Study On Multi-attribute Group Decision Making Of NIMBY Facility Location Scheme From The Perspective Of Public Participation

In the context of the continuous advancement of urbanization in China,the rapid growth of urban population and the continuous expansion of urban scale,the huge pressure brought by population,environment and energy,etc.,requires more and more urban functions and service levels,which also increases the demand for adjacent facilities such as garbage disposal projects,nuclear power plants,high-speed railway stations,etc.However,while promoting social and economic development and realizing social and public interests,NIMBY(Not in my backyard)facilities also have the risk of negative impact on the surrounding environment and the quality of life of residents.The government led site selection decision-making mechanism often ignores the expression of the interests of the general public.Therefore,the location and construction of NIMBY facilities are rejected and resisted by the public in varying degrees,the construction of NIMBY facilities is in the dilemma of demand and landing.Therefore,the introduction of feasible public participation mechanism in the site selection of NIMBY facilities will help the construction and operation of such facilities and promote the rapid and stable development of social economy.Based on this,from the perspective of public participation,this dissertation studies the location scheme of NIMBY facilities based on multi-attribute group decision-making.Firstly,this dissertation introduces the concept,characteristics and classification of NIMBY facilities,and defines the scope of public participation in the decision-making of NIMBY facilities location scheme;expounds in detail the location dilemma and the necessity of public participation;at the same time,it focuses on the multi-attribute group decision-making theory,defines the nature of multi-attribute group decision-making in this dissertation,and explains its applicability,It provides a theoretical basis for the follow-up study.Then,through the analysis of the general influencing factors of NIMBY facility location and the characteristics of NIMBY facility location from the perspective of public participation,29 indicators are preliminarily identified and compiled,and then the first round of indicators are screened by questionnaire method,and three indicators with low importance and two indicators with no unified understanding from experts’ opinions are eliminated.Through the second round selection of indicators by correlation analysis method,two indicators with strong correlation were eliminated.Finally,22 indicators from 5 aspects,including natural factors,economic factors,social factors,environmental factors and external support conditions,were constructed.The index system of NIMBY facilities location scheme from the perspective of public participation was constructed.Again,from the perspective of taking into account the preferences of experts and the public,C-OWA operator is used to weight each index to obtain the weight vector of experts and different public subgroups.Through game theory,multiple groups of weight vectors are re weighted to obtain the final combination weight.Using intuitionistic fuzzy set as evaluation language,similarity theory and interactive decision-making are introduced to carry out the information exchange process between experts and public subgroups respectively;then,the decision-making information is gathered by the weighted average operator of ifwa,and the weight of each decision-maker is determined according to the similarity theory.The todim method developed from prospect theory is used to deal with the index weight,decision-making matrix and decision-maker weight,the priority of each alternative is obtained,and then the multi-attribute group decision-making model of NIMBY facility location scheme under the perspective of reasonable public participation is constructed.Finally,a case study of a waste incineration power generation project is carried out by using the above model.The sensitivity analysis of the ranking results is carried out by setting different risk aversion coefficients,and the stability of the model is verified.