A method and system for quantitatively evaluating the risk of a retirement facility NIMBY conflict and a computer readable medium

Abstract

The application discloses a kind of old-age facilities neighborhood avoidance conflict risk quantitative evaluation method, system and computer readable medium, the method includes steps: (1) based on individual, community and regional dimension each emotional sensitivity influence factor, obtain resident emotional sensitivity parameter;(2) based on each environmental change influence factor in the whole life cycle of old-age facilities each stage, obtain the resident resistance emotion parameter corresponding to each stage environmental change factor;(3) based on each subject behavior influence factor in government, operator and resident, calculate the resident resistance emotion parameter corresponding to each stage subject behavior factor;(4) combining (1)~(3), calculate the cumulative neighborhood avoidance conflict risk index of each stage, obtain neighborhood avoidance conflict risk, and determine risk level accordingly.The application realizes whole life cycle dynamic evaluation, resident emotion accurate quantification, multi-subject linkage calculation, and forms a front-end pre-evaluation scheme that can be implemented.

Description

Technical Field

[0001] This invention relates to the field of NIMBY risk assessment technology, and in particular to a method, system, and computer-readable medium for quantitatively assessing NIMBY conflict risks associated with elderly care facilities. Background Technology

[0002] The NIMBY (Not In My Backyard) effect refers to the strong and resolute, sometimes highly emotional, collective opposition behavior of residents or local entities due to concerns that construction projects (such as landfills, nuclear power plants, funeral homes, etc.) will have many negative impacts on their health, environmental quality, and asset value.

[0003] There are already many existing technologies for assessing NIMBY (Not In My Backyard) risks, such as:

[0004] In her paper “A Study on Social Stability Risk Assessment of Environmental NIMBY Projects from the Perspective of ‘Risk Differential Pattern’ [J]. Journal of China Yan’an Cadre Academy, 2022, 15(2)”, Zhang Chunyan proposed a qualitative framework for risk assessment of environmental NIMBY projects based on the differential pattern theory, but did not form a quantitative model.

[0005] In their paper “Research on Environmental and Social Risk Assessment Method of ‘NIMBY’ Facilities Based on Big Data [J]. China Environmental Management, 2023, 15(3)”, Yang Jing, Wang Feng, Zhao Jing, et al. proposed to construct a static risk index system for environmental NIMBY facilities driven by big data. However, the static assessment method is not suitable for the scenario of community elderly care service facilities.

[0006] Huang Weiqiang and Lin Weibiao proposed a analytic hierarchy process (AHP) and fuzzy comprehensive evaluation model for constructing an early warning model in their paper “Research on the construction of an intelligence early warning evaluation model for ‘NIMBY’ incidents [J]. Journal of Political Science and Law, 2025, 42(3)”. However, the model focuses on post-event emergency early warning and lacks front-end pre-evaluation function.

[0007] Wu J, Wang Z, Bai X, et al. proposed a fuzzy hierarchical-radar chart evaluation method for power transmission and transformation projects in their paper "Comprehensive Evaluation of NIMBYPhenomenon with Fuzzy Analytic Hierarchy Process and Radar Chart[J].AppliedSciences,2024,14(6)". However, it is only applicable to power-related NIMBY facilities and does not have dynamic evolution analysis.

[0008] Jiang T, Xu Z, He X, et al. proposed a cloud model-based approach to assess the social licensing resilience of NIMBY facilities in their paper "Evaluating the resilience of social license to operate towards NIMBY facilities: A cloud model-based approach [J]. Environmental Impact Assessment Review, 2025", but it did not model the entire lifecycle of NIMBY conflict risks.

[0009] Zhao H, Ge Y, and Zhang J proposed an assessment of the effect of public participation in the decision-making of NIMBY facilities in their paper "Evaluation on the implementation effect of public participation in the decision-making of NIMBY facilities[J].PLOSONE,2022,17(2)". However, they only assessed the effect of public participation in the decision-making of NIMBY facilities and did not form a complete risk pre-assessment system.

[0010] In summary, existing NIMBY risk assessment technologies generally suffer from poor scenario adaptability, static assessment, low quantitative accuracy, lack of dynamic analysis throughout the entire life cycle and multi-stakeholder collaborative assessment, and lack of front-end pre-assessment capabilities and practicality, thus failing to meet the needs of precise prevention and control of NIMBY conflicts in community elderly care service facilities. Summary of the Invention

[0011] Purpose of the invention: To address the above-mentioned shortcomings, this invention proposes a method, system, and computer-readable medium for quantitative assessment of NIMBY (Not In My Backyard) conflict risks in elderly care facilities. This method enables dynamic assessment throughout the entire life cycle, accurate quantification of residents' emotions, and multi-stakeholder collaborative calculation, forming a feasible pre-assessment scheme that provides scientific decision-making support for facility planning, site selection, and operation.

[0012] Technical Solution: This invention provides a method for quantitatively assessing the risk of NIMBY (Not In My Backyard) conflicts related to elderly care facilities, including the following steps:

[0013] (1) Based on the factors influencing emotional sensitivity in the dimensions of individuals, communities and regions, the parameters of residents' emotional sensitivity are obtained by multi-dimensional weighted normalization;

[0014] (2) Based on the environmental change influencing factors in each stage of the life cycle of elderly care facilities, the residents' resistance parameters corresponding to the environmental change factors in each stage are obtained by weighted calculation.

[0015] (3) Based on the influencing factors of the behavior of the government, operators and residents, the residents' resistance parameters corresponding to the behavioral factors of the main body at each stage are obtained by subject-stage double weighting calculation;

[0016] (4) Combine steps (1) to (3) to calculate the static risk index of NIMBY conflict at each stage. Add the static risk index of NIMBY conflict at the current stage to the previous stages to obtain the NIMBY conflict risk at the current stage, and determine the risk level accordingly.

[0017] Specifically, in step (1), the factors affecting emotional sensitivity in the individual, community and regional dimensions are numbered according to the dimensions to obtain the factors affecting emotional sensitivity in each dimension.

[0018] The values ​​of the emotional sensitivity influencing factors are determined based on the same pre-obtained emotional sensitivity influencing factor assessment table;

[0019] Define the emotional sensitivity influencing factor in the individual dimension ind as a ind,i The emotional sensitivity influencing factor in the community dimension of .com is a. com, j The emotional sensitivity influencing factor in the regional dimension reg is a. reg,k Where i represents the index of the emotional sensitivity influencing factor in the individual dimension ind, j represents the index of the emotional sensitivity influencing factor in the community dimension com, and k represents the index of the emotional sensitivity influencing factor in the geographical dimension reg, the calculation of the resident emotional sensitivity parameter q obtained through multi-dimensional weighted normalization is as follows:

[0020] ;

[0021] Where, ω ind,i ω represents the weight coefficient corresponding to the i-th emotion sensitivity influencing factor in the individual dimension ind. com, j ω represents the weight coefficient corresponding to the j-th emotion sensitivity influencing factor in the community dimension com. reg,k ω represents the weight coefficient corresponding to the k-th emotion sensitivity influencing factor in the region dimension reg; I, J, and K represent the number of emotion sensitivity influencing factors in the individual dimension ind, the community dimension com, and the region dimension reg, respectively; ind ω com ω reg Let ω represent the dimension weight coefficients corresponding to the individual dimension (ind), the community dimension (com), and the geographic dimension (reg), respectively, and ω ind +ω com +ω reg =1.

[0022] More specifically, the factors influencing emotional sensitivity in the individual dimension include education level, economic level, family pressure for elder care, environmental justice demands, and media communication capabilities; among them, education level, economic level, and family pressure for elder care are negatively correlated with residents' emotional sensitivity parameters, while environmental justice demands and media communication capabilities are positively correlated with residents' emotional sensitivity parameters, thereby determining the values ​​of each emotional sensitivity influencing factor in the individual dimension.

[0023] The factors influencing emotional sensitivity in the community dimension include elderly population density, community organization level, history of NIMBY (Not In My Backyard) conflicts, and feelings towards the place of residence. Among them, elderly population density is negatively correlated with residents' emotional sensitivity parameters, while community organization level, NIMBY conflict history, and feelings towards the place of residence are positively correlated with residents' emotional sensitivity parameters. Thus, the values ​​of each emotional sensitivity influencing factor in the community dimension are determined.

[0024] The factors influencing emotional sensitivity in the regional dimension include the acceptance of traditional concepts and the atmosphere of respecting and caring for the elderly; among them, the atmosphere of respecting and caring for the elderly is negatively correlated with the residents' emotional sensitivity parameter, while the acceptance of traditional concepts is positively correlated with the residents' emotional sensitivity parameter, thereby determining the values ​​of each emotional sensitivity influencing factor in the regional dimension.

