Spatial Analysis and Prioritization of Solar Energy Development in South Khorasan Province, Iran: An Integrated GIS and Multi-Criteria Decision Analysis Framework

Abstract

The use of solar photovoltaic technology is among the most promising approaches to achieving SDG7—Affordable and Clean Energy—which seeks to provide modern, reliable, sustainable, and efficient energy for everyone globally, especially in developing areas with high irradiation, where both energy access and decarbonization are major challenges. South Khorasan Province, Iran, is one of the most highly irradiated regions in the world. However, despite the abundance of solar resources, most previous research in Iran on solar potential has focused on technical potential, with little emphasis on actual energy consumption patterns and economic viability. To the best of our knowledge, this is the first demand-driven assessment at the county level and the first national-scale implementation of the MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution) method for selecting solar energy sites in Iran. A spatially explicit integrated framework based on GIS-MARCOS was established for each of the eleven counties of South Khorasan Province, and five benefits were used as criteria (solar irradiance, population, per capita electrical consumption in residential, industrial, and agricultural sectors). Objective weights were calculated using Shannon’s Entropy. The analysis indicates that residential electricity demand emerges as the most influential factor in the prioritization process. Therefore, the counties of Birjand, Qaenat, and Tabas were identified as top priority counties, while counties with high irradiation levels but low demand (for example, Boshruyeh) received the least priority. These results clearly indicate the need to transition from irradiation-based to demand-based planning to minimize transmission losses and maximize the ability to integrate solar-generated electricity into the electric power grid. This proposed methodology provides a transferable decision-support tool for other high-irradiation, demand-heterogeneous regions around the globe.

1. Introduction

Renewable energy sources, especially solar energy, have evolved throughout the 21st century as a key solution to the environmental and economic challenges posed by the reliance on fossil fuels. The increasing population growth and rising energy requirements have heightened the urgency of transitioning to sustainable energy sources [1,2]. Solar energy has the potential to be a sustainable source of energy due to its clean, accessible nature and global abundance [3]. Therefore, solar energy not only reduces greenhouse gas emissions but also increases national energy independence.

On the other hand, as civilizations strive for a cleaner, more sustainable energy future, identifying and prioritizing viable energy sources becomes essential [4].

Although the transition to sustainable energy is a global need, the regional specificities of each area must be considered. The variability in energy requirements, the diversity of environmental challenges, and the complexity of regional specifics require a more nuanced approach [5]. It is therefore crucial to understand these regional complexities so that tailored strategies can be developed that not only meet the region’s energy requirements but also align with its environmental and socio-economic objectives [6].

Iran is located in the global solar belt (between latitudes 25° and 40° N) and receives annual solar irradiation of 1800–2200 kWh/m², with more than 280 sunny days per year, making it one of the best locations for solar energy use. However, despite this considerable potential for solar energy, the share of solar energy in Iran’s total electricity production was less than 1% in 2024, primarily due to the significant fossil fuel subsidy, the lack of adequate policy frameworks, limited financing options, and the supply-driven model of planning that prioritizes generation capacity over the proximity of demand [7]. South Khorasan Province, located in the east of Iran, illustrates this paradox: it has one of the highest levels of solar irradiation in the country (reaching up to 2300 kWh/m²/yr) and it contains large areas of unused desert land; however, the installed solar capacity in South Khorasan is negligible (less than 50 MW in 2024). Previous national- and provincial-scale studies in Iran that assessed the potential for solar energy in the country employed a technical-only, irradiance-based approach and mapped solar energy potential without accounting for spatial differences in electricity demand, population density, or economic viability [8]. Consequently, proposed solar energy projects are typically located far from load centers, resulting in high transmission losses, poor returns on investment, and the underuse of solar energy’s potential.

The growing international literature indicates the importance of regional prioritization for renewable energy projects based on demand characteristics. Studies conducted in China, India, and Saudi Arabia demonstrated that the integration of energy consumption patterns with resource maps can reduce the costs associated with grid connection by 15–30%, improve the project bankability [9,10]. Several studies have used GIS-based suitability analyses at the national or single-province level [11,12,13]. There is currently no study that has carried out a county-level prioritization of solar energy development in South Khorasan that simultaneously considers solar irradiation, population, and sectoral electricity demand (domestic, industrial, and agricultural) within a multi-criteria decision-making (MCDM) framework. This research gap is relevant because South Khorasan exhibits extreme spatial heterogeneity: urban centers such as Birjand account for more than 40% of the province’s electricity demand, whereas vast rural and desert counties have an electricity consumption of less than 5% despite higher irradiation levels. Without demand-sensitive investment prioritization, they risk being allocated to low-consumption areas, thereby delaying the province’s contribution to Iran’s 2030 renewable energy target of 10 GW of solar capacity. The main research question of this study is as follows: How can the development of solar energy be spatially prioritized across the counties of South Khorasan Province, taking into account both resource availability and actual energy demand? According to the research hypothesis, the primary goal of this study is to develop and implement an integrated GIS-MARCOS framework for ranking the 11 counties of South Khorasan based on their suitability and priority for photovoltaic development. The specific goals of this study are threefold: (i) to quantify and map the spatial variations in solar irradiation and sectoral electricity consumption, (ii) to calculate the objective criteria weights using Shannon’s Entropy, (iii) to obtain a demand-driven priority ranking using the MARCOS method. Based on the research hypothesis, we assume that, contrary to conventional irradiation-based approaches, countries with a greater population density and per capita domestic electricity consumption will be ranked as the top priorities, even though their solar irradiation is slightly lower than that in distant desert areas. The demand-driven methodology proposed in this study enables a shift in planning from a supply-based to a demand-based paradigm. This research provides policymakers, provincial authorities, and private investors with a scientifically valid roadmap for directing investments to major load centers. The proposed framework is reproducible across all other Iranian provinces and arid regions worldwide with similar resource–demand mismatches, which could lead to reduced losses, improved grid stability, and the accelerated penetration of clean energy.

This paper is also one of the few papers that incorporates demand-based variables (e.g., population, sectoral demand) into a GIS-based multi-criteria spatial analysis (MARCOS), whereas most other papers focus almost entirely on solar irradiance or similar supply-side factors.

As a result, this research supports a shift from strictly resource-focused renewable energy planning to more demand-oriented approaches in areas with substantial regional disparities in electricity demand. Moreover, this research shows how entropy-based weights can be used to integrate demand and supply factors together in a manner that reflects the relative uncertainty associated with each factor. The research results indicate that when using entropy-based objective weights, per capita residential electricity consumption is the primary driver of the prioritization process. This indicates that in regions with significant differences in electricity demand, proximity to demand is likely to have a greater influence on investment priorities than small differences in solar irradiance.

The five largest gaps based on the previous evaluation can be defined as follows, with regard to one of Iran’s most high-solar-irradiated and thus least-developed solar energy regions—South Khorasan Province:

The absence of any county-based studies to date prevents prioritized regional allocation based on an assessment of each county’s available solar resources. There is no use of the MARCOS method in the literature related to the selection of sites for solar energy development in Iran, although this methodology has already been shown to have utility in other locations [14] as well as being recognized throughout the scientific community for its ability to provide stable rankings and resist rank reversals [15]. In addition, there is a genuine need for a demand-driven ranking system to guide the provincial investment plan and reduce transmission energy losses. There is currently no quantitative analysis of the influence of per capita domestic energy consumption on decisions regarding the location of new energy developments, even in high-irradiance desert counties with very small populations. Additionally, there is no incorporation of real-sector electricity demand as a first criterion for selecting a site, alongside irradiance, at the sub-provincial level—a procedure now commonly used in leading international studies conducted in arid and semi-arid environments. To the best of the authors’ knowledge, this is the first time in the Iranian literature that an attempt has been made to provide answers to all the aforementioned gaps simultaneously as part of the following new developments and contributions:

The development of the first county-level, mandate-driven prioritization system for the development of solar PV systems throughout the eleven counties of South Khorasan Province.

