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Review

Research on Offshore Photovoltaic Project Site Selection Based on PRISMA: A Systematic Review

by
Zhenzhou Feng
1,2,
Jijing Chen
1,2,
Duian Lv
3,
Peng Wang
1,2,*,
Kaixuan Zheng
1,2,
Ziyan Guo
1,2 and
Xihe Yue
1
1
Marine Academy of Zhejiang Province, Hangzhou 310012, China
2
Key Laboratory of Ocean Space Resource Management Technology, Ministry of Natural Resources, Hangzhou 310012, China
3
Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(18), 8293; https://doi.org/10.3390/su17188293
Submission received: 13 August 2025 / Revised: 5 September 2025 / Accepted: 9 September 2025 / Published: 16 September 2025
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Abstract

The manuscript adopts the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol to conduct a comprehensive analysis of the methodologies and critical factors influencing the site selection for offshore photovoltaic (PV) projects. The study innovatively proposes a three-stage site selection method model of “exclusion–optimization–correction” and a four-dimensional factor framework, which encompasses “technology, economy, environment, and policy.” The study underscores the pivotal roles of solar radiation, water depth, and grid connection conditions in site selection, while also highlighting the “veto” impact of military exclusion zones and ecological redlines. Additionally, it identifies the challenges associated with data acquisition and standardization in site selection, advocates for the establishment of standardized assessment procedures, and offers theoretical underpinnings and decision-making guidance for the rational site selection of offshore PV projects.

Graphical Abstract

1. Introduction

Amid rising global energy demand and environmental challenges, renewable energy from natural sources is vital for clean, sustainable development [1].Unlike fossil fuels, renewable energy reduces harmful emissions, improving environmental quality and public health. Solar photovoltaic (PV) power, with its safety, reliability, and pollution-free operation, is highly promising [2]. However, limited land resources hinder terrestrial PV expansion, shifting focus to offshore PV [3]. Offshore PV addresses land scarcity, offers high efficiency, better water quality, and low maintenance costs, and can coexist with other marine activities like fisheries and shipping, aiding the energy transition [4].
Successful offshore PV projects depend on strategic site selection, which ensures technical feasibility, economic rationality, environmental friendliness, and social acceptance, impacting power generation costs and resource utilization [5]. Yet, research on offshore PV site selection is sparse, often focusing narrowly on specific locations without comprehensive global or regional analyses [6]. Existing studies are also methodologically limited, lacking thorough, systematic exploration of influencing factors, which constrains scientific and sustainable development in this area [7].
Challenges in offshore PV site selection include environmental impacts, ecosystem disturbances, high construction costs, and methodological limitations. Fan et al. (2025) highlighted the significant influence of natural factors like light, waves, currents, seawater, and tides on site selection [2]. Wang et al. (2024) noted potential ecological impacts such as changes in dissolved oxygen, marine plant photosynthesis, and fishery resources [6]. Methodologically, Hauger et al. (2025) proposed a GIS and AHP-based method, though it relies on expert judgment and complex weight calculations [8]; Besharati Fard et al. (2022) introduced a hybrid fuzzy method, but it faces subjectivity in membership function determination and complex calculations [9].
To address these challenges, this paper systematically analyzed 67 relevant studies (2000–2025) using the PRISMA method. It evaluated various site selection approaches, introduced a three-stage model (“exclusion–optimization–correction”), and established a “technology-economy-environment-policy” framework. The study emphasized the importance of solar radiation, water depth, and grid connection conditions, and the “veto” effect of military exclusion zones and ecological redlines. It also identified data and standardization challenges, proposed future research directions, and provided practical recommendations for policymakers, researchers, and industry practitioners. These contributions enhance the scientific and practical value of offshore photovoltaic site selection research.

2. Materials and Methods

2.1. An Overview of the Systematic Quantitative Literature Review (SQLR) Process

PRISMA8 (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) is a set of guidelines designed to enhance the transparency and quality of reporting in systematic reviews and meta-analyses. The primary purpose of PRISMA is to assist researchers in clearly and completely reporting the background, methods, results, and discussion of systematic reviews, thereby ensuring the reproducibility of the research and the reliability of the results [10].
While the PRISMA framework provides a structured and transparent approach for systematic reviews, existing PRISMA applications in offshore PV site selection are often descriptive, focusing primarily on literature collection without thoroughly evaluating the strengths and weaknesses of site selection methods or addressing multi-dimensional influencing factors. Moreover, these studies rarely provide practical insights or identify knowledge gaps that can guide future research and decision-making. In contrast, this study addresses these limitations by systematically analyzing 67 highly relevant studies from 2000 to 2024, quantitatively summarizing key influencing factors, comparing existing site selection methods, and highlighting knowledge gaps and practical implications. This approach enhances methodological rigor and provides actionable guidance for offshore PV site selection.

2.2. Record Identification

To identify relevant studies on offshore photovoltaic (PV) site selection, we conducted a comprehensive search of multiple databases and sources. Specifically, we searched Web of Science and ScienceDirect until 2 September 2025. Additionally, we searched Baidu Scholar and reviewed the reference lists of included studies until 30 June 2025. The search strategy focused on keywords related to “offshore photovoltaic site selection” OR “photovoltaic site selection.” This study only considered peer-reviewed technical and review papers published between 2000 and 2025.

2.3. Screening, Eligibility, and Inclusion

The EndNote 2025 (Clarivate, London, UK) reference management software was used to remove duplicate records from the database search. Two authors independently extracted data; a unified Excel form was designed. The remaining articles were then screened based on their titles/abstracts, with highly relevant articles selected for full-text screening. The titles/abstracts were assessed based on whether the articles discussed photovoltaic (PV) site selection. A total of 4227 articles were excluded during this screening, with 94 articles deemed relevant for full-text screening. During the full-text screening process, a further 27 articles that did not discuss site selection methods or site selection criteria were excluded. Finally, 67 articles that were considered to meet the criteria for content analysis were carefully reviewed to identify and select methods for PV site selection and site selection criteria. The specific process is shown in Figure 1.

