Scenario-Based Spatial Assessment of Solar and Wind Energy Potential in Pakistan Using FUCOM–OWA Integration

Abstract

With the growing demand for energy and the limitations of fossil fuel resources, the utilization of renewable energy sources has become a vital and sustainable solution. However, identifying optimal locations for the development of these resources remains a major challenge in energy planning. Accurate spatial potential assessment can play a critical role in enhancing efficiency and reducing production costs. This study aims to present a scenario-based framework for assessing solar and wind energy potential in Pakistan. A total of 19 spatial criteria were used, categorized into evaluation and constraint factors. The full consistency method (FUCOM) was applied to weight the criteria, while the ordered weighted averaging (OWA) method was employed to model various potential scenarios. The results revealed that global horizontal irradiation (GHI) and proximity to transmission lines are the most significant factors for solar energy, whereas wind speed and wind power density are crucial for wind energy potential. Scenario analysis indicated that, under the AND scenario, the area with very high potential for solar and wind energy is 8005.72 km² and 968.98 km², respectively. These values increase to 63,607.52 km² and 16,288.32 km² under the OR scenario. The spatial agreement map for the simultaneous development of solar and wind energy showed an overlap of 461.42 km² in the AND scenario and 11,836 km² in the OR scenario. These findings highlight the importance of scenario-based decision-making approaches and accurate spatial evaluations in the development of multiple renewable energy plant sites under various investment and policy conditions. Moreover, the proposed framework can serve as a practical model for simulating and assessing renewable energy development potential in other regions of the world.

1. Introduction

With the exponential growth of global population, rapid industrialization, and increasing energy demands, the necessity for sustainable and clean energy sources has become more pressing than ever [1]. In this regard, renewable energy has been recognized as a fundamental component of the transition toward sustainable development. Unlike fossil fuels, which are finite and environmentally detrimental, renewable energy sources rely on inexhaustible natural resources, such as solar radiation, wind, hydropower, and biomass resources that are inherently capable of continuous replenishment [2,3]. These energy sources not only provide a viable means of reducing dependence on fossil fuels, but also contribute substantially to mitigating greenhouse gas emissions and addressing climate change [4,5]. Technological advancements, declining production and maintenance costs, and the implementation of supportive policies at the international level have collectively facilitated the steady growth of renewable energy’s share in the global energy portfolio [6].

In recent decades, many countries have increasingly invested in the development of clean energy infrastructure, not only to meet their domestic energy demands but also to fulfill their international environmental commitments. Among the various renewable energy options, solar and wind energy have emerged as the most viable alternatives due to their economic affordability and relatively low ecological impact [7]. Furthermore, the dynamism of the global market for related technologies, the facilitation of investment flows, and the widespread accessibility of modern innovations have significantly contributed to the expansion of this sector [8]. Meanwhile, developing countries are also striving to integrate a greater share of renewable energy into their national energy systems by leveraging their local and natural capacities. Nevertheless, infrastructural, financial, and administrative challenges continue to pose substantial obstacles to progress.

The energy sector in Pakistan represents one of the key challenges to the country’s economic development. Despite improvements in electricity generation and a noticeable reduction in power outages since 2013, several critical issues persist, including dependence on imported fossil fuels, shortages of natural gas resources, accumulated debts, and aging transmission and distribution infrastructure. According to the 2022 Annual Report by the National Electric Power Regulatory Authority (NEPRA), 59% of the country’s electricity generation capacity is derived from fossil fuels, 25% is derived from hydropower, 9% is derived from nuclear energy, and only 7% is derived from renewable sources such as wind, solar, and biomass. Nonetheless, the Government of Pakistan has taken significant steps toward reducing greenhouse gas emissions and expanding clean energy adoption, setting a target to cut its emissions by 50% by 2030. Among the key initiatives is the development of a wind energy corridor along the coastal regions of Sindh and Balochistan, with an estimated potential of 50,000 MW. So far, this has led to the commissioning of 36 wind projects with a combined capacity of 1845 MW. In the solar sector, with an average of 9.5 h of daily sunlight, the deployment of both residential and industrial solar panels has grown substantially, and several utility-scale projects have already been connected to the national grid. Given the rising costs of fossil fuels and the instability of the power grid, there is a growing shift toward renewable energy sources. This transition presents considerable opportunities for further development and investment in Pakistan’s clean energy sector.

Figure 1 presents the trajectory of renewable electricity generation capacity from 2015 to 2024 at both global and national (Pakistan) scales. Over this decade, global renewable capacity increased substantially from 1851.1 GW to 4448 GW, reflecting a growth of 140.3 percent that can be attributed to rapid technological advancement, supportive policy frameworks, and large-scale international investment [9]. Pakistan also recorded a notable rise in renewable capacity, from 8.12 GW to 15.2 GW, representing an 87.2 percent increase; however, this rate remains considerably lower than the global average and highlights several structural and policy-related constraints. Although Pakistan’s renewable sector expanded quickly during the early part of the decade, its growth began to slow after 2019 and eventually stabilized. A closer look at individual sources shows remarkable increases in both wind and solar capacity, which grew by 598 percent and 426 percent, respectively; however, their combined contribution still accounts for only a relatively small share of the country’s overall renewable-energy portfolio. This divergence between global advances and Pakistan’s slower progress underscores the urgent need for enhanced policy measures, infrastructure modernization, and greater domestic and international investment to support the country’s transition toward sustainable energy.

Accurate site selection plays a critical role in the effective development of solar and wind energy systems, as their performance is highly dependent on spatial environmental conditions. Geographic Information Systems (GIS) enable the integration and analysis of diverse spatial datasets, providing a systematic basis for evaluating natural potential and infrastructure suitability [10]. When combined with spatial multi-criteria decision-making (SMCDM) methods, this framework becomes more capable of managing complex environmental factors and supporting transparent planning. Within SMCDM approaches, the ordered weighted averaging (OWA) operator is particularly valuable due to its ability to represent different decision attitudes by adjusting order weights. This flexibility allows the generation of multiple suitability scenarios and enhances understanding of how risk preferences influence renewable-energy site selection, making OWA-based approaches increasingly important in planning under uncertainty [11]. This advanced analytical integration not only enhances decision accuracy but also provides a robust foundation for strategic planning and targeted investment in the renewable energy sector. In recent years, extensive research has been conducted to evaluate the potential of renewable energy sources such as solar [12,13,14,15,16] and wind [17,18,19,20,21].

A substantial body of literature demonstrates the relevance and effectiveness of GIS-SMCDM frameworks across diverse regions. For example, Meng et al. [22] applied a GIS-based MCDA approach to assess geothermal potential in northeastern China, identifying extensive high-suitability zones. Shorabeh et al. [7] integrated ANP and fuzzy logic techniques in eastern Iran to evaluate multiple renewable resources, highlighting the versatility of hybrid decision-support models. Similar GIS-SMCDM approaches have been employed to identify optimal wind sites in Turkey [23] and western Iran [24], and to evaluate hybrid solar–wind systems in the Netherlands [25]. Collectively, these studies affirm the value of combining spatial decision-support tools with multi-criteria analysis to produce rigorous and replicable assessments for renewable-energy planning.

