1. bookVolume 6 (2021): Issue 2 (July 2021)
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Study on the social impact Assessment of Primary Land Development: Empirical Analysis of Public Opinion Survey on New Town Development in Pinggu District of Beijing

Published Online: 03 Feb 2021
Page range: 31 - 42
Received: 13 Sep 2020
Accepted: 02 Dec 2020
Journal Details
License
Format
Journal
First Published
01 Jan 2016
Publication timeframe
2 times per year
Languages
English
Abstract

This paper studies a land primary development project in Pinggu District as an example for analysis since the complete survey data and adequate data analysis are not available on the assessment of social impact from primary development projects of land. In this paper, we carry out regression analysis based on statistical analysis of survey data, explore the attitudes of stakeholders towards development projects, and find out the main factors and risk problems. Finally, the required policy changes based on the analysis are recommended and put forward to provide reference for impact assessment of social stability.

Keywords

Introduction

Primary land development is a business of capital-intensity, characteristic of long periods of development and operation, high land acquisition and removal expenses, involving multilateral interest groups and many uncertain factors [1]. Hence, a long-term problem of social stability is inherent, risky problem in the primary land development process. For current construction projects, strengthening social impact assessment is the guarantee for the idea of sustainable and stable development along with harmony throughout the construction project [2]. According to Notice on Interim Measures for Social Stability Risk Assessment of Major Fixed Asset Investment Projects of the National Development and Reform Commission (Development and Reform Investment (2012) 2492) [3] laid down by the NDRC on September 2012, as well as requirements of Notice on Printing and Distributing the Social Stability Risk Analysis Chapters and Evaluation Report Outline of the Major Fixed Asset Investment Projects (for trial) (NDRC Office Investment (2013) No. 428) [4], social impact focuses on the participation and interaction from the public and the social. In 2015, China's premier's government work report necessitated the implementation of a risk assessment mechanism for social stability in major decisions [5]. In the same year, the opinion on strengthening the system of social security prevention and control was issued to emphasise the implementation of a social stability risk assessment system for major decisions [6]. Subsequently, in 2019, the Chinese President further emphasised the need to assess social stability risks carefully in all major decisions involving the interests of the masses [7].

From the perspective of the actual project, now decision-makers of projects are getting realised that engineering projects are directly related to the critical interests of the masses [8], and the state of risks of stakeholders also plays an important part on the social stability influence [9]. But in actual projects, the quality and effectiveness of social stability assessment are affected due to issues such as ambiguous regulations on public participation in social impact assessment and low public participation [10]. From an international perspective, social impact assessment has drawn so much attention in the early stages of project planning and construction in developed countries [11,12]. Just at the very time, Western scholars have analysed the benefits of social impact assessment in developing countries [13] and also have conducted social impact assessment research from the perspectives of conflict management [14], participation methods [15], project types [16] and simulation [17]. Nevertheless, as far as related projects of primary land development in China are concerned, the present social impact assessment is conducted directly using the most direct statistical analysis, that is, descriptive statistical analysis on some indicators such as housing conditions and removing attitudes; then identify potential risk factors of the project. The approach will become too simple and extensive for using the survey data, without establishing a correlation between the attributes and attitudes of relevant interest groups, and without fully exploring the value of survey data.

Risk factors in a primary development vary with different risk factors for each and every project. The common ones include obstacles to relocation by the elderly people, traffic congestion, and noise pollution and environmental pollution caused by construction works. This research is focussed on a primary land development project of some villages in Pinggu District, Beijing. At first, research data and direct opinions of relevant interest groups are obtained through field surveys and intuitive factors worthy of concerns are summarised. Then statistical analysis and regression analysis are employed to recognise the probable risks of such projects, and further analysed the factors that need to be focussed on. The project is comprised of all the key elements that may be involved in the primary development of the land and further, it also covers the demands of different kinds of residents. Therefore, it is an appropriate typical case to delve into the focal points of public opinion and underlying risks. This paper considers that the study methods and research contents can provide references for the social impact assessment of this kind and the policy recommendations are beneficial to policy decision-making.

Opinions of Stakeholders in Primary Land Development Projects

The primary land development project chosen in this study is located at the west side of Xincheng, Pinggu District, Beijing. The total land use for land bank in the early stage is 58.44 hectares, in which 32.60 hectares is for total land consolidation, and the rest 25.84 hectares for relocation and resettlement. The total construction area is 15.17 hectares, while the building area is 25 million square meters and the living construction area is 213,700 m2.