2.4. Data Extraction and Variable Definitions

For each included study, we extracted three predefined items:
(1) site-selection factors (e.g., solar radiation, water depth, grid distance);
(2) weights or importance scores assigned to each factor (continuous 0–1 or discrete 1–9 scale);
(3) site-selection method(s) (e.g., GIS-AHP, GIS-MCDM, hybrid fuzzy);
In addition, we also recorded the publication year and research area of each document. All variables were transcribed verbatim from the original papers; no assumptions, unit conversions, or external data were applied. Missing or ambiguous information was recorded as “NR” (not reported). After independent extraction, we calculated Cohen’s κ for inter-rater agreement. Disagreements were resolved by a third reviewer. Cohen’s κ was 0.67 (95% CI 0.62–0.74), indicating substantial agreement [11].
We designed a six column table, including title, indicator, weight, site selection method, publication year, and research area, to display the collected data. (Supplementary File S1). In this study, we synthesized the influencing factors for offshore photovoltaic project site selection through data aggregation and tabulation. We extracted key information from individual studies and organized it into tables for clear presentation and comparison. Although traditional meta-analysis was not conducted, this approach effectively highlighted the commonalities and differences in site selection factors. Data processing was performed using Microsoft Excel to ensure the accuracy and readability of the results.
This study adheres to the methodological requirements of the PRISMA 2020 Statement [10]. Given that the review topic does not involve human or animal experiments, this review protocol has no registration or protocol was prepared.

3. Results

3.1. Overview of Papers Characteristics–Citation Metadata

3.1.1. Current Spatial and Temporal Distribution of Research

The citation metadata of recorded papers represents the first category of analyzed properties. We have compiled information on the publishers, authors, years, study regions, DOIs, and other details of 67 studies (see Supplementary Table S2). We analyzed the publication years and study regions of these research papers and demonstrated the spatiotemporal distribution of offshore photovoltaic site selection studies through Figure 2 and Figure 3.
In recent years, the number of research papers on renewable energy site selection has shown a significant upward trend. Based on an analysis of 67 papers, the field has evolved from sporadic publications to rapid development between 2014 and 2025. There were only 2 papers in 2014, 1 in 2000, 2 in 2018, 7 in 2020, 6 in 2021, 9 in 2022, 5 in 2023, a sharp increase to 12 in 2024, and 17 in 2025, accounting for 25.4% of all papers. This trend indicates that since 2020, research on renewable energy site selection has entered a phase of rapid development, with particularly notable growth in 2024–2025, reflecting the increasing importance of this field in the context of addressing climate change and energy transition. It is worth noting that the average annual output was only 0.5 papers between 1997 and 2017, while the number of studies began to rise significantly after 2018 and showed sustained growth after 2020, highlighting the growing global emphasis on spatial planning for renewable energy in recent years.
In terms of country/region distribution (Figure 3), China dominates the research on renewable energy siting with 18 papers, accounting for 26.9%, with a focus on photovoltaic power station siting and hybrid systems of offshore wind, photovoltaic, and hydrogen energy [5,12,13,14]. Turkey (7 papers, 10.4%) and the United States (6 papers, 9.0%) rank second and third, respectively, with Turkey’s research mainly involving photovoltaic siting in Kars Province [15] and Aegean wind power projects [16], while the United States focuses on floating photovoltaic technology assessment [17] and Gulf of Mexico wind power siting [18]. It is worth noting that global review studies (6 papers, 9.0%) provide important references for siting methodologies [19,20,21]. Research from other active countries such as India (4 papers), Greece (3 papers), and Germany (3 papers) focuses on photovoltaic siting in Punjab, Aegean wind-solar hybrid systems, and agri-photovoltaic projects, respectively [8,22]. In addition, emerging market countries such as Ghana, Spain, and Bangladesh have also begun to see related research in recent years [23,24,25], reflecting the increasing emphasis on renewable energy development in developing countries. This distribution pattern not only reflects China’s leading position in the new energy field, but also demonstrates the diversified development trend of global renewable energy research.

3.1.2. Bias Risk Assessment

A comprehensive bias risk assessment was conducted for the 67 included papers (Figure 4), covering selection bias, performance bias, detection bias, attrition bias, reporting bias, and other biases. The results showed that most studies had a low risk of detection bias, attrition bias, and reporting bias, but had some risk in selection bias and performance bias. The specific assessment results are shown in Supplementary Table S3.

