Polarization and Segmentation of Public Attitudes Toward Renewable Energy: A Cluster Analysis of Polish Consumers

Abstract

Public attitudes toward renewable energy sources (RES) have been widely studied at the household level. However, less is known about how citizens differ in their broader perceptions, knowledge, and behavioral orientations toward renewable energy. This study explores such heterogeneity within Polish society using survey data from a representative sample of 974 respondents. An exploratory factor analysis identified six dimensions of renewable energy attitudes: environmental concern, knowledge and awareness, social and economic support, perceived ease of use, perceived benefits, and behavioral intentions. Using these attitudinal dimensions, cluster analysis revealed two distinct consumer segments that differ in their overall level of engagement with renewable energy. The first cluster comprises pro-green and engaged individuals who express strong concern for environmental issues, have a greater awareness of the benefits of renewable energy, and are more ready to adopt such technologies. The second cluster represents respondents who are less engaged or skeptical, with weaker environmental and behavioral commitment. The comparison of sociodemographic characteristics across clusters showed no statistically significant differences in gender, age, education, or place of residence, and only a marginal effect for income. The findings suggest that support for renewable energy in Poland is not driven by demographics but somewhat shaped by cognitive and value-based factors, offering valuable insights for policymakers and communication strategists promoting the energy transition.

1. Introduction

The global energy system faces a critical challenge: ensuring a reliable energy supply while mitigating the environmental impacts associated with current production models. The global energy system has reached a turning point: countries are struggling to reconcile growing economic needs with the need to protect the climate and pursue the ideal of sustainable development. Nevertheless, fossil fuels continue to dominate the global energy mix. Their historical cost-effectiveness has enabled them to be a source of growth and prosperity for decades. However, problems related to their use are intensifying, such as ongoing climate change, volatile commodity markets, and rising geopolitical tensions [1].

Reports from the International Energy Agency (IEA) [2] indicate that as much as 73% of global greenhouse gas emissions come from the energy sector. This figure clearly demonstrates the scale of the challenge and the direction we must take. In the face of the global energy crisis and the intensifying effects of climate change, improving the efficiency of carbon dioxide emissions has become a crucial component of a strategy that balances environmental protection with economic development. Reducing emissions is no longer just an expression of concern for the planet, but also a prerequisite for sustainable growth [3]. The transition to cleaner, safer, and more sustainable energy sources is becoming increasingly urgent. There is need for both a secure energy supply and to address ecological requirements to ensure a sustainable future for future generations [4].

Renewable energy has become a central component of contemporary energy system transformation. This development is driven by two primary factors: growing concern about climate change and the need to establish an economy aligned with sustainable development principles [5]. Technological solutions have reached a level of maturity that enables their effective large-scale implementation. Low emissions are their hallmark: rooftop solar panels, powerful wind turbines, and reliable hydropower. These technologies are increasingly recognized globally and demonstrate growing economic competitiveness compared with conventional high-emission energy sources [6].

This transformation encompasses not only technological change but also a broader evolution in energy preferences, moving from reliance on fossil fuels toward greater integration of clean, accessible, and environmentally sustainable sources such as wind, solar, hydropower, and biomass. Although this direction is undoubtedly the right one, its implementation requires consistent, long-term, and responsible investments that will allow us to permanently anchor ourselves in a low-emission economy [7].

The energy transition involves several social challenges that require coordinated policy responses. In short, it can be said that highly developed societies overwhelmingly support the development and broader use of renewable energy sources (RES). This suggests that the concept of expanding renewable energy enjoys broad public support and is generally not considered a controversial issue. This pattern is linked to increasing public awareness of climate change and its recognized association with the use of fossil fuels [8]. However, the positive image of support for renewable energy does not always persist at the local level, where specific projects are implemented [9]. In such situations, understanding the attitudes of energy consumers becomes crucial, as their perceptions and opinions determine whether a project will be met with approval or resistance. Therefore, social acceptance is now considered one of the most important factors influencing the share of renewable energy in the national energy mix [10]. Without adequate public support, even the most modern technologies and best-planned policies may encounter severe difficulties in practical implementation.

In recent years, the Polish government has increasingly recognized the need to integrate RES into the national energy system. The growth in the use of biomass is particularly impressive, accounting for a larger share of our energy system than in many other European Union countries [11]. Unfortunately, the energy transition in Poland is uneven. While biomass is developing dynamically, wind and solar energy face significant obstacles. The primary barriers are legal regulations and inadequate infrastructure, which hinder the adoption of these technologies [12]. Although the official strategy, “Poland’s Energy Policy until 2040,” sets ambitious goals, experts emphasize that achieving them within the set deadline is complex, and full transformation before 2050 remains challenging [13]. Therefore, the development of RES in Poland is dynamic but disorganized, marked by constant regulatory changes and varying levels of support. Recent developments indicate growing engagement from financial institutions, which are increasingly recognizing the economic potential of low-carbon investments and demonstrating a greater willingness to allocate capital to such projects. This trend is consistent with the strategic objectives outlined in the European Green Deal [14].

Poland’s energy transition trajectory remains distinctive within the European Union. While many EU countries, such as Germany or Denmark, have advanced rapidly in integrating wind and solar power, Poland continues to rely heavily on coal, which still accounts for a majority of electricity generation. Regulatory barriers, delayed infrastructure investments, and political debates over sovereignty and EU climate policy have slowed the pace of renewable adoption. This uneven progress contrasts with the more coordinated and ambitious transitions observed elsewhere in Europe, underscoring the importance of examining how Polish citizens perceive renewable energy in this unique context.

In this context, public sentiment is mixed. Although renewable energy and energy efficiency are actively promoted, public attitudes remain influenced by a long-standing reliance on coal within the national energy system, which can limit the broader acceptance of related energy policy measures [15]. Therefore, increasing emphasis is placed on social participation and dialog—citizens are given a real opportunity to influence key decisions [16]. This public engagement is crucial today: only by building trust and gaining support for reforms can we ensure a more sustainable and secure energy future for Poland.

Previous research on attitudes toward RES has focused primarily on micro-level analysis. This means that their attention has focused primarily on households [17,18], individual purchasing decisions [19,20], and declared intentions to use renewable technologies. These studies have often relied on classical behavioral models, particularly the Theory of Planned Behavior [21,22].

The literature also includes studies that analyze differences in attitudes toward RES across various demographic groups, such as age, gender, education level, or place of residence [23,24]. However, they are primarily comparative in nature, demonstrating that differences exist but not attempting to understand them more deeply or how they interrelate. This approach captures fundamental differences but does not fully capture the complexity of social attitudes toward renewable energy, especially those related to values, beliefs, knowledge levels, or emotional attachment to the energy transition.

Consequently, there remains a lack of segmentation-based research that empirically distinguishes different types of attitudes and patterns of engagement with RES, independent of classic demographic variables. Our study fills this gap by identifying key dimensions of attitudes toward RES and distinguishing two distinct social segments. These segments differ in their awareness, engagement, and acceptance of renewable energy development, allowing for a much more comprehensive and humanized perspective on the social determinants of energy transition.

