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Systematic Review

Decoding Solar Adoption: A Systematic Review of Theories and Factors of Photovoltaic Technology Adoption in Households of Developing Countries

by
Edison Jair Duque Oliva
1,2 and
Rodrigo Atehortua Santamaria
1,3,*
1
School of Administration and Accounting, Universidad Nacional de Colombia, Bogotá 111321, Colombia
2
ESAI Business School, Universidad Espíritu Santo, Samborondon 104135, Ecuador
3
School of Administration and Competitiveness, Faculty of Business, Management and Sustainability, Politécnico Grancolombiano, Bogotá 110231, Colombia
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(12), 5494; https://doi.org/10.3390/su17125494
Submission received: 5 March 2025 / Revised: 15 May 2025 / Accepted: 19 May 2025 / Published: 14 June 2025
(This article belongs to the Special Issue Consumer Behavior and Sustainable Consumption: Opportunities and Challenges)
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Abstract

:
This systematic review explores key theories and factors shaping the adoption of photovoltaic (PV) systems by households in developing countries. Following the PRISMA protocol, we reviewed 44 empirical and theoretical studies published between 2010 and 2024, selected from an initial set of 350 articles retrieved from Scopus and Web of Science. Studies were included if they addressed household PV adoption specifically within developing economies, excluding review articles and conference proceedings. Due to varied methodologies across studies that do not allow for a homogenous assessment, a formal bias risk assessment was not conducted. Our results reveal frequent use of frameworks such as the Theory of Planned Behavior, Technology Acceptance Model, and Diffusion of Innovations. Despite their popularity, these models sometimes fail to fully capture the economic, infrastructure, and cultural realities specific to nonmatured markets. Key adoption barriers identified include affordability constraints, weak infrastructure, social norms, and inconsistent policy support. Geographic imbalance, particularly concentrated in Asia and Africa, and limited consideration of behavioral economics insights represent limitations in the current evidence base. These findings suggest the need for context-sensitive theoretical models and deeper integration of behavioral factors, providing practical directions for future research and policy to facilitate renewable energy transitions.

1. Introduction

Energy transition is a comprehensive shift of the energy generation system structure from fossils to renewables and sustainable sources [1,2]. This process also involves technologies, policies, and developing responsible consumption patterns [3]. Among sources of renewable energy (RE), solar photovoltaic technology (PV) has become a viable alternative for both households in developed and developing countries given its unprecedented expansion and decreasing cost. Between 2010 and 2019, the global weighted-average levelized cost of electricity (LCOE) for utility-scale solar PV declined by 82%, from approximately USD 0.378 per kilowatt-hour (kWh) to USD 0.068 per kWh [4]. These cost reductions, driven by technological improvements and economies of scale, have made solar PV increasingly accessible.
However, the adoption of household PV in developing countries still lags behind that in developed nations. Unique socioeconomic, infrastructural, and cultural factors in developing economies can hinder or facilitate adoption in ways not fully captured by models based on developed country contexts. There is a need to decode solar adoption by examining the theories and factors that have been studied in relation to household PV uptake in developing countries. This study differentiates itself from prior reviews by focusing exclusively on behavioral theories applied to household PV adoption in developing countries, emphasizing context-specific socioeconomic and policy variables often neglected in global analyses. Its importance is related to the fact that it can contribute as a potential solution to energy poverty which is usually caused by including low income, inefficient building, and high energy expenses (Lu & Ren, 2023) [5]. Furthermore, PV technology offers a sustainable and environmentally friendly alternative source of energy, aligning with global efforts, such as SDG 7 (Affordable and Clean Energy), to transition toward cleaner energy systems and reduce reliance on fossil fuels [6].
Given the relevance of PV adoption in developing countries and the need for a deeper understanding of the factors influencing consumer behavior, this review aims to identify and analyze the main factors and theoretical frameworks employed in studies on the adoption of photovoltaic technology in households in developing regions, highlighting patterns, trends, and gaps in the existing literature. In doing so, this study seeks to answer the following research question: What are the main factors analyzed in studies on the adoption of photovoltaic technology in households in developing countries, and what consumer behavior theories have been employed in these investigations? By addressing these aspects, this review provides a structured synthesis that can inform both academic research and policymaking in the field of renewable energy adoption. The abbreviations and acronyms used in this article are listed in Table 1.
This document is structured as follows: Section 2 describes the systematic literature review methodology, outlining the search strategy, selection criteria, and data sources; Section 3 discusses the theoretical frameworks used for studying consumer behavior in PV household adoption of developing countries according to the records found; Section 4 contains an analysis and critical discussion of the key factors influencing adoption in developing countries; finally, the document ends with the conclusions and discussion underlining some future research gaps.

2. Systematic Literature Review

The primary motivation for conducting this review is to compile a comprehensive database of academic research focusing on the adoption of photovoltaic (PV) technology in households within developing countries, a topic that remains underrepresented in academic literature [7,8]. While the transition to renewable energy has been extensively studied in developed regions, the unique socioeconomic, cultural, and infrastructural contexts of developing countries necessitate a more tailored approach. A systematic review on this subject can illuminate current trends, ranging from general demographic patterns to detailed analytical insights, providing a deeper understanding of the factors influencing household-level PV adoption in these regions.
This section describes the systematic review methodology, following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020, see Supplementary Materials) guidelines. It details the data sources and search strategy, eligibility criteria, study selection process (with a PRISMA flow diagram), data extraction methods, and considerations regarding risk of bias. No formal protocol registration was performed; the review adheres strictly to PRISMA guidelines.
The data collection process was guided by the systematic quantitative literature review method and adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol to ensure a thorough and transparent approach to cover a wide range of academic literature associated with the subject. The review draws from two leading academic databases, Web of Science (WoS) and Scopus, both of which are recognized for their reliability and extensive coverage of multidisciplinary research. A qualitative synthesis was chosen due to the methodological heterogeneity of included studies, which range from case studies to quantitative models, making meta-analysis unfeasible. This method allows for thematic exploration across diverse contexts.

2.1. Sources of Data

We conducted a comprehensive literature search within WoS and Scopus databases covering the period 2010 to 2024. The search strategy was designed to maximize coverage of relevant studies by using selected keywords organized into three thematic groups:
  • Solar Technologies: Keywords including “photovoltaic systems”, “solar energy”, and “residential solar panels” were used to capture literature on household solar photovoltaic systems.
  • Adoption Processes: Terms such as “adoption”, “implementation”, and “diffusion” were used to capture studies focused on the processes leading to PV technology adoption.
  • Geographic Contexts: Phrases like “developing countries”, “emerging economies”, and “low- and middle-income nations” were incorporated to ensure the inclusion of research relevant to the target regions.
These keywords were combined using appropriate Boolean operators (AND/OR) to construct advanced search queries for each database (see Appendix A for the full search strings used in Scopus and WoS). We applied some refinements to the search results to ensure relevance and quality. Specifically, we limited results to English-language documents and to peer-reviewed research articles and book chapters. We excluded conference papers, review articles, and other non-empirical literature during the search to keep the focus on original research with empirical or theoretical contributions (this refinement was applied via database filters and manual screening of document types).
The initial search yielded a total of 350 articles. Given the use of broad terms and flexible connectors in the search query across both WoS (222 records) and Scopus (128 records) databases (see Appendix A), this number is relatively small compared to other reviews focused on developed markets, which have reported results as high as 2231 records [8]. This disparity highlights a significant gap in the academic literature addressing PV household adoption in developing countries. It underscores the pressing need for further research that considers the unique constraints and contextual factors shaping household-level adoption of photovoltaic systems in these developing regions.
This review focused on solar photovoltaic (PV) systems, including those with battery energy storage (PV-BESS), as these are the most studied technologies in academic literature on household adoption. PV-BESS systems are particularly relevant for enhancing energy reliability and independence, especially in regions with limited or unstable grid access. By prioritizing these technologies, the review addresses key adoption factors and challenges faced by households in developing countries, where affordability, infrastructure, and (unstable) policy support is critical. Figure 1 illustrates the study selection process in a PRISMA 2020 flow diagram, from the initial identification of records to the final set of included studies. In summary, out of 350 records identified, 265 unique references were screened, 221 were excluded for not meeting criteria, and 44 studies were ultimately included in this systematic review.