The first novel integration of Shannon’s Entropy weighting with the robust and rank stable MARCOS method in a full GIS environment—a methodological combination that has never previously been reported in any Iranian renewable energy research.

The first empirical validation in Iran of the increasingly well-supported hypothesis that population-weighted per capita domestic electricity consumption will exceed the irradiance benefits in arid regions, thereby aligning Iranian research with the most recent international evidence [16].

Provides a directly actionable ranking system for use by provincial authorities, SATBA, and private sector investors, with significant influence over the forthcoming review of South Khorasan’s Renewable Energy Roadmap and national 2030 targets.

Presents a transparent, easy-to-replicate methodology that could be applied to other sunbelt provinces in Iran and similar arid regions in the Middle East, Central Asia, and North Africa.

Although some restrictions remain (notably the use of the currently available provincial statistical yearbooks and the lack of hourly demand profiles), the proposed framework undoubtedly constitutes a major paradigm shift from the conventional irradiance-dominated planning approach to a much more economically rational, grid-efficient, and policy-relevant planning paradigm.

The Photovoltaic Power Potential (PVOUT) data from the Global Solar Atlas has been used to determine the potential for using solar power. PVOUT represents the amount of energy generated as kilowatts per year per kilowatt. PVOUT is a commonly used expression in solar resource assessments. PVOUT is a representation of the expected total yearly electricity generation from a traditional photovoltaic array based on the climatic conditions of the area.

2. Literature Review and Gap Analysis

The growing need for energy worldwide has led many people to question how we generate it [17]. Most researchers agree that we must transition from finite resources like fossil fuels to clean, renewable energy sources [18]. This is especially true given the growing concern around the environment and the increasing need for energy [19]. In addition to the benefits of using renewable energy, such as reducing greenhouse gas emissions and decreasing the reliance on foreign energy sources, one type of renewable energy is gaining popularity: solar power [20]. Demand-oriented solar planning is also possible within a broader body of knowledge on energy system planning and spatial energy economics [21]. Spatially oriented integrated resource planning (IRP), which has been widely used in the electric power industry’s planning process, focuses on integrating new supply resources with the spatial pattern of demand to minimize total system costs and capital requirements [22]. Likewise, spatial energy economics emphasizes locating generation assets near areas of high consumption to reduce energy losses during transmission, improve grid stability, and ultimately increase the overall efficiency of the system [23]. When it comes to developing renewable energy, IRP implies that solar investments should be determined not only by the availability of solar resources but also by the spatial distribution of electrical energy demand [24]. Therefore, the demand-focused methodology employed in this research project is consistent with the broader planning traditions of IRP and related methodologies by considering both the solar resource potential and regional consumption patterns to efficiently allocate renewable energy infrastructure across territories [25].

A number of recent international studies have highlighted the need for incorporating demand-related variables into renewable energy planning methodologies. Studies focusing on China, India, and several Middle East nations indicate that the placement of renewable energy generation near the areas of highest demand can result in an approximate reduction of 15–30% in energy transmission losses and an improvement in economic viability through reduced costs associated with connecting to the grid and infrastructure requirements [26]. Additionally, studies have shown that demand-related methodologies can reduce the LCOE of distributed renewable energy systems by reducing the need for additional network expansion, thereby increasing energy utilization efficiency [27]. As such, the findings of these studies further demonstrate the necessity of combining resource-based and demand-based variables when developing renewable energy systems to produce more efficient, economically viable systems [28].

Solar Photovoltaics (PV) are a form of renewable energy that uses sunlight to produce electricity [29]. Solar PV has become increasingly popular due to the availability of sunlight, the lack of pollution associated with electricity generation, and the decline in PV technology prices [30]. Recently, international research has increased, shifting the paradigm from traditional irradiance-based methods to more complex demand-based methodologies [31]. Similar results were reported by Nassar et al. [14], who used GIS-integrated AHP-MARCOS in Egypt and found that, despite 83% of the land area being technically suitable for PV systems, only 23% remained economically viable when real consumption patterns were incorporated as the primary decision-making criterion. Saraswat et al. [9] also noted that including electricity demand data increased project bankability by approximately 25%. These results indicate that neglecting the demand side of the equation can significantly overestimate the economic potential and lead to recommendations for locations that do not meet optimal conditions.

The significant advances reported in the literature on this subject, however, have yet to be reflected in the vast majority of studies examining Iran’s suitability for PV development. While the majority of studies have focused on a supply-oriented methodology, utilizing solar irradiation as the most important criterion, a number of recent Iranian studies [32,33] have similarly treated actual electricity consumption as simply an exclusionary layer or marginal weighting factor, but have failed to utilize it as the key driver in determining the relative importance of different geographic locations for PV development. Such simplifications have been shown in recent international studies to result in 30–40% overestimates of economic viability and recommendations of locations that suffer from high transmission loss and poor capacity factors.

In addition to the general lack of consideration for the demand side of the equation in Iranian studies, the methods used to evaluate and compare the suitability of different locations for PV development have almost exclusively employed subjective weighting techniques (AHP, ANP, Fuzzy-AHP). Objective entropy-based or CRITIC methods [34,35] have been employed in other countries but have yet to be adopted in Iran. Also, as in many international studies, Iranian analyses are often conducted at the national or provincial level. However, such scales mask important intra-provincial variability in population density and sectoral demand. Several international researchers [36,37,38] have demonstrated that conducting analyses at sub-provincial scales provides a better understanding of regional differences in PV suitability. The recent emphasis on hybrid MCDM methods (e.g., DEA + FAHP + TOPSIS) aims to address a range of environmental, economic, and infrastructure issues when determining locations for solar energy infrastructure simultaneously [39,40,41]. Some researchers have used AI and fuzzy MCDM methods to develop regional prioritization models incorporating GIS to select sites for renewable energy generation simultaneously [42].

For example, ANFIS–FAHP–GIS models can be used to simultaneously evaluate environmental, economic, and spatial factors, thereby improving the accuracy and robustness of decisions on the placement of solar photovoltaic farms [43].

The recent literature emphasizes the value of GIS–MCDM frameworks for assessing potential locations for renewable energy resources by integrating spatial constraints, technical potential, environmental impacts, and social and economic factors [44].

These approaches will be very useful when designing integrated wind/solar systems with hydrogen energy storage systems within a multi-criteria decision environment.

The fuzzy MCDM models developed to address the issue of selecting locations for solar photovoltaic plants account for uncertainties in environmental and infrastructural data [45].

Decision makers can use techniques such as CRITIC and VIKOR to determine the relative priority of each location based on its balance between competing criteria (e.g., solar potential, distance to grid connection points, climate characteristics, etc.) and environmental constraints [46].

As well as this global progress in international solar PV development, most studies examining the potential for solar PV in Iran have still employed mainly supply-oriented methodologies to evaluate solar PV resources, with solar irradiance as the main determining factor for solar PV development. Many studies have treated electricity demand indicators as secondary factors or exclusionary constraints rather than as primary determinants of spatial prioritization for solar PV development. Consequently, such simplification may lead to a significant overestimation of economic viability and suggest locations for solar PV development that are affected by high transmission losses and poor grid connections.

In addition to the limitations of the above-mentioned literature, another constraint is the application of multi-criteria decision-making (MCDM) techniques in solar energy planning. A large number of MCDM techniques have been used worldwide in GIS-based decision-support systems for solar energy planning, including TOPSIS, ELECTRE, PROMETHEE, MARCOS, AHP, and others. The reasons why MCDM techniques are widely applied include their simple methodology, the transparency of the computational procedure, and the high level of interpretability for decision makers. Moreover, the majority of these techniques are robust when applied to spatial data, require relatively little computing time, and yield stable rankings. Additionally, some methods, including MARCOS, have been developed to address rank reversal and improve the reliability of comparative assessments of alternatives.

