Abstract
Residents’ satisfaction with post-disaster reconstruction in earthquake-stricken areas directly affects their quality of life, which cannot be ignored in post-disaster reconstruction. More than 10 years after the Wenchuan earthquake, we took ten randomly selected villages in the five areas hardest-hit by the Wenchuan earthquake as research objects and obtained 483 valid completed questionnaires. The villagers were randomly sampled and descriptive statistical analysis, factor analysis, and ordered logistic regression were used to explore the factors and relationships influencing villagers’ satisfaction with post-disaster reconstruction in Wenchuan earthquake-stricken areas. The results show that: 1) the more rural residents know about the post-disaster reconstruction, the greater their level of satisfaction; 2) the more the annual income of families increases after resettlement, the greater the satisfaction of rural residents with the post-disaster reconstruction; 3) six public factors, namely the village committee acts as, housing construction quality, public service, policy of benefiting farmers, cultural environment, and hardware environment, all significantly positively affect residents’ overall satisfaction with post-earthquake reconstruction. This study thus enriches the theory of residents’ satisfaction studies and the practice of post-earthquake reconstruction.
1 Introduction
Since the 20th century, there have been nearly a thousand earthquakes of magnitude 6 or above in China, and their seismic activities have been characterized by high frequency, high intensity, and wide distribution (Xie and Zhang, 2005; Ao et al., 2021). Among them, the Wenchuan earthquake occurred in the Longmenshan seismic belt, and was in the northeast–southwest direction, and there were six earthquakes of magnitude six or above, and the largest earthquake was the Wenchuan earthquake of magnitude 8.0 in 2008 (Jiang, 2009). According to statistics, the Wenchuan earthquake was an unprecedented disaster, with the hardest-hit area exceeding 100,000 square kilometers, involving 6 cities and counties, 88 counties and cities, 1,204 towns and villages, and 27.92 million people. In Sichuan Province alone, more than four million houses collapsed or were damaged, and the infrastructure for water, electricity, and transportation suffered serious damage (China Government Affairs Monitoring Center, 2008).
Research into the damage caused by earthquakes to human production and life and the associated coping strategies has been a focus of scholars all over the world (Bryant, 1991). High-intensity earthquakes can destroy urban and rural construction in disaster-hit areas to varying degrees. To restore order to life in disaster areas as soon as possible and explore scientific and efficient modes of reconstruction, post-disaster reconstruction has become an important concern of experts around the world (Shi, et al., 2021). At the same time, there is also the issue of people’s livelihoods that needs special attention in the post-disaster reconstruction of residential areas and when striving to improve rural residents’ life satisfaction. To date, research into the post-disaster reconstruction of settlements has mostly focused on large cities, with less attention paid to people’s satisfaction with the post-disaster reconstruction of settlements in rural areas (Li and Tian, 2015; Yang, 2017).
Satisfaction is an individual’s subjective experience of his or her own quality of life, an individual’s comprehensive cognitive judgment of life, which reflects an individual’s general evaluation of their overall life and is influenced both by their own factors and environmental factors (Song et al., 2019). Individual differences among rural residents will lead to differences in their perceptions of a centralized living style in post-disaster reconstruction settlements, resulting in different degrees of acceptance and satisfaction with post-disaster reconstruction settlements (Peng et al., 2018a). Blakely pointed out that the post-disaster reconstruction of residential areas not only involves disaster prevention and emergency rescue considerations but also the re-planning and reconstruction of a region. The first priority is definitely the emergency planning when a disaster occurs, but more important is the functional allocation of the region, such as infrastructure, environmental planning, economic development planning, new residence resettlement, and so on (Hu, 2008). These living environment factors will affect rural residents’ satisfaction with the post-disaster reconstruction of residential areas.
Most of the previous analyses conducted were from the perspective of the government. This study, on the other hand, analyzes post-disaster reconstruction settlements from the perspective of individual villagers and tries to understand rural residents’ satisfaction with post-disaster reconstruction settlements in Sichuan Province. We established a system of the factors influencing rural residents’ satisfaction with post-earthquake reconstruction of residential settlements, based on existing research, and explored rural residents’ overall satisfaction with post-disaster reconstruction settlements and the corresponding influencing factors and their relationships. The significant influencing factors were determined based on empirical studies, providing theoretical and practical support for post-disaster reconstruction practice.
The structure of this paper is shown in Figure 1: Section 2 reviews the literature, Section 3 describes the research methods and data sources, Section 4 presents the empirical results and discusses the results, Section 5 summarizes the full text and proposes prospects for future research, and the final section presents the limitations of this study.
FIGURE 1
2 Literature review
China has adopted a variety of reconstruction policies for post-earthquake reconstruction in rural areas, including three main ways of overall construction, overall self-construction, and self-construction and maintenance (Peng, et al., 2013). Under the joint action of the family characteristics and production and management goals of rural residents, different types of farmers will be formed, and different types of farmers will have different positioning of maximizing the comprehensive benefits of the family, which will directly affect the decision-making behavior and mode selection of rural settlements post-disaster reconstruction (Steinberg, 2007).
