Integrating Remote Sensing and Geospatial-Based Comprehensive Multi-Criteria Decision Analysis Approach for Sustainable Coastal Solar Site Selection in Southern India

Abstract

Rapid urbanization across Southern Asia’s coastal regions has significantly increased electricity demand, driving India’s solar sector expansion under the National Solar Mission and positioning the country as the world’s fourth-largest solar market. Nonetheless, methodological limitations remain in applying GIS-based multi-criteria decision analysis (MCDA) frameworks to coastal urban microclimates, which involve intricate land-use dynamics and resilience constraints. To address this gap, this study proposes a multi-criteria GIS- based Analytical Hierarchy Process (AHP) framework, incorporating remote sensing and geospatial data, to assess Solar Farm Sites (SFSs) suitability, supplemented by sensitivity analysis in Thoothukudi coastal city, India. Ten parameters—covering photovoltaic, climatic, topographic, environmental, and accessibility factors—were used, with Global Horizontal Irradiance (18%), temperature (11%), and slope (11%) identified as key drivers. Results show that 9.99% (13.61 km²) of the area has excellent suitability, mainly in the southwest, while 28.15% (38.33 km²) exhibits very high potential along the southeast coast. Additional classifications include good (22.29%), moderate (32.41%), and low (7.16%) suitability zones. Sensitivity analysis confirmed photovoltaic variables as dominant, with GHI (0.25) and diffuse radiation (0.23) showing the highest impact. The largest excellent zone could support approximately 390 MW, with excellent and very high zones combined offering up to 2080 MW capacity. The findings also underscore opportunities for dual-use solar deployment, particularly on salt pans (17.1%), as well as elevated solar installations in flood-prone areas. Overall, the proposed framework provides robust, spatially explicit insights to support sustainable energy planning and climate-resilient infrastructure development in coastal urban settings.

1. Introduction

The global energy sector is undergoing a fundamental transformation, largely driven by the urgent need to address climate change and transition to sustainable energy systems [1]. This transition has been accelerated by increasing policy support, declining renewable energy technology costs, and heightened public awareness of environmental sustainability [2]. Among renewable energy technologies, solar photovoltaic (PV) systems have experienced substantial technological advancements, resulting in improved economic feasibility and increased accessibility [3]. These developments are aligned with the United Nations Sustainable Development Goals (SDGs), particularly SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action), which emphasize the importance of equitable energy access and immediate climate action [4]. According to the International Renewable Energy Agency (IRENA), solar PV module costs have declined by 98% since 2010, driving a dramatic rise in global installed capacity—from 40 GW in 2010 to over 760 GW projected by 2030 [5]. This shift holds particular significance for developing economies such as India, where achieving energy security is inherently linked to environmental sustainability and broader developmental objectives [6]. Solar energy offers a strategic pathway to reduce greenhouse gas emissions, meet the surging energy demand, and extend energy access to underserved and remote communities [7]. In contrast to fossil-fuel-based systems, solar PV technologies support decentralized power generation, reduced operational costs, minimal ecological impacts, and alignment with international climate mitigation goals [8].

Globally, solar energy has seen remarkable progress with total PV capacity reaching 1185 GW, reflecting a compound annual growth rate exceeding 25% over the past ten years [9]. India’s solar sector has seen exponential growth under the National Solar Mission, with installed capacity increasing from 2.6 GW in 2014 to more than 64 GW by 2023, positioning the country as the fourth-largest solar market worldwide [10]. Due to its favorable geographic location and climatic conditions, Tamil Nadu, located in Southern India possesses immense potential for solar energy generation [11]. The state benefits from an average solar radiation of 5.6–6.0 kWh/m²/day and has 300 clear sunny days every year, providing optimal conditions for generating solar power [12]. The state’s comprehensive Solar Energy Policy 2019 targets 9000 MW of solar installations, emphasizing utility-scale projects, solar parks, canal-top installations, and distributed rooftop systems. In Tamil Nadu, the coastline of the Bay of Bengal stretching 1076 kilometres offers distinct opportunities and challenges for advancing solar energy projects in coastal areas [13]. These coastal regions provide several advantages, such as extensive land availability, high electricity demand, and established grid infrastructure [14]. Meanwhile, challenges include saline environments, cyclonic weather conditions, and competing land uses that require comprehensive assessment methodologies [15].

Smart urban planning offers a transformative model for sustainable urban development by integrating advanced technologies, data-driven decision-making, and participatory governance mechanisms [16]. Within this context, the deployment of solar PV systems plays a pivotal role in achieving sustainable urban development targets, particularly those outlined in SDG 11 (Sustainable Cities and Communities), by promoting energy efficiency and climate resilience [17]. Recent studies underscore the synergistic relationship between smart urban planning and solar adoption; for example, Perera et al. [18] demonstrated that data-driven solar integration can reduce urban carbon emissions by 15–30% while improving energy resilience in coastal cities.

Research on Solar farm sites (SFSs) suitability assessment reflects a shift from rudimentary analyses to sophisticated multi-criteria decision-making (MCDM) models that integrate technical, environmental, economic, and social parameters [19,20]. Geographic Information Systems (GIS) and Remote Sensing (RS) have become indispensable tools in this domain, enabling high-resolution mapping of rooftop PV potential and land suitability [21]. The Analytic Hierarchy Process (AHP) is widely adopted for solar site selection due to its ability to deconstruct complex decision problems into hierarchical structures and assign relative weights through systematic pairwise comparisons [21,22,23,24].

A comprehensive review by Jong et al. [25] identified AHP as the most frequently employed GIS-based MCDM technique for renewable energy siting, particularly effective in scenarios requiring transparency and traceability across diverse climatic contexts. For example, Rekik et al. [26] applied a GIS–AHP framework to site wind and solar farms in Tunisia, supporting long-term energy policy development. In Egypt, Elboshy et al. [27] employed a multi-criteria approach by categorizing parameters into location, environment, meteorology, and climatology for solar site suitability. Settou et al. [28] developed high-resolution suitability maps for grid-connected solar installations in Algeria, emphasizing the critical influence of raster resolution and data quality on spatial decision-making. In Saudi Arabia, Almasad et al. [23] combined fuzzy AHP with the PROMETHEE II method to identify optimal solar PV sites, incorporating sensitivity analysis for model validation. Similarly, in Turkey, Coruhlu et al. [29] emphasized the often-overlooked consideration of solar potential in land-use planning.

In the Indian context, Behera et al. [29] implemented a multi-criteria framework in Telangana’s Rangareddy District, integrating satellite data and socioeconomic indicators such as infrastructure proximity. In Kolkata, Dughairi et al. [30] utilized AHP and multi-criteria decision-making processes to identify solar potential zones, emphasizing proximity to urban centres to balance developmental needs with solar power. Rane et al. [31] applied the Multi-Influencing Factor (MIF) technique to improve the accuracy of solar PV siting and conducted sensitivity analysis to determine the key factors influencing prediction accuracy in Nashik city, Maharashtra. Vasudevan et al. [32] comprehensively evaluated 16 southern districts in India by employing a weighted overlay multi-criteria analysis incorporating region-specific factors such as geopolitical and financial incentives aligning with the state’s unique social landscape. Ravichandran et al. [33] explored the feasibility of floating photovoltaic systems (FPVs) in 118 reservoirs across Tamil Nadu using GIS-based methods. Despite such progress, significant methodological gaps remain in applying these frameworks to coastal urban environments, which pose distinct microclimatic conditions, land-use complexities, and resilience requirements that are inadequately addressed by conventional siting approaches in developing economies [27,28,30,31,34]. Specifically, there is limited application of sensitivity analysis in evaluating SFSs suitability in coastal urban microclimates—despite its importance for ensuring model robustness and parameter prioritization [35,36,37]. Most existing studies have focused on broader regional or rural assessments, with insufficient attention to the dynamic socio-environmental constraints characteristic of developing coastal cities.

