human capital. The range of the Q_i variable was then used to classify the objects.:

$$R\left(Q\right)=\underset{i=\mathrm{1,2},\dots ,r}{\max }{Q}_i-\underset{i=\mathrm{1,2},\dots ,r}{\min }{Q}_i$$

(5)

The classification of spatial units involves the complete and disjoint division of a given heterogeneous set of objects (in this case, countries) into a number of non-empty subsets that are more homogeneous internally. Based on this established range, five classes of objects of an equal size were determined. Applying the above procedure resulted in a hierarchical classification of municipalities according to their human capital index (HCI). The classification of spatial units was performed ex post [por. [63,69,70,71,72]]. The analysis of empirical data resulted in the identification of classes that optimally reflected the observed similarities and differences between the analysed spatial units (Figure 4).

The above procedure was also used to estimate the level of human capital for each structural component (component) separately. Innovation and Creativity (INN), Health (HLH), Education (E), Labour Market (LM) and Quality of Life (LQ) (Figure 5).

A key methodological challenge was establishing the weights for the individual structural components of the HCIe index, a task that is inherently problematic and controversial when constructing synthetic measures. This study used an equal weighting approach for all five synthetic components. This approach was adopted to maintain methodological neutrality and avoid arbitrariness in the underlying approach. Often recommended for constructing composite indicators in the absence of expert consensus on relative weighting [73] this methodology ensures that the final HCIe result is not biased towards any one component.

When estimating the main measure (HCIe) and the individual partial measures (INN, LM, HLH, EDU and LQ), it was assumed that all diagnostic variables were equivalent. No additional weights were introduced for the individual empirical variables or partial components. Many authors emphasise that the issue of assigning weights in the construction of synthetic measures is controversial. In this case, given the lack of similar research on human capital in the energy transformation economy and the potential for referencing expert knowledge, it was deemed challenging to maintain relative objectivity when assigning weights to individual empirical variables.

Furthermore, the following criteria were maintained when constructing synthetic measures for the model: data continuity, completeness, comparability and reliability. The empirical data were verified using statistical tests. The strength and direction of the interdependencies between the variables included in the overall HCI measure were examined, as were the relationships between the structural components of human capital (partial measures). Correlation analysis confirmed the presence of positive levels of interdependency between the adopted components, albeit varied. The observed strength and direction of these interdependencies are valid and do not duplicate the same information.

A multivariate analysis method, namely cluster analysis based on the k-means algorithm, was used to explore European countries in terms of their human capital structure for the energy transition economy. The k-means method is a non-hierarchical algorithm that involves searching for and isolating groups of similar objects (clusters). Calculations were performed using the Statistica 13.0 software package.

The k-means method was used to create k different clusters of countries, each as distinct as possible from the others, while optimising variability within and between clusters. Clearly, the similarity within each cluster should be as high as possible, while the clusters themselves should be as distinct from each other as possible. The advantage of this method is that it allows us to examine both the distribution of components and the relationships between them. It is possible to determine the strengths and weaknesses of individual components and identify the presence or absence of certain factors (features). The specific nature of the typology allows it to be used to describe many aspects of socio-economic space, such as the labour market, demographic structure, social and economic fabric, and other characteristics.

The cognitive specificity of each typological analysis is determined by the research objective and data availability. Furthermore, despite the use of statistical methods and the implementation of formally binding research procedures, each typology, including the author’s own, is conventional in nature.

The first step of the k-means algorithm involves assigning each object (country) to the nearest centroid (cluster centre). This is achieved by minimising the distance of each object from the nearest centroid. In the standard implementation, the algorithm uses the squared Euclidean distance, which eliminates the need for the square root and thus simplifies the calculations while minimising the time taken by the algorithm (while maintaining the same proximity order).

The optimisation criterion (WCSS) that the algorithm tries to minimise is the sum of the squared Euclidean distances between points and their centroids, i.e., the Intra-Cluster Distance Sum of Squares (WCSS). In this study, the optimal number of clusters (k) was determined in a stepwise manner by testing different values of k and ensuring consistency with domain knowledge. This means that the clusters created for the selected k were meaningful in the context of the discussed problem and provided a satisfactory basis for interpreting the results. The results obtained were enhanced by using the Elbow statistical test, which determines the optimal number of clusters while minimising total variability within clusters based on the following formula [74]:

$$\mathrm{W}\mathrm{C}\mathrm{S}\mathrm{S}(\mathrm{K})=\sum _{k=1}^K\sum _{i\in Ck}{║Xi-\mu k║}^2$$

(6)

The two methods used—hierarchical classification and non-hierarchical grouping—provide different information. The HCIe measure enables the level of resources to be determined. In this case, it may be found that different countries have similar HCIe levels, but this does not mean that they achieved them based on the same internal potential. As the adopted concept explores the structure of human capital in five areas—the so-called structural components—only the non-hierarchical grouping method reveals surpluses or deficits in a given area that characterise a given country. For this reason, using these two methods seems justified.

3.6. Justification for Selecting the Variables for the Model

Selecting diagnostic variables for constructing synthetic measures is always controversial. This case is no exception, with the absence of wider studies posing a significant barrier. Nevertheless, this article attempts to create a measure that expresses the overall level of human capital in HCI and the level of human capital in each of its components separately. The primary criterion for selecting the diagnostic variables for the model was the existing scientific literature on the role of human capital in socio-economic development processes. Furthermore, the author’s research and publication experience in this area presented challenges. Collecting data that would capture the essence of human capital for an economy undergoing energy transformation was problematic. Due to the limited number of publications in this area, some variables were arbitrarily selected for the model based on the author’s personal judgement and the available research results from other authors across various scientific disciplines. A descriptive justification for selecting the most sensitive, controversial and problematic variables is provided below. This justification is presented sequentially for each component of the human capital structure, as assumed in the model for the HCIe indicator.

3.6.1. Component: Innovation and Creativity (INN)

Based on previous experience of measuring human capital in an economy undergoing energy transformation, the classic variable of expenditure on research and development was used to capture the essence of this component. Additionally, to adapt the classic measure to an economy undergoing energy transformation, the number of renewable energy patents was used as a variable. According to data published by IRENA in Renewable Energy Patents Evolution [75] the number of patents worldwide is increasing, with solar energy (PV) accounting for the largest share. Figure 6 below presents data on patents worldwide in recent years, including all technologies and subtechnologies.

To compare European countries in terms of their level of innovation, data on renewable energy patents per 100,000 inhabitants over the last three years was used (see Figure 7).

In general, three countries stood out as leaders in innovation during the analysed period: Germany, Croatia and Denmark. The countries with the fewest patents per 100,000 inhabitants (i.e., less than two) are: Ireland, Latvia, Greece, Bulgaria and Belgium. Examining the diversity of technologies used in renewable energy patents, it was observed that, since 2018, most patents filed are essentially related to the area of ‘adaptation’, i.e., modifying and adapting existing patented technologies to improve them, expand their applications, or circumvent existing rights. This is a key element of continuous development and innovation in the clean energy sector. Some changes have been observed since 2021, with the exceptions to the observed trend being: Iceland (power and geothermal energy), Ireland (power and ocean energy in 2021 and wind energy in 2022), Estonia (wind energy in 2022 and building and enabling technologies in 2021) and Poland (building and solar energy PV in 2022) [76].