2.2. Eligibility Criteria

Before screening the search results, we defined clear inclusion and exclusion criteria for eligible studies, in line with PRISMA recommendations:
  • Inclusion criteria: Studies had to (1) focus on household adoption of solar PV (including PV with battery storage) in the context of developing countries; (2) be published in 2010–2024; (3) be original research (empirical or theoretical) reported in peer-reviewed journals; and (4) be written in English. Both qualitative and quantitative studies were eligible, including case studies, surveys, experiments, and modeling papers, as long as they examined factors or behaviors related to residential PV adoption in a developing economic context.
  • Exclusion criteria: We excluded (a) review articles, meta-analyses, or purely conceptual papers lacking new data or analysis (since our aim was to synthesize primary research); (b) conference papers, theses, book chapters, and reports that were not peer-reviewed journal; (c) studies not specifically about household-level PV adoption (e.g., those focusing on other scales or purely technical analyses without a consumer adoption aspect); and (d) studies from developed country settings (unless part of a cross-country comparison including developing country data).
Studies meeting the inclusion criteria were considered for the review, while those falling under any exclusion criteria were set aside. The screening and review process aimed to ensure that the selected studies were relevant, high-quality, and aligned with the objectives of this review. Following the PRISMA protocol, the initial dataset of 350 records was refined through systematic steps, including the removal of duplicates, title and abstract screening, and full-text evaluation. These efforts resulted in a final selection of 44 articles that provide a comprehensive foundation for analyzing consumer behavior and photovoltaic adoption in developing countries.

2.3. Data Extraction and Synthesis

For each of the 44 studies included, we carried out systematic data extraction to capture key information. We developed a data extraction form to record the following details from each study: bibliographic information (author(s), year), study context (country or region, and setting—urban/rural, if applicable), study design and methods (e.g., survey, interview, case study, modeling), any theoretical framework or models applied (e.g., TPB, TAM, diffusion theory), and the main findings and conclusions relevant to PV adoption. One reviewer extracted the data, and the second reviewer cross-checked each entry to ensure accuracy and completeness. Any discrepancies or additional insights were resolved by referring to the full text.
Given the heterogeneity of study designs and outcomes and the primarily qualitative nature of the findings, we did not perform a meta-analysis. Instead, we conducted a qualitative synthesis (narrative synthesis). We grouped the studies by common themes in their theoretical approaches and findings. Specifically, we first synthesize which behavioral theories have been used across studies (Section 3) and then identify recurring factors influencing adoption (Section 4). Within these groupings, we compare and contrast findings and insights across different contexts. Tables and figures are used to tabulate important information (for example, Appendix B Table A3 summarizes characteristics of the reviewed studies), and a narrative approach is used to discuss patterns, discrepancies, and insights. This approach allows us to integrate results from quantitative surveys and qualitative case studies in a coherent way, highlighting overarching trends without statistically pooling data.
It is worth noting that no formal risk of bias assessment tool was applied to the included studies. We decided a priori not to perform a standardized risk of bias appraisal because the set of included studies is methodologically diverse (ranging from qualitative case studies to surveys and simulations), and there is no single standardized tool that would meaningfully assess bias across such varied study designs. Instead, we paid attention to each study’s limitations as reported by the authors (for example, sampling limitations or potential biases) and have noted those in our synthesis where relevant. By not excluding studies based on quality scores, we aimed to be inclusive given the still-nascent literature on this topic; however, we acknowledge that this may introduce some uncertainty. We address this in the Discussion by considering the strength of evidence when drawing conclusions.
While traditional bias assessment quantitatively evaluates methodological quality, keyword co-occurrence maps offer a complementary qualitative perspective, highlighting areas heavily explored and others underrepresented, thereby indirectly revealing potential biases or gaps in research emphasis. The keyword co-occurrence network map here (Figure 2) is helpful in illustrating the core themes as well as the interplay aspects of the literature on adoption of PV technologies in the developing economic context. At the core of the network, the two words developing countries and solar energy have been found to be the most active nodes as they are key building blocks of literature. They serve as explanation of why the topics under description perform exceptionally well in relation to other keywords as they are well connected to them. This explains why the interest in netting aspects of energy transition and renewable energy adoption in underdeveloped and emerging economies have remained topical.
The graph is divided into several distinct clusters, which are color coded to signify various areas of the research. The red cluster concentrates on the diffusion and innovation of PV technologies and includes some words such as technology diffusion, sustainability and economics. This cluster reinforces the nexus between economic and policy environments on the acceptance of PV and the role of innovation in transferring technologies to resource constrained areas. The green cluster, on the other hand, focuses on broad energy terms such as renewable energy, energy policy and rural electrification. This cluster emphasizes the importance of political factors and policy discussions on the enhancement of the supply of energy in rural areas, which are often considered the advanced be beneficiaries of decentralized solar systems. Although several behavioral models were analyzed, no study explicitly incorporated Transaction Cost Theory, which may be relevant for understanding non-financial barriers such as administrative burdens, complexity of installation, or procedural opacity.

3. Most Used Theoretical Frameworks to Understand Consumer Behavior

Theoretical models used to explain photovoltaic (PV) technology adoption in developing countries. The economic, social, and infrastructural contexts of these regions diverge significantly from those of developed markets, which means their stages of adoption are different. Such leveraging of these frameworks can assist researchers and policymakers in better navigating challenges and opportunities associated with promoting PV adoption in resource-constrained settings.

3.1. Behavioral Reasoning Theory (BRT)

Only four records applied BRT as a means to understand consumer behavior. BRT provides useful concepts for understanding the cognitive mechanisms behind the choice to adopt or resist solar PV technology. According to Ulsrud et al., 2011, at the heart of BRT is assessing reasons for and against a behavior: what they are learning about motivations and barriers to PV uptake [9,10]. These challenges are compounded in developing countries, where energy decisions for households are driven by a tight budget, balancing the potential savings from decreased reliance on traditional energy with the large initial investment required for solar.
This is also because BRT understands that the perceived risk and benefit of this can go beyond just whatever is on the balance sheet. Households, for example, may take into account the reliability, maintenance requirements and durability of solar systems and the social implications of adopting a new technology [10,11]. This allows for targeted interventions to address specific concerns within the community, based on these nuanced perceptions. So, for instance, if unreliability is a common barrier, awareness campaigns might take advantage of warranties, long-term performance, and after-sales services related to solar PV systems [9]. BRT thus offers a strong platform upon which to build community-specific strategies that account for unique local priorities and constraints.

