Photovoltaic Solar Farms Site Selection through “Policy Constraints–Construction Suitability”: A Case Study of Qilian County, Qinghai

Abstract

The scientific selection of photovoltaic (PV) sites is essential for achieving sustainable development of renewable energy and ensuring regional ecological security. In western China, extensive land resources coexist with a fragile ecological environment. To this end, we propose a PV siting framework based on policy restrictions and construction suitability. This paper evaluated the PV construction suitability index (CSI) from four dimensions of topography, climate, location, and ecology and proposed typical “PV+” models. Then, Qilian County was selected as a case study. The results showed the following: (1) In Qilian, 59.97% (8333.18 km²) of the area is unsuitable for development due to policy restrictions, leaving 40.03% (5563.02 km²) available for PV construction. (2) The most suitable areas are approximately in the western and southern areas, where there is a lot in common with the reported PV sites under construction. (3) Three distinct PV development models are proposed according to policy guidelines and local circumstances, including the PV + pastoralism model, PV + mine rehabilitation model, and PV + hydropower model. The results can be used to determine the suitable areas for solar PV farms and the appropriate development model, as well as promote the sustainable development of renewable energy.

1. Introduction

Energy serves as a pivotal and strategic component of the national economy. Population growth and technological progress have steadily increased energy consumption, making net zero emissions a new focus of climate policy [1,2]. The development of renewable energy stands as a key means to achieve net zero emissions [3]. Between 2010 and 2020, the global average cost of electricity derived from photovoltaic (PV) projects plummeted by 85%, while costs for onshore and offshore wind power saw reductions of 56% and 48%, respectively [4]. This significant cost reduction has enhanced the competitive edge of renewable energy sources, as evidenced by their increased share in the global energy market from less than 16% in 2000 to approximately 30% in 2020 [4]. China’s commitment to exceed 1.2 billion kW of wind and solar power capacity by 2030 further exemplifies the global momentum towards renewable energy [5]. By June 2023, China had already achieved 859 million kW in combined wind and solar power capacity, reaching 71.58% of its 2030 goal [6]. This rapid progress highlights the critical role solar energy plays in China’s energy strategy, given its attributes of cleanliness, quiet operation, and cost-effectiveness. Despite addressing contemporary energy and environmental dilemmas, solar energy still faces challenges related to land utilization [7]. The expansive deployment of PV panels has intensified the competition for land resources, particularly in western China. This region, known for its vast deserts, Gobi, and other landscapes, offers significant potential for PV industry expansion. However, mainly affected by disasters and natural environmental factors, the ecology of western China is very fragile [8]. Compared with other regions, ecologically fragile areas are faced with complex natural environments, such as lack of water resources, climate drought, soil erosion, desertification, salinization, and frequent extreme weather events such as sandstorms [9,10]. At the same time, ecologically fragile areas play an important role in the construction of China’s ecological security barrier [11]. Therefore, as the green and low-carbon transformation of the industry accelerates, the ecological sensitivity and uniqueness of western China necessitate a balanced approach to PV solar farm development. Policies regarding land use and ecological conservation must be considered to mitigate potential conflicts. It is noteworthy that not only has China implemented policies to regulate land use for PV solar farms, but similar control measures are also in place in many other countries globally, including the United Kingdom [12], Italy [13], Iran [14], and India [15]. Furthermore, both renewable energy and ecological conservation are topics of global interest. Therefore, it is crucial to harness diverse datasets in developing an innovative model that assesses the suitability of land for PV solar farm construction, ensuring that renewable energy development aligns with ecological preservation objectives.

Site selection is a critical process that entails a scientific justification and decision-making approach for choosing a location before construction begins. Generally, construction suitability evaluation serves as a tool for site selection and represents a significant branch of land suitability evaluation. In the 1960s, Ian Lennox McHarg (1920–2001) formally introduced the land ecological suitability assessment method, aiming to emphasize the rational use of land [16]. In 1976, the Food and Agriculture Organization of the United Nations (FAO) published the Framework for Land Evaluation, which proposed grading land based on its suitability to support land use planning. This framework inspired countries around the world to develop their own land evaluation systems, leading to widespread land suitability assessments [17]. Construction suitability refers to the appropriate degree of conversion of land resources into construction land [18,19,20]. In 2009, the suitability evaluation techniques and methods developed by Fan Jie’s research team played a significant role in the site selection for the reconstruction of the Wenchuan and Yushu disaster areas [21,22]. Since then, suitability evaluation has become widely used in the site selection process for various construction projects. Specifically, the aim of evaluating potential construction areas (PCAs) for PV solar farms involves analyzing various parameters to ascertain their suitability for renewable energy development. A fundamental aspect of this evaluation is the establishment of a robust scientific index system and evaluation criteria, areas that have been explored in existing studies. Several previous studies had predominantly focused on solar power generation hotspots in regions characterized by low latitudes and prolonged sunlight exposure, including countries like Egypt [23], Algeria [24], and Morocco [25] in Africa, as well as Saudi Arabia [26] and Iran [27] in Asia. Within China, notable areas abundant in solar energy resources, such as Inner Mongolia [28], Qinghai [29], Xinjiang [30], and Ningxia [31], have been identified. These regions not only offer extensive solar energy potential but are also strategically located near areas of high-power demand, including the Beijing–Tianjin–Hebei [32] and the Yangtze River Delta regions [33], covering a wide spectrum from national to provincial and urban cluster scales.