The originality of this study lies in its application of a two-stage, advanced analytical strategy based on a representative, nationwide sample of 974 respondents. First, exploratory factor analysis (EFA) was used to empirically identify and define six key dimensions underlying attitudes (including environmental concern, knowledge and awareness, and socioeconomic support). Second, cluster analysis was used to segment respondents based on the resulting factors. The study provides a new, in-depth understanding of social heterogeneity in renewable energy engagement through this segmentation.

The contribution of this study extends beyond mere description of attitudes, offering important implications for application. The empirical finding that the structure of attitudes is primarily shaped by cognitive and evaluative factors, rather than sociodemographic variables (such as gender, age, or education), offers policymakers and strategic communicators valuable insights, enabling the development of more targeted and effective interventions in the energy transition process.

The main objective of this study is to empirically identify and describe the diversity of Poles’ attitudes toward RES. Specifically, the study aims to:

• Identify the main dimensions of attitudes toward renewable energy using exploratory factor analysis (EFA).
• Distinguish respondent segments based on the obtained factors using cluster analysis.
• Compare the sociodemographic structures of the identified segments.

The article is divided into several logical sections. The abstract outlines the main body of the work, followed by the Introduction (Section 1), which introduces the research context and defines its objectives. The literature review (Section 2) then discusses the theoretical framework and the current state of knowledge in the area under study. The Materials and Methods section (Section 3) describes the research sample and the analytical procedures, including factor analysis (EFA) and cluster analysis. Results (Section 3 and Section 4) are then presented, covering the factors and segments identified in the study. The Discussion (Section 4) interprets the results in a broader context, and finally, the Conclusions (Section 5) summarize the study and present its practical implications.

2. Literature Review and Research Questions

Attitudes toward RES are often analyzed using behavioral and technology acceptance models that capture their cognitive, emotional, and behavioral dimensions. The most relevant theoretical frameworks include the Technology Acceptance Model (TAM) [25], the Unified Theory of Acceptance and Use of Technology (UTAUT2) [26], and the Theory of Planned Behavior (TPB) [27]. Recent adaptations of these models incorporate ecological and social factors to reflect the broader context of sustainable energy adoption. For instance, Bhatia et al. [28] and Anser et al. [29] extend TAM to include environmental concern, social acceptance, and sustainability motivations. Studies using UTAUT2 highlight the roles of hedonic motivation, social influence, and habit in shaping behavioral intentions toward clean technologies [30,31]. TPB-based research confirms that attitudes, norms, and perceived control influence the adoption of renewable energy [21]. Overall, RES acceptance emerges as a multidimensional construct shaped by ecological concern, institutional trust, and perceived personal impact [32], supporting the need for exploratory identification of key attitude components within specific national contexts.

2.1. Dimensions of Attitudes Toward Renewable Energy Sources

Attitudes toward RES have been widely explored through psychological and behavioral frameworks that recognize their multidimensional nature. Research consistently indicates that these attitudes comprise cognitive, emotional (or normative), and behavioral components, reflecting how individuals think about, feel toward, and act upon issues related to renewable energy [33]. This tripartite conceptualization is well-established in environmental psychology and energy studies, forming the theoretical foundation for examining public acceptance of renewable technologies [34,35,36].

2.1.1. Cognitive and Informational Dimension

Knowledge and awareness of RES are key determinants of accepting renewable technologies. The cognitive aspect primarily relates to knowledge about renewable energy technologies, including their functionalities, advantages, limitations, and potential environmental impacts. Prior research indicates that enhanced understanding correlates positively with favorable attitudes towards RES. Informed individuals are more likely to recognize the benefits of renewable energy, such as reduced greenhouse gas emissions and enhanced energy independence, thus fostering a supportive attitude towards these technologies [37,38]. Educational initiatives that enhance cognitive awareness of RES have been observed to contribute to increased public acceptance by equipping individuals with knowledge about the advantages and sustainability of renewable energy [39,40].

Research shows that a higher level of knowledge of the concepts, operating principles, and potential benefits of RES leads to a more positive assessment of their usefulness and greater openness to investing in green technologies [17,33,41,42]. Włodarczyk and Herczakowska [43] found that residents of the Silesian region who were more informed about the energy transition expressed more substantial support for RES adoption. Meta-analyses confirm that economic awareness and perceived environmental benefits, such as energy savings and emission reductions, are important predictors of RES acceptance [44]. Moreover, the availability and clarity of information play a pivotal role in shaping public attitudes. Various studies suggest that increased exposure to information about renewable energy enhances public support and reduces skepticism or misconceptions about these technologies [45]. This underscores the necessity for effective educational campaigns and communication strategies that not only disseminate information but also engage individuals on both an emotional and cognitive level. Well-structured information can help counterbalance negative perceptions and present renewable energy as a viable and beneficial alternative to non-renewable sources [46,47].

At the same time, low levels of knowledge, lack of trust in information sources, or misconceptions can limit public support and willingness to change behavior [48,49,50]. For example, public perceptions influenced by misinformation or a lack of exposure can create concerns regarding the reliability and efficiency of renewable energy systems [51]. For this reason, the cognitive and informational component forms the foundation of attitudes toward renewable energy, shaping how consumers perceive the benefits and risks associated with its use.

2.1.2. Emotional and Normative Dimension

A second core dimension of attitudes toward renewable energy is environmental concern, shaped by personal values and a sense of moral responsibility toward nature.

The emotional dimension refers to the feelings and reactions people experience toward renewable energy and its applications in everyday life. Emotions significantly shape social attitudes. Positive feelings, such as pride in pro-environmental activities, hope for a better future, or satisfaction with environmental care, foster greater openness to environmentally friendly technologies. People who perceive renewable energy in a positive light are often also driven by a sense of moral obligation to support sustainable practices. Research indicates that the influence of positive emotions and perceived social benefits on the acceptance of renewable energy is stronger than the influence of negative emotions or concerns about risk. For example, research has shown that communities that form a positive emotional bond with renewable energy projects tend to exhibit higher levels of support and commitment to their implementation [52].

On the other hand, negative emotions—such as anxiety, fear, or skepticism—can pose a significant barrier to the acceptance of new energy solutions. People who feel uncertain about new technologies often react with distrust, which can lead to resistance to their implementation. Research conducted in the Canary Islands has shown that opponents of renewable energy often experience ambivalent emotions toward these technologies, making it difficult for them to accept them [52] entirely.

The normative dimension, on the other hand, concerns the role of social norms, values, and expectations that influence individual decisions and behaviors toward renewable energy. What is considered “appropriate” or “desirable” in a given community largely shapes how people approach renewable energy. Subjective norms, or perceived social pressure, play a key role here. When individuals perceive that their environment, including friends, neighbors, and coworkers, supports the use of renewable energy, they are more likely to adopt similar attitudes and engage in pro-environmental behavior [53].

Significantly, the influence of social norms can be reinforced by the institutional and political environment. When government policies align with social values and promote pro-environmental initiatives, there is a growing sense that supporting renewable energy is not only just but also socially expected. This alignment between public policy and social beliefs fosters a norm that sees the use of renewable energy sources as natural and desirable [46,47,54]. Collaboration between decision-makers and local communities is therefore crucial to building an environment in which renewable energy development is perceived not as an imposed solution but as a shared, normatively accepted choice.