3.6.2. Component: Health [HLH]

The next component of the human capital structure was the ‘Health’ component. This component was modified and new empirical variables were introduced to express the essence of this resource in the context of an energy transformation economy. Health is now considered one of the forms of human capital [6,77,78]. Good health is a fundamental prerequisite for human life and social well-being [79,80,81,82,83]. This section focuses on justifying the variables adopted in the ‘Health’ area in the context of the energy transformation economy.

The HCIe index in the ‘Health’ area comprises seven variables, five of which are directly related to causes of death. Analysing these causes of death reveals potential links with energy policy, although these are not direct and require further analysis and context. Nevertheless, there are several arguments that justify the selection of these variables and suggest that energy policy may impact the listed causes of death. The impact of energy policy on respiratory diseases can be assessed on the basis of pollutant emissions, for example. The main source of air pollution is the combustion of fossil fuels (coal, oil and natural gas) in power plants, heating systems and transport. Gaseous air pollutants include carbon dioxide, carbon monoxide, sulphur oxides, nitrogen oxides, ammonia and ground-level ozone. Particulate matter includes dust, soil, acids, organic molecules and some metals. Long-term exposure to these pollutants significantly increases the risk of cardiovascular diseases such as hypertension, atherosclerosis, heart attacks and strokes [84], as well as respiratory diseases including chronic obstructive pulmonary disease, asthma, pneumonia and lung cancer [85]. Exposure is also associated with adverse birth outcomes and obesity [86]. In 2019, outdoor air pollution in urban and rural areas was estimated to cause 4.2 million premature deaths worldwide each year. Additionally, certain industrial processes in the energy sector (e.g., fuel extraction and processing) can expose workers and local communities to carcinogens. There is growing evidence that widespread environmental contaminants known as endocrine-disrupting chemicals (EDCs) can have an adverse effect on the reproductive health of animals and humans, and are associated with infertility [87,88], as well as ageing and female reproductive diseases [89,90]. Energy policies that promote renewable energy sources, energy efficiency, electromobility, and cleaner combustion technologies can reduce emissions of these pollutants and thus improve air quality, reducing morbidity and mortality due to respiratory diseases [86,91]. As indicated by data on mortality from selected causes in European countries, there is huge variation in scale (see Figure 8).

In addition to the causes of death, two further indicators were used to illustrate human capital: healthy life years at birth and life expectancy. These data are presented in Figure 9 and Figure 10. The average life expectancy in the group of 26 European countries is 80.1 years. The longest life expectancy is found in the northern countries (more than 83 years): Sweden (83.9 years), Slovenia, Spain, Norway and Iceland. The shortest life expectancy (approximately 74 years) is found among the inhabitants of Bulgaria, Romania and Latvia. On average, people live in good health for 62.2 years. The shortest average was observed in Latvia at 53.8 years, while in southern European countries such as Spain and Greece, it is the longest at over 69 years.

Zandel et al. [92] have also implicated neurological and mental disorders, albeit less directly, in energy policy. Air pollution, particularly fine particulate matter, is increasingly associated with adverse neurological and mental health outcomes, especially in children and the elderly. It may ultimately contribute to the development of neurodegenerative diseases and poor mental health. There is growing evidence that exposure to air pollution affects the central nervous system [93,94,95], with studies showing adverse effects on cognitive and behavioural functioning, attention, intelligence quotient (IQ), memory and academic performance [96,97,98]. Recent studies have also shown that air pollution is a major risk factor for internalising psychopathology. For instance, a recent meta-analysis revealed a strong association between elevated levels of airborne particulate matter (PM2.5 and PM10) and an increased risk of anxiety, depression, and suicide, as well as changes in brain regions linked to psychopathological risk [99,100,101,102,103].

Anxiety and depression are the most common mental health conditions worldwide [104] and can increase the risk of suicide attempts and completion [105]. They can also have an adverse effect on family and social relationships and are associated with a significant economic burden for individuals and society. Indeed, these disorders cost the global economy approximately US$1 trillion per year in lost productivity [106]. Given that 99% of the world’s population lives in environments that do not meet World Health Organization (WHO) guidelines for air quality [107], understanding the potential role of air pollution in the risk of mental illness is a major public health concern. Furthermore, more than one in ten people worldwide lived with a mental health disorder in 2019 [104]. In addition to the above-listed diseases, endocrine, nutritional and metabolic diseases are also associated with energy policy, albeit indirectly. Some air pollutants have been found to have endocrine-active effects, which can potentially affect the hormonal system and increase the risk of metabolic diseases [108]. Furthermore, energy policy can influence the availability and cost of food through its impact on agriculture and transport, which may be significant for nutritional and metabolic diseases [107]. Promoting sustainable energy production and transport can have a positive indirect impact on these areas of health [106]. Switching to cleaner energy sources and implementing stricter regulations on industrial emissions could reduce exposure to these risk factors.

3.6.3. Component: Quality of Life (LQ)

A classic and widely used measure of human capital in the area of quality of life is GDP per capita, but it could also be the number of people living in poverty or the long-term unemployment rate. However, the focus of this study was on variables that would explain quality of life in the context of an economy undergoing energy transformation (see Table 7). The question of the social aspects of a just energy transformation is particularly important in this context. Does the transition to renewable energy sources and the decarbonisation of the economy have the potential to reduce or increase energy poverty? How can energy policy contribute to reducing the negative health effects associated with an inability to heat a home? How can programmes to improve the energy efficiency of buildings reduce demand and lower bills for poor households? In this context, one of the variables included in the expression of the essence of human capital was the indicator ‘Inability to keep home adequately warm’, which refers to energy poverty. This measures the percentage of the population or households that cannot afford to heat their homes to an adequate temperature for health and comfort. A high value of this indicator suggests that a significant proportion of society is struggling to meet their basic energy needs for heating. This highlights a social and economic problem.

According to the Energy Poverty Observatory [109], energy poverty is defined as a lack of access to basic energy services by households and individuals. This phenomenon has many consequences, affecting health [110,111], life satisfaction [112,113] and the environment [114]. It can also indirectly reflect the quality of housing, since poorly insulated properties require more energy to heat efficiently, thereby increasing the risk of energy poverty. In Central and Eastern Europe, district heating plays a significant role in providing heat to households. The material and social characteristics of district heating mean it can both prevent and cause energy poverty. Households in Central and Eastern Europe are often ‘trapped’ in unsatisfactory or unprofitable heating systems with limited options for change [115,116].

