Identifying Factors Influencing Local Acceptance of Renewable Energy Projects: A Systematic Review

Abstract

Renewable energy projects are critical for sustainable development, yet their success often hinges on local community acceptance. This study refines the Community Acceptance Framework to classify and better understand the social and behavioral factors that shape community responses to renewable energy projects. To support the reclassification, we draw selectively on three psychological concepts to refine definition and streamline categories. Based on a systematic review of 212 studies, we identified 29 influencing factors and categorized them into a seven-dimensional framework: social, economic, environmental, political, process, project details, and temporal. The findings reveal that financial capital, which reflects economic gains, emerges as the most frequently cited factor influencing local acceptance. However, when viewed dimensionally, the social dimension encompassing factors such as social capital, cognitive response, and cultural capital accounts for the largest share of influencing factors. Additionally, the often-overlooked political and temporal dimensions highlight the importance of governance quality and timely community engagement. While the framework offers a more robust and context-sensitive tool for analyzing acceptance dynamics, empirical validation is needed to assess its practical applicability. Nevertheless, the refined CAF can guide policymakers, researchers, and practitioners in designing renewable energy initiatives that are both technically sound, economically viable, and socially inclusive.

1. Introduction

The global transition to renewable energy sources is a critical strategy for mitigating climate change and ensuring long-term energy security [1]. Renewable energy projects (REPs), encompassing solar, wind, geothermal, hydropower, and biomass technologies, have experienced unprecedented growth, increasing by nearly 50% to 510 GW [2]. However, the widespread adoption of these technologies is often hindered by challenges that extend beyond technological and economic factors. Among these challenges, the social dimension has gained particular importance, as emphasized by the Social Acceptance Framework introduced by [3], which defines social acceptance as a multidimensional concept, with community acceptance identified as a key dimension.

Community acceptance, often conceptualized as the local-scale dimension of social acceptance [4,5], plays a vital role in determining whether REPs face resistance or gain the support needed for successful implementation. It is often studied in terms of how communities perceive, evaluate, and respond to the presence or proposal of REPs in their vicinity [6]. These responses are shaped by a complex interplay of factors including perceived environmental impacts, trust in project developers, procedural fairness, and alignment with local values and interests. Given this complexity, scholars have developed conceptual frameworks to understand human behavior and decision-making around renewable energy technology acceptance across disciplines such as sociology, psychology, and social sciences [7].

Prominent among these frameworks are the Theory of Planned Behavior, Diffusion of Innovations, Technology Acceptance Model, and Social Cognitive Theory, which have been extensively applied and extended in studies on renewable energy adoption and acceptance [7,8]. These models offer valuable insights into the individual and social determinants of behavioral intention, emphasizing constructs such as attitudes, subjective norms, perceived behavioral control, and self-efficacy. However, a key limitation lies in their predominant emphasis on socio-behavioral factors, often at the expense of other important factors—such as environmental concerns (e.g., ecological impacts of REPs) and economic considerations (e.g., perceived costs or benefits). These non-behavioral factors are sometimes only implicitly captured within broader constructs like perceived usefulness or relative advantage. As a result, the multidimensional nature of community responses is often underrepresented, limiting the explanatory depth of these models when applied to the complex and context-dependent dynamics of REP acceptance. In particular, there is a lack of integration between behavioral and non-behavioral factors, leaving a critical gap in understanding the full spectrum of influences on local acceptance.

To respond to this challenge, Roddis et al. (2018, 2020) introduced the Community Acceptance Framework (CAF), a conceptual model that systematically captures a broader range of factors influencing local responses to REPs [9,10]. The framework identifies and organizes key factors that influence community acceptance across several dimensions. While this data-driven approach effectively captures diverse community concerns, studies highlight the need for clear factor definitions and suggest integrating inclusive design processes to build trust [5,11]. Furthermore, the framework emphasizes external and project-level characteristics, offering limited integration of internal psychological processes or community-based capacities, which are also critical to understanding acceptance dynamics [6]. Incorporating selected concepts from psychological frameworks may help address these limitations by clarifying the socio-behavioral constructs embedded in the CAF.

This paper addressed these limitations by refining the CAF by drawing selectively on components from three well-established psychological frameworks, including the Dimensionality of Organizational Justice [12], the Community Capitals Framework [13], and the Cognitive–Affective Framework [14], to support the reclassification of factors and enhance definitional clarity. This approach uses psychological insights to improve the conceptual coherence of the framework and to better distinguish overlapping or ambiguously defined dimensions.

To complement this refinement, a systematic literature review was conducted, focusing on studies that examine the formation of local acceptance toward REPs. The review analyzes bibliometric trends and identifies key influencing factors, which are then categorized within the refined CAF. Through this process, the study contributes to a more structured and transparent classification of factors and offers practical insights for future research, policy development, and stakeholder engagement. By synthesizing a previously fragmented body of literature, the refined framework supports a clearer and more context-sensitive understanding of the socio-behavioral dynamics underlying community responses to renewable energy initiatives.