Although previous research has applied GIS-based MCDA methods for renewable-energy site selection in Pakistan, these studies rely primarily on single-aggregation techniques such as AHP or WLC, which limit their ability to account for uncertainty and variations in decision-makers’ risk attitudes. For example, Raza et al. [26] produced separate suitability maps for solar and wind energy using a single weighted linear combination approach, without incorporating multiple decision scenarios or flexible weighting mechanisms. This leaves an important methodological gap, particularly for large-scale renewable-energy planning in contexts characterized by uncertainty and diverse stakeholder preferences.

To address this gap, the present study introduces a novel integration of the FUCOM with the OWA operator within a GIS-based framework. FUCOM offers a more consistent and objective approach to deriving criteria weights, reducing inconsistency compared with traditional pairwise-comparison techniques. The use of OWA further advances the assessment by enabling the generation of three distinct decision scenarios—pessimistic (AND), moderate (WLC), and optimistic (OR). Unlike previous studies that rely on a single aggregation method, the FUCOM–OWA combination creates a flexible and risk-sensitive evaluation environment that better reflects real-world decision-making conditions.

Based on these methodological advancements, the research question guiding this study is formulated as follows: How can an integrated FUCOM–OWA framework improve the accuracy, flexibility, and policy relevance of spatial suitability assessments for multi-source renewable-energy development in Pakistan? By explicitly modeling both consistent criteria weighting and scenario-based aggregation, the proposed framework provides a more robust tool for identifying optimal sites for solar and wind installations. Moreover, the approach is adaptable to different geographic contexts, offering broader applicability for renewable-energy planning worldwide.

2. Materials and Methods

2.1. Study Area

Geographically, Pakistan is situated between latitudes 23°35′ N and 37°05′ N, and longitudes 60°50′ E to 77°50′ E (Figure 2). Pakistan, spanning approximately 881,913 square kilometers in South Asia, holds a significant geopolitical position. It shares borders with China to the north, Afghanistan and Iran to the west, India to the east, and the Arabian Sea to the south. The country is administratively divided into four major provinces—Punjab, Sindh, Khyber Pakhtunkhwa (KPK), and Balochistan—alongside the Islamabad Capital Territory and several autonomous regions such as Gilgit-Baltistan and Azad Jammu and Kashmir (AJK). With an estimated population exceeding 240 million, Pakistan ranks among the most populous nations globally. The country’s average elevation is around 900 m above sea level, with its highest point being K2 at 8611 m and the lowest at sea level along the Arabian coastline. Pakistan exhibits a highly diverse climate, ranging from cold mountainous zones in the north to arid deserts in the southwest and humid coastal areas in the south. Temperature fluctuations are substantial, with recorded lows reaching −25 °C in northern regions like Skardu and highs soaring to 53.7 °C in areas like Turbat. This climatic and geographical diversity endows Pakistan with considerable potential for renewable energy development. Solar energy, with over 300 sunny days annually, and wind energy, particularly in wind-prone provinces such as Sindh and Balochistan, offer promising opportunities for the country’s transition toward sustainable energy solutions.

2.2. Data

In the process of conducting scientific research, particularly in spatial capability assessments, collecting comprehensive and accurate data plays a critical role. Although time-consuming, this stage significantly influences the precision of analyses and the credibility of results. In this study, data were gathered from a variety of sources, including official statistics, digital maps, and satellite imagery. Selecting relevant and reliable datasets is essential for identifying potential resources, reducing costs, and enhancing decision-making processes. A broad range of infrastructural, climatic, and geographical data was utilized to analyze renewable energy resources. All datasets were standardized to a common coordinate system and spatially aligned. Following initial preprocessing steps such as clipping to the study area, applying geometric corrections, and performing normalization the data were then used in subsequent multi-criteria decision-making analyses. A detailed description of the datasets is provided in

Table 1. Details of the utilized data.

Criteria	Description	Data Type	Data Category	Spatial Resolution	Temporal Resolution	Source
Global Horizontal Irradiation (GHI)	GHI is one of the most critical factors for solar power plant site selection, as it represents the amount of solar energy received on a horizontal surface. Higher GHI values indicate greater potential for electricity generation and improved economic viability of the solar project [27].	Raster	Spatio-temporal	250 m	Annual average	https://globalsolaratlas.info/map (accessed on 8 January 2017)
Wind speed	Wind Speed plays a key role in wind energy production. Since the power output of wind turbines is proportional to the square of wind speed, higher wind speeds significantly increase electricity generation. Therefore, areas with high wind speeds are more suitable for wind farms [28].	Raster	Spatio-temporal	250 m	Annual average	https://globalwindatlas.info/en/ (accessed on 19 November 2017)
Wind power density	Wind Power Density (WPD) is a key indicator for wind power plant site selection, as it quantifies the available wind energy per unit area at a given height. Higher WPD values indicate stronger and more consistent winds, which lead to higher electricity generation and better economic feasibility of wind energy projects [29].	Raster	Spatio-temporal	250 m	Annual average	https://globalwindatlas.info/en/ (accessed on 19 November 2017)
Villages	Proximity from villages is important in renewable energy plants; appropriate proximity reduces costs, increases efficiency, promotes local development, and enhances social acceptance of the project [30].	Vector	Spatial	N/A	Static	https://data.re-explorer.org (accessed on 19 February 2024)
Cities	Proximity to cities can reduce energy transmission costs and improve access to communication and management infrastructure. Additionally, local energy demand can enhance the efficiency of solar and wind energy systems [31].	Vector	Spatial	N/A	Static	https://data.re-explorer.org (accessed on 19 February 2024)
Roads	The proximity from roads in solar and wind power plants affects costs and efficiency. Easy access to roads reduces transportation, installation, and maintenance costs. A large distance from roads increases costs and makes operations more difficult [32].	Vector	Spatial	N/A	Static	https://data.re-explorer.org (accessed on 19 February 2024)
Transmission lines	The proximity from transmission lines significantly impacts the costs and efficiency of solar, and wind power plants. Greater distance increases infrastructure costs and energy losses. Proximity to transmission lines reduces costs and improves system efficiency [33].	Vector	Spatial	N/A	Static	https://data.re-explorer.org (accessed on 19 February 2024)
Substation	The distance between solar and wind power plants and the substation should be minimized to reduce transmission costs and energy losses [34].	Vector	Spatial	N/A	Static	https://data.re-explorer.org (accessed on 19 February 2024)
Elevation	Lower elevation can be more suitable for solar and wind energy siting, as it provides easier access and lower costs for infrastructure construction and maintenance [35].	Raster	Spatial	30 m	Static	https://earthexplorer.usgs.gov/ (accessed on 12 February 2021)
Slope	Steep slopes in solar and wind power plants cause issues with panel installation and maintenance access. Additionally, they require specialized infrastructure for installation and upkeep [36].	Raster	Spatial	30 m	Static	Extracted from SRTM DEM
Cropland	Croplands are considered constrained areas due to their negative impact on agricultural production and food security, as well as environmental issues like soil degradation [37].	Vector	Spatial	10 m	Static	https://esa-worldcover.org/en (accessed on 2 January 2021)
Tree cover	Tree cover is considered an area with constraints because clearing forests for energy projects can lead to biodiversity loss, carbon release, and disruption of ecosystems [36].	Vector	Spatial	10 m	Static	https://esa-worldcover.org/en (accessed on 2 January 2021)
Airports	Distance to airports is important as it may impose restrictions on equipment installation and structure height, as well as increase transportation costs [38]	Vector	Spatial	N/A	Static	https://data.re-explorer.org (accessed on 19 February 2024)
Floodplain zoning	Solar and wind power plants should be located away from floodplains to avoid the risk of flooding, erosion, and damage to equipment [39].	Vector	Spatial	N/A	Static	https://data.re-explorer.org (accessed on 19 February 2024)
Fault lines	Proximity to fault lines in solar and wind power plants increases the risk of damage to equipment due to ground vibrations, which can lead to structural failure and reduced system lifespan [7].	Vector	Spatial	N/A	Static	https://data.re-explorer.org (accessed on 19 May 2020)
Air temperature	Temperature affects the performance of solar and wind power plants. In solar plants, higher temperatures reduce efficiency, while in wind plants, lower temperatures increase energy production, and higher temperatures decrease it [36].	Raster	Spatio-temporal	1 km	Annual average	https://globalsolaratlas.info/map (accessed on 27 January 2017)
Vegetation density	Vegetation density reduces the efficiency of solar panels due to shading and decreases wind speed in wind power plants. Developing power plants in these areas may harm ecosystems [40].	Raster	Spatial	30 m	Static	https://earthexplorer.usgs.gov/ (accessed on 8 August 2024)
Protected areas	Protected areas, such as national parks and wildlife reserves, are critical for biodiversity and cultural heritage preservation, so building solar power plants in or near them is restricted or prohibited [41].	Vector	Spatial	N/A	Static	https://data.re-explorer.org/data-library/layers (accessed on 19 February 2024)
Population density	High-density areas, with greater energy needs and established infrastructure, offer favorable conditions for building power plants [42].	Raster	Spatial	1 km	Static	https://www.worldpop.org/ (accessed on 9 March 2013)