These emotional and normative factors are reinforced by social norms [55], particularly through the influence of family, peers, and local communities [36]. Research indicates that both descriptive and injunctive norms have a significant impact on individuals’ support for renewable technologies [56,57,58,59,60]. When renewable energy is perceived as socially endorsed or widely adopted, individuals are more likely to engage in pro-environmental behavior. Nevertheless, it is essential to note that when examining social norms and their impact on attitudes toward RES, they cannot be considered in isolation from their cultural context. Remembering the strong link between culture and norms [61,62]. Thus, the social environment serves as a catalyst, translating ecological values into collective action and sustained support for energy transitions.

2.1.3. Behavioral and Intentional Dimension

The third dimension concerns readiness to use renewable energy technologies, encompassing declared intentions (e.g., willingness to adopt renewables in the future) and sustained behavioral engagement, such as long-term energy-saving practices and the use of renewable technologies. Decisions to adopt renewable technologies are often determined less by financial capabilities than by individuals’ beliefs in their own environmental impact and the efficacy of these technologies [63,64,65,66].

The behavioral dimension concerns the actual actions people take—or are willing to take—in relation to renewable energy sources. It encompasses various forms of engagement, from simple, everyday choices that are environmentally friendly to more organized actions such as participating in local energy projects, supporting policies that support the development of renewable energy, or investing in renewable technologies. In practice, this dimension reflects the extent to which individuals’ beliefs and emotions translate into specific decisions and behaviors. Research indicates that such actions are strongly influenced by both social norms and individual beliefs and emotional reactions to environmental and climate change issues [57]. This means that even a positive attitude toward renewable energy does not always lead to actual action—only a combination of beliefs, emotions, and social support fosters genuine engagement in the energy transition.

The intentional dimension of attitudes toward renewable energy refers to the decision-making process that leads individuals to undertake specific actions related to renewable energy. Within the Theory of Planned Behavior (TPB), intention, or the intention to act, is shaped by three main factors: individual attitudes toward a given behavior, perceived behavioral control (i.e., belief in one’s own ability to perform it), and social norms, i.e., the expectations of the environment [54,67]. The more substantial the belief that action on renewable energy sources is valuable, feasible, and socially supported, the greater the likelihood that an individual will actually take specific steps in this direction. In this way, the intentional dimension bridges beliefs and emotions with actual behavior, playing a key role in the process of moving from environmental awareness to actual action.

Recent integrative models, combining frameworks such as the Theory of Planned Behavior, Norm Activation Model, and Technology Acceptance Model, further demonstrate the importance of intention-behavior alignment in renewable energy adoption [68,69,70].

2.1.4. Integrated Perspective on Social Attitudes

Recent scholarship highlights that cognitive, emotional, and behavioral dimensions are interrelated and mutually reinforcing [71]. Building on this integrative view, the present study examines how these dimensions jointly shape attitudes toward renewable energy in the Polish context.

While previous studies have often examined cognitive, emotional, and behavioral dimensions in isolation, they are interdependent and mutually reinforcing in practice. This study employed exploratory factor analysis (EFA) to empirically identify which of these dimensions are most relevant in the Polish context. The results reveal that attitudes toward renewable energy are shaped by a dynamic interplay of knowledge, values, and behavioral readiness, offering a comprehensive framework for understanding public engagement with RES.

2.2. Attitudes Toward Renewable Energy in Poland and Other Countries

Segmentation analyses are increasingly used in research on energy behavior, allowing for the identification of consumer groups with similar attitudes, beliefs, and motivations. Studies conducted in Germany, Romania, and the United Kingdom [72,73,74,75,76,77,78] indicate that consumers differ in their level of environmental commitment and willingness to adopt renewable technologies. Hayn et al. [75] emphasize the importance of household segmentation in analyzing electricity load profiles, while Ozaki [76] explores behavioral changes in response to dynamic time-of-use tariffs. Scheller et al. [78] highlight stakeholder dynamics in solar energy adoption, and Engelmann et al. [74] examine perceived benefits and barriers of carbon capture technologies. Recent evidence from Southern Europe also underscores the significance of cognitive and value-based factors in shaping social acceptance. Tsiaras et al. [79] demonstrate that in the Epirus region of Greece, public perceptions of renewable energy projects are significantly influenced by environmental awareness, perceived visual impact, and educational level. At the same time, purely demographic factors play a limited role. These results align with broader European patterns and complement the Polish case by illustrating that acceptance of RES often depends on knowledge, values, and perceived local benefits rather than structural sociodemographic differences.

These studies demonstrate that segmentation enables a more nuanced understanding of energy-related behaviors and supports the development of targeted interventions. However, most of the work to date has been local in nature or based on small samples. Poland lacks extensive, representative studies that would allow for a comprehensive description of attitudes toward RES comparable to those in other countries [17,44,80,81].

2.3. Demographic and Ideological Determinants of Attitudes Toward RES

Demographic factors, such as age, gender, education, income, and place of residence, have long been examined as predictors of attitudes toward renewable energy; however, the results remain inconsistent. Research increasingly suggests that ideological and psychological variables better explain differences in environmental behavior [82,83].

Age, gender, and education significantly differentiate attitudes toward renewable energy sources—younger, better educated, and women are more likely to demonstrate positive pro-environmental attitudes and a greater willingness to support sustainable development [84,85,86]. However, these effects weaken when cognitive and value factors are taken into account [87].

Income and education influence the adoption of clean energy technologies, although their impact is partially mediated by perceived fairness and effectiveness [88,89,90]. Education increases knowledge about renewable energy sources and promotes sustainable development in local communities [21,91].

The place of residence also differentiates attitudes—city dwellers are more likely to accept renewable energy sources than people from rural areas. However, local conditions, such as landscape or proximity to installations, may influence the level of social acceptance [85,92]. Polish studies confirm similar dynamics [12]. Gajdzik et al. [44] found a link between higher economic awareness and greater sustainable energy consumption. Siuda and Grębosz-Krawczyk [17] demonstrated that green identity and sustainability motivation outweigh purely demographic predictors of purchase intentions.

While many studies in Western Europe emphasize demographic predictors of RES acceptance, research from Poland and other Central and Eastern European countries highlights the importance of contextual and political factors. Poland’s historical reliance on coal continues to shape public perceptions of energy security and climate neutrality goals [93]. Political discourse around sovereignty and tensions with EU Green Deal policies further influence attitudes toward RES independently of socio-demographic variables [94,95]. Comparative evidence from the Czech Republic and Hungary similarly shows that acceptance of renewable projects is shaped by community governance, local infrastructure, and value-based determinants rather than demographics [96,97,98,99,100]. These findings underscore that in CEE contexts, polarization in RES attitudes is often rooted in historical energy reliance and political framing, complementing and contrasting with Western European studies.

Overall, demographic traits remain relevant mainly as differentiating variables rather than primary causes of RES attitudes. Accordingly, the present study identifies attitude dimensions and respondent segments (via EFA and cluster analysis) and then verifies whether these segments differ by gender, age, education, income, and residence. This approach acknowledges that structural factors may distinguish attitudinally defined groups, even if deeper cognitive–ideological mechanisms drive the differences.

2.4. Research Questions and Hypothesis

Building on the reviewed literature, the present study integrates exploratory and confirmatory components to investigate the structure and determinants of attitudes toward renewable energy in Poland. Following the logic of exploratory verification, the study first seeks to uncover underlying attitude dimensions through exploratory factor analysis (EFA) and to identify distinct groups of respondents who share similar orientations through cluster analysis. Subsequently, it tests whether these empirically derived segments differ in demographic characteristics.

