Social stability risk assessment of NIMBY major projects by OWA,matter-element,and cloud model

Abstract

To solve the problem of Social Stability Risk (SSR) assessment for Not In My Back Yard (NIMBY) major projects, first, we established an SSR assessment index system in terms of legality, rationality, feasibility, and controllability. Then, we used the cloud model to determine the value of SSR assessment indicator and the Ordered Weighted Averaging (OWA) operator to weight SSR indicators. Finally, the SSR levels of NIMBY major projects were measured by the matter-element model. The case study shows that the SSR indicator system and assessment model developed in this work can assess the SSR of NIMBY major projects and provide a reference for the investment decisions of government departments.

Keywords

NIMBY major projects social stability risk assessment

1 Introduction

In recent years, as the urbanization has accelerated in China, public awareness on environmental protection and sustainability has continued to increase. The boycotts and conflicts often occur that are triggered by the site selection of Not In My Back Yard (NIMBY) major projects [1]. In order to solve these problems, since 2011, the Chinese government has made it obligatory to implement Social Stability Risk (SSR) assessment during the feasibility study of major projects. At the same time, the assessment results are required for decision-making to prevent and resolve social conflicts and ensure smooth implementation of projects [2]. However, while substantial effort has been devoted to addressing the SSR assessment for NIMBY major projects, the issues still occur; Wangzi sewage project was rejected in 2012 at Qidong, Jiangsu province; residents opposed to the waste incineration projects in 2014 at Hangzhou, Zhejiang province; and Yulong petrochemical project was rejected in 2016 at Longkou, Shandong province. One of the main reasons for the above-mentioned NIMBY events is that the SSR assessment mechanism for major projects is still lacking [3]. The current index system and assessment methods fall short of effectively guiding investment decisions for NIMBY major projects.

SSR assessment generally includes two aspects: assessment index system and assessment method. Regarding the construction of the assessment index system, the “Interim Measures of the National Development and Reform Commission on the SSR Assessment of Major Fixed Assets Investment Projects” (Development and Reform Investment [2012] No. 2492) requires the critical evaluation of the projects regarding legality, rationality, feasibility, and controllability. However, specific content for evaluation is not clearly defined. Based on this guidance framework, researchers have designed an SSR assessment index system based on four criteria: legality, rationality, feasibility, and controllability [4 –11]. At present, SSR assessment indicators are selected by scholars based on personal knowledge and experience under the guidance of the National Development and Reform Commission; thus, a comprehensive evaluation index system has not been formed. However, the references mentioned above can help us build an SSR assessment index system for NIMBY major projects.

SSR assessment methods include expert evaluation method [7], Analytic Hierarchy Process (AHP) and multi-objective linear weighting function method [8], Fuzzy Analytic Hierarchy Process (FAHP) [9], multidimensional synthetic evaluation method [10], and fuzzy clustering algorithm [11]. The index values are determined by the scoring method [7 , 10] and the fuzzy evaluation method [9, 11]. The index weights are determined by the AHP [8, 9] and expert scoring [10, 11]. The weighting method, which relies on subjective factors such as experts’ knowledge or judgment matrix, is insufficient to effectively deal with extreme scores. In addition, determining index values based on expert ratings does not fully consider the fuzziness and randomness of the index values. Although the fuzzy evaluation method considers the fuzziness of the index value, it is mostly determined sporadically, and determining the randomness of indicator values has not been solved to date.

In this study, we aim to establish an assessment system to evaluate the SSR level of NIMBY major projects. First, we established an assessment index system from the perspective of legality, rationality, feasibility, and controllability. Afterward, we used the cloud model to determine values of SSR indicators. Then, we used the OWA operator to calculate index weights to reduce the negative impact of extreme values. Finally, we built an SSR assessment approach based on the matter-element method.

2 Assessment approach

2.1 Research process

The research process on the SSR assessment of NIMBY major projects is shown in Fig. 1. In this study, we established an SSR assessment index system for NIMBY major projects and used the OWA operator to determine the weight of each SSR indicator. Then, we built an SSR assessment approach based on the matter-element method. The detailed description of the research process is given from Section 2.2 to Section 2.5.

Fig.1

The SSR assessment process of NIMBY major projects.

2.2 The SSR assessment index system

We built an SSR assessment index system for NIMBY major projects based on the existing literature [4 –11] from four aspects: legality, rationality, feasibility, and controllability (Table 1). The indicator set F is a set of first-level SSR assessment indicators, where F ={ F₁, F₂, ⋯ , F_j }; the indicator set C_i (i ∈ [1, n]) is a set of second-level SSR assessment indicators, where C ={ C₁ C₂ ⋯ C_n }.