[0025] Specifically, in step (2), the environmental change influencing factors in each stage of the entire life cycle of the elderly care facility are numbered according to the stage, and the influencing factors of the residents' resistance to the environmental change factors in each stage are obtained.

[0026] The values ​​of the influencing factors of the residents' resistance parameters corresponding to the environmental change factors are determined based on the pre-obtained assessment table of the same environmental change factors.

[0027] Define the weight coefficient for each stage as λ. t Let b be the influencing factor of the resident resistance parameter corresponding to the p-th environmental change in stage t. t,p Since the influencing factors of environmental change factors differ at each stage, the resident resistance sentiment parameter x corresponding to the environmental change factors at stage t is obtained through weighted calculation. b,t The details are as follows:

[0028] ;

[0029] Where φ represents the number of environmental change influencing factors in the corresponding stage, ω b,t,p This represents the weighting coefficient of the influencing factor corresponding to the resident resistance sentiment parameter of the p-th environmental change in stage t.

[0030] More specifically, the entire life cycle of the elderly care facility includes four stages: proposal, site selection, implementation, and operation.

[0031] The environmental change influencing factors in the proposal stage include the future number of elderly people, the future living situation of elderly people living alone, the suitability of consumption capacity and facility types, environmental carrying capacity, positive public infrastructure, negative municipal facilities, parks and green spaces or activity spaces, and environmental aesthetics. Among these, the future number of elderly people, the future living situation of elderly people living alone, the suitability of consumption capacity and facility types, environmental carrying capacity, positive public infrastructure, parks and green spaces or activity spaces, and environmental aesthetics are negatively correlated with the residents' resistance sentiment parameter, while negative municipal facilities are positively correlated with the residents' resistance sentiment parameter. Thus, the values ​​of the influencing factors of the residents' resistance sentiment parameter corresponding to each environmental change factor in the proposal stage are determined.

[0032] Environmental change factors influencing the site selection phase include the number of elderly people, the number of elderly people living alone, population density, green space and public space, availability of amenities, community organization level, history of NIMBY (Not In My Backyard) conflicts, number of residents in the same building, number of adjacent buildings, shared vertical transportation / entrances / exits, encroachment on existing public space, distance from residential buildings, parking space occupancy, visual visibility, and street environment damage. Among these, the number of elderly people, the number of elderly people living alone, green space and public space, availability of amenities, and distance from residential buildings are negatively correlated with the residents' resistance parameters, while population density, community organization level, history of NIMBY conflicts, number of residents in the same building, number of adjacent buildings, shared vertical transportation / entrances / exits, encroachment on existing public space, parking space occupancy, visual visibility, and street environment damage are positively correlated with the residents' resistance parameters. Based on this, the values ​​of the influencing factors of residents' resistance parameters corresponding to each environmental change factor in the site selection phase are determined.

[0033] Environmental changes during the implementation phase include distance from facilities, traffic obstruction, noise pollution from renovations, visual visibility, property depreciation, resistance to traditional values, occupation of public space, provision of shared space, and improvement of the community environment. Among these, the provision of shared space and improvement of the community environment are negatively correlated with residents' resistance parameters, while distance from facilities, traffic obstruction, noise pollution from renovations, visual visibility, property depreciation, resistance to traditional values, and occupation of public space are positively correlated with residents' resistance parameters. Therefore, the values ​​of the influencing factors of residents' resistance parameters corresponding to each environmental change factor during the implementation phase are determined.

[0034] Environmental change factors during the operation phase include the degree and range of noise impact, the degree and range of traffic interference, medical waste pollution, public space occupation, residential area security, visual visibility, independent traffic / entrance / exit, visual barriers, sound insulation devices, and pollution treatment systems. Among these, residential area security, independent traffic / entrance / exit, visual barriers, sound insulation devices, and pollution treatment systems are negatively correlated with residents' resistance parameters, while the degree and range of noise impact, the degree and range of traffic interference, medical waste pollution, public space occupation, and visual visibility are positively correlated with residents' resistance parameters. Based on this, the values ​​of the influencing factors of residents' resistance parameters corresponding to each environmental change factor during the operation phase are determined.

[0035] Specifically, in step (3), the influencing factors of the behavior of each subject among the government, operators and residents are numbered according to the subject to obtain the influencing factors of the residents' resistance emotion parameters corresponding to the behavior factors of each subject;

[0036] The values ​​of the influencing factors of the residents' resistance parameters corresponding to the subject's behavioral factors are determined based on the pre-obtained assessment table of the same subject's behavioral factors.

[0037] The weighting coefficients for the government, operators, and residents are defined as ω respectively. g ω o ω r ω g +ω o +ω r =1; Define the influencing factors of the resident resistance parameter corresponding to the behavioral factors of each subject among the government, operators, and residents as c. g,u c o,v c r,w u, v, and w represent the numbers of the influencing factors of the residents' resistance parameters corresponding to the main behavioral factors among the government, operators, and residents, respectively.

[0038] The subject-stage dual-weighted model is then used to calculate the weighted average of all influencing factors, yielding the resident resistance parameter x corresponding to the subject's behavioral factors in stage t. c,t The details are as follows:

[0039] ;

[0040] Where, ω c,g,u ω c,o,v ω c,r,w These are the weight coefficients corresponding to the influencing factors of the resident resistance emotion parameter, which are the behavioral factors of the government, operators, and residents respectively. U, V, and W represent the number of influencing factors of the resident resistance emotion parameter, which are the behavioral factors of the government, operators, and residents respectively.

[0041] More specifically, the behavioral influencing factors of government actions include public participation channels, information disclosure, democratic consultation models, fair and transparent decision-making procedures, responding to public needs, ensuring smooth public feedback channels, closed-loop decision-making, public opinion surveys, surveys on the aging population, establishment of compensation mechanisms, statistics on the elderly population in each community, establishment of facility operation systems, compliant operation under supervision, and clear allocation of rights and responsibilities. Among these, public participation channels, information disclosure, democratic consultation models, fair and transparent decision-making procedures, responding to public needs, ensuring smooth public feedback channels, public opinion surveys, surveys on the aging population, establishment of compensation mechanisms, statistics on the elderly population in each community, establishment of facility operation systems, compliant operation under supervision, and clear allocation of rights and responsibilities are negatively correlated with the resident resistance parameter, while closed-loop decision-making is positively correlated with the resident resistance parameter. This determines the values ​​of the influencing factors of the resident resistance parameter corresponding to the government action influencing factors.

[0042] The behavioral factors influencing the operator's actions include establishing communication and coordination channels with residents, information disclosure, technical explanation, promotion and explanation of elderly care services, evading responsibility / refusing to communicate, selecting sites suitable for the scale, reserving shared activity spaces, compensating residents whose interests are harmed, setting up sound barriers or green visual barriers, matching the supply and demand of services provided, providing public services to the community, organizing public activities, noise / garbage pollution, and traffic flow interference. Among these, establishing communication and coordination channels with residents, information disclosure, technical explanation, promotion and explanation of elderly care services, selecting sites suitable for the scale, reserving shared activity spaces, compensating residents whose interests are harmed, setting up sound barriers or green visual barriers, matching the supply and demand of services provided, providing public services to the community, and organizing public activities are negatively correlated with the resident resistance parameter. Evading responsibility / refusing to communicate, noise / garbage pollution, and traffic flow interference are positively correlated with the resident resistance parameter. Therefore, the values ​​of the influencing factors of the resident resistance parameter corresponding to the operator's behavioral factors are determined.

[0043] The behavioral influencing factors of residents include participation in project decision-making, participation in operation and management, participation in project supervision, participation in public welfare activities related to elderly care services, use of elderly care services, use of shared spaces in elderly care facilities, protests within homeowner groups, offline protest rallies, and online public opinion dissemination. Among these, participation in project decision-making, participation in operation and management, participation in project supervision, participation in public welfare activities related to elderly care services, use of elderly care services, and use of shared spaces in elderly care facilities are negatively correlated with the resident resistance emotion parameter, while protests within homeowner groups, offline protest rallies, and online public opinion dissemination are positively correlated with the resident resistance emotion parameter. Based on this, the values ​​of the influencing factors of resident resistance emotion parameters corresponding to the resident behavioral influencing factors are determined.

[0044] Specifically, step (4), in conjunction with steps (1) to (3), calculates the static risk index F of NIMBY conflict at each stage. t The details are as follows:

[0045] ;

[0046] Where q represents the resident emotional sensitivity parameter obtained in step (1), and x b,t This represents the resident resistance parameter corresponding to the environmental change factors in stage t obtained in step (2), x. c,t θ represents the resident resistance parameters corresponding to the subject's behavioral factors in stage t obtained in step (3), θ represents the interaction coupling coefficient between environmental change and subject behavior, and μ represents the risk prevention and control correction coefficient.