Aiming for a better understanding the opinions and demands of stakeholders on the project, and for a more accurate social impact assessment of the project implementation, the situation and attitudes of villagers are obtained through questionnaires. The research data in this paper are extracted from a random sample survey of residents in a village in Pinggu District. A sum of 284 non-collective dwellers were visited, and 217 valid questionnaires were altogether withdrawn.

The survey falls into three parts: the first part gives an introduction to the basic situation of the villagers, including gender, age and relationship with the householder, occupation, education level and sources of family income. The second includes the basic situation of the existing living conditions and living environment from the year of construction, the structure and the area of the house to the resident population, and whether they are satisfied with the existing living conditions or the existing living environment. The issues concerned with the implementation of this project comes the third. This section contains eight aspects such as house-removing concerns, requests for resettlement methods, opinions on current resettlement ways, compensations and other subsidies, opinions on present compensative standards, problems that may arise after relocation and rights protection methods that make waves. The statistic content and number of households in the third part are shown in Table 1.

Statistical table of stakeholders’ concerns

Issues concerned House removal Number of households Compensation ways Number of households Problems after relocation (optional) Number of households
Compensative standards and its reasonableness 100 Property right exchange plus monetary compensation 207 Pension problems 137
Implementation of compensation funds 48 Pure monetary compensation 10 Changes in living environment 86
Follow-up security issues of demolition 32 Other subsidies Number of households Employment issues 55
Openness and legality of removing information 37 Employment arrangement 88 Schooling problem of kids 43
Requests for Resettlement Methods Number of households Information of get-rich 47 Source of income 2
Resettlement nearby 140 Vocational skills training 43 No concerns 3
Relocation 49 Microloan discount 9 Rights protection ways that make waves (optional) Number of households
Obedient to arrangements 28 Without requests on this item 30 Report to government 127
Opinions on current resettlement ways Number of households Opinions on present compensative standard Number of households Self-negotiation 63
Very satisfied 9 Very satisfied 9 Legal solution 56
Satisfied 37 Satisfied 37 Petition way 16
Almost satisfied 47 Almost satisfied 52 Keep silent 24
Unsatisfied 35 Unsatisfied 28 By means of media or network 12
Unknown 89 Unknown 91 Protest by Uniting neighbouring residents 6

It was finally found that 54% of the stakeholders were for the primary land development project, accounting for the largest proportion; 34% of villagers also gave conditional support; only 4% were against it; apart from that, 8% of the families did not express their attitudes towards this project.

Through direct surveys and statistical analysis of villagers’ questionnaires, some important findings are needed to be highlighted:

First, the basic situation of the villagers is quite consistent. Most of them are middle-aged farmers or elderly people, whose main sources of income are from migrant working and pensions. Households are mostly concerned about pensions. For instance, some villagers support for the project, but it is suggested that the oldage care of the elderly should be resolved completely. Some villagers are opposed to the implementation of the project just because they believe that it is inconvenient for the elderly to live in high-rise buildings and hopes it will be properly resettled. Naturally, attention will be mostly paid to the issue of pensions in the relocation process.

Moreover, the villagers have been used to living here for a long time, and enjoy high satisfaction with the existing environment and living conditions. This may partly get in the way of dwellers’ support for demolition.

Lastly, villagers do not have access to multiple sources of information and rely much on information dissemination from village committees and street offices. As a result, the project needs close communication with the village committee and the sub-district office to explain and publicise the project compensation standards and other issues so as to reduce the risks caused by the asymmetry of information.

Generally speaking, the direct survey tells that the villagers offer a high support for the project. Most stakeholders deem that the project will make a greater difference to local construction and development, but also hold different ideas on various issues. It can be seen from this point that there are only a few risk factors for implementation of this project.

Analysis of Related Factors
In-depth Analysis on the Impact of Stakeholders on the Project

In line with the content of the survey, the dependent variable in this article is set in the degree of villagers’ support for the primary land development project, that is, the overall attitude of the interviewed villagers to the proposed primary land development project. This survey investigated respondents’ support rate What is your overall attitude towards this project? The answers against, indifferent, conditional support, support and other are assigned a value of 1, 2, 3, 4 and 5, respectively, as a quantitative measurement.

This study prescribes a limit to some related variables, which includes: gender, age, occupation, education, year of construction, population, living conditions, living environment, resettlement compensation method, re-settlement satisfaction and compensative satisfaction. To clarify the relationship between varied variables, factor analysis, regression analysis and other methods are employed to further analyse the obtained data. There are descriptive statistics of some variables in Table 2.

