A European Comparative Study of Public Perception and Evidence-Based Information on Energy Production Alternatives

Abstract

The accelerating global energy transition underscores the crucial role of societal perceptions and knowledge in the acceptance of sustainable energy technologies. This study assesses the accuracy of self-assessed knowledge of seven energy production alternatives—solar, wind, hydropower, gas, nuclear fission, and fusion—across four complex criteria (economy, environment, safety, and reliability) based on a large-scale European survey (n = 19,144). Their assessments were contrasted with literature-based reference values through a multi-criteria evaluation approach. Results reveal that public knowledge is most accurate for long-established technologies such as hydropower, gas, and nuclear, while knowledge of renewable and emerging technologies (wind, solar, and fusion) is less accurate. The decomposition of the four complex criteria revealed that public evaluations are predominantly influenced by single indicators: fixed costs for the economic criterion, air pollution for the environmental dimension, accident risk for safety, and flexibility or availability factor for reliability. Average self-assessed knowledge levels were relatively homogeneous across Europe (2.6–3.1 on a five-point scale), yet the correlation between perceived and actual knowledge accuracy was weak. In just over half of the countries, lower knowledge levels corresponded to greater self-assessment errors, while in others, no clear trend emerged. These findings underscore the importance of improving societal understanding of renewable and novel energy sources and strengthening knowledge dissemination to support the transition toward sustainable energy systems.

1. Introduction

Sustainability and sustainable development are widely discussed concepts in both scientific literature and public policy, emphasizing the need to balance the complex interactions between human society and the natural environment. To understand energy sustainability, it is first necessary to clarify the broader concept of sustainability, which entails the responsible use of natural resources in a way that does not compromise the ability of future generations to meet their needs. Energy sustainability is closely linked to the utilization of renewable energy sources, which are virtually inexhaustible and freely available, thereby ensuring long-term viability [1,2,3,4]. Nevertheless, sustainability extends beyond the production of clean energy and also encompasses social, economic, and environmental dimensions.

The transition from fossil fuels to renewable energy is indispensable, but achieving sustainability also requires a reduction in energy demand through the development of more efficient systems [5,6,7]. This represents a considerable challenge for most countries, as large segments of society continue to rely heavily on fossil fuels, whose negative effects on both the environment and human can truly ensure sustainability health are well documented [8]. In response, many governments have introduced new policies over the past decade to transform their energy systems into highly efficient, green, and sustainable infrastructures. However, without appropriate planning, such transformations remain difficult to implement. Whereas energy planning once relied on relatively simple, single-objective approaches, it has evolved into a much more complex process involving multiple reference values, stakeholders, and often conflicting objectives. As a result, contemporary planning scenarios require the integration of diverse goals, definitions, and criteria, which complicates the design of systems that can ensure truly sustainability. An appropriate planning framework must therefore incorporate political, social, economic, and environmental considerations to address the rising global energy demand in line with the principles of sustainable development. In this regard, multi-criteria decision-making (MCDM) has emerged as one of the most effective methodological tools to support energy planning [9,10,11,12,13].

A long-term research on the social acceptance of energy innovations have shown that few political issues are as divisive as energy [14]. Debates on energy occur simultaneously at global and local levels, where competing socio-economic interests, ideological perspectives, and political decisions clash. The transition to renewable energy systems must therefore also take into account broader geopolitical factors. Consequently, successful innovation in the energy sector increasingly requires the integration of public opinion into decision-making processes. Although MCDM provides a methodological framework for including such perspectives, large-scale public participation remains costly, which explains why comprehensive, representative population surveys are relatively rare. Instead, research more frequently relies on questionnaire-based surveys or structured interviews with selected stakeholders.

In the formulation of energy strategies, the perspectives of stakeholders play a primary role, particularly in supporting political and policy decisions. However, understanding the views of the public is equally essential, as effective policy implementation relies on accurately measured and representative societal attitudes.

To date, most existing studies are localized, no comprehensive research has systematically examined public energy preferences across multiple regions and cultural contexts. For example, in the study of Rijnsoever and Farla [15], the authors have identified five main criteria influencing household preferences in the Netherlands—disaster risk, economic security, private costs and convenience, spatial impact, and price—while Sovacool [16] emphasized the cultural embeddedness of energy security, showing that its perception varies widely across socio-economic and political contexts. Demski et al. [17] analyzed concerns related to energy security across Europe using nationally representative survey data. They found that public concern regarding energy security was higher in countries with lower levels of economic and social well-being. Despite these valuable insights, recent, country-specific, and Europe-wide representative surveys focusing on multiple evaluation criteria remain scarce.

Survey-based methods offer clear advantages, such as the ability to handle large sample sizes, differentiate subtle variations, increase the generalizability of findings, and enable the use of advanced statistical analyses [18]. However, ensuring the reliability and quality of responses poses significant methodological challenges. Several reliability checks have been developed to address this. One common method is test–retest reliability, where the same questionnaire is administered to the same respondents at two different times to measure the consistency of their answers, although this substantially increases survey costs [19]. Another is internal consistency, which measures how well a set of items within a questionnaire capture the same construct, often used in studies of attitudes, perceptions, or knowledge. Internal consistency has also been successfully used in energy-related surveys, for example when evaluating public perceptions of renewable energy [20,21]. The split-half method, which divides the questionnaire into two parts to compare results, can also be applied but requires longer surveys and higher research costs [22]. Triangulation, which relies on combining multiple sources or methods such as survey responses with administrative or observational data, can also enhance reliability and validity, but its complexity and resource requirements make it impractical for very large-scale surveys. Nevertheless, triangulation has been applied in urban energy studies [23,24]. More advanced statistical approaches such as Item Response Theory (IRT) or Rasch analysis provide opportunities for cross-national scaling and the evaluation of cultural differences in survey responses [25], although their application requires very large datasets and specialized expertise.

The present research seeks to contribute to this field by evaluating energy-related knowledge levels across Europe through a country-specific, representative survey. The survey presented in this study provides an important snapshot that highlights the current state of public awareness and indicates where we stand on the path toward broader knowledge dissemination and social acceptance. The study also addresses a critical knowledge gap concerning public understanding of fusion energy, an emerging technology that remains far less familiar to the general population than conventional or renewable sources. To this end, we combine self-assessment with multi-criteria evaluation in order to examine (i) whether the accuracy of public knowledge varies across different energy production alternatives, (ii) whether the four complex evaluation criteria—economy, environment, safety, and reliability—are interpreted consistently or show systematic differences, and (iii) to what extent opinions about these technologies are grounded in substantive knowledge and whether self-assessment provides a reliable measure of it. This study therefore provides new insights into the relationship between knowledge, perception, and acceptance of energy innovations in the context of the ongoing energy transition.

This paper is organized as follows. Section 2 presents the materials and methods, including the design of the international survey, the applied evaluation framework, and the data processing procedure. Section 3 combines the results and discussion in order to ensure greater transparency in the interpretation of findings. As the results consist of several interrelated sub-sections, each focusing on distinct aspects of knowledge accuracy, criterion interpretation, and self-assessment reliability, the discussion is presented alongside the presentation of results to facilitate direct and contextualized interpretation. Finally, Section 4 summarizes the main findings and limitations, highlights methodological implications, and outlines directions for future research related to public understanding and acceptance of sustainable energy technologies.

2. Materials and Methods

2.1. Survey

A self-administered questionnaire was designed to assess public attitude toward fusion. To obtain more meaningful results, fusion was not discussed as a standalone topic, but rather analyzed in relation to other energy technologies. The survey was implemented in 21 European countries (AU-Austria, BL-Belgium, BUL-Bulgaria, CZ-Czech Republic, DEN-Denmark, FIN-Finland, FR-France, GER-Germany, GRE-Greece, IT-Italy, LAT-Latvia, LIT-Lithuania, NL-The Netherlands, PL-Poland, PT-Portugal, RM-Romania, SL-Slovenia, SP-Spain, SW-Sweden, UK-United Kingdom, UKR-Ukraine) during the final quarter of 2023. The design and implementation of the survey was part of the research on public attitudes toward fusion of the EUROfusion Socio Economic Studies (SES) work programme [26]. The questionnaire was administered through online panels, all participating ensuring a standardized data collection process across countries.

In total, 19,144 European citizens aged 18 years and above participated in the survey, with the number of respondents per country ranging from 909 to 914. A comprehensive description of the questionnaire design, the associated sociodemographic variables and the discussion of nuclear acceptability in Europe based on the survey outcomes, can be found in [27,28].

The questionnaire included several items specifically addressing fusion energy; however, in the present study, we focus on the more general questions concerning the characterization of energy alternatives. For the purposes of the present analysis, however, we restrict our focus to five questions that are most pertinent to the research objectives (see Table 1). The aim of analyzing this concise set of five comparative questions was twofold: first, to develop a data-processing framework capable of decomposing the four complex criteria used in the questionnaire—economy, environment, safety, and reliability—into detailed indicators at the national level; and second, to validate respondents’ self-assessed energy-related knowledge by quantifying the magnitude of self-assessment error.

Table 1. Questions and corresponding response options considered in the present study.

Q2. How would you describe your level of familiarity with the following electricity generation and supply options? 1. Gas power plants 2. Solar plants 3. Residential/commercial solar panels 4. Wind farms 5. Hydroelectricity 6. Nuclear power (nuclear fission) 7. Fusion energy (nuclear fusion)	C1. Nothing, I have never heard of it C2. Nothing, I only know the name C3. Very little C4. A moderate amount C5. A lot
Q14. How harmful or harmless to the environment do you think each of the following power sources is? 1. Gas power plants 2. Solar plants 3. Residential/commercial solar panels 4. Wind power 5. Hydroelectricity 6. Nuclear power (nuclear fission) 7. Fusion energy (nuclear fusion)	C1. Very harmful C2. Rather harmful C3. Neutral C4. Rather harmless C5. Completely harmless
Q15. How positive do you consider the contribution of each of the energy sources to the reliable and consistent supply of energy and to meet the needs of a country? 1. Gas power plants 2. Solar plants 3. Residential/commercial solar panels 4. Wind power 5. Hydroelectricity 6. Nuclear power (nuclear fission) 7. Fusion energy (nuclear fusion)	C1. Very negative C2. Rather negative C3. Neutral C4. Rather positive C5. Completely positive
Q16. How safe (in terms of potential accidents and hazardous emissions or wastes) do you think it is to produce electricity with each of the energy options? 1. Gas power plants 2. Solar plants 3. Residential/commercial solar panels 4. Wind power 5. Hydroelectricity 6. Nuclear power (nuclear fission) 7. Fusion energy (nuclear fusion)	C1. Very dangerous C2. Rather dangerous C3. Neutral C4. Rather safe C5. Completely safe
Q17. How costly or cheap do you think it is to produce electricity with each of the energy options? 1. Gas power plants 2. Solar plants 3. Residential/commercial solar panels 4. Wind power 5. Hydroelectricity 6. Nuclear power (nuclear fission) 7. Fusion energy (nuclear fusion)	C1. Very costly C2. Rather costly C3. Not costly neither cheap C4. Rather cheap C5. Very cheap

Participants were first asked to rate their self-assessed level of knowledge regarding seven energy technologies—residential photovoltaic systems, utility-scale solar plants, wind farms, hydropower stations, nuclear power, gas power plants, and fusion energy—using a five-point scale. Subsequently, each of these energy generation alternatives was evaluated, again on a five-point scale, with respect to four predefined criteria: reliability, environmental impact, cost, and safety.

To limit the scope of the evaluation, only technologies currently in operation within the European Union and contributing more than 5% of the total electricity generation mix were included. Coal-fired power plants were excluded due to ongoing policy measures promoting their progressive phase-out. Fusion energy, however, was included as an alternative in light of the recent resurgence of interest in the field and the increasing involvement of private-sector research initiatives.

The selection of criteria was constrained by the design of the survey. Instead of employing single keywords, each criterion was introduced through a concise explanatory statement, and was therefore treated as a multidimensional concept. The international scope of the survey also necessitated this approach to ensure consistency in linguistic and conceptual interpretation. For instance, the environmental impact criterion was defined broadly to encompass climate-related consequences in addition to direct adverse physiological effects. Furthermore, the concept of safety was explicitly distinguished from supply security: safety referred to risks associated with accidents, hazardous emissions, or waste, whereas supply security was captured under the concept of reliability, framed as the “consistent and reliable and consistent supply of energy”. The economic dimension could not be elaborated in detail; thus, respondents were invited to assess the cost criterion in general terms, defined as “how costly or inexpensive you think it is to produce electricity.”

All responses were mandatory, ensuring a dataset without missing values.

2.2. Comparison Analysis

Our analysis builds on the international survey described above, in which respondents compared the characteristics of seven commonly used energy alternatives across four criteria, organized into a respondent matrix (Figure 1).

Figure 1. Response-based evaluation matrix.

Each row of the respondent matrix represents the respondents’ subjective assessment of energy production alternatives with respect to a given criterion. The initial matrix, based on the five-category responses of the questionnaire, takes integer values ranging from 1 to 5, where the value 1 corresponds to response C1, and the value 5 corresponds to response C5. Although these subjective evaluations can be compared with established values reported in the literature, precise comparability requires normalization. Accordingly, each row of the matrix was normalized to enable consistent cross-criterion analysis:

$$a_{ij}^{*}={\displaystyle \frac{a_{ij}}{\sum _{i=1}^7{a}_{ij}}}$$

(1)

Due to the limited scope of the representative survey, only general questions could be included concerning energy carriers and compact composite evaluation criteria (hereafter referred to as complex criteria). With regard to energy carriers, it was not possible to differentiate between technological variants or scales of deployment (with the sole exception of solar energy, where residential photovoltaic systems and utility-scale solar plants were distinguished). Similarly, no subdimensions of the four complex criteria—economic, environmental, safety, and reliability—could be explicitly distinguished. This restriction is not trivial, as responses may differ substantially depending on whether, for instance, the subject is a rooftop residential PV installation versus a ground-mounted system, or an onshore versus an offshore wind farm. Comparable difficulties arise for the criteria themselves: in the case of environmental impact, for example, respondents may emphasize different aspects such as carbon neutrality or biodiversity loss.

In this study, we focused on responses structured around the four complex criteria. As questions Q14–Q17 in the questionnaire (Table 1) were formulated with detailed explanatory statements to characterize these criteria, we found it necessary to include additional indicators in the evaluation process to refine their interpretation. The eleven indicators applied in our analysis reflect our specific research interests and should not be considered as a complete or independent set of evaluation parameters. Nevertheless, the proposed methodology allows for the inclusion of further indicators in future assessments, providing flexibility for broader or more specialized analyses.

Since reference data were also required for evaluation, we associated each criterion with measurable indicators (detailed in Table 2). Using data from the scientific literature, we then evaluated the energy production alternatives accordingly.

Table 2. Literature-based normalized values for each electricity generation alternative across complex criteria and indicators, combining data from residential photovoltaic systems and utility-scale solar plants.

Complex Criteria	Indicator	Solar	Wind	Hydro	Fossil Gas	Nuclear	Fusion
Economy	Fixed cost	0.0543	0.0831	0.2134	0.0631	0.2159	0.3701
	Variable cost	0.0238	0.1082	0.1552	0.3866	0.1148	0.2114
Environment	Climate change effect	0.0093	0.0074	0.0630	0.9085	0.0043	0.0074
	Air pollution	0.0953	0.0887	0.0582	0.5896	0.0718	0.0964
	Hazardous waste	0.0120	0.0023	0.0006	0.0012	0.8609	0.1230
	Waste quantity	0.4690	0.1858	0.2389	0.0177	0.0442	0.0442
Risk	Risk of accident	0.0010	0.0261	0.0314	0.9413	0.0001	0.0000
	Risk aversion	0.0000	0.0000	0.0549	0.0110	0.8791	0.0549
Reliability	Availability of fuel	0.0000	0.0000	0.0000	0.8889	0.1111	0.0000
	Flexibility	0.2703	0.2703	0.1351	0.0000	0.2162	0.1081
	Availability factor	0.3255	0.2471	0.2118	0.0588	0.0784	0.0784

The step sizes and the technology-independent alternatives introduced above were evaluated using eleven indicators. Two of these address economic aspects: fixed costs and variable costs. Fixed costs include the annualized investment expenditures together with the fixed portion of operation and maintenance (O&M) costs, whereas variable costs mainly account for fuel expenditures and the variable share of O&M costs, which depend on the level of electricity generation [29].

Four indicators were defined as strictly environmental. These include (i) the impact on climate change, based on life-cycle greenhouse gas emissions expressed in carbon dioxide equivalents [30], and (ii) air pollution, measured as the aggregate health burden associated with the key pollutants (NOx, SO₂, NMVOCs, PM_2.5) [31]. The remaining two indicator account for the total amount of waste generated over the entire life cycle of energy production, evaluated in terms of (iii) its hazardousness and (iv) its quantity. Hazardousness is primarily characteristic of the nuclear sector, where the environmental impact is dominated by long-lived isotopes associated with nuclear fission. In contrast, with the decline of coal-fired power generation, the quantity of waste is increasingly associated with low-energy-density renewable sources, which require substantially larger material inputs throughout their life cycle [32].

Two further indicators relate to accident risks. The first, risk of accident, quantified as the number of fatalities over the full life cycle. The second, risk aversion, reflects the maximum societal damage caused by accidents. Risk aversion is interpreted as a social dimension capturing the public perception and fear of severe accidents, which primarily concerns nuclear power but, to a lesser extent, may also affect reservoir-based hydropower [33].

Finally, three indicators capture the reliability of energy production. Fuel availability reflects the geopolitical dimension, namely, the extent to which fuel supply is affected by local political or economic conflicts. Accordingly, higher values are attributed to renewable, domestic, or storable resources. Flexibility denotes the upward and downward controllability (dispatchability) of electricity generation and auxiliary demand. The availability factor is expressed as the theoretical maximum number of full-load operating hours per year. In this respect, gas-fired power plants tend to perform more favorably than suggested by standard literature values [34]. Literature-based criterion values for the principal electricity generation alternatives are summarized in Table 2.

Individual responses were compared with the reference indicator values $\left(b_{i1}^{*};\dots ;b_{i7}^{*}\right)$, and, for each respondent, the minimum deviation was determined using the Euclidean distance (t_i) between the corresponding data series:

$$t_i=\sqrt{{\left(a_{i1}^{*}-b_{i1}^{*}\right)}^2+\dots +{\left(a_{i7}^{*}-b_{i7}^{*}\right)}^2}$$

(2)

Contrasting subjective responses with literature-based data allowed us to assess the accuracy of selected fundamental concepts of sustainability, by comparing them with the corresponding indicators of the four complex criteria included in the survey, and to explore potential sources of systematic deviations.

Respondents’ knowledge of energy production and the associated evaluation criteria can also be approached from a complementary perspective, namely by examining the columns of the response matrix. By normalizing the column values,

$$a_{ij}^{**}={\displaystyle \frac{a_{ij}}{\sum _{j=1}^4{a}_{ij}}},$$

(3)

the results can be examined from the perspective of individual energy alternatives. Comparing the normalized column values with the literature-based benchmarks $\left(b_{1j}^{**};\dots ;b_{4j}^{**}\right)$ enables an evaluation of the accuracy of knowledge related to each specific energy alternative. In this case, accuracy is again quantified using the Euclidean distance (d_j) between the two data columns:

$$d_j=\sqrt{{\left(a_{1j}^{**}-b_{1j}^{**}\right)}^2+\dots +{\left(a_{4j}^{**}-b_{4j}^{**}\right)}^2}$$

(4)

which provides an assessment of each energy carrier in terms of the four complex criteria.

This latter analysis is particularly relevant, as it allows for direct comparison with respondents’ self-assessments, in which they rated their knowledge of the seven energy alternatives on a five-point scale. For this purpose, we employed a vector constructed from the determined d_j values (D_k = (d_k1, …, d_k7), where k is the number of respondent, and a second vector derived from the self-assessed knowledge levels of the alternatives, obtained by normalizing the values of the 1–5 scale: R_k = (r_k1, …, r_k7). The Euclidean distance (p_k) between these two vectors was then calculated as

$$p_k=\sqrt{{\left(d_{k1}-r_{k1}\right)}^2+\dots +{\left(d_{k7}-r_{k7}\right)}^2}$$

(5)

which yielded an accuracy measure characterizing the self-assessment. Smaller value indicate that the respondent rated its own knowledge more accurately. The distribution of the individual p_k values is evaluated at the national level, and the accuracy of self-assessment is further analyzed across the different rating categories.

3. Results and Discussion

This study, based on the results of a specific section of an international survey, presents a methodology by which respondents’ knowledge of energy-related and associated sustainability aspects can be classified and compared with their self-assessment data. The part of the survey analyzed in the present study had a different original aim: to map public energy preferences and to establish a ranking of evaluation criteria. However, during the data analysis, the need arose to assess the quality of the responses, and through the application of the methodology outlined in the previous section, a new perspective for interpreting the dataset emerged.

3.1. Quality of Knowledge Regarding Energy Production Alternatives

In the aforementioned survey, each energy production alternative was evaluated according to multiple criteria. In addition, reference values from the scientific literature were available, as well as respondents’ self-assessments. Together, these data provided an opportunity to assess the quality of knowledge concerning the different alternatives. As a first step, we examined the accuracy of respondents’ evaluations of each alternative across the complex criteria. For each country, an alternative ranking was established based on the magnitude of respondent error, where higher positions indicated greater accuracy—that is, smaller deviations between individual responses and the corresponding values reported in the literature.

The results (Figure 2) demonstrate that, across all participating European countries (n = 21), respondents exhibited the most accurate knowledge with respect to hydropower, as this alternative consistently produced the lowest error values. Hydropower was clearly followed by fossil gas–based electricity generation, while nuclear power plants most frequently occupied the third position in the ranking. Interestingly, public knowledge was less accurate regarding renewable energy sources: wind power appeared only in fourth place, whereas residential photovoltaic systems and utility-scale solar plants were ranked fifth and sixth, respectively. Not surprisingly, respondents expressed the highest degree of uncertainty regarding fusion power, for which the scientific literature itself also provides only limited data. For example, when evaluating indicators such as availability factor and waste quantity, nuclear power data were often used as a proxy. Consequently, the observed deviation may not represent a true error, but rather an inherent reflection of the present limitations in available knowledge.

Figure 2. Ranked accuracy of knowledge regarding individual energy production alternatives across the examined European countries. Smaller rank values correspond to higher accuracy of knowledge; therefore, rank 1 represents the best result.

Overall, the findings indicate that society possesses more accurate knowledge of traditional energy production alternatives that have long been part of public awareness. Somewhat unexpectedly, this relative accuracy also applies to nuclear power plants, even though this type of alternative was generally expected to exhibit the greatest discrepancy between expert and public knowledge. By contrast, the results concerning renewable energy sources are more puzzling. One might assume that renewables—especially wind and solar power, which enjoy strong societal support and are often perceived as “close to people”—would be evaluated more accurately in terms of sustainability criteria, such as environmental and climate impact. However, the data suggest otherwise. This discrepancy may reflect the absence of a life-cycle perspective among respondents, or it may result from difficulties in interpreting the evaluation criteria. A definitive explanation, however, would require further research.

3.2. Evaluation of Complex Criteria and Indicators

In any survey, the precise definition of notions is of fundamental importance. In this study, our aim was to clarify the interpretation of the four complex criteria by linking them to specific indicators. The underlying objective was to identify potential differences in interpretation that may reveal which complex criteria are most prone to misjudgment. To this end, we examined whether inaccuracies in respondents’ evaluations could be better understood by decomposing each complex criterion into its constituent indicators.

For the comparison, a reference matrix was constructed on values reported in the scientific literature. Error calculations were then performed separately for each indicator, while the remaining criteria were represented by their complex values. This approach enabled us to investigate which indicator respondents were most likely to have considered when evaluating a given complex criterion. The results of this analysis are presented in Figure 3a–d.

Figure 3. Average error values by country for specific indicators substituting relevant complex criterion, separated according to the main criteria: (a) economic, (b) environmental, (c) safety, and (d) reliability.

Overall, the findings reveal a remarkably consistent pattern across the European countries examined: for each complex criterion, one dominant indicator emerges that appears to shape public perceptions. In the case of the economic criterion, fixed cost was clearly the most influential factor, as considerably smaller error was obtained when the cost was evaluated solely on the basis of this indicator.

This can be partly explained by a common misconception regarding renewable energy sources—namely, that in the absence of fuel inputs they entail negligible variable costs. In reality, expenditures for insurance and maintenance are required, and the inherently low energy density of renewables further worsens their cost profile.

For the environmental criterion, respondents’ evaluations were dominated by the perception of air pollution. This likely reflects limited public awareness of life-cycle impacts and the broader spectrum of environmental indicators. In terms of safety, the results were most strongly influenced by perceptions of risk of accident. This finding is somewhat unexpected, as safety is often associated with the avoidance of large-scale catastrophic events.

Country-level differences were observed only for the reliability criterion, where flexibility and availability factor produced similar error levels. This suggests that respondents may assign roughly equal importance to two indicators when assessing the reliability of energy production alternatives.

One might suspect that such results reflect insufficiently distinct patterns among the indicator values themselves. However, as shown in Table 2, flexibility and availability factor differ substantially between technologies such as natural gas and nuclear power plants. This highlights the importance of carefully formulating survey questions: complex criteria may carry divergent meanings for respondents, and such interpretive differences are almost impossible to reconstruct retrospectively. At best, clarification can be achieved through inclusion of well-designed control questions included in the questionnaire in advance.

3.3. Distribution of Self-Assessed Knowledge Levels by Country

An important element of the survey discussed in this paper was the respondents’ self-assessment of their knowledge regarding alternative energy production options (Table 1). Participants were asked to rate their knowledge on a five-point scale. This information proved valuable, as it enabled the filtering of potentially less competent responses during the evaluation of subsequent questionnaire items.

During the analysis, the question arose as to whether any regional or cultural differences—such as those discussed in Hofstede’s work [35]—could be identified that might systematically influence the national averages of self-assessed knowledge levels and thereby affect the filtering of less reliable responses. The national averages of self-assessed knowledge ranged between 2.6 and 3.1, with a maximum deviation of 10%, which cannot be considered substantial. Based on a reliable comparative evaluation of inter-country differences (Figure 4), no systematic variation or extreme rating were observed in the grade-based self-assessment; only minor differences appeared in the percentage distribution of the assigned grades across countries. Nevertheless, alongside these reassuring results, a new question arises: to what extent can respondents’ subjective perception of their knowledge be considered reliable?

Figure 4. Percentage distribution of the respondents’ knowledge grades assigned in each country, aggregated across all seven examined energy alternatives.

To assess the reliability and potential error of self-assessment, we employed the previously calculated quality measure of knowledge regarding energy production alternatives. For the purposes of statistical analysis, it was essential that respondents could be assigned to each knowledge level in every country with an adequate sample size, which was ensured by the survey data.

3.4. Evaluation of the Error in Self-Assessed Knowledge Level

To determine the error in self-assessment, we used the quality measure of knowledge regarding energy production alternatives. For the evaluation of this knowledge, literature-based reference values and respondents’ assessments were compared for each alternative (denoted as D_k and R_k). The distance between these two vectors defined the accuracy of self-assessment (p_k), the distribution of which was analyzed by country (see details in Figure 5).

Figure 5. Country-level distribution of the error in self-assessed knowledge level.

The results clearly indicate that there are no significant differences between the examined countries. A relatively small number of self-assessments were associated with low error values, whereas the distribution curve shows a shoulder toward higher error values. The maximum error ranged between 0.05 and 0.07. Interpreting these values on the five-point scale of the questionnaire, an error magnitude of 0.05–0.07 corresponds to a scenario in which respondents make a one-point mistake on the five-point scale in approximately three to four out of the seven alternatives. Thus, similarly to the self-assessment results themselves, the errors of self-assessment can also be considered relatively homogeneous across Europe.

It is worth considering to what extent the self-assessed knowledge level actually reflects respondents’ factual knowledge of energy production alternatives. When examining the errors associated with the alternatives (d_j) grouped by country and by the five-point self-assessment scale, the mean values of the (d_j) errors corresponding to the five scale levels show a rather mixed pattern across countries. Our results indicate that, for respondents in slightly more than half of the surveyed countries (see Figure 6), the anticipated relationship is partially observed—namely, the lower the knowledge level, the greater the self-assessment error. However, for the remaining countries, the results reveal a more variable pattern: instead of the expected strictly monotonic increase, no clear trend is apparent, and in some cases (e.g., the Netherlands), a slight decreasing tendency was observed, which is difficult to interpret. In other countries, the nearly constant error values across all knowledge levels raise concerns regarding the reliability of self-assessment.

Figure 6. National averages of self-assessment errors by self-assessed knowledge level ratings.

4. Conclusions

As the global energy transition accelerates, the evaluation of energy production alternatives through the lens of sustainability becomes increasingly critical. Beyond purely technical or economic considerations, societal perceptions, knowledge levels, and value judgments play a decisive role in shaping which technologies gain acceptance and societal support. Understanding how different energy sources are assessed in terms of sustainability, cost, safety, and environmental impact is therefore essential for guiding the development of future sustainable energy systems.

Our results show that the evaluation of energy production alternatives based on sustainability criteria is currently on more solid ground for large, centralized power plants (hydropower, gas, nuclear) than for decentralized, intermittent sources such as solar and wind. This asymmetry underscores the need for more comprehensive and transparent assessment frameworks tailored to the growing role of distributed renewable generation in future energy systems.

Fusion energy—widely regarded as a potential cornerstone of future sustainable energy supply—remains poorly understood by the general public. Addressing this knowledge gap will be essential for building social acceptance and informed policy support once the technology approaches maturity.

From an economic perspective, societal judgments are still largely based on capital cost considerations, while variable costs and long-term operational efficiencies are often neglected when evaluating renewable sources. Similarly, the full life-cycle environmental impacts of energy technologies remain insufficiently known to broader social groups, despite their central importance for achieving sustainability goals.

Safety perceptions continue to be strongly influenced by the occurrence of operational accidents, highlighting the enduring role of risk communication in shaping public acceptance. At the same time, the reliability of self-assessed knowledge—frequently employed in survey-based studies—remains highly uncertain. This raises critical methodological challenges for future research aimed at capturing societal understanding and acceptance of sustainable energy pathways.

The methodology presented in this study has several notable advantages. Its design allows for efficient processing of large datasets, while the quantified results are easily interpretable and adaptable for ranking additional criteria or energy alternatives. Moreover, the proposed approach is inherently flexible and can be extended to include further indicators in future analyses.

Nevertheless, several limitations should be acknowledged. First, the simplification of both the alternatives and the complex criteria represents an inherent constraint of the applied approach. Although additional factors could be considered in processing the questionnaire data, completeness and strict mutual exclusivity were not set as requirements. While the methodology could, in principle, accommodate a full system of evaluation criteria, such an extensive framework lies beyond the scope of the present study.

Second, the applied method is not designed to identify causal mechanisms underlying the observed patterns. Given the narrow focus of the research questions and the descriptive nature of the dataset, broad explanatory arguments would remain speculative. Furthermore, the analysis covers a limited subset of European countries; as Hofstede’s framework on cultural dimensions suggests, our results cannot be straightforwardly generalized to a global scale.

Third, the validation of the methodology was not feasible within the available financial and temporal constraints. Complementing the present survey with interviews, focus groups, or objective knowledge tests would substantially enhance the depth and robustness of the conclusions. Such qualitative extensions could also clarify the sources of respondents’ knowledge—for instance, whether it originates primarily from education or from media exposure. The lack of methodological validation implies that grading logic and self-assessment standards may differ across respondents, even though their statistical distributions appeared largely consistent.

These limitations naturally lead to several directions for future research. Further studies could explore to what extent public opinion is shaped by political or media influence, and how awareness and understanding of fusion energy can be strengthened as it moves toward technological maturity. Public understanding of fusion energy is indeed limited, not only because of the scarcity of operational projects and accessible scientific data, but also due to the high complexity and long-term nature of fusion research. Future efforts to improve awareness and understanding should focus on transparent communication, educational engagement, and interdisciplinary collaboration.

Expanding the analysis to include additional energy alternatives—such as coal-based generation or small modular reactors (SMRs)—would also be of interest, as these could reveal new patterns in public knowledge. Finally, integrating diverse national energy policies and examining why knowledge of renewable and emerging technologies (e.g., wind, solar, and fusion) tends to be less accurate could provide valuable insights into whether this gap arises from limited access to information, misconceptions, or the inherent complexity of these technologies.

Taken together, these findings point to the urgent need for improved information dissemination, more robust assessment metrics, and enhanced public engagement strategies. Such efforts are indispensable for guiding the transition toward resilient, low-carbon, and socially supported energy systems of the future.