The energy transition required to achieve decarbonisation goals is expected to radically alter the technologies and energy sources used in our economies, as well as how we consume energy. This could have significant impacts on low-income, vulnerable consumers, who may be unable to make the necessary investments or fuel changes, or who may suffer from higher prices. Some studies show that households with lower incomes, smaller sizes or lower levels of education are disproportionately affected by energy poverty during transitions to cleaner energy sources, such as electricity and gas [117]. Other studies highlight that, under these policies, households affected by energy poverty may have difficulty accessing basic energy services, which could deepen social inequalities. With the implementation of the EU CO₂ Emissions Trading System in 2027, transport costs could increase by almost one third, affecting the prices of goods and services and exacerbating energy poverty [118,119,120].

Figure 11 presents data on the scale of energy and social poverty. It should be noted that these are not identical phenomena. A high level of the ‘inability to keep home adequately warm’ indicator suggests a risk of growing social inequalities and primarily affects the poorest European countries (Romania and Bulgaria) as well as southern countries such as Greece, Spain and Portugal, and some Eastern European countries (Lithuania). The scale of social poverty is much higher and less diverse than that of energy poverty in all European countries. Even in highly developed and wealthy countries such as Sweden, a significant proportion of the population is at risk of social poverty. Despite theoretical cost advantages over fossil and nuclear energy sources, renewable energy deployment is currently associated with higher income needs and increased risk of energy poverty, mainly because commercial producers retain the economic surplus from renewable energy. At the same time, consumers are burdened with subsidies. For example, the renewable energy tax in Germany has disproportionately burdened households affected by energy poverty [121]. In summary, energy poverty, or the inability of a household to afford basic energy services, is an expression of a fundamental socio-political injustice with serious detrimental effects on equality, health and well-being [122] (On 1 July 2022, the EEG fee (Erneuerbare-Energien-Gesetz—Renewable Energy Sources Act) in Germany was completely abolished, which was intended to provide relief to consumers in the face of rising energy costs.).

Another indicator used in the empirical analysis of the “Quality of life” area is the “Overall share of energy from renewable sources”, which is closely linked to human capital in the transformation to a zero-emission economy (Figure 12). The first is the quality of public health, which was described in detail in an earlier section of the article. The literature indicates that energy transformation has a positive impact on public health by reducing air pollution. Studies such as the global analysis of cardiovascular diseases by Lelieveld et al. [123] emphasise the negative effects of pollution from the combustion of fossil fuels. The Lancet Countdown systematically documents the health benefits of switching to cleaner energy sources [124]. Raunio and Karjalainen’s [125] analysis of Nordic countries provides evidence of the positive local health effects of reducing energy-related emissions. Together, these publications argue that an energy transition leads to improved population health and increased productivity, as well as reduced healthcare costs, thereby enhancing human potential.

Another aspect of the quality of life in an economy undergoing energy transformation is the mitigation of climate change and the promotion of well-being. The Intergovernmental Panel on Climate Change’s extensive reports provide compelling scientific evidence of the key role of renewable energy sources [126]. The Stern economic analysis (2007) argues that investments in clean energy, including renewable energy sources, are economically justified in the long term [127]. These investments contribute to the protection of natural resources and a stable environment, both of which are essential for the sustainable well-being and development of future generations. It is impossible to talk about quality of life without considering social development itself. Reports in the International Energy Agency’s “Energy Access Outlook” [128] series emphasise the role of decentralised renewable energy solutions in providing access to energy for remote and underdeveloped communities. This has a direct impact on improving living conditions and enabling the development of human potential [128]. Bhattacharyya’s (2019) review examines the links between access to energy, often based on renewable energy sources in rural areas, and social development and poverty reduction [129].

3.6.4. Component: Labour Market (LM)

Quality of life is undoubtedly related to health and well-being, but it also affects the labour market and employment. From an individual’s perspective, economic security and a decent standard of living thanks to employment are essential for a good life. In macroeconomic terms, increasing the share of renewable energy sources requires suitably qualified staff to operate, develop and manage this new energy system and related sectors of the economy (‘green’ skills). Studies analyse how investments in clean energy, including RES, affect job creation and economic growth potential. Reports by the International Renewable Energy Agency (IRENA), such as the annual Renewable Energy and Jobs—Annual Review, provide global data confirming the creation of new jobs in the renewable energy sector. Pollin, Heintz and Garrett-Peltier’s (2009) study analyses the potential for economic growth and job creation through clean energy investments. These publications indicate that energy transformation is necessary not only from an environmental point of view, but also creates new economic and professional opportunities, thereby improving quality of life and strengthening human capital [130].

According to reports by [54] investments in renewable energy create jobs in the production, installation, operation and maintenance of equipment, while energy efficiency programmes for buildings, transport and industry generate employment opportunities in areas such as energy audits, modernisation and the production of energy-efficient technologies. A number of actions can ensure that the transition to a clean energy future is job-rich and fair. To demonstrate the role of human capital in the labour market of a transforming economy, the study uses data on ‘Labour productivity of market production in the renewable energy sector’, among others. This indicator measures the efficiency with which labour is used to produce goods and services in the renewable energy sector (see Figure 13). High labour productivity means that the sector is more competitive and generates more added value per employee. This allows for comparison of labour productivity in the renewable energy sector with labour productivity in other energy sectors (e.g., the fossil fuel sector) or in the entire economy. An increase in labour productivity in the renewable energy sector may indicate technological progress and innovation, enabling the production of more energy with fewer workers. Changes in labour productivity can affect employment in the renewable energy sector. Increases in productivity could lead to more energy being produced with fewer workers, but they could also stimulate growth in the sector and job creation in other areas (e.g., research and development, equipment manufacturing).

The second important indicator expressing the essence of human capital for the energy-transforming economy in the labour market was ‘Job creation in the environmental economy and protection of ambient air and climate’. This indicator measures the number of new jobs created in sectors related to environmental protection, specifically those created as a result of actions to improve air quality and counteract climate change. Additionally, the model includes detailed data on two other areas: waste management and management of energy resources. Figure 14 shows employment dynamics in 2021–2022.

3.6.5. Component: Education [EDU]

Selecting the variables to express the essence of human capital in education should not be difficult for researchers. However, in this study, limitations were encountered when trying to obtain data other than the classic indicators published by EUROSTAT. The OECD database provides a wide range of data on education. This includes data obtained from the Survey of Adult Skills (PIAAC) on the key information-processing skills of adults aged 16–65, i.e., literacy, numeracy, and problem-solving. These are the skills that individuals need to participate in society and develop economies [131]. Another OECD study provides some fascinating data [132]. In the 2022 cycle, PISA measured the creative thinking of 15-year-old students for the first time. PISA defines creative thinking as the ability to generate, evaluate, and improve ideas to create original and effective solutions, develop knowledge, and produce imaginative expressions [133].