Statistical analysis on variable descriptions of stakeholders (n = 217)

Variables Variable descriptions Mean value Standard deviation Minimum Maximum
Dependent variable Overall attitude 1=objection, 2=indifference, 3=conditional support, 4=support, 5= and others 3.44 0.809 1 5
Control variables and independent variables Sex 0= male, 1=female 0.65 0.478 0 1
Age 1=30 off, 2= 30~45, 3=45~60, 4=above 60 3.10 0.897 1 4
Occupation 1= workers, 2=farmers, 3= commercial servicemen 4=civil servants, 5= the self-employed, 6=professionals, 7= enterprise and public institution staff, 8=students, 9=the retired, 10=others 5.32 3.538 1 10
Education 1= high school or less, 2=college, 3=bachelor, 4=master and above 1.20 0.557 1 4
Year of building Numerical variables 1985.38 13.865 1903 2009
Population Numerical variables 4.13 2.467 0 11
Living conditions 1= unsatisfied, 2= general, 3= satisfied 2.94 0.341 1 3
Living environment 1= unsatisfied, 2=general, 3=satisfied 2.95 0.285 1 3
Resettlement and compensative ways 1=monetary, 2= house property right exchange, 3=monetary combined with house property right 2.55 0.584 1 4
Resettlement satisfaction 1=unsatisfied, 2= unknown, 3=almost satisfied, 4=obedient to arrangement, 5= satisfied, 6=very satisfied 2.75 1.428 1 6
Compensation satisfaction 1=unsatisfied, 2= almost satisfied, 3=satisfied, 4= very satisfied 2.77 1.382 1 6

In order to have effective control on variables, many factors such as gender, age, occupation are selected and factor analysis is used to eliminate some variables. The principle of factor analysis is to display fewer independent factors to reflect most of the information of the original variables. Suppose that there are p original variables, x1,x2,...,xpx1,x2,...,xp, each with a mean value of 0 and a standard deviation of 1. Now each original variable is represented by a linear combination of factors, f1, f2,..., fk, then {x1=a11f1+a12f2+a13f3++a1kfk+ε1x2=a21f1+a22f2+a23f3++a2kfk+ε2xp=ap1f1+ap2f2+ap3f3++apkfk+εp \left\{ {\matrix{ {{x_1} = {a_{11}}{f_1} + {a_{12}}{f_2} + {a_{13}}{f_3} + \cdots + {a_{1k}}{f_k} + {\varepsilon _1}} \cr {{x_2} = {a_{21}}{f_1} + {a_{22}}{f_2} + {a_{23}}{f_3} + \cdots + {a_{2k}}{f_k} + {\varepsilon _2}} \cr \vdots \cr {{x_p} = {a_{p1}}{f_1} + {a_{p2}}{f_2} + {a_{p3}}{f_3} + \cdots + {a_{pk}}{f_k} + {\varepsilon _p}} \cr } } \right.

Sampling moderation values and Bartlett's test

Kaiser-Meyer-Olkin metric of sampling sufficiency 0.726
Bartlett's test of sphericity Approximate to chi-square 964.658
df 66
Sig. 0.000

Prior to performing factor analysis, whether there is a certain linear relationship between the original variables and whether it is suitable to use factor analysis to extract factors are checked. This paper utilises the correlation coefficient matrix of variables, Bartlett's test of sphericity and KMO test to analyse.

Based on the correlation coefficient matrix test, it is found that most of the correlation coefficients are high, which implies strong linear relationship between the variables, and so the common factors can be extracted suitable for factor analysis. The corresponding probability value of the Bartlett sphericity test gets close to zero. If the significance level α = 0.05 and the probability value is less than α = 0.05, the null hypothesis is rejected and the correlation coefficient matrix is considered to be significantly varied from the identity matrix. At the same time, it can be derived that the original variables are proper for factor analysis based on the KMO test.

Based on results of Table 4, six components were extracted from the initial solution. Thus, the total variance of the original variable can merely explain six components only and the cumulative contribution rate is up to 74.79%.

Explicatory analysis of total variance

Elements Initial eigenvalue Extract square and load Rotate square and load
Total Of variance Cumulative Total Of variance Cumulative Total Of variance Cumulative
1 2.253 18.778 18.778 2.253 18.778 18.778 2.140 17.832 17.832
2 1.936 16.130 34.907 1.936 16.130 34.907 1.926 16.053 33.885
3 1.392 11.601 46.509 1.392 11.601 46.509 1.397 11.638 45.523
4 1.249 10.411 56.919 1.249 10.411 56.919 1.250 10.416 55.939
5 1.121 9.340 66.259 1.121 9.340 66.259 1.195 9.960 65.898
6 1.023 8.527 74.785 1.023 8.527 74.785 1.066 8.887 74.785
7 0.898 7.483 82.268
8 0.760 6.331 88.599
9 0.702 5.850 94.449
10 0.503 4.192 98.641
11 0.123 1.023 99.664
12 0.040 0.336 100.000

Extractive method: Principal component analysis.