In the establishment of evaluation models, the selection of indicators spans various dimensions, including solar energy resources, topography, location, and economic factors [15,24,34,35]. Studies have also explored the impact of the “Not In My Backyard” (NIMBY) effect [36] and flood erosion risks [37] on PV solar farms establishment. The Multiple Criteria Decision Method (MCDM), supported by Geographic Information System (GIS) technology, is commonly utilized for conducting suitability evaluations [38]. GIS technology enhances decision-making by allowing for the analysis, management, storage, and visualization of all geospatial information [39], thereby facilitating a comprehensive decision-making process. According to Malczewski J [40], the synergistic use of MCDM and GIS plays a complementary role in this context. Weight determination methods, such as the Analytic Hierarchy Process (AHP) [39], Analytic Network Process (ANP) [41], and Spatial Principal Component Analysis (SPCA) [42], are instrumental in this process. Additionally, decision-making evaluation methods like the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) [27], Weighted Linear Combination (WLC) [43], and Preference Ranking Organisation Method for Enrichment Evaluations II (PROMETHEE II) [44] are adept at addressing multi-factor decision-making challenges.

In summary, the ecologically fragile areas in western China boast significant solar energy potential. However, these areas also confront substantial environmental challenges, hence emphasizing the need for multi-dimensional evaluation of site selection.

Table 1. Studies on the site selection of solar power plants.

Method	Criteria	Location	Scale
AHP [26]	Solar irradiation, average temperature, slope, land aspects, dis. to urban areas, highways, power lines	Saudi Arabia	National
AHP [25]	GHI, slope, dis. to residential, road, railway network, electricity grid, waterways, dams, groundwater	Eastern Morocco	Regional
Fuzzy AHP, PROMETHEE II [44]	GHI, average temperature, precipitation, air pressure, surface albedo, relative humidity, slope, aspect, dis. to transmission grids, power lines, highways, major cities	Saudi Arabia	National
AHP [24]	Direct normal irradiation, dis. to roads, railways, high population density, electricity grid, waterways and damps, slope, slope orientation	Algeria	National
Fuzzy Boolean logic [45]	GHI, mean annual temperature, precipitation, average annual precipitation, slope, aspect, faults, elevation, rivers and lakes, road network, electric power transmission lines	Eastern Iran	Regional
AHP [46]	Solar irradiance, slope, slope orientation, land use, dis. to urban areas, roads, transmission lines	Peru	National
Fuzzy best–worst method [47]	GHI, slope, land use, wind, temperature, dis. to urban areas, rural areas, historical areas, main roads, power lines, faults, protected areas, surface water	Guilan province, Iran	Regional
Fuzzy–Boolean logic and AHP [48]	Annual horizontal solar irradiance, sunshine hours, air temperature, relative humidity, wind speed, slope, orientation, altitude, land use, dis. to roads, transmission lines, substations, urban areas, and villages	Khuzestan province, Iran	Regional

shows a research gap in siting PV sites at the county level in ecologically fragile areas. In China, counties are the primary administrative units for deploying PV solar farms, making county-level analysis crucial for decision-makers [13]. Previous studies often neglected policy constraints, focusing instead on solar energy endowment, location, and economic factors. Using Qilian County as a case study, we integrate policy considerations with ecological and other key indicators to develop a framework that balances policy constraints with construction suitability, promoting sustainable land use. To narrow the gap above, this study attempts to (1) identify the areas in Qilian County that are unsuitable for the construction of PV solar farms due to policy constraints; (2) determine the suitability of PCAs for PV solar farm development and classify the construction suitability index (CSI) from high to low; and (3) formulate distinctive models for the development of PV solar farms based on current industrial resources.

2. Materials and Methods

2.1. Research Framework

The methodology framework is presented in Figure 1. In general, there are three essential steps in this study. First, we formulated exclusion layer criteria according to the policy, used QGIS 3.32.2 software to quantitatively extract unsuitable layers, and then cut the exclusion layer from the study area to identify PCAs. Next, we provide an evaluation index system with four main and ten sub-criteria to evaluate construction suitability. Using the AHP, we calculate the weights of these criteria and apply the WLC method to determine the CSI. Finally, we propose distinct PV development models based on considering regional industrial development strategies and current land use.

2.2. Data Source and Processing

In recent years, GIS has been widely used in the field of renewable energy siting. This study uses GIS spatial analysis tools, including slope direction analysis, superposition analysis, and Euclidean distance analysis. These tools can process and calculate relevant data objectively. Preparation for the generation of land-suitable layers for photovoltaic power generation development.