3.2. Offshore Photovoltaic Site Selection Methods: A Critical Comparison and Suitability Assessment

3.2.1. Method Selection and Analysis

Based on a systematic review of the 67 collected literature (Supplementary Table S1), this study categorizes the methods for siting offshore photovoltaic (PV) systems into six major types: GIS-AHP (41.8%), GIS-MCDM (32.8%), hybrid fuzzy methods (17.9%), climate modeling (4.5%), exclusion-evaluation methods (3.0%), and other innovative methods such as machine learning and digital twins (4.5%). Among these, GIS-AHP and GIS-MCDM together account for nearly three-quarters of the studies and are mainly applied in large-scale or complex environment projects in Saudi Arabia, Turkey, China, and Pakistan [3,8,15,18,26,27,28,29,30,31,32]. The hybrid fuzzy methods are more common in high-uncertainty scenarios in countries like Bangladesh and Iran [9,12,20,30,33]. Climate modeling serves long-term strategic planning in China and the European Union [7,34,35]. The exclusion-evaluation method is used for rapid preliminary screening in Mauritius and Egypt [36,37,38]. The emerging technologies are still in the pilot stage and are concentrated in India, the Netherlands, and Singapore. The above distribution provides a solid evidence base for the subsequent critical comparison and regional applicability assessment. Refer to Table 1 for comprehensive details.
This study provides an introduction to the five mainstream methods, as detailed below.
(1)
Based on GIS and Analytic Hierarchy Process (GIS-AHP)
The GIS-AHP method integrates Geographic Information Systems (GIS) and the Analytic Hierarchy Process (AHP), it breaks down complex problems into hierarchical structures, uses expert judgment to determine the weights of various factors, and processes geographic data through GIS technology [3]. The results are ultimately presented in a visual format to assist decision-makers in identifying the optimal site selection solutions [26]. The advantages of this method are that it can comprehensively consider the impact of multiple factors, and the results are relatively objective and reliable. However, it relies heavily on expert experience and subjective judgment, and the weight calculation process is complex [8]. The technical roadmap is shown in Figure 5.
(2)
Based on GIS and Multi-Criteria Decision-Making (MCDM) Method
The GIS-MCDM method integrates various spatial data through GIS technology and employs multi-criteria decision-making methods to evaluate and rank site selection options [31]. The advantage of this method is that it can comprehensively assess the strengths and weaknesses of site selection options, with strong flexibility and adaptability [40]. However, the workload for data collection and processing is substantial, and the determination of evaluation criteria and weights has a degree of subjectivity [41]. The technical roadmap is shown in Figure 6.
(3)
Hybrid Fuzzy Method
The hybrid fuzzy method employs fuzzy logic to deal with the vagueness and uncertainties in the site selection process, constructs fuzzy sets and membership functions, and combines weights for fuzzy comprehensive evaluation to ultimately determine the optimal site selection plan. This method can effectively handle fuzzy information, but the determination of membership functions is subjective, and the computational process is relatively complex [9,33]. The technical roadmap is shown in Figure 7.
(4)
Long-term Analysis Based on Climate Models
The long-term analysis based on climate models utilizes climate models (such as CORDEX) to simulate future climate change and assess its impact on offshore photovoltaic resources, providing a long-term scientific basis for site selection decisions [34]. The advantage of this method is that it can provide long-term predictive information. However, climate model projections carry inherent uncertainties due to differences among models, assumptions about future emission scenarios, and limitations in spatial and temporal resolution, which may affect the reliability of site selection outcomes. Therefore, these uncertainties should be carefully considered when using climate models for long-term planning [42]. However, the simulation results of climate models are subject to uncertainties and the workload is relatively high. The technical roadmap is shown in Figure 8.
(5)
Method Based on Exclusion and Evaluation Criteria
The method based on exclusion and evaluation criteria identifies and evaluates potential site areas by setting exclusion criteria and evaluation criteria, and ultimately determines the optimal site area [6]. The advantage of this method is its strong operability, which allows for the rapid screening of areas that meet the requirements. However, it may overlook some potentially suitable site areas. The technical roadmap is shown in Figure 9.

3.2.2. Comprehensive Performance Evaluation of Site Selection Methods

To systematically assess the overall performance of various site selection methods, this study develops a comprehensive five-dimensional evaluation framework grounded in a thorough review of existing literature (Figure 10). A 1–5 point scale is employed to evaluate each method across five critical dimensions: accuracy, computational efficiency, robustness, interpretability, and dynamic adaptability (Figure 10). The findings reveal that the GIS-AHP method excels in interpretability (4.5) and computational efficiency (4.0) due to its intuitive hierarchical structure, but it is limited by its static weights, which result in the lowest dynamic adaptability (1.5). The GIS-MCDM method demonstrates robust performance in accuracy (4.2) and robustness (3.8), though it is burdened by a higher computational load (3.0). The hybrid fuzzy method achieves the highest scores in accuracy (4.5) and robustness (4.8), but its complex membership functions lead to the worst interpretability (2.0). The climate modeling method stands out with perfect dynamic adaptability (5.0) and strong robustness (4.5), yet it is constrained by high computational demands, yielding the lowest computational efficiency (1.5). The exclusion method boasts high computational efficiency (5.0) and interpretability (4.8), making it well-suited for rapid preliminary screening, but it falls short in accuracy and robustness (2.8, 2.0) and has the lowest dynamic adaptability (1.0). This evaluation matrix offers a quantitative foundation for guiding subsequent method selection and combination strategies.

3.2.3. Comparative Overview of Site Selection Methods

In the selection of offshore photovoltaic (PV) sites, five categories of methods exhibit distinct differences in their strengths, weaknesses, and application scenarios. The GIS-AHP method achieves macro-coordination through hierarchical weighting and spatial visualization. However, due to the subjective weighting by experts and static assumptions, it struggles to incorporate dynamic factors such as waves and salt spray [29]. As a result, it is best suited for the initial exclusion of no-build zones and policy communication [3]. (The detailed comparison is shown in Table 2).
The GIS-MCDM method leverages sub-methods like TOPSIS and ELECTRE to achieve pixel-level precise trade-offs [18]. It performs exceptionally well in nearshore areas with multi-objective conflicts (economic, ecological, and military), with case studies demonstrating a potential increase in benefits by 19%. However, it requires high data inputs and computational load [24].
Hybrid fuzzy methods, which rely on Pythagorean or interval type-2 fuzzy logic, demonstrate high robustness and accuracy in the far sea, especially in areas with frequent typhoons and scarce data. The trade-off is increased model complexity and weak interpretability [43].
Climate modeling methods provide long-term climate risk maps (30–50 years) through models such as CMIP6 and RegCM4. These are suitable for strategic assessments of projects with capacities of ≥100 MW but are limited by a resolution > 50 km and the high costs associated with supercomputing [42].
Exclusion-evaluation methods use Boolean logic to exclude large-scale no-build zones within days, requiring low computational resources. However, they may overlook suboptimal waters, such as land-water intertidal zones [6].
In practical applications, a cascaded approach of “exclusion–optimization–correction” is advisable. GIS-AHP or exclusion methods can be used to initially identify feasible sea areas [13]. GIS-MCDM and hybrid fuzzy methods can then be employed for fine-tuning multi-objective trade-offs [23]. Climate models and machine learning can be used for long-term dynamics and real-time correction. It is essential to continuously incorporate marine-specific factors such as waves and salt spray, as well as conduct interdisciplinary validation, to enhance the scientific rigor and sustainability of site selection decisions [7,34].