Table 5 shows that the gender, age, occupation, education level of the villagers, the year of house built and the number of residents, which demonstrate remarkable correlations.

Componential matrixa of related variables

Components
1 2 3 4 5 6
Gender 0.227 0.020 −0.145 −0.459 0.579 0.154
Age −0.139 0.150 −0.764 −0.064 0.034 −0.338
Occupation −0.027 −0.164 0.130 −0.200 −0.687 −0.019
Education level 0.181 0.069 0.776 −0.331 0.149 −0.052
Year of house built −0.199 −0.111 −0.139 0.129 −0.085 0.908
Number of inhabitants −0.172 ++++ 0.144 0.040 0.689 0.277 0.023
Satisfaction of existing living conditions −0.100 0.948 0.062 −0.068 −0.068 0.065
Satisfaction of current living environment −0.133 0.947 0.057 −0.027 −0.038 0.071
Ways of compensation and resettlement −0.034 −0.084 0.344 0.555 0.166 −0.182
Whether villagers are satisfied with methods and standards of compensation and resettlement 0.922 0.140 −0.089 0.191 −0.193 0.016
Whether the subsidy standard is satisfactory 0.920 0.144 −0.088 0.192 −0.177 0.044
Overall attitude 0.596 −0.068 −0.071 −0.085 0.324 0.110

Extraction method: Principal component analysis.

Six components have been extracted.

Therefore, the stakeholders of different gender, age, occupation, education level, the year of house construction and the number of residents give evident distinction on project attitudes. These factors can be used as the basis for the following steps.

Fig. 1

Lithograph of factor analysis.

Factors Related to Primary Land Development and Social Impact Assessment

For illustrating deeply the diversity of the influential factors such as gender, age, occupations, education levels, years of house construction and the number of residents on the attitude of the project among Baigezhuang villagers, the multiple linear regression analysis is performed on the data extracted by the above factor analysis. The multiple linear regression analysis is applied to disclose the linear relationship between the variables explained and many other explanatory variables. The mathematical model is y=β0+β1x1+β2x2++βpxp+ε y = {\beta _0} + {\beta _1}{x_1} + {\beta _2}{x_2} + \cdots + {\beta _p}{x_p} + \varepsilon There are p revealing variables in the above formula, and ɛ is the random error. The results of multiple linear regression are shown in Table 6.

Anovaa

Model Sum of squares df Mean square F Sig.
1 Regression 7.171 6 1.195 1.868 0.008b
Residual 134.359 210 0.640
Total 141.530 216

Dependent variables: Overall attitude.

Predictive variables: (constant), number of inhabitants, age, year of house construction, occupation, gender, education level.

The outputs of multiple linear regression indicate that the observed value of the F-test statistic was 1.868, and the corresponding P-value was approximately 0. When the significant level is 0.05, the null hypothesis of the significance test of the regression equation should be rejected, since the probability P-value is less than the significance level. The variables explained and explanatory variables, which are regarded important in the analysis, are ’likely to establish a linear model.

Table 7 manifests and presents the regression equation between each variable and the degree of project support: approval rate= 0.184 * gender-0.002 * age-0.056 * occupation + 0.075 education level - 0.026 * year of house built - 0.038 * residential population.

Coefficientsa of relevant social influence factors

Model Non-standardised coefficients Standardised coefficients t Sig.
B Standard error Testing edition
1 Constant 6.237 8.046 0.775 0.439
Sex 0.311 0.116 0.184 2.679 0.008
Age −0.002 0.066 −0.002 −0.030 0.976
Occupation −0.013 0.016 −0.056 −0.822 0.412
Education level 0.109 0.109 0.075 1.000 0.318
Year of house construction −0.002 0.004 −0.026 −0.376 0.708
Residential population −0.012 0.022 −0.038 −0.555 0.580

Dependent variables: Overall attitude.

Based on the obtained correlations between each variable and the project, the main six points of the study are concluded:

The important points are (1) women have higher support for the project than men; (2) the higher the education level is, the higher the support rate for the project; (3) occupations of different kinds are supporters for the project and to our surprise, workers and business servicemen give a higher supporting rate; (4) the longer you live here and the older you are, the lower your support rate will be, because the older the villagers are, the more adaptive they are to the existing living environment and moreover the elder people's legs and feet may be growing less flexible as the time goes on; (5) the longer the time the house is built, the more its owner will support the project and during the investigation, many old houses collapsed severely, and some of them in bad condition that makes difficult to move in again. So, these housekeepers expressed support for the project for safety reasons; and (6) larger the number of residents is, the lower the support rate is, which is mostly because dwellers like these live here for a long time, and have been accustomed to the current living environment. ’More importantly, they are worried that due to the large population, housing resettlement after demolition cannot be successfully resolved; hence they do not approve the project. The first three points are mainly observed in different interest groups. The fourth point emphasis the much worry on the convenience of elder's life. The fifth point shows that the living environment can objectively promote the launching of this project. The last is ascribed to the problem of living habits and the distribution of family benefits. The three conclusions mentioned at the end require immediate information gathering and resolve them via good communication.