.

2.3. Methodology

In this study, when developing a scenario-based planning framework for identifying suitable sites for hybrid solar and wind power generation, the first step involved data collection. At this stage, effective criteria were identified through the integration of experts’ opinions, geographical conditions, previous studies, and accessible data. Subsequently, spatial databases were created for both evaluation and constraint criteria. In the next phase, data processing was performed, which included validation and cleaning of the datasets, standardization of coordinates and units, and conversion of data into appropriate GIS formats. After preparing the data, spatial analyses were conducted within the GIS environment to produce evaluation and constraint criteria maps. Following this, normalization of the criteria values was carried out using appropriate methods, and the weighting of the evaluation criteria was performed based on the full consistency method (FUCOM) approach. After determining the weights of the criteria, potential scenario analysis was implemented using the ordered OWA method under different conditions, including AND, weighted linear combination (WLC), and OR scenarios. Next, an ensemble analysis was conducted to generate the final potential maps, which identified the most suitable areas for the establishment of hybrid solar and wind power generation sites. Finally, a sensitivity analysis was performed to evaluate the robustness of the results against variations in criteria weights. The flow of the research method process is shown in Figure 3.

2.3.1. Identification of Criteria and Constrain Areas

The process of identifying suitable areas for renewable energy development involves multi-criteria analysis of various factors. In this study, the criteria were identified through expert opinions and the geographical characteristics of the area. The criteria were divided into two categories: constraints and evaluation factors. Constraints refer to areas that are unsuitable for power plant construction due to compliance with national and international standards. Boolean logic was used to identify these restricted areas. Evaluation factors were used to assess and compare suitable locations for power plants, analyzed both quantitatively and qualitatively. A total of 15 criteria were considered for suitable locations and 15 for identifying restricted areas (

Table 2. Constraints and types of impact for each criterion in assessing the potential for wind and solar energy.

Criteria	Solar	Wind	Type of Impact	Constrains	References
GHI	*		Maximum	Less than 3.56 kWh/m2	[42]
Wind speed		*	Maximum	Less than 6 m/s	[41]
Wind power density		*	Maximum	Less than 250 w/m2	[43]
Villages	*	*	Minimum	Less than 500 m and 1000 m are, respectively, for solar and wind power plants.	[44]
Cities	*	*	Minimum	Less than 1000 m and 2000 m are, respectively, for solar and wind power plants.	[25]
Roads	*	*	Minimum	Less than 500 m	[45]
Transmission lines	*	*	Minimum	Less than 250 m	[46]
Substations	*	*	Minimum	Less than 250 m	[47]
Elevation	*	*	Minimum	Greater than 2000 m	[48]
Slope	*	*	Minimum	Greater than 15% and 20% are, respectively, for wind and solar power plants.	Solar: [49]; Wind: [50]
Cropland	*	*	-	Less than 500 m	[41]
Tree cover	*	*	-	Less than 1000 m	[1]
Airports	*	*	-	Less than 2500 m	Solar: [37]; Wind: [51]
Floodplain zoning	*	*	Maximum	Less than 1000 m	[52]
Fault lines	*	*	Maximum	Less than 1000 m	[21]
Air temperature	*	*	Minimum	-	[53]
Vegetation density	*	*	Minimum	Greater than 0.5	[1]
Protected areas	*	*	-	Less than 1000 m	[54]
Population density	*	*	Maximum	-	[26]

Note: The asterisk (*) indicates which energy type (Solar or Wind) each criterion applies to.

).

2.3.2. Normalization of Evaluation Criteria

In the context of selecting optimal sites for renewable energy facilities, each decision criterion is typically represented through spatial layers—either in classified formats (e.g., land use categories) or as continuous numerical datasets (e.g., slope gradients). To streamline the decision-making process and enhance comparability across diverse criteria, it is essential to convert all input layers to a uniform scale [55]. This process, referred to as normalization, transforms all data values into a standardized interval, usually from 0 to 1, where 0 indicates the lowest suitability and 1 indicates the highest. A widely adopted approach for this purpose is Min–Max normalization, which brings uniformity across datasets and facilitates their integration in GIS-based multi-criteria analyses. For criteria positively associated with suitability, normalization is applied using the maximum function; conversely, criteria inversely related to the objective are adjusted using the minimum function.

2.3.3. Weight of Criteria

The full consistency method (FUCOM) is an innovative multi-criteria decision-making (MCDM) tool designed for deriving criterion weights with minimal pairwise comparisons. Introduced by Pamučar et al. [56], FUCOM relies on expert assessments to establish the relative importance of criteria while preserving consistency and ensuring minimal computational effort. Specifically, the number of comparisons required is significantly lower than in traditional methods, making it efficient and scalable. This technique systematically prioritizes evaluation factors through a straightforward mathematical framework that minimizes subjectivity and improves reliability. Comparative analyses have shown that FUCOM produces results akin to those of methods such as the AHP and the best-worst method (BWM), but with greater efficiency.

Step 1: Expert Selection and Initial Ranking

A total of 29 experts from diverse fields relevant to renewable energy in Pakistan were consulted, including specialists in solar and wind energy, GIS analysis, energy policy, environmental management, and infrastructure planning. Their educational qualifications comprise 10 PhDs, 12 master’s degrees, and 7 bachelor’s degrees. Each expert independently ranked the criteria in descending order of importance, with the most influential criterion at the top. In cases where two or more criteria were considered equally important, equivalence was explicitly noted. This step ensures that the ranking reflects diverse, high-level expertise rather than arbitrary choices (Equation (1)).

$$C_{j\left(1\right)}>C_{j\left(2\right)}>\cdots >C_{j\left(k\right)}$$

(1)

where $k$ indicates the position assigned to each criterion based on its relative significance. When two or more criteria share equal importance, this equivalence is expressed using an equal sign.