Table 1
SSR Assessment Index System of NIMBY major projects

First-level indicators Two-level indicators

Legality indicators (F1) Information disclosure (C1)

The normative of initiation and approval (C2)

The legality of location selection (C3)

Rationality indicators (F2) Influence degree on the production and livelihood of the general public (C4)

Social order influence (C5)

The acceptance degree of resettlement policy (C6)

Feasibility indicators (F3) The feasibility of construction plan (C7)

The public acceptance degree of the project (C8)

The perfection degree of supporting and safeguard measures (C9)

The maturity level of construction condition (C10)

Controllability indicators (F4) The occurrence probability of group events (C11)

Early warning and contingency plans (C12)

The propaganda and explanation for NIMBY project (C13)

First-level indicators	Two-level indicators
Legality indicators (F1)	Information disclosure (C1)
	The normative of initiation and approval (C2)
	The legality of location selection (C3)
Rationality indicators (F2)	Influence degree on the production and livelihood of the general public (C4)
	Social order influence (C5)
	The acceptance degree of resettlement policy (C6)
Feasibility indicators (F3)	The feasibility of construction plan (C7)
	The public acceptance degree of the project (C8)
	The perfection degree of supporting and safeguard measures (C9)
	The maturity level of construction condition (C10)
Controllability indicators (F4)	The occurrence probability of group events (C11)
	Early warning and contingency plans (C12)
	The propaganda and explanation for NIMBY project (C13)

In the process of constructing the indicator system, we comprehensively considered the characteristics of NIMBY major projects and the difficulty of data acquisition and followed a systematic approach with comparability. We established the indicator system according to the research stages of literature review, expert consultation, and index selection. First, we determined the indicators of SSR assessment from the literature. Then, we consulted a group of experts on the rationality of the indicators through questionnaires. Finally, we selected SSR indicators based on expert evaluation results.

Legality indicators (F1). The legality indicators are used to assess the legitimacy of NIMBY major projects in initiation and approval, location selection, and information disclosure. Three indicators are selected: information disclosure (C1) [4, 7], the normative of initiation and approval (C2) [8 , 11], and the legality of location selection (C3) [5, 6].

Rationality indicators (F2). Because the implementation of NIMBY major projects has a significant impact on the production and livelihood of general public, this type of indicators is used to assess the project’s impact on public production and life, the rationality of compensation for land expropriation and house removal, and the rationality of resettlement policy. Three indicators are selected: influence degree on the production and livelihood of the general public (C4) [7 , 11], social order influence (C5) [4 , 8], and the public acceptance degree of compensation policy (C6) [5 , 9].

Feasibility indicators (F3). Feasibility indicators are used to assess project construction opportunity, construction plan, and supporting and safeguard measures. Four indicators are selected: the feasibility of construction plan (C7) [4, 10], the public acceptance degree of the project (C8) [5 , 9], the perfection degree of supporting and safeguard measures (C9) [3, 5], and maturity level of construction condition (C10) [7 , 11].

Controllability indicators (F4). Controllability indicators are used to assess the occurrence probability of NIMBY events, the measures of early warning, and emergency management. Three indicators are selected: the occurrence probability of group events (C11) [4 , 9], early warning and contingency plans (C12) [5, 8], the propaganda and explanation for NIMBY project (C13) [6 , 11].

2.3 Indicator value measurement by cloudmodel

2.3.1 The overview of cloud model

The cloud model is a model that transformsbetween qualitative concepts and quantitative descriptions proposed by Li et al. [12]. On the basis of traditional probability theory and fuzzy mathematics, the cloud model reflects the ambiguity and randomness of things through three numerical characters: Ex, En, and He. It has been applied to the fields of trust assessment [13], urban traffic flow forecasting [14], and risk evaluation [15]. In this paper, we used the cloud model to calculate the index value to overcome the deficiencies of existing methods in determining the index value.

(1) The numerical characters of cloud model

Cloud model achieves conversion between qualitative concepts and quantitative descriptions through three numerical characters: Ex, En, and He. [16], as shown in Fig. 2.

Fig.2

The numerical characters of cloud model.

Ex, originally derived from probability theory, refers to the expected value of variables in random sampling [17]. Ex, is the midpoint of the domain of discourse; it represents the expectation of cloud droplet distribution. It is the point that best represents the qualitative concept and the highest point of the cloud map.

En, is used to measure the uncertainty of the concept, reflecting the margin of the concept [18]. The randomness and ambiguity of the concept directly affect the value of En. The larger En is, the larger the value range of the cloud droplet is, i.e., the more blurred the concept is, the larger the cloud span shown by cloud diagram becomes.

He, the entropy of entropy, is a measure of the uncertainty of entropy, reflecting the dispersion degree of cloud droplets [19]. The bigger the He, the greater the dispersion of cloud droplets. Hyper-entropy combines the randomness and fuzziness of things and reflects the thickness of the clouds in the cloud diagram.

A vector C (Ex, En, He) consisting of Ex, En, and He is called a cloud vector.

(2) The cloud generator

In this section, we introduce three cloud generators used in this paper: the forward cloud generator, backward cloud generator, and condition and rule generator.