[0047] Therefore, the cumulative NIMBY conflict risk index F for the current stage and previous stages is calculated. total The current NIMBY (Not In My Backyard) conflict risk is as follows:

[0048] ;

[0049] Where n represents the current stage;

[0050] The specific steps for determining the risk level are as follows:

[0051] Set the risk perception threshold F low Individual opposition critical value F mid And the critical value of group resistance F high Based on this, the NIMBY (Not In My Backyard) conflict risk level at each stage is determined as follows:

[0052] 0≤F total <F low This is the stage where the risks are not yet perceived, and residents do not show any obvious sense of risk or resistance.

[0053] F low ≤F total <F mid This is the individual perception stage, characterized by residents developing initial resistance.

[0054] F mid ≤F total <F high This is the stage of individual opposition, characterized by residents exhibiting clear acts of opposition.

[0055] F total ≥F high This is the stage of group resistance, characterized by the formation of organized group resistance behavior.

[0056] This invention also provides a system for quantitatively assessing the risk of NIMBY (Not In My Backyard) conflicts in elderly care facilities, which applies the aforementioned method for quantitatively assessing such risks, comprising:

[0057] The data acquisition module is used to collect factors influencing emotional sensitivity at the individual, community, and regional levels; to collect factors influencing environmental changes at each stage of the entire life cycle of elderly care facilities; and to collect factors influencing the behavior of various stakeholders, including the government, operators, and residents.

[0058] The emotion sensitivity acquisition module is used to obtain residents' emotion sensitivity parameters by multi-dimensional weighted normalization based on the various emotion sensitivity influencing factors in the individual, community and regional dimensions collected by the data acquisition module.

[0059] The environmental factor quantification module is used to calculate the residents' resistance parameters corresponding to the environmental change factors in each stage of the entire life cycle of the elderly care facility, based on the environmental change influencing factors obtained by the data acquisition module.

[0060] The subject behavior quantification module is used to obtain the resident resistance emotion parameters corresponding to the subject behavior factors in each stage by using subject-stage dual weighted calculation based on the subject-stage influencing factors of the behavior of each subject among the government, operators and residents obtained by the data acquisition module.

[0061] The risk quantification module is used to calculate the static risk index of NIMBY conflict at each stage based on the residents' emotional sensitivity parameters, the environmental change factors at each stage, and the residents' resistance parameters corresponding to the subject's behavioral factors. The index is then added to the static risk index of NIMBY conflict at the previous stage to obtain the NIMBY conflict risk at the current stage.

[0062] The risk level determination module is used to determine the risk level of NIMBY conflict risk obtained by the risk quantification module by combining the set threshold value.

[0063] The present invention also provides a computer-readable medium having a computer program stored thereon, wherein when the computer program is executed by a processor, it implements the aforementioned method for quantitatively assessing the risk of NIMBY (Not In My Backyard) conflict in elderly care facilities.

[0064] Beneficial effects: This invention improves the universality and adaptability of assessment by accurately quantifying the heterogeneity of residents' emotions, restores the real conflict generation mechanism by coupling the dual dimensions of environment and multi-subject behavior, constructs a dynamic assessment system for the entire life cycle of elderly care facilities, and clearly tracks the risk evolution path by accumulating and dynamically updating the risk index in stages, identifying high-risk project nodes and key risk-causing events in advance, and providing a predictive basis for risk prevention and control throughout the entire process. Attached Figure Description

[0065] To more clearly illustrate the technical solutions in this invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely embodiments of this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0066] Figure 1 This is a flowchart of the method for quantitatively assessing the NIMBY (Not In My Backyard) conflict risk of elderly care facilities according to the present invention;

[0067] Figure 2 This is a schematic diagram of the pre-obtained assessment table of factors influencing emotional sensitivity in this invention;

[0068] Figure 3 This is a schematic diagram of the environmental change factor assessment table obtained in advance by the present invention;

[0069] Figure 4 This is a schematic diagram of the subject behavior factor assessment table obtained in advance by the present invention. Detailed Implementation

[0070] To make the objectives, technical solutions and advantages of the present invention clearer, the present application will be further described in detail below with reference to specific embodiments and accompanying drawings.

[0071] It should be noted that, unless otherwise defined, the technical or scientific terms used in the embodiments of the present invention should have the ordinary meaning as understood by one of ordinary skill in the art to which this invention pertains.

[0072] The present invention provides a quantitative assessment method for the risk of NIMBY (Not In My Backyard) conflicts related to elderly care facilities. Figure 1 As shown, the steps include:

[0073] (1) Based on the factors influencing emotional sensitivity in the dimensions of individuals, communities and regions, the parameters of residents' emotional sensitivity are obtained by multi-dimensional weighted normalization;

[0074] (2) Based on the environmental change influencing factors in each stage of the life cycle of elderly care facilities, the residents' resistance parameters corresponding to the environmental change factors in each stage are obtained by weighted calculation.

[0075] (3) Based on the influencing factors of the behavior of the government, operators and residents, the residents' resistance parameters corresponding to the behavioral factors of the main body at each stage are obtained by subject-stage double weighting calculation;

[0076] (4) Combine steps (1) to (3) to calculate the static risk index of NIMBY conflict at each stage. Add the static risk index of NIMBY conflict at the current stage to the previous stages to obtain the NIMBY conflict risk at the current stage, and determine the risk level accordingly.

[0077] In this invention, in step (1), the factors influencing emotional sensitivity in the individual, community, and regional dimensions can be numbered according to the dimensions to obtain the emotional sensitivity influencing factors in each dimension. Specifically, the values ​​of the emotional sensitivity influencing factors can be determined based on a pre-obtained evaluation table of the same emotional sensitivity influencing factors, as shown in the evaluation table for emotional sensitivity influencing factors. Figure 2 As shown, the specific method for determining the values ​​of the emotional sensitivity influencing factors is as follows:

[0078] S11. Assignment Basis: Based on the pre-obtained assessment table of factors influencing emotional sensitivity, a standardized 5-point Likert scale (1-5 points) was used to obtain the public values ​​of each factor influencing emotional sensitivity in the individual, community, and regional dimensions; where:

[0079] Positively correlated influencing factors are given low scores, such as 1-3 points;

[0080] Negatively correlated influencing factors are given higher scores, such as 3-5 points;

[0081] S12. Obtain the influencing factors of emotional sensitivity in the individual, community and regional dimensions through field research, and repeat S11 to obtain the research values ​​of the influencing factors of emotional sensitivity in the individual, community and regional dimensions.

[0082] S13. Set up authoritative experts and assign expert values ​​to the emotional sensitivity influencing factors in the individual, community and regional dimensions in combination with S11 and S12.

[0083] S14. The values ​​obtained from the publicized values, survey values, and expert assignments in S11-S13 are weighted and calculated to obtain the final value of the emotion sensitivity influence factor. The factor weights in the weighting calculation can be determined by a combination of the analytic hierarchy process (AHP) and the entropy weight method to balance expert experience and data objectivity.

[0084] In this invention, before performing weighted calculations on the public values, survey values, and expert values, they can be normalized within the 0-1 range to eliminate dimensional differences.

[0085] Specifically, please refer to Figure 1 , 2 :

[0086] The factors influencing emotional sensitivity in the individual dimension include education level, economic level, family pressure for elder care, environmental justice demands, and media communication capabilities. Among them, education level, economic level, and family pressure for elder care are negatively correlated with residents' emotional sensitivity parameters, while environmental justice demands and media communication capabilities are positively correlated with residents' emotional sensitivity parameters. Thus, the values ​​of each emotional sensitivity influencing factor in the individual dimension can be determined.

[0087] In this invention, data on education level can be obtained from the National Bureau of Statistics' population census data or local civil affairs department's household registration statistics; data on economic level can be obtained from publicly available information in community resident ledgers; data on family elderly care pressure can be obtained from civil affairs elderly care service statistics, community door-to-door survey questionnaires, or health commission's aging data; and data on environmental justice demands and media dissemination capabilities can be obtained from resident questionnaire surveys, community interviews, or online public opinion monitoring data.

[0088] The factors influencing emotional sensitivity in the community dimension include elderly population density, community organization level, history of NIMBY (Not In My Backyard) conflict resistance, and feelings towards the place of residence. Among them, elderly population density is negatively correlated with residents' emotional sensitivity parameters, while community organization level, NIMBY conflict resistance history, and feelings towards the place of residence are positively correlated with residents' emotional sensitivity parameters. Thus, the values ​​of each emotional sensitivity influencing factor in the community dimension can be determined.