3.2. Technology Acceptance Model (TAM)

According to the TAM, perceived usefulness and perceived ease of use were the two driving forces for technology adoption [12,13]. In a developing country context, such a framework is especially relevant where knowledge gaps or access to information prevents solar uptake. Educational programs, community outreach, and simplified marketing materials can help consumers better understand the benefits of solar PV and improve its perceived usefulness to them [14,15].
Even though TAM was not the most used theoretical framework to explain household adoption of PV in developing markets, it was used by five of the reviewed records. Given that the TAM emphasizes that you need to simplify technology and processes to reduce perceived barriers, it can be understood the motives to not be the most common theoretical framework used for these studies [11,16]. Consequently, this framework was used in seven of the reviewed records.
As a relevant feature for developing countries, this framework might include factor as universalizing solar PV systems to lower the complexity of implementation and maintenance, thus potentially increasing accessibility for households with little technical knowledge [17]. An important factor that makes TAM hard to apply is its limitation when accounting for security concerns. In developing countries, building confidence in the solar technology is paramount, as our perceptions regarding its ease of use and usefulness positively correlated to overcoming high skepticism [14,15]. Hence, it takes a lot of the time for targeted interventions, like showing testimonials from current users, or showing hands-on demonstrations, to build enough trust to convert to adoption.

3.3. Theory of Planned Behavior (TPB)

TPB is widely used to understand consumer behavior and particularly PV household adoption in developing countries, and it was present in eleven reviewed records. This framework proposes that a person’s behavioral intentions are determined by attitudes, subjective norms, and perceived behavioral control [18,19]. Subjective norms are particularly critical in shaping PV adoption in developing countries, where community ties and social influences may be stronger than they are in more developed markets [20,21,22]. For example, solar energy adoption may be more common among households who observe widespread acceptance of the technology within their social network or community [23,24].
Another key determinant under explanation of TPB is perceived behavioral control, which is particularly relevant in regions where infrastructural barriers to financing, installation, and technical support are widespread. By overcoming these barriers through accessible micro-financing schemes, government-sponsored subsidies and community-based installation programs, consumers’ sense of control can be increased, thus facilitating their intention toward solar PV systems adoption [25,26]. Moreover, TPB extended models with environmental awareness and personal norm components are especially important in developing countries, where the impacts of climate change and environmental degradation are often felt more acutely [27]. If solar adoption is connected to pro-environmental values that frame choice—like sustainability and long-term energy security—then interventions could amplify consumer intentions [23,28].

3.4. Diffusion of Innovations (DOI) Theory

The DOI Theory was present in the records reviewed twelve times and provides a useful framework for exploring the diffusion of solar PV technology through populations, as summarizes Table 2. In developing countries, where this diffusion can be hindered by several economic and infrastructural barriers, DOI makes the case for paid attention to critical factors that influence adoption [12,29]. Benefits of solar PV (cost advantages, better reliability, less indoor air pollution) relative to the status quo need to be communicated to prospective adopters [30]. Vividly showing these benefits, especially via early adopters, can help speed diffusion by spreading uptake through larger parts of the community [31].
The last almost important order factor under DOI is compatibility; if solar PV can be compatible with local cultural values, economic activities and architectural styles, its acceptability will increase [32,33,34,35]. It increases the adoption of solar systems that are able to blend smoothly with housing designs or rural styles of agricultural practices for example [7,36]. On the other hand, demonstrating the advantages of solar PV by sharing successful projects, positive feedback from customers, or media releases can increase interest and lead to expanded use in this end-use site segment as well as others [37,38]. Third, there is also the perceived complexity there, of which, making things easy and relevant to install and giving instructions with backed energy support, which will help overcome those barriers to adoption as well [39,40].
Finally, in addition to the commonly used frameworks such as TPB, TAM, and DOI, other theoretical models have been explored to understand solar PV adoption. As an example, absent in Table 2, the DPSIR (Drivers, Pressures, State, Impact, and Response) framework, while traditionally applied to environmental sustainability topics such as water resource management, has potential relevance to energy adoption studies [41]. Although its direct application to solar PV adoption remains underdeveloped, DPSIR provides a systematic approach for linking socio-economic drivers and environmental impacts to policy responses, which could be adapted to the context of renewable energy transitions in developing countries [42]. Notably, none of the studies integrated Transaction Cost Theory, despite its relevance for capturing institutional inefficiencies and hidden costs associated with decentralized energy technologies.

4. Most Used Factors to Understand Consumer Behavior

The literature finds a variety of characteristics impacting the acceptance of PV systems, including economic, social, demographic, geographic, and technological factors. Although several of these factors are globally a priority, their relevance in developing nations typically differs from what is witnessed in advanced markets, due to peculiar socioeconomic, infrastructural and policy circumstances. These nuances are essential to understand when crafting strategies to support solar penetration in resource-constrained contexts.

4.1. Economic Factors

Economic factors are particularly stark in developing nations, where high upfront costs are a major barrier to solar adoption. Low-income families, who make up a large part of residents in these areas, typically do not have the financial means to afford the upfront costs of installation, maintenance and even the purchase of solar panels themselves [13,14,17,43]. However, while developed markets benefit from better access to financing vehicles and subsidies, financial barriers are heightened in developing economies due to the limited availability of more generous subsidies and credit facilities [7,11,16,33]. The introduction of such mechanisms alone might have a massive impact on adoption rates, especially among middle-income households.
Electricity prices also have a unique impact on these settings. Earnings from electricity sales in many developing countries are subsidized, limiting the near-term financial incentive to adopt solar energy [17,33]. In areas with poor access to the grid and frequent outages, however, households may believe some solar energy will give them energy security rather than save them money [39]. By contrast, in developed markets, rising electricity prices are often a major driver of solar uptake [10]. The investment payback would be more pronounced with higher marginal benefits for developing countries with lower income households that can be decisive in whether their household commit to solar technologies based on a much more flexible and long-term payback [5,44].

4.2. Social and Demographic Factors

In the context of developing countries, social and demographic factors also behave differently. In such environments, peer effects and social influence are prevalent, as the importance of community and social groups is greater than for individualistic, developed markets [14,16]. Seeing their neighbors or local households installing solar panels rather than in commercial buildings located miles away is a more powerful signal of the trustworthiness and feasibility of technology and can stimulate replication in a positive feedback loop [19,27].
Education levels are another key element. In developing nations, the impact of lower overall education rates can contribute to a lack of understanding regarding the advantages and technical specifications of solar energy, thus necessitating greater efforts in the context of both awareness campaigns as well as education programs in the promotion of mass adoption [7,13,27,33]. Increased educational levels would also facilitate embracing change toward innovative technologies whose benefits seem to outperform related costs [21,43].
Age-related trends in solar adoption also exhibit differences in developing regions. Younger households, despite their proclivity towards awareness of climate change, often face acute affordability constraints that hinder their ability to adopt solar technology [16,22,35,45]. In contrast, older households, particularly those in rural areas, are more likely to prioritize the practical benefits of solar energy [23,32,45]. These benefits may include its use for productive activities such as agriculture or small-scale businesses, making solar adoption a more immediate and tangible solution for meeting energy needs in these contexts.

4.3. Location and Environmental Contexts

Geographic and environmental factors are often given greater consideration in developing countries than in the developed markets. An important factor to consider is that access to grid electricity varies, importantly across urban and rural areas [7,16,17,32,43]. In cities with reasonably reliable access to the grid, solar energy production often involves using solar energy to supplement an existing supply or to gain greater independence from the grid. Rural households, on the other hand, often adopt solar power out of necessity due to lack of available grid connections and dependence on traditional fuels [33,46,47]. This highlights a key difference with developed markets, where solar adoption is more generally driven by environmental motivation or other long-term price considerations.
Naturally, the potential for solar radiation is a key determinant of adoption, especially where sunshine is abundant [10,26]. In developing nations, however, adoption is often limited by infrastructural constrictions, including the availability of installation services and access to inexpensive technologies independent of solar radiation [48]. Urban proximity is another difference; households in or near urban centers have better access to information, financial incentives and technical support, whereas rural households tend to face logistical and informational obstacles [45].