Recently, researchers have emphasized the importance of incorporating environmental and ecosystem services into territorial energy planning frameworks. For instance, Hernández and Camerin [47] have demonstrated that spatial decision-support tools can integrate assessments of ecosystem services into land use planning processes. They highlight the need to assess functional ecological factors—for instance, biodiversity protection, landscape preservation, and ecosystem resilience—alongside socio-economic development goals to ensure sustainable territorial development.

Although the main focus of the current study is on the relationships between available solar resources and electrical consumption patterns, future extensions of the proposed GIS–Entropy–MARCOS framework can include indicators of ecosystem services as additional evaluation criteria. This will allow for a more integrated evaluation of the location for renewable energy investment, taking into account the efficiency of the electric grid, economic feasibility, and environmental sustainability.

To position the present research within the wider literature,

Table 1. Comparative overview of previous GIS–MCDM studies for solar energy planning.

Study	Study Area	Spatial Scale	Criteria Considered	Weighting Method	Ranking Method	Key Findings
[9]	India	Regional	Solar irradiation, land use, slope, infrastructure	AHP	GIS–MCDM	Identified optimal regions for solar farm development based on physical suitability criteria.
[11]	Thailand	Provincial	Solar radiation, land suitability, infrastructure	Fuzzy AHP	GIS–MCDM	Demonstrated the importance of integrating multiple environmental and infrastructural criteria in solar planning.
[12]	Iran	National	Solar irradiation, slope, land use	AHP	GIS–MCDM	Highlighted the significant solar potential of several Iranian regions based on resource-based criteria.
[14]	Egypt	Regional	Solar radiation, electricity demand, land use	AHP–MARCOS	GIS–MCDM	Showed that incorporating demand indicators improves economic feasibility of solar projects.
This study	South Khorasan Province, Iran	County level	Solar resource potential, population, sectoral electricity demand	Shannon’s Entropy (objective)	MARCOS	Introduces a demand-driven prioritization framework linking solar resource potential with spatial electricity demand patterns.

presents a comparative overview of a selection of GIS-MCDM studies on solar energy planning and emphasizes differences in geographical area of study, evaluation criteria, weighting procedures, and decision-making techniques.

Recent research is also focused on integrating environmental and ecological services into territorial energy planning frameworks. As an example, the work of Hernández and Camerin [47] demonstrated how spatial decision-support tools can incorporate ecosystem service assessments into land use planning processes. The authors emphasize that it is essential to evaluate environmental functions (such as biodiversity protection, landscape conservation, and ecosystem resilience) alongside socio-economic development objectives to support sustainable territorial development. Therefore, integrating ecosystem service indicators into GIS-based decision-making models will provide a more comprehensive basis for spatial planning. While this study has focused on the relationship between solar resource availability and electricity demand patterns, future applications of the proposed GIS–Entropy–MARCOS framework may include additional ecosystem service layers as criteria. This would allow for a more integrated prioritization of the territorial renewable energy projects based on grid efficiency, economic viability, and sustainability.

To compare the present research with previous GIS-MCDM research on solar energy, Table 1 summarizes several relevant GIS-MCDM studies on solar energy planning with respect to differences in spatial scale, evaluation criteria, weighting procedures, and decision-making techniques.

In particular, most previous studies have employed subjective weighting procedures (e.g., AHP) and have focused primarily on resource-based suitability criteria. The present study, by contrast, develops an objective weighting procedure based on entropy and focuses its analysis on demand-driven regional prioritization at the county level.

3. Research Methodology

3.1. Study Area

Compared to other provinces in Iran, South Khorasan is the third largest in terms of geographic area, with an approximate total land area of 150,800 km², and is located in the eastern part of the nation. The province of South Khorasan is bounded to the north by Khorasan Razavi Province, to the west by Yazd, Semnan, and Isfahan provinces, and to the south by Kerman and Sistan-Baluchestan provinces. To the east, it shares an international border of approximately 331 km with the Farah Province in Afghanistan. The province has a dry, arid desert climate, with an average annual precipitation of 98 mm. The province’s most recent census indicated that approximately 768,898 people lived in the province in 2016.

The province comprises 11 counties: Birjand (the province’s administrative capital), Boshruyeh, Darmian, Ferdows, Khusf, Nehbandan, Qaenat, Sarayan, Sarbisheh, Tabas, and Zirkuh. The majority of the province’s population lives in the city of Birjand, which is the province’s major urban and economic center and accounts for a large share of the province’s population and electrical load. The remaining counties are mostly desert and sparsely populated. A map of the county boundaries is shown in Figure 1.

In addition to its geographical and climatic features, South Khorasan Province has structural features that are important for energy planning. The regional electrical system is characterized by a relatively low grid density and long transmission distances between thinly populated villages; both factors can lead to increased technical losses and higher infrastructure costs. The majority of the region’s electricity demand is concentrated in a few cities (Birjand and Qaenat), whereas large desert regions with abundant solar energy resources are poorly connected to the current power distribution system. The regional economy is largely composed of agricultural production, mining, and small-scale manufacturing, resulting in a diverse pattern of electricity use across counties. For these reasons, South Khorasan is a good example for examining how well the spatial availability of solar resources matches the spatial patterns of electrical use within the province.

3.2. Data Sources and Criteria Selection

In the initial phase, an extensive review of the national and international literature on solar-energy regional prioritization and spatial prioritization was conducted to identify research gaps and inform criteria selection. Subsequently, to obtain accurate and up-to-date solar resource data for South Khorasan Province in 2024, annual and monthly global horizontal irradiation (GHI) values were extracted from the Global Solar Atlas (World Bank/IRENA), a widely recognized and validated data source. These raster datasets were then imported into ArcGIS Pro 3.2 for spatial processing, reclassification, and the generation of county-level solar irradiation maps.

Five benefit criteria were selected based on the literature review and the specific context of arid, demand-heterogeneous regions: (i) annual solar irradiation (kWh/m²/year), (ii) population, (iii) per capita domestic electricity consumption, (iv) industrial electricity consumption, (v) agricultural electricity consumption. All criteria except irradiation were treated as demand-side indicators to reflect the study’s explicit focus on consumption patterns. County-level electricity consumption data for 2023–2024 were obtained from the South Khorasan Regional Electric Company (Mashhad, Iran) and normalized per capita where appropriate.

Because the model evaluates population and per capita residential electricity usage (together), an additional screening method was developed to test for possible overlap among demand-based indicators. These two variables are associated but measure different aspects of the region’s total electrical demand. Population indicates the number of people available to meet total demand; per capita residential electricity usage is the average electrical demand per person. Therefore, one measures the “size” of the demand aggregation area, and the other measures residents’ behavior regarding their electrical usage.

A correlation analysis was also conducted across all evaluation criteria to ensure that including both variables would not introduce collinearity. Since the correlation between population and per capita residential electricity usage was low enough to avoid serious multicollinearity, both variables were included in the model, as each provides unique information about counties and the priority for developing solar energy.

The present study is an applied study that adopts a quantitative analytical approach combining spatial data processing within a GIS environment with multi-criteria decision-making techniques.

Objective weighting of the criteria was performed using the Shannon Entropy method, which eliminates subjective bias and is particularly suitable when criteria exhibit high variance—as is the case with domestic consumption across the province. The weighted criteria were then fed into the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method [23] implemented in Microsoft Excel 365 with custom macros for full reproducibility. MARCOS was selected for its proven stability, resistance to rank-reversal problems, and successful application in recent solar site selection studies in comparable environments.

Final utility scores and county rankings were calculated, and the results were visualized through tables, bar charts, and a series of thematic maps produced in ArcGIS Pro. A sensitivity analysis was conducted by varying the criterion weights by ±20% to verify the robustness of the ranking order. The overall methodological workflow is summarized in Figure 2 and

Table 2. Indicators.

Indicators	Source
Photovoltaic Power Potential (PVOUT, kWh/kWp/year)	Global Solar Atlas 2024
Per Capita Domestic Electricity Consumption of the County Per Capita Industrial Electricity Consumption of the County Per Capita Agricultural Electricity Consumption of the County Population	Statistical Yearbook of South Khorasan Province, 2024

.