As the most basic cloud model algorithm, the forward cloud generator [20] achieves the range and distribution of quantitative data obtained in qualitative representation. It is also an essential tool for representing qualitative language. Through the numerical characters of cloud model, it can generate several two-dimensional points of the normal cloud model—cloud droplet (x_i, u_i).

The backward cloud generator [21] effectively converts a certain number of precise values into appropriate qualitative language values C (Ex, En, He); thus, it is an inverse and indirect mapping from quantitative to qualitative data. Its role is to restore three numerical characters of a one-dimensional cloud from a given number of cloud droplets in order to convert from quantitative values to qualitative language values.

The condition and rule generator [22] is composed of X-conditioned cloud generator CG_A and Y-conditioned cloud generator CG_B based on the forward cloud generator. According to the numerical characters CG_A (Ex_A, En_A, He_A) and quantitative value x_A of X-conditioned cloud generator CG_A and the numerical characters CG_B (Ex_B, En_B, He_B) of Y-conditioned cloud generator CG_B, the quantitative value x_B of Y-conditioned cloud generator CG_B that satisfies the certainty degree y is obtained.

2.3.2 Determining the qualitative indicator byforward backward cloud

The qualitative indicator values are determined by the forward backward cloud and Delphi method. According to the scores of the expert group, the forward backward cloud is used to achieve the conversion between qualitative concepts and quantitative representations. First, the numerical characters C (Ex, En, He) of qualitative concept is generated by the backward cloud generator, and then the cloud image is generated by the forward cloud generator.

(1) The transformation of expert scores to qualitative concept based on backward cloud generator

First, we set up an expert group and formulate the evaluation criteria. After obtaining the scores from experts, a set of score data is formed. Then, this set of scores is converted into the numerical characters $\hat{C} (E \hat{x}, E \hat{n}, H \hat{e})$ of qualitative concepts through the backward cloud generator.

(2) Qualitative concept conversion to cloud map based on forward cloud generator

According to the numerical characters $\hat{C} (E \hat{x}, E \hat{n}, H \hat{e})$ of qualitative concepts, the forward cloud generator is used to perform mapping conversion between qualitative concept and quantitative representation. In this process, cloud droplets are generated and formed into a cloud map. With the help of the cloud map, we can visually analyze the range of the number domain, the gravity center of discourse domain, the thickness of cloud, and the condensation degree of cloud droplets.

(3) Determining qualitative indicator value with Delphi method

We used the Delphi method [23] to determine the value of qualitative indicators. In this process, we used cloud map to visually analyze the dispersion degree of the experts’ scores; and, we gave feedback to the experts on the distribution of scores using the Delphi method. We can get more consistent results of the experts’ scores through inviting experts to score multiple times and visually control the convergence direction, quality, and speed of the scores. Then, we obtained the numerical characters C^* (Ex^*, En^*, He^*), where Ex^* is the quantitative value x_i of the indicator C_i.

2.3.3 Determining the quantitative indicator by cloud uncertainty reasoning

Cloud uncertainty reasoning is a rule generator consisting of X-conditioned and Y-conditioned cloud generator according to the condition rule (If E, Then G) and forward cloud generator. This method is used in the determination of quantitative indicator values as follows:

(1) Determining the comment set of quantitative indicator

We used the qualitative comment E_k to describe the importance level of the Two-level indicators C_i. Let E be a comment set of E_k, denoted as E ={ E₁ E₂ ⋯ E_k }. The numerical characters of the indicator comment E_k is ${CM}_{i}^{E_{k}} ({Ex}_{i}^{E_{k}}, {En}_{i}^{E_{k}}, {He}_{i}^{E_{k}})$ .

(2) Calculating the membership degree of quantitative indicator values

If the quantitative indicator value is r_i, the membership degree of r_i is $y_{i}^{E_{k}}$ by running the X-conditioned cloud generator. It can be seen that the quantitative index C_i corresponds to thecomment E_k denoted as r_i ∈ E_k.

(3) Determining the comment set of score

Let G_d be a qualitative comment describing the level of quantitative indicator score, and G be a set of comment G_d, i.e., G ={ G₁ G₂ ⋯ G_d }. The numerical characters of the indicator comment B_d is CM^{G
_d} (Ex^{G
_d}, En^{G
_d}, He^{G
_d}).

(4) Measuring the score of quantitative indicator

We obtained the quantitative indicator value x_i through cloud uncertainty reasoning; if r_i ∈ E_k, then x_i ∈ G_d, and the relationship between k and d is one-to-one correspondence. ${CM}_{i}^{E_{k}}$ denotes X-conditioned cloud generator for inputting a quantitative indicator comment E_k, and CM^{G
_d} denotes Y-conditioned cloud generator for outputting a score comment G_d. When a specific value r_i triggers X-conditioned cloud ${CM}_{i}^{E_{k}}$ , a number $y_{i}^{E_{k}}$ is generated. Then, $y_{i}^{E_{k}}$ triggers Y-conditioned cloud CM^{G
_d} and produces cloud drop $Drop (x_{i}, y_{i}^{G_{d}})$ .