In summary, the main factors of the social impact assessment in the primary land development projects after thorough research are supposed to cover the issues of meeting the requirements of various groups, environmental adaptation and convenience for the elderly together with reasonable distribution of interests.

Policy Recommendations

Given the direct analysis of opinions from stakeholder groups and the study of major social influence factors, it can be noticed that the unimpeded flow of information and good communication are indispensable for land-level development projects. Furthermore, the requirements of various stakeholders should be considered, especially the elderly, and ensuring the most reasonable distribution of benefits. Subsequently, the paper here comes up with the following three policy recommendations to government regulators:

Open information. As for the problem of inadequate risk communication among stakeholders in the social impact assessment of major public policies, risk communication may as well be acted as the kernel of social impact assessment and racing through the whole process of policy formulation and enforcement [18]. The public ‘concern on the elderly and the adjustment to the new environment can be dealt immediately by effective information spreading. We should keep eyes on the entire process of risk assessment and risk communication, strengthen the negotiation and dialogue among plural subjects, and give full play of the media and by publicly responding to negative comments. Relevant government departments also ought to treat the negative emotions of the minority specifically on basis of their own reasons. As soon as there are negative voices, quick responses and effective guidance should be given.

Diversity of subsidies. Studies have indicated that differences in the age, occupation, family background, and education of residents differentiated their demands also. Therefore, while ensuring openness, fairness and no violation of relevant rules and regulations, the government can take advantage of the available resources to meet the various needs of residents as much as possible. Diversified subsidy options for the public may be utilised to try to solve some family interests, disputes and other issues. Relevant government departments may also form a coordinating group for public disputes to drive the development of the project.

Democratisation of decision-making. In spite of paying more attention to the importance of citizen participating in the current practice of social impact assessment, the channels for citizens are still waiting to be widened. Continuous improvement of field participation is on the road, while netizens should be admitted to get more access to decision-making. Correspondingly, it should be clear that the role of the government in risk assessment should not be ‘parental’ to guide citizens in favour of an administrative decision, but to allow citizens to fully understand the decision-making information and the results of ‘stable evaluation‘ from a neutral place. Determine whether the decision can be brought into force by the people themselves [19]. Procedural mechanisms such as prior investigations, public hearings, public participation, validity check, collective discussion and decisions, tracking decision-making effects, and error correction are the legal institutional basis for major administrative decision-making procedures [20].

The three policy measures mentioned above are all closely related to real interest rates for residents and scalable. All policy measures can be applied according to the characteristics of the project, and preference could be given to one or both of these measures. But for the current stable reviews on great decision-making projects, there are main steps to determine the project awaiting assessment, draw up an evaluation plan, fully give a hearing to opinions, make a comprehensively analytic demonstration, rank the risk level, set out precautionary measures, fix the evaluation report and use the evaluation results. Relevant government departments can take the forms of consulting documents, such as field surveys, questionnaires, opinion polls, interviews, internet public opinions, hearings, and public notices at the stage of hearing opinions and so on. Make sure that there are a variety of ways for residents to obtain information, and that the content of survey has a large amount of data and immediacy. The three types of policy recommendations above can be used in conjunction with existing processes.

Conclusion

The primary land development projects cannot be harmoniously and smoothly developed until risks and accidents get decreased or avoided in construction projects, accompanied by preventing and resolving project social impact and social conflicts. The foundation for the effective development of project is the social impact assessment, which is attributed to the close combination of theoretical analysis and practical application. This paper takes the primary land development project involved in a village in Pinggu District of Beijing as a backdrop, and extracts research data through questionnaire design and field survey. The opinions of stakeholders are statistically and intuitively analysed, and then a profound study on potential risk factors of the project is undertaken by making use of regression analysis. On one hand, this research can provide a reference for the techniques of social impact assessment of such kind. On the other hand, the risk factors identified in the research conclusions are the focuses of opinion polls as well and so it can lend some insight into similar projects. In the meantime, this paper puts forward some corresponding policy suggestions, with a humble eagerness to furnish referential for the social impact assessment of primary land development projects and for correlated government decision-making.

Fig. 1

Lithograph of factor analysis.
Lithograph of factor analysis.