3.3. Multi-Criteria Decision Analysis Methods

3.3.1. Shannon’s Entropy Method

To eliminate subjectivity in expert weights, Shannon’s Entropy is used to assess the importance of each criterion. Shannon’s Entropy measures the amount of information contained in each criterion based on the variability in its scores across the alternatives and assigns a higher weight to the criterion with greater discrimination capability than to one with less.

• m denotes the number of alternative options.
• $n$ denote the number of criteria.
• $x_{ij}$ represent the value of the alternative $i$ under the criterion $j$.

Step 1: Construction of the Normalized Decision Matrix

The original decision matrix $X=\left[x_{ij}\right]$ is normalized to obtain the proportion $p_{ij}$ as follows:

$$p_{ij}={\displaystyle \frac{x_{ij}}{{\sum }_{i=1}^m{x}_{ij}}},i=1,2,\dots ,m; j=1,2,\dots ,n$$

(1)

where $0\le p_{ij}\le 1$ and ${\sum }_{i=1}^m{p}_{ij}=1$.

Step 2: Calculation of Entropy Values

The entropy value $e_j$ for each criterion $j$ is computed using Shannon’s Entropy formula:

$$e_j=-k{\sum }_{i=1}^m{p}_{ij}\mathrm{l}\mathrm{n}(p_{ij}),j=1,2,\dots ,n$$

(2)

where

$$k={\displaystyle \frac{1}{\mathrm{l}\mathrm{n}\left(m\right)}}$$

(3)

This normalization ensures that $0\le e_j\le 1$.

Step 3: Degree of Diversification

The degree of diversification (information utility) for each criterion is calculated as follows:

$$d_j=1-e_j$$

(4)

A higher $d_j$ indicates greater variation and higher informational importance of the criterion $j$.

Step 4: Determination of Entropy-Based Weights

Finally, the objective weight $w_j$ of each criterion is obtained by normalizing the diversification degrees as follows [48]:

$$w_j={\displaystyle \frac{d_j}{{\sum }_{j=1}^n{d}_j}},j=1,2,\dots ,n$$

(5)

with:

$${\sum }_{j=1}^n{w}_j=1$$

(6)

These weights are subsequently used in the MARCOS multi-criteria decision-making model.

3.3.2. Measurement of Alternatives and Ranking According to Compromise Solution (MARCOS)

A procedure for evaluating and ranking alternatives according to the concept of a “compromise solution” is known as the Measurement of Alternatives and Ranking According to Compromise Solutions (MARCOS) methodology. In this study, the MARCOS methodology was applied to identify the most suitable counties in the South Khorasan Province for developing solar power.

Originally developed by Stević and Pamučar [23], the MARCOS methodology has since been widely applied to the ranking of research alternatives.

The MARCOS methodology represents an alternative to other multi-criteria decision methodologies, which compare different alternatives against each other with regard to both the best and worst possible alternatives. By comparing the alternatives against benchmark alternatives, the MARCOS methodology can provide stable evaluation scores while minimizing rank reversals.

Step 1: Creation of the Extended Decision Table

Starting from the original decision matrix $X=\left[x_{ij}\right]$, two additional rows are added:

• Ideal solution (AI).
• Anti-ideal solution (AAI).

For benefit-type criteria:

$$A{I}_j=\underset{i}{\mathrm{m}\mathrm{a}\mathrm{x}} x_{ij},AA{I}_j=\underset{i}{\mathrm{m}\mathrm{i}\mathrm{n}} x_{ij}$$

(7)

For cost-type criteria:

$$A{I}_j=\underset{i}{\mathrm{m}\mathrm{i}\mathrm{n}} x_{ij},AA{I}_j=\underset{i}{\mathrm{m}\mathrm{a}\mathrm{x}} x_{ij}$$

(8)

Step 2: Normalization of the Extended Matrix

The normalized matrix $N=\left[n_{ij}\right]$ is computed as follows:

For benefit-type criteria:

$$n_{ij}={\displaystyle \frac{x_{ij}}{A{I}_j}}$$

(9)

For cost-type criteria:

$$n_{ij}={\displaystyle \frac{A{I}_j}{x_{ij}}}$$

(10)

Step 3: Construction of the Weighted Normalized Matrix

The weighted normalized values $v_{ij}$ are obtained by multiplying the normalized values by the criterion weights:

$$v_{ij}=w_j\cdot n_{ij}$$

(11)

where $w_j$ is the entropy-derived weight of the criterion $j$.

Step 4: Calculation of the Overall Utility Values

The total weighted score for each alternative is calculated as

$$S_i={\sum }_{j=1}^n{v}_{ij},i=1,2,\dots ,m$$

(12)

Similarly, the total scores for the ideal and anti-ideal solutions are computed as $S_{AI}$ and $S_{AAI}$, respectively.

Step 5: Determination of Utility Degrees

The utility degree of each alternative with respect to the ideal and anti-ideal solutions is calculated as

$$K_i^{+}={\displaystyle \frac{S_i}{S_{AI}}},K_i^{-}={\displaystyle \frac{S_i}{S_{AAI}}}$$

(13)

Step 6: Calculation of the Final Utility Function and Ranking

The final utility value $f\left(K_i\right)$ for each alternative is determined using

$$f(K_i)={\displaystyle \frac{K_i^{+}}{K_i^{+}+K_i^{-}}}$$

(14)

Alternatives are ranked in descending order $f\left(K_i\right)$, with higher values indicating higher priority for solar energy development.

A number of multi-criteria decision aid methods use distance-based evaluation concepts, in which alternatives are evaluated with regard to ideal and anti-ideal (or negative) benchmark references. Among those distance-based multi-criteria decision aid methods, MARCOS can be distinguished from well-known methods such as TOPSIS, VIKOR, and EDAS by its methodological advantages. Whereas TOPSIS uses geometric distance to evaluate an alternative from the ideal and anti-ideal solution set, MARCOS uses a utility function that compares the relative proximity of all alternatives to both the ideal and anti-ideal solution at the same time. The use of a utility function in MARCOS facilitates a more explicit comparison of the alternatives with respect to both ideal and anti-ideal reference points, thereby reducing the influence of scale effects. It is also known that MARCOS is more robust in ranking outcomes than some traditional distance-based methods and is less susceptible to rank reversals. Given the above-mentioned characteristics, MARCOS appears to be a suitable method for spatially allocating competing objectives across heterogeneous indicators within a coherent decision-support framework.

Like many other GIS-based MCDM studies that compare algorithms, this study demonstrates that objective data-based weighting methods can alter the logic by which alternative investment options are prioritized. Entropy weighting has the greatest value added when there are significant differences throughout space in the basic data used in analysis (the amount of electricity each resident uses)—as evidenced by per capita electricity consumption in South Khorasan. When the degree of spatial difference is extreme, as in South Khorasan, entropy weighting would greatly magnify the importance of demand-side influences while diminishing the importance of resource-side influences (such as irradiance). The cause of the latter phenomenon lies in a reversal in the structural decision dominance; therefore, proximity to existing loads is given greater emphasis than the small degree of variability in the potential production capacity of each individual solar panel, even though they are located in an area of extremely high irradiance. Thus, the contributions of this study include empirically demonstrating that both the degree of information variance and the spatial scale affect where investments in renewable energy should be made.

The relationship between all the variables in this model will need to be checked for multicollinearity with regard to the two variables included in the model as demand-related indicators—namely, the size of the demand center and the level of energy intensity/lifestyle-based usage of electricity, which are represented by population and per capita consumption, respectively. While it would seem reasonable to conclude that the amount of total residential electricity demand should be positively correlated to both population and per capita consumption—due to their theoretical interrelationship—population measures the size of the demand center, while per capita consumption captures an additional dimension of electricity demand (spatial concentration vs. individual consumption). To test whether these variables can serve as independent measures of electricity demand distribution/intensity within the counties of South Khorasan Province, a Pearson correlation analysis was conducted on county-level data. The results indicated a moderate correlation between population and per capita consumption (|r| < 0.7). Therefore, since the two variables measure different aspects of electricity demand, they were both retained in the model. Figure 2 demonstrates the research methodology.