It emphasises the importance of students learning to generate ideas productively, assess their relevance and originality, and refine them until they achieve a satisfactory result. Further data is provided by the International Early Learning and Child Well-being Study [134]. This international study assesses children aged five who attend early years education and care centres and/or schools. It measures early literacy, early numeracy, self-regulation, empathy, trust and prosocial behaviour. Unfortunately, as the lists of OECD and European countries do not match, the study ultimately uses two classic measures of human capital related to education: early leavers from education and training and employment rates by sex, age, and educational attainment level. The former shows the percentage of young people (typically aged 18–24) who have not completed upper secondary school (e.g., general or technical secondary education) and are not engaged in further education or training. It provides information on the proportion of young adults who leave the education system with relatively low levels of formal qualifications. In reality, these individuals often experience unemployment, lower earnings and social exclusion. Comparing this indicator across regions or countries can highlight differences in, and the most effective policies for, education and training.

The second indicator shows the relationship between educational attainment and employment prospects. Higher levels of education are associated with higher employment rates. Changes in the indicator can be analysed to show the tangible benefits of education in terms of employment. On the one hand, it can indicate the extent to which the education system equips individuals with the skills and qualifications required by the labour market. On the other hand, changes in employment rates can reflect broader economic trends and demand for labour with different skill levels. This data is crucial for policymakers when designing strategies for employment, education and social integration aimed at improving labour market outcomes for different demographic groups.

3.7. Criteria for Selecting Objects for the Study

Initially, the study was planned to cover all European countries. However, this criterion was not met for several reasons. Firstly, the data planned for the model came from three different international databases: IRENA, EUROSTAT and OECD. However, it transpired that these databases publish data for different countries, making them not comparable. The second criterion for selecting objects was the completeness and continuity of the data. Some countries did not meet this criterion (Montenegro, Liechtenstein, Monaco, Serbia, Kosovo, Albania, the United Kingdom, Bosnia and Herzegovina, North Macedonia, Switzerland, Malta and Cyprus). Finally, the original assumption was verified based on the analysis of the availability of data on empirical variables, and the study sample was limited to 26 countries by eliminating those for which data were either unavailable or incomplete. The final group of countries examined included 24 EU member states and two non-EFTA countries: Norway and Iceland.

4. Research Results

4.1. The Effects of the Hierarchical Classification of Countries in Terms of the HCIe Index

Based on the HCIe index, for which the reference range is [0;1], a hierarchical classification of the 26 European countries under study was created. The countries were then grouped into five classes according to their HCIe index level. Equally spaced intervals were adopted, where class 1 represented countries with the highest level of the HCIe index and class 5 represented countries with the lowest level (

Table 8. Level of human capital in the energy-transforming economy (HCIe). Hierarchical classification of countries according to the [HCIe] indicator level.

Lp.	Country	EU Member	[HCIe] [0;1]	Class	Reference Range of the [HCIe] Indicator	[HCIe] Relative
1	Romania	Yes	0	Class 5	0.00–0.19	−49.9%
2	Bulgaria	Yes	0.050828	Class 5		−44.8%
3	Lithuania	Yes	0.090892	Class 5		−40.8%
4	Latvia	Yes	0.138677	Class 5		−36.0%
5	Hungary	Yes	0.194923	Class 5		−30.4%
6	Poland	Yes	0.253247	Class 4	0.20–0.39	−24.5%
7	Slovakia	Yes	0.304727	Class 4		−19.4%
8	Croatia	Yes	0.343029	Class 4		−15.6%
9	Estonia	Yes	0.351885	Class 4		−14.7%
10	Czechia	Yes	0.388392	Class 4		−11.0%
11	Ireland	Yes	0.402531	Class 3	0.40–0.59	−9.6%
12	Spain	Yes	0.466636	Class 3		−3.2%
13	Greece	Yes	0.466681	Class 3		−3.2%
14	Portugal	Yes	0.502439	Class 3		0.4%
15	Belgium	Yes	0.572397	Class 3		7.4%
16	Denmark	Yes	0.578994	Class 3		8.0%
17	Netherlands	Yes	0.614905	Class 2	0.60–0.79	11.6%
18	Italy	Yes	0.670988	Class 2		17.2%
19	France	Yes	0.693192	Class 2		19.5%
20	Slovenia	Yes	0.700659	Class 2		20.2%
21	Austria	Yes	0.721952	Class 2		22.3%
22	Iceland	No *	0.802644	Class 1	0.80–1.00	30.4%
23	Germany	Yes	0.840478	Class 1		34.2%
24	Finland	Yes	0.897525	Class 1		39.9%
25	Norway	No *	0.915303	Class 1		41.7%
26	Sweden	Yes	1	Class 1		50.1%

* Countries integrated with the EU through the European Economic Area and the Schengen Area. Source: own study.

).

Additionally, to improve the interpretative value of the results, the data were presented in relative terms, i.e., in relation to the average [HCIe] index value calculated for all 26 countries. Detailed data on the hierarchical classification of countries and their distance from the average [HCIe] index value (in percentage terms) are presented in Table 8 and Figure 15.

Based on the obtained data, the group with the lowest levels of human capital comprises mainly countries from Central and Eastern Europe. These countries were classified in groups 5 and 4, indicating low and very low levels of human capital (see Table 8). The only exception among Central and Eastern European countries was Slovenia, which was classified in group 2, indicating a high level of the HCIe indicator. Greece and Portugal achieved near-average levels of the human capital indicator.

All Western European and Scandinavian countries were classified into groups characterised by high and very high levels of the Human Capital Index (HCI). Let us take a closer look at the individual classes. Class 3 comprises countries with an average HCIe level. Geographically, this group is dominated by southern European countries such as Spain, Portugal and Greece, but it also includes Ireland, which has the lowest level of the index, and Denmark, which has the highest level and closes the group. The neighbourhood effect is also noticeable in Class 2. When we analyse the spatial distribution of human capital resources, we see that France, Italy, Austria and Slovenia, as well as the Netherlands, which does not directly border these countries, are on the western side. At the same time, this group of countries is the least diversified. Germany and the Scandinavian countries are classified in Class 1, which has the highest HCIe index levels. Denmark is classified in Class 3, which has an average HCIe index level. These countries have above-average levels of human capital. Sweden, Norway and Finland are the leaders. It is worth noting that Portugal has an average level of this resource (see Figure 15). The scale of the differences between countries can be observed using a relative approach. For example, the average HCIe value for the 26 countries studied is 0.473, which can be used as the “zero” level. A negative value indicates that a given country’s HCIe score is lower than the group average, while a positive value indicates the opposite. This presentation of the data illustrates the scale of the disparity more clearly than simply using the estimated HCIe value.

In summary, the analysis of the spatial distribution of human capital (HCIe) reveals significant disparities in human capital levels between European countries. Scandinavian countries, including two non-EU countries, lead the way in terms of resources for a transforming economy, while countries in Central and Eastern Europe and Southern Europe generally achieve lower HCIe index values. There is relatively high internal differentiation within each group of countries with a lower level of the HCIe index, and relatively low differentiation within the group of countries characterised by a high or very high level of the HCIe index.