Statistical analysis on variable descriptions of stakeholders (n = 217)

Variables Variable descriptions Mean value Standard deviation Minimum Maximum
Dependent variable Overall attitude 1=objection, 2=indifference, 3=conditional support, 4=support, 5= and others 3.44 0.809 1 5
Control variables and independent variables Sex 0= male, 1=female 0.65 0.478 0 1
Age 1=30 off, 2= 30~45, 3=45~60, 4=above 60 3.10 0.897 1 4
Occupation 1= workers, 2=farmers, 3= commercial servicemen 4=civil servants, 5= the self-employed, 6=professionals, 7= enterprise and public institution staff, 8=students, 9=the retired, 10=others 5.32 3.538 1 10
Education 1= high school or less, 2=college, 3=bachelor, 4=master and above 1.20 0.557 1 4
Year of building Numerical variables 1985.38 13.865 1903 2009
Population Numerical variables 4.13 2.467 0 11
Living conditions 1= unsatisfied, 2= general, 3= satisfied 2.94 0.341 1 3
Living environment 1= unsatisfied, 2=general, 3=satisfied 2.95 0.285 1 3
Resettlement and compensative ways 1=monetary, 2= house property right exchange, 3=monetary combined with house property right 2.55 0.584 1 4
Resettlement satisfaction 1=unsatisfied, 2= unknown, 3=almost satisfied, 4=obedient to arrangement, 5= satisfied, 6=very satisfied 2.75 1.428 1 6
Compensation satisfaction 1=unsatisfied, 2= almost satisfied, 3=satisfied, 4= very satisfied 2.77 1.382 1 6

Explicatory analysis of total variance

Elements Initial eigenvalue Extract square and load Rotate square and load
Total Of variance Cumulative Total Of variance Cumulative Total Of variance Cumulative
1 2.253 18.778 18.778 2.253 18.778 18.778 2.140 17.832 17.832
2 1.936 16.130 34.907 1.936 16.130 34.907 1.926 16.053 33.885
3 1.392 11.601 46.509 1.392 11.601 46.509 1.397 11.638 45.523
4 1.249 10.411 56.919 1.249 10.411 56.919 1.250 10.416 55.939
5 1.121 9.340 66.259 1.121 9.340 66.259 1.195 9.960 65.898
6 1.023 8.527 74.785 1.023 8.527 74.785 1.066 8.887 74.785
7 0.898 7.483 82.268
8 0.760 6.331 88.599
9 0.702 5.850 94.449
10 0.503 4.192 98.641
11 0.123 1.023 99.664
12 0.040 0.336 100.000

Coefficientsa of relevant social influence factors

Model Non-standardised coefficients Standardised coefficients t Sig.
B Standard error Testing edition
1 Constant 6.237 8.046 0.775 0.439
Sex 0.311 0.116 0.184 2.679 0.008
Age −0.002 0.066 −0.002 −0.030 0.976
Occupation −0.013 0.016 −0.056 −0.822 0.412
Education level 0.109 0.109 0.075 1.000 0.318
Year of house construction −0.002 0.004 −0.026 −0.376 0.708
Residential population −0.012 0.022 −0.038 −0.555 0.580

Sampling moderation values and Bartlett's test

Kaiser-Meyer-Olkin metric of sampling sufficiency 0.726
Bartlett's test of sphericity Approximate to chi-square 964.658
df 66
Sig. 0.000

Anovaa

Model Sum of squares df Mean square F Sig.
1 Regression 7.171 6 1.195 1.868 0.008b
Residual 134.359 210 0.640
Total 141.530 216

Componential matrixa of related variables

Components
1 2 3 4 5 6
Gender 0.227 0.020 −0.145 −0.459 0.579 0.154
Age −0.139 0.150 −0.764 −0.064 0.034 −0.338
Occupation −0.027 −0.164 0.130 −0.200 −0.687 −0.019
Education level 0.181 0.069 0.776 −0.331 0.149 −0.052
Year of house built −0.199 −0.111 −0.139 0.129 −0.085 0.908
Number of inhabitants −0.172 ++++ 0.144 0.040 0.689 0.277 0.023
Satisfaction of existing living conditions −0.100 0.948 0.062 −0.068 −0.068 0.065
Satisfaction of current living environment −0.133 0.947 0.057 −0.027 −0.038 0.071
Ways of compensation and resettlement −0.034 −0.084 0.344 0.555 0.166 −0.182
Whether villagers are satisfied with methods and standards of compensation and resettlement 0.922 0.140 −0.089 0.191 −0.193 0.016
Whether the subsidy standard is satisfactory 0.920 0.144 −0.088 0.192 −0.177 0.044
Overall attitude 0.596 −0.068 −0.071 −0.085 0.324 0.110