Previous research into post-earthquake reconstruction has emphasized keeping pace with the times and combining corresponding development policies for reconstruction areas. For urban post-disaster reconstruction, Zhou and Xia (2008) suggested that post-earthquake reconstruction planning should follow the development strategy of “shaping an international tourist city”, relocating urban functional departments and residents from severely damaged old cities to new areas, and relocating old urban areas to new areas, which should be transformed into economic development functional areas with cross-services of multiple industries. Yu et al. (2021) proposed that as rural social relations are complex, it is necessary to pay more attention to social connection and regional capacity building when planning post-earthquake reconstruction in rural areas and to maximize the flexibility of reconstruction strategies to form an adaptive mechanism for post-disaster recovery. When verifying the role and impact of participation in post-earthquake reconstruction, Wang (2018) believed that participatory post-disaster rural reconstruction could provide a basis for targeted poverty alleviation, rural revitalization, and urban and rural development.
Post-disaster reconstruction is a common behavior of social norms and government intervention. Guo and Fang (2019) investigated rural residents’ satisfaction with basic public services in rural areas. Their results showed that rural residents’ satisfaction is influenced by gender, age, culture, income level, farmers’ participation, and understanding of basic public services. Different individual and family characteristics of rural residents for post-disaster reconstruction residential satisfaction exist. Satisfaction with post-disaster reconstruction and resettlement shows a decreasing trend with increasing age. The higher the education level of rural residents, the greater their satisfaction with post-disaster reconstruction settlements (Yang, 2014). In addition, differences in income sources will have an impact on villagers’ satisfaction. Sun and Chen (2016) investigated the factors influencing rural residents’ satisfaction with resettlement in M Town, Jiangsu Province, and found that farmers with a larger proportion of non-agricultural income after resettlement in this area were less satisfied with this resettlement. The reason may be that for these farmers, the compensation of this resettlement is less attractive to them and their overall satisfaction is not high. Peng et al. (2018b) point out that the earthquakes cause serious damage to cultivated land, land consolidation and reclamation directly affected farmers’ economic income, and the process of rebuilding residential areas was an important opportunity to improve economic development. The diversification of rural residents’ incomes should be increased to make farmers’ income no longer unitary.
In addition to the above-mentioned personal factors, satisfaction with the post-disaster reconstruction of residential areas is also affected by living environment factors (Song et al., 2019; Yang, et al., 2021). Jansen (2014) suggested that living satisfaction depends on personal expectations and that when housing does not meet the needs of residents, it will directly reduce their living satisfaction. Cao (2016) analyzed satisfaction with the living environment, with categories including housing orientation, supporting facilities, housing quality, and neighborhood relationships. Aulia and Ismail, 2013 further categorized the external influencing factors into the natural environment, equipment conditions, property services, and traffic conditions, to analyze the factors influencing living satisfaction.
The actions of village committees or communities will have an impact on villagers’ satisfaction with reconstruction. In a study of new rural construction, Hu (2016) put forward the notion that the working ability of grassroots village committee cadres and rural residents’ awareness of rural construction policies will affect rural residents’ satisfaction with village-level democratic system construction and cultural construction. In the process of resettlement housing allocation, Xiao et al. (2014) evaluated satisfaction with the residential areas rebuilt after an earthquake from the perspective of the affected residents and found that the disclosure of the information channel of the housing redistribution system in the rebuilt residential areas had a major influence on the resettled residents’ satisfaction. Hu conducted a comparative study of the reconstruction of New Orleans in the United States and the reconstruction after the Wenchuan earthquake in China. He found that communication with local residents should be emphasized at the beginning of the reconstruction policy formulation process, and they should be invited to participate in the reconstruction process and have their opinions listened to, which is not only conducive to the application of the reconstruction policy but also helps to rebuild the confidence of local people (Hu, 2008). Paying attention to rural housing reconstruction for farmers is key to realizing sustainable recovery. The reconstruction policy has different decisions on the reconstruction of village houses damaged to varying degrees, while publicity about the reconstruction policy can clarify affected people’s perceptions of post-disaster reconstruction (Peng et al., 2018b). Wang et al. (2012) pointed out that the dissemination of knowledge about earthquake disaster prevention is an important factor affecting residents’ life satisfaction.
Housing construction is the top priority of post-earthquake reconstruction. According to the disaster reduction plan and regulations of the China Earthquake Administration (CEA) from 2007 to 2019, local rural housing construction planning should be supervised in terms of site selection, avoidance of earthquake prone areas, and construction quality to ensure housing safety, and skilled technicians should be trained to master earthquake knowledge (Wu and Wu, 2020). Cassidy (2007) proposed that when a disaster occurs, the maximum duration of temporary shelter provided by the government should be 5 years, so that the construction period of post-earthquake reconstruction settlements should not exceed the maximum duration that victims can bear. At the same time, post-disaster reconstruction projects should have clear start and completion dates (Davidson et al., 2007).
Steinberg (2007) summarized the experience of post-disaster reconstruction in Aceh and Nias in Indonesia and pointed out that the construction of residential buildings as part of post-disaster reconstruction was only the first step of reconstruction; the construction of the surrounding environment and public facilities should also be a focus of post-disaster reconstruction. Transportation is very important in post-disaster reconstruction. If roads are blocked, much of the transport of reconstruction materials will be affected. Therefore, post-disaster reconstruction of traffic systems should be a basic and pilot project (Kun, 2013). At the same time, public facilities, such as water, electricity, and communication, should also be a focus of the reconstruction of post-disaster settlements, and factors such as whether the quality of drinking water, the convenience of water use, and power supply and communication meet the needs of residents can also affect their satisfaction with post-disaster reconstruction settlements (Curti et al., 2008).