The remainder of this paper is structured as follows. The next section introduces the frameworks within the renewable energy and psychological domain, highlighting their relevance to REP acceptance. The third section outlines the methodology. The subsequent section presents the results focusing on publication trends, emerging themes, and the key factors of community acceptance. In the fifth section, we discuss the implications of our findings, followed by the main conclusion.

2. Conceptual Frameworks

2.1. Community Acceptance Framework

The CAF proposed by Roddis et al. (2020, 2018) systematically categorizes the factors influencing community acceptance into two overarching domains: material arguments and attitudinal/social influences [9,10]. This framework comprises 11 dimensions—aesthetic, environmental, economic, social, project details, temporal, political, process, construction, demographic, and geographical—encompassing a total of 35 distinct factors. Material arguments include tangible, project-specific features such as landscape impacts, environmental risk, economic consequences, and technical characteristics like noise, size, and ownership. Attitudinal and social influences capture deeper community traits, including political orientation, demographic composition, exposure to energy technologies over time, and levels of social capital. Grounded in the “triangle of social acceptance” [3], the framework bridges socio-political and community acceptance dimensions with empirical insights drawn from planning outcomes across the United Kingdom.

To expand the framework’s empirical scope, Enserink et al. (2022) applied it to synthesize findings from two overlapping domains: acceptance studies and landscape design [11]. Drawing upon 71 academic sources, the authors mapped a wider array of factors onto eight of the original dimensions. While this effort enriched the empirical inventory of influencing factors, it did not result in substantive conceptual refinement. Many factors lacked clear definitions, and the boundaries between dimensions remained ambiguous. This reflects a broader issue within the framework itself: several dimensions—originally developed inductively from case study data—lack theoretically grounded definitions, making it difficult to clearly distinguish and categorize factors. Such ambiguity limits the framework’s effectiveness for comparative analysis and increases the risk of conflating distinct drivers of acceptance or opposition. Additionally, the inductive origins of key dimensions contribute to interpretive uncertainty, constraining the framework’s applicability across diverse sociopolitical and geographic contexts [9,15].

The framework has also been critiqued for portraying communities as homogeneous entities, thereby neglecting internal social divisions, cultural diversity, and power asymmetries [16]. Such oversimplification may obscure intra-community conflicts and overlook localized sources of resistance or support. While the framework does acknowledge procedural justice, it falls short in offering operational clarity on critical process-related factors—such as stakeholder participation, fair distribution of risks and benefits, and inclusive decision-making—which are widely recognized as essential to the perceived legitimacy and long-term viability of renewable energy initiatives [17].

2.2. Psychological Frameworks

To support a more comprehensive and interdisciplinary approach for understanding community acceptance to REPs, it is essential to consider theoretical perspectives from a psychology field. While the CAF offers a robust structure for evaluating material and attitudinal factors, it leaves room for deeper exploration of the internal, subjective, and relational processes that shape individual and collective attitudes. Psychological frameworks provide well-established constructs for analyzing how people perceive fairness, interpret risks and benefits, experience emotional responses, and evaluate their environment. This section introduces three influential psychological frameworks that offer valuable lenses for understanding the behavioral and attitudinal dynamics relevant to community acceptance of REPs: the Dimensionality of Organizational Justice [12], the Community Capitals Framework [13], and the Cognitive–Affective Personality System [14].

Justice considerations are central to the discourse on community acceptance of REPs, particularly in addressing real-world concerns such as mistrust, lack of transparency, and perceived exclusion in decision-making. Procedural and distributive justice have been widely emphasized in the literature [18,19,20], forming the basis of the Energy Justice Framework. However, this study draws on the Dimensionality of Organizational Justice by Colquitt (2001), particularly the factor of informational justice [12], to confront the often-overlooked issue of poor communication. Informational justice focuses on the fairness, adequacy, and timeliness of information shared with stakeholders [21]. In REP development, communities frequently report feeling misinformed or excluded from key decisions, which undermines trust and fuels opposition [22,23]. By distinguishing informational justice as a standalone factor, the framework directly addresses the challenge of communication breakdowns between developers and local populations.

The Community Capitals Framework addresses the structural disparities and resource gaps that often shape uneven support for REPs. It provides a systems-based lens by categorizing community capacity into seven forms of capital—natural, cultural, human, social, political, financial, and built [13,24]. These domains directly relate to practical challenges such as weak social cohesion, insufficient access to political influence, or lack of financial investment in local benefit-sharing. For example, cultural capital helps explain resistance stemming from perceived threats to local identity or heritage; social capital links to trust and cooperation between community members and developers; and human capital relates to whether a project improves health, education, or well-being. The CCF thus allows policymakers to assess which community vulnerabilities must be addressed to foster local support. It moves beyond economic metrics to evaluate how REPs can empower communities through social transformation and capacity-building [25].