3.3. Site Selection Factors: Interactions, Trade-Offs, and Regional Differences

3.3.1. A Four-Dimensional Classification System for the Influencing Factors of Floating Photovoltaic (FPV) Site Selection

This study develops a four-dimensional classification framework, encompassing the core dimensions of technology, economy, environment, and policy, as well as their respective subsets, based on a comprehensive analysis of 67 relevant articles. (Detailed descriptions of each factor are provided in Table 3).
(1)
Technical Dimension
The technical dimension serves as the fundamental basis for siting offshore photovoltaic (PV) projects, encompassing two critical subsets: natural conditions and technical adaptability [24]. The subset of natural conditions, which includes solar radiation, water depth, wave conditions, and extreme weather events, directly determines the feasibility of a project [24]. Among these factors, solar radiation intensity and water depth are particularly decisive [44]. The subset of technical adaptability significantly influences long-term operational stability, especially with regard to grid connection requirements, which pose critical constraints on site selection [12,45]. Recent research has increasingly emphasized the assessment of extreme weather risks [39], with a general consensus in the literature recommending the use of 50-year return period events as design benchmarks.
(2)
Economic Dimension
The economic dimension centers on cost-effectiveness and investment returns, incorporating two subsets: technical-economic and regional-economic considerations [33]. Analysis of the technical-economic subset reveals that the selection of floating materials and variations in operation and maintenance (O&M) costs can cause substantial fluctuations in the levelized cost of electricity (LCOE) [46]. The regional-economic subset underscores the significance of locational economics, with most successful offshore PV projects situated in coastal economic zones characterized by high industrial electricity demand [47]. Notably, innovations in financing models are transforming traditional economic assessments of projects.
(3)
Environmental Dimension
The environmental dimension, which includes the environmental-social and climate resilience subsets, is gaining increasing importance in the context of sustainable development. Analysis of the environmental-social subset shows that avoiding ecological redlines has become an industry consensus [48]. Solutions to fishery conflicts have become more diverse, with PV-aquaculture symbiosis emerging as a best practice [49]. The climate resilience subset reflects the growing emphasis on environmental, social, and governance (ESG) requirements, with recent studies indicating that carbon footprint accounting can significantly reduce financing costs [50].
(4)
Policy Dimension
The policy dimension is a decisive factor for project implementation, comprising two subsets: policy-regulatory and international cooperation [51]. Analysis reveals that administrative approval cycles are the primary source of uncertainty [52]. Military restricted zones are identified as “veto” factors, effectively ruling out certain sites. The importance of international cooperation is growing, especially with the globalization of projects, including cross-border maritime initiatives [53].
Table 3. Table of the four-dimensional classification system for influencing factors.
Table 3. Table of the four-dimensional classification system for influencing factors.
DimensionFactorDescriptionReferences
Technical DimensionSolar RadiationAnnual global horizontal irradiance directly affects generation efficiency. Areas with <1400 kWh/m2 require additional PV panel area for compensation[5,13,15,24,44,50,54]
Water DepthOptimal range 5–50 m. <5 m increases ecological disturbance risks; >50 m substantially raises mooring system costs[23,36,41,43]
Wave ConditionsAnnual mean wave height > 2 m necessitates reinforced floating structures, increasing CAPEX by 15–25%[12,45,55,56]
Extreme WeatherTyphoon-prone regions (e.g., Northwest Pacific) require wind-resistant designs, elevating LCOE by 8–12%[57,58]
Economic DimensionCAPEX/OPEXDeep-water projects (>30 m) incur CAPEX of $1.2–1.8/W, 30–50% higher than shallow water[4,46,59]
Grid ConnectionEach additional 10 km offshore increases connection costs by $0.03/W, with optimal distance < 20 km[46,60]
Energy StorageSolar irradiance variability > 25% requires storage, increasing LCOE by $0.02–0.05/kWh[33,54]
O&M TechnologyAnti-biofouling coatings reduce OPEX by 15% but increase initial investment by 8%[12,14,45]
Environmental DimensionEcologically Sensitive AreasOverlap with protected areas increases permit rejection rates to 85%[13,61]
Fishery ConflictsAquaculture zone overlaps require compensation ($0.5–2/m2/year), accounting for 3–8% of OPEX[36,53]
Public AcceptanceProjects < 5 km from coast face 62% opposition rates, delaying approvals[14]
Carbon FootprintLifecycle emissions > 50 gCO2/kWh affect green certification[7]
Policy DimensionMaritime ApprovalsMulti-department approval averages 14–28 months, 3–5× longer than onshore projects[50,54]
Military Restricted ZonesCover 12–30% of available maritime areas (e.g., South China Sea, Persian Gulf)[5,62]
Carbon ConstraintsEach $10/ton carbon price increase boosts project IRR by 0.8–1.2 percentage points[6]
Transnational AgreementsRegional energy conventions reduce cross-border project risk premiums by 15–20%[61]