The existing research shows that relevant policies that benefit farmers will also have an impact on villagers’ satisfaction with reconstruction. Developing characteristic agriculture to diversify agriculture, developing agricultural training, and introducing non-agricultural industries will enable young laborers to engage in non-agricultural work, which will increase farmers’ income. This will help to make up for the increased cost of living after the disaster, which will improve rural residents’ satisfaction with the reconstruction of settlements following a disaster. In addition, as the land belongs to rural collectives, after many rounds of discussion by the village committee, even the land adjustment should not encounter any difficulties, which will help to reassure residents that problems of land reclamation or cultivated land demand can be solved following a disaster (Peng et al., 2013). At the same time, the human environment is also a key point that cannot be ignored. Earthquake disasters in Sichuan Province mostly occur in areas with superior natural conditions and profound cultural heritage. Post-earthquake reconstruction should pay attention not only to the protection of the natural environment but also to the reconstruction of national cultural traditions (Li and Shi, 2008).
Therefore, it is of great theoretical and practical significance to systematically explore residents’ degree of satisfaction with post-disaster reconstruction to further improve residents’ quality of life and enhance public participation in post-disaster reconstruction.
3 Methodology
3.1 Questionnaire design
For this study a questionnaire was designed according to the existing related research and the current situation of post-earthquake reconstruction of residential areas. The questionnaire comprised two parts: social-demographic information and a post-disaster reconstruction satisfaction survey scale.
Basic personal information of respondents (shown in Table 1) and family information was collected (shown in Table 2).
TABLE 1
| Variable | Variable declaration | Variable type |
|---|---|---|
| Gender | 1 = Male; 2 = Female | Categorical variable |
| Age | The corresponding numerical value is the corresponding age | Continuous variable |
| For example: 25 = 25 years old | ||
| Education level | 1 = Uneducated; 2 = Primary School; 3 = Junior High School; 4 = Senior High School; 5 = University or above | Categorical variable |
| Is the current place of residence the birthplace? | Yes = 1,No = 0 | Binary variable |
| Participate in the reconstruction decision-making process? | Yes = 1, No = 0 | Binary variable |
| Have you received education on disaster prevention and mitigation? | Yes = 1, No = 0 | Binary variable |
| Have you experienced secondary disasters after the earthquake? | Yes = 1, No = 0 | Binary variable |
| Understanding of post-disaster reconstruction management regulations | Very little understanding = 1, A little understanding = 2, General understanding = 3, Better understanding = 4, Very understanding = 5 | Sequence variable |
| Understanding of seismic fortification level of buildings | Very little understanding = 1, A little understanding = 2, General understanding = 3, Better understanding = 4, Very understanding = 5 | Sequence variable |
| Understanding of post-disaster reconstruction methods | Very little understanding = 1, A little understanding = 2, General understanding = 3, Better understanding = 4, Very understanding = 5 | Sequence variable |
Description of basic personal information of respondents.
TABLE 2
| Variable | Variable declaration | Variable type |
|---|---|---|
| Number of residential floors | 1 = 1, 2 = 2, 3–6 = 3, Layer 7 and above = 4 | Sequence variable |
| Was the rebuilt house completed on time? | Yes = 1, No = 0 | Binary variable |
| Post-earthquake reconstruction | Overall construction = 1, overall self-construction = 0 | Binary variable |
| Annual household income after earthquake | Ten thousand yuan | Continuous variable |
| Stability of main household income after resettlement | Unstable = 1, stable = 0 | Binary variable |
| Changes of annual household income after resettlement | Significant increase = 1, some increase = 2, no change = 3, some decrease = 4, significant decrease = 5 | Sequence variable |
| Main income sources of families before the earthquake | Farming/fruit and vegetable planting, poultry/aquaculture, farmhouse tourism, land circulation, working outside, and others | Categorical variable |
| Main source of family income after earthquake resettlement | Farming/fruit and vegetable planting, poultry/aquaculture, farmhouse tourism, land circulation, working outside, and others | Categorical variable |
Description of respondents’ family information.
The satisfaction scale measured respondents’ satisfaction with 28 elements of post-disaster reconstruction and was coded with a five-point Likert scale, with the lowest level of satisfaction being 1 and the highest level of satisfaction being 5 (Yang et al., 2020c). Details of the post-disaster reconstruction satisfaction scale and its measurement instructions are shown in Table 3.