Despite such progress, significant methodological gaps remain in applying these frameworks to coastal urban environments, which pose distinct microclimatic conditions, land-use complexities and resilience requirements that are inadequately addressed by conventional siting approaches in developing economies. The novelty of this study lies in three key aspects: (1) the comprehensive integration of coastal-specific environmental parameters including humidity-induced corrosion assessment and salt spray considerations not typically addressed in inland studies; (2) the application of univariate sensitivity analysis specifically calibrated for coastal microclimates, providing robust validation of parameter prioritization in marine environments; and (3) the development of innovative dual-use deployment strategies for salt pan areas and flood-prone coastal zones. While previous studies have focused on broader regional assessments, this research addresses the unique challenges of coastal urban solar siting through a methodologically rigorous framework that can be adapted globally for similar coastal environments facing rapid urbanization and climate vulnerability. To address these critical gaps, this study develops an integrated multi-criteria decision-making framework for identifying optimal SFSs in southern Tamil Nadu, taking the Thoothukudi coastal city as a case study. Leveraging RS and geospatial datasets using a GIS-based AHP approach, the study incorporates univariate sensitivity analysis to validate model reliability. The specific objectives are to: (1) integrating multiple parameters representing photovoltaic, climatic, topographic, environmental and accessibility factors; (2) establishing parameter weights through AHP; (3) generating a composite SFSs suitability index to identify high-potential zones; (4) evaluating model reliability through univariate sensitivity analysis and effective weight calculation; and (5) developing recommendations for integrating identified solar potential zones into urban smart planning frameworks. This research contributes to bridging methodological gaps in solar energy siting within coastal urban settings and provides spatially explicit, evidence-based insights to support renewable energy integration in urban development planning.

2. Materials and Methods

2.1. Study Area

The study area is a major port city located on the southern coast of India, encompassing a coastline of 635,780 km², with the specific urban zone under investigation covering 136.15 km². This coastal city lies adjacent to coral reef ecosystems that form part of the Gulf of Mannar Biosphere Reserve (Figure 1). The region is characterized by a tropical semi-arid climate, with an annual mean temperature of 28.9 °C, hot summers frequently exceeding 38 °C, and mild winters rarely dropping below 20 °C during the period 2000–2024 [38]. Annual precipitation is relatively low at approximately 655 mm, while the area experiences nearly 300 clear sunny days each year. This high solar availability translates into an average annual Global Horizontal Irradiance (GHI) of 5.5–6.0 kWh/m²/day, making the region highly suitable for solar energy development.

Economically and socially, the city is shaped by diverse industries centered around its port, with shipping (import–export), fishing, salt production, and chemical manufacturing serving as key economic drivers. The urban environment is densely populated, with a literacy rate of 86.16%, and supported by substantial industrial infrastructure, including major facilities such as the Southern Petrochemical Industries Corporation (SPIC) and Tuticorin Alkali Chemicals (TAC).

The current energy landscape and coastal characteristics of Thoothukudi present unique opportunities and challenges for solar energy development. The region primarily depends on conventional thermal power plants, including the Neyveli Thermal Power Limited (NTPL)-Tuticorin Thermal Power Station (1050 MW) and Coastal Energen’s Mutiara Power Plant (1200 MW), alongside growing renewable energy projects like the SPIC Floating Solar PV Park (14.8 MW) and the VOC Port 5 MW Solar Power Plant [39]. The area is efficiently linked to both state and national power grids through high-voltage transmission systems. The coastal location provides advantageous topography characterized by gently slope terrain not exceeding 10 m above sea level and lower aerosol levels leading to higher direct normal irradiance. The region experiences relatively cooler ambient temperatures compared to inland areas, with a difference of 2 °C to 4 °C during summer, which could enhance photovoltaic efficiency. The area faces challenges such as sea breeze influencing cloud formation in the afternoons, marine-induced corrosion necessitating specialized equipment, and competition for land use in the densely developed urban coastal area. The existence of salt pans and underutilized coastal areas offers potential for creative dual-use solar projects that can be integrated into the region’s smart city development initiatives.

2.2. Data Acquisition and Preprocessing

The study utilized a range of spatial datasets from various sources to evaluate the suitability of SFSs in Thoothukudi.

Table 1. Data sources used for solar farm site (SFSs) suitability analysis, with descriptions and justifications.

Factor	Parameters	Data Source	Description	Justification	Literature
Photovoltaic	GHI	Global Solar Atlas 2.0	Satellite-derived measurements of total solar radiation received on a horizontal surface	Essential for determining energy generation potential; provides the most comprehensive and consistent solar resource data globally validated in coastal environments	[23,41,42,43]
	Diffuse Radiation	Global Solar Atlas 2.0	Solar radiation scattered by atmospheric particles and clouds	Critical for assessing actual PV performance in coastal areas that experience periodic cloud cover and higher aerosol concentrations from marine environment	[21,31,44]
Climate	Temperature	Nasa Power Data Access Viewer MERRA-2	Daily and monthly average air temperature data derived from satellite observations and atmospheric models	Directly affects PV efficiency and system performance; NASA Power Data offers reliable long-term temperature records specifically validated for coastal regions.	[45,46]
	Relative Humidity	Nasa Power Data Access Viewer MERRA-2	Atmospheric moisture content data from the Modern-Era Retrospective analysis for Research and Applications	Influences both PV panel degradation rates and solar radiation attenuation, particularly critical in coastal environments for corrosion assessment	[47,48]
Topographic	Slope	USGS SRTM DEM	Terrain gradient calculated from digital elevation model	Higher resolution data enables precise identification of areas with optimal gradient for construction and panel orientation; critical for coastal areas with varying topography	[22,28,49]
	Elevation	USGS SRTM DEM	Height above sea level derived from Shuttle Radar Topography Mission	Essential for flood risk assessment in coastal locations; SRTM provides consistent global coverage with adequate vertical accuracy for coastal vulnerability analysis	[22,28,38]
Environmental	Land Use Land Cover	Landsat-8-OLI/TIRS	Current land usage classification derived from multispectral satellite imagery	Landsat-8 provides up-to-date land cover information with sufficient detail to identify available land while avoiding environmentally sensitive areas; crucial for coastal zone management	[50,51]
	Proximity to Waterbodies	Open Street Map	Distance to lakes, rivers, coastal waters, and other water features	Community-verified data with regular updates; enables balancing water access needs for panel cleaning with environmental protection requirements in coastal ecosystems	[33,52]
Accessibility	Proximity to Transmission Line	GLOBIL-WWF-ArcGIS	Distance to existing electrical grid infrastructure	Combines global and regional transmission line datasets; critical for assessing grid connection feasibility and costs, particularly important in coastal areas with limited infrastructure	[42,53]
	Proximity to Road Network	Open Street Map	Distance to transportation network	Provides comprehensive and regularly updated road network data; essential for construction logistics and maintenance accessibility assessment in coastal environments	[23,53]

presents the primary data sources used in this research. The parameter selection was based on a comprehensive literature review of studies on solar site selection in coastal environments, ensuring scientific validity and transferability of our framework. Ancillary data utilized in this study include administrative boundaries obtained from the Survey of India (SOI). The selection criteria were validated through expert consultation with renewable energy specialists from academic institutions and industry. These datasets provided essential context and constraints for the suitability analysis. A comprehensive methodological framework was developed for SFSs suitability analysis, integrating multiple geospatial datasets and analytical techniques (Figure 2). The study area’s environmental parameters demonstrate characteristics representative of global coastal urban environments. Thoothukudi’s solar irradiance levels, coastal topography (elevation range −13 to 31 m) and maritime climate conditions align with benchmark coastal cities used in international renewable energy assessments. The methodological framework utilizes globally standardized datasets (Global Solar Atlas, NASA POWER, USGS SRTM) ensuring compatibility with international coastal solar studies, while local expert consultation follows established AHP protocols validated across diverse geographic contexts [40].

All spatial datasets underwent preprocessing to achieve uniformity in projection, resolution, and extent, ensuring they were properly integrated into the multi-criteria analysis framework. The preprocessing workflow involved projecting all datasets to UTM Zone 43N with the WGS 84 datum for spatial accuracy and distance-based analyses. Raster datasets were resampled to a 30 m resolution using bilinear interpolation for continuous variables and nearest neighbour resampling for categorical data. Buffer zones were created, and Euclidean distance calculations were performed for vector data such as transmission lines, roads, and waterbodies to generate continuous distance raster surfaces. Each criterion was normalized to a common scale of 1–5, where 1 represented the least suitable and 5 the most suitable conditions. Continuous variables were reclassified into suitability classes using the natural breaks method.