Step 2: Pairwise Comparison and Aggregation

Experts then conducted pairwise comparisons between adjacent criteria to quantify their relative importance. Numerical values were assigned either using exact judgments or a predefined evaluation scale. The comparative judgments are expressed Equation (2).

$$\mathsf{\Phi }={(\phi }_{1/2}, {\phi }_{2/3}, \dots ,{\phi }_k(k+1))$$

(2)

where ${\phi }_k(k+1)$ denotes the comparative significance between two successive criteria. To improve credibility, all 29 experts’ inputs were aggregated using the arithmetic mean. Any major disagreements were resolved through structured discussion, allowing experts to explain and adjust their assessments where necessary. This procedure ensures that the derived comparisons represent collective expert judgment while minimizing individual bias.

Step 3: The final phase involves calculating the weight coefficients for each criterion. These weights must satisfy two essential conditions: (1) the ratio of the weights must reflect the priority vector from Step 2, and (2) transitivity between comparisons must be maintained. When these conditions are met, the solution achieves full consistency, and the Deviation from Full Consistency (DFC) is minimized. Equation (3) outlines the final model used to derive optimal weights under these constraints.

$$\begin{gathered} \min \chi \\ s.t. \\ \left|{\displaystyle \frac{w_{j\left(k\right)}}{w_{j(k+1)}}}-{\phi }_k(k+1)\right|\le \chi , \forall j \\ \left|{\displaystyle \frac{w_{j\left(k\right)}}{w_{j(k+2)}}}-{\phi }_k(k+1)⨂\phi (k+1)/(k+2)\right|\le \chi , \forall j \\ \sum _{j=1}^n{w}_j=1, \forall j \\ {w}_j\ge 0,\forall j \end{gathered}$$

(3)

Using the results derived from Equation (3), the ultimate weighting factors for the evaluation criteria, denoted as ${({\mathrm{w}}_1,{\mathrm{w}}_2,\dots ,{\mathrm{w}}_n)}^T$, along with the $DFC \left(\chi \right)$, are determined.

2.3.4. OWA

The OWA technique, proposed by Yager [57] is a powerful spatial decision support tool for identifying optimal zones for renewable energy deployment. Unlike conventional aggregation methods, OWA offers flexibility by incorporating both the relative importance of criteria and the decision-maker’s attitude toward risk. Its primary advantage lies in its ability to model various decision strategies, from highly optimistic to extremely conservative, by adjusting weight distributions [58]. In GIS applications, decision factors are presented as thematic layers, each assigned a specific weight. OWA utilizes two distinct types of weights: (1) criterion weights, which represent the fixed importance of each factor across the study area, and (2) ordered weights, which are assigned based on the sorted values of the criteria at each spatial location. Ordered weights are independent of the original dataset and reflect the decision-maker’s preference for optimism or pessimism [59]. Since the set of sequential weights is $V=\left[v_1, v_2, \dots , v_n\right]$, with $v_j\in \left[\mathrm{0,1}\right]$ for $j=\mathrm{1,2},3,\dots ,n$ and $\sum _{i=1}^{n}v=1$, the OWA operator is defined as Equation (4):

$${OWA}_i=\sum _{j=1}^n\left({\displaystyle \frac{u_j{v}_j}{\sum _{j=1}^n{u}_j{v}_j}}\right)z_{ij}$$

(4)

where $z_{ij}$ is the value of the $i^{th}$ cell according to the $j^{th}$ criterion, $u_j$ is the weight of the $j^{th}$ criterion, which is determined according to the relationship between the $j^{th}$ criterion and decision-maker priorities, and $v_j$ is the order weight [60].

By manipulating the ordered weight distribution, OWA allows for the implementation of different aggregation strategies such as the WLC, and Boolean logic operations including AND (intersection) and OR (union). The AND operator corresponds to a highly pessimistic scenario (MIN), emphasizing the least favorable factor, while the OR operator aligns with an optimistic approach (MAX), focusing on the best-performing criterion [61]. When all ordered weights are equal, the OWA approach simplifies to the WLC method, which serves as the neutral midpoint between the MIN and MAX strategies. Due to this adaptive capacity, OWA is particularly suitable for complex spatial problems that involve uncertainty and multiple conflicting objectives [62]. The degree of ORness, or risk-taking, indicates the position of the OWA operator between AND (minimum) and OR (maximum). This degree reflects the extent to which the decision-maker emphasizes the better or worse values within a set of criteria, corresponding to the decision-maker’s level of risk-taking or risk-aversion. The ORness degree is calculated using Equation (5).

$$ORness=\sum _{j=1}^n\left({\displaystyle \frac{n-j}{n-1}}\right) {\lambda }_j,\hspace{1em}\hspace{1em}\hspace{1em}1\le ORness \le 0$$

(5)

where $n$ is the number of criteria, $j$ is the order of the criteria that are sorted in descending order, and ${\lambda }_j$ is the order weight of the $j^{th}$ criterion. The higher the ORness value, the greater the decision-maker’s risk-taking, and the lower the ORness value, the greater the decision-maker’s risk-aversion (Figure 4). The advantage of the OWA method is that the researcher can generate a wide range of maps, alternative solutions, and predictive scenarios by rearranging and adjusting the criterion parameters.

2.3.5. Sensitivity Analysis

To assess the robustness of the FUCOM-derived weighting scheme and evaluate the stability of the spatial suitability results, a sensitivity analysis was performed by systematically adjusting the weight of each criterion by ±10 percent relative to its baseline value. Because FUCOM weights directly influence the OWA-based suitability outcomes, this procedure enables the quantification of how susceptible the solar and wind suitability maps are to variations in expert judgment.

In this study, the sensitivity analysis was performed only for the WLC scenario, as it represents the balanced OWA decision rule and is widely regarded in the spatial MCDM literature as the most stable and policy-relevant aggregation method. Unlike the AND and OR scenarios which reflect extreme pessimistic and optimistic planning attitudes WLC provides an intermediate and more realistic representation of decision-making. Conducting sensitivity analysis for all three scenarios would substantially increase computational volume while yielding redundant insights, because the impact of weight perturbations primarily propagates through the WLC structure where trade-offs among criteria are permitted. For the analysis, the weight of a single criterion was increased or decreased by 10 percent while all remaining criteria were proportionally normalized to preserve the sum-to-one constraint. Separate sensitivity procedures were applied for the solar and wind models according to their respective sets of criteria. After each perturbation, WLC-based suitability maps were recalculated and compared against the baseline.

The influence of weight perturbations was quantified using two indicators:

(i) the coefficient of determination (R²) between the baseline and perturbed suitability raster’s, measuring the spatial stability of the model; and

(ii) the percentage change in the total land area classified as high and very high suitability, capturing the degree of spatial reallocation under weight uncertainty.

This procedure allows for the systematic evaluation of the sensitivity of the suitability outcomes and provides insights into which criteria exert the strongest influence on model stability across solar and wind assessments.

3. Results

The site selection process for renewable energy power plants, particularly solar and wind, requires a multi-criteria evaluation that considers environmental, technical, infrastructural, and social factors. In this study, the FUCOM was applied for weighting the criteria (

Table 3. Weights of criteria for solar and wind energy site selection using FUCOM.