TABLE 3
| Variable | References | Satisfaction | Variable type | ||
|---|---|---|---|---|---|
| Minimum | Minimum | ||||
| F1 | Subsidy guarantee | Alparslan et al. (2008)4 | 1 | 5 | Sequence variable |
| Morimoto (2012) | |||||
| F2 | Information channel | Xiao et al. (2014) | 1 | 5 | Sequence variable |
| Ye et al. (2017) | |||||
| F3 | Education and publicity of disaster prevention and mitigation | Wang et al. (2012) | 1 | 5 | Sequence variable |
| Zhou and Liao (2015) | |||||
| Li and Shi. (2008) | |||||
| F4 | Reconstruction policy propaganda | Peng et al. (2018b) | 1 | 5 | Sequence variable |
| Qu et al. (2012) | |||||
| Li and Shi. (2008) | |||||
| F5 | Reconstruction decision-making participation | Li and Shi (2008) | 1 | 5 | Sequence variable |
| Zhou and Liao (2015) | |||||
| Wang (2018) | |||||
| F6 | Type of layout of apartment | Cinicioglu et al. (2007) | 1 | 5 | Sequence variable |
| Feyza et al. (2007) | |||||
| F7 | House safety | Ergonul (2005); Wu and Wu (2020) | 1 | 5 | Sequence variable |
| F8 | Quality of building materials | Ergonul (2005); Wu and Wu (2020) | 1 | 5 | Sequence variable |
| Cinicioglu et al. (2007) | |||||
| F9 | Technology of constructors | Ergonul (2005); Cinicioglu et al. (2007); Wu and Wu (2020) | 1 | 5 | Sequence variable |
| F10 | Reconstruction duration | Ergonul (2005); Davidson et al. (2007) | 1 | 5 | Sequence variable |
| F11 | Drinking water quality | Curti et al. (2008) | 1 | 5 | Sequence variable |
| F12 | Water convenience | Curti et al. (2008) | 1 | 5 | Sequence variable |
| F13 | Power supply demand | Curti et al. (2008) | 1 | 5 | Sequence variable |
| F14 | Communication requirements | Curti et al. (2008) | 1 | 5 | Sequence variable |
| F15 | Planting space around housing | Li and Shi (2008) | 1 | 5 | Sequence variable |
| F16 | Cultivated land distance | Li and Shi (2008) | 1 | 5 | Sequence variable |
| F17 | Land reclamation | Ansal et al. (2009); Peng et al. (2013) | 1 | 5 | Sequence variable |
| Mahdi and AsgharAlesheikh (2011) | |||||
| F18 | Agricultural training | Ansal et al. (2009); Peng et al. (2013) | 1 | 5 | Sequence variable |
| Mahdi and AsgharAlesheikh (2011) | |||||
| F19 | Agricultural diversification | Ansal et al. (2009); Peng et al. (2013) | 1 | 5 | Sequence variable |
| Mahdi and AsgharAlesheikh (2011) | |||||
| F20 | Non-agricultural industry introduction | Ansal et al. (2009); Peng et al. (2013) | 1 | 5 | Sequence variable |
| Mahdi and AsgharAlesheikh (2011) | |||||
| F21 | Talent education | Speare (1974) | 1 | 5 | Sequence variable |
| F22 | Policies and systems | Li and Shi (2008) | 1 | 5 | Sequence variable |
| F23 | Cultural tradition | Speare (1974); Li and Shi (2008) | 1 | 5 | Sequence variable |
| F24 | Earthquake shelter | #FF0000 | 1 | 5 | Sequence variable |
| Wen (2001) | |||||
| F25 | Road planning | Kun (2013) | 1 | 5 | Sequence variable |
| (Zhou et al., 2019) | |||||
| F26 | Road quality | Steinberg (2007); Kun (2013) | 1 | 5 | Sequence variable |
| F27 | Sanitary environment/village appearance | Inneke et al. (2013) | 1 | Sequence variable | |
| MacAskill and Guthrie (2015) | |||||
| F28 | Natural environment | Curti et al. (2008); Li and Shi (2008) | 1 | 5 | Sequence variable |
Post-disaster reconstruction satisfaction survey scale variables.
3.2 Model specification
Several types of models have been used to study people’s satisfaction with the built environment, including multiple regression models (Yang et al., 2022; Xu, 2020), structural equation models (Song et al., 2019; Kostas, 2020; Wang et al., 2020; Seongyeon and Christine, 2009; Chen et al., 2014; Margareta et al., 2018), a CCSI model (Zhou and Wang, 2022), ordered logistic regression analysis (Mao, 2022; Junghwa et al., 2020), and a Bayesian multilevel ordinal response model (Zhai et al., 2021). However, there has been limited research into the factors influencing residents’ satisfaction and their relationships in post-disaster reconstruction, which restricts the rationality of the formulation of post-disaster reconstruction policies. The dependent variable used in the present study was satisfaction, a categorical variable with differences in degrees. Therefore, factor analysis was mainly used to reduce the dimension of influencing factors (Ao, et al., 2020), and an ordered logistic regression model was used to analyze the relationship between influencing factors and the satisfaction with post-earthquake reconstruction. In logistic regression analysis, when the variable level is greater than two and it is an ordered variable, ordered logistic regression analysis can be used. As the dependent variable in this study was satisfaction, the options were completely dissatisfied, not very satisfied, generally satisfied, comparatively satisfied, and very satisfied, which were suitable for ordered logistic regression analysis. The logistic regression model used in this study is expressed as follows:where j = 1, 2, 3, 4, and 5, representing the five levels of satisfaction; y is residents’ satisfaction with reconstruction; xi is the explanatory variable and control variable that affects farmers’ life satisfaction; αj is the intercept parameter; and βj is the regression coefficient, which indicates the direction and degree of influence of explanatory variables on the explained variables.
3.3 Sample selection and data collection
According to the degree of the Wenchuan earthquake disaster, the population, economy, industry, and employment status, combined with the vigilance of rural residents and the degree of cooperation reflected, this study randomly selected 10 sample villages in the 5 hardest-hit areas in Sichuan Province, with the geographical location of each village shown in Figure 2.
