# GIS-Based Site Suitability Analysis for Solar Power Systems in Mongolia
## 1 Introduction  
The Energy Information Administration (EIA) expects global energy consumption to grow by approximately 50% between 2018 and 2050 [1]. Fossil fuels are becoming increasingly expensive and elusive, and limited access to fossil fuels in the long run will lead to high prices. Therefore, in recent years, many countries have formulated policies aiming at reducing their energy costs and reliance on fossil fuels. With increasing awareness regarding the risks of climate change, many countries have undertaken strategies for a transition to low-carbon economies. This has led to a focus on the use of renewable energy sources, as they are generally considered \"clean\", with a considerably lower impact on the environment than other energy sources [2]. Renewable energy accounted for 26.2% of global electricity generation in 2018, which is expected to grow to 45% by 2040, making renewable energy the fastest growing source of electricity [3]. Solar energy is an unlimited and clean renewable energy source, which is gradually replacing non-renewable energy sources [4]. Solar power generation has become one of the fastest growing renewable energy sources. As the price of solar panels is steadily declining, and various stimulus policies are being implemented, the growth rate of photovoltaic (PV) energy is increasing. PV energy is considered one of the most promising energy alternatives, owing to its ubiquitous and sustainable nature [5]. In recent years, many studies have identified suitable sites for PV power plants. A suitable site for solar installation depends not only on the amount of solar radiation received, but also on other technical, economic, environmental, and social factors--suchas local topography, the need to conserve protected areas, environmental impact, water availability, and urban development. Therefore, site selection is not simple. All of these factors--as well as the scope for expansion, proximity to urban areas and roads, and access to power grids--need to be considered [6]. To address these issues, multiple-criteria decision analysis (MCDA; also known as multiple-criteria decision-making or MCDM) has been widely used, in conjunction with geographic information systems (GIS), in many studies [7]. MCDA has been applied to energy issues such as energy planning, site selection, resource allocation, energy exploitation, energy policy, building energy management, and transportation energy management [8]. Among the various MCDA methods, the analytic hierarchy process (AHP) has been commonly used for evaluating site suitability for solar power plants (Table 1). [PERSON] [9] analyzed site suitability for PV solar farms in Karapinar, Turkey, using GIS and the AHP. Using the AHP, [PERSON] et al. [10] determined the weights of criteria for analyzing site suitability for concentrated solar power (CSP) plants. In addition, a combination of GIS and the AHP has been used to identify the most suitable sites for PV system installations in southern England [11], Tanzania [12], Saudi Arabia [5], West Africa [13], and Morocco [2; 14]. Some studies used an integrated method of fuzzy logic and the AHP in order to evaluate site suitability for PV systems in Iran [15; 16], Korea [17], and Spain [18]. However, no study has investigated site suitability for PV systems, using GIS and the AHP, in Mongolia. The purpose of this study was to use a combination of GIS and the AHP to map the most suitable sites for PV power plants in Mongolia. Mongolia has enormous solar energy resources that can potentially be used to strengthen its energy security, reduce pollution, meet its global climate commitments, and develop regional electricity exports. To place Mongolia's abundant solar energy resources into economic circulation and attract foreign investors, it is initially necessary to identify suitable PV system installation sites. A large national-scale GIS database was constructed, which includes seven criteria layers representing site characteristics and their relative weightings calculated using the AHP.