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Sci Rep
Sci Rep
Scientific Reports
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Nature Publishing Group UK London

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70719
10.1038/s41598-024-70719-6
Article
The measurement of rural community resilience to natural disaster in China
Li Yuheng liyuheng@igsnrr.ac.cn

12
Wang Shengye 4
Zhang Yun 3
Du Guoming 3
1 https://ror.org/04t1cdb72 grid.424975.9 0000 0000 8615 8685 Key Laboratory of Regional Sustainable Development Modeling, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, 100101 China
2 https://ror.org/05qbk4x57 grid.410726.6 0000 0004 1797 8419 University of Chinese Academy of Sciences, Beijing, 100049 China
3 https://ror.org/04v3ywz14 grid.22935.3f 0000 0004 0530 8290 College of Humanities and Development Studies, China Agricultural University, Beijing, China
4 https://ror.org/0515nd386 grid.412243.2 0000 0004 1760 1136 Present Address: College of Public Administration and Law, Northeast Agricultural University, Harbin, China
2 9 2024
2 9 2024
2024
14 2032229 11 2023
20 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Comparing with cities, rural communities especially those declining rural communities have become vulnerable to natural disasters owing to their backward socioeconomic conditions. Taking Xun County of China’s Henan Province as the study area, the paper aims to evaluate rural community resilience to flood by unveiling the connection between individuals’ cognition, follow-up actions and the community resilience. Research results show that: (1) The logic chain exists as individual’s cognition to disaster leads to their constructive actions to cope with disaster, which contribute to community resilience. (2) At the cognition dimension, individual’s knowledge reserve of disaster prevention and their recognition to local authority are playing an important role in their decision making and follow-up behaviors when disaster occurs. (3) At the action dimension, individual’s familiarity with the disaster preparedness, efficient information transmission when disaster occurs and villagers’ following order and their unity of action all contribute to community resilience to disaster. The paper proposes ways to improve rural community resilience to disasters based on the research findings.

Keywords

Rural community resilience
Cognition
Natural disaster
China
Subject terms

Sustainability
Climate-change adaptation
501100001809 National Natural Science Foundation of China (National Science Foundation of China) 42171208 Li Yuheng issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

As the world has transformed from agriculture-based rural society to capital, industry and knowledge-based urban society, rural decline owing to depopulation and other related factors like industrial recession has become a global tendency which has swept both developed and developing countries1. This has further aggravated rural vulnerability to cope with those increased risks and unexpected challenges such as natural disasters associated with anthropogenic climate change. Within this background, sustainable communities have drawn public attention for the sake of ensuring people’s livelihood, welfare and socioeconomic stability etc. According to Hsine2 and Haight3, sustainable communities are characterized as ecologically friendly, socially harmonious, and economically efficient places where people have strong sense of belonging, community participation, and shared ecological and cultural awareness. Further, sustainable communities are also believed to meet the needs of current and future generations, and to provide opportunities and choices to enable local residents to achieve sustainable development4,5.

Generally, rural community is a complex and dynamic system which consists of both material elements like space, natural endowments and immaterial elements like norms, institutions. It is the interactions between rural internal system and the external environment that promote rural evolution which may turn into different development patterns like growth, stagnation or decline6. Thus, there exists no single model to revitalize and sustain rural communities especially in a context of increasing incidences of natural disasters across the world. As a result, there is widespread interest from both academic and governmental spheres in understanding the patterns of resilience exhibited by rural communities to mitigate the impact of natural disasters and create adaptive measures for future risks.

The concept of resilience which is widely used in the fields of ecology, material science and biology etc. has become an essential aspect of sustainability to describe the ability of a complex system characterized by the nonlinear interactions of economic, social, environmental and institutional subsystems to withstand, adapt and continue to develop when facing unexpected shocks and risks7–10. Community resilience reflects the capacity of rural communities to proactively resist, recover and adapt to adversity and disturbances and it tightly relates to the decision making and behaviors of local stakeholders who operate within the community’s economic, social and environmental subsystems11–15. Arbon.16 described community resilience to disasters as “…where members of its population are connected to one another and work together, so that they are able to function and sustain critical systems, even under stress; adapt to changes in the physical, social or economic environment…”. Case studies also proved the importance of local stakeholders’ self-organized actions with shared values and attitude in carrying out rural revitalization initiatives17. Besides, literatures also mentioned other necessary components to contribute to community resilience, including economic and social capacity, organizational capacity and community competence as well as information flows and communication capacity14,18–23,24.