FIGURE 2
The field investigation part of this study was conducted from January 1 to January 5, 2019. The research team was divided into five groups, each of which was responsible for data collection in two sample villages. The research team entered the village and randomly selected residents of the village to complete the questionnaire survey. If a resident did not accept the invitation to take part in the survey, the researchers randomly selected the next household. In this study, 516 face-to-face questionnaires were completed, of which 33 questionnaires with missing information or internal inconsistencies were excluded. In total, 483 valid questionnaires were included. Table 4 shows the sample villages and the number of questionnaires collected from each, while Table 5 presents the basic statistical information about the respondents. The changes in the main sources of household income before and after the earthquake are shown in Figure 3.
TABLE 4
| Investigation site | Degree of disaster | Reconstruction of settlement pattern | Number of questionnaires | |
|---|---|---|---|---|
| Deyang city | Jixian Community, Hanwang Town, Mianzhu City | Severe disaster | Centralized residence | 50 |
| Hongming Village, xinan town, Mianzhu City | Severe disaster | Decentralized residence | 49 | |
| Dujiangyan city | Heming Village, LiujieTown | Severe disaster | Centralized residence | 48 |
| He Jia Village, Anlong Town | Severe disaster | Decentralized residence | 47 | |
| Guangyuan city | Dongfang Village, Qingxi Town, Qingchuan County | Severe disaster | Centralized residence | 52 |
| Yinping Village, Qingxi Town, Qingchuan County | Severe disaster | Centralized residence | 45 | |
| Aba Autonomous Prefecture | Guojiaba Village, Shuimo Town, Wenchuan County | Severe disaster | Centralized residence | 49 |
| Lianshanpo Village, Shuimo Town, Wenchuan County | Severe disaster | Decentralized residence | 42 | |
| Mianyang city | Laochang Village, Chenjiaba Town, Beichuan Qiang Autonomous County | Severe disaster | Centralized residence | 51 |
| Qinglin Village, Chenjiaba Town, Beichuan Qiang Autonomous County | Severe disaster | Centralized residence | 50 | |
Sample villages and the numbers of questionnaires collected.
TABLE 5
| Variable | Variable declaration | Percentage (%) | Number of samples | Variable | Variable declaration | Percentage (%) | Number of samples |
|---|---|---|---|---|---|---|---|
| Gender | Male | 45.25 | 219 | Level of education | Without education | 14.49 | 70 |
| Female | 54.75 | 264 | Primary school | 30.85 | 149 | ||
| Age | 15–30 years old | 16.56 | 80 | Junior school | 30.85 | 149 | |
| 30–45 years old | 21.33 | 103 | Technical secondary school | 4.97 | 24 | ||
| 45–60 years old | 35.20 | 170 | Senior high school | 10.14 | 49 | ||
| 60–75 years old | 22.57 | 109 | Junior college | 5.59 | 27 | ||
| More than 75 years old | 4.34 | 21 | University or above | 3.11 | 15 | ||
| Number of residential floors | 1 | 36.44 | 176 | Sources of family income before the earthquake | Farming/fruit and vegetable growing | 30.23 | 147 |
| 2 | 48.45 | 234 | Poultry/aquaculture | 13.04 | 63 | ||
| 3–6 | 14.70 | 71 | Farmhouse tourism | 3.52 | 17 | ||
| 7 or more | 0.41 | 2 | Land circulation | 1.24 | 6 | ||
| Annual household income after earthquake | <10,000 | 22.57 | 109 | Working outside | 36.65 | 177 | |
| 10,000–50,000 | 62.32 | 301 | Other | 15.32 | 73 | ||
| 50,000–100,000 | 12.84 | 62 | Sources of household income after the earthquake | Farming/fruit and vegetable growing | 15.32 | 74 | |
| >100,000 | 2.27 | 11 | Poultry/aquaculture | 3.73 | 18 | ||
| Changes in annual household income after resettlement | Some increase | 54.24 | 262 | Farmhouse tourism | 3.73 | 18 | |
| No change | 34.37 | 166 | Land circulation | 3.52 | 17 | ||
| Some decrease | 9.32 | 45 | Working outside | 50.31 | 243 | ||
| Significant decrease | 2.07 | 10 | Other | 23.39 | 113 |
Basic statistical information about respondents.
FIGURE 3
4 Results and discussion
4.1 Exploratory factor analysis
In this study, SPSS software version 23.0 was used to conduct exploratory factor analysis (EFA) of 28 variables relating to post-disaster reconstruction satisfaction, to determine the influence of each factor on the overall satisfaction with post-disaster reconstruction. To test the applicability of the factor analysis, we used the Kaiser–Meyer–Olkin (KMO) test and Bartlett’s test to explore the applicability of factor analysis of the 28 satisfaction measurement variables. The test results showed that the KMO value was 0.882 and the p-value was 0.000. Thus, the results showed that there was a high correlation among the 28 satisfaction measurement variables, which indicated that these data were suitable for the EFA method. The factor analysis results of the 28 satisfaction indexes are shown in Table 6 Variables with a factor load of less than 0.4 were considered to be nonsignificant variables, so F15 was deleted (indicated by “–” in the Table). EFA finally determined six common factors.