The following research questions (RQs) guide the exploratory stage:

• RQ1: What dimensions of attitudes toward RES can be identified among Polish respondents?
• RQ2: What respondent segments emerge based on these attitudes?

In light of previous research on the acceptance and adoption of renewable energy sources, it can be inferred that demographic variables significantly influence individual attitudes and behaviors toward renewable energy sources. Research conducted in various countries and social contexts confirms that characteristics such as age, education, income, gender, and place of residence differentiate both the level of environmental knowledge and awareness and the propensity to invest in green technologies [17,18,19,20,23,101]. With this in mind, in the verification stage, a testable hypothesis (H1) is formulated to examine demographic differentiation across the identified segments:

H1: 

The identified segments differ significantly in demographic characteristics, including gender, age, education, income, and place of residence.

This structure allows the study to maintain methodological coherence, progressing from discovery to validation. The exploratory phase provides empirical grounding for the typology of attitudes, while the verification phase assesses the socio-demographic correlates of these patterns. Together, these stages demonstrate that the study employs a mixed-methods approach, combining exploratory and confirmatory logic to understand how Polish consumers perceive and relate to RES comprehensively.

3. Materials and Methods

3.1. Sample and Data Collection

The research presented in this article is part of a broader project aimed at examining public attitudes towards RES and identifying the factors that shape social acceptance of the energy transition in Poland. The purpose of this study was to capture the perceptions and opinions of Polish citizens regarding renewable energy within the context of the ongoing European energy transformation.

Poland was deliberately chosen as the research setting due to its distinctive energy profile. The country remains one of the EU member states with a relatively low share of renewables in its energy mix, and the pace of transition toward sustainable energy remains moderate compared to Western Europe [2,102]. The absence of long-term regulatory stability and comprehensive support mechanisms makes public attitudes and perceptions toward RES particularly important for understanding the social dimension of the energy transition.

Data were collected in June 2025 using the CAWI method. A standardized and pre-validated online questionnaire was distributed among respondents by an external professional research agency managing a nationwide panel of Polish citizens. The target population consisted of adult residents of Poland (18 years and older).

A quota sampling procedure was applied to ensure that the demographic structure of the sample reflected the national distribution in terms of age and gender. In total, 1000 responses were obtained, out of which 974 correctly completed questionnaires were retained for further analysis after data quality verification (time consistency, detection of patterned or inconsistent responses). The sample was socio-demographically diverse, allowing for reliable comparisons across age and gender groups. The main characteristics of the sample are presented in

Table 1. Sample characteristics.

Characteristics	Category	Frequency	Percentage
Generation	Baby-boomers	264	27.11%
	Generation X	290	29.77%
	Generation Y	252	25.87%
	Generation Z	168	17.25%
Gender	Male	473	48.56%
	Female	501	51.44%
Education	Below secondary education	350	35.93%
	Secondary education	358	36.76%
	Higher education	266	27.31%
Place of residence	Village—City with up to 20,000 inhabitants	432	44.35%
	City with 20,000–100,000 inhabitants	220	22.59%
	City with over 100,000 inhabitants	322	33.06%
Income	Below 5000 PLN	240	24.64%
	5000–10,000 PLN	495	50.82%
	10,000–20,000 PLN	207	21.25%
	Above 20,000 PLN	32	3.29%

.

3.2. Analytical Strategy

The analysis was conducted using a two-stage procedure. An Exploratory Factor Analysis (EFA) was applied to identify variables based on 37 statements describing attitudes toward renewable energy. The resulting factors were then used in a cluster analysis to identify segments of Polish respondents.

To identify the main segments of Polish attitudes toward RES, a multivariate approach was employed—Exploratory Factor Analysis (EFA) and Cluster Analysis (CA). Exploratory Factor Analysis is one of the approaches within factor analysis used when the underlying data structure is unknown [103]. It enables the extraction of latent factors that are internally consistent by regrouping observed variables into a smaller set based on shared variance [104]. EFA allows for data simplification by separating and reducing the number of variables while considering the intercorrelations among observed indicators. In this study, an Exploratory Factor Analysis with oblimin rotation was applied. The cut-off value for factor loadings was set at 0.50 [105], and loadings above this threshold were considered significant. The resulting factors were used in a Cluster Analysis to identify segments of Polish respondents.

Cluster Analysis is a set of techniques that enable the grouping and classifying of individual observed data based on their similarity into relatively homogeneous structures [106,107]. The primary goal of Cluster Analysis is to divide a dataset into groups, identifying clusters whose elements are similar and, at the same time, distinct from those in other groups. Depending on the method of cluster formation, hierarchical and non-hierarchical approaches can be distinguished. Hierarchical methods create a cluster structure in the form of a hierarchy by successively merging or dividing observations into subgroups [108]. In this study, Ward’s method was applied using the Euclidean distance, which is based on variance analysis and forms clusters by minimizing the sum of squared deviations within clusters [109]. In turn, non-hierarchical methods, e.g., the k-means method employed in this study, assign observations to a predetermined number of clusters [107].

The chi-square test of independence was used to compare results and identify potential differences between clusters. This test evaluates the relationship between two categorical variables to determine whether a statistically significant association exists between them [110]. This study conducted statistical tests at a 5% significance level. The analysis was performed using R software (Version 4.0.3).

3.3. Measurement and Variables

The measurement model included 37 indicators adapted from Gârdan et al. [32]. In this study, an identical set of indicators was chosen, based on the psychosocial and structural similarities between the transformational challenges in Poland and Romania. The indicators focused on universal psychosocial dimensions such as risk perception, trust, and social acceptance, rather than on specific regulations. Accordingly, the adaptation of the questionnaire was limited exclusively to linguistic and terminological adjustments for the Polish context.

The items captured various dimensions of attitudes toward renewable energy, including cognitive, emotional, and behavioral aspects, as well as perceptions of benefits, barriers, knowledge, and social influence. They were measured using a five-point Likert scale (1 = strongly disagree, 5 = strongly agree). Consistent with the original study by Gârdan et al. [32], these indicators were initially grouped into nine theoretical dimensions. However, in the present study, an Exploratory Factor Analysis (EFA) was conducted to empirically determine the number of underlying constructs and the indicators forming each construct, given the Polish population’s distinct cultural and contextual setting.

The Exploratory Factor Analysis was initiated by evaluating data adequacy through the Kaiser–Meyer–Olkin (KMO) statistic, which determines whether the data are appropriate for factor analysis. The KMO is calculated based on partial correlations between variables and ranges from 0 to 1. A KMO value ≥ 0.5 is acceptable, while values ≥ 0.8 indicate an excellent sample suitable for factor analysis [111]. In this analysis, the obtained KMO value was 0.973 (

Table 2. KMO and Bartlett’s Test of Sphericity.

Kaiser–Meyer–Olkin Measure (KMO) of Sampling Adequacy		0.973
	Approx. Chi-Square	31,667.751
Bartlett’s Test of Sphericity	Df	360
	Significance	0.000

), suggesting excellent sample adequacy.