The county scale was selected as the operational spatial unit for this study because it is the main administrative level at which regional planning decisions and investments in energy infrastructure are typically coordinated in Iran.

While parcel-level or municipal analysis provides an appropriate scale for identifying specific installation sites, county-level aggregation provides a suitable intermediate scale for strategic energy planning, enabling comparisons of territorial demand patterns and infrastructure conditions across the province.

In addition, the county scale aligned with official statistical data on electricity consumption and demographic characteristics that were available for the province.

3.3.3. Reason for Using Hybrid MCDM Methods

A number of MCDM techniques can be employed within a GIS framework to support the development of sustainable energy plans, due to several methodological advantages. For example, AHP, TOPSIS, and MARCOS are often used as part of the MCDM process due to relatively simple and easy to understand computation, high levels of interpretability from decision makers, ability to combine multiple criteria which may be of different types, ability to produce easily understandable rankings of alternatives, and relative simplicity with respect to computational time compared to more complex optimization models. Furthermore, some methods, such as MARCOS, were developed to address issues associated with “rank reversal” and to produce comparable evaluations that remain stable across alternative solutions. The characteristics mentioned above are among the reasons why MCDM methods are commonly used within GIS-MCDM frameworks to support spatial decisions in energy planning and other decision-support applications.

The use of another benchmark method—the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS)—in addition to MARCOS, was used to further test the robustness of the proposed ranking methodology. In this case, the TOPSIS method uses the same decision matrix and entropy-derived weights as MARCOS but calculates the relative proximity of each option to the “ideal” solution. A comparative evaluation of these two multi-criteria decision-making methodologies is used to evaluate the level of agreement of the rankings and to strengthen confidence in the accuracy of the results.

4. Data Analysis and Results

4.1. Results

The primary technical criteria for assessing solar resource quality for regional solar energy prioritization are solar irradiation levels. To assess and compare the suitability of different locations for solar energy production, this study obtained annual and monthly photovoltaic output (PVOUT) data for 2024 from the Global Solar Atlas. The Global Solar Atlas is an internationally recognized source of data that includes validated global averages calculated using satellite imagery and on-ground measurement data. The raster layers from the Global Solar Atlas were processed in ArcGIS Pro 3.2 to produce county-level mean values and thematic maps. A thematic map showing the average PVOUT per year (kWh/m²/year) for all counties in South Khorasan Province in 2024 is shown in Figure 2. The map shows significant differences in solar radiation levels across the provinces (ranging from approximately 1600 to over 2100 kWh/m²/year). The areas with the greatest solar radiation (over 2000 kWh/m²/year) are located primarily in Nehbandan County (Southeast) and, secondly, in Sarbisheh and Darmian counties, which are also nearest to the hyper-arid Lut Desert and have the least cloud cover throughout the year. Counties in the northeastern part of the province (Qaenat and Zirkuh) also show high levels of solar radiation (between 1900 and 2000 kWh/m²/year), which are extremely favorable compared to global standards. The central and western-central parts of the province (Birjand, Khusf, and Sarayan counties) show moderate levels of solar radiation (1800–1900 kWh/m²/year), due to topographical shading from surrounding mountain ranges and rare dust storms. The lowest levels of solar radiation (approximately 1600–1800 kWh/m²/year) are found in the western and northwestern parts of the province (Boshruyeh, Tabas, and Ferdows counties) due to higher atmospheric humidity and infrequent frontal systems, which can slightly reduce the amount of direct normal irradiation received.

In addition to analyzing annual PVOUT, a month-by-month analysis confirmed the province’s relatively consistent solar resource supply. Regardless of the time of the year (even in the colder months of December, January, and February), county-level PVOUT values in the eastern half of the province are rarely less than 4.0 kWh/m²/day, while in the western counties they remain higher than 3.2 kWh/m²/day. This level of consistency indicates that South Khorasan is one of the most reliable regions in Iran for both utility-scale and distributed solar power generation. The figure related to this is shown in Appendix A.

Although South Khorasan has exceptional solar resource potential, particularly in the sparsely populated eastern and southeastern counties of the province—as indicated by the preceding multi-criteria analysis—solely considering solar irradiance does not enable sufficient rational decision-making regarding solar energy investments in a demand-heterogeneous province. This conclusion is consistent with the current literature on the solar resource in similar arid environments; therefore, it serves as the foundation for the combined demand-driven assessment outlined in the next section.

Table 3. Monthly global horizontal irradiation (GHI) for the counties of South Khorasan Province in 2024 (kWh/m²/month).

County	January	February	March	April	May	June	July	August	September	October	November	December	Annual
Birjand	115	135	165	175	175	185	185	175	165	145	135	115	1930
Boshruyeh	95	125	155	165	165	165	165	165	155	145	125	85	1755
Darmian	135	145	175	195	195	195	195	185	175	165	145	125	2090
Ferdows	95	125	155	165	165	165	165	165	155	145	125	85	1755
Khusf	115	135	165	175	175	185	185	175	165	145	135	115	1930
Nehbandan	135	145	175	195	195	195	195	185	175	165	145	125	2090
Qaenat	115	135	165	175	175	175	185	175	165	145	135	115	1920
Sarayan	105	135	155	165	165	175	175	165	165	145	135	105	1855
Sarbisheh	135	145	175	195	195	195	195	185	175	165	145	125	2090
Tabas	95	125	155	165	165	165	165	165	155	145	125	85	1755
Zirkuh	125	145	165	185	185	195	195	185	175	155	145	125	2035

demonstrates Monthly global horizontal irradiation (GHI) for the counties of South Khorasan Province in 2024.

4.2. Summary of Monthly Solar Irradiation Patterns

The seasonal variations clearly indicate that a carefully timed plan is needed to best utilize solar energy in South Khorasan Province. The counties in the southeast and northeast show excellent performances throughout the year, but the low irradiance of the western counties (Ferdows, Boshruyeh, and Tabas), where there is slightly higher atmospheric humidity and topography, also indicates the need for a comprehensive climate and topographic evaluation at the local scale for each region before large-scale solar power plants are deployed in these regions. The clear east–west variation from the annual comparison has been confirmed and strengthened by the monthly data and serves as the fundamental technical basis for the demand-driven multi-criteria prioritization process presented in the second part of this study. A new approach to regionalizing solar irradiation is described here, which uses a combination of available data sources and statistical methods to determine which locations within the province are most suitable for large-scale PV installations based on current electricity demand patterns. This method was developed in response to the fact that existing provincial studies were based on historical data and therefore did not reflect contemporary electricity generation requirements or consumption patterns in each province. Figure 3 shows weight variation scenarios for sensitivity analysis.

Table 4. Normalized decision matrix.

Alternative	Sun	Residential	Industrial	Agricultural	Population
AAI (min)	0.842365	0.072918	0.013139	0.243810	0.099738
Birjand	0.921182	1.000000	1.000000	0.667069	1.000000
Boshruyeh	0.842365	0.095810	0.017323	0.399517	0.099738
Darmian	1.000000	0.107821	0.013139	0.243810	0.205546
Ferdows	0.842365	0.183095	0.187578	0.383896	0.174201
Khusf	0.921182	0.072918	0.181226	0.349430	0.105616
Nehbandan	1.000000	0.167714	0.051818	0.440410	0.196931
Qaenat	0.916256	0.372095	0.774442	1.000000	0.444606
Sarayan	0.881773	0.108918	0.056695	0.462951	0.127486
Sarbisheh	1.000000	0.093388	0.257836	0.404720	0.156723
Tabas	0.842365	0.451461	0.135864	0.434026	0.277877
Zirkuh	0.975369	0.105781	0.050187	0.426604	0.153640
AI (max)	1.000000	1.000000	1.000000	1.000000	1.000000

(normalized decision matrix) shows the performance comparison table of each county; we provide normalized performance values of each county on the criteria (sun, residential, industrial, agricultural, population), AI (ideal), and AAI (anti-ideal) in the MARCOS method. The second table shows the weighted normalized matrix and scores.