Statistical table of stakeholders’ concerns

Issues concerned House removal Number of households Compensation ways Number of households Problems after relocation (optional) Number of households
Compensative standards and its reasonableness 100 Property right exchange plus monetary compensation 207 Pension problems 137
Implementation of compensation funds 48 Pure monetary compensation 10 Changes in living environment 86
Follow-up security issues of demolition 32 Other subsidies Number of households Employment issues 55
Openness and legality of removing information 37 Employment arrangement 88 Schooling problem of kids 43
Requests for Resettlement Methods Number of households Information of get-rich 47 Source of income 2
Resettlement nearby 140 Vocational skills training 43 No concerns 3
Relocation 49 Microloan discount 9 Rights protection ways that make waves (optional) Number of households
Obedient to arrangements 28 Without requests on this item 30 Report to government 127
Opinions on current resettlement ways Number of households Opinions on present compensative standard Number of households Self-negotiation 63
Very satisfied 9 Very satisfied 9 Legal solution 56
Satisfied 37 Satisfied 37 Petition way 16
Almost satisfied 47 Almost satisfied 52 Keep silent 24
Unsatisfied 35 Unsatisfied 28 By means of media or network 12
Unknown 89 Unknown 91 Protest by Uniting neighbouring residents 6

Jiang Nongjuan, Lai Lei, Zheng Guangsan. Research on the Enterprise Profit Model Transformation Design of Primary Land Development[J]. Construction Economy, 2014 (3): 78–81. JiangNongjuan LaiLei ZhengGuangsan Research on the Enterprise Profit Model Transformation Design of Primary Land Development [J]. Construction Economy 2014 3 78 81 Search in Google Scholar

Dong Xiaolin. Several Issues on Social Stability Risk Assessment of Major Construction Projects [J]. Social Scientist, 2018, No.249 (01): 100–106. DongXiaolin Several Issues on Social Stability Risk Assessment of Major Construction Projects [J]. Social Scientist 2018 249 01 100 106 Search in Google Scholar

National Development and Reform Commission. Interim Measures for Social Stability Risk Assessment of Major Fixed Asset Investment Projects [EB/OL]. (2012-09) http://www.whdpc.gov.cn/art/2014/5/27/art_4166_359879.html. National Development and Reform Commission Interim Measures for Social Stability Risk Assessment of Major Fixed Asset Investment Projects [EB/OL]. 2012 09 http://www.whdpc.gov.cn/art/2014/5/27/art_4166_359879.html. Search in Google Scholar

National Development and Reform Commission. Notice on the Preparation of the Social Stability Risk Analysis Chapter and Evaluation Report outline of major fixed asset investment projects (for trial) [EB/OL]. (2013-02-17) http://www.intpec.com/zhengcefagui/20.html. National Development and Reform Commission Notice on the Preparation of the Social Stability Risk Analysis Chapter and Evaluation Report outline of major fixed asset investment projects (for trial) [EB/OL]. 2013 02 17 http://www.intpec.com/zhengcefagui/20.html. Search in Google Scholar

People.cn. Premier of the State Council of China: Government work report. [EB/OL]. (2015-03-17) http://cpc.people.com.cn/n/2015/0317/c64094-26702593.html. People.cn Premier of the State Council of China: Government work report [EB/OL]. 2015 03 17 http://cpc.people.com.cn/n/2015/0317/c64094-26702593.html. Search in Google Scholar

State Council of China. Opinions on Strengthening the System of Public Security Prevention and Control. [EB/OL]. (2015-04-13) http://www.gov.cn/xinwen/2015-04/13/content_2846013.htm. State Council of China Opinions on Strengthening the System of Public Security Prevention and Control [EB/OL]. 2015 04 13 http://www.gov.cn/xinwen/2015-04/13/content_2846013.htm Search in Google Scholar

People.cn. Chinese President: important remarks on risk governance and its significance. [EB/OL]. (2019-10-29) http://www.china.com.cn/opinion/theory/2019-10/29/content_75351879.htm. People.cn Chinese President: important remarks on risk governance and its significance [EB/OL]. 2019 10 29 http://www.china.com.cn/opinion/theory/2019-10/29/content_75351879.htm Search in Google Scholar

Hua Jian, Deng Li, Zhang Changzheng. Evolutionary Mechanism of Public Participation in Stability Evaluation of Major Engineering Projects [J]. Journal of Systems Science, 2017, 25 (02): 46–50. HuaJian DengLi ZhangChangzheng Evolutionary Mechanism of Public Participation in Stability Evaluation of Major Engineering Projects [J]. Journal of Systems Science 2017 25 02 46 50 Search in Google Scholar