2.4 Determining the indicator weights by OWA operator method

OWA operator is an information aggregation method proposed by Yager and is suitable for dealing with uncertain decision problems [24]. The OWA operator method can better reflect the risk preferences of the decision-makers and reduce the influence of the extreme values of the decision data on the weighting results [25]. Its essence is to sort the data elements in descending order and make a weighted set based on their location to ensure the scientific rationality of information aggregation in the determination of weights [26].

Although the traditional OWA operator method is simple and clear, the calculation process of weights is relatively rough and involves shortcomings in the calculation of the weight vector. Therefore, many scholars have studied the extended algorithm of the OWA operator to improve its inadequacies [27 –30]. The weighting method adopted in this paper is an improved OWA operator method based on the combination number [31]. The improved OWA operator method uses combination numbers to weight decision data and calculates the weight of each indicator based on the traditional OWA operator, thereby making the calculation process of the weight vector more accurate and reasonable by reducing the influence of extreme values and better reflecting the preferences of decision makers. The improved OWA operator method has been applied in many fields, such as risk assessment [32] and performance evaluation [33]. The specific steps are listed below:

2.4.1 Calculating first-level indicator weight

(1) Determining the dataset for scores

First, we determined the evaluation criteria; then, n experts scored the importance level of the first-level indicator F_i, and we obtained the dataset (a₁, a₂, ⋯ , a_τ). Finally, we sorted the dataset from the largest to the smallest and numbered the data starting from 0. In doing so, we obtained the new dataset (β₀, β₂, …, β_τ-1), and β₀ ⩾ β₁ ⩾ ⋯ ⩾ β_k ⩾ ⋯ ⩾ β_τ-1.

(2) Calculating the weight vector

We weighted dataset A by the combination number. The weight vector is determined by: $g_{n + 1} = \frac{C_{τ - 1}^{n}}{\sum_{i = 0}^{τ - 1} C_{τ - 1}^{i}} = \frac{C_{τ - 1}^{n}}{2^{τ - 1}}, n = 0, 1, 2, \dots, τ - 1$ (1)

(3) Calculating the absolute weights

The weight vector g_n+1 weights the scores of indicator importance level to obtain the absolute weight $η_{i}^{'}$ of first-level indicator F_i by: $η_{i}^{'} = \sum_{i = 1}^{τ} g_{n} β_{n}, n \in [1, τ]$ (2)

(4) Calculating the relative weights

We calculated the relative weight $η_{i}^{'}$ of the first-level indicator F_i by: $η_{i} = \frac{η_{i}^{'}}{\sum_{i = 1}^{m} η_{i}^{'}}, i = 1, 2, \dots, t$ (3)

2.4.2 Calculating second-level indicator weight

Our method for calculating the weight of second-level indicators is similar to the process for determining the weight of first-level indicators. First, the relative weights of second-level indicators belonging to each first-level indicators were obtained. Then, the final weight values of second-level indicators were obtained by: $W = η_{i} • Z_{e}$ (4)

The final weight value of second-level indicator is W, which is the product of the relative weight of the first-level indicator and second-level indicator.

2.5 Assessment of SSR level by matter-elementmodel

Cai et al. pioneered matter-element analysis, which is a new discipline that finds ways to solve problems through matter transformation [34, 35]. Matter-element analysis mainly studies the variability of things, explores the process of transformation of things, and analyzes the condition, channel, and regularity of the change of things. It has been applied to the land suitability evaluation [36], risk assessment [37], and project site selection [38]. The basic structural form of the matter-element analysis model is R = (N, C, V), where N denotes a thing, C represents a characteristic, and V represents a value [39].

(1) Determining the matrix of classical domain and joint domain

The matrix of classical domain

The classic domain matrix of SSR is defined as:

$\begin{matrix} - 24 pt R_{j} & = (N_{j}, C_{i}, V_{ji}) = [\begin{matrix} N_{j}, & c_{1}, & ν_{j 1} \\ c_{2}, & ν_{j 2} \\ ⋮ & ⋮ \\ c_{n}, & ν_{jn} \end{matrix}] \\ - 24 pt & = [\begin{matrix} N_{j}, & c_{1}, & 〈 a_{j 1}, b_{j 1} 〉 \\ c_{2}, & 〈 a_{j 2}, b_{j 2} 〉 \\ ⋮ & ⋮ \\ c_{n}, & 〈 a_{jn}, b_{jn} 〉 \end{matrix}] \end{matrix}$ (5)

In Equation 5, N_j denotes the j- th risk level; c_i (i = 1, 2, ⋯ , n) denotes the characteristics of the risk level N_j; ν_ji is the value range of the indicator c_i corresponding to the risk level N_j, i.e., the classical domain 〈a_ji, b_ji〉 a_ji and b_ji are the boundary values of ν_ji .