Additionally, Bartlett’s test of sphericity was conducted to assess sample adequacy further. This test enables the examination of the interrelationships among variables and verifies whether the correlation matrix differs from an identity matrix [112]. The p-value ≤ 0.000 (Table 2) indicates that the correlation matrix is not an identity matrix, justifying the application of factor analysis. Thus, both tests confirm the appropriateness of using factor analysis.

Parallel Analysis (PA), proposed by Horn [113], was used to determine the optimal number of factors. This simulation-based method generates random datasets with properties similar to those of the observed data but without an underlying factor structure. The eigenvalues extracted from the observed data are then compared with those from the simulated datasets. Factors with eigenvalues greater than those from the random data are retained.

Based on the Parallel Analysis (PA), six factors were suggested. Exploratory Factor Analysis (EFA) was then conducted using principal axis factoring, which is appropriate for identifying latent constructs based on shared variance. An oblimin rotation was applied to facilitate correlations among the factors. After cleaning the factor structure by removing factor loadings below 0.50 and eliminating items exhibiting significant cross-loadings (to ensure a simple and interpretable structure), the final set of factors was achieved. A total of 28 items were retained, forming six distinct factors, which together explained 72.41% of the variance in the variables. The obtained factor loadings are presented in

Table 3. The matrix of rotational components—factor loadings.

Item *	Variable
	V1	V2	V3	V4	V5	V6
I am concerned about the environment when I am making a new source of energy purchase.	0.764
I am concerned about the way we use the environment (air, water, and land use).	0.900
I am anxious about the pollution in the environment.	0.842
I am anxious about the environmental problems caused by the exaggerated consumption of energy.	0.836
I am anxious about classic energy sources depletion (oil, gas, coal).	0.699
I am familiar with the notion of renewable energy.		0.870
I know that renewable energy-based solutions are available in Poland.		0.772
I am aware of the benefits of renewable energy utilization.		0.714
I can comfortably spend a part of my income on renewable energy.			0.532
My family thinks that I should purchase RE over conventional energy.			0.919
My close friends think that I should purchase RE over conventional energy sources.			0.925
Most people who are important to me think I should buy renewable energy sources.			0.828
I believe that the equipment used to produce renewable energy is relatively easy to install.				0.688
Installations that produce renewable energy are easy to use.				0.825
I can master using renewable energy equipment easily.				0.545
I believe that by using renewable energy, we contribute to a better environment.					0.534
Renewable energy contributes to environmental benefits more than other sources of energy.					0.809
I believe that the use of renewable energy reduces carbon emissions and improves the structure of the energy system.					0.792
I believe that employment opportunities can increase with the development of new energy projects.					0.638
I believe that generating energy from renewable sources is easier than from fossil fuels (oil, gas, coal, etc.).					0.681
I believe that the use of renewable energy reduces the cost of the energy supply.					0.728
I think renewable energy utilization is safe.					0.699
I would like to use renewable energy as a way to protect the environment.					0.598
I am planning to use renewable energy technologies.						0.580
I enjoy using renewable energy.						0.666
I plan to continue using renewable energy in the future.						0.856
I believe that I will continue receiving benefits from the use of renewable energy in the future.						0.866
I prefer to use renewable energy over other energy sources.						0.557

* Items were considered as components of a factor if their absolute loadings were equal to or greater than 0.5.

.

The communalities of the items ranged from 0.55 to 0.89. Since an oblimin rotation was applied, factors were allowed to correlate. The factor correlation matrix (see Appendix A) reveals moderate correlations among certain factors (e.g., V1 with V3, r = 0.66; V2 with V5, r = 0.67), while others exhibit weak correlations (e.g., V2 with V4, r = 0.18), consistent with theoretical expectations.

The internal consistency of the factors was assessed using Cronbach’s alpha, a measure developed by Cronbach [114]. A factor is considered reliable if its alpha is at least 0.70. The analyzed data showed that Cronbach’s alpha for each factor exceeded 0.80, indicating excellent reliability.

The six variables extracted through exploratory factor analysis (EFA) were further characterized and described in

Table 4. Description of Extracted Factors (EFA results).

Variable	Label	Description
V1	Environmental Concern (EC)	Reflects respondents’ emotional and cognitive concern for environmental degradation, resource depletion, and pollution associated with energy production and consumption. This factor captures the general environmental awareness that influences attitudes toward renewable energy.
V2	Knowledge and Awareness of Renewable Energy (KRE)	Refers to the level of familiarity with the concept, availability, and benefits of renewable energy. It represents the cognitive foundation upon which other attitudes toward renewable energy are formed.
V3	Social and Economic Support for Renewable Energy (SES)	Combines perceived financial readiness to invest in renewable energy with social endorsement from family, friends, and peers. It represents both economic capacity and social encouragement to adopt renewable energy solutions.
V4	Perceived Ease of Use of Renewable Energy (EURE)	Measures the extent to which respondents perceive renewable energy technologies as easy to install, operate, and manage. It captures the usability and accessibility dimension of renewable energy adoption.
V5	Perceived Benefits and Attitude toward Renewable Energy (PBA)	Represents the belief that renewable energy use provides multiple benefits, including environmental protection, economic efficiency, safety, and employment growth. It also captures respondents’ positive personal attitudes toward renewable energy.
V6	Behavioral Intentions and Continued Use of Renewable Energy (BICU)	Reflects both the intention to adopt and the willingness to continue using renewable energy in the future, including enjoyment, preference, and perceived long-term benefits from its use.

, providing a concise explanation and corresponding labels for each factor. These variables represent distinct yet complementary dimensions of attitudes toward renewable energy—ranging from environmental concern and general ecological awareness, through the level of knowledge and social and economic support for renewable energy use, to the perceived ease of use, perceived benefits, and positive attitudes toward renewable energy, as well as intentions and willingness to continue using renewable solutions in the future.

Significantly, the empirically derived six-factor structure differs from the nine theoretical dimensions proposed by Gârdan et al. [32]. While the original model identified nine separate constructs, our analysis reveals a more condensed configuration. This revised structure more accurately captures the context-specific patterns of attitudes toward risk, perceived benefits, and the social perception of the energy transition.

The obtained factor structure is clear, stable, and internally consistent, confirming that Polish respondents’ attitudes toward renewable energy are multidimensional. Each factor reflects a coherent thematic area that captures cognitive, affective, and behavioral components of public perceptions of renewable energy. The results are consistent with prior theoretical and empirical findings, demonstrating both the adequacy of the applied measurement instrument and the interpretability of the six-factor solution.

4. Results

To identify groups of respondents with similar attitudes toward renewable energy, a two-stage clustering procedure was applied. In the first stage, the hierarchical Ward’s method, using the Euclidean distance metric, was employed to explore the natural grouping tendency in the data. The Elbow Method plot (Figure 1) indicated a clear inflection point at k = 2, suggesting that the optimal solution should include two clusters. The selection of the two-cluster solution was further validated using internal clustering validation methods. The examined indices—including the Calinski–Harabasz Index, the Average Silhouette Width, and the Davies–Bouldin Index—consistently indicated, in most evaluations, that the two-cluster solution exhibited the highest quality and cohesion. For instance, the value obtained by the Average Silhouette Width for k = 2 was 0.315, indicating a relatively weak clustering structure. Nevertheless, compared to solutions with more than two clusters, the silhouette values were lower, thereby reinforcing the choice of a two-cluster partition. Therefore, solutions with a greater number of clusters (k > 2) were rejected due to a sharp decline in the separation index, suggesting that the additional clusters did not represent sufficiently distinct or stable segments.