Table 2. The data source used in the paper.

Dataset	Year	Type	Resolution	Data Source
Global Horizontal Irradiance	2020	Grid	1 km	Solar GIS Database (https://solargis.com/maps-and-gis-data/download/china, accessed on 2 August 2023).
Sunshine duration	2020	Grid	1 km	Resource and Environment Science and Data Center (http://www.resdc.cn, accessed on 5 August 2023).
Wind speed	2023	Grid	0.0025°	Global Wind Atlas (https://globalwindatlas.info/zh, accessed on 8 August 2023).
DEM	-	Grid	30 m	NASA Earth Science Data Network (https://nasadaacs.eos.nasa.gov/, accessed on 11 August 2023).
Land use	2020	Grid	30 m	Resource and Environment Science and Data Center (http://www.resdc.cn, accessed on 16 August 2023).
Roads	2021	Vector	-	National Catalogue Service for Geographic Information (https://www.webmap.cn/main.do?method=index, accessed on 22 August 2023).
Potential geohazard sites	2010	Vector	-	GLOBAL DISASTER DATA PLATFORM (https://www.gddat.cn, accessed on 5 September 2023).
Protect areas	2021	Vector	-	National Catalogue Service for Geographic Information (https://www.webmap.cn/main.do?method=index, accessed on 12 September 2023).
Rivers	2021	Vector	-	National Catalogue Service for Geographic Information (https://www.webmap.cn/main.do?method=index, accessed on 17 September 2023).

lists the data used in the methodology. All data are unified into a coordinate system using projection tools and resampled to a uniform pixel size. The geographic reference is unified to WGS_1984_UTM_zone_47N, and grid data are resampled to a 30 m × 30 m resolution. Figure 2 shows the data processing flow. Data layers in this study can be divided into two categories: one is the data of the exclusion layer, and the other is the data of the evaluation layer. After the combination of layers containing all exclusion areas, the exclusion layer is removed from the study area by clipping tool, and the potential construction areas are obtained. The suitability evaluation is carried out in potential construction areas, and the evaluation index system layer is obtained by using reclassification and Euclidean distance tools.

2.3. Study Area

Qilian County (98°05′35″~101°02′06″ E, 37°25′16″~39°05′18″ N) is located in the northwest part of Haibei Tibetan Autonomous Prefecture, Qinghai Province and occupies an area of 13,896.2 km². It consists of seven townships: Yanglong, Yeniugou, Mole, Zhamashi, Babao, Arou, and Ebao (Figure 3). The Global Horizontal Irradiance (GHI) spans from 989.8 to 1801.3 kWh/m². According to the Solar Energy Resource Assessment Method (GB/T 37526-2019) delineated by the China Meteorological Administration [49], approximately 96.79% of the region falls within the highly abundant range (1400 kWh/m² to 1750 kWh/m²). With the Sunshine duration (SSD) ranging from 2625 h to 3133 h, there exists substantial potential for solar energy exploitation. According to Chen et al. [50], the Koppen-climate classification of Qilian County in the case area includes BSk, DWc, and DWb, and most areas belong to BSk, which can provide a reference for similar climate regions. Qilian County is rich in water conservancy resources, with a theoretical hydroelectric potential of 56.77 GW, and 22,000 kW of hydroelectric power has been harnessed [51]. Qilian is also rich in coal resources, and there have been a lot of mining activities throughout its history. Qinghai Province is dedicated to becoming a leader both nationally and internationally in ecological civilization and renewable energy development. Remarkably, 84% of Qinghai’s annual electricity consumption is sourced from renewable energy. Among its regions, Qilian County holds a pivotal ecological position. Since 2021, Qilian has been included in the national pilot program for rooftop distributed PV systems and has been designated as a national demonstration area for ecological civilization. As a region abundant in energy resources and a critical zone for ecological conservation, Qilian’s approach to developing the renewable energy sector offers invaluable lessons. Therefore, Qilian serves as an exemplary case study for investigating the location of PV solar farms, which can provide valuable insights for the development of renewable energy in ecologically fragile areas in China and even in the world.

2.4. Identification of Unsuitable Area Based on Policy Constraints

Authorities at various levels have implemented policies to safeguard flood safety, ecological integrity, and food security. Table 3 illustrates China’s regulatory policies regarding land use for PV sites. In general, the exclusion criteria developed in this paper based on policy constraints include special protected areas and restricted land use types. Specially protected areas refer to provincial forest parks, national nature reserves, national parks, etc., while excluded land use types include cultivated land, closed forest land, high-coverage grassland, wetlands, and construction land. We quantitatively express policy constraints in spatial by GIS technology and Boolean overlay technique [45,52]. Any unit meeting a constraint condition is categorized as an unsuitable area. The logical expression is as follows:

$$PCs=\left\{A_1,A_2,A_3,\left.\dots A_{\mathrm{n}}\right\}\right.$$

(1)

$$UCAs=A_1\cup A_2\cup A_3\cup \dots A_{\mathrm{n}}$$

(2)

$$PCAs=TA-UCAs$$

(3)

where $PCs$ represents the set of all policy constraints; $UCAs$ is the unsuitable construction areas, indicating the union of all policy constraints; $PCAs$ is the potential construction areas; $TA$ is the total area.