3.3.2. Analysis of Floating Photovoltaic Power Station Site Selection Factors: High-Frequency Factors and Key Factors for Scalability

Based on the analysis of 67 papers and Supplementary Table S1 (with a 95% confidence level), this study identified key factors for the siting of offshore photovoltaic power station.
(1)
High-Frequency Factors
solar radiation with a 100% occurrence rate) and water depth (93%) are the “dual core” of almost all studies; grid connection distance (87%) and CAPEX (82%) follow closely, forming the “technical-economic foundation” for project feasibility. Extreme weather (85%), ecological red lines (78%), and military exclusion zones (71%) have slightly lower occurrence rates but have a “veto effect”: once triggered, they can lead to project termination or relocation [63]. Fishery conflicts (68%) and carbon constraint policies (65%) have seen a 40% increase in mention rates in papers after 2020, indicating that policy and environmental issues are rapidly heating up.
(2)
Key factors for scalability.
To facilitate large-scale expansion, offshore photovoltaic systems must surmount three major challenges: technological, economic, and environmental-social constraints. Technologically, floating structures experience an annual corrosion loss of 1.5% in seawater, and their failure rate increases by 30% when wave heights exceed 4 m. This necessitates an additional investment of 0.12 USD/W in nanocoatings to enhance durability. Economically, the capital expenditure (CAPEX) for offshore photovoltaics ranges from 1.3 to 2.1 USD/W, approximately 40% higher than that of terrestrial systems. Although clustering developments of 500 MW or more can reduce unit costs by 18%, the financing interest rate remains 1–2 percentage points higher. Environmentally and socially, issues such as fisheries compensation (e.g., 2 million USD in Indonesia) and policy fluctuations (e.g., a 30% annual drop in the US carbon price) have already caused delays in about 30% of projects. To systematically mitigate these risks, long-term power purchase agreements (LTPAs) and community dividend mechanisms (≥5%) are essential.

3.3.3. An Analysis of Economic-Environmental Coupling from the Perspective of Interactions and Trade-Offs: A Framework for Balancing Techno-Economic Factors with Environmental and Social Considerations

(1)
The relative importance of site selection factors.
Through co-occurrence analysis, significant associations among various factors have been identified. The coupling between technical and economic factors is the most pronounced (The correlation is shown in detail in Figure 11.). For instance, a 10 m increase in water depth corresponds to a 15–25% rise in CAPEX [64]. The interaction between environmental and policy factors exhibits a veto effect. For example, the correlation coefficient of 0.82 between ecological red lines and approval cycle can lead to an average delay of 6–12 months in the approval process [41]. The interaction between economic and policy factors demonstrates a lever effect. For example, the correlation coefficient of 0.73 between carbon constraints and financing costs translates into a financing cost advantage, where a $10/t increase in carbon price results in a 0.5–1.5% reduction in green bond interest rates [33].
(2)
The trade-off mechanism between technological economy and environmental society
This study proposes a three-tier progressive framework to balance techno-economic and environmental-social factors in maritime area selection. Initially, rigid constraints such as ecological red lines and military exclusion zones eliminate about 30% of potential areas [26]. In the second stage, core techno-economic indicators like high solar radiation and suitable water depth are prioritized, while soft thresholds of ≤15% loss in fishery economy and ≤20% community opposition rate are set to ensure social acceptability. Finally, for controversial sites, compensation measures such as mangrove restoration (approximately $50,000/MW) or technical compromises like articulated floating bodies (with a 12% cost increase) are implemented to achieve a sustainable optimal solution that balances technical feasibility with environmental and social permissions [19].

3.3.4. Regional Differences in the Site Selection of Offshore Photovoltaic Projects

The literature review highlights that the factors influencing the site selection of offshore photovoltaic projects significantly differ between Asia and Europe, primarily in three dimensions: the priority given to technological and economic factors, the intensity of environmental constraints, and the level of social acceptance [23]. In Asia, particularly in regions such as China, India, and Southeast Asia, the decision-making process for site selection is highly influenced by technological and economic considerations, which account for 65–75% of the weight [36]. Key factors include the intensity of global horizontal irradiance (GHI > 1800 kWh/m2) and water depth conditions (<30 m). Nearshore shallow water areas are often favored to minimize grid connection costs, despite potential overlaps with fishing zones, which result in a conflict rate of approximately 40% [30]. Policy approval processes in these regions are relatively efficient, but the ecological compensation mechanisms are less robust. For instance, China follows a “build-first, compensate-later” approach, permitting projects to commence construction prior to the completion of environmental impact assessments [36]. In contrast, Europe, especially in areas like the North Sea and Southern European countries, places a greater emphasis on environmental and social factors, which collectively account for 45–55% of the weight [60]. Environmental protection is stringent, with Germany, for example, prohibiting construction within 10 km of the shore [41]. Community opposition rates exceeding 15% can lead to project rejections. Technological and economic optimization in Europe is often achieved through floating technologies, such as the hinged platforms used in the Netherlands, which are designed to accommodate deeper water areas (30–50 m), although this approach increases costs by 20–30%. These differences result in an average project development cycle in Asia that is 1.5 years shorter than in Europe [23]. However, European projects face a 60% lower risk of environmental litigation.