4.2. Assessment of the Relationship Between the HCIe Indicator and HCI: Effects of Grouping Countries

The next step in the analysis was to assess whether countries with a high level of human capital also demonstrate high levels of resourcefulness in the transforming economy. To this end, the level of the HCIe indicator, which provides information on human capital resources for the energy-transforming economy, was compared with the HCI level, which expresses human capital in the classical approach as estimated according to the World Bank methodology. Figure 16 and Figure 17 present detailed calculations showing the scale of differentiation between countries in relative terms, i.e., in relation to the average values for both indicators. Another variable was included in the analysis: GDP per capita. The results are interesting.

The general conclusion is that countries with a higher level of human capital are better prepared in terms of human resources for the energy-transforming economy. However, there are some exceptions to this rule: Poland, the Czech Republic, Estonia, Ireland and Italy, which presents an unusual case.

Examining the detailed data on empirical variables, Ireland and Estonia’s poor performance is likely associated with their low patent indexes of 7.2 and 6.2, respectively (compared to a median of 203.5). In Poland and the Czech Republic, the unfavourable situation is mainly linked to exceptionally high mortality rates. For example, the rate for malignant neoplasms of the trachea, bronchus, and lung is 52 and 42.1, respectively (compared to a median of 39.7), and high figures persist across individual causes of death. For circulatory diseases, the respective rates are 426 and 430 (median 280). For Estonia, this indicator is even higher, at 470.

Poland and Ireland have particularly unfavourable indicators for mortality due to asthma (1.46 and 1.56, respectively, median 1.03), as well as for chronic obstructive pulmonary diseases, with Poland’s indicator (41.3) being one of the highest in the entire analysed group of 26 countries (median 22.9).

Italy is the only country that exhibits atypical behaviour because, despite having negative human capital (HCI), it has a positive human capital indicator in the area of the transforming economy (HCIe). This is driven by above-average health indicators, with the highest life expectancy (83 years) and life expectancy in good health (67.8) in the group, alongside one of the lowest mortality rates for all analysed causes. The situation on the labour market is also notable: the above-average labour productivity of market production in the renewable energy sector is 217.2, compared to a median of 128.1.

In several countries, the level of the HCIe indicator exceeded that of the HCI. These countries were: Italy, Iceland, Austria, Germany and Norway. Geographically, the level of human capital in the energy transformation economy (HCIe) was relatively higher than ordinary capital (HCI), as measured by the World Bank, in all Eastern and Central Eastern European countries except Slovenia. It is also worth noting that the smallest disparities in the level of the indicators were observed in Sweden and Norway.

A similar attempt was made to group countries in relation to the measure of well-being, which is GDP per capita (pc) expressed in purchasing power standards (PPS) (EU 100%). As a result of the grouping procedure, two groups of countries were obtained that differ in the characteristics of the analysed variables and are arranged along the conventional East–West line of Europe: group A and group D. Only a few countries were found in group B, with just one in group C. The detailed results of the analysis are presented graphically in Figure 18 and Figure 19, and in

Table 9. Effects of grouping countries.

	Characteristics of changes
Changes in indicators	+HCI/+[Chine]/+GDPpc	−HCI/−[HCIe]/−GDPpc	+HCI/−[HCIe]/+GDPpc
Countries	Austria, Belgia, Dania, Finlandia, Francja, Niemcy, Iceland, Netherlands, Sweden, Norway	Bulgaria, Croatia, Greece, Hungary, Latvia, Lithuania, Romania, Slovakia, Spain	Ireland
Changes in indicators	+HCI/−[HCIe]/−GDPpc	+HCI/−[HCIe]/+GDPpc	+HCI/+[HCIe]/−GDPpc
Countries	Poland, Estonia	Italy	Slovenia

Source: own study.

.

Statistical tests confirm a positive strong or medium relationship between the analysed variables [HCI], [HCIe] and [GDP pc], which, in light of scientific knowledge, should be considered correct (

Table 10. Correlation relationship.

	[HCI] [WB)	[HCIe]	GDP in PPS
[HCI] [WB)	1
[HCIe]	0.71926	1

Source: own study.

).

In summary, the above research results indicate a certain spatial regularity in the distribution of the three variables: HCI, HCIe and GDP per capita. The level of wealth is positively correlated with the level of human capital in both the energy transformation economy and the classical approach. Additionally, a clear effect of spatial polarisation was observed, whereby highly developed countries in Western Europe and Scandinavia have highly developed human capital resources, while poorer countries in Central and Eastern Europe and Southern Europe have less developed human capital resources. The neighbourhood effect is also visible, resulting directly from the geographical location of a given country.

4.3. Exploring the Structure of Human Capital in an Energy-Transforming Economy: The Effects of a Non-Hierarchical Classification

The internal structure of human capital in energy-transforming economies was explored based on an analysis of five partial human capital indicators (HCIs), which were adequately estimated in each of the five components of the structure: innovation and creativity, health, labour market, education, and quality of life (see Table 8). The outcome of the exploration was the identification of different characterological types among European countries, each characterised by a certain internal coherence.

To demonstrate the degree of differentiation within the analysed group of 26 European countries, the k-means clustering algorithm was used. As a result, five distinct groups of countries were obtained, each exhibiting a clear differentiation in human capital structure. The optimal k value was confirmed by the Elbow method statistical test (Figure 20).

The estimated values for the individual components of the HCIe indicator can be found in

Table 11. Characteristics of the types of municipalities in terms of the characteristics of the human capital components (Average values for clusters of countries).

	Structural Components [HCIe]
	INN	LM	LQ	EDU	HLH
Type A (6 countries)	0.239261	0.068826	0.304500	0.379394	0.396972
Type B (5 countries)	0.332346	0.154078	0.396716	0.726413	0.719082
Type C (7 countries)	0.619249	0.268387	0.738118	0.499733	0.703330
Type D (4 countries)	0.253151	0.700688	0.189937	0.469503	0.956767
Type E (4 countries)	0.132482	0.390052	0.362793	0.282997	0.039191
Average (26 countries)	0.315298	0.316406	0.398413	0.471608	0.563068
min	0.132482	0.068826	0.189937	0.282997	0.039191
max	0.619249	0.700688	0.738118	0.726413	0.956767
Coeff. ratio	4.674212	10.18057	3.88612	2.566858	24.41293

Source: Own study.

and

Table 12. Correlation relationships between partial measures of the human capital index [HCIe].

Components of [HCIe]	INN	LM	LQ	EDU	HLH
INN	1
LM	0.051553	1
LQ	0.511679	−0.28331	1
EDU	0.286368	−0.17324	0.126175	1
HLH	0.33443	0.24777	0.153557	0.337036	1

Source: Own study.

, as well as in Figure 21. The detailed characteristics of the individual types of countries according to the components of the HCIe structure are presented in

Table 13. Characteristics of types of countries with regard to the internal structure of human capital, according to its components [HCIe].