The Cognitive–Affective Framework, proposed by Mischel and Shoda (1995) [14], tackles the psychological divergence often observed within communities exposed to the same REP. This framework explains how stable Cognitive–Affective units such as attitudes, beliefs, awareness, and emotions interact with external project attributes to shape behavior. It offers insights into real-world phenomena such as fear of nuclear power due to safety concerns or aversion to biomass because of odor and pollution [26,27]. These emotional reactions are not merely irrational responses but form part of how individuals assess risk and acceptability. The framework also accounts for why some residents embrace a REP while others resist it, despite receiving the same information. This understanding can guide tailored communication strategies and help avoid one-size-fits-all approaches to public engagement.

Together, these psychological frameworks offer a robust foundation for understanding and responding to real-world challenges that hinder REP acceptance. By integrating these theories into the refined CAF, the study provides a more context-sensitive and actionable tool for designing REPs that are not only technically viable but also socially resilient and publicly legitimate.

3. Methodology

To synthesize factors influencing local acceptance of REPs from different disciplines, this study employed a systematic review focusing on the constructs of local opinion, acceptance, and behavior. The methodology adhered to the PRISMA guidelines to ensure transparency, rigor, and consistency in the review process [28]. PRISMA involves documenting each stage of the review process, ensuring a replicable and methodological approach. A systematic three-step approach [29] was followed: (1) development and implementation of a comprehensive search strategy across the Scopus and Web of Science databases using keywords related to the constructs; (2) assessment of the relevance and quality of the identified studies based on predefined inclusion and exclusion criteria; and (3) extraction and synthesis of data to identify key trends, relationships, and frequently studied factors.

3.1. Search Strategy Development and Implementation

The review was performed using a targeted search strategy to identify and synthesize factors related to the three key constructs: opinion, acceptance, and behavior. Three separate search queries were designed to pair each construct with the “renewable energy project” field, ensuring the retrieved articles were directly relevant to the study’s focus [30]. The constructs were operationalized using carefully selected keywords and Boolean operators (e.g., “AND” and “OR”) to capture their conceptual scope (

Table 1. Keyword search in Scopus and Web of Science.

Search	Keyword Combinations
1	((“collective opinion” OR “community opinion” OR “local opinion” OR “group opinion” OR “public opinion” OR “social opinion”) AND (“renewable energy project” OR “renewable energy projects” OR “renewable energy initiative” OR “renewable energy initiatives” OR “renewable energy technology” OR “renewable energy technologies”))
2	((“community acceptance” OR “community support” OR “local acceptance” OR “local support” OR “social acceptance” OR “public support” OR “collective support”) AND (“renewable energy project” OR “renewable energy projects” OR “renewable energy initiative” OR “renewable energy initiatives” OR “renewable energy technology” OR “renewable energy technologies”))
3	((“community behavior” OR “community behaviour” OR “collective behavior” OR “collective behaviour” OR “local behavior” OR “local behaviour” OR “social behavior” OR “social behaviour” OR “community response” OR “community action”) AND (“renewable energy project” OR “renewable energy projects” OR “renewable energy initiative” OR “renewable energy initiatives” OR “renewable energy technology” OR “renewable energy technologies”))

). This structured approach maximized the relevance and specificity of the identified literature, enabling a comprehensive analysis of community acceptance formation.

Searches were conducted using Scopus and Web of Science, two leading academic databases widely used in renewable energy and social sciences research [31]. These databases provide extensive coverage of high-quality, peer-reviewed English-language journals. The review focused on articles published between August 2004 and August 2024, ensuring a 20-year perspective on research trends. To ensure methodological rigor and consistency in quality appraisal, only peer-reviewed journal articles and review papers were included. Books, conference proceedings, and gray literature were excluded, as these sources often lack standardized peer review and reporting structures, which could compromise the reliability and comparability of findings.

3.2. Relevance and Quality Assessment

The PRISMA flow diagram in (Figure 1) illustrates the systematic process undertaken in this review, including record identification, screening, eligibility assessment, and final inclusion. A total of 428 records were retrieved from Web of Science (n = 236) and Scopus (n = 198). As both databases contain overlapping content, duplicate entries were identified and removed using R software (version 4.3.2), resulting in a refined dataset of 250 unique records for further analysis. The inclusion criteria focused on studies addressing REP acceptance at the community level, particularly those that examined factors related to the formation of local opinion, acceptance, and behavior. While not all studies explicitly referenced community-governed projects, the criteria were expanded to include those involving collaboration with local communities or projects designed to deliver environmental, economic, or social benefits at the local level.