4. Challenges and Recommendations for Future Works

4.1. Challenges in Data Acquisition and Model Accuracy

High-resolution marine environmental data, such as wave dynamics, ocean currents, and long-term climate projections, are difficult to obtain, limiting the accuracy and reliability of offshore PV site selection models. The dynamic changes of waves and currents directly affect the stability of PV modules, but existing models often fail to capture these variations due to technical and cost constraints [35]. Furthermore, current models do not adequately account for extreme weather and complex marine environments, making it difficult to fully reflect actual conditions. Future research should advance data acquisition technologies, including satellite remote sensing and drone-based monitoring, while improving modeling algorithms to simulate dynamic environmental changes more accurately, thereby enhancing the scientific rigor and precision of site selection decisions [35].

4.2. Optimization of Multi-Criteria Decision-Making Methods and Technological Integration

Although multi-criteria decision-making (MCDM) methods have been applied to offshore PV site selection, they are often limited by static evaluation and poor adaptability to temporal variations, such as seasonal solar radiation fluctuations and long-term climate change [32]. Future studies should integrate advanced technologies such as fuzzy logic and machine learning to construct intelligent and dynamic decision-making frameworks [60]. These technologies can enhance predictive capabilities, manage uncertainties, and provide a more reliable scientific basis for offshore PV siting in complex and evolving marine environments [16].

4.3. Necessity of Interdisciplinary Collaboration and Integrated Research Frameworks

Offshore PV site selection involves multiple disciplines, including marine engineering, environmental science, and energy economics [10]. Current research is often confined to a single-discipline perspective, limiting comprehensive optimization. For example, marine engineering focuses on installation and stability, environmental science emphasizes ecosystem protection, and energy economics addresses economic feasibility. Future research should strengthen interdisciplinary collaboration by establishing multi-disciplinary teams that integrate technical, ecological, and economic knowledge [6]. Such collaboration can optimize PV module design and siting, evaluate project sustainability, and promote a holistic approach to offshore PV development [65].

5. Discussion

This study provides a comprehensive analysis of 67 papers to elucidate the critical methodologies and determinative factors in the site selection process for floating photovoltaic (FPV) systems [32]. The findings indicate that GIS-AHP and GIS-MCDM are the predominant methods utilized in site selection, collectively representing nearly three-quarters of the reviewed studies [30]. Regarding site selection factors, solar radiation, water depth, and grid connection conditions emerge as the most crucial elements, each occurring with a frequency of at least 87%. Although military exclusion zones and ecological redlines have relatively lower frequencies, they possess a “veto effect” [41]. These insights offer a robust theoretical framework for the scientific site selection of FPV projects.
The study’s implications extend to policy, technology, and future research directions. From a policy perspective, it is essential to develop a dynamic approval mechanism that integrates climate change factors, such as alterations in typhoon frequency, into site selection criteria [2]. Technologically, there is a pressing need to innovate new anti-biofouling materials (e.g., graphene coatings) and intelligent monitoring systems [12]. In terms of research, the creation of a global FPV database and the standardization of performance evaluation indicators are urgently needed [36].
For policymakers, investors, and developers, this study offers practical guidance. Policymakers should prioritize the implementation of ecological compensation mechanisms in ecologically sensitive areas, such as mangrove coasts [59], and establish cross-departmental coordination platforms. Investors need to focus on region-specific risks, such as the typhoon risk in Southeast Asia projects (with an average annual loss rate of 2.3%) and the environmental assessment costs in European projects (3–5% of total investment) [30]. Developers can employ a hybrid decision-making tool combining GIS-AHP-TOPSIS, and by adjusting the weight configuration (e.g., increasing the weight of community acceptance from 10% to 20%), they can significantly enhance project success rates by up to 40% [32].
Compared with existing reviews, this study’s innovations are evident in two main aspects: (1) the development of a three-stage “site selection decision tree” model that can recommend the optimal site selection method based on the stage of FPV site selection; (2) the establishment of the first FPV site selection literature database, which includes standardized data from 67 studies. These innovations distinguish this study from existing reviews and fill the research gap in systematic methodology and policy responsiveness for FPV site selection.
Despite the significant contributions of this study, several limitations remain: (1) uneven geographical coverage, with 65% of the literature originating from Asia and a lack of data from Africa and South America; (2) language bias, as only English-language literature was included [30]; and (3) insufficient long-term performance data, with only 15% of the studies containing data spanning over five years. Future directions for improvement include the following: (1) expanding the scope of literature coverage, particularly including studies from non-English-speaking and developing countries; (2) establishing a multilingual literature database; and (3) conducting longer-term tracking studies. These enhancements will further elevate the scientific value and practical guidance of FPV site selection research.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su17188293/s1, Table S1: Extraction table of site-selection methods and key factors reported in the 67 included studies; Table S2: Basic characteristics of the 67 included studies; Table S3: Risk-of-bias assessment for the 67 included studies; File S1: PRISMA Checklist. Reference [66] is cited in the Supplementary Materials.

Author Contributions

Conceptualization, Z.F. and P.W.; methodology, J.C.; formal analysis, D.L.; data curation, K.Z.; writing—original draft preparation, J.C.; writing—review and editing, Z.G. and X.Y. All authors have read and agreed to the published version of the manuscript.

Funding

Supported by the Pioneer and Leading Goose + X S&T Program of Zhejiang (2025C02016) and the Ministry of Natural Resources Cooperation Project (2024ZRBSHZ146).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new raw data were generated or analyzed in support of this research. Three Supplementary tables (Tables S1–S3) containing the aggregated information are provided with the online version of the article.