Type	Countries	Components of [HCIe]
		INN	LM	LQ	EDU	HLH
Type A	Bulgaria, Croatia, Czechia, Lithuania, Slovakia, Estonia	-	---	-	-	-
Type B	Belgium, Ireland, Netherlands, Portugal, Slovenia	-	---	II	+	+
Type C	Sweden, Norway, Denmark, Finland, Iceland, Germany, Austria	+	-	+	II	+
Type D	France, Greece, Italy, Spain	-	+	---	II	+++
Type E	Latvia, Hungary, Poland, Romania	---	-	-	-	---

Legend: Assessment of human capital in a given component (HCIe): Very good: +++; Good: +; Average: II; Poor: -; Very bad: ---. Source: Own study.

.

The obtained correlation results are correct. There are no overly strong connections between the variables.

Classifying countries according to characteristic types (A–E) reflects the actual differences in human capital levels and characteristics observed between these groups. It also assesses the degree of differentiation within individual groups (types A–E), as illustrated graphically in Figure 22.

Type Amainly comprises Central and Eastern European countries. This group generally has low values in all structural components (HCIe), but has a particularly significant deficit in the labour market (LM). The group is characterised by a lack of the integrated human capital necessary for transformation. Type B comprises countries with above-average levels of education and health. Despite having good underlying resources, a key weakness is the unfavourable situation in the labour market. This group is in an intermediate position, with potential but facing clear structural barriers. Type C is a group of leaders characterised by an overall favourable situation in all components (HCIe). This group achieves the highest average scores in the areas of innovation and quality of life, making it the best prepared for transformation. The only relative weakness is the labour market.

Type D comprises Southern European countries. This group is distinguished by the highest level of health in the entire sample. This contrasts with a particularly unsatisfactory assessment of quality of life (LQ) and low innovation (INN), which are below average for the entire sample. Despite favourable labour market indicators, this structure suggests an imbalance between basic resources and adaptive capacity. Type E is a group with extreme deficits, characterised by the lowest values in the entire sample for key components, particularly health and innovation. This profile indicates the lowest readiness for an energy transition of all the analysed types, as reflected by low scores for most of the other components.

Assessing the differences in the values of individual partial measures across country types, the greatest disparities are found in the Health component (Type D vs. Type E), while the Education component shows the least disparity (see Figure 23 and

Table 14. Maximum and minimum values of partial measures of human capital within a given component of the structure.

The Partial Index Level for a Given Component Is [HCIe].	INN	LM	LQ	EDU	HLH
max [1;0]	Germany	Italy	Norway	Netherlands	Italy
min [0;0]	Romania	Slovakia	Greece	Romania	Romania

Source: Own study.

) of human capital.

5. Discussion

The hierarchical classification and delimitation of countries, by exploring the internal structure of human capital, reveals differences in countries’ readiness to implement the principles of a transforming energy economy. In light of the research conducted, there is strong evidence to support the statement that the European socio-economic space exhibits certain differentiation features with regard to the general level of human capital in the energy transformation economy (HCIe), as well as in terms of the individual components of its structure: Health (HLH), education (EDU), quality of life (LQ), innovation and creativity (INN) and the labour market (LM). Analysis of the relationship between the distribution of the human capital index (HCIe) and the wealth of individual countries, as well as the level of human capital in the classical approach (HCI), seems to confirm this trend. Richer societies are characterised by a higher level of human capital and capital for the energy transformation economy. In terms of spatial distribution, poorer countries with less human capital tend to be found in Eastern and Central Eastern Europe, while richer countries with more human capital tend to be found in Western and Northern Europe. This is confirmed by the results of the analysis of the links between GDP and HCI, as estimated by the World Bank.

It can be concluded that geographical factors play an important role in shaping human capital within individual European countries. In Europe, it should be noted that geographical factors are closely linked to historical and socio-economic factors. These issues appear to be strongly correlated. The history of Europe, as well as historical facts and events since World War II, provides ample evidence that political factors have strongly influenced the polarisation of wealth in European societies. Despite some of the so-called ‘post-communist’ countries having caught up quite quickly since 1990, there are still serious deficits in the social fabric. This can be considered a consequence of the political situation between 1945 and 1990. This historical polarisation is evident today in the form of structural path dependence in human capital development. Former institutional barriers and investment delays in Central and Eastern European countries (clusters A and E) are strongly linked to chronic deficits in key HCI components, particularly innovation (INN) and the labour market (LM). These deficits constitute a key element of the observed clustering patterns. The level and distribution of the HCIe indicator, which reflects human capital resources in a transforming economy, confirms this trend, although Italy and Ireland are exceptions.

Interestingly, categorising countries according to their internal human capital structure revealed slightly different information. The typology revealed differences between country types and a clear link between geographical location and human capital structure. Examining the individual structural components reveals that countries in Central and Eastern Europe (types A and E) generally have an unfavourable situation in each component of the Human Capital Index (HCI). Type A countries experience severe labour market deficits, while Type E countries have dramatically high mortality rates and short life expectancy. Both types are characterised by an extremely low level of innovation.

Type C and Type D countries, on the other hand, are characterised by an opposite position on the North–South line. While southern European countries fare relatively well in terms of health, their quality of life assessment is very low. However, these two types of countries are united by a relatively favourable assessment in the area of education. Type B and Type C countries can be described as intermediate; however, they can also be considered completely different due to their varying levels of partial indicators for individual components (HCIe). What distinguishes Type B countries, albeit negatively, is their low labour market resource assessment. Type C countries did not receive any extreme assessments and were the only typological group to receive a favourable innovation assessment.

Another finding of the analysis is that particular attention should be given to the issues of energy poverty and reducing inequalities. These results are particularly significant in the context of disparities in quality of life and labour market deficits, as they corroborate the alarming reports of the European Fuel Poverty Network [55] concerning the escalating energy poverty in Europe, particularly in nations with lower levels of human capital. This implies an urgent need for political action, such as the introduction of energy support programmes or investment in the energy efficiency of buildings. The observed spatial polarisation of human capital in the energy-transforming economy suggests that EU cohesion policy and the Just Transition Fund will be crucial in eliminating existing disparities. To eliminate these disparities and ensure a just transition for all regions in line with the objectives of the European Green Deal, it is necessary to invest in education and develop digital and ecological skills, as well as strengthen healthcare systems in Central and Eastern European countries.

Energy transformation poses a fundamental challenge to the European Union. It is a process with far-reaching implications that extend beyond technological and economic considerations, given its profound impact on social structures. This study clearly shows that the success of this endeavour is closely linked to the amount and type of human capital available. The observed polarisation, whereby Western and Northern European countries possess higher levels of human capital, while Central and Eastern European countries struggle with deficits, has direct social consequences. The low results observed in Central and Eastern European countries in areas such as the labour market, health, and innovation are not merely related to technological progress but also represent serious social problems. Constraints on the labour market present challenges relating to retraining the workforce for green jobs and risk excluding communities that depend on high-emission industries. Health deficits (e.g., a high ‘inability to keep home adequately warm’ indicator) reflect energy poverty and its negative impact on citizens’ well-being and ability to participate in change.