To ensure methodological transparency and reproducibility, the full review protocol, PRISMA checklist, and validation documentation are available in the Open Science Framework (OSF) registry. The screening process was led by the primary reviewer, with regular consultation and oversight from a secondary reviewer or academic supervisor to ensure consistency. Any discrepancies during the inclusion process were discussed and resolved collaboratively. Out of the 250 records retrieved, ten records were excluded because of unavailability, resulting in 240 eligible records for abstract screening. Abstracts were rigorously reviewed to ensure relevance to criteria selection and the objectives of the study. At this stage, 19 records were excluded for focusing on unrelated themes. Records categorized under “policy-focused” were excluded because they concentrated on economic modeling or policy design without addressing local acceptance dynamics. Records grouped under “others” were similarly excluded, as their abstracts emphasized national or regional policies, environmental or technical performance assessments, innovation systems, or entrepreneurial strategies. Subsequently, 221 records underwent a full-text eligibility assessment, during which 9 additional records were excluded for not aligning with the criteria. Ultimately, 212 studies were included in the final analysis, providing a comprehensive dataset for understanding local responses to REPs.

3.3. Data Extraction and Analysis

The next step involved systematically extracting and organizing the factors using Microsoft Excel to ensure consistency, accuracy, and transparency. The data collected from each selected study included the study title, author(s), year of publication, journal name, and keywords. Bibliometric analysis is among the various methods used to quantitatively analyze the literature trends [32]. This study employed bibliometric analysis using the Bibliometrix package in R and the VOSviewer (version 1.6.20) to examine publication trends, leading journals, and keyword co-occurrence patterns. These techniques aim to uncover quantitative trends and thematic clusters within the literature, which provide the current research directions and gaps in existing studies. Additionally, the factors influencing community acceptance towards REPs were identified and systematically categorized onto the refined CAF, which is explained in Section 4.

4. Refined Community Acceptance Framework

The refinement of the CAF adopted a definition-based construct mapping approach to enhance conceptual coherence and reduce dimensional redundancy in the original framework [33]. Each of the 35 factors was systematically reassessed against the original dimension definitions and cross-referenced with established psychological constructs.

This deductive process led to the consolidation of overlapping and ambiguously defined dimensions, resulting in a refined framework comprising 7 dimensions and 29 factors (

Table 2. The definitions of the dimension and factors of refined CAF.

Categories	Dimensions	Definition	Factors
Material arguments	Environmental	Concerns the relationship between the project and the natural ecosystem, including its visual integration with the landscape and impacts on local environments and resources.	Natural capital
			Environmental concerns and values
	Economic	Relates to financial resources and the project’s impact on local income, spending, and economic value.	Financial capital
			Tourism
			Property values
			Willingness to pay
	Project details	Covers specific technical and structural characteristics of the project that affect perceptions of feasibility, safety, and relevance. Encompasses impacts during the project construction phase, such as noise, traffic, visual changes, and infrastructure development.	Built capital
			Energy security
			Project size
			Technology
			Business model
			Design
			Safety plant
			Nuisance
Attitudinal and social influences	Temporal	Refers to the influence of time-bound factors such as historical experiences, past project outcomes, and media narratives on current perceptions.	Social/media influence
			Past experiences and exposure
	Social	Refers to the social and cultural fabric of the community, including relationships, knowledge, traditions, and shared values, which shape how people perceive and respond to the REP.	Human capital
			Social capital
			Cultural capital
			Cognitive response
			Affective response
			Demographic characteristics
	Process	Addresses the fairness and inclusivity of planning, decision-making, and implementation processes related to the project.	Informational justice
			Procedural justice
			Distributive justice
	Political	Reflects the influence of governance systems, policy frameworks, and political ideologies on project acceptance and stakeholder engagement.	Policy and governance
			Incentives and subsidies
			Political values and beliefs
			Support for political party

). These factors were reorganized based on the categorization by Roddis et al. (2018), as outlined in Section 2.1 [10]. The refined CAF groups three dimensions—environmental, economic, and project details—under material arguments, focusing on tangible, project-specific aspects. In contrast, four dimensions—process, social, political, and temporal—fall under attitudinal and social influences, capturing psychological and contextual factors that shape community acceptance of REPs. This approach aligns with established methods in conceptual synthesis, similar to how corporate sustainability management dimensions were redefined using strategy and organizational theories [34]. The clear, structured method ensures replicability and lays a strong foundation for interdisciplinary research, particularly for understanding community acceptance of renewable energy.

As part of the refinement process, several dimensions in the original CAF—namely aesthetic, demographic, geographical [10], and construction [9]—were identified as candidates for integration into broader categories based on the definitional scope and the nature of their associated factors. For instance, the aesthetic dimension, which included factors such as visual impact, landscape changes, and glint/glare effects, was consolidated under the environmental dimension due to its strong alignment with natural capital. Human perceptions of landscape aesthetics are often rooted in environmental quality and place-based identity [13]. This dimension further distinguishes between natural capital referring to tangible ecological assets like land, water, and biodiversity, and environmental concerns and values, which reflect individuals’ beliefs, perceptions, and attitudes toward environmental protection.