Rural China has been facing depopulation and other emerging socioeconomic problems as the state promotes its rapid urbanization and industrialization development during the past decades. In this process, those agriculture, forestry or other natural resources-based communities which are situated outside the positive influence of the metropolitan regions are inclined to decline25. As a result, rural decline which is manifested as rural hollowing and other related recessions has become the focal point of China’s rural revitalization strategy which was launched in 201726. Being a country of huge population and large territory of complicated and diverse socioeconomic as well as climatic conditions, China has been suffering various climatic disasters. The past 30 years have witnessed around 358 million people who were swept by various natural disasters in China27. Generally, comparing with cities, rural communities especially those declining communities have become vulnerable to natural disasters owing to their backward socioeconomic and infrastructure conditions. It thus becomes necessary to evaluate and enhance rural community resilience so as to better cope with natural disasters in the future.

In this study, we consider rural community resilience as a capacity which not only recognizes and anticipates the scope of damages, but also integrates multiple actors (public, private, nonprofit organizations) and coordinates their actions when facing natural disasters and other shocks. What’s more, besides those external support from the public organizations and governments, a key hypothesis underpinning the research of the paper is that local stakeholders’ cognition and their actions before, during and after disasters play an essential role in building community resilience. In this sense, the paper aims to examine rural community resilience to disaster from the perspective of individuals’ cognition to disaster and their follow-up actions. The study area of this paper is Xun County which is located in Central China’s Henan Province. In July 2021, Xun County suffered extremely heavy rainstorm and flood disaster while 94% of its population were affected. This flood was unusual and can be used as a suitable research case to examine rural communities’ disaster response, post-disaster recovery as well as local residents’ cognition to disaster.

The structure of the paper is as follows. After the introduction, the paper reviews related literatures and creates a theoretical framework by integrating people’s cognition, follow-up action and community resilience. The third section illustrates the study area, research methodology and data source. The fourth section displays and analyzes the research results. In the end, the paper closes by discussing research findings and conclusions.

Literature review and theoretical framework

To date, scholars have used various methods to measure community resilience given the fact that there are no universally accepted measurement tools. Data limitation and diverging indicators of resilience are also challenging the measurement of community resilience. Kapucu et al.28 conducted mail and online survey of emergency management professionals in eight Central Florida counties of the US to investigate the characteristics of disaster management in rural communities and ways to strengthen emergency management systems to develop and improve community disaster resilience. Boon13 carried out individual and group interviews of four disaster-impacted rural communities in Australia and examined significant generic factors to community resilience based on people’s views and attitudes. Hong et al.29 used people’s mobility data (800,000 mobile devices) before, during and after the hurricane Harvey in the US in 2017 and measured inequality of community resilience which was considered as a function of the magnitude of impact and time to recovery.

Su et al.30 depicted the evolution of rural community resilience in China’s Qinghai-Tibetan Plateau and found changing stages of resilience given to the differences of governmental interventions and community self-organization abilities. What’s more, there are also studies focusing on local stakeholders’ perceived community resilience which is measured by assessing individual’s perceptions of their community’s reactions to natural disasters20,31–33.

Given the above descriptions about community resilience, we believe that it is the local villagers’ views, awareness and attitudes as well as their behaviors that form and support community disaster resilience. As Imperiale and Vanclay34,35 indicated, community resilience has both cognitive and interactional dimensions. Resilient communities are normally considered to be the places where residents tend to have better cognition and awareness of local vulnerabilities and stronger intention and bottom-up initiatives to change the situation17. This is manifested by their decision making and actions when dealing with challenges and shocks. And this further explains why some rural communities are more resilient than others when disasters occur.

Generally, individuals’ cognition towards disaster consists of four dimensions. The first dimension is their awareness and knowledge of potential disasters to local communities. This tightly relates to disaster prevention, preparedness and mitigation programs before disaster occurs. Sim et al.36 highlighted that comparing with material aid, the awareness of disaster prevention and public participation are the key variables which are significantly and positively correlated with community disaster resilience. The second cognition dimension is individual’s authority recognition under which the local villagers will follow orders and arrangements issued by the community committee during the disaster resistance and recovery periods. This helps to guarantee the unity of local residents’ action to mitigate disaster instead of staying in chaos. The importance and effectiveness of authority recognition were proved in Yang et al.’s33 study of measuring community disaster resilience in China’s Loess Plateau. Research findings showed that resilient communities are those where village committees and residents can work cooperatively and coherently. The third dimension of cognition is individuals’ awareness of self-participation and active involvement in resisting and mitigating disasters. This tightly connects to individuals’ sense of place and their local identity which are considered as the fourth dimension of cognition. The stronger of residents’ sense of place and local identify, the more active they will be to participate in disaster mitigation and recovery.

All the above-mentioned aspects of individuals’ cognition are expected to lead to local residents’ proactive actions and response to cope with disasters. Thus, we draw Fig. 1 to capture the relationship between individuals’ cognition, their behavior and community resilience in response to disasters.Fig. 1 Theoretical framework of individuals’ cognition, behavior and community resilience.