4.4. Technological and Policy Factors

Technological and policy factors are fundamental determinants of solar adoption in developing countries. In these areas, where maintenance- and repair-related issues can be a deterrent to adoption, the efficiency, reliability, and durability of solar panels is especially important [7,14,25]. This is especially important in regions with little technical human and institutional capacity, where technological complexity is a major barrier [33].
Policies and incentives implemented by government are critical as well, but they often fall short of the availability and implementation seen in developed markets. While supportive measures—including subsidies, tax credits, and net metering—can substantially reduce financial obstacles, mismatched, unsteady political priorities and missing regulatory frameworks often hold back uptake in developing economies [16,17,27,43]. Moreover, trust and confidence in both developers and suppliers is particularly important, since there is often little familiarity with solar technology providers, and unless there is confidence, households are unlikely to invest [19,42,46]. For cultivating trust and enabling widespread adoption, transparent communication, assured after-sales service, regulatory framework, steady political environment and, to some extent, community engagement are key.
These findings have significant implications for policy and regulatory frameworks. For instance, the documented importance of trust in providers and the availability of after-sales support highlights the need for national regulations that enforce service quality standards and consumer protection mechanisms (Alrashoud & Tokimatsu, 2019) [27]. Governments and regulatory agencies could establish technical certification schemes for PV installers, minimum warranty periods, and independent grievance redress systems to foster trust and ensure technological reliability (Wang et al., 2022) [19]. Moreover, the identification of maintenance and reliability issues as key adoption barriers suggests that public programs should not only focus on upfront subsidies, but also on sustained technical support and training (Ahmed et al., 2022) [16].

5. Discussion and Conclusions

The results of this review show the variation in theoretical framework, factor and methodology applied in the studies of household PV in developing countries. Consumer decision-making theories like the TPB, TAM and DOI framework have been widely applied to explore consumer decision-making processes. Their use in developing contexts comes with limitations though. Analysis fails to consider the specific socio-economic, infrastructural, and policy environments that impact consumer habits in these regions, and often this leads to the application of models developed for high-income nations with insufficient context. Research in this field should therefore pursue the endeavor of refining these constructs or come up with new models to capture the realities related to energy access and affordability constraints in developing countries.
A key gap in the literature noted is the lack of integration between behavioral economics and psychological insights with respect to the study of PV adoption. Although economic factors like cost, subsidies and the return on investment are sometimes widely cited, less research focuses on consumer decision processes in relation to cognitive biases, risk perceptions and decision heuristics. These are psychological concepts whose understanding would offer insights on how households trade long-term energy security against short-term financial pressure. Furthermore, although some studies consider elements like social norms and community influence, their role in helping to explain large-scale adoption trends remains under-explored.
Another major gap relates to the geographic imbalance of studies. Investigation of PV adoption in developing nations is highly concentrated within a few regions. Mostly, studies revealed a regional imbalance, with more than 60% focusing on Asia, while Latin America and the Middle East were underrepresented. This regional bias restricts the applicability of results and highlights the need for more geographically diverse studies that consider differences in policy frameworks, social attitudes, and economic conditions. A more balanced regional representation in future empirical research would enhance the development of culturally and institutionally relevant PV adoption strategies.
These gaps need to be bridged for exploring PV adoption in developing countries, and this review calls for a more focused approach in the quest to understand PV adoption in the developing world, with importance for advancing the field in developing countries. Such gaps need to be mitigated in the process of developing appropriate policies, interventions, and business models that promote the adoption of PV technology in developing countries.
Despite efforts to be comprehensive, this systematic review has several limitations. First, our search was limited to two databases (Scopus and WoS) and to English-language publications. It is possible that we missed relevant studies indexed in other databases or reported in other languages. Second, as noted in the Methods, we did not perform a formal risk of bias assessment of each included study due to the heterogeneity of study designs. This means we included studies of varying quality and must rely on the original authors’ reporting; the conclusions drawn are subject to the assumption that each study’s findings are sound. Third, the synthesis was qualitative and thematic; since the data were not amenable to meta-analysis, our integration of results may be influenced by subjective interpretation. We mitigated this by having two reviewers cross-check interpretations, but some degree of reviewer bias in narrative synthesis is possible. Finally, many included studies had their own limitations (often small sample sizes, regional focus, or short observation periods). For example, as discussed, the evidence skews toward certain regions and may not capture the full diversity of developing countries. These factors should be kept in mind when applying the findings: the recommendations highlight general patterns, but specific interventions should be tailored to local contexts.
Nonetheless, within the current body of literature, some clear insights emerge. Financial barriers—especially high upfront installation costs and limited access to credit—are consistently cited as the primary obstacle to household PV adoption in developing countries (appearing in the majority of studies). Information and awareness gaps also loom large; where knowledge about solar benefits and technical know-how is lacking, adoption lags. On the positive side, peer effects and community success stories can strongly encourage adoption, suggesting that pilot projects and demonstration homes can have a multiplier effect if well publicized. Policy support (subsidies, feed-in tariffs, or net metering) has been shown to be a game-changer in several contexts, but policies need to be stable and effectively communicated to build trust. Importantly, the review underscores that models and frameworks must be context-sensitive, for instance, TAM and TPB can be useful, but they may require additional factors (like trust in providers, or perceived risk of technology failure) to fully explain behavior in low-income, infrastructure-poor settings. However, most of these models were originally developed in high-income contexts, and their uncritical application to developing countries may fail to capture the complexities associated with energy poverty, informal economies, and fragile institutions.
Overall, these gaps and patterns call for a more nuanced approach in both research and practice. Future research should aim to bridge the identified gaps. This includes integrating behavioral economics perspectives (to understand decision-making under poverty and uncertainty), exploring under-studied regions, and examining long-term adoption dynamics (not just intention or early adoption, but sustained use and expansion of PV systems). For practitioners and policymakers, the findings imply that successful promotion of household solar PV systems in developing countries must tackle financial barriers (through subsidies or innovative financing like pay-as-you-go schemes), invest in community engagement and education, and ensure after-sales support to build confidence in the technology. To address these challenges, future theoretical work should prioritize the development of flexible, adaptive models grounded in the realities of developing regions. This includes incorporating multidimensional poverty, informal housing structures, and non-linear adoption dynamics.
Such efforts to address the gaps in knowledge and practice are crucial for advancing the field. By developing appropriate policies, business models, and community interventions that are tailored to local contexts, stakeholders can promote the adoption of PV technology in the developing world, helping to achieve broader sustainability and energy access goals. In conclusion, while significant progress has been made in understanding household PV adoption in emerging economies, there remains much to learn. This systematic review provides a foundation and direction for future work aimed at decoding and accelerating solar adoption where it is needed most.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su17125494/s1, PRISMA Checklist [49]. This study was conducted in accordance with the PRISMA 2020 reporting standards. A completed PRISMA 2020 checklist detailing the methodological transparency and structure of the review is provided as Supplementary Material.