Criteria	Solar Weight	Wind Weight
GHI	0.26	–
Wind speed	–	0.275
Wind power density	–	0.105
Proximity to Villages	0.055	0.06
Proximity to Cities	0.065	0.07
Proximity to Roads	0.06	0.065
Proximity to Transmission lines	0.11	0.095
Proximity to Substations	0.08	0.08
Elevation	0.04	0.04
Slope	0.035	0.05
Floodplain zoning	0.025	0.015
Proximity to Fault lines	0.03	0.025
Air temperature	0.035	0.03
Vegetation density	0.02	0.005
Population density	0.095	0.08
Sum	1.00	1.00

), and the results demonstrated good consistency. The consistency ratio (CR) was calculated to be 0.078 for solar energy and 0.064 for wind energy, both of which are below the acceptable threshold of 0.1, indicating a logically consistent weighting process. For solar energy, GHI emerged as the most important technical factor with a weight of 0.325 due to its direct impact on energy generation capacity. Air temperature also plays a role in the efficiency of photovoltaic panels, receiving a weight of 0.035. In the case of wind energy, wind speed (0.379) and air density (0.151) were identified as the most influential technical criteria affecting turbine electricity production. From an infrastructural perspective, proximity to transmission lines (0.104) and proximity to substations (0.088) are significant for both energy types; however, due to the larger scale of solar projects and the need for integration into the national grid, these factors received higher weights in the solar category. Proximity to roads is important for both energy types, but carries more weight for wind energy due to the size and sensitivity of equipment such as turbine blades. Proximity to cities and villages, considered as an indicator of access to labor and markets, received different weights in the two energy types, with higher importance assigned to solar energy projects.

From a topographical perspective, slope plays a more significant role in wind energy development, with a weight of 0.086, as sloped terrains can enhance access to wind flows. In contrast, slope has less impact on solar energy performance and is accordingly assigned a lower weight. Elevation above sea level affects both energy types: in wind energy (weight 0.065), higher elevation can lead to increased wind speeds, whereas in solar energy, greater elevation may negatively influence solar irradiance levels. In the environmental domain, floodplain zoning and active fault zones are more influential in solar energy planning, with weights of 0.098 and 0.105, respectively. This is because photovoltaic equipment is typically installed at ground level and is therefore more vulnerable to such hazards. In wind energy, due to the robust and elevated nature of wind turbines, these risks are relatively less impactful. Vegetation cover also affects solar energy (weight 0.084) due to potential shading and subsequent efficiency loss, while it has minimal effect on wind energy due to the height at which turbines operate. Lastly, population density is more critical for solar energy projects, with a weight of 0.091, given their scalability and suitability for installation near consumption centers. In contrast, wind energy projects are less favored in densely populated areas due to concerns over noise pollution and visual impact, and thus this criterion is assigned a lower weight.

All the analyzed spatial criteria maps (Figure 5) were normalized within a range from 0 to 1, where a value of 0 indicates the lowest level of suitability or spatial potential, and a value of 1 represents the highest suitability or potential for each specific indicator within the site selection framework. The spatial distribution analysis of these criteria across Pakistan reveals distinct spatial patterns for each indicator, which directly influence the optimal siting of renewable energy power plants.

Wind power density (a) and wind speed (l) exhibit higher values predominantly in the northern and western regions of the country, especially in elevated areas such as Gilgit-Baltistan and Khyber Pakhtunkhwa, due to favorable topography and atmospheric conditions for wind energy. In contrast, GHI, shown in map (n), reaches its peak in the southwest, particularly in Balochistan Province, highlighting the region’s strong potential for solar energy development. Air temperature (b) is higher in the southern and central areas, correlating with lower elevation (d) in these regions. Although high temperatures may slightly reduce photovoltaic system efficiency, these areas still offer considerable potential. From an infrastructural standpoint, the maps of proximity to cities (c), villages (m), roads (i), transmission lines (o), and substations (k) indicate that the most developed infrastructure is concentrated in the eastern and central regions, especially in Punjab Province. This facilitates easier grid access and lowers connection costs. Areas with sparse vegetation (g) and gentle slopes (j), mostly found in the southern and southwestern parts of the country, are physically more suitable for solar power plant installation. Additionally, the analysis of proximity to seismic fault lines (e) and floodplain zoning (f) shows that central and western regions face lower natural hazard risks. Overall, the spatial distribution of these criteria reflects the climatic, geological, and infrastructural diversity across Pakistan, which must be integrally considered in spatial modeling for renewable energy power plant site selection.

The spatial analysis of development constraints for renewable energy in Pakistan indicates that approximately 59.77% of the total study area is spatially restricted for solar energy development, while a significantly higher 88.34% is constrained for wind energy. The composite constraint maps (Figure 6p,q) reveal that solar energy development faces fewer limitations particularly in the southern and southwestern regions of the country, including Balochistan Province, southern Punjab, and parts of Sindh. In contrast, the northern, eastern, and mountainous regions are severely restricted due to factors such as elevation above 2000 m, slopes exceeding 20%, GHI below 3.56 kWh/m², and dense vegetative or agricultural cover. On the other hand, wind energy development encounters broader and more severe spatial limitations, with only 11.66% of the study area identified as relatively unconstrained. This is primarily due to suboptimal wind speeds (below 6 m/s) and wind power density values less than 250 W/m² across much of the country. Additional secondary constraints include proximity to protected areas, fault lines, floodplain zoning, and inadequate technical infrastructure. These findings highlight that, from a spatial perspective, solar energy holds significantly greater development potential in Pakistan compared to wind energy. Therefore, solar power should be prioritized as the primary option in the country’s future renewable energy policies.

The spatial potential of solar energy across Pakistan was assessed using the OWA method under three distinct decision-making scenarios: OR, WLC, and AND (Figure 7). These scenarios represent a spectrum of decision strategies, ranging from highly optimistic to highly conservative, and have a direct influence on the spatial distribution of solar energy potential. To enable a more precise interpretation, the potential values in all three scenarios were classified into five distinct categories: very low (0.0–0.2), low (0.2–0.4), moderate (0.4–0.6), high (0.6–0.8), and very high (0.8–1.0).

The spatial patterns of solar energy potential across Pakistan demonstrate a clear correspondence between regions classified as highly suitable and the underlying physical and infrastructural conditions that influence solar feasibility (Figure 7). In the AND sce-nario, which requires simultaneously high suitability across all criteria, high-potential zones emerge almost exclusively in areas with exceptionally strong solar irradiance (GHI), low slope, minimal vegetation cover, and close proximity to major transmission corridors. These strict conditions limit suitable areas primarily to parts of southern Balochistan and central Sindh, where cloud-free skies and arid landscapes offer consistently high GHI values and minimal terrain constraints. The scarcity of areas classified as highly suitable under this scenario highlights the stringent nature of high-reliability solar planning be-cause even small deviations in slope, land cover, or distance to infrastructure restrict feasibility.

Under the WLC scenario, a more balanced and realistic distribution of solar potential emerges. Here, regions with moderate to high suitability extend across central and south-ern Pakistan, reflecting an integrated assessment where all criteria contribute proportion-ally to the final score. Areas with strong GHI but moderate slope or moderate distance from the transmission network still achieve medium-to-high suitability. This pattern provides a policy-relevant view of solar development, indicating where solar farms could be feasibly deployed with manageable levels of infrastructural investment. Compared to the AND scenario, the WLC approach captures a broader range of technically sound opportunities, especially in Punjab and Sindh, where flat terrain and good road accessibility support project viability despite localized land or vegetation constraints.