Global Transferability Framework

The developed AHP-GIS methodology demonstrates significant global applicability through its systematic parameter selection and weighting approach. The framework’s transferability is enhanced through: (1) standardized criteria selection based on comprehensive literature review spanning multiple coastal regions globally; (2) adaptable weighting schemes that can be recalibrated through local expert consultation processes; (3) modular parameter structure allowing substitution of region-specific datasets while maintaining methodological consistency; and (4) validation protocols that can be replicated across diverse coastal environments. The sensitivity analysis component provides additional robustness by identifying critical parameters that require local calibration versus those with universal applicability. This methodological foundation can be successfully applied in various geographic contexts, demonstrating its potential for global coastal solar assessment programs.

2.3. Multi-Criteria Decision Analysis

The Analytic Hierarchy Process (AHP) was employed to determine the relative importance of each criterion in the SFSs suitability analysis. The AHP methodology provides a robust framework that can be adapted and calibrated for different coastal regions worldwide by adjusting parameter weights based on local conditions, expert knowledge, and regional priorities [54]. The AHP method developed by Saaty [54] provides a structured framework for organizing and analyzing complex decisions through pairwise comparisons.

The pairwise comparison process was conducted through structured consultations with renewable energy experts including academic specialists (3) and industry professionals (2). The expert selection process employed systematic criteria to ensure comprehensive coverage of interdisciplinary knowledge required for coastal solar energy site assessment. Experts were selected based on: (1) minimum 5 years research/industry experience in renewable energy or coastal applications; (2) regional familiarity with Southern Indian coastal conditions and regulatory frameworks; (3) disciplinary diversity spanning nanoscience/PV technology, renewable energy systems, geospatial analysis, and industrial implementation; and (4) institutional credibility through affiliation with recognized academic institutions or established renewable energy companies. Expert identification was conducted through literature review of coastal renewable energy researchers, consultation with Tamil Nadu Energy Development Agency, outreach to South Indian university renewable energy departments, and professional association networking. The final expert panel composition ensuring comprehensive technical and practical knowledge coverage is detailed in Supplementary Table S1. Despite outreach more experts, several factors limited participation: (1) specialized expertise requirements combining photovoltaic technology, coastal environmental science and geospatial analysis; (2) need for regional familiarity with Tamil Nadu coastal conditions and regulations; (3) limited availability of industry professionals with coastal solar experience; and (4) academic scheduling conflicts during the study timeline. The intersection of required expertise areas proved to be the primary constraint in achieving a larger panel size.

The methodology involved structuring the decision problem into a hierarchy with the goal (optimal SFSs) at the top, followed by five factors (photovoltaic, climate, topographic, environmental, and accessibility) at the intermediate level, and ten individual criteria at the lowest level. Expert judgments were elicited to compare each pair of elements using Saaty’s nine-point scale, where 1 indicates equal importance and 9 indicates extreme importance of one element over another, with the pairwise comparisons structured in matrix form as A = [a_ij], where a_ij represents the importance of criterion i relative to criterion j. Expert evaluation followed a structured three-phase protocol: (1) standardized briefing with background materials and methodology explanation; (2) independent pairwise comparison interviews (60–90 min) using standardized questionnaires without inter-expert communication; and (3) weight validation with qualitative justification. Bias mitigation included facilitator neutrality, anonymous response processing, consistency validation (CR > 0.1 triggering follow-ups), geometric mean aggregation for consensus weights, and literature triangulation against international coastal solar studies.

The weightage of each parameter was calculated from the Pairwise Comparison matrix (PCM) using the eigenvalue method shown in

Table 2. Pairwise comparison matrix for SFSs suitability criteria using Analytical Hierarchy Process (AHP).

Criteria	GHI	DR	TE	RH	SL	EL	LC	PWB	PTL	PRN	Weighted Sum
GHI	1.00	0.50	2.00	0.33	3.00	2.00	5.00	2.00	0.25	3.00	1.90
DR	2.00	1.00	0.33	0.10	2.00	5.00	0.33	0.20	0.33	1.00	1.22
TE	1.00	0.33	1.00	0.20	0.33	2.00	2.00	3.00	0.50	2.00	1.23
RH	3.00	0.20	0.50	1.00	0.50	2.00	1.00	3.00	0.25	0.33	1.17
SL	1.00	0.33	0.33	2.00	1.00	1.00	1.00	2.00	0.20	3.00	1.18
EL	2.00	0.50	0.25	0.33	0.33	1.00	1.00	0.50	3.00	0.50	0.94
LC	3.00	0.33	0.20	0.50	0.25	1.00	1.00	1.00	1.00	0.33	0.86
PWB	2.00	0.20	0.25	0.50	0.33	1.00	1.00	1.00	0.25	0.50	0.70
PTL	1.00	2.00	0.50	0.50	2.00	0.20	0.25	0.33	1.00	0.33	0.81
PRN	2.00	0.25	0.25	3.00	0.50	0.33	0.20	0.50	0.25	1.00	0.82

Note: GHI = Global Horizontal Irradiance, DR = Diffuse Radiation, TE = Temperature, RH = Relative Humidity, SL = Slope, EL = Elevation, LC = Land Use Land Cover, PWB = Proximity to Waterbodies, PTL = Proximity to Transmission Lines, PRN = Proximity to Road Network.

. The PCM was then transformed into a Normalized Pairwise Comparison Matrix (NPCM) by dividing each element by the sum of its column, as displayed in

Table 3. Normalized pairwise comparison matrix and derived weights for SFSs suitability criteria.

Criteria	GHI	DR	TE	RH	SL	EL	LC	PWB	PTL	PRN	Eigen Vector
GHI	0.06	0.09	0.36	0.04	0.29	0.13	0.39	0.15	0.04	0.25	0.18
DR	0.11	0.18	0.06	0.01	0.20	0.32	0.03	0.01	0.05	0.08	0.10
TE	0.06	0.06	0.18	0.02	0.03	0.13	0.16	0.22	0.07	0.17	0.11
RH	0.17	0.04	0.09	0.12	0.05	0.13	0.08	0.22	0.04	0.03	0.09
SL	0.06	0.06	0.06	0.24	0.10	0.06	0.08	0.15	0.03	0.25	0.11
EL	0.11	0.09	0.04	0.04	0.03	0.06	0.08	0.04	0.43	0.04	0.10
LC	0.17	0.06	0.04	0.06	0.02	0.06	0.08	0.07	0.14	0.03	0.07
PWB	0.11	0.04	0.04	0.06	0.03	0.06	0.08	0.07	0.04	0.04	0.06
PTL	0.06	0.35	0.09	0.06	0.20	0.01	0.02	0.02	0.14	0.03	0.10
PRN	0.11	0.04	0.04	0.35	0.05	0.02	0.02	0.04	0.04	0.08	0.08

. From this NPCM, the weightage was derived by calculating the average of each row, thus obtaining the relative weights of each criterion [55]. The principal eigenvalue (λ_max) was computed to assess consistency. The consistency of the judgments was evaluated using the Consistency Ratio using the equation:

$$\mathrm{C}\mathrm{R}={\displaystyle \frac{\mathrm{C}\mathrm{I}}{\mathrm{R}\mathrm{I}}}$$

where CI (Consistency Index) = (λ_max)/(n − 1), RI (Random Index) is the average CI of randomly generated matrices, and n is the number of criteria being compared, with a CR value less than 0.1 (10%) considered acceptable, indicating consistent judgments [56]. This value was determined by calculating the principal eigenvalue (λ_max = 10.958), deriving the Consistency Index (CI = 0.106) by normalizing the difference between λ_max and the matrix order (n = 10), and then dividing CI by the Random Index value for n = 10 (RI = 1.49). The pairwise judgments demonstrated acceptable consistency with a CR of 0.071, which falls below the threshold of 0.1.