In the second stage, the non-hierarchical k-means clustering method was employed to refine the classification and determine the final cluster membership. This approach allowed for greater precision in assigning cases to clusters and the identification of segments with distinct profiles of attitudes toward renewable energy. In robustness tests, comparing the k-means method with the previously applied Ward’s hierarchical clustering and additionally with the k-medoids approach, the resulting Adjusted Rand Index (ARI) [115] scores, exceeding 0.92 (0.947 and 0.923, respectively), attest to the strong reliability and stability of the identified two-group partition.

The variables used to define these two segments were the centroids of the six key attitude factors previously derived from exploratory factor analysis (EFA). These centroids are presented in

Table 5. Cluster centers.

Variable	Cluster/Segment
	I	II
V1	0.665	−0.537
V2	0.572	−0.461
V3	0.696	−0.562
V4	0.679	−0.548
V5	0.759	−0.612
V6	0.770	−0.622

, forming the basis for interpreting the established attitude profiles.

Cluster I is characterized by positive centroid values for all six factors (ranging from 0.57 to 0.77), whereas Cluster II displays negative centroid values (from −0.62 to −0.46). This pattern suggests that respondents in Cluster I exhibit stronger pro-renewable attitudes across all dimensions. In contrast, those in Cluster II demonstrate lower or even neutral-to-negative evaluations of the same aspects.

The visualization of the two-cluster structure obtained from the k-means clustering is presented in Figure 2 (Cluster Plot). The figure clearly separates the two groups along the main factor dimensions, confirming that the data naturally form two distinct segments. Cluster I occupies the area corresponding to higher scores on all attitudinal dimensions, whereas Cluster II is concentrated in the opposite region, characterized by lower or more neutral evaluations. This graphical evidence supported the decision to adopt a two-cluster solution for subsequent analysis.

Descriptive statistics for the two clusters are provided in

Table 6. Descriptive statistics.

Factor	Mean		Median		SD		Skewness		Kurtosis
	I	II	I	II	I	II	I	II	I	II
EC	4.229	3.056	4.200	3.000	0.616	0.895	−0.876	−0.406	2.267	0.079
KRE	4.519	3.708	4.667	3.667	0.506	0.783	−0.739	−0.261	−0.315	−0.178
SES	3.622	2.211	3.750	2.250	0.917	0.841	−0.399	0.137	−0.136	−0.649
EURE	3.711	2.552	3.667	2.667	0.801	0.703	−0.312	−0.225	−0.095	0.111
PBA	4.305	3.069	4.250	3.125	0.524	0.751	−0.330	−0.687	−0.734	0.797
BICU	4.044	2.576	4.000	2.800	0.685	0.817	−0.373	−0.495	0.041	−0.460

. Cluster I includes 539 participants (55.34% of the sample), while Cluster II contains 435 respondents (44.66%). The mean and median values confirm that Cluster I systematically achieves higher scores across all six factors—Environmental Concern (EC), Knowledge about Renewable Energy (KRE), Social and Economic Support (SES), Ease of Use and Renewable Experience (EURE), Perceived Benefits and Attitudes (PBA), and Behavioral Intention toward Clean Use (BICU). In contrast, Cluster II shows substantially lower mean values, indicating limited engagement and weaker support for renewable energy initiatives.

The standard deviations suggest moderate variability within clusters, implying a relatively homogeneous structure in each segment. The distributions for most variables are slightly left-skewed, reflecting the predominance of higher response values, particularly within Cluster I. The kurtosis coefficients remain close to zero, indicating that the distributions are approximately normal.

Overall, the k-means classification confirmed the presence of two well-defined groups of respondents:

• Cluster I (Pro-renewable segment): individuals expressing consistently favorable attitudes, higher awareness, and greater support for renewable energy solutions.
• Cluster II (Less-engaged segment): respondents with more neutral or skeptical views and lower levels of knowledge and perceived benefits.

The comparison of sociodemographic distributions between the two identified clusters is presented in

Table 7. Comparison of sociodemographic distributions across clusters.

Characteristic	Category	Cluster I (%)	Cluster II (%)	Chi-Square Test
				χ2	p-Value
Generation	Baby Boomers	27.8	26.5	1.09	0.78
	Generation X	30.1	29.5
	Generation Y	26.2	25.6
	Generation Z	15.9	18.4
Gender	Female	52.9	50.3	0.55	0.459
	Male	47.1	49.7
Education	Below secondary school	34	37.5	1.75	0.417
	Secondary school	38.9	35.1
	Higher education	27.1	27.5
Place of residence	Village/town ≤ 20 k	42.5	45.8	1.07	0.586
	City 20 k–100 k	23.4	21.9
	City > 100 k	34	32.3
Income	<5000 PLN	22.1	26.7	6.86	0.076
	5000–10,000 PLN	52.6	49.4
	10,000–20,000 PLN	20.7	21.7
	>20,000 PLN	4.6	2.2

. Percentages within each cluster were calculated so that the total equals 100%, allowing for a direct comparison of internal group structures.

The analysis shows that, for most of the examined characteristics—including generation, gender, education level, and place of residence—the proportions of respondents are very similar across both clusters. For instance, women account for approximately half of the respondents in both clusters (52.9% and 50.3%, respectively). Likewise, educational profiles are nearly identical: respondents with secondary education represent about 39% in Cluster I and 35% in Cluster II, while those with higher education constitute around 27% in both clusters.

The lack of clear differentiation in the sociodemographic structure is supported by the chi-square test results, which did not reveal statistically significant differences between clusters for any of the analyzed variables. Only income showed a slight deviation, with lower-income respondents appearing somewhat more frequently in Cluster I; however, the difference was close to but did not reach statistical significance. The proximity to the threshold of statistical significance suggests that income may have potential relevance for differentiating attitudes toward renewable energy sources. This suggests the need for further exploration in subsequent research.

In summary, the distributions for all analyzed demographic characteristics are closely aligned between the clusters, indicating that the two groups are broadly similar in their sociodemographic composition.

5. Discussion

The primary aim of this study was to conduct an in-depth analysis of the diversity of attitudes toward RES among Polish citizens in the context of the ongoing energy transition. The study was designed to address two research questions (RQ1 and RQ2) and one hypothesis (H1).

Regarding RQ1, the exploratory factor analysis (EFA) confirmed that attitudes toward RES are multidimensional. Based on 37 indicators adapted from Gârdan et al. [32], six dimensions were identified, encompassing the cognitive, emotional, and behavioral components of attitudes toward renewable energy. These include environmental concern, knowledge and awareness of RES, social and economic support, perceived ease of use and renewable experience, perceived benefits and positive attitudes, and behavioral intentions toward clean use. Together, these factors reflect the informational and evaluative aspects of energy perceptions, as well as the readiness to engage in pro-environmental behaviors. This structure is consistent with prior theoretical models that describe social acceptance of renewable energy as a multidimensional construct encompassing various factors, including awareness, perceived benefits, and behavioral engagement [35,36]. Djurisic et al. [116] also identified a multidimensional approach. They used Structural Equation Modeling to explore attitudes, perceptions, and behaviors toward Montenegro’s RES. These dimensions provided a framework for analyzing causal relationships in a study of 1012 respondents conducted in 2019–2020.