Research methodology and data source

Overview of the study area

Xun County is located in the northern part of Central China’s Henan Province. This county covers a total area of 966 square kilometers while 82% of its territory is plain and 18% is hilly areas (Fig. 2). With 748,000 population, Xun County administrates 7 townships and 4 urban districts as well as 438 villages. In 2021, the county’s gross domestic product (GDP) reached 29.38 billion RMB while the per capita GDP was 23,922 RMB.Fig. 2 Location and administration of Xun County.

Xun County is located in the middle and lower reaches of China’s Yellow River while the Wei River runs through its entire territory. The county is susceptible to floods due to its low terrain. In July 2021, the upper reaches of the Wei River experienced extensive and lasting heavy rainfall which resulted in soaring water level of the Wei River and the Communist Canal. As a result, big flood occurred and swept Xun County. 94% of its total population were affected with the direct economic loss reaching over 9.76 billion RMB.

Beside the downtown area, the major part of Xun County is rural where local economy and people’s livelihood are based on agriculture. In the meantime, Xun County faces risks of flood because of the Wei River. Thus, many villages are vulnerable to flood. By selecting Xun County as the study area, we aim to contribute to the literature on rural development strategies and resilience building in disaster vulnerable regions. The devastating flood which occurred in July 2021 highlighted the vulnerability of the county and the urgent need for effective disaster management strategies. The study aims to analyze the impact of this event on the local rural communities and explore ways to build rural disaster resilience. Lessons drawn from Xun County are expected to generate enlightenments for rural places which are facing climatic disasters across the world.

Research methodology and data

Based on the theoretical framework, the paper hypothesizes that individuals’ cognition leads to their proactive actions when disaster occurs. These will finally contribute to rural community resilience to disasters and demonstrate the coping ability of communities to withstand disasters. To measure rural community resilience, the first step is to select and determine indicators which are depicting people’s cognition and follow-up behaviors. Then, the research team made questionnaires and carry out rural survey in Xun County. The final step is to sort data and measure community resilience with entropy methodology.

The survey design aims to measure the resilience of rural communities by selecting indicators that reflect people's cognitive processes and behavior in disaster scenarios. An index system has been constructed for evaluating resilience based on the four dimensions of cognition. Each dimension was chosen based on their theoretical importance for understanding how individuals and communities prepare for, respond to, and recover from disasters.

First, disaster prevention awareness and knowledge reserve refer to villagers’ awareness and knowledge of potential disasters to local communities. In this study, it is manifested as relevant knowledge in the pre-disaster phase, such as household emergency plans and mitigation programs making before disaster occurs, and their awareness of disaster knowledge reserve.

Second, authority recognition refers to the degree of individual’s following orders and arrangements issued by community committee when facing disasters. In this study, this dimension is assessed by a series of villagers’ behaviors and opinions, such as whether they can obey commands in an orderly manner, whether they trust the village cadres, and whether they can actively cooperate with reconstruction after the disaster.

Third, self-participation awareness refers to the enthusiasm of local residents’ active involvement in resisting and mitigating disasters. This dimension is evaluated by local residents’ behavior during post-disaster reconstruction period. The survey questions focus on whether villagers can help each other, whether they can actively participate in discussions and management of public affairs.

Finally, people’s sense of place leads to their participation in disaster mitigation. In this study, this dimension is examined from the perspectives of folk customs and residents’ attention to the community development.

After establishing the evaluation index system, it is necessary to further quantify the weights of indicators. Entropy method is used to determine the weights of evaluation indicators to eliminate subjective factors. Entropy is a concept derived from physics, originally referring to a measure of the disordered state of a system. In information theory, entropy is a measure of uncertainty, which can be calculated based on the characteristics of entropy to determine the degree of dispersion of a certain indicator and then determine its weight. In general, the greater the dispersion of indicators, the greater their impact on comprehensive evaluation. By referring to Sovacool et al.’s 37 methodological review, the design of the research and the above-mentioned methodology tend to increase the robustness and novelty of this study.

The following are the detailed calculation steps for entropy method:

Build the original indicator matrix,

There are n villages with h evaluation indicators, and the original indicator matrix is established as X=xijn×h1≤i≤n,1≤j≤h, which xij is the value of the j indicator in the i village.

Non dimensional processing,

Use the deviation standardization method to convert the original indicators in the index system into dimensionless indicators Zij.1 Zλij=Xij-XijminXijmax-Xijmin.

The indicators involved in this study are all positive indicators. The values of each evaluation indicator after standardization are within the range of [0, 1].

Determine the weight of each evaluation indicator,

To ensure a comprehensive evaluation of rural resilience level, this article determines the weights of the four dimensions of resilience indicators as 1/4, and then uses the entropy method to assign weights to the 10 indicators to evaluate the rural community resilience level.