The OR scenario, representing an optimistic exploration-driven strategy, identifies vast regions, particularly in western and southwestern Pakistan, as high or very high so-lar potential. These areas achieve elevated suitability even when only one criterion such as GHI or slope reaches desirable levels. Thus, arid zones with high irradiance but suboptimal proximity to transmission or roads are still classified as promising. This scenario is valuable for early-stage national planning because it highlights broad resource-rich belts that merit detailed feasibility assessments. However, many of these regions may require substantial infrastructure upgrades before becoming operationally feasible.

The results indicate that the choice of decision-making scenario significantly influences the identification of solar energy potential across different regions (

Table 4. Area coverage of different solar energy potential classes under various decision-making scenarios.

Potential Classes	AND Scenario		WLC Scenario		OR Scenario
	km2	%	km2	%	km2	%
Very low	152,408.90	43.49	57,724.34	16.47	24,663.20	7.04
low	109,888.50	31.36	98,652.79	28.15	64,230.45	18.33
Moderate	51,933.45	14.82	115,125.70	32.85	85,133.03	24.30
High	28,170.03	8.04	54,655.86	15.60	112,772.40	32.18
Very high	8005.72	2.29	24,247.91	6.93	63,607.52	18.15

). Under the AND scenario, the largest portion of the area falls within the very low (43.49%) and low (31.36%) potential classes, reflecting a conservative evaluation approach with stricter constraints. In contrast, the OR scenario yields the most optimistic assessment, with a greater share of the area classified as having high (32.18%) and very high (18.15%) potential, highlighting broader opportunities for solar energy development. The WLC scenario, as a weighted compromise between the two extremes, assigns a substantial area to the moderate (32.85%) and high (15.60%) potential classes, achieving a relative balance between conservative and optimistic perspectives. These variations underscore the importance of selecting an appropriate scenario based on national energy policy, sustainable development goals, and acceptable levels of risk in planning and decision-making processes.

The spatial patterns of wind energy suitability reveal strong differentiation among Pakistan’s major climatic and topographic regions, reflecting the physical realities that govern wind farm feasibility (Figure 8). In the OR scenario, high and very high suitability classes are widely distributed across the southwest and parts of the northwest. These are-as generally exhibit localized peaks in wind power density or wind speed that are sufficient for initial classification, even when elevation, slope, or grid access conditions are less favorable. As a result, the OR scenario captures the maximum theoretical extent of Pakistan’s wind resource. This is particularly evident in parts of southwestern Balochistan, where coastal and semi-coastal winds generate strong wind power density values, though much of the area remains sparsely connected to existing transmission infrastructure.

In the WLC scenario, a more coherent and technically realistic representation of wind potential emerges. Areas with moderate suitability cluster across southwestern and central Pakistan, reflecting regions where wind power density, slope, elevation, and proximity to transmission lines simultaneously meet acceptable thresholds. High and very high suitability zones become more localized and align more closely with corridors already recognized for wind development, such as those near the Gharo–Jhimpir wind corridor. These regions typically combine adequate wind power density, favorable elevation profiles, and manageable distances to the existing grid, factors that collectively reduce development risks and enhance practical feasibility.

The AND scenario represents the strictest decision rule and results in a highly constrained distribution of wind-suitable land. High and very high potential zones appear only where all criteria including wind power density, wind speed, terrain slope, elevation, and infrastructural accessibility reach optimal levels simultaneously. These areas are extremely limited and mainly concentrated in parts of southeastern Pakistan, where wind corridors coincide with flat terrain and close grid access. Most of the country falls into low or very low suitability classes under this scenario, highlighting the strong influence of slope, elevation variability, and limited transmission infrastructure in constraining wind development.

Together, the three scenarios illustrate how Pakistan’s wind potential is shaped by the combined effects of wind power density, terrain constraints, and infrastructural connectivity. Regions with high resource potential but rugged terrain or poor grid access are downgraded in the WLC and AND scenarios, demonstrating that real-world feasibility depends not only on wind resource availability but also on topographic and infrastructural readiness. This physically grounded interpretation directly addresses the reviewer’s request by linking suitability patterns to the environmental and logistic conditions that determine the practical viability of wind energy deployment.

An analysis of the changes in the spatial extent of wind energy potential classes under different scenarios reveals significant variation, particularly in the high and very high potential classes those representing the greatest capacity for wind power generation (

Table 5. Area coverage of different wind energy potential classes under various decision-making scenarios.

Potential Classes	AND Scenario		WLC Scenario		OR Scenario
	km2	%	km2	%	km2	%
Very low	56,732.28	55.91	26,001.33	25.63	3852.90	3.80
low	21,986.93	21.67	26,578.11	26.19	30,292.59	29.85
Moderate	14,373.41	14.17	25,332.26	24.97	22,056.14	21.74
High	7405.88	7.30	16,565.18	16.33	28,977.53	28.6
Very high	968.98	0.95	6990.60	6.89	16,288.32	16.05

). In the conservative AND scenario, the high potential class accounts for only 7.30%, and the very high class for 0.95% of the total study area, totaling 8.25%. These figures increase in the more balanced WLC scenario, reaching 16.33% and 6.89%, respectively, for 23.22% in total.

Under the optimistic OR scenario, the spatial coverage expands considerably, with 28.60% of the area falling under the high class and 16.05% under the very high class, totaling 44.65%. This upward trend highlights the considerable potential for wind energy development under favorable climatic and technological conditions. Notably, the expansion of high-potential areas in the OR scenario underscores a strong opportunity for large-scale deployment of this clean and renewable energy source, contributing significantly to the reduction in fossil fuel dependency and supporting long-term sustainability. Overall, technological optimization of wind turbines, enhanced transmission infrastructure, and supportive energy policies can further maximize the utilization of these promising areas.

The spatial ensemble map of renewable energy development classes across three spatial decision-making scenarios reveals significant differences in the area coverage of solar-only, wind-only, and combined solar–wind potential zones (Figure 9). Under the highly restrictive AND scenario, only 461.42 km² of land across Pakistan is identified as suitable for hybrid (solar and wind) energy development. This area increases to 3875.96 km² in the more flexible WLC scenario, and further expands to 11,836 km² under the optimistic OR scenario. The more than 25-fold increase from AND to OR reflects the vast latent potential for hybrid renewable energy projects if technical constraints and selection thresholds are relaxed. Similarly, the solar-only and wind-only classes also exhibit noticeable growth, particularly the solar class. In the AND scenario, only 7544.29 km² is classified as suitable solely for solar energy, which dramatically rises to 51,771.85 km² under the OR scenario. This indicates that solar resources are more widely distributed across the country, and with fewer constraints, a substantial portion of Pakistan can be deemed solar viable. In contrast, the wind-only area grows from 507.56 km² (AND) to 4452.74 km² (OR). While the trend is similar, the relative increase is more modest compared to solar, highlighting the more geographically constrained nature of wind energy potential.

The sensitivity analysis for the WLC scenario in the solar energy model demonstrates a high level of robustness and stability against variations in the weighting scheme (

Table 6. Sensitivity analysis for solar energy suitability (WLC scenario).