It should be noted that not all land use data listed in Table 3 have open access from national authorities and the municipality. The ecological conservation red lines, basic grassland, and I-level protected forest land are difficult to obtain. Therefore, we matched the meaning of these land use types with the existing land use database to facilitate the subsequent processing work. Based on the “Territorial Spatial Ecological Restoration Plan of Qinghai Province (2021–2035)”, the spatial distribution of ecological conservation red lines in Qilian County is almost the same as that of special protected areas [53]. High-cover grassland is one of the subcategories of grassland with better water conditions and dense grass cover, which is used to represent the basic grassland. The I-level protected forest land is defined as the forest land within national nature reserves and world natural heritage. In this paper, the closed forest land is also regarded as the exclusion layer.

Table 3. Policy constraints for PV power generation industry land use.

Department Issuing the Policy	Description of the Information	Restricted Land Types
Office of the Ministry of Natural Resources, Office of the Forestry and Grassland Agency, General Division of the Energy Agency, 2023 [54]	New construction and expansion of photovoltaic power generation projects shall not occupy permanent basic farmland, basic grassland, or I-level protected forest land.	Permanent basic farmland, basic grassland, I-level protected forest land.
Qinghai Provincial Energy Bureau, Qinghai Provincial Department of Natural Resources, 2023 [55]	Occupying permanent basic farmland, ecological conservation red lines, and other areas where development and construction activities are explicitly prohibited by national laws and regulations is strictly prohibited.	Permanent basic farmland, ecological conservation red lines.
The People’s Republic of China, 2023 [56]	Consolidate the ecological functions of the Qilian Mountain glacier and water conservation ecological function area and other national key ecological function areas such as water conservation and biodiversity protection.	Protected areas.
Ministry of Water Resources of the People’s Republic of China, 2022 [57]	Photovoltaic power plants and wind power projects are not allowed to be built in rivers, lakes, and reservoirs.	Water area.
Ministry of Natural Resources of the People’s Republic of China, 2022 [58]	Utilize wasteland and unused land as much as possible, occupy little or no cultivated land or forest land, and avoid special protection areas as much as possible.	Cultivated land, forest land, special protected areas.
Ministry of Natural Resources, Ministry of Agriculture and Rural Development, State Forestry and Grassland Administration, 2021 [59]	Permanent basic farmland shall not be converted into other land such as forest land, grassland, garden land, and the construction of agricultural facilities.	Permanent basic farmland.
State Forestry and Grassland Administration, 2020 [60]	Except for construction projects approved and agreed to by the State Council, provincial people’s governments, and their relevant departments, basic grasslands may not be occupied.	Basic grassland.

Table 3. Policy constraints for PV power generation industry land use.

Department Issuing the Policy	Description of the Information	Restricted Land Types
Office of the Ministry of Natural Resources, Office of the Forestry and Grassland Agency, General Division of the Energy Agency, 2023 [54]	New construction and expansion of photovoltaic power generation projects shall not occupy permanent basic farmland, basic grassland, or I-level protected forest land.	Permanent basic farmland, basic grassland, I-level protected forest land.
Qinghai Provincial Energy Bureau, Qinghai Provincial Department of Natural Resources, 2023 [55]	Occupying permanent basic farmland, ecological conservation red lines, and other areas where development and construction activities are explicitly prohibited by national laws and regulations is strictly prohibited.	Permanent basic farmland, ecological conservation red lines.
The People’s Republic of China, 2023 [56]	Consolidate the ecological functions of the Qilian Mountain glacier and water conservation ecological function area and other national key ecological function areas such as water conservation and biodiversity protection.	Protected areas.
Ministry of Water Resources of the People’s Republic of China, 2022 [57]	Photovoltaic power plants and wind power projects are not allowed to be built in rivers, lakes, and reservoirs.	Water area.
Ministry of Natural Resources of the People’s Republic of China, 2022 [58]	Utilize wasteland and unused land as much as possible, occupy little or no cultivated land or forest land, and avoid special protection areas as much as possible.	Cultivated land, forest land, special protected areas.
Ministry of Natural Resources, Ministry of Agriculture and Rural Development, State Forestry and Grassland Administration, 2021 [59]	Permanent basic farmland shall not be converted into other land such as forest land, grassland, garden land, and the construction of agricultural facilities.	Permanent basic farmland.
State Forestry and Grassland Administration, 2020 [60]	Except for construction projects approved and agreed to by the State Council, provincial people’s governments, and their relevant departments, basic grasslands may not be occupied.	Basic grassland.

2.5. Construction Suitability Evaluation

2.5.1. The Evaluation Criteria Definition

Combined with the characteristics of Qilian County and the relevant literature [15,25,26,31,41,43], the four criteria are set: topography, climate, location, and ecology. Each evaluation indicator is scored from 20 to 100 (Table 4). The detailed explanation of each indicator is described as follows:

(1)

Topography

Topography emerges as a pivotal element in the construction and operational phases of PV sites, influencing both construction costs and the efficacy of photovoltaic power generation. Characterization of topography typically hinges on two metrics: slope and aspect. The slope indicates the steepness of the land and directly affects the complexity and cost of construction. Slopes under 3° are generally preferred for the development of photovoltaic sites [47,61]. Aspect refers to the orientation of the slope, which affects the length of sunlight exposure and the intensity of solar radiation. In the northern hemisphere, south-facing slopes are advantageous, receiving the most intense sunlight, whereas north-facing slopes receive the least. Data for slope and aspect are derived from DEM and processed using QGIS 3.32.2 software.

(2)

Climate

Key climatological factors for solar power plant installation include GHI, SSD, and wind speed (WS). GHI represents the annual solar radiation intensity on the horizontal plane, reflecting the solar availability of PV panels. SSD quantifies the annual duration of direct solar irradiance above 120 W/m², signifying the stability of solar resources. Wind can cool PV panels, which in turn boost energy conversion efficiency [62]. This study makes WS an indicator of the beneficial cooling influence on PV power generation, which is consistent with relevant literature [47,48].

(3)

Location

Location refers to the spatial relationship between PV sites and other geographic entities. This includes proximity to roads (PTR), proximity to settlements (PTS), and proximity to potential geohazard sites (PTPGS). Proximity to roads, including national highways and local roads, mitigates construction costs by reducing the need for new road development [63]. Proximity to settlements can lower project capital costs [31]. Given Qilian’s vulnerability to geohazards such as debris flows and avalanches, the distance from such hazards is crucial. Distance measurements were obtained using the Euclidean distance tool in QGIS 3.32.2 software.

(4)

Ecology

Ecology considers the overall health and balance of the ecosystem, advocating for the selection of PV site locations that minimize impact on areas of significant ecological value. Ecological criteria include sources and corridors. Ecological sources (Figure S2), identified through the Habitat Quality module of the InVEST model, are vital for biodiversity maintenance. Ecological corridors (Figure S2), essential for biological migration and material exchange, were constructed using the Minimum Cumulative Resistance (MCR) with cost path analysis. The identification process and results are detailed in Sections S1 and S2 of Supplementary Materials.

Table 4. Standardized value and weights of evaluation indicators.

Evaluation Criteria	Evaluation Indicators	Indicator Type	Weight	20	40	60	80	100	Reference
Topography	Slope (°)	−	0.147	>25	15~25	8~15	3~8	<3	[31]
	Aspect	/	0.135	North	Northwest, Northeast	East, west	Southeast, Southeast	Horizontal and South	[43,61]
Climate	GHI (kWh/m2)	+	0.127	<1400	1400~1500	1500~1600	1600~1700	≥1700	[35]
	SSD (h)	+	0.087	<2200	2200~3000	3000~3200	3200~3300	>3300	[49]
	Wind speed (WS) (m/s)	+	0.070	<2	2~3	3~4	4~5	>5	[62]
Location	Proximity to roads (PTR) (km)	−	0.070	>5	3~5	1~3	0.1~1	<0.1	[46,64]
	Proximity to settlements (PTS) (km)	−	0.067	>10	5~10	3~5	1~3	<1	[25]
	Proximity to potential geohazard sites (PTPGS) (km)	+	0.101	<2	2~3	3~5	5~8	>8	/
Ecology	Proximity to ecological sources (PTES) (km)	+	0.088	<2	2~3	3~5	5~8	>8	/
	Proximity to ecological corridor (PTEC) (km)	+	0.108	<1	1~2	2~4	4~6	>6	/

Note: “+” indicates that the increase in the index data will enhance the construction suitability, “−” indicates that the increase in the index data will weaken the construction suitability, and “/” indicates that there is no distinction between positive and negative.

Table 4. Standardized value and weights of evaluation indicators.

Evaluation Criteria	Evaluation Indicators	Indicator Type	Weight	20	40	60	80	100	Reference
Topography	Slope (°)	−	0.147	>25	15~25	8~15	3~8	<3	[31]
	Aspect	/	0.135	North	Northwest, Northeast	East, west	Southeast, Southeast	Horizontal and South	[43,61]
Climate	GHI (kWh/m2)	+	0.127	<1400	1400~1500	1500~1600	1600~1700	≥1700	[35]
	SSD (h)	+	0.087	<2200	2200~3000	3000~3200	3200~3300	>3300	[49]
	Wind speed (WS) (m/s)	+	0.070	<2	2~3	3~4	4~5	>5	[62]
Location	Proximity to roads (PTR) (km)	−	0.070	>5	3~5	1~3	0.1~1	<0.1	[46,64]
	Proximity to settlements (PTS) (km)	−	0.067	>10	5~10	3~5	1~3	<1	[25]
	Proximity to potential geohazard sites (PTPGS) (km)	+	0.101	<2	2~3	3~5	5~8	>8	/
Ecology	Proximity to ecological sources (PTES) (km)	+	0.088	<2	2~3	3~5	5~8	>8	/
	Proximity to ecological corridor (PTEC) (km)	+	0.108	<1	1~2	2~4	4~6	>6	/

Note: “+” indicates that the increase in the index data will enhance the construction suitability, “−” indicates that the increase in the index data will weaken the construction suitability, and “/” indicates that there is no distinction between positive and negative.