On the basis of normalizing the indicators according to formula (2), calculate the entropy values of each indicator Ej, as shown in formulas (3) and (4).2 Pij=Zij/∑i=1nZij,

3 Ej=-k∑i=1nPijlnPij,

4 k=1/lnh×n.

Determine the resilience level of various dimensions of communities and the comprehensive resilience level of rural communities.

Calculate the entropy redundancy Dj using formula (5) and obtain the weight with the support of formula (6).5 Dj=1-Ej,

6 Wj=Dj/∑j=1hDj.

Calculate the standardized values and weights of each dimension according to formula (7) to obtain the resilience level of each indicator Rk.7 Rk=∑ZijWj.

The standardized processing results of the 10 sub indicators in the rural resilience evaluation index system are overlaid with their weights to obtain the comprehensive resilience level of rural areas R.8 R=∑j=116ZijWj.

To depict this index system, the rural community disaster resilience evaluation index system is shown in Table 1.Table 1 Evaluation index of rural community disaster resilience.

Community disaster resilience	Indicators	Explanation	Weights	
Disaster prevention awareness and knowledge reserve	Do rural households have emergency plans and measures for flood disasters	To test whether the residents have relevant knowledge reserve in the pre-disaster phase	0.084	
Are villagers familiar with the village’s disaster prevention and mitigation programs and measures	To examine the extent to which residents have acquired the relevant knowledge reserve in the pre-disaster phase	0.083	
In the event of a flood, villagers are well aware of what to do	To test the ability of residents to call on relevant knowledge reserves when faced with a disaster	0.083	
Authority recognition	When flood occurs, villagers can follow the command of the village committee and act in an orderly manner	To examine the extent to which residents trust authorities when faced with a disaster	0.084	
In the process of flood relief, villagers trust the village cadres	To examine the extent to which residents trust the interests behind the decisions of authorities when faced with a disaster	0.083	
In the process of post-disaster reconstruction, villagers can cooperate actively	To examine residents’ trust and understanding of authority’s decision-making priorities after a disaster	0.083	
Self participation awareness	In the process of flood relief and post-disaster reconstruction, villagers can help and care for each other	To test the willingness of residents to proactively engage in helping their neighbors in the face of disasters	0.125	
In the process of post-disaster reconstruction, villagers are keen to participate in the management of public affairs in the village and express their views	To test the willingness of residents to participate actively in discussions and construction in the process of disaster recovery	0.125	
Sense of place	The village’s folk customs are good	To examine whether the local human environment and customs are conducive to the formation of stronger community residential bonds	0.125	
Villagers are concerned about the development of the village	To test whether local people identify with the community in which they live and see its development as an important part of their lives	0.125	
All indicators are calculated using the entropy method, with scores expressed as any value between 0 and 1. The resilience of each dimension and the total resilience are obtained by adding the weights of each indicator, with the same value range.

Xun County is of high population density (774 people per square kilometer) and each township consists of over 60 villages. Overlap of the research results at village level will occur if each village community is used as the research unit. For the sake of map quality, the paper takes township as the unit to explicitly present the research findings.

The authors conducted household survey which covering 504 administrative villages in Xun County. The survey was carried out by scholars from the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences in March and April, 2022. This survey consists of detailed investigation before, during and after the flood. The dataset with questions covering individuals’ multiple cognitive opinions and behaviors, cooperating attitudes, and social capital, serves as a robust foundation for achieving the study's research objectives.

The survey included self-reported household information on demographics, livelihood capital, multiple shocks, and coping strategies. Questions are consistent with the evaluation index system in Table 1. The village committee director who are familiar with their village were invited to answer the questionnaire. In this process, the committee directors were asked to complete the survey independently and objectively.

To improve our empirical estimations, we cleaned the data rigorously in three steps. First, samples with missing or abnormal values on social capital were excluded. Second, we deleted samples that reported missing values for variables representing cooperating attitude. Third, we excluded samples that reported missing or abnormal answers on the personal identity information. We finally obtained 418 valid questionnaires.

The survey questions are based on the index system in Table 1, which integrates the main viewpoints of previous literatures on community disaster resilience. Through statistical analysis, the survey results have well presented the process and logic from individual’s cognition to action when disaster occurs. It is in consistency with existing theories of community resilience.

Research results

Disaster damage and post-disaster recovery

According to the damage of the villages, the paper divides the surveyed villages into three categories: mostly affected, partially affected and little affected. Statistics show that there are 162 mostly affected villages which account for 38.76% of the total surveyed villages. 144 villages are partially affected, accounting for 34.45% while 112 villages were little affected, accounting for 26.79% of the surveyed villages. According to Fig. 3, the north and south parts of Xun County suffered higher level of damage than other parts. We can see that those mostly affected villages are mainly located along the Wei River and Communist Canal. These include Xiaohe Township, Xinzhen Township and Wangzhuang Township. However, Pishan District, Shantang Township and Liyang District which are further away from Wei River and the Communist Canal were relatively less affected.Fig. 3 Flood damage in Xun County.