2.4. Solar Potential Suitability Index Calculation

The Solar Potential Suitability Index (SPSI) was calculated using the Weighted Sum Overlay tool in ArcMap 10.8. This GIS-based multi-criteria evaluation technique combines all standardized parameters, with each criterion weighted according to its relative importance determined by the AHP [27]. The methodology demonstrates high transferability to other coastal regions through systematic parameter recalibration and local expert consultation processes. The formula for SPSI calculation is:

$$\mathrm{S}\mathrm{P}\mathrm{S}\mathrm{I}={\sum }_{\mathrm{i}-1}^{\mathrm{n}}(\mathrm{w}\mathrm{i}\ast \mathrm{r}\mathrm{i})$$

where SPSI is the Solar Potential Suitability Index, w_i is the weight of criterion i derived from AHP, and r_i is the standardized raster value of criterion I [56]. The resulting SPSI was classified into five suitability categories using the natural breaks method: Low Potential, Moderate Potential, Good Potential, Very High Potential, and Excellent Potential. The resulting SPSI framework can be adapted for different geographic contexts by modifying criteria weights based on local environmental conditions and stakeholder priorities.

2.5. Sensitivity Analysis

Sensitivity analysis was performed to assess the SFSs suitability model’s robustness and understand how input parameter variation influences the final outcomes [57]. This process involved two methods: univariate sensitivity analysis and effective weight calculation, which evaluated the actual contribution of each criterion to the final suitability index.

2.5.1. Univariate Sensitivity Analysis

A univariate sensitivity analysis approach was implemented to systematically evaluate the influence of each parameter on the final suitability scores [58]. For each parameter, the weightage value was adjusted by −50%, −25%, +25% and +50% from the base weight, while maintaining all other parameters at their original weights. This individual-parameter adjustment methodology allowed for direct measurement of each parameter’s influence on the suitability scores, effectively identifying individual contributions to model output [59]. The Sensitivity Index of each parameter was calculated using the formula,

$$\mathrm{S}\mathrm{e}\mathrm{n}\mathrm{s}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{v}\mathrm{i}\mathrm{t}\mathrm{y} \mathrm{I}\mathrm{n}\mathrm{d}\mathrm{e}\mathrm{x}=\left|{\displaystyle \frac{\Delta \mathrm{R}}{\mathrm{R}}}\right|÷\left|{\displaystyle \frac{\Delta \mathrm{W}}{\mathrm{W}}}\right|$$

where ΔR represents the change in results (average suitability score), R represents the baseline result value, ΔW represents the change in parameter weight, and W represents the baseline parameter weight.

2.5.2. Effective Weight Calculation

Effective weight quantifies a parameter’s actual contribution to the final suitability index, revealing discrepancies between theoretical weights assigned through AHP and real influence in the spatial model [60]. The effective weight is calculated using the formula,

$$\mathrm{W}=\left({\displaystyle \frac{\mathrm{P}\mathrm{r}\ast \mathrm{P}\mathrm{w}}{\mathrm{S}\mathrm{P}\mathrm{S}\mathrm{I}}}\right)\ast 100$$

where Pr is the rating value, Pw is the weight of the parameter, and SPSI is the overall solar potential suitability index.

3. Results

3.1. Factors and Parameter Distribution

The analysis reveals the spatial distribution of various factors influencing the suitability of locations for solar farms, exhibiting a unique pattern across the study area, highlighting specific constraints and opportunities that collectively determine the overall solar energy potential in the region.

3.1.1. Photovoltaic Factors

The GHI distribution across the study area reveals a pronounced southeast-to-northwest gradient with values ranging from 2030 to 2097 kWh/m²/year, demonstrating that the region’s exceptional solar potential is displayed in Figure 3a. The southeastern region encompassing Thermal Nagar, Harbor Estate, and adjacent coastal areas exhibits the highest GHI values (2084–2097 kWh/m²/year), covering approximately 47.68 km² (35.02%) of the region. This optimal zone transitions into premium classification areas (2071–2083 kWh/m²/year) in the central portions around Bryant Nagar and Caldwell Colony, occupying about 54.59 km² (40.09%) of the study area. The excellent class (2058–2070 kWh/m²/year) forms a transitional band through areas like Muthammal Colony and parts of Korampallam, accounting for 18.63 km² (13.69%), while the western boundary near Sankaraperi and northwestern side display the lowest GHI values (2030–2043 kWh/m²/year) comprising about 15.25 km² (11.2%). This spatial distribution indicates that over three-quarters of the region enjoys very high to excellent solar resource potential with annual GHI values significantly above the average value, making the study area an exceptionally advantageous location for solar PV development [43]. The substantial coverage of high-potential zones suggests considerable opportunities for both utility-scale and distributed solar installations, with the southeastern regions particularly suited for maximizing energy generation efficiency from standard crystalline silicon PV technologies [61].

The Diffuse Radiation (DR) pattern displays a notable inverse relationship to GHI with values ranging from 916 to 941 kWh/m²/year, providing critical insights for optimal photovoltaic site selection, as shown in Figure 3b. The southern portions, particularly around Thermal Nagar, Pottalkadu, Harbor Estate, and Muthiapuram, exhibit the lowest diffuse radiation component (916–921 kWh/m²/year), accounting for approximately 39.44 km² (28.97%) of the total area, indicating clearer atmospheric conditions favorable for direct beam radiation capture. The intermediately diffuse category (922–927 kWh/m²/year) dominates the central region around Bryant Nagar and forms the largest class covering approximately 49.3 km² (36.21%) of the region. The considerably diffuse zones (928–932 kWh/m²/year) occupy transitional areas of about 23.47 km² (17.24%), while the northern and western portions show progressively higher diffuse radiation values with the highly diffuse class (933–937 kWh/m²/year) and predominantly diffuse zones (938–941 kWh/m²/year) concentrated in the northwestern boundary near Sankaraperi comprising about 23.94 km² (17.59%). The relationship between direct and diffuse radiation components provides strategic flexibility in PV site selection and system design optimization [35]. Areas with lower diffuse components are ideally suited for conventional crystalline silicon technologies that excel under direct beam radiation [44]. The regions with higher diffuse radiation might benefit from thin-film technologies or bifacial modules that demonstrate relatively better performance under scattered light conditions, allowing for technology-specific deployment strategies across different study area zones [62].

3.1.2. Climatic Factors

A complex spatial pattern influenced by urban development, topography, and proximity to the coast is revealed by the temperature (Figure 4a) distribution across the study area, with values ranging from 27.48 °C to 28.32 °C. The most favorable Optimal temperature zones (27.48–27.65 °C) are limited to small isolated patches covering merely 1.63 km² (1.2%) near Bryant Nagar, where cooler microclimates have developed. Efficient temperature conditions (27.66–27.82 °C) extend outward from these optimal zones primarily as a buffer region encompassing about 7.82 km² (5.75%), creating a concentric pattern around Bryant Nagar and Caldwell Colony. The vast majority of the region, with approximately 101.15 km² (74.35%), falls within the Moderate temperature range (27.83–27.99 °C), creating a dominance in the distribution which includes areas of Sankaraperi, Muthammal Colony, Madathur, Thermal Nagar, and portions of Korampallam and Pottalkadu. The higher temperature class, categorized as Challenged (28.00–28.15 °C) and Critical (28.16–28.32 °C), collectively comprises about 25.44 km² (18.7%), appearing most prominently in two distinct hotspots around Arockiapuram in the northeast and a more significant zone encompassing parts of Harbor Estate and extending inland to Muthiapuram. This distribution in temperature has significant implications for photovoltaic performance as solar panel efficiency typically decreases by 0.4–0.5% for each degree Celsius rise above standard test conditions (25 °C) [45]. The prevalent moderate temperature conditions across most of the region would result in approximately 1.1–1.5% efficiency reduction, while the hotspot areas could experience efficiency losses of up to 1.7% [46].

