# Comprehensive Evaluation of NIMBY Phenomenon with Fuzzy Analytic Hierarchy Process and Radar Chart
## 1 Introduction
The NIMBY phenomenon refers to the fear of residents or local units that construction projects, such as transmission and transformation projects, waste-disposal plants, nuclear-power plants and funeral parlors, will bring about a number of negative impacts on physical health, environmental quality and asset value, thus stimulating people's dislike of the complex and fostering the mentality of 'do not build it in my backyard', which means they will take a strongly collective opposition and even demonstrate resistance behavior [11; 12; 13]. Research on the NIMBY phenomenon in the world focuses on the construction of waste-disposal plants, nuclear power plants and funeral parlors, and seldom deals with the aspects of transmission and transformation projects. Moreover, the planning and construction of transmission and transformation projects are highly different from those mentioned above, which are mainly reflected in the planning of power grids, construction periods, line direction, substation-layout locations and social-demand levels. In addition, the little research on the NIMBY phenomenon of transmission and transformation projects are mostly approached from the aspects of project management, technological innovation and economic compensation, and seldom explores it from the direction of analyzing the existing cases, researching the relationship between the relevant stakeholders and establishing a risk model to solve the problem [14; 15; 16; 17]. For example, [PERSON] [18] discusses the factors affecting the siting problem of the 80 km 400 kV Lackenby-Pictor-Shipton line in the northeast of England through questionnaires and hierarchical linear regression analysis. [PERSON] [19] theoretically explored the impact of general values and beliefs on wind energy. [PERSON] et al. [20] proposed the genetic algorithms to minimize the total degree of NIMBY sentiments. For the risk assessment of transmission and transformation projects, the impact variables are qualitative, not quantitative. Therefore, the use of fuzzy comprehensive evaluation is extremely appropriate. Meanwhile, the fuzzy comprehensive evaluation refers to the use of fuzzy mathematics methods to assess the possibility of evaluating fuzzy objects influenced by multiple factors based on certain evaluation criteria [21; 22; 23]. The method has been widely used in comprehensive evaluation in various fields and has achieved good results [24; 25; 26]. A risk assessment is carried out with reference to a number of evaluation indicators, which are assigned different weights depending on their level of importance. The weight calculation is generally divided into subjective and objective weights, because the evaluation indicators of the risk assessment are qualitative, so the subjective weights like the analytic hierarchy process (AHP) method is commonly used as a useful multiple-criteria decision-making tool or a weight-estimation technique in various areas of human needs and interests [27]. The conventional AHP is a simple and explicit method to acquire a judgement matrix by exact ratios or numbers. However, due to the complexity, vagueness and uncertainty involved in real-world decision-making problems, decision makers discuss how the fuzzy judgement is more practical than explicit comparisons [28]. Nowadays, the combination of the conventional AHP and fuzzy methods to form a fuzzy AHP has been widely used. The method, called the fuzzy analytic hierarchy process (FAHP), first introduced by [PERSON] and [PERSON], is a powerful tool to treat uncertainty in the case of incomplete or vague information [29]. [PERSON] et al. [30] proposed a fuzzy AHP-TOPSIS framework for ranking the solutions of knowledge-management adoption. [PERSON] et al. [31] compared the fuzzy AHP and AHP in an urban land-use planning decision and proposed to choose different methods depending on the complexity of the planning. [PERSON] et al. [32] used the fuzzy comprehensive evaluation method to solve the problem of the resource evaluation of a port shoreline.