According to the degree of socioeconomic recovery after the flood, the paper divides the research villages into three categories: fully recovered, partially recovered and unrecovered villages. According to Fig. 4 the peripheral townships of Xun County are comparatively better than the central places in terms of post-disaster recovery. However, the overall level of post-disaster recovery of Xun County is still low during the survey time especially in Xunzhou District, Pishan District and Tunzi Township.Fig. 4 Post flood recovery in Xun County.

Spatial distribution of rural community disaster resilience

As shown in Fig. 5, the rural community disaster resilience of Xun County is at high level as a whole and the average resilience value is between 79 and 95%. There are obvious spatial differences in the community disaster resilience at the township level. The community resilience in the central part of Xun County is higher than that of the surrounding areas. For example, the community resilience of the Xun zhou District, Baisi Township and Pishan District are higher than other areas, with their resilience values at 0.95, 0.94 and 0.93, respectively.Fig. 5 Rural community disaster resilience of Xun County.

The paper further maps the spatial distribution of the four aspects of individuals’ cognition in Fig. 6. The average value of disaster prevention awareness and knowledge reserve of all townships is 0.90, which is at high level. And the western and northern parts of Xun County have higher values of disaster prevention awareness and knowledge reserve than other places (Fig. 6a). Among all the townships, Baisi Township and Weixian Township are of the highest scores at 0.93 and 0.94, respectively.Fig. 6 Four dimensions of rural community disaster resilience of Xun County.

As shown in Fig. 6b, the central areas of Xun County have higher values of authority identification than other places. In addition, the index value of Xiaohe Township, Xinzhen Township and Liyang District are obviously lower than other areas.

The average value of self-participation awareness of all townships is 0.8.8 As Fig. 6c shows, the central and west parts of Xun County have higher level of residents’ self-participation awareness than other places. However, Xiaohe Township has lower level of self-participation awareness (0.81).

As for the sense of place, beside Xiaohe Township, Liyang District, Tunzi Township and Weixi District, all the other townships have higher values of residents’ sense of place (Fig. 6d).

This suggests that these four aspects of cognition have directly built up and contributed to local residents’ reaction to the flood disaster, and thus play a positive role in improving community disaster resilience.

Influencing factors to rural disaster resilience

Disaster prevention awareness and knowledge reserve

Peasants’ knowledge of disaster prevention measures before the disaster are the key factors to reduce risk. This survey selected three indicators: whether the peasants’ families in the village have emergency plans and pre-disaster measures for flood, whether villagers are familiar with the disaster prevention and mitigation plans and measures, and whether the villagers clearly know what preparations need to be made when flood occurs.

As shown in Table 2, only 18.77% of the rural households of Xun County have formulated emergency plans and measures for flood while 49.52% of the villagers are very familiar with the disaster prevention and mitigation plans and measures. According to Table 2, the variance at township level is not significant. It is worth noting that knowledge reserves are limited to the collective level. Compared to their own families, villagers have a more sufficient knowledge reserve about collective action. However, ignoring individual level contingency plans tends to cause greater loss of life and property.Table 2 Indicators of disaster prevention awareness and knowledge reserve.

Township	According to your understanding, the village households have emergency plans and measures for flood disasters	As you know, the villagers are very familiar with the village’s disaster prevention and mitigation programs and measures	In the event of a flood, the villagers are well aware of what needs to be done to prepare and respond	
Xunzhou district	0.20	0.52	0.22	
Baisi township	0.19	0.51	0.22	
Pishan district	0.19	0.49	0.22	
Weixian Township	0.20	0.48	0.22	
Shantang Township	0.19	0.50	0.22	
Weixi district	0.19	0.49	0.22	
Tunzi township	0.19	0.51	0.21	
Wangzhuang Township	0.19	0.51	0.21	
Xinzhen Township	0.19	0.52	0.22	
Liyang district	0.17	0.43	0.20	
Xiaohe township	0.18	0.49	0.22	

At the time of floods, whether people have mastered the response measures has a decisive impact on whether they can effectively mitigate disaster impact. The survey results show that only 21.64% of the villagers of Xun County clearly know what preparation work and countermeasures need to be done. For taking correct actions to deal with disaster losses, more emphasis is needed on the spread of knowledge.

Authority recognition

When flood occurs, whether the villagers can follow the command of the village committee and act in an orderly manner reflects the villagers’ recognition of local authority. Cooperation and trust among the community residents are crucial when floods occur. Better command effect leads to better disaster countermeasure.