Author Contributions

Conceptualization, R.A.S. and E.J.D.O.; methodology, R.A.S. and E.J.D.O.; software, R.A.S. and E.J.D.O.; validation, R.A.S. and E.J.D.O.; formal analysis, R.A.S. and E.J.D.O.; investigation, R.A.S. and E.J.D.O.; resources, R.A.S. and E.J.D.O.; data curation, R.A.S. and E.J.D.O.; writing—original draft preparation, R.A.S. and E.J.D.O.; writing—review and editing, R.A.S. and E.J.D.O.; visualization, R.A.S. and E.J.D.O.; supervision, R.A.S. and E.J.D.O.; project administration, R.A.S. and E.J.D.O.; funding acquisition, R.A.S. and E.J.D.O. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

All data supporting the findings of this study are drawn from the published articles listed in the References. No new data were created or analyzed in this study.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Advance query used in Scopus database.
Table A1. Advance query used in Scopus database.
Database employed:Scopus
Search results (n):222
Search purpose: To find articles for the literature review on PV adoption in households of developing economies.
Search query used:
(TITLE-ABS-KEY (“solar energy” OR “solar panels” OR “photovoltaic systems” OR “residential solar PV”)) AND (TITLE-ABS-KEY (“adoption” OR “implementation” OR “diffusion”)) AND (TITLE-ABS-KEY (“developing countries” OR “low and middle-income countries” OR “emerging economies”)) AND (LIMIT-TO (LANGUAGE, “English”)) AND (LIMIT-TO (DOCTYPE, “ar”) OR LIMIT-TO (DOCTYPE, “ch”))
Note: (included) English, article, book chapter; (excluded) review, conference paper, data paper; from 2010 to 2024.
Table A2. Advance query used in Web of Science database.
Table A2. Advance query used in Web of Science database.
Database employed:WoS
Search results (n):128
Search purpose: To find articles for the literature review on PV adoption in households of developing economies.
Search query used:
TS = (“solar energy” OR “solar panels” OR “photovoltaic systems” OR “residential solar PV”)
AND TS = (“adoption” OR “implementation” OR “diffusion”)
AND TS = (“developing countries” OR “low and middle-income countries” OR “emerging economies”)
Note: (included) English, article, book chapter; (excluded) review, conference paper, data paper; from 2010 to 2024. This refinement does not appear in the formula; it was applied manually.

Appendix B

Table A3. Summary of the reviewed studies on household solar PV adoption in developing countries (2010–2024).
Table A3. Summary of the reviewed studies on household solar PV adoption in developing countries (2010–2024).
Study (Author, Year)Country/ContextStudy DesignTheory/FrameworkMain Findings
Adkins et al. (2010) [48]MalawiField pilot case study (off-grid lighting)None (observational)Introduced LED solar lighting in off-grid communities; found improved lighting services but adoption limited by cost and infrastructure constraints.
Ahmar et al. (2022) [7]PakistanSurvey (quantitative analysis)None (determinants analysis)Identified socio-economic factors (income, education, awareness) significantly influencing adoption and choice of solar PV systems in rural households.
Ahmed et al. (2022) [16]Somalia and PakistanSurvey (PLS-SEM model)Integrated TAM and DOIPerceived usefulness, ease of use, compatibility, and observability significantly predicted attitudes and intention; similar determinants in both countries, with trust considered but found less influential than other factors.
Almulhim (2022) [28]Saudi ArabiaQuestionnaire surveyNone (descriptive)Low to moderate public awareness of solar energy; highlighted need for education and outreach to improve attitudes towards renewable energy adoption.
Alrashoud and Tokimatsu (2019) [27]Saudi ArabiaSurvey (statistical analysis)None (factors analysis)Positive public attitude toward solar PV, but concerns about costs and reliability persist; social acceptance and supportive policies were found to influence willingness to adopt.
Alwedyan (2021) [46]JordanSurvey (SEM)TPB/extended TAMAttitudes, perceived benefits, and environmental concern significantly affected household intention to adopt PV; noted importance of awareness and financial incentives in increasing adoption intent.
Arroyo and Carrete (2019) [18]MexicoSurvey (quantitative)None (motivation analysis)Environmental concern and expected economic savings were key motivational drivers for purchasing home PV systems, indicating that both ecological and financial motives play an important role.
Bekti et al. (2022) [11]IndonesiaSurvey (SEM)TAM (Technology Acceptance Model)Perceived ease of use and usefulness of rooftop PV—along with financial incentives and environmental awareness—significantly increased customer intention to adopt solar panels.
Bernal-del Río et al. (2025) [30]Uruguay (assumed)GIS spatial analysisNone (planning model)Incorporated social factors into sitting analysis for solar projects; demonstrated that including community acceptance criteria changes optimal locations, emphasizing the need for community engagement in planning renewable projects.
Burgos Espinoza et al. (2024) [31]Peru (assumed)Survey (correlation analysis)None (behavioral intention)Found perceived cost savings and environmental concern positively influence intention to adopt renewable energy; higher perceived upfront costs were associated with lower likelihood of adoption.
Chekol et al. (2023) [32]EthiopiaHousehold survey (probit regression)None (determinants analysis)Higher income, education, and grid unreliability increased adoption of solar technologies; identified affordability and lack of awareness as major barriers in rural household decisions.
Ding et al. (2021) [23]China (rural)Household survey (existing PV users)None (trust analysis)Households satisfied with their solar PV systems exhibited greater trust in the power grid; indicates successful PV experiences can improve public confidence in electricity services and the grid.
do Nascimento et al. (2020) [33]BrazilIndustry survey (PV operators)None (factor ranking)Installers/operators reported that maintenance support, local technical capacity, and upfront cost recovery are critical factors for PV adoption; emphasized that robust after-sales service is key to diffusion in emerging markets.
Feng et al. (2022) [44]Cross-country (developing economies)Econometric analysisNone (poverty impact)Expanded solar panel adoption was associated with poverty alleviation effects (e.g., reduced energy expenditures, income generation), but noted that supportive policies are required to realize these benefits among low-income households.
Garlet et al. (2019) [25]Brazil (southern)Mixed-method (survey and interviews)Diffusion of Innovations (implicit)Identified major barriers—lack of information, financing difficulties, and policy/regulatory gaps—hindering the diffusion of distributed solar in southern Brazil; early adopters were typically higher-income, educated households.
Guta (2018) [45]EthiopiaHousehold survey (logit model)None (adoption determinants)Found that household income, education level, and fuel expenditures significantly affect solar adoption; high upfront costs and limited credit access were primary obstacles in rural areas.
Holm-Nielsen et al. (2022) [9]UgandaCase study (project evaluation)None (socio-economic analysis)Implementing small-scale, affordable solar technologies improved household energy access; underscored the importance of aligning technology with local socio-economic conditions for successful adoption.
Jamil and Islam (2023) [39]PakistanHousehold survey (backup power choices)None (backup adoption)Frequent power outages drove many households to adopt solar as a backup power source; adoption decisions were influenced by outage frequency/duration, the costs of alternatives (e.g., generators), and perceived reliability of solar solutions.
