**1. Introduction**

In recent years, environmental problems, such as global warming, climate change, pollution, and problems with traditional fossil resources (such as increased extraction costs and non-renewability) that have been a source of human energy for many years, have caused doubt about the use of these resources and increased the tendency to employ more renewable resources [1]. Countries ratified the Paris Climate Agreement in 2015 to control this critical situation. Under the agreement, Iran voluntarily pledged to reduce greenhouse gas emissions by 4% and 8% by 2030 and 2050, respectively [2]. Using renewable energy resources, such as solar, wind, waves, and biofuels, play a crucial role in reaching this goal.

Utilizing wind energy can play an important role in enabling Iran to meet the standards set for this country in the Paris Agreement. Studies have also shown that hybridizing wind turbines with other energy sources reduces carbon emissions [3]. Applying wind energy creates employment and helps reduce CO2 [4]. Research shows that wind energy use in Canada, Sweden, China, and Germany will increase significantly by 2025 [5]. Meanwhile, it has a more extended history than other renewable sources in Iran. It is also low-cost and attractive to investors and can reduce dependence on fossil fuels [6].

**Citation:** Yousefi, H.; Motlagh, S.G.; Montazeri, M. Multi-Criteria Decision-Making System for Wind Farm Site-Selection Using Geographic Information System (GIS): Case Study of Semnan Province, Iran. *Sustainability* **2022**, *14*, 7640. https://doi.org/10.3390/ su14137640

Academic Editors: Farhad Taghizadeh–Hesary and Han Phoumin

Received: 8 May 2022 Accepted: 20 June 2022 Published: 23 June 2022

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Wind energy also has adverse effects, such as noise, unpleasant visual impact (tourism industry), habitat occupation, and the extinction of some bird species [7]. Therefore, it is necessary to minimize these adverse effects according to the existing criteria for locating and constructing wind farms.

MCDM is a method to evaluate multiple conflicting criteria in decision-making problems. MCDM could be applied for various applications, some of which are mentioned in references [8–10]. In addition, it is a suitable solution to deal with different and sometimes contradictory criteria for choosing the best places to use renewable resources [11]. There are several methods for weighing criteria in MCDM. Some of these methods include AHP [12], analytic network process (ANP) [13], fuzzy measures [14], Entropy [15], Swara [16], Dematel [17], Standard deviation [18], etc.

The ANP is a generalization of AHP. ANP is used to solve more complex decisionmaking problems, which AHP is not suitable for solving [19]. According to some scientists, the use of an exact number to compare the alternatives, unbalanced scale of judgment, inability to manage uncertainty, and inaccuracy in pairwise comparisons have caused some doubts about AHP [20,21]. Nevertheless, it is no secret that AHP is one of the most essential and widely used methods in MCDM. In some papers, due to interval judgment instead of fixed judgment [22], the Fuzzy AHP method is used to weigh the criteria. In 2021, Nguyen et al. used a hybrid fuzzy AHP MCDM to organize the priorities of the medical community and government during the COVID-19 crisis in Vietnam [23]. In another study by Nguyen, fuzzy AHP and machine learning approaches were used to predict vaccination intention against COVID-19 [24]. Entropy is another weighing method that has been widely used in recent years. The main difference between Entropy and other methods is to remove the human factor from the decision-making process [25], which enhances this method's accuracy. In 2015, Zhao and Gou developed a hybrid MCDM system based on Entropy to evaluate China's economic, social, and environmental benefits of the renewable energy sector [26]. In other studies, MCDM systems based on Entropy were used to investigate the sustainable development factors worldwide [27–30]. There are two general views on the Entropy method. According to some literature, Entropy is reliable and effective [31]. However, from the other point of view, Entropy results do not always consider the importance of the indexes [32]. The Swara method is similar to AHP in that the expert's opinion specifies the importance and prioritization of the alternatives. In the end, the weight of the attributes calculates by considering two main features. According to this method, all the attributes are compensatory and independent [16]. The Dematel is similar to Swara, except that Dematel is used to solve very complex subjects. In the decision-making process of Dematel, the expert's opinion uses to develop the pairwise comparison matric, and it has three main features. The attributes are compensatory and independent from each other. The qualitative attributes convert to quantitative attributes [16]. Moreover, the Swara and Dematel methods have been extensively used in MCDM problems, especially in the renewable energy sector [33–35]. In this study, the AHP method was used to solve the site-selection problems due to following reasons:


9. In site-selection problems, where the main goal is to select the best places, simple methods such as AHP are sufficient, and more complicated methods such as fuzzy AHP do not necessarily lead to different results [39].

In this study, GIS is employed to create and use map layers. GIS is a powerful tool for MCDM, and it can utilize topological, structural, and ecological information to perform calculations based on criteria and sub-criteria. Topological, structural, and ecological information can be displayed as layers in GIS, and by overlapping these layers, criteria, and restrictions, the final suitable and inappropriate locations can be determined. Afterward, a suitable area can be weighed according to various criteria. These criteria are determined by the type of problem and the area. After classifying the suitable areas according to these criteria, the importance and value of each area were determined, and decision-making was carried out according to the categorized final map. Figure 1 illustrates how the GIS tool works in integrating information layers.

**Figure 1.** Integrating information layers using GIS [40].