The trust in leadership and the willingness to cooperate in post-disaster reconstruction have shown poor performance (10%). Differences of residents’ authority recognition among townships mainly focused on the accessibility of information.

According to Table 3, at the time of the flood, 77.34%, 69.98% and 68.75% of the villagers in Xunzhou District, Baisi Township and Pishan District were able to follow the command of the village committee and act in an orderly manner. The villagers in Xiaohe Township, the worst-hit township, have the worst performance in this regard. Only 45.65% of the villagers can follow the command of the village committee in an orderly manner. Most townships can reduce disaster losses by taking collective actions.Table 3 Indicators of authority recognition.

Township	When the flood occurs, the villagers can follow the command of the village committee and act in an orderly manner	In the process of flood relief, the villagers trust the village cadres very much. In the process of flood relief, the villagers trust the village cadres very much	In the process of post-disaster reconstruction, the villagers can cooperate actively	
Xunzhou district	0.70	0.09	0.12	
Baisi township	0.77	0.09	0.12	
Pishan district	0.58	0.08	0.11	
Weixian Township	0.69	0.09	0.13	
Shantang Township	0.60	0.09	0.12	
Weixi district	0.62	0.08	0.12	
Tunzi township	0.64	0.08	0.11	
Wangzhuang township	0.65	0.09	0.11	
Xinzhen Township	0.64	0.09	0.12	
Liyang district	0.46	0.08	0.10	
Xiaohe township	0.58	0.08	0.12	

The results further show that in the process of flood relief, only 8.5% of the villagers of Xun County have great trust in village cadres. Among the 11 townships, the lowest value of the research results is still Xiaohe Township, only 7.6%. In the process of post-disaster reconstruction, 11.67% of the villagers of Xun County were able to cooperate actively. Among them, the villagers in Pishan District, Baisi Township, Xunzhou District and Shantang Township performed relatively well, which were 13%, 12.29%, 12.07% and 11.94%, respectively, which were higher than the average value of 11.67% in the county.

In short, among the 11 townships of Xun County, the villagers in Xunzhou District, Baisi Township and Pishan District have relatively high recognition of the authority identification, while Xiaohe Township has the lowest recognition. The empirical results show that the higher the villagers’ recognition of the authority identification, the more conducive it is to the smooth development of disaster prevention and post-disaster reconstruction. The cadres of villages and townships of Xun County should be further strengthened in obtaining villagers’ trust.

Self participation awareness

When the flood occurs, the villagers are the main body to bear the disaster risk. Villagers’ high sense of independent participation is the key factor to deal with the disaster, which is conducive to the formation of strong collective social capital and the improvement of the government’s work efficiency. The questionnaire survey focuses on whether the villagers can help and care for each other in the process of flood relief and post-disaster resettlement, whether the villagers are keen to participate in the management of public affairs in the village and express their views, and evaluates the villagers’ awareness of independent participation.

As shown in Table 4, in the process of flood relief and post-disaster resettlement, the situation that the villagers can help each other and care about each other is generally poor. Only 34.89% of the villagers of Xun County can help and care for each other in the process of flood relief and post-disaster resettlement. In the process of post-disaster reconstruction, 53.43% of the villagers of Xun County are keen to participate in the management of public affairs in the village and express their views. Although the proportion is generally higher than that of the villagers who can help and care for each other, the overall level is at a general level. This shows that when disasters occur, villagers rely more on state and social assistance, and their subjective initiative is low. Post-disaster reconstruction relies on people. On the one hand, we should focus on the recovery of ‘hardware’ in the disaster area, and on the other hand, we should pay attention to the mental health of the victims. Every farmer’s own active disaster relief is more effective than government assistance and social support to reduce psychological pressure, and to a certain extent, it can prevent social problems such as order imbalance or instability caused by post-disaster.Table 4 Indicators of self participation awareness.

Township	In the process of flood relief and post-disaster resettlement, the villagers can help and care for each other	In the process of post-disaster reconstruction, the villagers are keen to participate in the management of public affairs in the village and express their views	
Xunzhou district	0.36	0.56	
Baisi township	0.37	0.56	
Pishan district	0.34	0.50	
Weixian township	0.37	0.55	
Shantang township	0.35	0.54	
Weixi district	0.34	0.53	
Tunzi township	0.34	0.52	
Wangzhuang township	0.34	0.53	
Xinzhen township	0.36	0.54	
Liyang district	0.32	0.48	
Xiaohe township	0.34	0.55	

The enthusiasm level of local people’s participation in public affairs reaches 50%. While level of mutual assistance among residents is only around 30%. This is strongly related to the political system of grassroots communities in China, where residents place greater emphasis on collective knowledge and action. This characteristic was also reflected in the authority identification section.