TABLE 6
| Common factor | Variable | Load | |
|---|---|---|---|
| X1 The village committee acts as | F1 | Subsidy guarantee | 0.444 |
| F2 | Information channel | 0.594 | |
| F3 | Education and publicity about disaster prevention and mitigation | 0.829 | |
| F4 | Reconstruction policy propaganda | 0.835 | |
| F5 | Reconstruction decision-making participation | 0.711 | |
| X2 Housing construction quality | F6 | Type of layout of apartment | 0.641 |
| F7 | House safety | 0.693 | |
| F8 | Quality of building materials | 0.865 | |
| F9 | Technology of constructors | 0.832 | |
| F10 | Reconstruction duration | 0.538 | |
| X3 Public services | F11 | Drinking water quality | 0.798 |
| F12 | Water convenience | 0.871 | |
| F13 | Power supply demand | 0.845 | |
| F14 | Communication requirements | 0.742 | |
| — —— | F15 | Planting space around housing | — |
| X4 Policy of benefiting farmers | F16 | Cultivated land distance | 0.629 |
| F17 | Land reclamation | 0.758 | |
| F18 | Agricultural training | 0.779 | |
| F19 | Agricultural diversification | 0.67 | |
| F20 | Non-agricultural industry introduction | 0.507 | |
| X5 Cultural environment | F21 | Talent education | 0.527 |
| F22 | Policies and systems | 0.731 | |
| F23 | Cultural tradition | 0.736 | |
| X6 Hardware environment | F24 | Earthquake shelter | 0.512 |
| F25 | Road planning | 0.846 | |
| F26 | Road quality | 0.799 | |
| F27 | Sanitary environment/village appearance | 0.456 | |
| F28 | Natural environment | 0.469 | |
Molecular results of exploratory factors.
4.2 Multiple collinearity analysis
The multiple collinearity problem may lead to a low level of significance of various spatial variables. Therefore, it is necessary to investigate the multiple collinearity of these independent variables (Ding, et al., 2017; Yang, et al., 2020a; Yang et al., 2020b; Zhao, et al., 2020; Yang, et al., 2022). To test multiple collinearity, we mainly used the variance inflation factor (VIF). With a higher VIF value, a specific explanatory variable is more likely to be expressed by the linear function model of other explanatory variables, and there may be multiple collinearity problems in the model. The maximum VIF value of the explanatory variable in this study was 2.094, which showed that there was no multiple collinearity problem. The results of multivariate multiple collinearity tests are shown in Table 7.
TABLE 7
| Variable | Model 1 | Model 2 | Collinearity test | |||
|---|---|---|---|---|---|---|
| B | p-value | B | p-value | Tolerance | VIF | |
| Gender | −0.208 | 0.237 | — | — | — | — |
| Age | −0.004 | 0.573 | — | — | 0.562 | 1.778 |
| Education level | −0.004 | 0.959 | — | — | 0.554 | 1.804 |
| Is the current place of residence the birthplace? | −0.255 | 0.195 | — | — | 0.952 | 1.050 |
| Participate in the reconstruction decision-making process? | −0.035 | 0.846 | — | — | 0.887 | 1.128 |
| Have you received education on disaster prevention and mitigation? | −0.081 | 0.658 | — | — | 0.870 | 1.150 |
| Have you experienced secondary disasters after the earthquake? | −0.360 | 0.106 | −0.411* | 0.053 | 0.887 | 1.127 |
| Understanding of post-disaster reconstruction management regulations | 0.138 | 0.244 | — | — | 0.518 | 1.930 |
| Understanding of seismic fortification level of buildings | −0.137 | 0.272 | — | — | 0.478 | 2.094 |
| Understanding of post-disaster reconstruction methods | 0.212* | 0.051 | 0.223** | 0.012 | 0.623 | 1.604 |
| Was the rebuilt house completed on time? | −0.341 | 0.124 | — | — | 0.937 | 1.068 |
| Post-disaster reconstruction mode of housing is overall construction | 0.018 | 0.935 | — | — | 0.854 | 1.171 |
| Annual income of families after resettlement | 0.024 | 0.316 | — | — | 0.946 | 1.057 |
| Family income stability after resettlement | −0.049 | 0.783 | — | — | 0.942 | 1.062 |
| Changes in annual household income before and after resettlement | −0.226* | 0.052 | −0.217* | 0.057 | 0.937 | 1.067 |
| X1 The village committee acts as | 0.548*** | 0.000 | 0.525*** | 0.000 | 0.979 | 1.021 |
| X2 Housing construction quality | 0.447*** | 0.000 | 0.434*** | 0.000 | 0.973 | 1.028 |
| X3 Public services | 0.365*** | 0.000 | 0.372*** | 0.000 | 0.955 | 1.047 |
| X4 Preferential agricultural policy | 0.393*** | 0.000 | 0.393*** | 0.000 | 0.950 | 1.052 |
| X5 Cultural environment | 0.595*** | 0.000 | 0.594*** | 0.000 | 0.950 | 1.053 |
| X6 Hardware environment | 0.649*** | 0.000 | 0.637*** | 0.000 | 0.982 | 1.018 |
Ordered logistic regression results of villagers’ satisfaction.
*, **, *** represent significance levels of 10%, 5%, and 1%, respectively.