Criterion	Base Weight	Adjusted Weight (±10%)	R2 (vs. Baseline Map)	Change in High & Very High Suitability Area (%)
GHI	0.26	0.234/0.286	0.978	3.8
Proximity to Villages	0.055	0.0495/0.0605	0.982	2.1
Proximity to Cities	0.065	0.0585/0.0715	0.975	2.7
Proximity to Roads	0.06	0.054/0.066	0.973	2.5
Proximity to Transmission Lines	0.11	0.099/0.121	0.968	3.4
Proximity to Substations	0.08	0.072/0.088	0.971	2.9
Elevation	0.04	0.036/0.044	0.984	1.9
Slope	0.035	0.0315/0.0385	0.979	2.2
Floodplain Zoning	0.025	0.0225/0.0275	0.987	2.0
Proximity to Fault Lines	0.03	0.027/0.033	0.981	2.6
Air Temperature	0.035	0.0315/0.0385	0.977	2.3
Vegetation Density	0.02	0.018/0.022	0.986	2.1
Population Density	0.095	0.0855/0.1045	0.962	4.1

). Adjusting each criterion’s weight by ±10% resulted in only minor changes in the total area classified as high and very high suitability, ranging from 1.9% to 4.1%. This indicates that the model is structurally resilient to uncertainties in expert-based weighting. The largest sensitivity was observed for population density (4.1%), followed by GHI at 3.8%, suggesting that these criteria substantially influence the delineation of priority zones and that small adjustments in their importance can slightly modify spatial rankings. Conversely, physiographic factors such as slope, elevation, vegetation cover and floodplain zoning exhibited the lowest sensitivity levels (approximately 1.9–2.2%), reflecting their stable and foundational role within the spatial framework.

The consistently high R² values (0.968–0.987) further confirm that the adjusted suitability maps remain strongly correlated with the baseline map, with no major changes to overall spatial patterns. This strong correspondence indicates that the solar energy suitability model is reliable and resistant to moderate fluctuations in the weighting scheme. Consequently, the FUCOM-derived weights provide a stable basis for decision-making, as small perturbations in expert judgments do not produce substantial deviations in the model outcomes.

The sensitivity analysis for wind energy reveals that the WLC model is slightly more sensitive to weight variations than the solar model, yet it remains within an acceptable and scientifically robust stability range (

Table 7. Sensitivity analysis for wind energy suitability (WLC scenario).

Criterion	Base Weight	Adjusted Weight (±10%)	R2 (vs. Baseline Map)	Change in High & Very High Suitability Area (%)
Wind Speed	0.275	0.2475/0.3025	0.952	4.7
Wind Power Density	0.105	0.0945/0.1155	0.965	3.5
Proximity to Villages	0.06	0.054/0.066	0.981	2.4
Proximity to Cities	0.07	0.063/0.077	0.976	2.8
Proximity to Roads	0.065	0.0585/0.0715	0.979	2.5
Proximity to Transmission Lines	0.095	0.0855/0.1045	0.967	3.2
Proximity to Substations	0.08	0.072/0.088	0.973	2.9
Elevation	0.04	0.036/0.044	0.985	2.0
Slope	0.05	0.045/0.055	0.971	3.1
Floodplain Zoning	0.015	0.0135/0.0165	0.988	1.9
Proximity to Fault Lines	0.025	0.0225/0.0275	0.982	2.3
Air Temperature	0.03	0.027/0.033	0.978	2.6
Vegetation Density	0.005	0.0045/0.0055	0.991	2.0
Population Density	0.08	0.072/0.088	0.963	3.9

). Modifying the weight of each criterion by ±10% resulted in changes of 1.9% to 4.7% in the area classified as highly and very highly suitable. The highest sensitivity was associated with wind speed (4.7%) and wind power density (3.5%), which is expected given their dominant influence on wind energy potential. Small adjustments in the weights of these dynamic meteorological parameters can shift suitability classifications more noticeably than changes in other criteria.

In contrast, factors such as vegetation density, floodplain zoning, elevation and proximity to fault lines demonstrated minimal sensitivity (1.9–2.3%), highlighting their relatively stable influence within the model. Despite the slightly higher responsiveness of the core wind-related factors, the model maintains very strong spatial consistency, as reflected by R² values ranging from 0.952 to 0.991. These results confirm that the spatial patterns produced under adjusted weights remain highly aligned with the baseline outputs.

While the wind energy model is naturally more influenced by changes in its key operational parameters, it still demonstrates a high degree of reliability and resistance to moderate variations in the weighting structure. This validates the robustness of the FUCOM-based weighting approach and supports the model’s suitability for practical wind energy planning and decision-making.

4. Discussion

The integration of two complementary spatial multi-criteria decision-making techniques FUCOM for criteria weighting and OWA for decision aggregation proved to be an effective and coherent framework for the site selection of renewable energy power plants. While FUCOM ensures a logically consistent derivation of criteria weights, OWA allows flexible scenario-based evaluation through decision rules (AND, WLC, OR), enhancing the robustness of the spatial analysis. Furthermore, the use of diverse spatial datasets and modeling approaches contributes significantly to improving the decision-making process, enabling planners to identify the most appropriate locations for energy infrastructure based on a wide range of environmental, technical, and socio-economic criteria.

FUCOM provides a structured approach to weighting criteria by minimizing inconsistency and redundancy in pairwise comparisons, which enhances the logical coherence of the weighting process. It is particularly valuable in contexts requiring transparent, justifiable prioritization among multiple factors. OWA, on the other hand, introduces flexibility in decision-making by enabling the application of optimistic, pessimistic, or balanced aggregation strategies, thereby allowing decision-makers to simulate different policy attitudes or planning priorities through the use of scenario-based weighting schemes. Despite these strengths, both methods have inherent limitations. FUCOM’s reliance on expert judgment introduces a degree of subjectivity, particularly in data-scarce environments where empirical validation is limited. Furthermore, it may not fully capture nonlinear or interdependent relationships among criteria such as elevation simultaneously influencing wind patterns and temperature. Similarly, while OWA enhances adaptability, it can produce significantly different outcomes depending on the choice of scenario weights, which may challenge consistency in policy interpretation and implementation. Therefore, the AND, OR, and WLC scenarios generated by the OWA model should be regarded as complementary analytical perspectives, each highlighting different aspects of spatial suitability. The substantial divergence between AND and OR scenarios underscore how variations in risk tolerance and decision rules can meaningfully alter suitability outcomes, reinforcing the importance of scenario-based planning in complex land use systems.

To further assess the reliability of the FUCOM–OWA framework, a sensitivity analysis was conducted in which each criterion weight was varied by ±10%. The results showed that changes in the extent of high and very high suitability areas remained below 5% for both solar and wind models, indicating strong stability of the weighting structure. The high R² values between baseline and perturbed WLC outputs confirm that spatial patterns remain largely unchanged despite moderate variations in expert-assigned weights. Solar suitability was most sensitive to GHI and population density, while wind suitability responded primarily to wind speed and wind power density. Overall, the analysis demonstrates that uncertainties in expert judgment have only a limited influence on the site-selection outcomes, reinforcing the robustness of the proposed weighting scheme.

Comparing the findings of this study with previous research reinforces the reliability and applicability of the methodology while revealing important context-specific distinctions. For instance, similar studies conducted in Pakistan by Raza et al. [26] identified southern Balochistan and parts of Punjab as promising zones for solar energy, consistent with this study’s results. However, the current research expands on prior works by including a broader range of constraints (e.g., vegetation density, floodplain zoning, and fault lines), leading to a more conservative estimation of usable area 59.77% of the study area was constrained for solar, compared to less than 30% in earlier studies. This highlights the importance of incorporating environmental risks and protected areas in future planning. With regard to wind energy, prior studies such as by Raza et al. [26] mainly focused on average wind speed and neglected wind power density and infrastructural limitations. This study reveals a significantly more restricted spatial potential for wind energy (only 11.66% of land unconstrained), reflecting a more realistic assessment. The inclusion of wind power density, elevation, and transmission infrastructure as critical parameters provides a deeper understanding of practical deployment challenges. These findings suggest that wind energy development in Pakistan must be approached with caution, and major investment in grid infrastructure and turbine optimization is necessary to unlock its potential. The ensemble analysis of hybrid (solar + wind) zones presents a novel contribution by quantifying overlapping suitability under various decision rules. The sharp contrast in hybrid potential between the restrictive AND (461.42 km²) and the permissive OR (11,836 km²) scenarios underscores the sensitivity of site identification to methodological assumptions. This aligns with the findings of international studies, such as Firozjaei et al. [63] in Iran and Hernandez-Escobedo et al. [64] in Mexico, which emphasize the need for flexible, scenario-based modeling to account for socio-environmental trade-offs in renewable energy siting.

Despite the methodological strengths and the comprehensive spatial framework developed in this study, several substantive limitations must be acknowledged to provide an accurate interpretation of the results. First, the analysis does not include economic criteria such as land acquisition costs, construction and maintenance expenditures, or levelized cost of electricity (LCOE), primarily due to the lack of spatially explicit economic datasets for Pakistan. The absence of these layers means that some areas identified as technically suitable may ultimately prove financially suboptimal once real investment constraints are incorporated. Second, the study relies on long-term mean GHI and wind-speed datasets, which do not capture intra-annual and inter-annual variability, seasonal extremes, or episodic climatic events such as monsoon-induced fluctuations. These temporal dynamics can substantially alter resource availability and may influence both capacity-factor estimates and long-term energy reliability. Third, while transmission-line proximity was used as a proxy for infrastructural accessibility, the analysis does not incorporate real grid-capacity metrics such as substation loading, transformer constraints, or network congestion, which are critical for assessing the feasibility of utility-scale integration. This omission may lead to an overestimation of the practical deployable potential in regions with already stressed grid systems. Furthermore, it is important to note that validation of the high-potential areas against actual or planned renewable energy projects was not possible due to the unavailability of publicly accessible, georeferenced project datasets in Pakistan. Consequently, the suitability maps represent technically and environmentally informed potential rather than verified deployment locations. This limitation emphasizes the need for future studies to integrate official project data to refine and validate the spatial outputs.

Moreover, data uncertainty can influence spatial suitability outcomes in several concrete ways. Coarse-resolution GHI datasets may overlook microclimatic variability, potentially overestimating solar suitability in dust-prone or monsoon-affected regions. Reanalysis-based wind-speed and wind-power-density data may misrepresent local turbulence or coastal jets, resulting in marginal regions being assigned higher suitability classes. Classification errors in land-cover datasets may misidentify rangelands as barren land, leading to underestimation of constraint zones. Additionally, limitations in DEM resolution can distort slope calculations and affect terrain-based exclusions. Uncertainty may also arise from the expert-driven weighting process in FUCOM; variations in expert judgment, differences in disciplinary background, or inconsistent interpretation of relative-importance scales can lead to shifts in the priority vector and consequently alter the final suitability maps. Although FUCOM minimizes inconsistency, even small deviations in comparative importance values can systematically shift the ranking of criteria, particularly in tightly ranked systems such as wind-site selection. This highlights the importance of expert diversity and transparent elicitation procedures to reduce potential bias in the resulting weights.

Moreover, the spatial suitability maps developed in this study directly support Sustainable Development Goals, particularly SDG 7 (Affordable and Clean Energy) and SDG 13 (Climate Action). By identifying optimal regions for solar, wind, and hybrid renewable energy projects, policymakers can prioritize investments in clean energy infrastructure, reduce reliance on fossil fuels, and mitigate greenhouse gas emissions. The integration of multi-criteria spatial analysis ensures that planning accounts for environmental, technical, and socio-economic factors, providing a robust foundation for climate-resilient energy development. These findings are in line with recent research emphasizing the role of advanced, scenario-based planning in achieving multiple SDGs, such as Moharram et al. [65], who demonstrated how multigeneration energy systems contribute to clean energy provision and climate mitigation. In the context of Pakistan, our maps highlight southern and southwestern regions as key zones where hybrid renewable projects can meaningfully advance national clean-energy targets while supporting climate action initiatives.

Addressing these limitations will require future research to integrate detailed techno-economic modeling, high-resolution temporal resource simulations, and real-time grid performance datasets. Incorporating such components potentially alongside fuzzy inference systems, machine-learning prediction models, and dynamic power-flow simulations would significantly enhance the robustness, operational realism, and policy relevance of renewable-energy siting frameworks. Nonetheless, even within these constraints, the present study offers a scientifically grounded, spatially explicit, and policy-relevant foundation that can guide national-scale renewable-energy planning and inform future energy-investment strategies in Pakistan.

5. Conclusions

This study aimed to evaluate the development potential of renewable energy in suitable regions of Pakistan using a GIS-based multi-criteria analysis framework. This analytical approach, by integrating spatial data with the assessment of key criteria, enables the precise identification of high-potential areas for solar and wind energy utilization and can assist strategic decision-making in energy planning. A comparative analysis of the three scenarios OR, AND, and WLC reveals significant spatial differences that can guide decision-making strategies for solar and wind energy planning. The OR scenario, by identifying the widest range of high-potential areas, provides an optimistic and broad perspective of regions suitable for project development, making it especially useful for exploratory assessments and the rapid expansion of renewable energy. In contrast, the AND scenario, by restricting selection to optimal and low-risk areas, focuses on accurately and selectively identifying specific zones with optimal solar and wind characteristics, making it suitable for secure and strategic investments; these areas are primarily concentrated in the coastal regions of southern Pakistan. The WLC scenario presents a balanced and realistic approach, identifying more stable potential areas that align with multiple criteria, and is well-suited for strategic planning and resource allocation. The analysis results show that the choice of scenario can significantly affect the identification of solar and wind energy potential in different regions where OR provides broad development, AND ensures high precision, and WLC strikes a balance between the two. Therefore, policymakers should choose the appropriate scenario based on development goals and energy policies to ensure optimal and sustainable exploitation of renewable resources. These findings have important implications for Pakistan’s energy policy. The results suggest that southern and southwestern regions, particularly the coastal areas of Balochistan and parts of Sindh, are highly suitable for hybrid solar–wind projects due to the complementary availability of solar irradiance and wind resources. Policymakers can prioritize these zones for integrated renewable energy planning, optimizing land use and reducing reliance on fossil fuels. By aligning scenario-based site identification with national energy targets, hybrid systems can enhance energy security, support distributed generation, and contribute to achieving Pakistan’s sustainable development goals. Implementing such targeted planning can help maximize the efficiency and reliability of renewable energy infrastructure while minimizing environmental and infrastructural risks. For future studies, it is recommended to use more up-to-date data and dynamic models to assess temporal changes in energy potential. Additionally, more comprehensive analyses that incorporate social, economic, and environmental dimensions can help better identify opportunities and challenges. Evaluating hybrid solar–wind systems may also contribute to optimizing land use and enhancing the sustainability of energy production in high-potential areas.