Addressing RQ2, the subsequent cluster analysis revealed the existence of two distinct and clearly defined groups of respondents, despite the identified factors representing different thematic areas. This finding reveals a significant polarization of attitudes toward RES within Polish society. Rather than forming a gradual continuum of opinions ranging from supportive to skeptical, the data reveal a bifurcated structure: one group is highly engaged and supportive of renewable energy, while another is more skeptical, passive, or indifferent toward the energy transition.

The first segment, Cluster I (pro-RES group), exhibited high scores across all six factors, reflecting strong pro-environmental awareness, positive emotions toward renewable technologies, and conviction in their social and economic benefits. Members of this group also demonstrate a readiness to support policies that advance renewable energy and sustainability. In contrast, Cluster II (the less-involved group) displayed lower scores across all factors, indicating weaker trust in institutions, lower awareness of the benefits of RES, and a limited willingness to engage in pro-environmental activities. This pattern mirrors similar segmentation results reported in prior research on renewable energy acceptance [35].

Within the broader Central and Eastern European (CEE) context, the identified segmentation patterns in Poland are consistent with findings reported for other Visegrad Four (V4) countries. Studies conducted in Hungary, the Czech Republic, Slovakia, and Romania suggest that attitudes toward renewable energy are more strongly influenced by cognitive and psychosocial factors than by structural socio-demographic differences. For example, Szeberényi et al. [117] show that among young citizens across the CEE region, pro-environmental intentions are primarily driven by individual attitudes and subjective norms, while demographic predictors play a marginal role. Similarly, Maruszewska et al. [118] find that although young CEE residents express support for climate and energy transition policies, their readiness to accept higher financial contributions—such as energy-related taxes—remains moderate, reflecting similar cost–risk perceptions across the region. Research from Hungary and the Czech Republic further demonstrates that public support for renewable energy coexists with a persistent preference for traditional or nuclear-based energy solutions, highlighting a shared regional ambivalence rooted in historical energy structures and a lack of trust in institutions. These parallels suggest that the Polish pattern of attitudinal polarization is not unique but reflects broader CEE-specific dynamics that differentiate the region from Western European contexts.

Importantly, however, our analysis reveals that these differences between clusters cannot be attributed to demographic variables, thereby leading to the rejection of Hypothesis H1. This is a critical finding: polarization in Poland does not align with socio-demographic lines, which highlights the need to incorporate psychographic profiling in future research. Rather than attributing causality to unmeasured factors, our results highlight an empirical gap. Future studies should examine how values, institutional trust, and ideological orientations contribute to the disengagement observed in Cluster II, thereby extending the explanatory framework beyond demographics.

To address H1, the study examined whether the two clusters differed significantly in terms of socio-demographic variables, including age, gender, education, income, and place of residence. Statistical tests (chi-square and non-parametric comparisons) showed no significant differences in the distribution of these variables between clusters. This result provides empirical evidence that socio-structural, demographic, or socioeconomic characteristics cannot explain the polarization of attitudes toward renewable energy in Poland. Instead, it likely stems from cognitive and ideological differences, including environmental awareness, perceptions of energy policy fairness, and broader worldview orientations.

Other studies on attitudes toward RES also indicate that demographic factors significantly differentiate individuals’ opinions and behavioral intentions. Nassar [23] demonstrated that attitudes toward renewable energy in Qatar are shaped by sociodemographic factors, revealing the complexity of public opinion, which depends on the social structure of respondents. Almrafee and Akaileh [20] confirmed the existence of statistically significant differences in renewable energy purchase intentions based on age, income, and education level, demonstrating that demographic variables are an important predictor of attitudes toward RES. Comparatively, Rana et al. [19], analyzing data from several South Asian countries, also found variation in RES adoption intentions depending on the sociodemographic context of the surveyed households. Similar observations were presented by Asli et al. [24], who noted that demographic data (gender, age, city, education, marital status, and economic status) do not play a moderating role in the relationships between knowledge, attitudes, and behaviors toward RES. This result demonstrates the complex interplay of sociocultural, cognitive, and behavioral factors. The renewable energy sphere appears to transcend traditional demographic boundaries—its universal nature means that differences in age, gender, or social status do not significantly impact knowledge or attitudes toward renewable energy. This may indicate a society in which the discourse on renewable energy reaches a broad audience, regardless of their demographic characteristics. Demographic factors were also considered by Oh et al. [119], who indicated that they shape public attitudes toward supporting energy transition policies toward RES in different ways. A positive attitude supporting RES (regarding nuclear energy) is more often expressed by women than men, as well as by individuals in their 30 s and 40 s, and by those with higher levels of education and belonging to higher social classes. In contrast, household income alone does not significantly differentiate support for this policy. These results support hypothesis H1, which states that attitudes toward RES vary significantly based on demographic characteristics such as gender, age, education, income, and residence. However, in the Polish dataset analyzed here, Hypothesis H1 was not confirmed: no significant differences were observed across gender, age, education, income, or place of residence. This divergence suggests that, unlike in Qatar or South Asia, where demographics play a role, polarization in Poland is shaped more by cognitive and ideological orientations.

Our analysis shows that demographic variables do not account for the segmentation of attitudes toward RES in Poland. This contrasts with prior studies that found demographic predictors of renewable energy acceptance [23,24,84]. One explanation may be that the current energy crisis and climate concerns have created a more universal awareness across groups [12,44]. Another possibility is methodological differences in sampling or national context [32]. In contrast, Cluster I respondents display strong environmental concern and readiness, while Cluster II remains less engaged, with weaker trust and limited willingness to adopt RES. This mirrors segmentation patterns reported elsewhere [35], but in Poland emerges independently of socio-demographics, underscoring the need for future psychographic profiling [36,71].

In the Polish context, these findings may also be interpreted through the lens of ideological and political polarization. In recent years, energy and climate policy issues have become integral to public discourse on national sovereignty, economic security, and relations with the European Union [2,102]. Consequently, attitudes toward RES may increasingly reflect trust in institutions and information sources, rather than objective socio-economic circumstances. Similar dynamics have been observed in other countries, where environmental support aligns with ideological and value-based divisions rather than demographic attributes [82,83].

The lack of demographic differentiation in our results is particularly significant in the Polish case, where the energy transition is slower and more contested than in many other EU countries. This suggests that public polarization around RES in Poland is not rooted in socio-demographic divides but in ideological and cognitive factors shaped by the country’s distinctive energy pathway and political discourse.

From a theoretical perspective, these findings contribute to the broader discussion on the social acceptance of renewable energy by illustrating that in countries undergoing slower transitions, such as Poland, attitudes toward RES are complex, value-laden, and strongly intertwined with trust in institutions and political narratives. From a practical standpoint, this pattern represents a significant challenge for policymakers and communicators. Since demographic segmentation does not explain differences in attitudes, interventions cannot be tailored to specific population groups. Instead, promoting RES requires broad-based, ideologically neutral, and transparent communication emphasizing common societal benefits, long-term energy security, and credible institutional engagement.