Sense of place

The sense of place belonging represents the villagers’ recognition and attachment to local infrastructure, environmental sanitation, living conditions, and the masses. After the disaster, a good sense of place belonging is conducive to stimulating the villagers’ strong sense of home, which plays an important role in promoting post-disaster reconstruction. The questionnaire survey contains two indicators: whether the village style and folk customs are good, and whether the villagers are very concerned about the development of the village, reflecting the status quo of the villagers’ sense of local belonging.

According to the survey data shown in Table 5, 49% of the villagers of Xun County think that the village style and folk customs are good, and 47.46% of the peasants are very concerned about the development of the village, which is at a lower-middle level. In the context of urban and rural development and diversified interests, the sense of place belonging of villagers in some areas has weakened, which is detrimental to the effective response to major disasters and risks. A strong sense of place belonging helps villagers work together in the face of common disasters and risks.Table 5 Indicators of public sense of place.

Township	The village style and folk customs are good	The villagers are very concerned about the development of the village	
Xunzhou district	0.50	0.48	
Baisi township	0.51	0.49	
Pishan district	0.47	0.45	
Weixian township	0.50	0.49	
Shantang township	0.50	0.48	
Weixi district	0.48	0.47	
Tunzi township	0.49	0.47	
Wangzhuang township	0.49	0.48	
Xinzhen township	0.50	0.48	
Liyang district	0.46	0.45	
Xiaohe township	0.50	0.48	

Sense of place shows the best overall performance among four dimensions. Half of the residents are able to view social networks from a positive perspective, and willing to pay attention to community development. This indicates that the community has a high level of social capital reserves and a structural foundation for collective action and mutual assistance.

Test of resistance and recovery

Based on the above spatial distribution of disaster situation, except for a few townships, there is not much absolute difference in resilience in terms of self participation awareness and sense of place among the townships in Xun County. Therefore, what truly widens the overall resilience gap is the weak factors of each township, especially the authority identification. Compared to further leveraging advantageous factors, reinforcing weaknesses plays a more important role in improving the disaster resilience of rural communities.

Comparing with the disaster situation and overall resilience, townships with lower knowledge reserves may also exhibit higher resilience (such as Xunzhou District), and townships with severe disaster situations may also have higher knowledge levels (such as Weixi District). However, there is almost a significant positive correlation between authority identification and overall resilience. We can conclude that there are differences between theoretical knowledge and practical knowledge in the process of responding to natural disasters in rural communities. Theoretical knowledge determines the upper limit of practical knowledge, but it is practical knowledge that truly determines the performance of community resilience. If theoretical knowledge is difficult to effectively transform into practical knowledge, prevention work cannot be effectively applied when disasters occur.

According to the definition in this article, the higher the resilience of rural communities to disasters, the better their performance in combating disasters should be. This performance is divided into resistance to actively responding to disasters and recovery to quickly restart after disasters. These two factors together create a rural community’s performance in facing disasters, and resistance, as the actual first response to disasters, should have a greater impact on the overall performance of rural communities than the impact of post disaster recovery. Combining the two, the better the overall performance of rural communities, the smaller the losses caused by disasters.

Next, based on this inference, we constructed an evaluation index system for resistance and recovery from other questions in the previous questionnaire survey (see Tables 6 and 7), and used the entropy method to calculate the performance scores of each township (out of 1).Table 6 Indicators of community resistance.

Township	When a disaster occurs, important affairs in the village can be notified to all villagers in a timely manner	In the process of flood rescue and post disaster resettlement, the villagers can timely understand each other's status and assist each other	Resilience	
Xunzhou district	0.78	0.21	0.95	
Baisi township	0.74	0.21	0.94	
Pishan district	0.78	0.22	0.93	
Weixian township	0.75	0.21	0.91	
Shantang township	0.74	0.20	0.90	
Weixi district	0.72	0.20	0.90	
Tunzi township	0.69	0.20	0.89	
Wangzhuang township	0.67	0.20	0.89	
Xinzhen township	0.71	0.20	0.88	
Liyang district	0.68	0.20	0.86	
Xiaohe township	0.62	0.19	0.79	

Table 7 Indicators of community recovery.

Township	The current economic and social conditions in this village have returned to pre-disaster levels	The current production and living conditions of villagers have returned to pre-disaster levels	Resilience	
Xunzhou district	0.52	0.40	0.95	
Baisi township	0.50	0.39	0.94	
Pishan district	0.53	0.42	0.93	
Weixian township	0.49	0.39	0.91	
Shantang township	0.49	0.39	0.90	
Weixi district	0.48	0.38	0.90	
Tunzi township	0.51	0.40	0.89	
Wangzhuang township	0.43	0.36	0.89	
Xinzhen township	0.48	0.38	0.88	
Liyang district	0.51	0.40	0.86	
Xiaohe township	0.44	0.34	0.79	

From the above-mentioned figures, we can see that the total disaster resilience is consistent with the representative indicators of community resistance to natural disasters. Rural communities with relatively high resilience will inevitably take the lead in the aspect of resistance, while the correlation of recovery is not that significant. This also demonstrates in the comparison between data and reality that resistance, as the first response, plays a more important role in the resilience level of rural communities when disasters occur.