[0089] In this invention, the data on the density of the elderly population can be obtained from local statistical yearbooks, public announcements of the elderly population by the civil affairs department, or community population statistics reports; the data on the level of community organization can be obtained from neighborhood committee filing materials or community social organization registration information; the data on the history of NIMBY (Not In My Backyard) conflicts can be obtained from government petition records, local news and public opinion, or community historical dispute archives; and the data on feelings towards the place of residence can be obtained from resident questionnaires or community interviews and surveys.

[0090] The factors influencing emotional sensitivity in the regional dimension include the acceptance of traditional concepts and the atmosphere of respecting and caring for the elderly. Among them, the atmosphere of respecting and caring for the elderly is negatively correlated with the residents' emotional sensitivity parameter, while the acceptance of traditional concepts is positively correlated with the residents' emotional sensitivity parameter. Thus, the values ​​of each emotional sensitivity influencing factor in the regional dimension can be determined.

[0091] In this invention, data on the acceptance of traditional concepts and the atmosphere of respecting and caring for the elderly can be obtained through regional folk customs statistics, local cultural survey data, or resident questionnaire scores.

[0092] In this invention, the emotional sensitivity influencing factor in the individual dimension ind can be defined as a. ind,i The emotional sensitivity influencing factor in the community dimension of .com is a. com, j The emotional sensitivity influencing factor in the regional dimension reg is a. reg,k Where i represents the index of the emotional sensitivity influencing factor in the individual dimension ind, j represents the index of the emotional sensitivity influencing factor in the community dimension com, and k represents the index of the emotional sensitivity influencing factor in the geographical dimension reg, the calculation of the resident emotional sensitivity parameter q obtained through multi-dimensional weighted normalization is as follows:

[0093] ;

[0094] Where, ω ind,i ω represents the weight coefficient corresponding to the i-th emotion sensitivity influencing factor in the individual dimension ind. com, j ω represents the weight coefficient corresponding to the j-th emotion sensitivity influencing factor in the community dimension com. reg,k ω represents the weight coefficient corresponding to the k-th emotion sensitivity influencing factor in the region dimension reg; I, J, and K represent the number of emotion sensitivity influencing factors in the individual dimension ind, the community dimension com, and the region dimension reg, respectively; ind ω com ω reg Let ω represent the dimension weight coefficients corresponding to the individual dimension (ind), the community dimension (com), and the geographic dimension (reg), respectively, and ω ind +ω com +ω reg =1.

[0095] The range of the previously obtained resident emotional sensitivity parameter q is [0,1].

[0096] This invention uses multi-dimensional weighted normalization to determine residents' emotional sensitivity parameters, coupling the three-dimensional emotional heterogeneity characteristics of individuals, communities, and regions. It unifies the assessment benchmarks of different communities, eliminates the differences in the dimensions of multi-dimensional factors, and solves the problems of poor model adaptability, insufficient accuracy, and inability to be applied across communities caused by existing technologies that do not consider the differences in residents' emotions. It can be applied to the risk assessment of elderly care facilities in different regions and different types of communities.

[0097] In this invention, in step (2), the entire life cycle of elderly care facilities includes four stages: proposal, site selection, implementation, and operation.

[0098] Specifically, environmental change influencing factors at each stage of the entire life cycle of elderly care facilities can be numbered according to the stage, yielding the influencing factors of residents' resistance parameters corresponding to the environmental change factors at each stage. Specifically, the values ​​of the influencing factors of residents' resistance parameters corresponding to environmental change factors can be determined based on a pre-obtained environmental change factor assessment table, such as... Figure 3 As shown, the method for determining the values ​​of the influencing factors of residents' resistance parameters corresponding to specific environmental change factors is as follows:

[0099] S21. Assignment Basis: Based on the pre-obtained assessment table of environmental change influencing factors, a standardized 5-point Likert scale (1-5 points) is used to obtain the public values ​​of the influencing factors of residents' resistance to emotional distress corresponding to each environmental change influencing factor at each stage of the entire life cycle of the elderly care facility; where:

[0100] Positively correlated influencing factors are given low scores, such as 1-3 points;

[0101] Negatively correlated influencing factors are given higher scores, such as 3-5 points;

[0102] S22. Obtain the influencing factors of environmental changes in each stage of the entire life cycle of elderly care facilities through field research. Based on this, repeat S21 to obtain the survey values ​​of the influencing factors of residents' resistance to emotions corresponding to each environmental change in each stage of the entire life cycle of elderly care facilities.

[0103] S23. Set up authoritative experts to assign expert values ​​to the resident resistance parameters corresponding to the environmental change factors in each stage of the entire life cycle of elderly care facilities, in conjunction with S21 and S22.

[0104] S24. The values ​​obtained from the publicized values, survey values, and expert assignments in S21-S23 are weighted and calculated to obtain the values ​​of the influencing factors of residents' resistance to environmental changes. The factor weights in the weighted calculation can be determined by a combination of the analytic hierarchy process (AHP) and the entropy weight method to balance expert experience and data objectivity.

[0105] In this invention, before performing weighted calculations on the public values, survey values, and expert values, they can be normalized within the 0-1 range to eliminate dimensional differences.

[0106] Specifically, please refer to Figure 1 , 3 :

[0107] The environmental change influencing factors at the proposal stage include the future number of elderly people, the future living situation of elderly people living alone, the suitability of consumption capacity with facility types, environmental carrying capacity, positive public infrastructure (such as convenient medical points, standardized bus stops, convenient access facilities, libraries, etc.), negative municipal facilities (such as garbage transfer stations, sewage treatment plants, substations, open garbage collection points, etc.), parks and green spaces or activity spaces, and environmental aesthetics. Among these, the future number of elderly people, the future living situation of elderly people living alone, the suitability of consumption capacity with facility types, environmental carrying capacity, positive public infrastructure, parks and green spaces or activity spaces, and environmental aesthetics are negatively correlated with the residents' resistance sentiment parameter, while negative municipal facilities are positively correlated with the residents' resistance sentiment parameter. This allows us to determine the values ​​of the influencing factors of the residents' resistance sentiment parameter corresponding to each environmental change factor at the proposal stage. In this invention, the future number of elderly people, the future living situation of elderly people living alone, and the suitability of consumption capacity with facility types all belong to the resident impact category, while environmental carrying capacity, positive public infrastructure, negative municipal facilities, parks and green spaces or activity spaces, and environmental aesthetics all belong to the physical environment impact category.

[0108] In this invention, data on the future number of elderly people can be obtained from the National Bureau of Statistics, publicly available data from the Ministry of Civil Affairs / National Health Commission, or GIS population maps. Data on the future living conditions of elderly people living alone, their consumption capacity, and the suitability of facility types can be obtained from the National Bureau of Statistics, publicly available data from the Ministry of Civil Affairs / National Health Commission, GIS population maps, or door-to-door surveys. Data on environmental carrying capacity can be obtained from environmental impact assessment reports, public announcements from ecological and environmental departments, or on-site monitoring data. Data on positive public infrastructure, parks, green spaces, or activity spaces can be obtained from planning documents from the Natural Resources Bureau or public information from municipal authorities. Data on negative municipal facilities can be obtained from facility design plans, operational environmental impact assessments, or operation and maintenance public announcements. Data on environmental aesthetics can be obtained from interviews, surveys, or on-site inspections.

[0109] Environmental factors influencing site selection include the number of elderly residents, the number of elderly people living alone, population density, green space and public space, availability of amenities, community organization level, history of NIMBY (Not In My Backyard) conflicts, number of residents in the same building, number of adjacent buildings, shared vertical transportation / entrances, encroachment on existing public space, distance from residential buildings, parking space occupancy, visual visibility, and street environment degradation. Among these, the number of elderly residents, the number of elderly people living alone, green space and public space, availability of amenities, and distance from residential buildings are negatively correlated with residents' resistance. Population density, community organization level, history of NIMBY conflicts, number of residents in the same building, number of adjacent buildings, shared vertical transportation / entrances, encroachment on existing public space, parking space occupancy, and visual visibility are also negatively correlated with these factors. Visibility and street environment damage are positively correlated with residents' resistance parameters, which can determine the values ​​of the influencing factors of residents' resistance parameters corresponding to various environmental change factors during the site selection stage. In this invention, the number of elderly people, the situation of elderly people living alone, and population density all belong to the population information influence category; the availability of green space, public space, and convenience facilities all belong to the physical environment influence category; the level of community organization and the history of NIMBY conflicts all belong to the resident behavior influence category; the number of people in the same residential building, the number of adjacent residential buildings, and shared vertical transportation / exit all belong to the residential replacement influence category; the encroachment on the original public space and the distance of facilities from the residential building all belong to the public service support influence category; and the occupation of parking spaces, visual visibility, and street environment damage all belong to the commercial ground floor influence category.