4.3 Ordered logistic regression
In this study, ordered logistic regression was used to analyze the influence of the above factors on rural residents’ satisfaction with post-disaster reconstruction. Two models were fitted in this study. Model one contained all of the above variables, while model two was obtained by re-fitting after deleting nonsignificant variables from model 1. The results is models one and two are shown in Table 7. The -2log-likelihood values are 1360.778 and 1178.072, respectively, while both of their Sig. values were 0.000, which means that model two fit the data better. Finally, model two was selected to interpret and analyze the data. Meanwhile, the Cox and Snell and Nagelkerke R2 values were 0.315 and 0.335, respectively, which means that the model fit the data well and had statistical significance.
4.3.1 The influence of demographic variables on post-disaster reconstruction satisfaction
Villagers’ understanding of post-disaster reconstruction methods (B = 0.223, p = 0.012) had a positive and significant influence on their overall level of satisfaction at a significance level of 5%; that is, the more fully rural residents understood reconstruction methods in residential areas, the higher their satisfaction level. Whether the residents had experienced a secondary earthquake disaster (B = -0.411, p = 0.053) was negatively correlated with their overall satisfaction at the 10% significance level, indicating that residents who had not experienced an earthquake disaster in the current reconstructed residential area had a high level of satisfaction with the reconstructed residential area. Indirectly explain the importance of reconstruction of residential areas in avoiding secondary earthquake disasters.
In addition, the change in annual household income before and after post-disaster reconstruction (B = -0.217, p = 0.057) had a negative correlation with overall satisfaction at a significance level of 10%. This option in the questionnaire of this study is designed as (significant increase = 1, some increase = 2, no change = 3, some decrease = 4, significant decrease = 5), that is, the annual household income after resettlement is higher. This is consistent with the findings of Shi et al. (2018) when they studied urban–rural migration and resettlement and found that increased income had a positive correlation with residents’ life satisfaction.
4.3.2 The influence of six common factors on satisfaction
The more satisfied the villagers in the post-disaster reconstruction area were with the village committee’s actions (X1,B = 0.525, p = 0.000) during the post-earthquake resettlement process, the higher their level of satisfaction with the post-disaster reconstruction of residential areas. The implementation of government policies is directly related to the style and ability of village cadres, which shows that improving these cadres’ sense of responsibility and their ability is an important part of improving rural residents’ satisfaction with the post-disaster reconstruction of residential areas. Huang et al. (2020) believed that village cadres should improve their own skills, use information to improve the efficiency of rural community governance, and achieve the goal of rural governance informatization. The more stable a cadre’s network is, the higher the rural residents’ evaluation of village cadres will be.
The quality of housing construction (X2, B = 0.434, p = 0.000) significantly affected villagers’ overall satisfaction with post-disaster reconstruction. The better the quality of housing construction in post-disaster reconstruction areas, the higher the overall satisfaction of villagers with the post-disaster reconstruction. This shows that good earthquake-resistance and the comfort of the house itself are an important factor that determines rural residents’ satisfaction with the post-disaster reconstruction of residential areas. Xiao et al. (2014) found that in the built environment, the greater the degree of completion of residential reconstruction and the shorter the construction period, the more satisfied disaster-affected people are with the post-disaster reconstruction. Therefore, the quality and efficiency of housing construction in a post-disaster reconstruction area play an important role in improving villagers’ satisfaction with post-disaster reconstruction.
The public services in the post-disaster reconstruction area (X3, B = 0.372, p = 0.000) had a significant positive correlation with the villagers’ overall satisfaction with post-disaster reconstruction, which showed that rural residents pay attention to the level of public services at resettlement sites. The higher the level of public services, the higher the rural residents’ satisfaction with the post-disaster reconstruction settlements (Wang and Li, 2019). In a study of rural medical and health services, Wang and Li (2019) found that rural residents with better self-rated health status were more satisfied with public health services and that the higher the satisfaction of rural residents with medical and health services, the higher their life satisfaction. Therefore, in the process of post-disaster reconstruction, not only should the construction work be done well but also the role of public services should not be ignored.
Preferential agricultural policies (X4, B = 0.393, p = 0.000) had a significant positive impact on villagers’ overall satisfaction with the post-disaster reconstruction area, indicating that the greater the implementation of preferential agricultural policies, the higher the villagers’ overall satisfaction with post-disaster reconstruction settlements. This is consistent with the view of Tian and Zhao (2010), that the intensity of implementation of agricultural benefit policies and the two exemption and one subsidy policies can have a great and positive impact on farmers’ life satisfaction.
The higher the level of satisfaction of residents with the construction of the human environment (X5, B = 0.594, p = 0.000) in the post-earthquake reconstruction area, the higher the overall satisfaction of villagers with the post-earthquake reconstruction residential area. This showed that improving rural residents’ satisfaction from the perspective of rural policy environment, rural talent environment, and rural cultural environment will be conducive to improving rural residents’ satisfaction with post-disaster reconstruction settlements. Ye (2015) suggested that there are many problems in the construction of the rural cultural environment, which reduce the happiness of rural residents to varying degrees. They proposed that corresponding policies should be formulated for different problems, to improve the life satisfaction of rural residents (Ye, 2015), which is consistent with the conclusion of this study.