This interpretation also aligns with the broader European context, in which a successful energy transition depends on technological development, cultivating public trust, and fostering a shared sense of participation. Therefore, future research should investigate how political orientation, institutional trust, and information framing influence public acceptance of renewable energy in Poland. Understanding these mechanisms could help policymakers design strategies that reduce ideological divides and strengthen public support for green transformation.

5.1. Practical Implications: Tailored Interventions for the Two Segments

The segmentation results allow for the development of differentiated policy tools tailored to the needs of the two identified clusters. For the less engaged or skeptical segment, targeted interventions should focus primarily on reducing cognitive barriers and addressing perceived risks. First, communication strategies should prioritize clear, simplified information that explains the functioning of RES technologies—particularly wind and photovoltaic systems—using visual materials, real-world demonstrations, and locally relevant examples. Second, messages should highlight concrete, place-based economic benefits such as lower household energy bills, municipal revenues from RES installations, and local job creation in the operation and maintenance of biomass or solar facilities. Third, policy tools should include low-cost access programs, micro-grants for small RES installations, and community-level support schemes, reducing the upfront financial burden that often reinforces skepticism. These measures can help bridge the knowledge gap and gradually build trust among less engaged households.

For the pro-green and engaged cluster, whose members already exhibit strong behavioral intentions and positive attitudes toward RES, interventions should emphasize mechanisms that amplify their impact on social norms. Policymakers should support community-led energy projects, such as energy cooperatives or local prosumer groups, that enable highly engaged citizens to become catalysts of change within their communities. Communication campaigns may also highlight visible collective actions—e.g., neighborhood solar initiatives, community heat pumps, or citizen panels on local energy planning—to strengthen descriptive and injunctive norms around renewable energy. Furthermore, this segment may be encouraged to participate in consultation processes, pilot programs, and educational activities to increase the visibility of pro-environmental behavior and thus facilitate wider social diffusion.

To illustrate how the above strategies can be implemented, it is worth referencing existing precedents. German energy cooperatives (e.g., BürgerEnergie) demonstrate how citizen ownership and governance increase local acceptance [120]. National prosumer grant programs such as Poland’s Mój Prąd show how micro-grants boost household PV uptake [121]. Czech grant schemes and developing legal frameworks for energy communities provide additional models for micro-granting and community pilots [122].

5.2. Alignment with EU and National Policy Frameworks

These recommendations are directly aligned with the strategic direction of both the European Green Deal and Poland’s Energy Policy until 2040 (PEP2040). The observed attitudinal polarization poses challenges to achieving key national targets, particularly those relating to the expansion of wind and solar capacity, the accelerated development of distributed energy, and the continued use of sustainable biomass. For example, skepticism within the less engaged segment may undermine local acceptance of onshore wind installations or large-scale photovoltaic farms, slowing progress toward the RES share envisaged in PEP2040 and the Green Deal’s decarbonization trajectory. Conversely, the strong pro-environmental orientation of the engaged cluster offers an opportunity to accelerate community-driven prosumer development and support the EU objective of empowering consumers in the energy transition.

The segmentation approach, therefore, provides policymakers with actionable insights: Poland’s RES targets cannot rely on one-size-fits-all communication. Instead, policies should combine trust-building narratives and financial facilitation for the less engaged cluster with empowerment-oriented strategies for the pro-green segment. Such a dual approach can enhance public acceptance, mitigate polarization, and enhance the implementation potential of both national and EU-level energy transition goals.

6. Conclusions

This study provides an empirical insight into the diversity and polarization of attitudes toward RES among Polish citizens, contributing to a broader understanding of public engagement with the ongoing energy transition. Using 37 validated indicators adapted from Gârdan et al. [32], we identified six key dimensions of RES-related attitudes—cognitive, affective, and behavioral—and revealed, through cluster analysis, two distinct societal segments: one strongly supportive of renewable energy and another characterized by skepticism and low involvement.

The key theoretical conclusion is that attitudinal polarization toward RES in Poland does not result from demographic or socio-structural differences. The absence of statistically significant variations across gender, age, education, income, or place of residence demonstrates that traditional socio-demographic variables are no longer effective predictors of pro-environmental behavior. Instead, the division appears to stem from ideological and cognitive factors, such as trust in institutions, perceptions of fairness in energy policy, and individual values concerning modernization and sustainability.

From a practical perspective, this finding poses a challenge to policymakers and communication strategists. Since no specific demographic profile can be identified as particularly resistant or supportive of renewable energy, actions promoting RES adoption cannot rely on selective targeting. Instead, communication and policy interventions should be inclusive, transparent, and ideologically neutral, emphasizing shared benefits, credibility, and social trust. Promoting renewable energy in Poland thus requires engagement across all social groups, using clear information, education, and consistent policy signals that transcend political divisions.

6.1. Limitations

While the study offers valuable insights, several limitations should be acknowledged. First, the research design employed a cross-sectional approach, capturing attitudes at a single point in time (June 2025). This limits the ability to assess how attitudes evolve in response to changing economic or political circumstances, such as fluctuations in energy prices or regulatory reforms. Second, all measures were self-reported, which introduced the risk of social desirability bias—respondents may have overstated their pro-environmental attitudes. Third, the study focused exclusively on the Polish context, where specific institutional, cultural, and policy factors shape public perceptions of renewable energy. Additionally, data were collected using the CAWI method, which may lead to the underrepresentation of individuals with limited internet access or lower digital skills. No additional weighting was applied for variables such as education or region, which may somewhat limit the generalizability of the results beyond the controlled quotas. Consequently, the findings should be interpreted within this national framework and not directly generalized to other countries. Additionally, while the quantitative approach provides structural insights, it does not capture the qualitative depth of individual reasoning and motivations behind energy-related attitudes. Finally, although the use of self-reported survey data may introduce social desirability bias, future studies could employ methods to mitigate this limitation. Examples include indirect questioning or randomized response techniques, which increase respondents’ perceived anonymity. Researchers may also use more neutral item wording and incorporate short social-desirability control scales to statistically adjust for this bias. Finally, complementing declarative data with simple behavioral indicators (e.g., actual use of RES solutions) could further reduce reliance on self-reported attitudes.

6.2. Future Research Directions

Future studies should seek to overcome these limitations in several ways. First, conducting longitudinal studies would enable the tracking of changes in public attitudes over time and understanding how major events—such as energy crises, new incentives, or geopolitical shifts—influence perceptions of renewable energy. Second, cross-national comparative research could examine whether the polarization observed in Poland is also present in other Central and Eastern European countries with similar institutional legacies or energy dependencies. Third, future research should incorporate psychological and ideological variables, including political orientation, trust in institutions, media exposure, and perceived risk, to better capture the deeper drivers of public acceptance. Mixed-method approaches combining quantitative segmentation with qualitative interviews or focus groups could further enrich understanding of how individuals interpret and internalize renewable energy policies. Furthermore, self-reported data may overstate pro-environmental attitudes. To reduce the potential overestimation of respondents’ pro-environmental attitudes, future studies should consider applying statistical adjustments to account for the influence of social desirability.

Finally, extending this line of research to include behavioral indicators, such as actual energy consumption patterns or participation in renewable programs, would strengthen the connection between declared attitudes and observable actions. Exploring these dimensions could help design more effective strategies to reduce polarization, enhance public trust, and support a socially inclusive path toward Poland’s green transition.