Acknowledgments

Thank you to the Marine Academy of Zhejiang Province and Key Laboratory of Ocean Space Resource Management Technology for their technical support.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. PRISMA Flow Diagram.
Figure 1. PRISMA Flow Diagram.
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Figure 2. Interannual Variations in the Number of Renewable Energy Site Selection Research Papers from 2000 to 2025. Based on the statistical results of 67 studies, research on renewable energy site selection has shown a significant growth trend from 2014 to 2025. There were only 2 papers in 2014, and the research entered a period of rapid development after 2020, increasing to 12 papers in 2024 and reaching 17 papers in 2025 (accounting for 25.4% of the total).
Figure 2. Interannual Variations in the Number of Renewable Energy Site Selection Research Papers from 2000 to 2025. Based on the statistical results of 67 studies, research on renewable energy site selection has shown a significant growth trend from 2014 to 2025. There were only 2 papers in 2014, and the research entered a period of rapid development after 2020, increasing to 12 papers in 2024 and reaching 17 papers in 2025 (accounting for 25.4% of the total).
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Figure 3. Region Distribution of Renewable Energy Siting Studies (2000–2025). Analysis of 67 publications reveals China’s dominance (26.9%, 18 papers) in renewable energy siting research, followed by Turkey (10.4%, 7 papers) and the USA (9.0%, 6 papers). Global review studies account for 9.0% (6 papers), while India, Germany, and Greece show notable activity. Emerging economies (e.g., Ghana, Bangladesh) reflect the field’s global diversification.
Figure 3. Region Distribution of Renewable Energy Siting Studies (2000–2025). Analysis of 67 publications reveals China’s dominance (26.9%, 18 papers) in renewable energy siting research, followed by Turkey (10.4%, 7 papers) and the USA (9.0%, 6 papers). Global review studies account for 9.0% (6 papers), while India, Germany, and Greece show notable activity. Emerging economies (e.g., Ghana, Bangladesh) reflect the field’s global diversification.
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Figure 4. Distribution of Bias Risk in Offshore Photovoltaic Siting Studies: A Systematic Review Based on 67 Studies (n = 67). This stacked bar chart illustrates the bias risk assessment results of 67 research papers in the field of renewable energy. The Y-axis represents six types of bias (selection bias, performance bias, detection bias, attrition bias, reporting bias, and other bias), and the X-axis represents the proportion of three risk levels (high, medium, and low). The color segments indicate the risk levels.
Figure 4. Distribution of Bias Risk in Offshore Photovoltaic Siting Studies: A Systematic Review Based on 67 Studies (n = 67). This stacked bar chart illustrates the bias risk assessment results of 67 research papers in the field of renewable energy. The Y-axis represents six types of bias (selection bias, performance bias, detection bias, attrition bias, reporting bias, and other bias), and the X-axis represents the proportion of three risk levels (high, medium, and low). The color segments indicate the risk levels.
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Figure 5. Technical roadmap for photovoltaic site selection based on GIS and Analytic Hierarchy Process. The diagram illustrates the hierarchical structuring of influencing factors, the assignment of weights through expert judgment, and the integration of spatial data in GIS to identify optimal sites. This figure highlights how the method balances multi-factor considerations with visual decision support.
Figure 5. Technical roadmap for photovoltaic site selection based on GIS and Analytic Hierarchy Process. The diagram illustrates the hierarchical structuring of influencing factors, the assignment of weights through expert judgment, and the integration of spatial data in GIS to identify optimal sites. This figure highlights how the method balances multi-factor considerations with visual decision support.
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Figure 6. A Roadmap for Offshore Photovoltaic Site Selection Based on GIS and Multi-Criteria Decision-Making (MCDM) Methods. This figure illustrates how spatial data are integrated with multiple evaluation criteria to generate and rank potential sites. The approach allows flexible consideration of diverse factors, but also highlights the subjectivity and workload associated with weight assignment and data collection.
Figure 6. A Roadmap for Offshore Photovoltaic Site Selection Based on GIS and Multi-Criteria Decision-Making (MCDM) Methods. This figure illustrates how spatial data are integrated with multiple evaluation criteria to generate and rank potential sites. The approach allows flexible consideration of diverse factors, but also highlights the subjectivity and workload associated with weight assignment and data collection.
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Figure 7. A Roadmap for Offshore Photovoltaic Site Selection Based on Hybrid Fuzzy Methods. This figure demonstrates how fuzzy logic is used to handle vagueness and uncertainty in site selection by constructing fuzzy sets and membership functions. It shows the integration of weighting schemes and fuzzy comprehensive evaluation to identify optimal sites. While effective for uncertainty management, the method also reflects the challenges of subjective membership function design and computational complexity.
Figure 7. A Roadmap for Offshore Photovoltaic Site Selection Based on Hybrid Fuzzy Methods. This figure demonstrates how fuzzy logic is used to handle vagueness and uncertainty in site selection by constructing fuzzy sets and membership functions. It shows the integration of weighting schemes and fuzzy comprehensive evaluation to identify optimal sites. While effective for uncertainty management, the method also reflects the challenges of subjective membership function design and computational complexity.
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Figure 8. A Roadmap for Offshore Photovoltaic Site Selection Based on Long-term Analysis Methods Using Climate Models. This figure illustrates how climate models (e.g., CORDEX) are applied to simulate future climate change and evaluate its impacts on offshore PV resource availability. It highlights the strength of providing long-term predictive information for strategic planning, while also acknowledging key limitations such as model uncertainty, emission scenario assumptions, and the high computational workload.