[0110] In this invention, data on the number of elderly people and population density can be obtained from statistical bulletins, publicly available data from the Ministry of Civil Affairs / Health Commission, or GIS population maps. Data on the living conditions of elderly people living alone can be obtained from statistical bulletins, publicly available data from the Ministry of Civil Affairs / Health Commission, GIS population maps, or door-to-door surveys. Data on green spaces and public spaces can be obtained from planning documents from the Natural Resources Bureau, publicly available municipal information, or on-site surveys. Data on the availability of convenient facilities can be obtained from publicly available drawings from the Planning Bureau, map software, GIS surveying, or on-site survey data. Data on the level of community organization can be obtained from neighborhood committee filing materials or community social organization registration information. Data on the history of NIMBY (Not In My Backyard) conflicts can be obtained from government petition records, local news and public opinion, or community historical dispute archives. Data on the number of people in the same residential building can be obtained from door-to-door surveys. Data on the number of adjacent residential buildings, the distance of facilities from residential buildings, parking space occupancy, and visual visibility can be obtained from on-site surveys. Data on shared vertical transportation / entrances and exits can be obtained from on-site surveys or community design plans. Data on encroachment on existing public spaces and damage to the street environment can be obtained from on-site surveys and interviews. Environmental changes during the implementation phase include distance from facilities, traffic obstruction, noise pollution from renovations, visual visibility, property depreciation, resistance to traditional values, occupation of public space, provision of shared space, and improvement of the community environment. Among these, the provision of shared space and improvement of the community environment are negatively correlated with residents' resistance parameters, while distance from facilities, traffic obstruction, noise pollution from renovations, visual visibility, property depreciation, resistance to traditional values, and occupation of public space are positively correlated with residents' resistance parameters. This allows us to determine the values ​​of the influencing factors for residents' resistance parameters corresponding to each environmental change factor during the implementation phase. In this invention, distance from facilities, traffic obstruction, noise pollution from renovations, and visual visibility all belong to the category of individual impacts, while property depreciation, resistance to traditional values, occupation of public space, provision of shared space, and improvement of the community environment all belong to the category of community impacts.

[0111] In this invention, data on distance from facilities, traffic obstruction, noise pollution from renovations, visual visibility, and public space occupancy can be obtained through on-site surveys; data on housing price depreciation can be obtained through transaction data from housing and construction departments or information published on formal real estate platforms; data on resistance to traditional concepts can be obtained through interviews and surveys; data on shared space provision can be obtained through facility design plans; and data on community environment improvement can be obtained through on-site testing data or interviews and surveys.

[0112] Environmental change factors during the operation phase include the degree and range of noise impact, the degree and range of traffic interference, medical waste pollution, public space occupation, residential area security, visual visibility, independent traffic / entrances / exits, visual barriers (such as greenery), sound insulation devices, and pollution treatment systems. Among these, residential area security, independent traffic / entrances / exits, visual barriers, sound insulation devices, and pollution treatment systems are negatively correlated with residents' resistance parameters, while the degree and range of noise impact, the degree and range of traffic interference, medical waste pollution, public space occupation, and visual visibility are positively correlated with residents' resistance parameters. Therefore, the values ​​of the influencing factors for residents' resistance parameters corresponding to each environmental change factor during the operation phase can be determined. In this invention, the degree and range of noise impact, the degree and range of traffic interference, medical waste pollution, public space occupation, residential area security, and visual visibility all belong to the category of facility interference impact, while independent traffic / entrances / exits, visual barriers, sound insulation devices, and pollution treatment systems all belong to the category of improvement measures impact.

[0113] In this invention, the data sources for the degree and scope of noise impact and medical waste pollution can be obtained from environmental impact assessment reports, public announcements by the ecological and environmental departments, or on-site monitoring data; the data sources for the degree and scope of traffic interference can be obtained from on-site monitoring data; the data sources for public space occupation and visual visibility can be obtained from on-site exploration; the data sources for residential area security can be obtained from interviews and surveys; and the data sources for independent traffic / entrances and exits, visual barriers, sound insulation devices, and pollution treatment systems can be obtained from facility design plans, operational environmental impact assessments, or operation and maintenance public announcement materials.

[0114] In this invention, the weight coefficient for each stage can be defined as λ. t Where t=1, 2, 3, 4, corresponding to the proposal, site selection, implementation, and operation stages respectively; the influence factor of the resident resistance parameter corresponding to the p-th environmental change in stage t is defined as b. t,p Since the influencing factors of environmental change factors differ at each stage, the resident resistance sentiment parameter x corresponding to the environmental change factors at stage t is obtained through weighted calculation. b,t The details are as follows:

[0115] ;

[0116] Where φ represents the number of environmental change influencing factors in the corresponding stage, ω b,t,p This represents the weighting coefficient of the influencing factor corresponding to the resident resistance sentiment parameter of the p-th environmental change in stage t.

[0117] In this invention, in step (3), the influencing factors of the behaviors of the government, operators, and residents can be numbered according to the subject to obtain the influencing factors of the residents' resistance parameters corresponding to each subject's behavioral factors. Specifically, the values ​​of the influencing factors of the residents' resistance parameters corresponding to the subject's behavioral factors can be determined based on a pre-obtained assessment table of the same subject's behavioral factors, as shown in the subject's behavioral factor assessment table. Figure 4 As shown, the method for determining the values ​​of the influencing factors of the residents' resistance parameters corresponding to specific subject behavioral factors is as follows:

[0118] S31. Assignment Basis: Based on the pre-obtained assessment table of factors influencing the subject's behavior, a standardized 5-point Likert scale (1-5 points) is used to obtain the probabilistic values ​​of the influencing factors of the residents' resistance emotion parameters corresponding to the subject's behavior factors; where:

[0119] Positively correlated influencing factors are given low scores, such as 1-3 points;

[0120] Negatively correlated influencing factors are given higher scores, such as 3-5 points;

[0121] S32. Obtain the behavioral factors of each subject through field research, and repeat S31 to obtain the research values ​​of the influencing factors of the residents' resistance emotion parameters corresponding to the subject's behavioral factors.

[0122] S33. Set up authoritative experts and assign expert values ​​to the influence factors of the residents' resistance emotion parameters corresponding to the main behavioral factors, in conjunction with S31 and S32.

[0123] S34. The values ​​obtained from the publicized values, survey values, and expert assignments in S31-S33 are weighted and calculated to obtain the values ​​of the influence factors of the residents' resistance emotion parameters corresponding to the main behavioral factors. Among them, the factor weights in the weighted calculation can be determined by a combination of the analytic hierarchy process (AHP) and the entropy weight method to take into account both expert experience and data objectivity.

[0124] In this invention, before performing weighted calculations on the public values, survey values, and expert values, they can be normalized within the 0-1 range to eliminate dimensional differences.

[0125] Specifically, please refer to Figure 1 , 4 :

[0126] Factors influencing government actions include public participation channels, information disclosure, democratic consultation models, fair and transparent decision-making procedures, responsiveness to public needs, unimpeded public feedback channels, closed-loop decision-making, public opinion surveys, surveys on aging populations, establishment of compensation mechanisms, statistics on the elderly population in each community, establishment of facility operation systems, compliant supervision and operation, and clear allocation of rights and responsibilities. Among these, public participation channels, information disclosure, democratic consultation models, fair and transparent decision-making procedures, responsiveness to public needs, unimpeded public feedback channels, public opinion surveys, surveys on aging populations, establishment of compensation mechanisms, statistics on the elderly population in each community, establishment of facility operation systems, compliant supervision and operation, and clear allocation of rights and responsibilities are negatively correlated with the parameter of resident resistance. Closed-loop decision-making is positively correlated with the parameter of residents' resistance, which can be used to determine the value of the influencing factor of the parameter of residents' resistance corresponding to the influencing factors of government behavior. In this invention, public participation channels, information disclosure, democratic consultation model, fair and transparent decision-making procedures, response to public needs, smooth public feedback channels and closed-loop decision-making are common behavioral influencing factors. Public opinion surveys and surveys on the aging status are behavioral influencing factors in the proposal stage. The establishment of compensation mechanisms and the statistics of the elderly population in each community are behavioral influencing factors in the site selection stage. The establishment of facility operation system is a behavioral influencing factor in the implementation stage. Supervision of compliant operation and clear allocation of rights and responsibilities are behavioral influencing factors in the operation stage.