Similarly, the demographic and geographical dimensions—originally separated to highlight how social deprivation, population density, and administrative geography influence planning decisions—were integrated into the social dimension. This decision is supported by the literature recognizing that demographic and spatial variables are not isolated drivers but are embedded within the socio-cultural structures of communities [35,36]. Factors such as demographic characteristics, human, social, and cultural capital, along with cognitive and affective responses, are interrelated and collectively shape how communities perceive and respond to REP. Consolidating them under the social dimension provides a more holistic and contextually grounded understanding of community composition and dynamics in relation to renewable energy acceptance.

The construction dimension capture concerns arising during project development were also reclassified. Although initially framed as pertaining to temporary disruptions, factors such as noise and dust pollution can persist into the operational phase of certain technologies (e.g., wind turbines). These impacts were therefore reassigned to the project details dimension, which more appropriately reflects how various project characteristics—such as design, size, technology type, business model, nuisance, and safety features—can influence acceptance throughout the entire project lifecycle. This dimension also incorporates the perceived benefits of REP implementation, including contributions to local energy security, as well as built capital—defined as the physical infrastructure developed to support and sustain innovation [24].

The economic dimension retains the factors on tourism and property values from the original framework and expands to incorporate financial capital and willingness to pay. Financial capital is defined as the monetary resources that can be mobilized to support community development and capacity-building efforts [13], while willingness to pay serves as an economic indicator reflecting the value individuals assign to the perceived benefits of REPs or the compensation they would require to tolerate associated impacts [11]. Collectively, these factors capture how communities evaluate the economic implications of REP implementation, including potential gains or losses in local income and asset values. Perceptions of economic benefit or burden are known to play a significant role in shaping local acceptance of renewable energy initiatives [17,37,38].

The process dimension addresses perceptions of fairness and legitimacy throughout the planning and implementation phases. It includes procedural, distributive, and informational justice, reflecting the extent to which communities feel meaningfully included in deliberative processes, adequately informed about project impacts, and equitably treated in the distribution of benefits and burdens. While trust is often linked to procedural justice in the literature [39,40], it is positioned within the social dimension of this framework under social capital to align with definition by [13,24]. Nevertheless, trust remains conceptually linked to the process dimension, as this refinement clarifies the interdependencies between dimensions.

The temporal dimension redefines the factors to time-based influences on perception, including past experiences, prior exposure to similar projects, and social or media narratives. This dimension acknowledges that local acceptance to REPs is not static but evolves over time, shaped by historical context, narrative framing, and accumulated experience [41,42]. Lastly, the political dimension considers the broader governance and policy context in which REPs are embedded. It includes policy frameworks, the role of incentives and subsidies, and residents’ political values and affiliations, all of which shape how institutional credibility and energy transitions are interpreted at the local level [3,43]. The factors within these dimensions are adopted from the study of [11].

The reorganization of the refined framework into 7 dimensions and 29 factors enhances the internal coherence of each dimension and facilitates a more integrated understanding of the complex drivers of community acceptance of REPs. The data extracted from 212 peer-reviewed sources, identified through a systematic PRISMA-guided review, were mapped to the corresponding factors based on thematic relevance and keyword association. This categorization process aimed to capture the frequency with which each factor has been discussed within the research field. In the analysis, each factor mentioned in a study was counted as a single occurrence, regardless of the depth of discussion or emphasis within the original source. Although this method does not reflect the relative significance or contextual richness of individual factors, it offers a systematic overview of their prevalence in the literature [44], providing useful insights into dominant themes and underexplored areas.

5. Results

5.1. Bibliometric Analysis

5.1.1. Publication Trends and Leading Journals

Growing academic interest has been directed toward the study of local acceptance of REPs. Figure 2 presents the annual trend in scholarly publications from 2004 to 2024. The blue line represents the actual number of publications per year, starting with one record in 2004. The red dashed line indicates the 3-year moving average, which smooths short-term fluctuations to highlight long-term trends. A marked increase in publication output is observed from 2014 onward, with notable peaks in 2016 (15 publications), 2021 (29 publications), and a sharp rise to 33 publications in 2024—the highest annual output recorded. Despite minor declines in 2017, 2022, and 2023, the overall trajectory remains upward, indicating sustained and growing scholarly engagement.

Table 3. Ranking of the ten most productive and influential journals (sorted by number of publications).