The higher the villagers’ recognition in the construction of hardware environment (X6, B = 0.637, p = 0.000), the higher the villagers’ overall satisfaction with the post-disaster reconstruction, and the greatest influence of hardware facilities construction in the post-disaster reconstruction area. This shows that the construction of infrastructure for post-disaster reconstruction settlements is the most important content that affects rural residents’ satisfaction with these settlements. Routes for rapid evacuation and earthquake shelters are basic requirements necessary to improve the level of seismic resilience in the new era. The timeliness of evacuation routes and the reliability of earthquake shelters during disasters will reduce casualties. Therefore, rational road planning and the safety and accessibility of earthquake shelters had a significant impact on rural residents’ satisfaction with the rebuilding of settlements after a disaster, which is consistent with the conclusions of a study by Ma et al. (2021).
5 Conclusion
Rural residents’ overall satisfaction with the post-disaster reconstruction of residential areas is influenced by many factors. Based on a literature search, combined with information about the current situation in ten post-disaster reconstruction settlements in Sichuan Province, this study summarized the factors that affect rural residents’ overall satisfaction with post-disaster reconstruction settlements. In this questionnaire survey, 483 valid questionnaires were collected following face-to-face completion of the questionnaires by village residents. This research uncovered the following insights:
1) The more the villagers in earthquake-stricken areas know about post-disaster reconstruction methods, the greater their overall level of satisfaction with post-disaster reconstruction. Therefore, attention should be paid to improving rural residents’ awareness of methods used to reconstruct residential areas and strengthening the publicity and education around post-disaster reconstruction methods.
2) Following earthquake disaster reconstruction, if annual household income increases to more than that before the reconstruction, the villagers in the disaster area will be more satisfied with the overall reconstruction. Therefore, we should pay attention to employment issues following reconstruction and increase the income of rural residents after a disaster.
3) Six public factors, such as the village committee acts as, housing construction quality, public service, policy of benefits for farmers, cultural environment, and hardware environment, all significantly positively affect residents’ overall satisfaction with post-earthquake reconstruction. This study is of great importance for enriching the theory of residents’ satisfaction and the practice of post-earthquake reconstruction.
Through the post-earthquake reconstruction of Wenchuan residential satisfaction survey, this research has revealed the influence of rural residents for post-earthquake reconstruction overall satisfaction of the key factors of residential area, reveals the relationship between human activities and residents of the ecological environment. It also reveals the unreasonable planning in the construction of post-disaster reconstruction settlements from the perspective of rural residents, complements the theory development of post-disaster reconstruction settlement. More scientifically, this research describes the construction status and problems of post-disaster reconstruction settlements. This has practical significance for maintaining the spatial stability of post-disaster reconstruction settlements.
6 Limitations
Despite the innovative essence and significant findings of this research, this study does have some limitations, as elaborated below.
1) The variables collected were limited. Although this study combined the current situation in post-disaster reconstruction settlements and put forward a variety of factors based on existing research, the content cannot fully cover all aspects of rural residents’ satisfaction with post-disaster reconstruction settlements. Therefore, there are limitations due to the variables collected in this study.
2) Data values are limited. When respondents are satisfied with each indicator, their understanding of the questions set in the questionnaire may have been biased due to their own educational level and mood at the time, so the data value has certain limitations. In future research, the formulation of the questionnaire should take this objective phenomenon into account, and the language should be as concise as possible, easy to understand, and avoid any redundancy.
3) The scope of the investigation also had limitations. This study selected rural residents from ten sample villages in Sichuan Province as the research objects, and the survey was conducted in the form of on-site household visits, so it was difficult to avoid contingency and regional distribution limitations. In future research, it will be necessary to increase the number of areas investigated to make the spatial distribution more representative.
Statements
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
Ethical review and approval were not required for the study involving human participants in accordance with the local legislation and institutional requirements. The patients/participants provided their written informed consent to participate in this study.
Author contributions
Conceptualization: YA, LH; data collection and analysis: LH, JZ, ZZ; writing the original draft: JZ, ZZ, TW, YW, YA; revising: TW, YC; resources: YA, TW; supervision: YA, TW, YC.
Funding
This study is jointly supported by the National Natural Science Foundation of China (72171028), China Postdoctoral Science Foundation (2022T150077, 2022M710496), Sichuan Provincial Social Science Planning Project (22GL086), Development Research Center of Sichuan Old Revolutionary Base Area (SLQ2022SB-23) The article processing costs are funded by Delft University of Technology.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
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Summary
Keywords
earthquake-stricken area, post-disaster reconstruction, satisfaction analysis, factorial analysis, ordered logistic regression
Citation
Ao Y, Zhong J, Zhang Z, Han L, Wang Y, Chen Y and Wang T (2022) Determinants of villagers’ satisfaction with post-disaster reconstruction: Evidence from surveys ten years after the Wenchuan earthquake. Front. Environ. Sci. 10:952700. doi: 10.3389/fenvs.2022.952700
Received
25 May 2022
Accepted
22 July 2022
Published
06 September 2022
Volume
10 - 2022
Edited by
Ge Wang, Huazhong Agricultural University, China
Reviewed by
Zhe Cheng, Xi'an University of Architecture and Technology, China
Zezhou Wu, Shenzhen University, China
Updates
Copyright
© 2022 Ao, Zhong, Zhang, Han, Wang, Chen and Wang.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yan Wang, wangyan@scac.edu.cn; Tong Wang, t.wang-12@tudelft.nl
This article was submitted to Environmental Economics and Management, a section of the journal Frontiers in Environmental Science
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All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.