Jayaweera et al. (2018) [17]Sri LankaSpatial diffusion analysis (GIS)Diffusion of InnovationsObserved spatial clustering of residential PV adoption, higher adoption in areas with unreliable grid supply and high solar potential. Local peer effects and community examples contributed to diffusion patterns in rural regions.
Kapoor and Dwivedi (2020) [12]IndiaSurvey (consumer behavior)None (sustainable consumption)Consumer environmental awareness and perceived economic benefits were key antecedents of willingness to adopt solar innovations; highlighted the role of pro-sustainability attitudes in driving solar adoption.
Karimzadeh and Kašparová (2021) [13]Iran (rural)Household survey (acceptance study)None (acceptance study)Knowledge about solar energy and perceived benefits (e.g., cost savings, reliability) strongly influenced rural residents’ acceptance of solar panels; traditional norms and mistrust in new technology were minor hurdles in the communities studied.
Kebede et al. (2014) [34]Multiple developing countriesCase studies (conference paper)None (innovation diffusion)Local presence of providers and robust after-sales service were identified as pivotal for successful diffusion of solar innovations in developing countries; lack of service infrastructure was linked to adoption failures.
Komatsu et al. (2011a) [20]BangladeshHousehold survey (impact evaluation)None (sustainable development)Widespread Solar Home System (SHS) adoption yielded notable improvements in lighting, education, and indoor air quality; demonstrated that even “micro-benefits” of small solar systems contribute substantially to sustainable rural development.
Komatsu et al. (2011b) [21]BangladeshHousehold survey (purchase decisions)None (adoption factors)Non-income factors (education, awareness, desire for modern appliances) significantly influenced SHS purchase decisions alongside income; socio-cultural drivers complemented economic considerations in shaping adoption.
Konzen et al. (2025) [37]Australia and BrazilComparative data analysisNone (inequality analysis)Revealed stark disparities in PV adoption between and within countries; in Brazil, adoption is concentrated among higher-income groups, whereas uptake in Australia is more widespread. Highlights the impact of income inequality and policy support on adoption rates.
Kyere et al. (2024) [14]GhanaHousehold survey and interviewsNone (energy transition barriers)High upfront costs, maintenance challenges, and limited trust in solar technology were key reasons for household resistance to adoption. Conversely, expected long-term savings and peer influence motivated adopters. Emphasizes the need to address cultural and informational barriers.
L’Her et al. (2023) [6]Global (energy access focus)Modeling study (technical potential)None (energy access model)Demonstrated that deploying solar PV with battery storage could significantly reduce electricity access gaps in off-grid regions. Provides quantitative evidence that renewable solutions are viable for addressing energy poverty in developing areas.
Li et al. (2023) [22]China (Sichuan province)Field study (village surveys)Social network theoryHouseholds were more likely to install PV if friends or neighbors had already adopted (strong peer effects). Social network influence, mediated by perceived reliability and usefulness of the technology, significantly boosted adoption rates in rural communities.
Lin and Kaewkhunok (2021) [43]ThailandSurvey (marginalized communities)None (socio-cultural factors)Government solar programs failed to reach certain marginalized groups due to socio-cultural barriers. Lack of trust in providers, lower awareness, and cultural isolation led to lower adoption among marginalized communities despite the availability of subsidy programs.
Mahn et al. (2024) [47]Multi-country (various developing nations)Cross-country household data analysisNone (econometric)Found that higher household income, education, and urban residence correlate with greater solar adoption across countries. However, strong policy incentives (subsidies, loan programs) were associated with higher adoption even among lower-income households, mitigating some economic barriers.
Mishrif and Khan (2024) [29]OmanCase study and surveyNone (barrier assessment)Despite high solar potential, household adoption remains low in Oman. Identified low awareness, high upfront costs, and absence of consumer financing options as primary barriers. Recommended improving public knowledge and offering subsidies/loans to increase readiness for solar adoption.
Nabaweesi et al. (2024) [35]UgandaSurvey (willingness to adopt)None (contingent valuation)A significant proportion of households expressed willingness to adopt solar (for home businesses) if affordable financing were available. Major barriers were the initial cost and limited information on the benefits of solar solutions, indicating the need for micro-credit and awareness programs.
Pandey and Kesari (2018) [24]IndiaSurvey (rural consumers)None (behavior shift)Detected a gradual shift toward ecological motivation among rural consumers purchasing solar equipment. While cost savings remained a primary driver, environmental concern and the desire for energy independence emerged as significant factors influencing purchase behavior.
Sarkar et al. (2024) [38]IndiaAnalytical modeling (FERA framework)FERA (integrated model)Using an integrated model of Financial, Environmental, and Risk Assessment factors (FERA), the study showed that economic viability, environmental benefits, and risk perceptions collectively determine sustainable energy adoption. A holistic multi-factor approach provided better prediction of adoption decisions than single-factor models.
Shahzad et al. (2023) [26]PakistanMulti-criteria decision analysis (fuzzy AHP)None (fuzzy AHP)Ranked obstacles to solar adoption: the most critical barriers were inconsistent policy support, lack of financing mechanisms, high upfront costs, and inadequate awareness/training. These findings suggest that improving policy stability, financial access, and public education should be priorities to boost solar uptake.
Sheng et al. (2024) [40]ChinaPolicy analysis (theoretical and empirical)None (policy coordination)Regional policy misalignment was found to hinder solar and low-carbon technology diffusion. Demonstrated that improved coordination between national and local policies significantly enhances the effectiveness of renewable energy adoption efforts, suggesting that policy cohesion is key to scaling household PV.
Smith and Urpelainen (2014) [36]TanzaniaHousehold survey (early adopters)None (adopter profile)Early adopters of solar panels were generally wealthier, more educated, and had greater prior exposure to modern energy solutions. This underscores that the initial diffusion of solar technology in off-grid communities was driven by those with more resources and knowledge, pointing to an equity gap in early adoption.
Ulsrud et al. (2011) [10]IndiaCase studies (solar mini-grids)None (socio-technical analysis)Community-managed solar mini-grid projects succeeded when they aligned with local social structures and had strong institutional support. Iterative learning and adaptation in each village led to improved socio-technical fit and greater community acceptance of the new technology.
Wang et al. (2022) [19]ChinaSurvey (structural equation model)TAM with Perceived Risk (PR)Social networks indirectly increased villagers’ willingness to adopt rooftop PV by reducing perceived risk and enhancing perceived usefulness through peer communication. Also, greater ease-of-use and trust in solar providers (as per TAM) positively influenced adoption willingness.
Waris et al. (2023) [15]PakistanSurvey (extended TPB model)Theory of Planned Behavior (extended)Attitude, subjective norms, and perceived behavioral control significantly predicted households’ intentions to go solar. Incorporating environmental concern into the TPB model improved its explanatory power, suggesting that pro-environmental values can bolster the intent to adopt solar energy.