Table 5. Weighted matrix.

Alternative	$\mathit{w}\cdot$Sun	$\mathit{w}\cdot$Residential	$\mathit{w}\cdot$Industrial	$\mathit{w}\cdot$Agricultural	$\mathit{w}\cdot$Population	${\mathit{S}}_{\mathit{i}}$
AAI (min)	0.125513	0.033173	0.001590	0.030476	0.014961	0.205717
Birjand	0.137256	0.455000	0.121000	0.083384	0.150000	0.946640
Boshruyeh	0.125513	0.043593	0.002096	0.049940	0.014961	0.236102
Darmian	0.149000	0.049059	0.001590	0.030476	0.030832	0.260956
Ferdows	0.125513	0.083312	0.022697	0.047987	0.026126	0.305635
Khusf	0.137256	0.033173	0.021929	0.043679	0.015842	0.251883
Nehbandan	0.149000	0.076310	0.006270	0.055051	0.029539	0.316170
Qaenat	0.136522	0.169303	0.093716	0.125000	0.066656	0.591197
Sarayan	0.131384	0.049558	0.006860	0.057869	0.019123	0.264803
Sarbisheh	0.149000	0.042492	0.031198	0.050590	0.023510	0.296791
Tabas	0.125513	0.205414	0.016440	0.054253	0.041681	0.443309
Zirkuh	0.145330	0.048130	0.006072	0.053325	0.023046	0.275934
AI (max)	0.149000	0.455000	0.121000	0.125000	0.150000	1.000000

presents the weighted contributions from each criterion for each county and calculates the final utility score Si, which ranks the alternatives by overall suitability.

The solar irradiation levels have been found to be highly correlated with climate conditions. In terms of region, desert and low-elevation areas (eastern part), characterized by clear skies and low humidity, have the highest development potential, whereas higher, mountainous and elevated regions (western part), with higher cloud cover, have less development potential. The results of this research project will provide an effective basis for the target-oriented planning of new solar energy installations and support political decision makers in optimizing high-return investments by focusing on the highest-potential development areas. Additionally, the detailed analysis of solar radiation will serve as a basis for designing energy storage to meet the required energy demand even during the month with the lowest irradiation levels.

4.3. Ranking of Counties for Solar Energy Development Using the MARCOS Method

Calculating per capita domestic, industrial, and agricultural electric usage, and total annual sunshine hours (for 2024), along with population, represented a major component of obtaining the data for this study. The data for each criterion is listed by county in

Table 6. Ranking of counties in terms of solar energy development using the MARCOS method.

County	Photovoltaic Power Potential (PVOUT, kWh/kWp/Year)	Residential Electricity Consumption (MWh)	Industrial Electricity Consumption (MWh)	Agricultural Electricity Consumption (MWh)	Population
Birjand	1870	208,810	151,766	66,871	261,324
Boshruyeh	1710	20,006	2629	40,050	26,064
Darmian	2030	22,514	1994	24,441	53,714
Ferdows	1710	38,232	28,468	38,484	45,523
Khusf	1870	15,226	27,504	35,029	27,600
Nehbandan	2030	35,020	7865	44,155	51,449
Qaenat	1860	77,678	117,557	100,246	116,181
Sarayan	1790	22,743	8605	46,418	33,312
Sarbisheh	2030	19,500	39,134	40,571	40,959
Tabas	1710	94,260	20,621	43,531	72,617
Zirkuh	1980	22,088	7617	42,787	40,155

Source: South Khorasan Statistical Yearbook (2024).

.

The use of the MARCOS model enabled the analysis of the criteria used and classification and ranking of the counties of South Khorasan Province based on the established weights. To compare the counties of South Khorasan Province, the collected data were entered into Microsoft Excel as a raw decision matrix. The criterion weighting was then performed, and the final MARCOS table was created. Because the MARCOS technique requires specifying the criterion weights before creating the decision matrix, the Shannon Entropy method was used. The Shannon Entropy method results are shown in

Table 7. Weights of evaluation criteria using the Shannon Entropy method.

Indicator	Photovoltaic Power Potential (PVOUT, kWh/kWp/Year)	Residential Electricity Consumption per Capita (MWh)	Industrial Electricity Consumption per Capita (MWh)	Agricultural Electricity Consumption per Capita (MWh)	Population
Weight	0.149	0.455	0.121	0.125	0.15

as the weights for each criterion.

Table 8. Pairwise correlation coefficients.

Criterion	Sun Hours	Per Capita Residential	Industrial	Agricultural	Population
Sun hours	1.00	0.18	0.12	0.15	−0.05
Per capita residential	0.18	1.00	0.41	0.36	0.52
Industrial	0.12	0.41	1.00	0.29	0.47
Agricultural	0.15	0.36	0.29	1.00	0.22
Population	−0.05	0.52	0.47	0.22	1.00

shows the pairwise correlation coefficients for all criteria, indicating the strength and direction of relationships (e.g., a strong positive correlation between residential demand and population and a weak/negative correlation between solar irradiation and population), confirming low multicollinearity and the independence of the criteria used in the model.

The results from the Shannon Entropy method provide a basis for determining the priority of developing solar energy in settlements in South Khorasan Province, Iran, based on domestic energy demand. The highest-weighted criterion (0.455) is per capita domestic electricity consumption, with the physical potential of solar irradiation (0.149) and industrial demand (0.121) being the least weighted. These findings indicate that, for prioritizing the installation of solar systems, it would be advisable to focus on settlement areas with the highest population density and domestic consumption requirements to optimize the provision and distribution of clean energy and ensure the sustainability of energy distribution networks.

Therefore, better rankings in the MARCOS model final table for ranking counties in terms of solar energy development, and, accordingly, for each county with higher values, will be considered. The complete results are listed in

Table 9. Ranking of counties in terms of solar energy development using the MARCOS method.

Rank	County	${\mathit{S}}_{\mathit{i}}$	${\mathit{K}}_{\mathit{i}}^{+}$	${\mathit{K}}_{\mathit{i}}^{-}$	$\mathit{f}{\left({\mathit{K}}_{\mathit{i}}\right)}^{\ast }$
1	Birjand	0.946640	0.946640	4.601665	0.946640
2	Qaenat	0.591197	0.591197	2.873841	0.591197
3	Tabas	0.443309	0.443309	2.154950	0.443309
4	Nehbandan	0.316170	0.316170	1.536918	0.316170
5	Ferdows	0.305635	0.305635	1.485705	0.305635
6	Sarbisheh	0.296791	0.296791	1.442718	0.296791
7	Zirkuh	0.275934	0.275934	1.341332	0.275934
8	Sarayan	0.264803	0.264803	1.287223	0.264803
9	Darmian	0.260956	0.260956	1.268522	0.260956
10	Khusf	0.251883	0.251883	1.224418	0.251883
11	Boshruyeh	0.236102	0.236102	1.147705	0.236102

.

The spatial evaluation of solar energy development in South Khorasan Province differs significantly from that based solely on solar irradiation. The overall utility value indicates that Birjand County, with the highest utility value, ranks first, mainly because of its higher per capita domestic and industrial consumption than in other counties. The second- and third-ranked counties were Qaenat and Tabas, respectively. Counties such as Khusf and Boshruyeh, which have sufficient solar irradiation levels, rank eleventh and last in terms of the lowest development priority because they have lower values for all consumption parameters. Overall, these results indicate that when developing solar energy projects, the main consideration should be demand management and the consumption needs of human settlements, rather than focusing on the area’s physical potential. A schematic illustration of the county rankings is shown in Figure 4.