Discussion

Climate change and its induced disaster events have arisen public concerns to sustainable development of human being and calls for trials to enhance human resilience in every aspect. As rural decline has become a global tendency, it becomes necessary to improve rural community resilience against natural disasters and other unexpected shocks. The study of this paper proves the important roles of individual’s cognition towards disaster and their follow-up actions in constructing rural community resilience. The research findings have several implications for policy making.

First and foremost, it is necessary to provide local residents with more training and publicity about disaster prevention and preparedness. Villagers’ knowledge about disaster directly leads to their actions in terms of households’ disaster prevention and resistance when disaster occurs. This will contribute to individual’s emergency response and disaster relief. In the meanwhile, each community should make its own disaster prevention and mitigation program which includes emergency materials reserve, disaster relief training and publicity as well as infrastructure construction like public shelters and water supply and drainage facilities.

Reshaping and establishing authority of local governments or village committee is needed to consolidate residents’ trust on the governing agencies. This helps to transmit disaster information and relief orders or arrangements down to each household. With the trust and authority recognition, local residents thus tend to follow the orders issued by the local governments or village committee, and respond with unity of actions when disaster occurs. In this process, trust and proximity in social networks are of great importance in developing sustainable communities. Local actions and interactions are expected to contribute to solving climatic disasters38.

Finally, there need to improve local residents’ sense of place. For one thing, residents are encouraged to participate in community governance to cultivate their self-participation awareness of community issues. For another thing, rural social capital—mutual trust, social norms and attitudes should be cultivated among villagers and village committee so as to enhance neighborhood interaction and deepen their sense of community identify and belonging. All these measures are expected to contribute to people’s participation in community recovery construction and care about others like those vulnerable group of people when coping with disasters.

The evaluation index system used in the paper considers rural community resilience to natural disaster as a comprehensive capability which is shown before, during and after the disaster. It integrates multiple actors (public, private, non-profit organizations) and coordinates their actions to cope with disasters. In addition, this model can better elucidate the spatial differences of community disaster resilience based on the four cognition dimensions and derive key influencing factors of different communities. Therefore, these four evaluation dimensions of cognition can be applied to community disaster resilience studies in other background. And the specific evaluation indicators could be adjusted according to research needs.

Conclusion

With community-based surveys in China’s Xun County, the paper evaluates rural community resilience to natural disaster based on the hypothesis that local stakeholders’ cognition and follow-up actions play an important role in resisting disasters. The research findings have proved the hypothesis.

To conclude the study of the paper, we believe that rural community resilience to disaster relies on villagers’ cognition to disaster and their follow-up actions before, during and after the disaster. For one thing, individual’s knowledge of disaster prevention and their authority recognition decide how they behave when disaster occurs. For another thing, individual’s familiarity with the disaster preparedness and mitigation programs, disaster information transmission and villagers’ unity of actions all contribute to community resilience to disaster.

Author contributions

Yuheng Li and Guoming Du wrote the “Introduction” part and guided the overall design of the article, constructing a theoretical framework based on literature review. Shengye Wang and Yun Zhang finished the rest main manuscript text and prepared Figs. 1, 2, 3, 4, 5 and 6 and Tables 1, 2, 3, 4, 5, 6 and 7. All authors reviewed the manuscript.

Data availability

The datasets generated and analyzed during the current study are not publicly available due to the consideration of high accuracy of administrative village survey data involves privacy and trust issues for interviewees, but are available from the corresponding author on reasonable request.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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References

1. Liu YS Li YH Revitalize the world’s countryside Nature 2017 548 275 277 10.1038/548275a 28816262
Liu, Y. S. & Li, Y. H. Revitalize the world’s countryside. Nature 548, 275–277 (2017).28816262 10.1038/548275a
2. Hsine R Guidelines and Principles for Sustainable Community Design: Keith Grey 1996 Florida A&M University
Hsine, R. Guidelines and Principles for Sustainable Community Design: Keith Grey (Florida A&M University, 1996).
3. Haight, T.D. A process for the development of sustainable Canadian communities. University of Guelph (Canada) (2001).
4. Portney KE Taking Sustainable Cities Seriously 2003 The MIT Press
Portney, K. E. Taking Sustainable Cities Seriously (The MIT Press, 2003).
5. Lee YJ Huang CM Sustainability index for Taipei Environ. