Citizens and Energy Transition: Understanding the Role of Perceived Barriers and Information Sources

Abstract

By investing in renewable energy sources (RES), citizens can participate actively in energy transition. The problem, however, is that citizen investment decisions are highly complex, while most strategies for capital mobilization rely on generic incentives or broad campaigns. To provide a new approach to mobilizing citizen capital, this study considers perceived barriers, as it is important to address aspects that disincline citizens from investing, and their preferred information sources, because attitudes are shaped and actions are empowered or disempowered through these channels. Drawing on a representative sample of Greek citizens, we used k-means clustering to segment citizens; the first cluster was inhibited to invest by loaning conditions, highlighting the need for banks to offer better terms for loans, while the second cluster was inhibited by a wide array of technical, economic, and systemic concerns requiring different stakeholders to address the barriers underlying these concerns. The third cluster was inhibited by barriers related to the technology of renewables and the availability of experts for installing and maintaining the systems, indicating the need to address such. Results also showed that several information sources can have a negative effect, suggesting that there should be policy intervention to enhance the accuracy of information.

1. Introduction

The long-standing reliance on coal as the main energy source and the affordability of traditional energy carriers have been hindering the deployment of renewable energy sources. At the same time, the recent geopolitical crises emphasize the strategic value of energy security and affordability. In the European Union, the energy system is experiencing a radical energy transition that is facilitated by energy market liberalization and various incentivizing climate policies [1]. Consequently, energy markets are now becoming decentralized modes of energy service provision and are open to new technologies, actors, operation schemes, and management models [2,3]. In response to the challenges caused by geopolitical instability, the critical need for energy security, and the escalating impacts of climate change, the European Union has been adopting lofty renewable energy targets [4]. The aspiring targets reflect the EU’s progress; renewables’ share in the EU electricity mix achieved a new all-time high of 48% in 2024 as compared to 41% in 2022 and 45% in 2023. At the same time, the share of fossil fuels dropped considerably from 28% in 2023 to 24% in 2024 [5]. Over the longer term, the share of renewables in the EU has more than doubled from 2005 to 2023. This impressive progress has been propelled by the EU’s solid policy framework and, more recently, by the European Green Deal and the relevant measures, including the Fit for 55 Package as well as the Green Deal Industrial Plan [5].

Despite the political support and the incentivizing policies at place, the deployment of renewable energy has not achieved the scale, reliability, and systemic transformation required to ensure a sustainable energy future. Significant investment from both public and private actors is necessary to transition to a renewable-based energy system. Yet, large-scale investors are typically interested in utility-scale renewable projects and tend to refrain from investing in decentralized and small-scale projects mainly due to the comparatively low-equity return rates [1,6,7]. To overcome these challenges and to secure the progress towards renewable energy targets, it is necessary to leverage alternative financing concepts [6,7,8,9,10]. A possible alternative could involve citizen financial participation, whereby private individuals would invest in renewable energy projects via diverse business models and financing mechanisms [8,11,12,13]. As opposed to large-scale investors, who are typically interested in high-equity return rates, private individuals tend to regard more favorably the yield expectations that are offered. From this perspective, citizens may not only provide a part of the capital required to transition to a low-carbon energy system, but can also serve as supporters for the renewable technologies [14,15]. Moreover, citizen investors act as “energy citizens”; that is, they are active rather than passive stakeholders whose actions are determined by notions around equitable rights and responsibilities [16]. At the same time, public engagement in RES deployment may take the edge off social opposition to renewable energy by sending the message that citizens are, in practice, included in the energy transition and treated as its key components [6,13,14,17,18].

It would be an understatement to say that mobilizing citizen capital is easy; typically hesitant to invest their capital, citizens exhibit pronounced sensitivity to the investment climate and engage in risk-averse behaviors [7,19,20]. To understand citizens’ investment behaviors, much research on the subject has applied approaches of rational risk–return that have not yet managed to fully explain the observed investment behaviors; to maximize the efficacy of policies, and to devise new ones in order to mobilize citizen capital, it is critical to examine citizen investment decisions through new approaches [12,21].

A novel approach would be to focus on the perceived barriers, as it is important to address those investment aspects that disincline citizens from investing, as well as the information sources they use, because it is through these channels that citizen attitudes are shaped and their actions are empowered or disempowered. In contrast to traditional approaches to understanding citizen investment decisions, which may not fully explain RES investment decisions, segmentation analysis can transform abstract citizens into discernible, actionable groups. In this way, it is feasible for policymakers to devise targeted, effective, and engaging policies, which mobilize citizen capital and ultimately accelerate the energy transition. The importance of segmentation as a tool for designing differentiated policies based explicitly on segment characteristics has already been acknowledged in the context of RES deployment [22,23]. Building on this, a segmentation analysis based on perceived barriers and preferred information channels can open up significant opportunities for targeted intervention. Not only can it inform communication and policy strategies, but it can also identify specific concerns that remain unaddressed by current policies. While a substantial number of studies have focused on citizens’ demographic characteristics (such as the studies of [10,15,24,25,26]), the present study aims to group citizens with the same attitudes towards investment barriers and information sources together into clusters, thereby revealing naturally occurring patterns without predefined outcomes. Segmenting citizens based on perceived barriers and information sources reveals why different groups hesitate to invest and how they can be most effectively reached, enabling policymakers to design highly targeted measures that directly address each group’s concerns through the channels they trust most. In other words, instead of relying on generic incentives or broad campaigns, segmentation may enable policymakers to formulate precisely targeted interventions that appeal to each segment’s actual hesitations through their trusted sources.

2. Theoretical Background

This section reviews the relevant literature on barriers and information sources, thereby establishing the theoretical foundation that underpins our methodological choice of cluster analysis to identify distinct citizen types.

2.1. Barriers to Citizen Investment in Renewables

In terms of barriers, a substantial body of the literature highlights the high upfront capital cost as the primary impediment to citizen investment, particularly among low- and middle-income groups. Empirical studies consistently demonstrate that even when long-term cost savings are available, the initial financial cost functions as a critical constraint. This barrier is often accompanied by limited access to affordable financing mechanisms, and perceptions of financial risk, as well as competing short-term household expenditures, all of which disproportionately affect socioeconomically vulnerable citizens [24,26,27,28]. Specifically, the study of Vasseur and Kemp [27] indicated that most respondents regarded the investment cost as the most important barrier; moreover, evidence suggests that many individuals exhibit a conditional willingness to invest, based on a substantial reduction in costs and the availability of more favorable subsidy schemes [7]. This indicates that investment decisions are not solely determined by awareness of long-term benefits, but are also highly sensitive to the structure of financial incentives and the policy design [25,26,27].

Subsidies function not merely as cost-reduction tools, but also as mechanisms that can change risk–return perceptions while enhancing the perceived affordability of renewable investments [29]. In their study, Ebers Broughel and Hampl [28] observed that despite expressing a willingness to invest, most respondents lacked the necessary financial capacity to participate, underscoring a structural gap between intention and actual investment behavior. This discrepancy highlights the critical need for designing more accessible and affordable financing mechanisms tailored to citizens, particularly those in lower income groups. Such mechanisms would not only bridge the affordability gap but also enhance social equity in the diffusion of investment opportunities [7].

Even though the cost has emerged as the most prominent barrier, there are further financial barriers that inhibit citizen RES investments. The most important of these involve the lack of loan access, grants, and subsidies [11,12,13,24]. More analytically, middle-income households interested in investing in renewables encounter credit constraints and high interest rates, which inhibit investments, even in cases where citizens possess substantial savings [7,30]. In addition, lenders experience price regulation uncertainty due to changes in net-metering and tariffs, while there is considerable price volatility of the equipment, all of which make the investment more risky [31,32]. Therefore, it seems that even when citizens are interested in investing in renewables or are supportive of the idea of citizens participating financially in energy transition, in practice, there are various financial barriers that are inhibiting decisions. This may also be part of the reason that citizens with high economic capacity exhibit a higher willingness-to-invest in renewables [24]. Moreover, renewable investments often involve indirect costs related to the processes of licensing, inspection, grid connection, and insurance. These costs considerably increase the overall investment cost and thus represent another barrier that inhibits citizen RES investment [31,32].

In addition to the economic barriers discussed above, citizen RES investment is often negatively affected by the policy and regulatory framework in place. This often materializes as the administrative and procedural dimensions of investment; specifically, the complexity of the permit process for renewable energy installations, the level of stability of the regulatory framework, and the efficacy of licensing mechanisms are pivotal factors. Lengthy approval periods, extensive documentation, and time-consuming grid-connection processes create a high degree of uncertainty, which deters potential investors, particularly citizens who are vulnerable to investment risks. In practice, these additional difficulties operate as structural impediments, which, in turn, weaken the investor’s confidence, inhibiting the pace of renewable energy deployment [24,25,27,28].

Another category of barriers involves the inconsistency of market instruments for renewable investments and the instability of energy policy [25,33]. The regulations in place for feed-in tariffs and other models in which citizens participate constantly change and differ considerably among EU member states. In certain member states, for example, feed-in tariffs and premiums are not especially considered for citizens and energy communities. In addition, a problem that remains in many member states is the lack of remuneration for the surplus of electricity production. In such contexts, the inability to receive extra remuneration for the surplus of electricity production and the lack of mechanisms to adjust tariffs and premiums discourage citizen investments [34].

The technical aspects of renewable systems compose another notable group of barriers affecting citizen investment decisions. It is interesting that even when citizens are willing to invest, technical barriers simply prevent them from investing. Citizens may be unfamiliar with renewable technology and may find it challenging to navigate new technical terms which may result in limited trust in renewable systems [35,36,37]. In addition, concerns over the availability of technicians, installers, and maintenance services have been found to be a source of unease for potential investors. In the face of such obstacles, citizens simply feel that it is not feasible to invest in renewables and, in this way, the presence of technical barriers decreases the perceived feasibility of RES investment, thereby leading potential investors to forgo the investment, which is seen as uncertain or high-risk [26,36,38,39]. At the same time, the installation of small-scale renewable systems may be inhibited by various factors, such as the lack of space required or the lack of suitable infrastructure (e.g., the absence of rooftops or unsuitable rooftop orientation in the case of residential photovoltaics) [40,41]. In addition, local energy systems often require the development of new infrastructure, which in many cases must be located on privately owned land outside the direct control of investors. This kind of spatial requirement creates further layers of complexity that extend beyond solely technical considerations. They often lead to conflicts of interest between stakeholders, particularly when landowners, communities, and developers have different priorities. Moreover, these arrangements can give rise to unforeseen legal and regulatory challenges, many of which remain insufficiently addressed by existing policy frameworks [37].

2.2. The Role of Information in Investment Decisions

Information exerts a comparable influence to that of the perceived barriers discussed above. Yet, the role of information in citizen investment decisions is ambivalent, as it can motivate or inhibit investments; regardless of its positive or negative effect, information has a strong influence on decisions, emphasizing the need to direct research attention to the effect of information sources [42,43].

Citizens depend on a broad spectrum of information sources, ranging from mass media outlets (such as websites, television, and radio networks) to more specialized channels, such as academic and research institutions. The credibility and robustness of the information disseminated through these sources play a critical role in shaping public perceptions. Drawing on the Diffusion of Innovations theory, it may be stated that trusted information sources are critical for reducing uncertainty about new technologies. When such information is reliable and based on scientific evidence, it can effectively address misinformation and mitigate misconceptions related to contentious issues, particularly those associated with the technological efficiency of renewable energy systems and the economic feasibility of RES investments [44]. In addition, information regarding the cost and the return on investment can be highly influential on investment decisions; if preferred information sources disseminate relevant information in an unclear or confusing manner, citizens are likely to feel less confident in making investment decisions, avoiding the investment altogether despite their interest [45,46]. It, therefore, seems that information about RES investment shapes attitudes, inhibits or reinforces investment willingness, affects perceptions regarding perceived risks and benefits, and, in this way, can either facilitate investments or hinder them [45,46].

In the context of RES investment, a significant barrier to household investment in renewable energy systems is the presence of information asymmetries and associated behavioral frictions. That is because households often lack reliable, relevant, and timely information regarding expected energy savings, system performance, maintenance requirements, and financing options. This kind of asymmetry exacerbates potential investors’ perceived risk, which may be disproportionate to the real economic outcomes; in the long term, the continued information asymmetry may lead to underinvestment despite positive net present value projections [47,48].

Behavioral economics further exemplify this phenomenon; that is, households exhibit a strong tendency to emphasize the upfront cost and investment complexities while putting less emphasis on the long-term benefits [49]. At the same time, householders demonstrate status quo bias and resist changes, especially if the investment decisions involve a considerable degree of uncertainty or multiple procedural steps (such as the selection of installers, the application for loans or subsidies, and the issuance of a license) [50,51]. Studies suggest that citizen investment can increase significantly with improvements in the transparency of information, the provision of information regarding the technological performance of renewable systems, and guidance on financial returns. It can be inferred that addressing informational friction can not only mitigate the perceived risks, but also improve the efficacy of the existing incentivizing policies [7,50].

The above suggests that the development of citizen types on the basis of perceived barriers and information sources can provide a strategic lens for acknowledging behavioral diversity, addressing key barriers, ensuring the dissemination of effective messages, and countering misinformation.

3. Materials and Methods

3.1. Research Design

The study was conducted in Greece, an EU member state that stands out for its impressive renewable potential and the significant progress it has made towards policy frameworks promoting the decarbonization of the national energy mix [52,53]. It is, however, important to maintain progress not only to support the country’s role as a significant EU energy producer, but also to limit the expensive imports of fossil fuels [54]. To perform this study, a structured questionnaire was designed; all items drew on the previous literature on the attitudes and views among citizens toward renewable energy investments (some examples include the studies of Dinica et al. [21]; Willis et al. [55]; Vasseur and Kemp [27]; and Salm et al. [13]). The questionnaire was structured in eight sections; specifically, the first section included introductory questions on citizens’ views on various energy topics, the second section examined their knowledge levels about renewable energy investments, the third section explored their willingness-to-invest in renewables [7], the fourth section involved items that required respondents to evaluate various financial, environmental, and social reasons for investing in RES (results based on data from this section published in Karasmanaki et al. [56]), the fifth section required respondents to evaluate barriers to RES investments, the sixth section measured their satisfaction with different stakeholder groups’ actions to facilitate RES investments [57,58], the seventh section examined their environmental attitudes and the information sources they use for acquiring information about environmental and energy topics, and the eighth section gathered respondents’ sociodemographic information.

The analysis presented in this paper is based on the data collected through the fifth, seventh, and eighth sections. In order to enable respondents to provide precise responses and to facilitate the statistical analysis, all multivariate items used five-point Likert scales [59]. To test the coherence and accuracy of the questionnaire, a pilot study with thirty citizens from varied socio-economic backgrounds was performed. The pilot study showed that certain phrases in the items had to be revised to improve clarity, while the response scale of an item required modification. Moreover, the order of a few items was rearranged to improve the flow and coherence of the questionnaire. Conforming to Law 4521/2018 and its 23rd Article, this research received approval by the Research Ethics Committee of the Democritus University of Thrace (Decision No. 3/09–12-2019).

In order to yield results that would represent the population under study, that is Greek citizens, simple random sampling was used; this sampling method was also considered the most suitable because it has limited requirements regarding the knowledge about the population under study [59,60]. According to this technique, all Greek households constituted the population under study and functioned as sampling units; that is, each chosen household was represented by one number that was randomly chosen. To estimate the sample size, the simple random sampling formula without replacement was used. Then, in accordance with research principles, the researchers performed pre-sampling with fifty subjects, which enabled the estimation of the proportions of the population for every variable, thereby making sure that the questionnaire was accurate [59,61]. As variable ‘Gender’ was found to require the largest sample size, it was used to determine the final sample size; 1536 citizens had to participate in the study. In order to make sure that all questionnaire items were comprehended by the respondents, the completion of the questionnaires was conducted via face-to-face interviews. To protect the anonymity of respondents, at the end of the interviews, respondents placed their questionnaires in envelopes and then into a box.

In order to check the quality of data, reliability testing was conducted using Cronbach’s alpha coefficient. Reliability tests, according to research principles, can be conducted prior to or after questionnaire collection [62]. Here, reliability tests were conducted after the survey and, for the items used in this analysis, the values of alpha coefficients were found to be higher than 0.8, indicating that all items were reliable and the questionnaires were acceptable. After collecting the questionnaires, we also compared as many findings as possible with those of the Hellenic Statistical Authority. For example, the male participants in our sample were 48.4% while their female counterparts were 51.6%; the respective rates of the Hellenic Statistical Authority were 49% for male and 51% for female citizens. In addition, married and unmarried participants in our sample were 51% and 36.9%, respectively, whereas the Hellenic Statistical Authority’s rates were 50.3% for married and 39.1% for unmarried citizens [63].

3.2. Data Processing

The Statistical Package for the Social Sciences (SPSS, version 22) was used for data analysis. To achieve the aim of this study, which was to develop targetable groups of citizens that share similar obstacle patterns and information-seeking behaviors, factor analysis with varimax rotation was applied on barriers and information sources in order to reduce the dimensionality of the data and examine the correlations among the observed variables [64]. There are different ways to extract the underlying factors, and here principal component analysis (PCA) was used because the aim was to analyze the data to extract the minimum number of factors necessary to represent the data set. Specifically, principal component analysis was applied to the multivariates ‘Perceived barriers to renewable energy investments’ and ‘Information sources’, with the former, according to the literature, inhibiting investments and the latter having emerged as citizens’ primary channels for acquiring information. With first-order factor analysis, the variables were reduced into latent factors while the initial data set maintained most information. The orthogonal (uncorrelated) and oblique (correlated) are the two basic approaches for factor rotation, which are much easier for interpretation and reporting [65]. In terms of rotation, we used orthogonal factor rotation as it produces solutions that are much easier for interpretation and reporting. Specifically, we used varimax rotation because it maximizes the differences among the squared pattern structure coefficients. The orthogonal factor analysis model assumes the form X = μ + λF + ε, while assuming that F ~ (0, 1 m); meaning that the latent factors have mean zero, unit variance, and are uncorrelated, Ε ~ (0, Ψ) where Ψ = diag (Ψ₁, Ψ₂, … Ψ_p) with Ψi which denotes the jth specific variance, and ε_j and F_k are independent of one another for all pairs, j, k [65].

In addition, the Guttman–Kaiser criterion was used in order to select the principal components which would have eigen-values ≥ 1 and, in order to optimize results, this was followed by Varimax rotation. The level that the obtained factors account for the variance relies on the degree to which the factors represent the original data. Moreover, factor analysis yields many numerical variables, and every variable is linked to a unique estimated factor score. It is these scores that can then be utilized in more advanced analyses [64]. Afterwards, in order to reveal higher-level latent constructs which would explain the relationships among the first-order factors, second-order factor analysis on the factors from first-order factor analysis was performed. This facilitated a precise understanding of the connection between barriers and information sources.

Then, in order to develop the citizen segments, cluster analysis was performed using the factors that had been obtained through factor analysis. It should be noted that Latent Class Analysis would be the most suitable method to estimate the probability of an individual belonging to different latent subgroups; however, our variables were continuous and, therefore, we performed k-means cluster analysis as it can handle continuous variables [66]. While there are many techniques for calculating the distances and combining the observations within the clusters, k-means clustering was used because it is recommended for big samples [67]. The unsupervised k-means algorithm exhibits a flexible relationship to the k-nearest neighbor classifier, which is a popular supervised machine learning classification technique that is often confused with k-means due to the name [68,69]. The application of the 1-nearest neighbor classifier to the cluster centers obtained by k-means manages to classify new data into existing clusters. This is referred to as the nearest centroid classifier or the Rocchio algorithm [68].

Assuming a set of observations as (x₁, x₂, …, x_n), where every observation is a-dimensional real vector, the k-means clustering seeks to partition the n observations into k (≤n) sets S = {S₁, S₂, …, S_k} in order to minimize the sum of squares within the clusters.

Formally, the aim is to discover the following:

$$\underset{S}{\arg \min }{\displaystyle \sum _{i=1}^k{\displaystyle \sum _{x\in S_i}{‖x-{\mu }_i‖}^2}=}\underset{S}{\arg \min }{\displaystyle \sum _{i=1}^k\left|S_i\right|Var S_i}$$

here μ_i expresses the mean, i.e., the centroid of the following point:

$${\mu }_i={\displaystyle \frac{1}{\left|S_i\right|}}{\displaystyle \sum _{x\in S_i}x,}$$

$\underset{\dots \dots }{\left|{\mathrm{S}}_{\mathrm{i}}\right|}$ refers to the size of $\underset{\dots }{{\mathrm{S}}_{\mathrm{i}}}$, and $\underset{\dots \dots }{||·||}$ is the usual L² norm, which is equivalent to minimizing the pairwise squared deviations of points in the same cluster:

$$\underset{S}{\arg \min }{\displaystyle \sum _{i=1}^k\frac{1}{\left|S_i\right|}{\displaystyle \sum _{x,y \in S_i}{‖x-y‖}^2}}$$

It is possible to deduce the equivalence from identity $\left|S_i\right|\underset{S}{{\displaystyle \sum _{x\in S_i}{‖x-{\mu }_i‖}^2}={\displaystyle \frac{1}{2}}}{\displaystyle \sum _{x,y \in S_i}{‖x-y‖}^2}$

Because the total variance is constant, it is equivalent to the maximization of the sum of squared deviations among points in the different clusters. This kind of deterministic relationship also has an association with the law of total variance in probability theory.

Regarding the cluster solution, the k-means method classifies the observations in a specific number of clusters that was defined previously; specifically, it maximizes the inter-cluster differences, while minimizing the variance within the clusters [70,71]. It is an iterative method that enhances cluster assignments according to the proximity of the observations to the cluster centroids. The number of clusters is chosen on the basis of interpretability, meaning that the number of clusters ought to provide distinct and coherent groupings with characteristics that are meaningfully interpreted in relation to the study aim. In this analysis, different clustering combinations were considered, and the three-cluster solution gave better results. In order for a cluster solution to be valid, it is necessary that the clusters differ notably from each other in terms of demographics; the chi-squared test was used for clustering validation. In particular, the chi-squared test was used to assess the association between the clusters and the variables. Moreover, ANOVA examined whether the means of the clusters differed significantly. Then, the citizen typology was established, and, finally, the types were “named” in order to summarize and interpret their characteristics, thereby communicating the results meaningfully.

4. Results

4.1. Sociodemographic Characteristics

In terms of gender, female participants outnumbered (51.6%) the male ones, while most respondents belonged to the 41–50 age group (27.9%). In relation to their occupation, most participants reported being private (21.2%) and public employees (19.9%), whereas the percentages of respondents reporting other occupations were notably lower. In terms of their educational level, most participants were university degree holders (22.3%), and a considerable percentage reported having completed upper secondary school (20.8%). As for their family status, married respondents made up around half of the sample (51%), and a significant share had two children (28.3%). Finally, 28.5% of respondents reported an annual income between EUR 10001 and EUR 20000, as well as 20.1% between EUR 5001 and EUR 10000.

4.2. Profiling Citizens Based on Perceived Barriers and Information Sources

To examine the underlying structure of respondents’ perceived barriers to renewable energy investments and the information sources they use for their information on environmental and energy topics, principal component analysis was performed on the multivariates ‘Perceived barriers to RES investments’ and ‘Information sources’.

Table 1. The internal consistency of multivariates.

Multivariate	Cronbach’s Alpha
Barriers to RES investment	0.886
Barriers of financial, institutional and bureaucratic nature Barriers related to renewable technology and technical staff Unfavorable loan terms, weak market competitiveness and limited information availability
Information sources	0.844
Internet-based sources Academic institutions and close environment Private stakeholders Mass media

shows the internal consistency of the multivariates whose factors were used in the analysis.

Regarding “Perceived barriers to RES investments”, Cronbach’s alpha value (0.886), Kaiser–Meyer–Olkin index (0.871), and Bartlett’s test of sphericity (Chi-Square = 10,376.640, with df = 78 and p = 0.000) showed that the data were eligible for factor analysis. Variables related to investment cost, institutional stability, and bureaucracy were loaded on the first factor. In specific, the variables ‘Lack of subsidies’, ‘High capital required for investment’, ‘High taxation’, ‘Instability of institutional framework for energy’, and ‘Bureaucracy in licensing process’ fell under the first factor, which can be labeled ‘Barriers of financial, institutional and bureaucratic nature’. The second factor contained variables which were related to the technological aspect of the investment; that is, ‘Lack of staff for the maintenance of renewable systems’, ‘Lack of trust in the technological quality and efficiency of renewable systems’, ‘Lack of staff for the installation of renewable systems’, and ‘Operational problems due to weather’ were loaded on PC2 which can be labeled ‘Barriers related to renewable technology and technical staff’. Finally, ‘Complex loan application process’, ‘High level of interest rates on bank loans intended for RES investments’, ‘Fossil fuels’ reduced production costs making RES less competitive in market’, and ‘Difficulty finding information on RES investments’ were loaded on the third factor, which can be labeled ‘Unfavorable loan terms, weak market competitiveness and limited information availability”.

Regarding the variable “Information sources”, prior to factor analysis, the suitability of data was checked with Cronbach’s alpha value (0.844), the Kaiser–Meyer–Olkin index (0.825), and Bartlett’s test of sphericity (Chi-Square = 6193.234, with df = 55 and p = 0.000). Then, the performance of factor analysis with Varimax rotation resulted in the extraction of four factors (

Table 2. Factor analysis (with Varimax rotation) regarding perceived barriers and information sources.

Variables Loaded in Each Factor	Loadings	Eigenvalue	Variance (%)
Barriers of financial, institutional and bureaucratic nature		5.540	42.618
Lack of subsidies	0.836
High capital required for investment	0.812
High taxation	0.801
Instability of institutional framework for energy	0.678
Bureaucracy in licensing process	0.587
Barriers related to renewable technology and technical staff		2.068	15.905
Lack of staff for the maintenance of renewable systems	0.873
Lack of trust in the technological quality and efficiency of renewable systems	0.841
Lack of staff for the installation of renewable systems	0.825
Operational problems due to weather	0.685
Unfavorable loan terms, weak market competitiveness and limited information availability		1.062	8.171
Complex loan application process	0.753
High level of interest rates on bank loans intended for RES investments	0.703
Fossil fuels’ reduced production costs making RES less competitive in market	0.680
Difficulty finding information on RES investments	0.566
Total variance (%): 66.694
Kaiser-Meyer-Olkin = 0.871, Bartlett χ2 = 10,376,640, df 78, p < 0.001
Internet-based sources		4.329	39.350
Official organizations’ websites	0.837
News media websites	0.813
Multi-topic websites	0.793
Academic institutions and close environment		1.349	12.265
Education	0.874
Universities and Research organizations	0.788
Family and friends	0.699
Private stakeholders		1.179	10.719
Banks	0.821
Companies’ leaflets	0.729
Environmental organizations	0.607
Mass media		1.033	9.391
Television-radio	0.865
Newspapers and magazines	0.780
Total variance (%): 71.725
Kaiser-Meyer-Olkin = 0.825, Bartlett Chi-square = 6193.2339, df 55, p < 0.001

). Under the first factor, which can be named ‘Internet-based sources’, the following variables were loaded: ‘Official organizations’ websites’, ‘News media websites’, and ‘Multi-topic websites’. The second factor comprised the variables ‘Universities and Research Organizations’, ‘Education’, and ‘Family and friends’, and thus could be named ‘Academic institutions and close environment’. The third factor includes the variables ‘Companies’ leaflets’, ‘Banks’, and ‘Environmental organizations’ and thus can be named ‘Private stakeholders’. Finally, the fourth factor involves variables ‘Television and radio’ and ‘Newspapers and magazines’ and can be labeled ‘Mass media’.

To identify the structure underlying barriers to renewable investments and information sources, second-order factor analysis was carried out (

Table 3. Second-order factor analysis.

Principal Components	Loadings	Eigenvalues	Variance (%)
Technological barriers and mass media (P1)		1.172	19.535
Barriers related to renewable technology and technical staff	0.723
Mass media	0.718
Economic and institutional barriers and the Internet (P2)		1.134	18.901
Barriers of financial, institutional and bureaucratic nature	0.715
Internet-based sources	0.708
Loaning, competitiveness, and information through education and close environment (P3)		1.076	17.935
Unfavorable loan terms, weak market competitiveness, and limited information availability	−0.716
Academic institutions and close environment	0.720
Total variance (%) 56.371
Kaiser–Meyer–Olkin = 0.497, Bartlett Chi-square = 94.660, df 21, p < 0.001

). Prior to performing second-order factor analysis, the adequacy of sampling and correlation among variables had to be checked. Thus, the Kaiser–Meyer–Olkin index gave a value of 0.497, and Bartlett’s test of sphericity rejected the null hypothesis (Chi-square = 94.660 with df = 21, p < 0.001). Second-order factor analysis yielded three factors. The first factor can be named ‘Technological barriers and mass media’, the second factor can be named ‘Economic and Institutional barriers and information from the Internet’, and the third factor can be named ‘Loaning, competitiveness and information through education and close environment’. Results showed that citizens, who perceived that investments are inhibited by economic, institutional, and bureaucratic barriers, use the Internet for their information. Finally, respondents, who did not perceive that investments are inhibited by loaning conditions, meet their information needs through their family and friends, as well as education.

K-means cluster analysis was then performed for the identification of the associations of each member to the cluster. In addition, ANOVA was conducted to examine whether respondents could be clustered according to perceived barriers and information sources. The analysis of variance indicated notable differences and thus the proposed clustering was acceptable.

In order to explore the natural groupings of respondents, the procedure of k-means was used and solutions with two, three, and four clusters were tested. The best solution in terms of interpretability was provided by the three-cluster solution; this clustering was selected because it gave an optimal balance between within-cluster homogeneity, as well as between-cluster heterogeneity.

Table 4. Mean factor loadings for each cluster.

	CL1 (40.1%) Inhibited by Loaning Conditions	CL2 (27.8%) Inhibited by RES Technology, Technical Staff, and Economic and Institutional Barriers	Cl3 (32.1%) Inhibited By Res Technology And The Perceived Lack Of Technical Staff	Cluster		Error
				Mean Square	df	Mean Square	df
P1	0.85635	−0.26787	−0.83915	414.450	2	0.461	1533
P2	0.26729	−1.16435	0.67823	425.288	2	0.446	1533
P3	−0.10934	0.11784	0.03439	6.945	2	0.992	1533

shows the dimensions’ average scores and offers a description of the clusters. The contribution of factors to each cluster can also be seen. More analytically, Cluster 1 can be labeled as ‘Inhibited by loaning conditions’ and is the largest cluster, as it represents about 40.1% of the sample. Its members are positively affected by P1 and P2, but negatively affected by P3. Cluster 2, which can be designated as ‘Inhibited by RES technology, technical staff and economic and institutional barriers’, accounts for 27.8% of the sample, and its members are affected negatively by P1 and P2, but positively affected by P3. Finally, Cluster 3, which can be named ‘Inhibited by RES technology and the perceived lack of staff’, represents 32.1% of the sample, and its members are affected negatively by P1 and positively by P2 and P3.

The profiles of the clusters are shown in

Table 5. Results of chi-squared tests.

Variable	Scale	Cluster 1	Cluster 2	Cluster 3
Age	18–30	14.9%	25.9%	27.2%
	31–40	24.0%	19.4%	22.2%
	41–50	32.3%	22.9%	26.6%
	51–60	18.3%	19.4%	17.3%
	>60	10.4%	12.4%	6.7%
Occupation	Public employee	22.2%	14.7%	21.5%
	Private employee	19.0%	20.6%	24.4%
	Freelancer	12.8%	11.0%	12.0%
	Entrepreneur	4.9%	4.0%	3.9%
	Homemaker	7.3%	5.4%	4.9%
	Crop farmer	7.0%	5.4%	4.7%
	Livestock farmer	1.6%	3.0%	1.0%
	Retired	16.4%	17.8%	13.4%
	Unemployed	8.8%	18.2%	14.2%
Education level	Primary school	14.6%	25.5%	14.4%
	Lower secondary school	7.6%	10.5%	4.1%
	Technical school	2.8%
		2.6%	3.0%
	Vocational training school	7.0%	9.1%	6.3%
	Upper secondary school	20.0%	21.7%	20.9%
	Vocational education and training	10.9%	8.2%	11.4%
	University	25.3%	14.5%	25.2%
	Master’s degree	10.2%	6.5%	12.8%
	Doctoral degree	1.6%	1.4%	1.8%
Family status	Unmarried	31.2%	37.9%	43.3%
	Married	55.7%	50.5%	45.5%
	Divorced	5.2%	3.7%	5.7%
	Widowed	8.0%	7.9%	5.5%
Income	Less than 5000 Euros	9.9%	14.7%	9.1%
	5001–10,000 Euros	19.8%	18.0%	22.2%
	10,001–20,000 Euros	32.3%	22.9%	28.5%
	20,001–30,000 Euros	9.9%	3.7%	8.7%
	More than 30,000 Euros	2.4%	4.4%	5.1%
Knowledge about RES investments	No knowledge at all	4.4%	10.0%	3.7%
	Slight knowledge	29.9%	34.3%	30.1%
	Moderate knowledge	39.9%	37.1%	45.7%
	Much knowledge	18.8%	14.5%	17.1%
	Very much knowledge	7%	4%	3.5%

and are based on the statistically significant differences between the three clusters. More analytically:

Cluster 1 ‘Inhibited by loaning conditions’:

Although all clusters are composed of significant percentages of citizens, CL1 is the largest cluster since it accounts for 40.1% of the sample. In this cluster, most members are aged between 31–40 and 41–50. In relation to their occupation, most members are public and private employees as well as pensioners. In terms of educational level, many citizens in this cluster have graduated from higher education and high school. In addition, the overwhelming majority are married and earn between 10,001 and 20,000 as well as between 5001 and 10,000 Euros per year. Members of Cluster 1 are positively affected by P1 and P2 and negatively affected by P3. In other words, they have a positive view on the economic and technological barriers to investments in renewable energy, but are negatively affected by the loaning conditions for loans intended for investments in RES. In terms of their knowledge about investments, most have a moderate (39.9%) as well as a slight level of knowledge (Table 5).

Cluster 2 ‘Inhibited by RES technology, technical staff, and economic and institutional barriers’

Cluster 2 presents a more even distribution in terms of its members’ age, and there are slightly more citizens in the ages between 18 and 30 and between 41 and 50. Most members in this cluster are private employees, unemployed, and pensioners, and, in terms of their education level, most are primary and high school graduates. Similar to the previous cluster, most are married and unmarried. Moreover, most members in this cluster have an annual income of 10,001–20,000 or 5001–10,000 Euros a year. Members in this segment are negatively affected by P1 and P2 and positively affected by P3. To put this differently, it seems that they are skeptical about the technological efficiency and quality of renewable energy systems and their ability to operate in extreme weather conditions, as well as the technical staff conducting the installation and maintenance of renewable systems. At the same time, they have a positive attitude towards loans for investments in renewables, cost production, and the ability to obtain relevant information about investments. A considerable share of respondents in this cluster have slight (34.3%) or moderate (37.1%) knowledge about investments in renewable energy (Table 5).

Cluster 3 ‘Inhibited by RES technology and the perceived lack of technical staff’

Finally, Cluster 3 involves citizens aged between 18–30, 41–50, and 31–40, while very few are over the age of 60. This cluster includes a considerable number of citizens who are private and public employees, as well as a notable number of citizens who are unemployed and pensioners. Most are university graduates and high school graduates. Most are married and earn between 10,001 and 20,000 or 5001 and 10,000 Euros a year. Members in this cluster are positively affected by P2 and P3 but negatively affected by Ρ1. Namely, they are positively affected by the economic, institutional, and loaning aspects of investments in RES but negatively affected by the technological aspects. In comparison to the other clusters, the members of this cluster present the highest level of moderate knowledge (45.3%) about investments in renewable energy (Table 5).

5. Discussion

The investment decision of the members of the first cluster, the largest one, was not inhibited to a high degree by financial barriers, which comes in contrast with the study of Broughel and Hampl [28], which indicated that financial means are the most significant barrier to citizen financial participation in RES projects. Other previous studies have also pointed to the negative effect of financial, but also technical and institutional, barriers [24,27,28]. The members of this cluster were rather prevented by barriers related to loaning conditions, the low competitiveness of renewables in the energy market, and the unavailability of information about renewable energy investments. In relation to loaning, the study of Menyeh [25] had also concluded that loan access has a negative effect on investment decisions, and therefore, this study corroborates the negative role of loan unavailability in the context of citizen investment. It is possible that, if the members of this group were considering using loans to invest in renewables, in practice, the conditions on loans for RES investments would prevent them from investing. This highlights the need for banks to offer better terms for loans intended for RES investments or to develop specific loan programs for private persons, thereby ensuring that financing options are accessible for citizens. The low competitiveness of renewables is likewise seen as a barrier for the members of this group, suggesting that citizens may be doubting the financial viability and profitability of RES investments. An explanation of this kind resonates with studies showing that investors are predominantly driven by the expected gains and that, if renewables are viewed as uncompetitive, then the investment motive may weaken [7,19]. Moreover, information from universities, research institutes, family, and friends has a negative effect on the investment decision of this cluster. It is possible that information from these sources is presented in a highly technical manner and is seen through the lens of the Theory of Planned Behavior [72,73]; excessive technical information may increase individuals’ cognitive overload and, in this way, reduce their perceived behavioral control, rendering them less confident in their ability to make an informed investment decision. The strong effect of citizens’ close environment (family and friends) on investment decision seems reasonable if one considers the influence of descriptive norms (perceptions of whether other people engage in a certain behavior themselves) and injunctive norms (perceptions of what other people approve or disapprove) [74]. If potential investors’ family and friends express skepticism toward renewable energy investments (due to perceived costs, investment uncertainty about returns, or technology unfamiliarity), then a negative social reference point is established. In other words, individuals are inclined to conform to the opinions and behaviors of their close environment, even when this conflicts with their private attitudes.

In contrast to the first cluster, the members of the second cluster were not affected negatively by loaning terms. The difference from the previous cluster, however, is that it is affected by several and varied factors; that is, respondents were inhibited by the lack of technical staff for the installation and maintenance of the systems, the perceived technological efficiency of renewable systems, and the operational problems due to weather, but also the unavailability of subsidies, high upfront cost, the uncertainty of the institutional framework, and the bureaucratic licensing process. Citizens in this segment, therefore, would be hesitant or unwilling to invest due to a combination of technical, economic, and systemic concerns. To address the multifaceted concerns of this cluster, it is recommended to adopt a collaborative model that would involve tripartite agreements among government bodies, renewable companies, and local communities. This may help streamline all steps involved in citizen renewable energy investments. In relation to the concerns over the availability of staff for the installation and maintenance of renewable systems, it is possible that citizens were aware of the labor shortages in the sector of electric equipment manufacturing; even though shortages have decreased from 20% in 2023 to 15% in 2024, they still remain a concern that can have a negative effect on the development of the renewable sector [75].

Moreover, the members of the second cluster are negatively affected by the information obtained through Internet-based sources and mass media, suggesting that exposure to information from these sources may have led citizens to perceive numerous and varied barriers to RES investments. It is also possible that citizens in this cluster may have been exposed to inaccurate information on the Internet, not only about the economic feasibility of RES investments, but also key technological aspects such as renewable systems’ performance in poor weather or the availability of efficient technical staff for the installation and maintenance of the systems. This points to the need to take steps to mitigate the spread of false information by empowering civil society to identify misinformation and verify sources. In the meantime, it would be effective to establish a dedicated independent body that would be responsible for the communication of accurate information about RES investments, including the existing policies, investment schemes for citizens, and available loans, as well as the technological aspects of the systems and the available renewable energy service providers. If supported by academics and scientists, such a body may help build trust and reduce the propagating misinformation that is detrimental to citizen investment.

The second cluster is negatively affected by mass media information, suggesting that mass media may not be effective in communicating clear messages regarding RES investments in the study area. For example, mass media may be using terminology when covering laws and legislative amendments without, however, tailoring this information to meet the general public’s level of understanding. It is also possible that journalists do not tailor and simplify information on RES investments simply because they lack expert knowledge. If that is the case, then it is important that academics and experts in the renewable sector provide journalists with support for communicating technological and financial information about RES investments.

The barriers that negatively affect the investment decision of the members of the third cluster involve barriers related to the technology of renewables and to the availability of experts for installing and maintaining the systems. The difference from the previous cluster is that this cluster is not affected negatively by economic and institutional barriers, implying that it may be easier to mobilize investments from this cluster. Yet, it appears that concerns regarding technical aspects are the most influential, and, therefore, to mobilize investments from this cluster, it is necessary to address misconceptions and concerns about these aspects. This cluster is negatively affected by information from mass media, showing that, much like the second cluster, mass media may be ineffective in communicating the necessary information on RES investments, which resonates with previous studies showing that insufficient information acts as an inhibiting factor and discourages potential investors from investing in renewables [42,43,44].

Results from this study point to specific aspects that currently inhibit citizen investments, which, if leveraged, may help achieve crucial national energy targets. This should not mean, however, that other actions would not be required. Most importantly, it is necessary to consider the geographical differentiation of RES deployment in the study area, as there are stark differences among Greek regions in terms of RES development. As an example, small- and large-scale hydroelectric plants have been deployed in only four of the thirteen regions of the country, while biomass plants have been deployed in only two regions. From this perspective, the planning of future energy policy should aim for a more balanced RES deployment and, to that end, segmentation analyses such as the one reported in the paper, but also previous ones on the geographical differentiation of RES installed capacities and deployed renewable types, can be used as useful policy tools [22,23]. The reported differentiation also provides strong evidence against one-size-fits-all approaches while pointing to the need to differentiate actions and measures. Apart from national energy targets, the country’s energy planning should also be based on targets specific to regional development, which should be re-evaluated on a frequent basis. Therefore, the characteristics of citizen clusters, along with information on the regions of RES implementation, the deployed renewable types, and their installed capacities ought to be the basis for energy planning [22,23].

Moreover, in order to create a more favorable investment climate, it is necessary to restructure the RES institutional framework so that it is simple, effective, and transparent. To that end, it would be useful to leverage experiences from other European countries that managed to reduce investment costs, the licensing processes, and the access to energy grid. In other words, insights into the socio-economic reality, as well as the planning relations, can point to the ways that RES deployment can support social and economic targets in different regions [22,23].

Finally, in the study area, there is currently a lot of debate on the environmental impacts of renewables, which has led a substantial part of the public to express opposition to renewable energy sources. Even though the opposition is driven by several factors, it is primarily fueled by the siting of large-scale renewable projects within, or adjacent to, protected, or recently burned, forest areas. For this reason, the legal framework regarding the siting of large-scale energy projects needs to be revised in ways that ensure environmental protection and build citizen trust.

6. Conclusions

This study sought to classify, for the first time, citizens as potential RES investors based on their perceived barriers and preferred information sources in order to propose a new approach to mobilizing citizen investment in renewable energy. The analysis resulted in a new citizen typology and identified three clusters, named as “Inhibited by loaning conditions”, “Inhibited by RES technology, technical staff and economic and institutional barriers”, and “Inhibited by RES technology and the perceived lack of technical staff” (representing 40.1%, 27.8%, and 32.1% of the sample, respectively). The clusters exhibit notable differentiation, capturing an interesting association between perceived barriers and information sources. The recorded differentiation provides evidence in favor of formulating precisely targeted interventions that appeal to each segment’s actual hesitations through their trusted sources. Specifically, the members of the first cluster “Inhibited by loaning conditions” were mostly inhibited to invest due to loaning conditions, highlighting the need for banks to offer better terms for loans intended for RES investments or to create financial programs that are tailored to citizens who typically prefer to invest low sums of money. The second cluster “Inhibited by RES technology, technical staff and economic and institutional barriers” is perhaps the group that requires the most policy attention as it is inhibited by several and varied factors, including the perceived lack of technical staff, the technological efficiency of renewable systems, and operational problems due to weather, but also economic barriers such as the availability of subsidies, high upfront cost, the instability of the institutional framework, and the bureaucratic licensing process. To mobilize investments from this group, it is necessary that different actors take steps to address the barriers underlying their concerns. The third cluster, “Inhibited by RES technology and the perceived lack of technical staff” was inhibited by barriers related to the technology of renewables and to the availability of experts for installing and maintaining the systems, indicating the need to address concerns regarding the technological efficiency of renewable systems and the availability of experts to perform the installation and maintenance of the systems.

The study has also raised concerns regarding the effect of information sources on investment decisions. Results show that several information sources can have a negative effect, suggesting that there should be policy intervention to enhance the communication of information. Notably, mass media had a negative effect for two clusters, showing that for a significant part of potential investors, information through mass media negatively affects citizen investment, pointing to the need to pay attention to the ability of mass media to disseminate technical and legal information in ways tailored to the public. Along with addressing the barriers to investments and addressing the effectiveness of information sources, it is also necessary to consider the wider context within which renewable energy projects are implemented and to create a more favorable investment environment by basing the planning of energy policy on citizen differentiation, as well as the geographical differentiation of RES installed capacities and deployed renewable types.

Finally, the results of this study should be seen within certain limitations. Most importantly, our analysis included a considerable number of barriers and information sources, but this cannot exclude the possibility that there may be other barriers that were not considered in this analysis. In addition, even though this study drew on a representative citizen sample, its results apply only to Greece; reported results can only be interpreted in relation to the policy and institutional framework, along with the broader investment conditions in the study area. Finally, regarding the generalizability of the sample, it should be noted that even though the sociodemographic variables were compared with the official data of the Hellenic Statistical Authority, the comparison was not possible for all categories because the Hellenic Statistical Authority uses different scales. For this reason, it is possible that there are discrepancies for some categories.