The phenomenon of disconnected human capital is observed in Central and Eastern European countries (clusters A and E). This is characterised by a low conversion of formal education into innovation capacity and labour market effectiveness, both of which are critical for transformation. While the level of formal education may be moderate, its quality, particularly with regard to digital and ecological competencies, remains low. This phenomenon is strongly correlated with investment trends, particularly the low level of research and development (R&D) funding in both the public and private sectors. This leads to lower professional mobility of workers towards green technologies, resulting in stagnant innovation indicators and deepening labour market deficits. This suggests that HCI components are operating in isolation, failing to generate the necessary synergies for active participation in the transformation process.

In light of these findings, it is reasonable to suggest that education and quality of life are essential for fostering environmental awareness and social acceptance of the transformation. A lack of investment in these areas can lead to social resistance and a lack of understanding of the necessary sacrifices, which in turn slows down the entire process. Empirical analysis also reveals that historical and political factors continue to shape social and economic inequalities in Europe, impacting countries’ contemporary capacity to develop human capital.

The disparities revealed in the human capital landscape for the energy-driven economy in European Union countries highlight the importance of considering all these factors when developing practical recommendations in the form of specific scenarios. When preparing these scenarios, we ensured consistency with human capital theory, while also accounting for differences in the level and structure of green human capital across countries. The proposed actions directly address the theory and definition of human capital, which is defined as investment in education, vocational training, awareness-raising and health. All of these are key to increasing human capital. Therefore, they are essential to accelerating and streamlining the energy transition, as set out in the article’s main thesis. Furthermore, it is assumed that the direction of the proposed actions should be consistent with EU goals. Proposals for EU countries are fully aligned with EU goals and programmes such as the ‘European Green Deal’ and ‘Fit for 55’. Many of the proposals concern a just transition, smart grids, and moving away from fossil fuels, all of which are already under consideration at the European level. Additionally, practicality was considered to ensure that the proposed actions were not merely abstract concepts. Sample scenario proposals for each of the selected country groups are included in

Table 15. Examples of activities and action scenarios for individual groups of countries.

Group of countries: ‘Leaders’ Countries with very High Human Capital and Advanced Energy Transformation (e.g., Denmark, Sweden, Finland)
Challenges:	Scenario:
These countries face the challenge of maintaining their leadership position and accelerating the pace of change in the face of new challenges. These include integrating new technologies, such as green hydrogen and energy storage, and minimising social resistance to large-scale infrastructure projects.	The focus is on investing in innovation and research. Create public programmes that support green technology start-ups. Launch educational campaigns to promote advanced, pro-ecological attitudes in society and the private sector. Additionally, we export solutions and knowledge to developing and emerging markets.
Activities:
Legal and institutional: introducing legal provisions to facilitate the aggregation of energy from prosumers, enabling citizens to actively participate in the energy market. Creating standards for smart grids to enable effective management of the increasing production of renewable energy. Socio-educational: Launching national educational programmes that promote the ‘digital energy transformation’ (e.g., using applications to monitor energy consumption), as well as providing training for engineers and technicians in new technologies such as green hydrogen and energy storage. Economic: Introducing tax breaks for companies investing in green technology research and development. Establishing investment funds to support clean technology start-ups.
Group of countries: ‘Aspiring’ Countries with high human capital that have experienced delayed transformation, such as Germany and France.
Challenges:	Scenario:
Breaking down bureaucratic and social barriers. Implementing solutions rapidly and on a large scale, while managing costs and social acceptance (e.g., protests against wind farms).	The focus is on the effective management of transformation processes. Simplify administrative procedures for renewable energy projects and energy grid modernisation. Invest in training programmes for workers in traditional sectors (e.g., mining), helping them to find employment in the green economy and minimising the risk of unemployment and social unrest.
Activities:
Legal and institutional: introducing ‘fast-track’ procedures for obtaining permits for the construction of renewable energy infrastructure, particularly in areas with high energy potential. Socio-educational: Creating public consultation platforms for large energy projects to minimise social resistance. Organising information campaigns that demonstrate the advantages of renewable energy for local communities, such as job creation. Economic: Introducing financing mechanisms to support redeployment of workers from high-emission sectors (e.g., mining and heavy industry), as well as subsidy programmes to encourage the thermal modernisation of buildings.
Country group: ‘Developing’ This group comprises Central and Eastern European countries (e.g., Poland, the Czech Republic and Hungary) which are characterised by average human capital and significant delays in the transformation process.
Challenges:	Scenario:
Addressing energy poverty and overcoming dependence on fossil fuels. Raising public awareness, which is often lower than in Western countries.	The policy of introducing a “fair transformation” of healthcare and the national economy, based on the termomodernisation of buildings, support for the poorest households in energy sources and extensive education on the benefits of OZE. A priority should also be given to risky cases, which cause sources of energy to emerge.
Activities:
Legal and institutional: Streamlining regulations to facilitate the development of energy networks that can accommodate more energy from distributed sources. Implementing policies to combat energy poverty, such as subsidies for energy bills and programmes to support energy efficiency. Socio-educational: Launching wide-reaching media campaigns to promote energy conservation and renewable energy sources. Environmental education is introduced in schools at all levels. Economic: Implementing subsidy programmes for photovoltaic installations and heat pumps in individual households. Using EU funds to modernise energy infrastructure and reindustrialise coal-dependent regions.
Group of countries: ‘Candidate’ These are countries with low human capital and very limited progress in energy transformation, such as some Western Balkan countries.
Challenges:	Scenario:
There is a lack of adequate institutional, technological and social support. The need to develop everything from scratch, including legal regulations, infrastructure, and widespread environmental awareness.	A strategy based on cooperation and knowledge transfer must be implemented. Support from the European Union and ‘leader’ countries, who could contribute know-how, technology and financial resources, is crucial. The focus should be on providing basic environmental education and implementing simple, low-cost solutions (e.g., small-scale photovoltaic installations and energy efficiency measures) that will deliver quick and visible results.
Activities:
Legal and institutional: introducing a basic legal framework for renewable energy and energy efficiency that is consistent with EU regulations. Establishment of independent energy agencies. Socio-educational: Training officials and local leaders in managing sustainable development projects. Public events and workshops will be organised to educate the public on the benefits of renewable energy. Economic: Obtaining funds from international financial institutions (e.g., the World Bank and the European Bank for Reconstruction and Development) for the construction of basic energy infrastructure. Providing preferential loans to small and medium-sized enterprises that wish to invest in renewable energy sources.

Source: own elaboration.

.