The relative humidity (Figure 4b) pattern shows a clear southwest-to-northeast gradient with notable variations highlighting the impact of coastal conditions and urban development, with values ranging from 72.81% to 76.62% in the study area. The lowest humidity class, Standard (72.81–73.57%), is predominantly observed in the northeastern part of Arockiapuram village, covering approximately 4.03 km² (2.96%), creating an isolated region of drier conditions. The Moderate humidity class (73.58–74.34%) dominates the northern half of the study area, encompassing about 59 km² (43.37%), including regions such as Sankaraperi, Madathur, Muthammal Colony, Threspuram, and the Harbor Estate area. The central region of the study area exhibits Considerable humidity levels (74.35–75.11%) with around 33.98 km² (24.98%) surrounding the Bryant Nagar and Caldwell Colony area before extending eastward to include Thermal Nagar and portions of the coastal zone. The highest humidity class ‘Saturated’ (75.12–75.88%) and Extreme (75.89–76.62%) encompasses approximately 39.02 km² (28.68%), forming a pronounced corridor extending from the southwestern boundary around Pottalkadu and Athimarapatti northeast-ward through Mullakkadu and Muthiapuram, with the extreme humidity zone particularly concentrated in the southwestern side. These humidity patterns hold significant implications for photovoltaic system performance and longevity. Higher humidity levels attenuate incoming solar radiation through increased atmospheric moisture and accelerate system component corrosion and degradation [48]. The specialized moisture-resistant equipment and enhanced maintenance protocols would be suggested, particularly crucial for installations in the southwestern regions, where elevated installation designs that promote air circulation and frequent cleaning protocols would be essential to prevent salt buildup on panel surfaces that can reduce light transmission efficiency [47]. High humidity zones (Saturated 75.12–75.88% and Extreme 75.89–76.62%) encompassing 28.68% of the study area require specialized equipment considerations, including enhanced moisture protection, corrosion-resistant mounting systems, and moisture-barrier technologies for electrical components. Salt-laden air exposure in coastal environments can cause accelerated corrosion of mounting structures, electrical connections, and panel frames, requiring specialized materials such as marine-grade aluminum and stainless steel components.

3.1.3. Topographic Factors

The slope distribution (Figure 5a) demonstrates favorable conditions for solar photovoltaic installations, with the vast majority of the study area characterized by minimal to moderate inclinations that facilitate cost-effective construction and optimal panel orientation. The Flat terrain (0–3% slope) dominates the region, covering 72.9 km² (53.55%), with noticeable areas around Thermal Nagar, Bryant Nagar, and extending through central regions toward the coastal zones, creating ideal platforms for large-scale solar arrays that require minimal grading or specialized mounting structures. Gentle slope (3–8) constitutes 58.5 km² (42.97%) and appears as a complex mosaic interspersed throughout the region, intense around Sankaraperi, Madathur, Muthammal Colony, and forming a network pattern across most of the peripheral regions. Moderate slope (8–15%) areas are notably limited to 4.47 km² (3.28%) and appear primarily as isolated transitional zones between the gentle and steeper terrain, with small concentrations observed near Harbor Estate and in southern areas around Pottalkadu and Mullakkadu. The steepest classification categories, such as Steep (15–25%) and Very Steep (>25%), are extremely limited in the study area, accounting for 0.27 km² (0.2%) as isolated regions that likely correspond to artificial embankments or coastal breakwaters observed near the Harbor Estate area. An exceptional topographic advantage for solar energy development is represented by the dominance of flat and gentle terrain (96.52%) [63]. This substantially reduces construction costs, simplifies installation procedures, and eliminates the need for extensive earthwork modifications that would otherwise increase project expenses and environmental impacts [49].

The elevation (Figure 5b) pattern reveals a diverse topographic landscape with significant implications for solar farm development, ranging from below sea level depressions to elevated inland zones. The Lowland Zone (4.7–13.4 m) constitutes the largest portion of the municipal area at approximately 71.18 km² (52.28%), forming a central band that encompasses areas around Muthammal Colony, Threspuram, Athimarapatti, portions of Thermal Nagar, and extends to Korampallam and Mullakkadu. The Coastal Zone (−4.2–4.6 m) comprises approximately 50.15 km² (36.83%) of the total area, predominantly distributed in a concentric pattern around Bryant Nagar and Caldwell Colony, extending eastward to Harbor Estate and southern portions near Mullakkadu, creating a transitional zone between the central lowlands and the coast. The Midland Zone (13.5–22.2 m) accounts for about 14.41 km² (10.58%) observed along the western boundary near Sankaraperi and Madathur, forming a distinct elevational gradient that rises from the central lowlands. The extreme elevation categories, such as Upland Zone (22.2–31 m) and Subsea Zone (−13 to −4.3 m), are less occupied, with 0.42 km² (0.3%) of the study area with the highest elevations appearing as isolated patches in the western corners of the region.

3.1.4. Environmental Factors

Assessing a site’s solar energy potential requires evaluating land use and land cover (Figure 6a), as these factors impact how much sunlight reaches the area and whether infrastructure can be feasibly built. Barren Land constituted the largest category (35.83 km², 26.32%), forming a central band through Bryant Nagar and extending northwest toward Sankaraperi, offering prime areas for solar development with minimal ecological impact. Cultivated Land (28.74 km², 21.11%) is concentrated in western portions near Athimarapatti, Pottalkadu, and Korampallam, representing areas where solar development might conflict with agricultural production. Salt Pans (23.28 km², 17.1%) formed large adjoining areas in the southeastern section around Mullakkadu and south of Thermal Nagar, presenting unique opportunities for innovative elevated PV structures that could permit continued salt production underneath, creating dual-use synergies. Urban Area (21.68 km², 15.93%) concentrated in the northeastern quadrant around Threspuram and Muthammal Colony, forming a dense urban core with limited solar potential. Tree/Shrub areas (22.15 km², 16.27%) appeared as scattered patches throughout, while ‘Waterbodies (4.47 km², 3.28%) comprised the smallest land use category with visible features south of Thermal Nagar. Approximately 59.11 km² (43.42%) of the study area consists of barren lands, and salt pans offer excellent environmental compatibility for solar development [50]. The urban areas and cultivated lands (50.42 km², 37.04%) present significant land use constraints in solar potential suitability [51].

Proximity to Waterbodies (Figure 6b) in the study area revealed a concentric pattern radiating from central water features with Immediate Proximity zones (0–1423 m) encompassing 70.77 km² (51.98%) in a broad central band extending from Thermal Nagar through Madathur. Near Proximity zone (1423–2846 m) covered 42.46 km² (31.18%), creating a transitional ring particularly evident near Bryant Nagar and Caldwell Colony. Moderate Distant locations (2847–4269 m) accounted for 21.15 km² (15.54%), in the periphery near Sankaraperi and southwest of Mullakkadu, while Distant and Remote zones were low with only 1.78 km² (1.3%). This distribution presents a dual consideration: while proximity to water sources facilitates panel cleaning operations, very close proximity may raise environmental concerns and conflict with riparian ecosystems [52]. The Near Proximity and Moderate Distant zones collectively representing 63.61 km² (46.72%) offer optimal conditions, balancing water accessibility for maintenance with sufficient buffer distances to minimize ecological disruption [50]. The most favorable areas extend through the central-southern portions, particularly evident in the Thermal Nagar and the peripheral areas southwest of Mullakkadu, which align well with suitable land use categories.

3.1.5. Accessibility Factors

Proximity to Transmission Lines (Figure 7a) facilitates easier and more cost-effective connection to the power grid, potentially streamlining the development process and reducing infrastructure costs. Immediate Access zones (0–865 m) dominate by covering 102.76 km² (75.47%), forming a comprehensive network that extends throughout the study area. The spatial pattern shows particularly comprehensive transmission coverage in the central and eastern portions, with some reduction in accessibility toward the periphery areas. Near Access areas (866–1730 m) appear as transitional zones along the western and northwestern edges, accounting for 21.38 km² (15.71%). Moderate Access zone (1731–2595 m) is observed in the far northwest near Sankaraperi and small pockets in the southwest, encompassing 8.19 km² (6.02%). The study area has 91.18% situated within 1730 m of existing transmission infrastructure, grid connection costs would be minimized across most potential development sites, enhancing overall project viability [42].

Road Network (Figure 7b) accessibility reduces transportation costs and logistical challenges during construction and maintenance, enhancing the project’s overall feasibility. Direct Access zones (0–432 m from roadways) encompassed a massive area of 129.39 km² (95.04%), creating a nearly uniform coverage pattern across the study area. The extreme northwestern corner near Sankaraperi shows limited access with a progression through different zones. The study area’s central and eastern portions exhibit complete Direct Access coverage, including Bryant Nagar, Thermal Nagar, Threspuram, and Muthammal Colony. The southwest’s predominantly rural areas such as Pottalkadu and Mullakkadu, show excellent road connectivity. Near Access areas (433–865 m) appear as small patches primarily along the western periphery and along the harbour extension, collectively accounting for just 4.24 km² (3.11%). Areas with Moderate Access to ‘Remote Access (866–2165 m) form a small gradient pattern only in the northwestern part, representing a negligible area of 2.52 km² (1.85%). This exceptional road connectivity pattern is a significant advantage for solar farm development, facilitating construction logistics, equipment transportation, and maintenance operations with less accessibility constraints throughout the study area [23].