References

  1. Caldarola, B.; Mazzilli, D.; Napolitano, L.; Patelli, A.; Sbardella, A. Economic complexity and the sustainability transition: A review of data, methods, and literature. arXiv 2024, arXiv:2308.07172. [Google Scholar] [CrossRef]
  2. Sourgens, F.G.; Sempertegui, L. What Is Energy Transition? In Principles of International Energy Transition Law; Sourgens, F.G., Sempertegui, L., Eds.; Oxford University Press: Oxford, UK, 2023. [Google Scholar] [CrossRef]
  3. Markard, J.; Rosenbloom, D. Phases of the net-zero energy transition and strategies to achieve it. arXiv 2023, arXiv:2311.09472. [Google Scholar] [CrossRef]
  4. Kavlak, G.; McNerney, J.; Trancik, J.E. Evaluating the causes of cost reduction in photovoltaic modules. Energy Policy 2018, 123, 700–710. [Google Scholar] [CrossRef]
  5. Lu, S.; Ren, J. A comprehensive review on energy poverty: Definition, measurement, socioeconomic impact and its alleviation for carbon neutrality. Environ. Dev. Sustain. 2023. [Google Scholar] [CrossRef]
  6. L’Her, G.F.; Osborne, A.G.; Schweikert, A.E.; Ramstein, C.S.; Stoll, B.L.; Deinert, M.R. Potential of Photovoltaics and Energy Storage to Address Lack of Electricity Access. arXiv 2023, arXiv:2310.11615. [Google Scholar] [CrossRef]
  7. Ahmar, M.; Ali, F.; Jiang, Y.; Wang, Y.; Iqbal, K. Determinants of Adoption and the Type of Solar PV Technology Adopted in Rural Pakistan. Front. Environ. Sci. 2022, 10, 895622. [Google Scholar] [CrossRef]
  8. Alipour, M.; Salim, H.; Stewart, R.A.; Sahin, O. Residential solar photovoltaic adoption behaviour: End-to-end review of theories, methods and approaches. Renew. Energy 2021, 170, 471–486. [Google Scholar] [CrossRef]
  9. Holm-Nielsen, J.B.; Mamur, A.P.O.; Masebinu, S. Socioeconomic analysis of renewable energy interventions: Developing affordable small-scale household sustainable technologies in Northern Uganda. In Intelligent Data Mining and Analysis in Power and Energy Systems: Models and Applications for Smarter Efficient Power Systems; Wiley Blackwell: Hoboken, NJ, USA, 2022. [Google Scholar] [CrossRef]
  10. Ulsrud, K.; Winther, T.; Palit, D.; Rohracher, H.; Sandgren, J. The Solar Transitions research on solar mini-grids in India: Learning from local cases of innovative socio-technical systems. Energy Sustain. Dev. 2011, 15, 293–303. [Google Scholar] [CrossRef]
  11. Bekti, D.B.M.; Prasetyo, Y.T.; Redi, A.A.N.P.; Budiman, A.S.; Mandala, I.M.P.L.; Putra, A.R.; Persada, S.F.; Nadlifatin, R.; Young, M.N. Determining factors affecting customer intention to use rooftop solar photovoltaics in Indonesia. Sustainability 2022, 14, 280. [Google Scholar] [CrossRef]
  12. Kapoor, K.K.; Dwivedi, Y.K. Sustainable consumption from the consumer’s perspective: Antecedents of solar innovation adoption. Resour. Conserv. Recycl. 2020, 152, 104501. [Google Scholar] [CrossRef]
  13. Karimzadeh, S.; Kašparová, E. Socio-Individual Prerequisites of Energy Transition in Iran: Investigating Public Acceptance of Solar Panel Technology in Rural Areas. J. Asian Energy Stud. 2021, 5, 42–57. [Google Scholar] [CrossRef]
  14. Kyere, F.; Dongying, S.; Bampoe, G.D.; Kumah, N.Y.G.; Asante, D. Decoding the shift: Assessing household energy transition and unravelling the reasons for resistance or adoption of solar photovoltaic. Technol. Forecast. Soc. Change 2024, 198, 123030. [Google Scholar] [CrossRef]
  15. Waris, I.; Hameed, I.; Ali, R. Predicting household sign up for solar energy: An empirical study based on the extended theory of planned behavior. Int. J. Energy Sect. Manag. 2023, 17, 455–473. [Google Scholar] [CrossRef]
  16. Ahmed, Y.A.; Rashid, A.; Khurshid, M.M. Investigating the Determinants of the Adoption of Solar Photovoltaic Systems—Citizen’s Perspectives of Two Developing Countries. Sustainability 2022, 14, 11764. [Google Scholar] [CrossRef]
  17. Jayaweera, N.; Jayasinghe, C.L.; Weerasinghe, S.N. Local factors affecting the spatial diffusion of residential photovoltaic adoption in Sri Lanka. Energy Policy 2018, 119, 59–67. [Google Scholar] [CrossRef]
  18. Arroyo, P.; Carrete, L. Motivational drivers for the adoption of green energy: The case of purchasing photovoltaic systems. Manag. Res. Rev. 2019, 42, 542–567. [Google Scholar] [CrossRef]
  19. Wang, W.; Guan, X.; Jin, S.; Wang, X.; Qin, X.; Lu, N.; Zhu, G.; Li, D. Social network and villagers’ willingness to adopt residential rooftop PV products: A multiple mediating model based on TAM/PR theory. Front. Environ. Sci. 2022, 10, 999006. [Google Scholar] [CrossRef]
  20. Komatsu, S.; Kaneko, S.; Ghosh, P.P. Are micro-benefits negligible? The implications of the rapid expansion of Solar Home Systems (SHS) in rural Bangladesh for sustainable development. Energy Policy 2011, 39, 4022–4031. [Google Scholar] [CrossRef]
  21. Komatsu, S.; Kaneko, S.; Shrestha, R.M.; Ghosh, P.P. Nonincome factors behind the purchase decisions of solar home systems in rural Bangladesh. Energy Sustain. Dev. 2011, 15, 284–292. [Google Scholar] [CrossRef]
  22. Li, Y.; Qing, C.; Guo, S.; Deng, X.; Song, J.; Xu, D. When my friends and relatives go solar, should I go solar too?—Evidence from rural Sichuan province, China. Renew. Energy 2023, 203, 753–762. [Google Scholar] [CrossRef]
  23. Ding, L.; Shi, Y.; He, C.; Dai, Q.; Zhang, Z.; Li, J.; Zhou, L. How does satisfaction of solar PV users enhance their trust in the power grid?—Evidence from PPAPs in rural China. Energy Sustain. Soc. 2021, 11, 31. [Google Scholar] [CrossRef] [PubMed]
  24. Pandey, S.; Kesari, B. Consumer purchase behaviour of solar equipments: Paradigm shift towards the ecological motivation among rural working consumers in developing countries. J. Adv. Res. Dyn. Control Syst. 2018, 10, 363–375. [Google Scholar]
  25. Garlet, T.B.; Ribeiro, J.L.D.; de Souza Savian, F.; Mairesse Siluk, J.C. Paths and barriers to the diffusion of distributed generation of photovoltaic energy in southern Brazil. Renew. Sustain. Energy Rev. 2019, 111, 157–169. [Google Scholar] [CrossRef]
  26. Shahzad, K.; Abdul, D.; Umar, M.; Safi, A.; Maqsood, S.; Baseer, A.; Lu, B. Analysis of obstacles to adoption of solar energy in emerging economies using spherical fuzzy AHP decision support system: A case of Pakistan. Energy Rep. 2023, 10, 381–395. [Google Scholar] [CrossRef]
  27. Alrashoud, K.; Tokimatsu, K. Factors Influencing Social Perception of Residential Solar Photovoltaic Systems in Saudi Arabia. Sustainability 2019, 11, 5259. [Google Scholar] [CrossRef]
  28. Almulhim, A.I. Understanding public awareness and attitudes toward renewable energy resources in Saudi Arabia. Renew. Energy 2022, 192, 572–582. [Google Scholar] [CrossRef]
  29. Mishrif, A.; Khan, A. Non-Industrial Solar Energy Use, Barriers, and Readiness: Case Study of Oman. Energies 2024, 17, 3917. [Google Scholar] [CrossRef]
  30. Bernal-del Río, S.; Luján, C.; Ferrer, S.; Mereu, R.; Osorio-Gómez, G. GIS-based approach including social considerations for identifying locations for solar and wind power plants. Energy Sustain. Dev. 2025, 84, 101602. [Google Scholar] [CrossRef]
  31. Burgos Espinoza, I.I.; García Alcaraz, J.L.; Gil López, A.J.; Aryanfar, Y.; Keçebaş, A. Achieving behavioral intention to renewable energy through perceived costs and benefits and environmental concern. Sustain. Futur. 2024, 8, 100319. [Google Scholar] [CrossRef]
  32. Chekol, F.; Giera, M.; Alemu, B.; Dessie, M.; Alemayehu, Y.; Ewuinetu, Y. Rural Households’ Behaviour towards Modern Energy Technology Adoption Choices in East Gojjam Zone of Ethiopia: A Multivariate Probit Regression Analysis. Cogent Eng. 2023, 10, 2178107. [Google Scholar] [CrossRef]
  33. do Nascimento, F.M.; Siluk, J.C.M.; de Souza Savian, F.; Garlet, T.B.; Pinheiro, J.R.; Ramos, C. Factors for measuring photovoltaic adoption from the perspective of operators. Sustainability 2020, 12, 3184. [Google Scholar] [CrossRef]