The proposed scenarios and action recommendations presented above are tailored to the specific needs and conditions of each analysed group of countries. Innovation-based strategies are recommended for Leaders and Aspiring Countries, while Developing and Candidate Countries are advised to implement policies that address structural challenges such as energy poverty and low environmental awareness.

It is now appropriate to refer to energy transition models in selected non-EU countries in order to place the study in a broader context. Three countries were selected for comparison: Japan, which pursues a model of technological innovation; China, which is based on a model of centralised development; and the United States, which pursues a model based on market innovation and private capital.

Japan, a country with a high level of human capital and high fuel import dependence, adopted a transition model based on technological innovation. Following the Fukushima disaster in 2011, the country intensified its search for alternative energy sources [135]. In this model, human capital in the form of highly skilled engineers, scientists and innovators drives the development of hydrogen technologies, energy efficiency and advanced energy storage systems [136,137]. Japan demonstrates that, even in the absence of domestic resources, appropriate investment in human capital and innovation can lead to the achievement of sustainable development goals.

The United States is characterised by a market-driven approach and a significant proportion of private capital. In this model, the energy transition is primarily driven by innovation and financing in the private sector, particularly in the form of technology start-ups in Silicon Valley, as well as by strong university research centres [138]. The US’s human capital, characterised by high mobility and entrepreneurship, drives the development of new technologies and business models which then spread around the world.

China, on the other hand, presents a completely different model. As a country with a rapidly growing economy that is simultaneously grappling with significant environmental challenges, it has focused on centralised development on a large scale. Government policies in education and research, combined with substantial financial resources, have enabled China to swiftly establish itself as a global leader in the production of solar panels, wind turbines and batteries [138,139]. In this model, human capital is viewed as a strategic resource that enables the achievement of economic and environmental goals on an unprecedented scale through appropriate educational programmes and technology transfer.

Comparing these three models with the European Union’s approach, which is based on regulation, community and a just transition, reveals the diversity of global strategies. This analysis confirms that there is no single, universal transformation model. Success depends on an appropriate combination of public policies, investments and human capital development, tailored to the specific circumstances of each country. It is precisely this flexibility and adaptability that are crucial in the transition to a low-carbon economy.

6. Conclusions

6.1. Concluding Remarks

This article provides a crucial diagnosis of the level and structure of human capital within the context of green energy transformation challenges in European countries. The original model for measuring human capital (HCIe), which takes into account five key constructs—innovation and creativity, the labour market, health, education, and quality of life—significantly extends classical approaches by adapting them to the specific requirements of a dynamically changing economy. The developed measure was applied to European countries as an example.

This study makes two contributions: theoretical and empirical. In terms of theory, the study provides new insights into how human capital can be conceptualised and measured, embedding it within a broader framework of transition theory (MLP/TIS). Empirically, it demonstrates that regional disparities in Europe are no longer primarily driven by deficits in basic health and education, as in classical HCI, but rather by structural deficits in innovation capacity and quality of life issues (e.g., energy poverty). This provides crucial new information for designing targeted Just Transition policies.

Using HCI measurement tools and selecting appropriate methods enabled us to answer the research questions, providing significant knowledge in the process. The empirical study clearly indicates significant social differentiation in EU countries’ preparation for decarbonisation. Richer countries in Western and Northern Europe have a higher level of human capital for the energy-transforming economy, making them more adaptable. Conversely, countries in Central and Eastern Europe still face significant deficits, particularly in terms of the labour market, health, and innovation. These disparities, often resulting from historical and socio-economic factors, emphasise the urgent need for targeted action to ensure a just transition. In light of these results, the key conclusion is that the success of the green energy transition hinges not only on technological and economic progress, but also on societies’ ability to adapt, accept change, and participate actively.

6.2. Policy Implications and Typology of Recommendations

The key policy recommendations formulated in this study are typological in nature and consistent with the adopted application objective. The recommendations are differentiated and closely tailored to the identified clusters (A, B and C), in accordance with the structural application logic presented in the empirical section. This approach ensures full consistency between the empirical diagnosis and the prescriptive conclusions, eliminating reliance on arbitrary expert interpretation. The recommendations are as follows:

For clusters with a dominant VBN (Behavioural Capital) deficit, priority should be given to informational and educational interventions rather than R&D investments. Programmes should focus on raising public awareness of and acceptance for the transformation in order to strengthen the weakened behavioural dimension of human capital (VBN).

For clusters with a dominant TIS (Innovation Systems) deficit, the key recommendation is financial and innovation intervention. Targeted funding for applied research and technology transfer is recommended to activate weakened TIS functions and commercialise knowledge.

For clusters with a dominant MLP and HCT deficit, intervention is essential in the structural and reform dimensions. This requires comprehensive investment in improving skills (through reskilling and upskilling), educational reform to adapt to the green labour market, and stabilising and simplifying the regulatory environment.

6.3. Limitations and Future Research Agenda

A key strength of the study is its diagnostic and typological value. This can provide a roadmap for policies that support the development and implementation of targeted actions to develop human capital for a specific group within an energy-driven economy. However, it should be acknowledged that the nature of the analysis has limitations that affect the inferences made for diagnosis and associations. These limitations form the basis of the research agenda for future work, which is aimed at further validating the HCIe model and transforming it into a causal tool. These limitations are as follows:

• Dynamic and temporal analysis (cross-sectional gap): The static nature of the presented analysis is a weakness. However, it should be noted that, by its nature, human capital is a structural and qualitative variable that changes much more slowly than quantitative and cyclical variables (e.g., GDP or investments in renewable energy sources). Therefore, the static picture obtained is diagnostically significant. A priority for further research, however, is to develop panel analyses to track the evolution of HCI over time, and examine the relationship between the development of human capital and changes in the pace of energy transition. Such research would facilitate the development of endogenous econometric models for studying human capital feedback loops (e.g., Myrdal’s problem).
• Taxonomic stability and weighting: To increase methodological transparency, future research should focus on verifying cluster stability by testing alternative distance measures and documenting rigorous sensitivity tests of weighting schemes. This could be achieved through the use of expert opinion or engagement analysis, for example, rather than assuming a neutral equal weighting scheme.
• Integration of Granular and Behavioural Data: Further work should focus on overcoming the limitations of country-level data aggregation, which masks regional disparities. Future analyses should utilise subnational (NUTS) data to better target Just Transition (JT) goals. At the same time, it is crucial to strengthen the VBN dimension by integrating microdata and data from public sentiment surveys to bridge the current gaps resulting from the lack of official behavioural statistics.
• Sample representativeness: Due to data limitations, the analysis covered 26 of the 38 European countries. Future research should seek proxy data for the twelve excluded countries. Completing the sample would allow regional conclusions to be verified and generalised more widely, while reducing potential sample bias and ensuring a more comprehensive representation of the results for the entire European continent.

In summary, this structural diagnosis of human capital (HCIe) provides a vital basis for formulating personalised, targeted energy transition policies, closing the gap between empirical diagnosis and effective public intervention and opening up new strategic avenues for future green transition research.