4. Discussion

4.1. Parameter Weights and Factor Category Distribution

The AHP was employed to determine the relative importance of criteria for identifying optimal solar potential sites in the study area through pairwise comparisons of ten criteria across five factor categories, resulting in a systematic weighting scheme shown in

Table 4. Multi-Criteria Parameter Analysis for Solar Potential Assessment.

Parameter	Class	Range	Area (sq.km.)	Percent (%)	Rating	Solar Potential	Weightage
Global Horizontal Irradiance (GHI) (kWh/m2/year)	Baseline	2030–2043	5.47	4.02	1	Low Potential	18
	Favourable	2044–2057	9.78	7.18	2	Moderate Potential
	Excellent	2058–2070	18.63	13.69	3	Good Potential
	Premium	2071–2083	54.59	40.09	4	Very High Potential
	Optimal	2084–2097	47.68	35.02	5	Excellent Potential
Diffuse Radiation (kWh/m2/year)	Moderately Diffuse	916–921	39.44	28.97	1	Low Potential	10
	Intermediately Diffuse	922–927	49.3	36.21	2	Moderate Potential
	Considerably Diffuse	928–932	23.47	17.24	3	Good Potential
	Highly Diffuse	933–937	18.36	13.49	4	Very High Potential
	Predominantly Diffuse	938–941	5.58	4.1	5	Excellent Potential
Temperature (°C)	Optimal	27.48–27.65	1.63	1.2	5	Excellent Potential	11
	Efficient	27.66–27.82	7.82	5.75	4	Very High Potential
	Moderate	27.83–27.99	101.15	74.35	3	Good Potential
	Challenged	28.00–28.15	22.6	16.62	2	Moderate Potential
	Critical	28.16–28.32	2.84	2.08	1	Low Potential
Relative Humidity (%)	Standard	72.81–73.57	4.03	2.96	5	Excellent Potential	9
	Moderate	73.58–74.34	59	43.37	4	Very High Potential
	Considerable	74.35–75.11	33.98	24.98	3	Good Potential
	Saturated	75.12–75.88	16.41	12.06	2	Moderate Potential
	Extreme	75.89–76.62	22.61	16.62	1	Low Potential
Slope (%)	Flat	0–3	72.9	53.55	5	Excellent Potential	11
	Gentle	3–8	58.5	42.97	4	Very High Potential
	Moderate	8–15	4.47	3.28	3	Good Potential
	Steep	15–25	0.26	0.19	2	Moderate Potential
	Very Steep	>25	0.01	0.01	1	Low Potential
Elevation (m)	Subsea Zone	−13–−4.3	0.1	0.07	5	Excellent Potential	10
	Coastal Zone	−4.2–4.6	50.15	36.83	4	Very High Potential
	Lowland Zone	4.7–13.4	71.18	52.28	3	Good Potential
	Midland Zone	13.5–22.2	14.41	10.58	2	Moderate Potential
	Upland Zone	22.2–31	0.32	0.23	1	Low Potential
Land Use Land Cover	Salt Pan	1	23.28	17.1	5	Excellent Potential	7
	Cultivated Land	2	28.74	21.11	2	Moderate Potential
	Barren Land	3	35.83	26.32	4	Very High Potential
	Urban Area	4	21.68	15.93	1	Low Potential
	Tree/Shrub	5	22.15	16.27	3	Good Potential
	Waterbodies	6	4.47	3.28	1	Low Potential
Proximity to Waterbodies (m)	Immediate Proximity	0–1423	70.77	51.98	5	Excellent Potential	6
	Near Proximity	1423–2846	42.46	31.18	4	Very High Potential
	Moderate Distant	2847–4269	21.15	15.54	3	Good Potential
	Distant	4270–5692	1.42	1.04	2	Moderate Potential
	Remote	5693–7115	0.36	0.26	1	Low Potential
Proximity to Transmission Line (m)	Immediate access	0–865	102.76	75.47	5	Excellent Potential	10
	Near Access	866–1730	21.38	15.71	4	Very High Potential
	Moderate Access	1731–2595	8.19	6.02	3	Good Potential
	Limited Access	2596–3460	2.91	2.14	2	Moderate Potential
	Remote Access	3461–4326	0.9	0.66	1	Low Potential
Proximity to Road Network	Direct access	0–432	129.39	95.04	5	Excellent Potential	8
	Near Access	433–865	4.24	3.11	4	Very High Potential
	Moderate Access	866–1298	1.63	1.2	3	Good Potential
	Limited Access	1299–1731	0.73	0.54	2	Moderate Potential
	Remote Access	1732–2165	0.16	0.12	1	Low Potential

. Global Horizontal Irradiance (GHI) emerged as the most influential parameter with a weight of 18%, reflecting its fundamental importance as the primary determinant of photovoltaic electricity generation potential. Temperature and Slope each received weights of 11%, acknowledging their significant impacts on system performance and installation feasibility, respectively, while Diffuse Radiation, Elevation, and Proximity to Transmission Lines were each assigned weights of 10%. Relative Humidity received a weight of 9%, followed by Proximity to Road Network (8%), Land Use Land Cover (7%), and Proximity to Waterbodies (6%). Photovoltaic factors collectively received the highest allocation at 28%, confirming the primacy of solar resource availability, followed by Topographic factors at 21%, Climatic factors at 20%, Accessibility factors at 18% and Environmental factors at 13%. This weighting scheme reflects a balanced approach that prioritizes solar resource potential while appropriately considering technical, environmental, and logistical factors, providing a reliable framework for the subsequent multi-criteria site suitability analysis [53].

This coastal urban solar assessment addresses global renewable energy deployment challenges, with findings applicable to coastal cities worldwide facing similar land availability and infrastructure constraints. The identified parameter weights provide international benchmarks: Global Horizontal Irradiance dominance (18%) aligns with Mediterranean [26], Middle Eastern [23], and Southeast Asian coastal studies [30,31]. The sensitivity analysis revealing photovoltaic factor dominance (GHI: 0.25, DR: 0.23) demonstrates parameter relationships consistent with coastal solar installations globally, supporting the framework’s international applicability. The dual-use salt pan integration approach (17.1% of study area) offers solutions for global coastal regions with similar land-use patterns.

4.2. Solar Potential Suitability Zones

The integration of all parameters through weighted overlay analysis reveals the comprehensive spatial pattern of solar potential suitability across the study area with varying potential that can directly inform strategic renewable energy planning and investment prioritization within the urban coastal context [24,64]. The Weighted Sum Overlay method in ArcGIS generated the comprehensive Solar Potential Suitability Index (SPSI), with weights derived from AHP analysis. The SPSI values were classified into five distinct categories representing different levels of suitability for solar farm development (Figure 8). The distribution of solar potential suitability zones across the study area is presented in

Table 5. Solar Potential Classification Distribution Across the Study Area.

Class	Area	Percentage
Low Potential	9.74	7.16
Moderate Potential	44.13	32.41
Good Potential	30.34	22.29
Very High Potential	38.33	28.15
Excellent Potential	13.61	9.99

. Moderate Potential zones emerged as the most prevalent category, covering 44.13 km² (32.41%), predominantly seen in the northwestern part around Sankaraperi and Madathur with scattered patches near Thermal Nagar. Very High Potential zones constitute the second largest category at 38.33 km² (28.15%), located primarily in the southeastern coastal sections near Mullakkadu and Pottalkadu, as well as in patches distributed throughout the eastern portions near Arockiapuram and Threspuram. Good Potential areas cover 30.34 km² (22.29%), forming extensive zones throughout the central areas around Bryant Nagar and Caldwell Colony, extending into the harbour area. Excellent Potential zones representing optimal locations for solar farm development encompass 13.61 km² (9.99%), concentrated primarily in the southwestern portions, including areas south of Mullakkadu and Athimarapatti. Low Potential areas comprise 9.74 km² (7.16%), appearing as scattered patches primarily in the northwestern section in constrained locations.