2.5.2. Suitability Evaluation

AHP is one of the most popular MCDM methods in suitability assessment and is employed to determine the weights of the indicators [65]. In AHP, the evaluation indicators need to be compared in pairs according to the 1–9 scale method. In this study, there are 10 criteria, and a 10 × 10 pairwise judgment matrix A is constructed, where the element $a_{ij}$ represents the relative importance of the indicator $i$ compared to the indicator j (Tables S1 and S2). With QGIS 3.32.2 software support, we use the WLC method to calculate the CSI for each grid unit. The formula is as follows:

$${CSI}_i=\sum _{j=1}^{10}{X}_{ij}{W}_j$$

(4)

where ${CSI}_i$ is the construction suitability index of grid unit i; $X_{ij}$ is the standardized value of indicator j in grid i; W_j is the weight of evaluation indicator j.

Finally, the ${CSI}_i$ is classified into three grades, from high to low, using the natural breakpoint method: most suitable, highly suitable, and moderately suitable. This method maximizes the similarity within each group and the difference between groups [66].

2.6. Models Design towards the Photovoltaic Land Use

China encourages the optimization of land use models for renewable energy projects.

Table 5. Relevant policies for integrated development in solar energy in China.

Department Issuing the Policy	Supported PV Development Models
National Development and Reform Commission, 2022 [67]	Wind solar complementary, livestock PV complementary, PV hydropower complementary.
General Office of the State Council of China, 2022 [5]	Support the development of renewable energy projects in coal mining subsidence areas.
Qinghai Province, 2022 [68]	Advocates for the establishment of integrated projects promoting multi-energy synergy and amalgamated energy storage with grid integration in Qilian.
National Energy Administration, 2021 [69]	Renewable energy + ecological restoration and mine management, forest PV complementary, and pastoral PV complementary.
National Development and Reform Commission, 2021 [70]	PV hydropower complementary, livestock PV complementary, plantation PV complementary.
Qilian County government, 2021 [71]	In the next five years, the construction of Babao 330 kV and Arou 110 kV voltage grade power lines is proposed to help deliver renewable energy.
Qinghai Province, 2021 [72]	Promote the photovoltaic sand control model in Qinghai according to local conditions.

lists the latest policies issued by China to support PV complementary development models. To design typical PV development models, we consider construction suitability alongside comprehensive policy guidance and the current land use situation.

3. Results

3.1. Identification of Unsuitable Area

Figure 4 represents all exclusion criteria as unsuitable areas. About 59.97% of Qilian consists of unsuitable areas, mainly distributed in the west. The remaining 40.03% are considered PCAs, primarily located in the eastern region. The western part of the region mainly consists of a provincial forest park, a national nature reserve, and a national park. In contrast, the unsuitable areas in the east are primarily restricted land use types, such as cultivated land, closed forest land, and construction land, etc.

3.2. Evaluation Results of Construction Suitability

3.2.1. Spatial Distribution of Evaluation Indicators

The scores for all evaluation units were classified based on Table 4. Figure 5 shows the varied spatial distribution characteristics among the evaluation criteria. It is worth noting that in the high score stage (≥80), PTPGS, PTES, and PTEC occupy a larger area relative to the other sub-criteria. Conversely, the PTR scored the lowest, reflecting the inconvenient transportation conditions within Qilian. Specific townships such as Yanglong, Yeniugou, and Mole are distinguished by higher scores across slope, aspect, GHI, PTPGS, and PTES. Meanwhile, the PTS achieves higher scores in Babao, Arou, and Ebao, which is attributed to their roles as significant economic and cultural hubs.

In the western region, the topographical scores are higher than in the eastern region. This is mainly due to the region’s high-altitude plains landscapes, which contrast with the alpine landscapes found in other areas. In terms of climate, Qilian’s northwest region has higher scores compared to its southeast. From a locational perspective, areas such as Babao and Zhamashi, which receive lower scores, are primarily associated with geohazard sites situated in foothill plains. This association underscores the heightened risk of landslides and debris flows, thereby signaling increased susceptibility to geological hazards. Regarding ecology, the PCAs contain ecological sources primarily in the southeastern region, marked by rich vegetation, significant ecological value, and strong ecological functions. Thus, units with higher scores in PTES and PTEC are situated farther from areas of high ecological significance, implying that construction activities in these regions would impose a lesser ecological impact.

3.2.2. Spatial Distribution of Construction Suitability

Figure 6a shows the construction suitability of PV solar farms. The CSI varies from 34.59 to 94.54 and is divided into three categories: most suitable [69.86, 94.54], highly suitable [59.05, 69.86), and moderately suitable [34.59, 59.05).

The most suitable areas comprise 42.09% (2341.38 km²) of the PCAs (Figure 6b), while the moderately suitable areas occupy the smallest extent. The most suitable and highly suitable areas are mainly concentrated in the northwest part of Qilian. These areas have flat terrain and are away from the ecological value zone, which is mainly characterized by grassland and unused land. Conversely, the moderately suitable areas, primarily located in the southeast, are closer to ecologically valuable regions and mostly covered by shrubland.

Figure 6 reveals that Mole encompasses the largest portion of the most highly suitable regions, accounting for 50.26% and 42.05%, respectively, and possesses the largest PCAs (Figure 6a). Consequently, Mole emerges as the most promising township for PV development. Yanglong ranks second due to the favorable conditions and excellent construction suitability.

3.3. Typical Models of the Photovoltaic Land Use

Three typical “PV+” models were designed with the goal of enhancing the deployment of PV solar farms (Figure 7).

(1)

PV + pastoralism model in the plain pasture areas of Yanglong and Mole.

This model is mainly distributed in the west of Qilian County, in the pastoral areas of Yanglong and Mole. The pasture areas of Yanglong and Mole, primarily devoted to livestock, are experiencing pasture degradation due to the arid climate, low precipitation, and high evaporation rates. Consequently, the PV + pastoralism model offers a strategy to mitigate pasture degradation while generating economic benefits, promoting the synergistic advancement of the PV sector and ecological restoration.

(2)

PV + mine rehabilitation model in coal mining subsidence areas of Yanglong, Mole, and Yeniugou.

This model is primarily concentrated in the southern part of Qilian County, specifically in the coal mining subsidence areas of Yanglong, Mole, and Yeniugou. These areas are facing the challenge of ecological restoration. In this model, PV modules installed on damaged land in subsided coal mining areas indirectly promoted vegetation growth by reducing evaporation and increasing soil moisture through regular cleaning. This approach effectively utilized idle land with fragile ecological vegetation while achieving three-dimensional land utilization.

(3)

PV + hydropower model in the moderately suitable areas in Ebao and Arou.

This model is distributed in the east of Qilian County, in the moderately suitable area in Mole and Ebao. The model integrates the rich water conservancy resources with the moderately suitable location of the surrounding PV solar farms. Through large-scale development to achieve complementary advantages and improve the production capacity of renewable energy. This is an important path for the high-quality development of renewable energy.

4. Discussion

4.1. Research Innovation under Policy Constraints

This study constructs an integrated framework for identifying suitable sites for PV solar farms and carries out an empirical study in Qilian. The study presents two primary advantages. First, it achieves quantification and identification of unsuitable construction areas based on policies and regulations. Although previous research identified areas where large-scale PV construction should be restricted, it lacked quantitative discriminant rules. For instance, Sun et al. [31] designated forests, agriculture, housing construction, and rivers as restricted. However, recent policies have further defined that PV farms are not permissible within basic grassland and areas delineated by the ecological protection red line. Similarly, Bao et al. [73] considered water bodies as potential sites for PV construction, omitting consideration of policy influences. Notably, policies are in a state of flux, with administrative departments across national, provincial, and municipal levels revising land use policies. Specifically, the 2022 “Guidance on Strengthening Spatial Control of River and Lake Waters and Shorelines” prohibits PV project construction in rivers, lakes, and reservoirs, allowing exceptions only in surrounding waters, reservoir branches, and areas affected by coal mining subsidence [74]. As a result, many aquatic PV power stations have been renovated or dismantled. Hence, this study noted the changes resulting from policy adjustments that are expected to promote the legal and sustainable development of PV projects.

Secondly, the evaluation index system of this paper has made certain improvements and innovations compared with previous research, particularly in the ecological aspect. For instance, Scognamiglio et al. and Yang et al. considered regional ecological security patterns changes before and after large-scale PV construction [75,76]. Nevertheless, previous research has seldom considered photovoltaic site selection from this perspective. Therefore, to proactively avoid ecological sources and ecological corridors, this study incorporates PES and PEC as sub-criteria, aiming to minimize ecological disturbance. Similar to Lv et al., who integrated land suitability with ecosystem service value for site planning, it can be observed that prior emphasis on ecological impacts is essential before PV site selection [77]. Additionally, this study introduces the distance indicator to potential geohazard sites, which ensures the safety of construction projects. This aspect is similar to Kutay et al. [37], who considered the influence of flood susceptibility on PV construction. Overall, these improved and innovative evaluation index systems contribute to more comprehensive assistance in PV site selection.

4.2. Feasibility of the Evaluation Model and the Designed Typical Models

The result of the spatial analysis shows that the most suitable and highly suitable areas for large-scale PV are mainly distributed in Yanglong, Yeniugou, and Mole. In the actual scenario, we found that photovoltaic projects are being built in the three towns—Yanglong, Yeniugou, and Mole [78]. The areas where construction started had strong consistency with our study results, reflecting the effectiveness of our proposed PV site selection suitability model, which could provide application value for renewable energy development. Given the arid climate and abundant solar energy resources in the case area, the model proposed in this paper can be extended to other ecologically fragile regions or areas with similar climate types, such as the northwest region of China, Spain [79], Morocco [80], and other similar regions.

The PV development model is increasingly favored in China. In the PV + pastoralism model, it has been proved that PV arrays may reduce surface irradiance, decrease soil temperature, or increase soil moisture, aiding in the restoration of degraded grasslands for grazing purposes [81,82,83]. In the PV + mine rehabilitation model, coal mining subsidence areas have a significant adverse impact on local land use and livelihoods, making ecological restoration a challenging task. Interestingly, coal mine subsidence treatment projects primarily involve slope greening measures and land reclamation work. Similarly, the construction of PV power stations also requires site leveling. In the PV + hydropower model, the variability of solar power generation can be offset by the flexibility of the existing hydropower system, greatly improving the efficiency of solar energy utilization [84,85]. In recent years, the PV + pastoralism model and PV + mine rehabilitation model have been adopted in the Inner Mongolia region adjacent to Qilian. The Longyangxia hydro-PV complementary stations in Qinghai Province have achieved great benefits and gained some visibility, which can provide valuable references for photovoltaic development in Qilian [86]. This ensures that the evaluation model and development models align with the goals and needs of renewable energy development in the current era.

4.3. Limitations and Prospects for Further Research

This study demonstrates a certain way to determine the location of PV solar farms through policy constraints and construction suitability. Under the premise of land use control, this paper adopts the method of combining qualitative analysis with quantitative analysis to reflect the policy constraints in geographic space. In addition, the selection of indicators considers the requirements of the construction of ecological civilization.

Nevertheless, there is still room for future research. First, the AHP used in the model, while widely employed in suitability evaluation studies, has certain limitations. For example, AHP involves a degree of subjectivity, and the evaluation results may be influenced by the evaluator’s personal knowledge, experience, values, and other factors. In this study, the construction of a judgment matrix in AHP combines the results of many existing pairwise judgment matrices. This integration aims to minimize the influence of subjectivity and improve the objectivity and consistency of evaluation results. However, other methods, such as ANP [41], SPCA [42], fuzzy Analytic Hierarchy Process (FAHP) [87], and Fuzzy TOPSIS [88], also involve a certain degree of uncertainty.

Second, the development of PV construction is influenced by many factors. Beyond conventional indicators like terrain, climate, and location, different scholars incorporate various additional indicators based on the specific case area. For instance, Wiesinger et al. [80] included dust impact indicators in their study of Morocco, while Simal Pérez et al. [79] considered the effects of particulate matter (PM 2.5 and PM 10) in their research on Spain. Future studies could incorporate factors such as land prices and public willingness to fully understand the possibilities of PV construction. This study did not consider the influence of precipitation and cloudy weather because the PV site selection is mainly targeted at arid climates with few rainfall days, and these climate factors have a certain correlation with GHI and SSD. To ensure the independence of factor selection, other factors are not considered. On the other hand, although using a newer database would provide greater insight, we were unable to extend our database due to data accessibility issues. For the annual mean climate data, there is little trend difference in relative sizes. In the siting model, all indicators are standardized to represent relative sizes within the range of 20 to 100. Therefore, the new data would not have a significant impact on our argument.

Finally, while the “PV+” model offers notable benefits, its complexity presents a significant challenge. The details of each model can be researched more deeply in the future. For example, the arrangement spacing design of photovoltaic panels on the prairie and how to optimize the matching of photovoltaic and hydropower will help to promote the development of the PV composite model more clearly.

5. Conclusions

This study introduces a novel approach, constructing a PV site selection framework based on policy constraints and construction suitability to provide typical development models and promote PV deployment of land resources in Qilian. The main conclusions are as follows: (1) The unsuitable area resulting in policy constraints is 8333.18 km² (59.97% of the total area). The PCAs occupy 5563.02 km² (40.03% of the total area). (2) The most suitable areas are concentrated in the west and south of Qilian, which have a lot in common with the reported PV under construction. (3) Three typical “PV+” models (PV + pastoralism, PV + mine rehabilitation, and PV + hydropower) are proposed for Qilian County.

Our study integrates PV site selection with land use policies and ecological considerations, providing a framework that supports legal land utilization and preserves valuable ecological environments in counties in ecologically fragile areas. This framework not only promotes the sustainable deployment of PV solar farms but also offers insights into renewable energy strategies in ecologically fragile areas in China and beyond.