The joint domain matrix

The joint domain matrix of SSR is defined as:

$\begin{matrix} R_{p} & = (P, C, V_{pi}) = [\begin{matrix} P, & c_{1}, & ν_{p 1} \\ c_{2}, & ν_{p 2} \\ ⋮ & ⋮ \\ c_{n}, & ν_{pn} \end{matrix}] \\ = [\begin{matrix} P, & c_{1}, & 〈 a_{p 1}, b_{p 1} 〉 \\ c_{2}, & 〈 a_{p 2}, b_{p 2} 〉 \\ ⋮ & ⋮ \\ c_{n}, & 〈 a_{pn}, b_{pn} 〉 \end{matrix}] \end{matrix}$ (6)

In Equation (6), P denotes all risk levels, and V_pi denotes the value range of P corresponding to indicator c_i, i.e., the joint domain 〈a_pi, b_pi〉 a_pi and b_pi are the boundary values of V_pi.

(2) Determining the object to be evaluated

$R_{o} = (P_{o}, C, X) = [\begin{matrix} P_{o}, & C_{1}, & x_{1} \\ C_{2}, & x_{2} \\ ⋮ & ⋮ \\ C_{n}, & x_{n} \end{matrix}]$ (7)

In Equation (7), P_o is the NIMBY major project to be evaluated, C_i (i = 1, 2, ⋯ , n) is the indicator of SSR, and x_i (i = 1, 2, ⋯ , n) is the value of indicator c_i.

(3) Determining correlation function values

The correlation function value k_j (x_i) reflects the membership degree of SSR indicators at each risk level and is obtained by:

$k_{j} (x_{i}) = {\begin{matrix} \frac{- ρ (x_{i}, ν_{ji})}{| b_{ji} - a_{ji} |}, x_{i} \in ν_{ji} \\ \frac{ρ (x_{i}, ν_{ji})}{ρ (x_{i}, ν_{pi}) - ρ (x_{i}, ν_{ji})}, x_{i} \notin ν_{ji} & ρ (x_{i}, ν_{pi}) \neq 0 \\ - ρ (x_{j}, ν_{ji}) - 1, x_{i} \notin ν_{ji} & ρ (x_{i}, ν_{pi}) = 0 \end{matrix}$ (8)

In Equation (8), k_j (x_i) is the membership degree of SSR at each risk level; ρ (x_i, ν_ji) is the distance from point ν_i to the range of value ν_ji =〈 a_ji, b_ji 〉; ρ (x_i, ν_pi) is the distance from point ν_i to the range of value ν_pi =〈 a_pi, b_pi 〉; x_i is the indicator value of SSR; 〈a_ji, b_ji〉 is the classical domain, and $ρ (x_{i}, ν_{ji}) = | x_{i} - \frac{1}{2} (a_{ji} + b_{ji}) | - \frac{1}{2} (b_{ji} - a_{ji})$ , $ρ (x_{i}, ν_{pi}) = | x_{i} \frac{1}{2} (a_{pi} + b_{pi}) | - \frac{1}{2} (b_{pi} - a_{pi})$ . Whenk_j (x_i) = max k_j (x_i), we obtainedthe risk level of the SSR indicator C_i.

(4) Comprehensive evaluation

According to the correlation function value and the weight value, we can obtain the comprehensive evaluation value of SSR indicator at each risk level.

$K_{j} = \sum_{i = 1}^{n} ω_{i} k_{j} (x_{i})$ (9)

After calculation, we obtained the comprehensive evaluation value of each indicator at each risk level. And we determined the level of SSR through the maximum value K^* = max K_j.

3 Case study

In order to strengthen the management of the solid waste, the YZ city government has decided to start ZF power generation project through domestic waste incineration and adopted a resource-based, reduced-quantity, and harmless approach to solving the domestic waste problem. The site of the project is located in the village of TZ, LQ Town. The total planned investment is $ 54.69 million, and 1400 tons of garbage will be disposed daily.

The project construction includes waste incineration system, waste heat utilization system, flue gas purification system, leachate treatment system, slag and fly ash handling system, and supporting facilities. The implementation of the project may lead to environmental pollution and loss of land and income for villagers. The project may trigger villagers to block the project through boycotts. This article studied this project as an example to conduct SSR assessment based on the above-mentioned index system and analytical models.

3.1 Indicator value measurement by cloudmodel

3.1.1 Determining the qualitative indicator byforward backward cloud

(1) Forming an expert group and determining the scoring criteria

We invited ten experts to form a group of experts, including three government officials, four university scholars, and three experienced project managers, all of whom have at least six years of experience in NIMBY projects management and consulting. The comment domain of SSR was “Very High, High, Middle, Low, Very Low,” and the corresponding comment domain of expert score were “Very Low, Low, Center, High, Very High” with the score ranges {[0, 20], [21, 40], [41, 60), 61–80], [81, 100]}.