The spatial distribution reveals distinct patterns contrary to the expected east–west gradient, with considerable heterogeneity influenced by the interaction of multiple parameters. The northwestern parts display predominantly Moderate potential with interspersed Low potential zones primarily due to limitations in photovoltaic factors (lower GHI values) and reduced accessibility (greater distance from transmission infrastructure), while the central portion shows a complex mosaic of Good and Moderate potential zones reflecting variable conditions in land use and climatic parameters. The southeastern and southwestern regions demonstrate the most favorable conditions, with substantial contiguous blocks of Excellent and Very High potential zones visible around Mullakkadu and extending westward toward Pottalkadu where optimal photovoltaic conditions coincide with favorable topography (predominantly flat terrain), advantageous environmental factors (salt pan regions and barren lands offering minimal land use conflicts) and excellent infrastructure connectivity. The largest contiguous Excellent potential zone south of Mullakkadu covers approximately 7.8 km², capable of supporting a utility-scale solar installation of approximately 390 MW (assuming 5 hectares per MW of capacity), while the combined Excellent and Very High potential zones (51.94 km², 38.14%) could support approximately 2080 MW of solar PV capacity, positioning Thoothukudi as a potential solar energy hub in southern Tamil Nadu.

4.3. Sensitivity Analysis

The sensitivity of different factors highlights important considerations for locating solar potential sites in the study area. The dominance of photovoltaic factors such as GHI (0.25) and DR (0.23) highlights their role in solar siting by suggesting that they should be prioritized over other factors. Topographic and infrastructure factors, including slope (0.17), proximity to transmission lines (0.16), elevation (0.15), and road proximity (0.14), act as both facilitators and barriers depending on their specific characteristics. The environmental factors like land use (LULC) and water proximity (PW) have a lower influence (0.08), indicating they play a smaller role in distinguishing suitable sites in this urban-adjacent area. The analysis shows that reducing weights significantly impacted site classification more than increasing them, revealing threshold effects where minimum radiation and maximum slope constraints set critical boundaries for feasibility [23,50].

The effective weight analysis uncovered significant gaps between the theoretical and actual influence of parameters in the suitability model. The parameters such as Global Horizontal Irradiance (GHI), Relative Humidity, and Diffuse Radiation, had a much greater practical impact than initially expected, with GHI increasing from 0.18 to 0.31, and the others rising to 0.23 and 0.21, respectively. These findings emphasize the critical role of radiation and climatic factors in the model. On the other hand, Proximity to Road Network and Slope saw their influence diminish, dropping to 0.03 and 0.10, respectively. These discrepancies underscore the need to assess actual parameter contributions rather than relying solely on initial weighting schemes, as certain factors, particularly those related to radiation and climate, played a much more significant role in this coastal urban setting than anticipated [51]. The relationship between effective weights and sensitivity indices shows a positive correlation (R² = 0.672), represented by the equation y = 0.5923x + 0.0495, indicating that parameters with higher weights tend to be more sensitive, though other factors also influence sensitivity [24,31]. The comparative analysis of baseline weights, average effective weights, and sensitivity indices across all parameters (Figure 9a) reveals that global horizontal irradiance (GHI) demonstrates the highest importance with maximum effective weight. The positive correlation (R² = 0.672) between effective weights and sensitivity indices (Figure 9b) validates the consistency of the parameter prioritization in the GIS-AHP integration framework.

The comprehensive sensitivity analysis conducted in this study demonstrates robustness by systematically varying the original expert-derived weights by ±25% and ±50%. Results demonstrate remarkable stability in site suitability rankings, with the top 20% of identified excellent potential zones maintaining their classification across all weight variation scenarios. The southeastern coastal areas (Mullakkadu region) consistently ranked in the highest suitability category regardless of weight adjustments, while only marginal sites showed classification changes between adjacent categories (e.g., “Very High” to “Good” potential). This robustness suggests that despite the five-expert panel size, the fundamental spatial patterns of solar suitability are driven by objective environmental and technical factors rather than subjective expert preferences, lending confidence to the framework’s validity and transferability. To validate the robustness of our five-expert panel findings, we conducted comprehensive weight sensitivity testing across four scenarios:

• Scenario 1: Photovoltaic Factor Emphasis (+50% weight adjustment)

GHI weight increased from 0.18 to 0.27

DR weight increased from 0.10 to 0.15

Result: 94.3% of excellent zones maintained classification; southeastern coastal priority areas unchanged

• Scenario 2: Infrastructure Factor Emphasis (+50% weight adjustment)

Transmission proximity weight increased from 0.10 to 0.15

Road proximity weight increased from 0.08 to 0.12

Result: 91.7% of excellent zones maintained classification; minor improvements in northwestern accessibility

• Scenario 3: Environmental Factor Emphasis (+50% weight adjustment)

Land use weight increased from 0.07 to 0.105

Water proximity weight increased from 0.06 to 0.09

Result: 89.2% of excellent zones maintained classification; salt pan areas showed enhanced suitability

• Scenario 4: Balanced Reduction (−25% across all parameters)

All weights proportionally reduced by 25%

Result: 96.8% classification stability; spatial patterns virtually unchanged

The sensitivity analysis reveals that the largest contiguous excellent zone (7.8 km² south of Mullakkadu) maintained its optimal classification across all scenarios, indicating that this site’s superiority is robust regardless of expert weight variations. Only 3.2–10.8% of marginal areas experienced classification changes, primarily between adjacent suitability categories, confirming that our expert-derived weights capture fundamental site characteristics rather than arbitrary preferences.

4.4. Implications for Urban Smart Planning

Translating technical suitability findings into actionable planning frameworks is essential for the practical implementation of renewable energy development [65]. The identified solar potential zones have significant implications for urban smart planning in the study area, offering strategic renewable energy integration opportunities addressing unique challenges of coastal urban development [53].

4.4.1. Integration with Urban Development Plans

The spatial distribution of solar potential zones aligns with urban development patterns, with the highest potential areas in peripheral and coastal regions facing traditional development challenges. A strategic approach would involve designating specific solar energy zones in the region’s master plan, prioritizing the 15.37 km² of Excellent Potential areas. Approximately 8.4 km² in the southeastern coastal section could be designated as a “Solar Energy Priority Zone” without conflicting with other urban functions or environmental constraints. Effective planning should go beyond spatial designations to include integrated infrastructure development, particularly enhancing transmission capacity in high-potential areas with moderate grid connectivity [42]. Coordinating these infrastructure investments with broader urban development would optimize efficiency, minimize disruption, and fully leverage the generation potential of the southeastern coastal zones [66].

4.4.2. Innovative Deployment Models for Coastal Urban Contexts

The study area’s coastal urban environment offers unique opportunities for innovative solar energy solutions that address land limitations while boosting resilience. The significant salt pan areas (23.28 km², 17.1%) with high solar potential can be utilized for dual-purpose development through elevated photovoltaic structures, allowing salt production to continue underneath, thus preserving existing livelihoods while generating renewable energy [67]. Approximately 3.6 km² of urban rooftops with moderate suitability could support distributed solar power initiatives driven by community efforts, thereby enhancing and complementing a comprehensive solar strategy [64]. For coastal regions with moderate flood risks, approximately 6.8 km² could be used for innovative elevated solar installations designed with climate adaptation in mind, transforming vulnerable areas into resilient energy-producing zones [68]. This approach aligns with global best practices, promoting multifunctional infrastructure that can withstand periodic flooding.

4.4.3. Policy Recommendations

The findings suggest several policy recommendations to facilitate strategic solar energy development in the Thoothukudi Municipal Corporation:

(1)

Zoning Policy: Create solar overlay zones aligned with ‘Excellent’ and ‘Very High’ potential areas, with streamlined permitting for qualifying projects.

(2)

Dual-Use Incentives: Develop policies encouraging dual-use solar development in salt pan areas through tax benefits, accelerated permitting, or production incentives.

(3)

Infrastructure Coordination: Establish a framework aligning transmission enhancements with high-potential zones through public–private partnerships, sharing costs and benefits.

(4)

Climate Resilience: Require adaptation features in coastal solar developments, including elevated structures and flood-resistant design, enhancing local resilience.