[0127] The behavioral factors influencing the operator's actions include establishing communication and coordination channels with residents, information disclosure, providing technical explanations to reduce resident concerns, promoting and explaining elderly care services, avoiding responsibility / refusing to communicate, selecting appropriately sized venues, reserving shared activity spaces, compensating residents whose interests are harmed, installing sound barriers or green visual barriers, matching the supply and demand of services provided, providing public services to the community, organizing public activities, noise / garbage pollution, and traffic flow interference. Among these, establishing communication and coordination channels with residents, information disclosure, providing technical explanations to reduce resident concerns, promoting and explaining elderly care services, selecting appropriately sized venues, reserving shared activity spaces, compensating residents whose interests are harmed, installing sound barriers or green visual barriers, matching the supply and demand of services provided, providing public services to the community, and organizing public activities are negatively correlated with resident resistance parameters. Correspondingly, evasion of responsibility / refusal to communicate, noise / garbage pollution, and traffic flow interference are positively correlated with the resident resistance parameter. Therefore, the values ​​of the influencing factors of the resident resistance parameter corresponding to the operator's behavioral factors can be determined. In this invention, establishing resident communication and coordination channels, information disclosure, technical explanations to reduce resident concerns, promotion and explanation of elderly care services, and evasion of responsibility / refusal to communicate are common behavioral influencing factors. Selecting a site of suitable size and reserving shared activity space are behavioral influencing factors in the site selection stage. Compensating residents whose interests are harmed and setting up sound barriers or green visual barriers are behavioral influencing factors in the implementation stage. Matching the supply and demand of services provided, providing public services to the community, organizing public activities, noise / garbage pollution, and traffic flow interference are behavioral influencing factors in the operation stage.

[0128] The behavioral factors influencing residents' behavior include participation in project decision-making, participation in operation and management, participation in project supervision, participation in public welfare activities related to elderly care services, use of elderly care services, use of shared spaces in elderly care facilities, protests within homeowner groups, offline protest rallies, and online public opinion dissemination. Among these, participation in project decision-making, participation in operation and management, participation in project supervision, participation in public welfare activities related to elderly care services, use of elderly care services, and use of shared spaces in elderly care facilities are negatively correlated with the resident resistance emotion parameter, while protests within homeowner groups, offline protest rallies, and online public opinion dissemination are positively correlated with the resident resistance emotion parameter. Therefore, the values ​​of the influencing factors of the resident resistance emotion parameter corresponding to the resident behavioral factors can be determined.

[0129] In this invention, the weighting coefficients for the government, operators, and residents can be defined as ω. g ω o ω r ω g +ω o +ω r =1; Define the influencing factors of the behaviors of each entity among the government, operators, and residents as c. g,u c o,v c r,wLet u, v, and w represent the influencing factors of the main behaviors of the government, operators, and residents, respectively. A subject-stage dual-weighted model is then used to weight all influencing factors to obtain the resident resistance parameter x corresponding to the main behavioral factors in stage t. c,t The details are as follows:

[0130] ;

[0131] Where, ω c,g,u ω c,o,v ω c,r,w These are the weighting coefficients corresponding to the behavioral influencing factors of each entity among the government, operators, and residents, respectively. U, V, and W represent the number of behavioral influencing factors among the government, operators, and residents, respectively.

[0132] In this invention, for any stage of the entire life cycle of elderly care facilities, the corresponding weight coefficients can be determined based on the corresponding subject behavior factors in the pre-obtained subject behavior factor assessment table. The general subject behavior factors have preset weight coefficients. In specific stages, this invention can also preset corresponding weight coefficients, or obtain measured data of the influencing factors of the corresponding subject behavior factors through surveys to participate in the weighted quantification calculation. That is, the weight coefficients of the influencing factors of the subject behavior factors for the corresponding stage are obtained through surveys. If there is no valid survey data, the influencing factors for the corresponding stage are discarded and not included in the calculation process. In this invention, in step (4), combined with steps (1) to (3), the static risk index F of NIMBY conflict for each stage is calculated. t The details are as follows:

[0133] ;

[0134] Where θ represents the interaction coupling coefficient between environmental change and subject behavior, and μ represents the risk prevention and control correction coefficient;

[0135] Therefore, the cumulative NIMBY conflict risk index F for the current stage and previous stages is calculated. total The current NIMBY (Not In My Backyard) conflict risk is as follows:

[0136] ;

[0137] Where n represents the current stage, specifically, the proposal stage n=1, the site selection stage n=2, the implementation stage n=3, and the operation stage n=4, that is, the current F total It equals the sum of the static risk indices of NIMBY (Not In My Backyard) conflicts in the current stage and all previous stages.

[0138] This invention incorporates environmental change factors and the behavioral factors of three main stakeholders—government, operators, and residents—into a unified assessment framework. It introduces an interaction coupling coefficient between environmental change and stakeholder behavior to achieve dual-weighted quantification of the stakeholder and the stage. This addresses the shortcomings of existing technologies, such as a single assessment dimension, failure to consider the game between multiple stakeholders, and inability to identify risk transmission paths. It can accurately locate core risk-causing factors and achieve risk tracing. At the same time, this invention introduces a risk prevention and control correction coefficient, which is compatible with the risk reduction effect of pre-emptive prevention and control measures. The risk index value range is unified and the judgment criteria are clear, solving the problem of existing technologies being disconnected from theory and practice and unable to be directly applied to engineering practice. The assessment results can directly support the scientific decision-making of community elderly care facility planning, site selection, and operation.

[0139] In this invention, an individual perception threshold F can be set. low Individual opposition critical value F mid And the critical value of group resistance F high Therefore, the risk level of NIMBY conflict at each stage can be determined as follows:

[0140] 0≤F total <F low This is a stage where the risk of NIMBY conflict is extremely low, as residents have not developed a strong sense of risk or resistance.

[0141] F low ≤F total <F mid This is the individual perception stage, where the risk of NIMBY (Not In My Backyard) conflict is low, manifested in residents' initial resistance.

[0142] F mid ≤F total <F high This is the stage of individual opposition, where the risk of NIMBY (Not In My Backyard) conflict is high, manifested in residents exhibiting clear opposition behavior.

[0143] F total ≥F high This is the stage of collective resistance, where the risk of NIMBY (Not In My Backyard) conflict is extremely high, manifesting as the formation of organized collective resistance behavior.

[0144] This invention divides risks into four stages—unperceived, perceived by individuals, opposed by individuals, and resisted by groups—by combining a dynamic cumulative risk index with preset three-level risk thresholds. This solves the problem that existing technologies cannot classify risk levels or predict the timing and intensity of conflict outbreaks. It can intuitively identify key nodes in the outbreak of conflict and provide quantitative basis for proactive risk prevention and control.

[0145] This invention also provides a system for quantitatively assessing the risk of NIMBY (Not In My Backyard) conflicts in elderly care facilities, which applies the aforementioned method for quantitatively assessing such risks, comprising:

[0146] The data acquisition module is used to collect factors influencing emotional sensitivity at the individual, community, and regional levels; to collect factors influencing environmental changes at each stage of the entire life cycle of elderly care facilities; and to collect factors influencing the behavior of various stakeholders, including the government, operators, and residents.

[0147] The emotion sensitivity acquisition module is used to obtain residents' emotion sensitivity parameters by multi-dimensional weighted normalization based on the various emotion sensitivity influencing factors in the individual, community and regional dimensions collected by the data acquisition module.

[0148] The environmental factor quantification module is used to calculate the residents' resistance parameters corresponding to the environmental change factors in each stage of the entire life cycle of the elderly care facility, based on the environmental change influencing factors obtained by the data acquisition module.

[0149] The subject behavior quantification module is used to obtain the resident resistance emotion parameters corresponding to the subject behavior factors in each stage by using subject-stage dual weighted calculation based on the subject-stage influencing factors of the behavior of each subject among the government, operators and residents obtained by the data acquisition module.

[0150] The risk quantification module is used to calculate the static risk index of NIMBY conflict at each stage based on the residents' emotional sensitivity parameters, the environmental change factors at each stage, and the residents' resistance parameters corresponding to the subject's behavioral factors. The static risk index of NIMBY conflict at the current stage is accumulated with the static risk index of NIMBY conflict at the previous stages to obtain the NIMBY conflict risk at the current stage.

[0151] The risk level determination module is used to determine the risk level of NIMBY conflict risk obtained by the risk quantification module by combining the set threshold value.

[0152] The present invention also provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the aforementioned method for quantitatively assessing the risk of NIMBY (Not In My Backyard) conflict in elderly care facilities.

[0153] This invention, through a modular NIMBY (Not In My Backyard) risk quantification assessment system for elderly care facilities and a computer-readable medium, can automatically complete the entire process of data collection, parameter calculation, index determination, and result output through a program. It replaces the existing manual qualitative analysis method, automates the dynamic pre-assessment of the entire life cycle, reduces assessment complexity, and improves assessment efficiency. It can be directly applied to the entire process of risk early warning in the planning, construction, and operation of community elderly care facilities, and has strong engineering feasibility and large-scale promotion value.