(2) Determining the value of a qualitative indicator

Taking the “Information Disclosure” indicator as an example, we ran the backward cloud generator and the forward cloud generator successively according to the scores of ten experts. We obtained the numerical characters (83.47, 0.8292, 0.0716) of this indicator through three rounds of expert scoring. The expected value (Ex = 83.47) was used as the value of “Information Disclosure” indicator.

The process was as follows: ➀ The first time of scoring by the expert group. Due to the influence of personal background, experts had different understanding of the “Information Disclosure” indicator, and they needed to score this indicator for the second round. The cloud map showed that the scores of various experts were relatively scattered, and the En and He were large in the numerical characters, and the cloud showed a fog state, as shown in Fig. 3a. ➁ The second time of scoring by the expert group. We provided feedback for the scores of this indicator to the experts and invited them to score again. The cloud map showed that En and He decreased compared with the first round, and the cloud condensed from the fog to the normal cloud, as shown in Fig. 3b. ➁ The third time of scoring by the expert group. After the third round of scoring, En and He further decreased, and the cloud map showed that the coherence of normal clouds was significantly enhanced, indicating the normal cloud of expert scores was gradually formed, as shown in Fig. 3c.

Fig.3

(a) The first, (b) second, and (c) third time of scoring by the expert group.

3.1.2 Determining the quantitative indicator by cloud uncertainty reasoning

(1) Determining the comment set of a quantitative indicator

The quantitative indicators in this paper included “the Public Acceptance Degree of Compensation Policy (PADCP) C6” and “the Public Acceptance Degree of the Project (PADP) C8,” and their original index value were determined by sampling survey. We randomly selected 50 villagers from the TZ village for statistics. The original values of the two indicators were 92% and 88%, respectively. Taking the indicator PADCP as an example, we let the risk level of the indicator PADCP be “Very Low, Low, Middle, High, Very High,” and the corresponding numerical characters were “(0.95, 0.0425, 0.005).), (0.85, 0.0425, 0.005), (0.75, 0.0425, 0.005), (0.65, 0.0425, 0.005), (0.55, 0.0425, 0.005). The cloud map of membership degree is shown in Fig. 4.

Fig.4

The cloud map of membership degree of the indicator PADCP.

(2) Determining the comment set and indicator scores

We let the comment set G = {Very Low, Low, Center, High, Very High} with a score range of {[0, 20),21, 40),41, 60),61–80),81, 100]}. The numerical characters were “(90, 8.49, 1.01), (70, 8.49, 1.01), (50, 8.49, 1.01), (30, 8.49, 1.01), (10, 8.49, 1.01)”. The score of a quantitative indicator was determined by cloud uncertainty reasoning. Taking the indicator PADCP as an example, we established reasoning rules as follows:

If the risk level of indicator PADCP is “Very High,” then score comment is “Very Low;”

If the risk level of indicator PADCP is “High,” then score comment is “Low;”

If the risk level of indicator PADCP is “Middle,” then score comment is “Center;”

If the risk level of indicator PADCP is “Low,” then score comment is “High;”

If the risk level of indicator PADCP is “Very Low,” then score comment is “Very High.”

We obtained (84.01) for indicator PADCP through the single condition single rule cloud generator. Similarly, the quantified score of indicator PADP was 75.99, as shown in Table 2.

Table 2

The weights and values of second-level SSR indicators

Indicator	C1	C2	C3	C4	C5
Indicator value	83.47	91.35	87.66	58.47	82.31
weight	0.0738	0.1054	0.0844	0.0921	0.0747
Indicator	C6	C7	C8	C9	C10
Indicator value	84.01	82.91	75.99	54.1	73.25
weight	0.0821	0.0748	0.0671	0.0568	0.0594
Indicator	C11	C12	C13
Indicator value	77.46	53.98	57.28
weight	0.0642	0.0918	0.0734

3.2 Determining the indicator weights by OWA operator method

3.2.1 Calculating first-level indicator weights

(1) Determining the rating and score range of indicators’ importance level

We invited six experts to determine the importance of indicators, including three university scholars and three project managers, all of whom have at least six years of experience in NIMBY projects management and consulting. We set the score for the importance level of the indicator to be between 0 and 10, and the increasing trend of the importance level of the indicator is consistent with the increasing trend of the scores. The rating of importance level was “First-Level, Second-Level, Third-Level, Fourth-Level, Fifth-Level”, and score range of ratings were {, (2, 4], (4, 6], (6, 8), (8, 10)}.

(2) Scoring the importance level of first-level indicators

Based on the above, we invited the expert team to score the importance level of the first-level indicators. The scores of indicator importance levels were designated as integral multiples of 0.5 to facilitate expert expression and simplify score statistics, as shown in Table 3.