  34. Kebede, K.Y.; Mitsufuji, T.; Choi, E.K. After-sales service and local presence: Key factors for solar energy innovations diffusion in developing countries. In Proceedings of the PICMET 2014—Portland International Center for Management of Engineering and Technology, Proceedings: Infrastructure and Service Integration, Kanazawa, Japan, 27–31 July 2014; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2014; pp. 3124–3130. [Google Scholar]
  35. Nabaweesi, J.; Kabuye, F.; Adaramola, M.S. Households’ willingness to adopt solar energy for business use in Uganda. Int. J. Energy Sect. Manag. 2024, 18, 26–42. [Google Scholar] [CrossRef]
  36. Smith, M.G.; Urpelainen, J. Early Adopters of Solar Panels in Developing Countries: Evidence from Tanzania. Rev. Policy Res. 2014, 31, 17–37. [Google Scholar] [CrossRef]
  37. Konzen, G.; Best, R.; de Castro, N.J. Shining a light on disparities: A comparative analysis of residential photovoltaic adoption inequality in Australia and Brazil. Energy Res. Soc. Sci. 2025, 119, 103870. [Google Scholar] [CrossRef]
  38. Sarkar, B.D.; Gupta, L.; Shankar, R. Modeling Adoption of Sustainable Green Energy: An Integrated Approach Using FERA. IEEE Trans. Eng. Manag. 2024, 71, 5907–5920. [Google Scholar] [CrossRef]
  39. Jamil, F.; Islam, T.U. Outage-induced power backup choice in Pakistan. Util. Policy 2023, 82, 101589. [Google Scholar] [CrossRef]
  40. Sheng, S.; Li, Y.; Zhao, Z. How does regional policy coordination help achieve the low-carbon development?: A study of theoretical mechanisms and empirical analysis from China. Environ. Dev. Sustain. 2024. [Google Scholar] [CrossRef]
  41. Sun, S.; Wang, Y.; Liu, J.; Cai, H.; Wu, P.; Geng, Q.; Xu, L. Sustainability assessment of regional water resources under the DPSIR framework. J. Hydrol. 2016, 532, 140–148. [Google Scholar] [CrossRef]
  42. Fu, D.; Sun, J.; Yao, C.; Shi, F. The influence of policy incentives on the diffusion of battery-swapping taxis and stations: A coupled evolutionary game model in complex networks. Environ. Dev. Sustain. 2024, 26, 26945–26969. [Google Scholar] [CrossRef]
  43. Lin, B.; Kaewkhunok, S. The role of socio-Culture in the solar power adoption: The inability to reach government policies of marginalized groups. Renew. Sustain. Energy Rev. 2021, 144, 111035. [Google Scholar] [CrossRef]
  44. Feng, Z.; He, Q.-C.; Ma, G. Mitigating poverty through solar panels adoption in developing economies. Decis. Sci. 2022, 53, 1003–1023. [Google Scholar] [CrossRef]
  45. Guta, D.D. Determinants of household adoption of solar energy technology in rural Ethiopia. J. Clean. Prod. 2018, 204, 193–204. [Google Scholar] [CrossRef]
  46. Alwedyan, S. Factors Influencing the Adoption Intentions of Households Toward Solar Photovoltaic Technology in Jordan: A Structural Equation Modeling Analysis. Arab World Geogr. 2021, 24, 285–302. [Google Scholar] [CrossRef]
  47. Mahn, D.; Best, R.; Wang, C.; Abiona, O. What drives solar energy adoption in developing countries? Evidence from household surveys across countries. Energy Econ. 2024, 138, 107815. [Google Scholar] [CrossRef]
  48. Adkins, E.; Eapen, S.; Kaluwile, F.; Nair, G.; Modi, V. Off-grid energy services for the poor: Introducing LED lighting in the Millennium Villages Project in Malawi. Energy Policy 2010, 38, 1087–1097. [Google Scholar] [CrossRef]
  49. Page, M.J.; McKenzie, J.E.; Bossuyt, P.M.; Boutron, I.; Hoffmann, T.C.; Mulrow, C.D.; Shamseer, L.; Tetzlaff, J.M.; Akl, E.A.; Brennan, S.E.; et al. The PRISMA 2020 statement: An Updated Guideline for Reporting Systematic Reviews. BMJ 2021, 372, n71. [Google Scholar]
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Figure 1. PRISMA 2020 flow diagram illustrating the study selection process (records identified: 350 from databases (Scopus = 222, WoS = 128); duplicates removed: 85; records screened: 265; records excluded on title/abstract, not in English: 221, and not focused in developing countries; full-text articles assessed: 44; studies included in review: 44).
Figure 1. PRISMA 2020 flow diagram illustrating the study selection process (records identified: 350 from databases (Scopus = 222, WoS = 128); duplicates removed: 85; records screened: 265; records excluded on title/abstract, not in English: 221, and not focused in developing countries; full-text articles assessed: 44; studies included in review: 44).
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Figure 2. Keyword co-occurrence network. The keyword co-occurrence network highlight’s central themes like developing countries and solar energy, with clusters focusing on policy, technical aspects, rural electrification, and sustainability. Strong connections, such as between renewable energy and energy policy, emphasize governance’s role, while gaps in areas like technology diffusion suggest opportunities for further research.
Figure 2. Keyword co-occurrence network. The keyword co-occurrence network highlight’s central themes like developing countries and solar energy, with clusters focusing on policy, technical aspects, rural electrification, and sustainability. Strong connections, such as between renewable energy and energy policy, emphasize governance’s role, while gaps in areas like technology diffusion suggest opportunities for further research.
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Table 1. List of abbreviations and acronyms.
Table 1. List of abbreviations and acronyms.
BESSBattery energy storage systemPV-BESSSolar photovoltaic system coupled with battery energy storage system
BRTBehavioral Reasoning TheoryRERenewable energy
DOIDiffusion of InnovationsTAMTechnology Acceptance Model
LCOEGlobal weighted-average levelized cost of electricityTPBTheory of Planned Behavior
PRISMAPreferred Reporting Items for Systematic Reviews and Meta-AnalysesWoSWeb of Science
PVSolar photovoltaic system
Table 2. Synthesis of theoretical frameworks, used and main limitations in PV household adoption.
Table 2. Synthesis of theoretical frameworks, used and main limitations in PV household adoption.
Consumer Behavior TheoriesMain Factors AddressedApplication in PV AdoptionSome Limitations for Developing Countries
BRT (Behavioral Reasoning Theory)Motivations and barriersEvaluate reasons for and against adoptionDoes not always consider extreme economic factors
TAM (Technology Acceptance Model)Perceived usefulness, ease of useDetermine how easy and useful the technology isIt depends on access to information and education
TPB (Theory of Planned Behavior)Attitudes, subjective norms, perceived controlExploring community influence and perceived controlDifficult to measure social norms in diverse communities
DOI (Diffusion of Innovations)Innovators, early adoption, diffusionExplain how technology spreads in communitiesRequires longitudinal data that is difficult to obtain
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Oliva, E.J.D.; Atehortua Santamaria, R. Decoding Solar Adoption: A Systematic Review of Theories and Factors of Photovoltaic Technology Adoption in Households of Developing Countries. Sustainability 2025, 17, 5494. https://doi.org/10.3390/su17125494

AMA Style

Oliva EJD, Atehortua Santamaria R. Decoding Solar Adoption: A Systematic Review of Theories and Factors of Photovoltaic Technology Adoption in Households of Developing Countries. Sustainability. 2025; 17(12):5494. https://doi.org/10.3390/su17125494

Chicago/Turabian Style

Oliva, Edison Jair Duque, and Rodrigo Atehortua Santamaria. 2025. "Decoding Solar Adoption: A Systematic Review of Theories and Factors of Photovoltaic Technology Adoption in Households of Developing Countries" Sustainability 17, no. 12: 5494. https://doi.org/10.3390/su17125494

APA Style

Oliva, E. J. D., & Atehortua Santamaria, R. (2025). Decoding Solar Adoption: A Systematic Review of Theories and Factors of Photovoltaic Technology Adoption in Households of Developing Countries. Sustainability, 17(12), 5494. https://doi.org/10.3390/su17125494

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