Xie Qihui, Peng Zongchao. An Empirical Study on Netizens’ Negative Emotions and Mentality Risk Perception——from the Perspective of Social Stability Risk Assessment for Major Decisions [J]. Scientific Decision, 2018, No.249 (04): 41–58. XieQihui PengZongchao An Empirical Study on Netizens’ Negative Emotions and Mentality Risk Perception——from the Perspective of Social Stability Risk Assessment for Major Decisions [J]. Scientific Decision 2018 249 04 41 58 Search in Google Scholar

Dong Youhong. Practice and Improvement of Social Stability Risk Assessment System for Important Events [J]. China Administration, 2011 (12): 80–83. DongYouhong Practice and Improvement of Social Stability Risk Assessment System for Important Events [J]. China Administration 2011 12 80 83 Search in Google Scholar

Burdge R J. The social impact assessment model and the planning process[J]. Environmental Impact Assessment Review, 1987, 7(2):141–150. BurdgeR J The social impact assessment model and the planning process [J]. Environmental Impact Assessment Review 1987 7 2 141 150 10.1016/0195-9255(87)90033-3 Search in Google Scholar

Becker H A. Social impact assessment[J]. European Journal of Operational Research, 2001, 128(2):311–321. BeckerH A Social impact assessment [J]. European Journal of Operational Research 2001 128 2 311 321 10.4324/9781315072432 Search in Google Scholar

Burdge R J. The benefits of social impact assessment in Third World development[J]. Environmental Impact Assessment Review, 1990, 10(1–2):123–134. BurdgeR J The benefits of social impact assessment in Third World development [J]. Environmental Impact Assessment Review 1990 10 1–2 123 134 10.1016/0195-9255(90)90012-O Search in Google Scholar

Prenzel P V, Vanclay F. How social impact assessment can contribute to conflict management[J]. Environmental Impact Assessment Review, 2014, 45(45):30–37. PrenzelP V VanclayF How social impact assessment can contribute to conflict management [J]. Environmental Impact Assessment Review 2014 45 45 30 37 10.1016/j.eiar.2013.11.003 Search in Google Scholar

Becker D R, Harris C C, Mclaughlin W J, et al. A participatory approach to social impact assessment: the interactive community forum[J]. Environmental Impact Assessment Review, 2003, 23(3):367–382. BeckerD R HarrisC C MclaughlinW J A participatory approach to social impact assessment: the interactive community forum [J]. Environmental Impact Assessment Review 2003 23 3 367 382 10.1016/S0195-9255(02)00098-7 Search in Google Scholar

Xiao-Zheng C, Xiang-Ming H. Study of social risk assessment on major projects based on risk perception[C]//International Conference on Management Science & Engineering. IEEE, 2013. Xiao-ZhengC Xiang-MingH Study of social risk assessment on major projects based on risk perception [C] International Conference on Management Science & Engineering IEEE 2013 10.1109/ICMSE.2013.6586524 Search in Google Scholar

Karami S, Karami E, Buys L, et al. System dynamic simulation: A new method in social impact assessment (SIA)[J]. Environmental Impact Assessment Review, 2017, 62:25–34. KaramiS KaramiE BuysL System dynamic simulation: A new method in social impact assessment (SIA) [J]. Environmental Impact Assessment Review 2017 62 25 34 10.1016/j.eiar.2016.07.009 Search in Google Scholar

Zhu Zhengwei, Liu Yingying. Construction of Risk Communication Mechanism in Social Stability Risk Assessment of Major Public Policies [J]. Party and Government Horizons, 2016 (12): 73. ZhuZhengwei LiuYingying Construction of Risk Communication Mechanism in Social Stability Risk Assessment of Major Public Policies [J]. Party and Government Horizons 2016 12 73 Search in Google Scholar

Zhang Shichen, Liao Xiujian. Reflection and Improvement of the Social Stability Risk Assessment Mechanism for Major Decisions: an Empirical Analysis based on 30 Group Incidents of Environment[J]. E-government, 2017 (04): 103–113. ZhangShichen LiaoXiujian Reflection and Improvement of the Social Stability Risk Assessment Mechanism for Major Decisions: an Empirical Analysis based on 30 Group Incidents of Environment [J]. E-government 2017 04 103 113 Search in Google Scholar

Tong Bin. Study on the Legal System of Major Administrative Decision-making Procedures of the Government [J]. Social Scientist, 2017 (09): 55–60. TongBin Study on the Legal System of Major Administrative Decision-making Procedures of the Government [J]. Social Scientist 2017 09 55 60 Search in Google Scholar

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