Table 3
The scores of the importance level of first-level indicators

Indicator Expert Expert Expert Expert Expert Expert

-1 -2 -3 -4 -5 -6

F1 9 9 8.5 8.5 9 9.5

F2 8.5 9 8 8.5 8 8.5

F3 8 9 8.5 9 9 8.5

F4 7.5 8 7.5 8.5 7.5 8

Indicator	Expert	Expert	Expert	Expert	Expert	Expert
F1	9	9	8.5	8.5	9	9.5
F2	8.5	9	8	8.5	8	8.5
F3	8	9	8.5	9	9	8.5
F4	7.5	8	7.5	8.5	7.5	8

(3) Determining the weight vector of first-level indicators

First, we ranked the scores of indicator importance level from the largest to the smallest. Taking the first-level indicator F1 as an example, we obtained β_k = (9.5, 9, 9, 9, 8.5, 8.5) by ranking the scores. Then, the weight vector of first-level indicators was calculated by Formula 1: $g = (0.03125, 0.15625, 0.3125, 0.3125, 0.15625, 0.03125)$ (10)

(4) Determining the weights of first-level indicators The weight vector weights the scores of the importance level of the first-level indicator to obtain the absolute weights $η_{i}^{'}$ by Formula 2. The absolute weights of each first-level indicators were $η_{i}^{'} = (8.922, 8.422, 8.734, 7.766)$ . Finally, we obtained the weight value of the first-level indicator by Formula 3: η = (0.2636, 0.2488, 0.2581, 0.2295).

3.2.2 Calculating the second-level indicator weights

Our method for calculating the weight of the second-level indicators was similar to the process for determining the weight of the first-level indicators. First, the relative weights of the second-level indicators belonging to each first-level indicator were obtained. Then, the final weight values of second-level indicators were finally obtained by Formula 4. The weight values of the second-level indicators are shown in Table 2.

3.3 Assessment of the SSR level by matter-element model

The SSR level of the ZF project was {Very High, High, Centre, Low, Very Low}, corresponding to N_j = {N₁, N₂, N₃, N₄, N₅}. By constructing the classical domain and section field of matter-element analysis, we calculated the degree of membership of the ZF project at each risk level. Finally, we determined the SSR level of the ZF project by maximum membership principle.

(1) Determining the object to be evaluated

The object to be evaluated was R_o = (P_o, C, V), where P_o is ZF power generation project through domestic waste incineration, C_i (i = 1, 2, ⋯ , n) is the SSR indicators, and x_i (i = 1, 2, ⋯ , n) is the value of SSR indicators. In the Section 4.1, the values of SSR indicators were obtained from the cloud reasoning and forward backward cloud model, i.e., the indicator values of the object were evaluated, as shown in Table 2.

(2) Determining the classical domain and joint domain

We first determined the scoring criteria for the SSR assessment. In order to be consistent with the score range of the indicators, we set the classical domain to {Very High [0, 20], High (20 40], Center (40 60], Low (60 80], Very Low (80 100]} and the joint domain to [0, 100].

(3) SSR assessment results

According to formulas 8 and 9, the SSR level of the first-level indicators was obtained as shown in Table 4. Then, the comprehensive SSR level of the ZF project was determined. The assessment results corresponding to the SSR levels {Very High, High, Centre, Low, Very Low} were {– 2.6206, – 1.6206, – 0.6206, – 0.0576, – 0.3794}.

Table 4
The SSR assessment results of ZF Project

Indicator Very High High Middle Low Very Low Risk Level Changing Trends

F – 2.6206 – 1.6206 – 0.6206 – 0.0576 – 0.3794 Low Very Low

F1 – 0.8188 – 0.5552 – 0.2916 – 0.0280 0.0280 Very Low Low

F2 – 0.6465 – 0.3976 – 0.1487 – 0.0060 – 0.1002 Low Very Low

F3 – 0.6781 – 0.4200 – 0.1619 0.0059 – 0.0962 Low Very Low

F4 – 0.4772 – 0.2478 – 0.0184 – 0.0295 – 0.2110 Middle Low

Indicator	Very High	High	Middle	Low	Very Low	Risk Level	Changing Trends
F	– 2.6206	– 1.6206	– 0.6206	– 0.0576	– 0.3794	Low	Very Low
F1	– 0.8188	– 0.5552	– 0.2916	– 0.0280	0.0280	Very Low	Low
F2	– 0.6465	– 0.3976	– 0.1487	– 0.0060	– 0.1002	Low	Very Low
F3	– 0.6781	– 0.4200	– 0.1619	0.0059	– 0.0962	Low	Very Low
F4	– 0.4772	– 0.2478	– 0.0184	– 0.0295	– 0.2110	Middle	Low

(4) Analysis of the SSR assessment results

From Table 4, we know that the SSR assessment result of ZF project is K^* = -0.0576 by maximum membership principle. The comprehensive SSR level is “Low,” and the SSR level of ZF project shows a downward trend that tends to the risk level of “Very Low.” ➀ The SSR assessment result of indicator F1 is K_F1 = 0.0280, and the SSR level is “Very Low.” ➁ The SSR assessment result of indicator F2 and F3 are K_F2 = -0.0060 and K_F3 = 0.0059, respectively. Their risk level is “Low,” and the risk level of ZF project shows a downward trend that tends to the risk level of “Very Low.” ➂ The SSR assessment result of indicator F4 is K_F4 = -0.0184, the SSR risk level is “Middle,” and the risk level of ZF project shows an upward trend that tends to the risk level of “Low.”