Rank	Journal	Publications	h-Index	Total Citations
1	Energy Policy	34	272	1945
2	Energy Research and Social Science	34	113	985
3	Renewable and Sustainable Energy Reviews	18	421	796
4	Sustainability	12	169	152
5	Energies	12	152	172
6	Renewable Energy	8	250	355
7	Journal of Cleaner Production	4	309	88
8	Energy, Sustainability, and Society	4	42	100
9	Energy	3	251	167
10	Solar Energy	3	224	61

outlines the top journals that published studies on the local acceptance of REPs. As this research field is interdisciplinary, the journals belong to various academic disciplines, such as energy, sustainability, economics, social science, and environmental science. However, the top ten journals were associated with energy and sustainability. Of the 83 journals, 2 journals published more than 30 articles on the subject.

The h-index is a widely used metric for assessing both the productivity and citation impact of a scholar or journal, as it captures the balance between the number of publications and the frequency with which they are cited [45]. A higher h-index indicates greater scholarly influence within a field. In this study, while some journals published a higher volume of articles, the Journal of Renewable and Sustainable Energy Reviews and the Journal of Cleaner Production stood out, suggesting that their publications played a significant role in shaping discussions on the social acceptance of REPs, even with fewer articles compared with the most productive journals.

5.1.2. Keyword Co-Occurrence Analysis

To identify underexplored themes within the field, a keyword co-occurrence analysis was conducted using VOSviewer, with a specific focus on socio-behavioral terms related to community acceptance of REPs. Manually selected keywords such as opinion, acceptance, behavior, perception, and attitude were analyzed to explore their relationships within the literature. To maintain this socio-behavioral focus, keywords related to environmental and economic dimensions were excluded from the analysis. Figure 3 presents the visualization, where node size indicates keyword frequency, with larger nodes representing more frequently studied keywords. The results reveal three major thematic clusters, as follows:

• Cluster 1 is centered on social acceptance and renewable energy, and includes keywords such as local acceptance, attitudes, perceptions, support, and barriers. This cluster reflects the dominant discourse on public support for renewable energy initiatives.
• Cluster 2 is oriented around renewable energy technologies and their association with public attitudes, and public opinions. Notably, the weak direct connection between acceptance and opinion highlights a research gap in exploring how opinion formation influences acceptance.
• Cluster 3 revolves around terms such as REPs, perception, local participation, and public engagement. The smaller node sizes in this cluster suggest these topics are less frequently examined compared to those in Clusters 1 and 2.

5.2. Key Factors Identification

Figure 4 presents a bar chart illustrating the frequency of the 29 factors identified across the 7 dimensions of the refined CAF: economic, environmental, political, process, project details, social, and temporal. The findings reveal the ten most frequently cited factors influencing local acceptance of REPs. The highest reported factor is financial capital (n = 108), environmental concerns and values and procedural justice, both tied in second place (n = 94). Other prominent factors include social capital (n = 87), cognitive response (n = 80), cultural capital (n = 54), policy and governance (n = 51), distributive justice (n = 46), natural capital (n = 45), and REP design (n = 38). Among these top ten factors, three belong to the social dimension, emphasizing the importance of trust, norms, perceptions, and cultural attachment in shaping acceptance. Environmental and process dimensions each contribute two key factors, reinforcing the relevance of ecological values and procedural fairness. Meanwhile, the economic, project details, and political dimensions each contribute one highly cited factor, suggesting their influence is present but more narrowly concentrated.

The radar chart in Figure 5 illustrates the proportional focus of each dimension in the refined CAF, revealing that the social dimension emerges as a key driver in the academic discourse of local acceptance of REPs, accounting for 29% of all identified factors. This is followed by the process (18%), environmental (14%), economic (14%), project details (13%), political (9%), and temporal (3%) dimensions. While a previous study identified the process dimension as a primary influence [11], the current findings offer a revised perspective. This shift is largely attributed to the reorganization of factors within the refined framework—specifically, the relocation of trust from the process dimension to the social dimension, in alignment with its conceptual definition with social capital. This adjustment provides a clear representation of the underlying social dynamics that influence local acceptance.

6. Discussion

In this study, we propose a refinement of the CAF by drawing selectively on concepts from psychological frameworks, including the Dimensionality of Organizational Justice, the Community Capitals Framework, and the Cognitive–Affective Framework to support the reclassification of factors and improve definitional clarity. The refined CAF organizes 29 factors across 7 dimensions—social, economic, environmental, political, process, project details, and temporal—each offering clearer interpretive pathways for examining local acceptance of REPs.

A bibliometric analysis was conducted based on 212 studies identified through the literature review to strengthen the refined CAF validity by grounding it in evidence-based trends rather than relying solely on theoretical abstraction, offering a comprehensive view of how academic attention has shaped our understanding of the social dimensions of renewable energy acceptance. As shown in Figure 2, the development of the field can be interpreted across three distinct phases. Phase 1 (2004–2014) represents the formative stage, characterized by low and sporadic publication output. This period laid the theoretical foundations for the field, most notably with the introduction of Wustenhagen et al.’s (2007) influential social acceptance triangle [3]. Phase 2 (2015–2021) marks a phase of accelerated growth and critical reframing, coinciding with heightened policy emphasis on public engagement and the global momentum following the Paris Agreement in 2015 [46]. Research during this phase increasingly adopted critical perspectives, highlighting issues of inequality and proposing more socially just approaches to renewable energy deployment [18]. Phase 3 (2022–2024) reflects the post-COVID adjustment period, characterized by sustained high publication levels, culminating in a peak in 2024. This phase coincided with a temporary pause in global energy investment due to the COVID-19 pandemic [47], followed by a reorientation of energy priorities toward resilience and sustainability. Additionally, the volatility of fossil fuel markets—exacerbated by the COVID-19 crisis and geopolitical instability such as the war in Ukraine—further increased the attractiveness of renewable energy [48], reinforcing community acceptance as a central and urgent focus in the energy transition discourse.

The dominance of energy and sustainability journals, as shown in Table 3, highlights the growing prominence of social acceptance research within the broader energy transition discourse. These journals play a critical role in shaping energy policy and practice, making it essential to integrate community-driven perspectives into their agendas. However, social acceptance processes are inherently complex and often difficult to quantify, which has led many studies to focus on identifying and cataloguing influencing factors rather than exploring their dynamic interactions [37,49]. This underscores the need for a more adaptive and integrative approach—one that moves beyond static factor lists to consider how community perceptions evolve over time, across contexts, and in response to ongoing technological, political, and social changes. Such an approach would help bridge the gap between empirical observation and actionable policy design, ensuring that energy transitions are not only technically sound and economically viable but also socially grounded.

One key observation in the keyword co-occurrence analysis was the overlapping use of terms such as acceptance, perception, attitude, and participation, as well as those linked to renewable energy, such as technologies, projects, and resources. This overlap indicates the need for clearer definitions and more structured investigations of the relationships among these constructs. The inconsistent use of constructs can lead to varying methodologies and measurements. For instance, a study by Azarova et al. (2019), which measured acceptance through survey questions, included attitudes toward specific renewable energy technologies [50], while Devine-Wright and Wiersma (2020) defined acceptance based on observed community support for a project [6]. This ambiguity hinders the development of effective community engagement strategies. For example, policymakers may focus on improving perception without fully understanding how it translates into acceptance or actionable support. Addressing these gaps is critical for advancing theoretical and practical insights into renewable energy adoption.

The mapping of synthesized factors into the refined CAF in Figure 4 revealed not only the relative frequency of each factor but also the depth of attention each dimension has received over time. However, it must be noted that this method may potentially overrepresent frequently mentioned but superficially discussed factors and limit insights into their interactions or contextual variations.

The prominence of financial capital as the key factor underscores the pivotal role of perceived economic gains in shaping public support for REPs. Financial capital spans direct and indirect benefits, including job creation, lower energy costs, and local revenue generation [51,52]. While often framed as co-benefits, these economic outcomes frequently serve as the primary motivators for local support—often taking precedence over environmental considerations [53]. Job opportunities can revitalize rural economies and enhance the perceived legitimacy of REPs [54], while anticipated energy savings support strong approval for renewables [55]. Nevertheless, the prominence of procedural justice and environmental concerns and values as the second-highest factors signals that communities seek more than just financial returns—they also demand inclusive decision-making and address the potential harm to their environment. The refined CAF helped to clarify how these factors interact, highlighting that fairness in decision-making processes is intertwined with how environmental risks are perceived and accepted.

The social and process dimensions in Figure 5 collectively account for a substantial portion of the total factor mentioned. Within the social dimension, social capital, cognitive response, and cultural capital emphasize the critical role of psychological and relational dynamics. Social capital, in particular, facilitates trust-building, collective action, and information sharing—key elements in enhancing project legitimacy and community ownership [56,57]. The findings of Broska (2021) further strengthen this point, showing that the motivations for participating in sustainable community initiatives are primarily driven by social needs and environmental concerns, with social capital functioning as a critical motivator at multiple stages—from initial engagement to behavior spillover [58]. This interplay among social norms, shared values, and trust networks enables participation, sustains commitment, and promotes broader lifestyle transformations.

The factors on procedural, distributive, and informational justice within the process dimension play a vital role in mediating how these social dynamics are institutionalized. Fair and participatory processes foster perceptions of legitimacy and accountability, particularly when communities are engaged early and meaningfully in project design and decision-making. As emphasized in previous studies, the absence of procedural fairness often fuels mistrust and opposition, regardless of the project’s technical or environmental merits [27,59]. The refined CAF allows these procedural elements to be distinguished from broader social constructs, highlighting that while trust is socially rooted, its reinforcement often depends on the perceived fairness and openness of institutional processes. Therefore, the process dimension not only supports the operationalization of social capital but also determines how responsive institutions are to community voices—making it a critical lever in fostering acceptance.

In contrast, the temporal and political dimensions remain underexplored in both empirical studies and conceptual frameworks, although their relative absence does not imply lesser importance. The political dimension, particularly policy and governance, incentives, and political values, plays a subtle yet influential role in shaping community acceptance. Regulatory clarity, institutional coordination, and the simplicity of administrative procedures can either enable or hinder local participation [43,60]. Bureaucratic delays and inconsistent policy signals often weaken public confidence, while responsive governance can encourage engagement. For example, Przygódzka et al. (2024) found strong expectations among farmers in Eastern Poland that the state should actively support renewable energy and energy efficiency, suggesting that government involvement is often seen as essential [61]. These findings highlight that acceptance is not purely bottom-up but also depends on the accessibility and credibility of governance structures.

The temporal dimension, meanwhile, shapes how communities interpret current REP initiatives—either as a continuation of past experiences or as responses influenced by evolving media narratives and public discourse. As Evans (2011) emphasizes, early and meaningful engagement is crucial not only for ensuring transparency but also for demonstrating respect and accountability [62]. When communities are engaged too late or inconsistently, it can lead to perceptions of tokenism or manipulation, undermining trust. The refined CAF makes these temporal factors explicit, recognizing that acceptance is influenced not only by what is offered but also by when communities are involved, how the initiative is communicated and managed, and who is perceived to be in control. This highlights the importance of timing, continuity, and institutional credibility in fostering long-term acceptance.

7. Practical Implications and Limitations

The refined CAF serves as a practical tool for policymakers, planners, and developers to better understand and prioritize the key factors that influence local acceptance of REPs. By organizing 29 influencing factors across 7 dimensions, the framework enables stakeholders to systematically assess which aspects warrant greater attention in a given locality. For example, in economically disadvantaged regions, emphasizing financial capital through job creation or community reinvestment may enhance support. In culturally sensitive areas, prioritizing cultural capital and inclusive decision-making processes can help build trust and legitimacy. The prominence of procedural and informational justice within the framework further underscores the need for transparent, participatory engagement strategies from early planning stages. Practitioners can leverage the CAF to develop toolkits, including checklists for assessing social, economic, and environmental impacts or guidelines for equitable benefit distribution. These strategies ensure REP implementation is technically sound, economically viable, and socially inclusive, supporting a sustainable energy transition.

However, the CAF’s theoretical foundation presents limitations that require attention. The use of a frequency-based counting method to identify key factors introduces a potential bias by prioritizing factors based on how often they appear in the literature, rather than their actual influence or contextual relevance. This approach may overrepresent frequently cited but less impactful factors and underrepresent emerging or nuanced ones. Future studies should consider weighted evaluation techniques, such as the Delphi method or the Analytic Hierarchy Process, which enable more context-sensitive prioritization through expert consensus or stakeholder input. Furthermore, the refined CAF currently lacks empirical validation, highlighting the need for fieldwork, surveys, or case studies across diverse socio-cultural and geographic contexts to assess the framework’s robustness and predictive value.

The scope of the literature reviewed may also be broadened in future research by incorporating keywords such as “attitude” and “perception”, which are foundational constructs in technology acceptance studies. While this study focused on terms like “opinion” and “acceptance”, this keyword set may have excluded influential energy behavioral works by scholars such as Perlaviciute and Steg (2014) and Huijts et al. (2012, 2018) that discuss the critical role of cognitive appraisals and affective response in shaping public attitudes toward renewable energy technologies [63,64,65]. The inclusion of these keywords may alter the relative prominence of the influencing factors identified in this study. For instance, the current finding that financial capital is the most frequently cited factor could shift if affective and psychological drivers were more comprehensively captured. Expanding the scope of analysis in this way would enhance the theoretical depth of the CAF and provide a more holistic understanding of the factors driving local acceptance of REPs.

8. Conclusions

This study advances the discourse on renewable energy acceptance by enhancing the conceptual precision and interdisciplinary relevance of the CAF. Informed by psychological concepts and a systematic review of 212 studies, the refined CAF distinguishes between overlapping constructs and achieve the aim of this study by categorizing 29 influencing factors across 7 dimensions: social, economic, environmental, political, process, project details, and temporal. This refinement enables clearer distinctions between overlapping constructs such as trust and procedural fairness, making the framework more applicable to real-world stakeholder analysis. While financial capital remains the most frequently cited individual factor, the findings highlight that broader influence resides within the social and process dimensions. These dimensions emphasize the vital roles of trust-building, inclusive decision-making, and culturally grounded values in securing community support. Overall, this study contributes to the literature by clarifying how communities form opinions, translate these into acceptance or resistance, and ultimately behave in relation to REPs. It presents a theoretically grounded and empirically informed framework for analyzing these dynamics across diverse contexts. The refined CAF serves not only as a tool for academic inquiry but also as a practical guide for developers, planners, and policymakers to anticipate community responses and design more inclusive, context-sensitive energy transitions.