(5)

Smart Grid Integration: Develop standards and incentives for advanced grid features, including storage and smart inverters, supporting broader smart city objectives.

4.4.4. Framework Adaptability and Extensions

The developed AHP-GIS framework demonstrates significant potential for adaptation to other renewable energy applications:

(1)

Wind Energy Assessment: The five-factor structure can be modified by substituting photovoltaic parameters (GHI, diffuse radiation) with wind-specific criteria (wind speed, wind power density, turbulence intensity, wind direction consistency).

(2)

Hybrid Renewable Systems: The framework can simultaneously assess multiple renewable sources by incorporating both solar and wind parameters within the same multi-criteria structure.

(3)

Geographic Transferability: The methodology can be applied to inland regions by adjusting environmental constraints (removing coastal-specific factors) and modifying accessibility parameters based on local infrastructure conditions.

(4)

Technology-Specific Applications: Different solar technologies (crystalline silicon, thin-film, bifacial) can be assessed by adjusting efficiency parameters and environmental sensitivity factors.

4.4.5. Implementation Roadmap and Validation Framework

The pilot testing was beyond the scope of this study; future work will focus on validating and implementing the findings through a stakeholder-led framework involving the Tamil Nadu Energy Development Agency, local solar developers, and the Thoothukudi Municipal Corporation. The identified excellent potential zones (13.61 km²), notably the largest contiguous area of ~7.8 km² south of Mullakadu, are prime locations for demonstration projects. We propose a phased approach comprising detailed site assessments and environmental impact studies for the top-priority zones, pilot installations (50–100 MW) to verify model predictions, performance monitoring to compare observed and predicted outcomes, and adaptive model refinement based on operational data. This roadmap will enhance the scientific robustness of the site selection framework and provide empirical evidence to support the large-scale advancement of coastal solar power development in Tamil Nadu.

4.4.6. Coastal-Specific Design and Maintenance Considerations

The identified high-potential coastal zones require specialized design approaches to address environmental challenges inherent to marine environments. Installation in high-humidity areas (28.68% of study area) should incorporate corrosion-resistant materials, enhanced drainage systems, and moisture-protection technologies. Regular maintenance schedules should account for salt spray cleaning requirements, with recommended monthly panel cleaning in high-exposure zones compared to quarterly cleaning in inland installations. Equipment selection should prioritize marine-grade components with enhanced weatherproofing ratings, while monitoring systems should include humidity and corrosion sensors to enable predictive maintenance protocols.

4.5. Study Limitations

While this research provides a robust framework for coastal solar site selection, several limitations should be acknowledged:

4.5.1. Scope of Sustainability Assessment

The study addresses sustainability through the context of SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action), focusing on technical and environmental sustainability dimensions. While GIS–AHP framework currently optimizes site identification based on technical potential and environmental compatibility, it can be extended to include a sixth factor category encompassing social (e.g., population density, community acceptance, land ownership, displacement risk), economic (e.g., land cost, income levels, employment potential, local economic impact) and institutional (e.g., policy support, regulatory compliance, governance capacity) parameters. These can be incorporated through expanded stakeholder consultation and supported by participatory GIS, census-based spatial analysis, economic impact modelling, and survey-derived public acceptance layers. Such integration would enable a comprehensive three-pillar sustainability assessment, ensuring that technically optimal sites achieve social acceptability and economic viability, thereby advancing sustainable coastal renewable energy development.

4.5.2. Temporal Analysis Constraints

The analysis represents a snapshot assessment based on available data periods (2019–2023). Coastal environments experience dynamic changes in climate patterns, land use, and infrastructure development that may affect long-term suitability. Implementation of temporal monitoring and adaptive assessment protocols would enhance framework reliability.

4.5.3. Ground-Truth Validation

The model demonstrates internal consistency through sensitivity analysis, and field validation through pilot installations in identified high-potential zones would strengthen model reliability. Collaboration with local stakeholders and energy developers is planned to address this limitation.

4.5.4. Regional Comparative Analysis

The study focuses specifically on coastal environments to comprehensively address unique challenges, including salt spray exposure, marine influences, and coastal regulatory frameworks. While our AHP-GIS framework can be adapted to inland regions through parameter recalibration, this study deliberately emphasizes coastal-specific applications. Future research should conduct parallel inland studies using the adapted framework to enable comparative analysis of coastal versus inland solar potential, providing valuable insights for comprehensive regional renewable energy planning across diverse geographic contexts.

4.5.5. Scale and Resolution Limitations

The 30 m spatial resolution, while appropriate for regional planning, may not capture micro-scale variations important for individual project sites. Higher resolution analysis and site-specific assessments would be needed for detailed project development.

4.5.6. Expert Panel Size Limitations

A key limitation of this study is the reliance on evaluations from five experts for MCDA criteria weighting. While these experts were carefully selected to represent diverse expertise in coastal solar energy planning, the relatively small sample size may limit generalizability beyond Tamil Nadu’s coastal context. The limited expert pool reflects the specialized interdisciplinary nature of coastal solar planning, requiring expertise in photovoltaic technology, coastal environmental considerations and geospatial analysis combined with regional familiarity. This limitation implies parameter weights may reflect regional preferences rather than universal principles, potentially underrepresenting certain perspectives and reducing statistical power for consensus detection. However, methodological safeguards including consistency checks (CR = 0.071), comprehensive sensitivity analysis demonstrating stable rankings (89.2–96.8% classification stability) and alignment with international studies provide confidence in framework validity. Future research should incorporate larger, geographically diverse expert panels through online collaboration. Regarding representativeness, the expert panel achieved balanced coverage across essential disciplinary domains (photovoltaic technology, coastal environmental science, geospatial analysis, industrial implementation) and incorporated both academic and industry perspectives. However, the regional focus on Tamil Nadu expertise, while essential for contextual validity, may limit representativeness of broader coastal solar planning perspectives. The geometric mean aggregation method and literature triangulation against international studies helped mitigate potential regional biases, though future studies would benefit from geographically diverse expert panels to enhance global representativeness.

5. Conclusions

This research developed a comprehensive multi-criteria framework for identifying optimal SFSs in Thoothukudi coastal city, addressing critical knowledge gaps regarding solar siting methodologies in coastal urban contexts. The framework directly contributes to achieving United Nations Sustainable Development Goals, particularly SDG 7 (Affordable and Clean Energy), SDG 11 (Sustainable Cities and Communities), and SDG 13 (Climate Action), by providing evidence-based spatial guidance for sustainable renewable energy deployment. The integration of photovoltaic, climatic, topographic, environmental, and accessibility factors through the Analytical Hierarchy Process revealed that 38.14% (51.94 km²) of the study area possesses excellent to very high potential for solar energy development, capable of supporting approximately 2080 MW of solar PV capacity. The sensitivity analysis demonstrated the dominance of photovoltaic factors in determining site suitability, with Global Horizontal Irradiance showing the highest sensitivity index (0.25) and greatest effective weight (0.31). This analysis also uncovered significant discrepancies between theoretical and actual parameter contributions, highlighting the need for robust validation in spatial decision models, particularly in coastal environments where complex microclimates influence solar resource availability.

The identified solar potential zones offer substantial strategic renewable energy integration opportunities within urban smart planning frameworks. The study proposes innovative deployment models tailored to the coastal urban context, including dual-purpose development in salt pan areas (23.28 km², 17.1%), distributed solar initiatives across moderate-suitability urban rooftops (3.6 km²), and climate-adaptive elevated installations in flood-prone regions (6.8 km²). The methodological approach developed in this study provides a robust foundation for sustainable coastal solar development that can be adapted for other regions pursuing SDG-aligned renewable energy strategies. The framework demonstrates how technical sustainability assessment can serve as the essential first step toward comprehensive sustainability evaluation. With its univariate sensitivity analysis and effective weight validation specifically adapted to a rapidly urbanizing, microclimate-sensitive coastal city, this study introduces the globally transferable framework for solar site suitability assessment in a setting that is increasingly representative of fast-growing, climate-vulnerable coastal megacities worldwide. The study establishes the technical and environmental sustainability foundation; future work should integrate socioeconomic dimensions—including social acceptance, economic viability, regulatory alignment, climate adaptation, and pilot validation—to achieve a comprehensive, three-pillar sustainability assessment.