We conclude that the comprehensive SSR level of ZF project is “Low.” For the first-level indicators, the risk level of the legality indicators (F1) is “Very Low;” the rationality indicators (F2) and the feasibility indicators (F3) have a “Low” risk rating, and the risk level of the controllability indicators (F4) is “Middle” with tendency towards “Low.” By analyzing the SSR second-level indicators, the following indicators have a relatively high level of risk: the risk level of second-level indicator “Influence degree on the production and livelihood of the general public (C4)” in the rationality indicators (F2), the risk level of second-level indicator “The perfection degree of supporting and safeguard measures (C9) ” in the feasibility indicators (F3), and the risk level of second-level indicator “Early warning and contingency plans (C12)” and “The propaganda and explanation for NIMBY project (C13)” in the feasibility indicators (F3).

In the investment decision-making process of the project, attentions should be paid to the controllability indicators (F4), and secondary indicators such as C4, C9, C12, and C13, and the above-mentioned shortcomings should be compensated by formulating countermeasures. We propose the following suggestions to reduce the SSR of this project through consulting experts and reviewing literature. The construction unit should minimize the negative impact of the project on the production and public life in the decision-making phase, construction phase, and operation phase. As the project site is far from the city, the construction unit should pay attention to the construction of supporting facilities for water, electricity, heating, and gas to ensure the smooth operation of the project. The construction unit also needs to fully consider the risks that the project may face in the process of construction and operation to improve the early warning measures and emergency plans. In addition, the construction unit can further introduce the project situation to the public through media propaganda and expert explanations, so that the public can clarify the specific conditions of the project in real time, thereby reducing the SSR of the project.

4 Conclusions

At present, SSR assessment mechanisms for NIMBY major projects are still lacking, and no specific indicator system and assessment method exist for the risk assessment of NIMBY major projects to avoid frequent occurrences of boycott and conflict events during the decision-making process of NIMBY major projects.

In response to these problems, we established an SSR assessment indicator system for NIMBY major projects based on legality, rationality, feasibility, and controllability. We used the cloud model to calculate the values of qualitative and quantitative SSR indicators, the OWA operator to calculate the weights of SSR indicators, and the matter-element model to determine the level of the project’s SSR. The case study shows that our indicator system and evaluation model are suitable for the risk assessment of the NIMBY major projects. Our work can determine the risk level of the NIMBY major projects, identify key risk factors, and provide a reference for the investment decisions of NIMBY major projects.

Because the SSR assessment model includes three methods, the calculation process is relatively complicated. However, the SSR assessment model has strong operability using MATLAB. At the same time, for the SSR assessment of NIMBY major projects, the integration of sustainable development factors based on this work will be a meaningful research direction that requires further exploration.

Disclosure Statement

Authors declare no conflict of interest.

Footnotes

Acknowledgments

We would like to thank our anonymous reviewers and editors for their input. This research is supported by the Fundamental Research Funds for the Central Universities (DUT18RW208) and the National Social Science Funding Program (15BGL023).

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Social stability risk assessment of NIMBY major projects by OWA,matter-element,and cloud model

Abstract

Keywords

1 Introduction

2 Assessment approach

2.1 Research process

2.3.1 The overview of cloud model

2.3.3 Determining the quantitative indicator by cloud uncertainty reasoning

2.4 Determining the indicator weights by OWA operator method

2.4.1 Calculating first-level indicator weight

3.1 Indicator value measurement by cloudmodel

3.1.1 Determining the qualitative indicator byforward backward cloud

3.2.1 Calculating first-level indicator weights

Table 3 The scores of the importance level of first-level indicators Indicator Expert Expert Expert Expert Expert Expert -1 -2 -3 -4 -5 -6 F1 9 9 8.5 8.5 9 9.5 F2 8.5 9 8 8.5 8 8.5 F3 8 9 8.5 9 9 8.5 F4 7.5 8 7.5 8.5 7.5 8

3.3 Assessment of the SSR level by matter-element model

Disclosure Statement

Footnotes

Acknowledgments

References

Table 3
The scores of the importance level of first-level indicators

Indicator Expert Expert Expert Expert Expert Expert

-1 -2 -3 -4 -5 -6

F1 9 9 8.5 8.5 9 9.5

F2 8.5 9 8 8.5 8 8.5

F3 8 9 8.5 9 9 8.5

F4 7.5 8 7.5 8.5 7.5 8