[0154] This invention enhances the universality and adaptability of assessments by accurately quantifying the heterogeneity of residents' emotions. By coupling the environmental and multi-agent behavioral dimensions, it reconstructs the real conflict generation mechanism. By constructing a dynamic assessment system for the entire life cycle of elderly care facilities, and through phased cumulative quantification and dynamic updating of risk indices, it can clearly track the risk evolution path, identify high-risk project nodes and key risk-causing events in advance, and provide predictive basis for risk prevention and control throughout the entire process. The values ​​of each factor in this invention are derived from objective survey data of the target community and preset assessment forms, without subjective assignment. Risk is calculated cumulatively through a transmission amplification coefficient, breaking through the limitations of existing technologies that can only perform static analysis, focus on a single stage, or handle events after the fact. It can completely restore the dynamic evolution and accumulation patterns of risks, enabling early risk prediction in the early stages of facility construction. The assessment results have real-time value and decision-making guidance.

[0155] Those skilled in the art should understand that the discussion of any of the above embodiments is merely exemplary and is not intended to imply that the scope of the invention (including the claims) is limited to these examples; within the framework of the invention, the technical features of the above embodiments or different embodiments can also be combined, the steps can be implemented in any order, and there are many other variations of different aspects of the embodiments of the invention as described above, which are not provided in the details for the sake of brevity.

[0156] The embodiments of this invention are intended to cover all such substitutions, modifications, and variations falling within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the embodiments of this invention should be included within the protection scope of this invention.

Claims

1. A method for quantitatively assessing the risk of NIMBY (Not In My Backyard) conflicts related to elderly care facilities, characterized in that, Including the following steps: (1) Based on the factors influencing emotional sensitivity in the dimensions of individuals, communities and regions, the parameters of residents' emotional sensitivity are obtained by multi-dimensional weighted normalization; (2) Based on the environmental change influencing factors in each stage of the life cycle of elderly care facilities, the residents' resistance parameters corresponding to the environmental change factors in each stage are obtained by weighted calculation. (3) Based on the influencing factors of the behavior of the government, operators and residents, the residents' resistance parameters corresponding to the behavioral factors of the main body at each stage are obtained by subject-stage double weighting calculation; (4) Combine steps (1) to (3) to calculate the static risk index of NIMBY conflict at each stage. Add the static risk index of NIMBY conflict at the current stage to the previous stages to obtain the NIMBY conflict risk at the current stage, and determine the risk level accordingly.

2. The method for quantitatively assessing the NIMBY (Not In My Backyard) conflict risk of elderly care facilities according to claim 1, characterized in that, In step (1), the factors influencing emotional sensitivity in the individual, community and regional dimensions are numbered according to the dimensions to obtain the factors influencing emotional sensitivity in each dimension. The values ​​of the emotional sensitivity influencing factors are determined based on the same pre-obtained emotional sensitivity influencing factor assessment table; Define the emotional sensitivity influencing factor in the individual dimension ind as a ind,i The emotional sensitivity influencing factor in the community dimension of .com is a. com, j The emotional sensitivity influencing factor in the regional dimension reg is a. reg,k Where i represents the index of the emotional sensitivity influencing factor in the individual dimension ind, j represents the index of the emotional sensitivity influencing factor in the community dimension com, and k represents the index of the emotional sensitivity influencing factor in the geographical dimension reg, the calculation of the resident emotional sensitivity parameter q obtained through multi-dimensional weighted normalization is as follows: ； Where, ω ind,i ω represents the weight coefficient corresponding to the i-th emotion sensitivity influencing factor in the individual dimension ind. com, j ω represents the weight coefficient corresponding to the j-th emotion sensitivity influencing factor in the community dimension com. reg,k ω represents the weight coefficient corresponding to the k-th emotion sensitivity influencing factor in the region dimension reg; I, J, and K represent the number of emotion sensitivity influencing factors in the individual dimension ind, the community dimension com, and the region dimension reg, respectively; ind ω com ω reg Let ω represent the dimension weight coefficients corresponding to the individual dimension (ind), the community dimension (com), and the geographic dimension (reg), respectively, and ω ind +ω com +ω reg =1.

3. The method for quantitatively assessing the risk of NIMBY (Not In My Backyard) conflict in elderly care facilities according to claim 2, characterized in that, The factors influencing emotional sensitivity in the individual dimension include education level, economic level, family pressure for elder care, environmental justice demands, and media communication capabilities. Among them, education level, economic level, and family pressure for elder care are negatively correlated with residents' emotional sensitivity parameters, while environmental justice demands and media communication capabilities are positively correlated with residents' emotional sensitivity parameters. Thus, the values ​​of each emotional sensitivity influencing factor in the individual dimension are determined. The factors influencing emotional sensitivity in the community dimension include elderly population density, community organization level, history of NIMBY (Not In My Backyard) conflicts, and feelings towards the place of residence. Among them, elderly population density is negatively correlated with residents' emotional sensitivity parameters, while community organization level, NIMBY conflict history, and feelings towards the place of residence are positively correlated with residents' emotional sensitivity parameters. Thus, the values ​​of each emotional sensitivity influencing factor in the community dimension are determined. The factors influencing emotional sensitivity in the regional dimension include the acceptance of traditional concepts and the atmosphere of respecting and caring for the elderly; among them, the atmosphere of respecting and caring for the elderly is negatively correlated with the residents' emotional sensitivity parameter, while the acceptance of traditional concepts is positively correlated with the residents' emotional sensitivity parameter, thereby determining the values ​​of each emotional sensitivity influencing factor in the regional dimension.

4. The method for quantitatively assessing the NIMBY (Not In My Backyard) conflict risk of elderly care facilities according to claim 1, characterized in that, In step (2), the environmental change influencing factors in each stage of the entire life cycle of the elderly care facility are numbered according to the stage to obtain the influencing factors of the residents' resistance to the environmental change factors in each stage. The values ​​of the influencing factors of the residents' resistance parameters corresponding to the environmental change factors are determined based on the pre-obtained assessment table of the same environmental change factors. Define the weight coefficient for each stage as λ. t Let b be the influencing factor of the resident resistance parameter corresponding to the p-th environmental change in stage t. t,p Since the influencing factors of environmental change factors differ at each stage, the resident resistance sentiment parameter x corresponding to the environmental change factors at stage t is obtained through weighted calculation. b,t The details are as follows: ； Where φ represents the number of environmental change influencing factors in the corresponding stage, ω b,t,p This represents the weighting coefficient of the influencing factor corresponding to the resident resistance sentiment parameter of the p-th environmental change in stage t.

5. The method for quantitatively assessing the NIMBY (Not In My Backyard) conflict risk of elderly care facilities according to claim 4, characterized in that, The entire life cycle of the elderly care facility includes four stages: proposal, site selection, implementation, and operation. The environmental change influencing factors in the proposal stage include the future number of elderly people, the future living situation of elderly people living alone, the suitability of consumption capacity and facility types, environmental carrying capacity, positive public infrastructure, negative municipal facilities, parks and green spaces or activity spaces, and environmental aesthetics. Among these, the future number of elderly people, the future living situation of elderly people living alone, the suitability of consumption capacity and facility types, environmental carrying capacity, positive public infrastructure, parks and green spaces or activity spaces, and environmental aesthetics are negatively correlated with the residents' resistance sentiment parameter, while negative municipal facilities are positively correlated with the residents' resistance sentiment parameter. Thus, the values ​​of the influencing factors of the residents' resistance sentiment parameter corresponding to each environmental change factor in the proposal stage are determined. Environmental change factors influencing the site selection phase include the number of elderly people, the number of elderly people living alone, population density, green space and public space, availability of amenities, community organization level, history of NIMBY (Not In My Backyard) conflicts, number of residents in the same building, number of adjacent buildings, shared vertical transportation / entrances / exits, encroachment on existing public space, distance from residential buildings, parking space occupancy, visual visibility, and street environment damage. Among these, the number of elderly people, the number of elderly people living alone, green space and public space, availability of amenities, and distance from residential buildings are negatively correlated with the residents' resistance parameters, while population density, community organization level, history of NIMBY conflicts, number of residents in the same building, number of adjacent buildings, shared vertical transportation / entrances / exits, encroachment on existing public space, parking space occupancy, visual visibility, and street environment damage are positively correlated with the residents' resistance parameters. Based on this, the values ​​of the influencing factors of residents' resistance parameters corresponding to each environmental change factor in the site selection phase are determined. Environmental changes during the implementation phase include distance from facilities, traffic obstruction, noise pollution from renovations, visual visibility, property depreciation, resistance to traditional values, occupation of public space, provision of shared space, and improvement of the community environment. Among these, the provision of shared space and improvement of the community environment are negatively correlated with residents' resistance parameters, while distance from facilities, traffic obstruction, noise pollution from renovations, visual visibility, property depreciation, resistance to traditional values, and occupation of public space are positively correlated with residents' resistance parameters. Therefore, the values ​​of the influencing factors of residents' resistance parameters corresponding to each environmental change factor during the implementation phase are determined. Environmental change factors during the operation phase include the degree and range of noise impact, the degree and range of traffic interference, medical waste pollution, public space occupation, residential area security, visual visibility, independent traffic / entrance / exit, visual barriers, sound insulation devices, and pollution treatment systems. Among these, residential area security, independent traffic / entrance / exit, visual barriers, sound insulation devices, and pollution treatment systems are negatively correlated with residents' resistance parameters, while the degree and range of noise impact, the degree and range of traffic interference, medical waste pollution, public space occupation, and visual visibility are positively correlated with residents' resistance parameters. Based on this, the values ​​of the influencing factors of residents' resistance parameters corresponding to each environmental change factor during the operation phase are determined.