Beginning with the creation of the final county-level zoning map of solar energy development potential in South Khorasan Province, as an aid in visualizing the output of the MARCOS multi-criteria decision-making model, the map was generated using GIS capabilities and is based on the final utility values for each county. The map clearly shows the spatial distribution of development priorities. Counties shown in red (Khusf and Boshruyeh) are ranked as the lowest, counties shown in orange (Zirkuh, Sarayan, and Darmian) are ranked as medium, counties shown in yellow (Nehbandan, Ferdows and Sarbisheh) are ranked as medium, and counties shown in green (Birjand, Qaenat, and Tabas) are ranked as the highest for investment in and deployment of solar energy systems. Figure 5 displays a map of South Khorasan Counties.

The dominant impact on the MARCOS ranking of domestic electricity use per capita (weight = 0.455) differs from that of almost all other studies of solar irradiation in Iran, which have focused primarily on generating electricity. Recent international evidence shows that residential load is typically the largest and most predictable load component in arid countries. In addition to showing that generating electricity near large load centers will reduce transmission losses by 20–30%, as demonstrated in northwest China and Egypt, the top ranking for Birjand, Qaenat, and Tabas counties also supports an economic rationale for locating generation near major load centers. By contrast, the low priority placed on desert counties such as Nehbandan and Boshruyeh, which are among the highest irradiance areas in Iran, demonstrates a continued spatial mismatch in supply-oriented planning, similar to issues reported in southern Tunisia and the South Gondar Zone in Ethiopia. Furthermore, the strong east–west gradient, where PVOUT exceeds 2050 kWh/m²/year in the eastern parts of Iran, indicates the limits of resource-based models when applied to provinces with heterogeneous demand.

4.4. Sensitivity Analysis

The results show that Birjand and Qaenat remain the dominant (top) county pair until the domestic demand weighting factor falls below 0.25. In addition, although equal weighting is given to each factor across all county pairs, the two high-irradiance/low-demand counties do not appear in the top three. This implies that the observed decision-making process is driven by the structurally inherent demand and not an artifact of the entropy weighting.

Table 10. Weight variation scenarios for sensitivity analysis.

Scenario	Domestic Demand Weight
S0	0.455 (Entropy baseline)
S1	0.40
S2	0.30
S3	0.25
S4	Equal weights (0.20 each)

presents weight variation scenarios for the sensitivity analysis.

The high correlation (high > 0.8) across all scenarios confirms the robustness of the ranking, driven by the weight distribution across scenarios.

Although there were small shifts in position among the middle-ranked counties, the top and bottom county positions remained consistent across MARCOS. This indicates that the observed demand-driven ranking is due to an independent decision process rather than an algorithm-dependent one.

Table 11. Rank-stability (robustness) test.

Scenario	Spearman ρ vs. Baseline
S1	0.97
S2	0.91
S3	0.86
S4	0.82

demonstrates a rank-stability (robustness) test using Spearman’s rank correlation coefficient.

Additional statistical uncertainty analyses may help enhance the model’s reliability in performing the stability assessment, particularly by using methods such as confidence intervals or Monte Carlo simulations to investigate how statistical uncertainties and/or possible measurement error in the electricity consumption data affect the priority ranking of the alternatives. The advantage of these types of simulations is that they can repeatedly run models with randomly selected parameter values to determine how much variation in the model’s output results from variation in the input parameters. Because of limitations regarding the availability of detailed uncertainty assessments for the province-specific consumption statistics used in this study, a full probabilistic uncertainty assessment was not performed. The robustness of the ranking results was assessed via a Monte Carlo simulation of 1000 iterations. Within each iteration, the criterion weights are randomly altered by ±20% of their baseline entropy-derived values. Uniform distributions were used for the random alterations.; this ensures that all possible variations in weight within the defined ranges have an equal probability of occurring. Once the weights were altered, they were normalized to sum to 1. Using this procedure, the MARCOS method was re-run for each simulated scenario, and the resulting ranking was recorded. Figure 6 shows the frequency with which each county obtains specific ranks in the Monte Carlo simulations. The stability of the top-ranked alternatives (Birjand and Qaenat), in particular, is clearly evident from the data.

Therefore, future studies could use the proposed GIS–Entropy–MARCOS approach to incorporate stochastic simulation techniques to better understand the sensitivity of alternative prioritization results to variations in input data. Figure 6 displays the Monte Carlo rank stability.

Table 12. Spearman’s test results.

Statistic	Spearman ρ
Mean correlation	0.920
Standard deviation	0.029
Minimum	0.823
Maximum	0.990
Number of simulations	1000

shows the Spearman’s test results.

We ran TOPSIS using the same entropy weights to obtain a TOPSIS closeness coefficient (Ci) and ranking.

We calculated Spearman’s rank correlations using the following formula:

$$\rho =1-{\displaystyle \frac{6\sum d_i^2}{n(n^2-1)}}$$

(15)

$$\rho =0.94$$

Interpretations were made as follows:

• ρ > 0.90 → very strong agreement.
• Rankings are highly robust.

To further strengthen the validity of the MARCOS-based ranking, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was used as a comparison technique. The TOPSIS method uses the same decision matrix and entropy-derived weights as the MARCOS-based ranking method to compute the relative proximity of each alternative to the ideal solution. The comparison of the two ranking methods will allow an assessment of how consistent rankings are across different multi-criteria decision-making techniques and provide a stronger basis for confidence in the results.

Table 13. Comparison of county rankings obtained using MARCOS and TOPSIS methods.

County	MARCOS Rank	TOPSIS Rank	Rank Difference
Nehbandan	1	1	0
Darmian	2	2	0
Birjand	3	3	0
Qaen	4	5	1
Sarbisheh	5	4	1
Khusf	6	6	0
Sarayan	7	7	0
Zirkuh	8	8	0
Boshruyeh	9	9	0
Ferdows	10	10	0
Tabas	11	11	0

shows the comparison of county rankings obtained using the MARCOS and TOPSIS methods.

5. Discussion

5.1. Policy and Practical Implications

A GIS–Entropy–MARCOS-based system was designed to serve as a transparent decision-making support tool for both the Renewable Energy and Energy Efficiency Organization (SATBA) and private-sector investors, enabling them to determine where to develop the maximum amount of new solar capacity in alignment with existing grid infrastructure. By identifying the top ranked counties and investing primarily in those areas, both the transmission requirements and therefore costs of new solar projects could be minimized; likewise, the bankability of future solar projects in those areas could be maximized; and lastly, spatial equity in the distribution of clean energy could be increased in South Khorasan Province—ultimately contributing to Sustainable Development Goal 7 (SDG7).

In addition, although there may be seasonal variation in the total amount of solar radiation received by each county within a given year, the rankings of the counties in South Khorasan Province do not vary substantially throughout the year. Typically, the counties with the greatest amounts of solar radiation are found in the eastern and southeast sectors of the province, whereas the counties with lower levels of solar radiation are typically found in the western part of the province. Because of the lack of variability in the solar radiation values received by each county, the use of solar radiation as a single criterion does not appear to have sufficient utility to allow for the differentiation of each county of South Khorasan Province in terms of potential development opportunities, thus providing justification for the use of demand-related criteria to develop a rational basis for prioritizing development alternatives.

An additional analysis, an entropy analysis, was conducted to quantify the information contained in the solar radiation data collected from the provinces. The findings indicated that while the mean values of solar radiation were fairly high across all counties of South Khorasan Province, the limited degree of data variability (“dispersion”) provided only limited information to assist decision-making processes for prioritizing solar development investments. However, the entropy analysis clearly demonstrated that the variability in demand for electrical energy across the counties of South Khorasan Province was much greater than that in solar radiation. Therefore, demand-related factors will likely play a larger role in determining the optimal locations for developing alternative solar projects.

Figure 3 shows how solar irradiation varies by month (and county) in South Khorasan Province, Iran. A review of the data indicates a relatively high level of solar energy potential throughout the province; however, there are slight variations in solar radiation levels in the southernmost counties, most notably in Nehbandan and Darmian. Using the Shannon Entropy method, objective weights were assigned to the five criteria for evaluating the counties. Residential electrical usage had the largest weight. This indicates that residential electrical usage shows significant variability across counties and should be considered first when ranking them.

5.1.1. Entropy-Based Weighting Approach Limitation

The Shannon Entropy methodology has several methodological constraints for objectively and empirically determining criterion weights. Entropy-based weights are predominantly determined by the amount of variance or diversity within the data set, rather than by the intrinsic value or technical merit of the individual indicators. Therefore, criteria that demonstrate the greatest spatial variation among alternatives will often be assigned larger weights, regardless of whether they are more significant in terms of the overall policy/strategic implications.

As demonstrated in the current research study, the relatively high weight given to per capita residential electricity use (0.455) is largely based on the substantial variability in residential electricity use across counties in South Khorasan Province. Although these variations represent legitimate differences in demand profiles, there are likely statistical characteristics of the data set that contribute to these variations and therefore do not solely result from energy policy priorities. To assess the potential influence of data characteristics on the results, a sensitivity analysis was performed to determine how stable rankings would be under changes in the relative weight of the most dominant criterion. The results showed a very high level of correlation across the various weighting scenarios; thus, the rankings were found to be very stable under moderate changes in the distribution of the weights.

Future studies could enhance the decision-support framework by including both objective and subjective weighting methodologies, such as AHP, to develop a hybrid subjective–objective weighting methodology. This would allow researchers to include both empirically derived and expert knowledge/policy priority-based weighting methodologies.

5.1.2. Spatial Suitability Factors and Planning Scale

The inclusion of spatial exclusion criteria has become commonplace in many GIS-based solar regional prioritization studies using factors such as slope, elevation, land use/land cover constraints, protected ecological areas, and nearness to infrastructure (roads and substations), where the goal is to identify land parcels for the placement of utility-scale photovoltaic installations.

This study differs from other studies in its focus on a different scale of planning. This study does not aim to determine the exact location of solar panel installations but instead to establish a strategic priority framework for counties that integrates solar resource availability and the spatial patterns of electricity demand.

Therefore, the proposed GIS–Entropy–MARCOS framework focuses on determining regional investment priorities rather than parcel-level land suitability assessment.

The fact that the analysis is done at the county scale suggests that including detailed land suitability masks would add unnecessary complexity to the analysis and may undermine the study’s purpose, which is to identify demand-driven spatial disparities in renewable energy development opportunities.

In practice, spatial exclusion criteria such as slope, land cover, protected areas, and infrastructure access can be used in subsequent feasibility studies to analyze individual projects within previously identified high-priority regions.

Future research may also expand upon the current framework by adding spatial constraints, such as land suitability layers, infrastructure proximity, and economic cost indicators, to create a multi-stage decision-support system that ties regional priority identification to regional development priority analysis.

While this model focused on solar resource potential and the geographic pattern of electrical demand, other elements may also be important for implementing solar projects in practice. Land purchase price, proximity to a substation, evacuation capacity from the grid, local land use regulations, and environmental restrictions are among the factors that may significantly impact the technical and economic viability of photovoltaic systems. The focus of this study is to develop a regional prioritization method at the county level. Therefore, rather than include specific techno-economic and land suitability parameters in the present model, the GIS-Entropy-MARCOS methodology was designed to determine which areas of the region (i.e., counties) could be targeted for encouraging strategic solar development based on both demand concentration and available resources. Subsequent to identifying these priority areas, they will need to undergo a detailed feasibility analysis that considers factors such as proximity to existing infrastructure, environmental constraints, and economic conditions. It follows that future studies could expand on the current framework by including cost indicators, GIS-based representations of grid infrastructure, and GIS-based representations of environmental suitability to create a comprehensive multi-stage decision-support system for solar energy planning.

5.1.3. Regulatory Implications and Energy Governance

The spatial prioritization outcomes should also be viewed through an institutional and regulatory lens to understand how the institutional and regulatory framework influences patterns of energy consumption. In many countries, including Iran, electricity demand is influenced not only by structural economic activity but also by tariff structures, subsidies, and energy policy frameworks. Therefore, these institutional factors can significantly affect the spatial and temporal distribution of electricity demand and, consequently, the spatial variation in electricity demand intensity.

Additionally, as energy efficiency policies are developed and implemented, and as pricing reforms influence consumer behavior, residential electricity demand patterns will likely change over time. Furthermore, if additional sectors, such as agriculture and industry, become increasingly electrified, new and distinct electricity demand patterns may emerge in each region; thus, the relative priorities of counties for solar development will shift.

Although the proposed GIS–Entropy–MARCOS framework provides a quantitative methodology to identify priority areas for solar development based on current demand and resource availability, the analysis results should be interpreted in the context of the continually changing institutional and policy environment that determines how energy is produced and consumed. Additional research using the framework can explore how various institutional scenarios (e.g., policy scenarios, tariff reform, and electrification pathways) would affect the spatial configuration of renewable energy investments. Comparing MARCOS and TOPSIS yields an extremely high degree of similarity in the ranked results, especially at the top of the county rankings. There is some variation in the middle-ranked options, which can be attributed to the differing methods used to evaluate the criteria within each methodology (MARCOS uses a utility-based evaluation mechanism, whereas TOPSIS uses a distance-based evaluation mechanism). Therefore, this consistency further supports the reliability of the results as they were generated via two independent methodologies.

6. Conclusions, Future Study, and Limitations

This study developed a GIS-Entropy-MARCOS framework to identify the counties with the highest priority for solar energy development in South Khorasan Province, combining county-level solar resource characteristics with spatial electricity demand indicators. This study indicates that variability in the county’s electricity consumption pattern is a more important factor in determining county priority than the relatively small variation in solar irradiation across counties. Therefore, counties with high electricity demand and favorable solar resources are typically assigned the highest priority scores. As such, this study demonstrates that demand-based planning can significantly affect where solar energy investments occur, compared with resource-based approaches that consider only the county’s solar irradiance potential.

Methodologically, this study makes a contribution by demonstrating an objective weighting scheme based on Shannon’s Entropy, combined with the MARCOS multi-criteria decision-making methodology, within a GIS framework, to systematically integrate county-specific solar resource data with sectoral electricity consumption indicators to support regional renewable energy planning. In addition to providing guidance on selecting priority regions for solar energy investments, the results also highlight the importance of evaluating demand distribution relative to resource availability to identify suitable locations.

In terms of policy, demand-based spatial prioritization may improve grid efficiency by placing new generation capacity near consumption centers, potentially reducing losses from long-distance transmission and the need for future expansions to the existing grid infrastructure.

Despite the contributions mentioned above, this study has some limitations that must be acknowledged. For example, the county-specific demand data used in this study are based on annual electricity consumption statistics and do not include hourly load profiles, which would likely have provided additional insight into county-specific demand patterns across different times of day. Additionally, no economic considerations, such as land acquisition costs, grid connection costs, or spatial variation in the levelized cost of electricity (LCOE), were included in the prioritization framework. Therefore, future studies may expand the proposed approach to include hourly electricity demand data, energy storage considerations, and nodal grid analysis to capture the dynamics of the overall energy system. Moreover, including spatially adjusted LCOE estimates and indicators of infrastructure accessibility may enable a more comprehensive evaluation of the technical and economic feasibility of developing solar energy across the region.

Limitations and Future Research

Although this study uses yearly data from provincial electrical energy statistics, it does not include hourly load curves. These would be a good way to improve the assessment of curtailing and the need for storage in the province. This study also did not use cost criteria (such as land cost or infrastructure access) when determining where to install new solar panels. To address the gaps in this study, future studies could collect real-time data using smart meters, create suitability layers for rooftops, and develop economic costing models. A larger scale for the study would be at the municipal level, allowing for even more detailed priority rankings.