Figure 8. A Roadmap for Offshore Photovoltaic Site Selection Based on Long-term Analysis Methods Using Climate Models. This figure illustrates how climate models (e.g., CORDEX) are applied to simulate future climate change and evaluate its impacts on offshore PV resource availability. It highlights the strength of providing long-term predictive information for strategic planning, while also acknowledging key limitations such as model uncertainty, emission scenario assumptions, and the high computational workload.
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Figure 9. A Roadmap for Offshore Photovoltaic Site Selection Based on Exclusion and Evaluation Criteria Methods. This figure illustrates the process of defining exclusion and evaluation criteria to screen potential sites and determine the most suitable areas for offshore PV deployment. The method is operationally simple and allows rapid assessment of candidate sites; however, it may overlook some potentially suitable locations.
Figure 9. A Roadmap for Offshore Photovoltaic Site Selection Based on Exclusion and Evaluation Criteria Methods. This figure illustrates the process of defining exclusion and evaluation criteria to screen potential sites and determine the most suitable areas for offshore PV deployment. The method is operationally simple and allows rapid assessment of candidate sites; however, it may overlook some potentially suitable locations.
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Figure 10. Five-dimensional performance radar chart for offshore PV siting methods. Note: Based on 67 empirical studies, the relative performance of six mainstream methods is quantified on a 1–5 scale across five dimensions—accuracy, computational efficiency, robustness, interpretability, and dynamic adaptability—and visualized. Values closer to the outer ring denote superior performance in the respective dimension.
Figure 10. Five-dimensional performance radar chart for offshore PV siting methods. Note: Based on 67 empirical studies, the relative performance of six mainstream methods is quantified on a 1–5 scale across five dimensions—accuracy, computational efficiency, robustness, interpretability, and dynamic adaptability—and visualized. Values closer to the outer ring denote superior performance in the respective dimension.
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Figure 11. Factor Co-occurrence Network-Offshore PV Farm Siting.
Figure 11. Factor Co-occurrence Network-Offshore PV Farm Siting.
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Table 1. The distribution of the six types of methods in the 67 papers.
Table 1. The distribution of the six types of methods in the 67 papers.
Method CategoryPercentageTypical Application Cases
GIS-AHP41.80%Large-scale PV projects in Saudi Arabia, Turkey, Spain [27,29]
GIS-MCDM32.80%Complex environments in China, Turkey, Greece [5,16]
Hybrid Fuzzy Methods17.90%High-uncertainty environments in Morocco [33]
Climate Model Analysis4.50%Long-term planning in Eastern China, EU [7]
Exclusion-Evaluation Method3.00%Rapid assessments in Mauritius, Egypt [36]
Other Methods4.50%Emerging technologies like machine learning, digital twins [39]
Table 2. Comparative Overview of Offshore Photovoltaic Site Selection Methods.
Table 2. Comparative Overview of Offshore Photovoltaic Site Selection Methods.
Method NameAdvantagesLimitationsOffshore SuitabilityReferences
GIS & Analytic Hierarchy Process (GIS-AHP)Structured decision-making, supports multi-dimensional criteria (e.g., solar radiation, water depth); strong spatial visualization, well-established.Subjective weight assignment (expert-dependent), static analysis struggles with dynamic changes (e.g., climate shifts).Moderate-High Suitability, suitable for near-shore (<20 km) macro-site selection; requires marine environmental data.[13]
GIS & Multi-Criteria Decision-Making (GIS-MCDM)Flexible adaptation to different decision scenarios (e.g., TOPSIS/ELECTRE), high-precision grid analysis (e.g., 100 m × 100 m).High data requirements, computational complexity, reduced efficiency with conflicting criteria.High Suitability, ideal for complex marine areas (e.g., hybrid wind-PV projects).[23]
Hybrid Fuzzy MethodsEffectively handles uncertainty (e.g., fuzzy linguistic variables), robust against outliers.High implementation barrier (requires fuzzy mathematics expertise), poor interpretability.High Suitability, suitable for deep-sea or extreme climate zones (e.g., typhoon-prone areas).[43]
Climate Model-Based Long-Term AnalysisForward-looking predictions (e.g., 30–50-year climate trends), mitigates long-term risks (e.g., sea-level rise).Low resolution (>50 km), high computational cost, requires supercomputing.Scenario-Specific Suitability, best for large-scale projects (>100 MW) and long-term planning.[7,34]
Exclusion & Evaluation Criteria MethodRapid screening of exclusion zones (e.g., shipping lanes, military areas), low computational cost.Overly simplistic, ignores marginally suitable areas (e.g., low ecological impact zones).Low Suitability, only for preliminary screening; requires refined follow-up methods.[6]
Notes: Offshore Suitability is categorized as Low (preliminary screening only), Moderate-High (nearshore applications), High (complex or deep-sea zones), and Scenario-Specific (large-scale, long-term planning).
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Feng, Z.; Chen, J.; Lv, D.; Wang, P.; Zheng, K.; Guo, Z.; Yue, X. Research on Offshore Photovoltaic Project Site Selection Based on PRISMA: A Systematic Review. Sustainability 2025, 17, 8293. https://doi.org/10.3390/su17188293

AMA Style

Feng Z, Chen J, Lv D, Wang P, Zheng K, Guo Z, Yue X. Research on Offshore Photovoltaic Project Site Selection Based on PRISMA: A Systematic Review. Sustainability. 2025; 17(18):8293. https://doi.org/10.3390/su17188293

Chicago/Turabian Style

Feng, Zhenzhou, Jijing Chen, Duian Lv, Peng Wang, Kaixuan Zheng, Ziyan Guo, and Xihe Yue. 2025. "Research on Offshore Photovoltaic Project Site Selection Based on PRISMA: A Systematic Review" Sustainability 17, no. 18: 8293. https://doi.org/10.3390/su17188293

APA Style

Feng, Z., Chen, J., Lv, D., Wang, P., Zheng, K., Guo, Z., & Yue, X. (2025). Research on Offshore Photovoltaic Project Site Selection Based on PRISMA: A Systematic Review. Sustainability, 17(18), 8293. https://doi.org/10.3390/su17188293

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