6. The method for quantitatively assessing the NIMBY (Not In My Backyard) conflict risk of elderly care facilities according to claim 1, characterized in that, In step (3), the influencing factors of the behavior of each subject among the government, operators and residents are numbered according to the subject, and the influencing factors of the residents' resistance emotion parameters corresponding to the behavior factors of each subject are obtained. The values ​​of the influencing factors of the residents' resistance parameters corresponding to the subject's behavioral factors are determined based on the pre-obtained assessment table of the same subject's behavioral factors. The weighting coefficients for the government, operators, and residents are defined as ω respectively. g ω o ω r ω g +ω o +ω r =1; Define the influencing factors of the resident resistance parameter corresponding to the behavioral factors of each subject among the government, operators, and residents as c. g,u c o,v c r,w u, v, and w represent the numbers of the influencing factors of the residents' resistance parameters corresponding to the main behavioral factors among the government, operators, and residents, respectively. The subject-stage dual-weighted model is then used to calculate the weighted average of all influencing factors, yielding the resident resistance parameter x corresponding to the subject's behavioral factors in stage t. c,t The details are as follows: ； Where, ω c,g,u ω c,o,v ω c,r,w These are the weight coefficients corresponding to the influencing factors of the resident resistance emotion parameter, which are the behavioral factors of the government, operators, and residents respectively. U, V, and W represent the number of influencing factors of the resident resistance emotion parameter, which are the behavioral factors of the government, operators, and residents respectively.

7. The method for quantitatively assessing the NIMBY (Not In My Backyard) conflict risk of elderly care facilities according to claim 6, characterized in that, The influencing factors of government actions include public participation channels, information disclosure, democratic consultation models, fair and transparent decision-making procedures, responding to public needs, ensuring smooth public feedback channels, closed-loop decision-making, public opinion surveys, surveys on aging populations, establishment of compensation mechanisms, statistics on the elderly population in each community, establishment of facility operation systems, compliant operation under supervision, and clear allocation of rights and responsibilities. Among these, public participation channels, information disclosure, democratic consultation models, fair and transparent decision-making procedures, responding to public needs, ensuring smooth public feedback channels, public opinion surveys, surveys on aging populations, establishment of compensation mechanisms, statistics on the elderly population in each community, establishment of facility operation systems, compliant operation under supervision, and clear allocation of rights and responsibilities are negatively correlated with the resident resistance parameter, while closed-loop decision-making is positively correlated with the resident resistance parameter. Therefore, the values ​​of the influencing factors of resident resistance parameters corresponding to the influencing factors of government actions are determined. The behavioral factors influencing the operator's actions include establishing communication and coordination channels with residents, information disclosure, technical explanation, promotion and explanation of elderly care services, evading responsibility / refusing to communicate, selecting sites suitable for the scale, reserving shared activity spaces, compensating residents whose interests are harmed, setting up sound barriers or green visual barriers, matching the supply and demand of services provided, providing public services to the community, organizing public activities, noise / garbage pollution, and traffic flow interference. Among these, establishing communication and coordination channels with residents, information disclosure, technical explanation, promotion and explanation of elderly care services, selecting sites suitable for the scale, reserving shared activity spaces, compensating residents whose interests are harmed, setting up sound barriers or green visual barriers, matching the supply and demand of services provided, providing public services to the community, and organizing public activities are negatively correlated with the resident resistance parameter. Evading responsibility / refusing to communicate, noise / garbage pollution, and traffic flow interference are positively correlated with the resident resistance parameter. Therefore, the values ​​of the influencing factors of the resident resistance parameter corresponding to the operator's behavioral factors are determined. The behavioral influencing factors of residents include participation in project decision-making, participation in operation and management, participation in project supervision, participation in public welfare activities related to elderly care services, use of elderly care services, use of shared spaces in elderly care facilities, protests within homeowner groups, offline protest rallies, and online public opinion dissemination. Among these, participation in project decision-making, participation in operation and management, participation in project supervision, participation in public welfare activities related to elderly care services, use of elderly care services, and use of shared spaces in elderly care facilities are negatively correlated with the resident resistance emotion parameter, while protests within homeowner groups, offline protest rallies, and online public opinion dissemination are positively correlated with the resident resistance emotion parameter. Based on this, the values ​​of the influencing factors of resident resistance emotion parameters corresponding to the resident behavioral influencing factors are determined.

8. The method for quantitatively assessing the NIMBY (Not In My Backyard) conflict risk of elderly care facilities according to claim 1, characterized in that, In step (4), combined with steps (1) to (3), the static risk index F of NIMBY conflict at each stage is calculated. t The details are as follows: ； Where q represents the resident emotional sensitivity parameter obtained in step (1), and x b,t This represents the resident resistance parameter corresponding to the environmental change factors in stage t obtained in step (2), x. c,t θ represents the resident resistance parameters corresponding to the subject's behavioral factors in stage t obtained in step (3), θ represents the interaction coupling coefficient between environmental change and subject behavior, and μ represents the risk prevention and control correction coefficient. Therefore, the cumulative NIMBY conflict risk index F for the current stage and previous stages is calculated. total The current NIMBY (Not In My Backyard) conflict risk is as follows: , where n represents the current stage; The specific steps for determining the risk level are as follows: Set the risk perception threshold F low Individual opposition critical value F mid And the critical value of group resistance F high Based on this, the NIMBY (Not In My Backyard) conflict risk level at each stage is determined as follows: 0≤F total <F low This is the stage where the risks are not yet perceived, and residents do not show any obvious sense of risk or resistance. F low ≤F total <F mid This is the individual perception stage, characterized by residents developing initial resistance. F mid ≤F total <F high This is the stage of individual opposition, characterized by residents exhibiting clear acts of opposition. F total ≥F high This is the stage of group resistance, characterized by the formation of organized group resistance behavior.

9. A system for quantitatively assessing the risk of NIMBY (Not In My Backyard) conflicts in elderly care facilities, using the method described in any one of claims 1-8, characterized in that, include: The data acquisition module is used to collect factors influencing emotional sensitivity at the individual, community, and regional levels; to collect factors influencing environmental changes at each stage of the entire life cycle of elderly care facilities; and to collect factors influencing the behavior of various stakeholders, including the government, operators, and residents. The emotion sensitivity acquisition module is used to obtain residents' emotion sensitivity parameters by multi-dimensional weighted normalization based on the various emotion sensitivity influencing factors in the individual, community and regional dimensions collected by the data acquisition module. The environmental factor quantification module is used to calculate the residents' resistance parameters corresponding to the environmental change factors in each stage of the entire life cycle of the elderly care facility, based on the environmental change influencing factors obtained by the data acquisition module. The subject behavior quantification module is used to obtain the resident resistance emotion parameters corresponding to the subject behavior factors in each stage by using subject-stage dual weighted calculation based on the subject-stage influencing factors of the behavior of each subject among the government, operators and residents obtained by the data acquisition module. The risk quantification module is used to calculate the static risk index of NIMBY conflict at each stage based on the residents' emotional sensitivity parameters, the environmental change factors at each stage, and the residents' resistance parameters corresponding to the subject's behavioral factors. The static risk index of NIMBY conflict at the current stage is accumulated with the static risk index of NIMBY conflict at the previous stages to obtain the NIMBY conflict risk at the current stage. The risk level determination module is used to determine the risk level of NIMBY conflict risk obtained by the risk quantification module by combining the set threshold value.

10. A computer-readable medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for quantitatively assessing the NIMBY (Not In My Backyard) conflict risk of elderly care facilities as described in any one of claims 1-8.

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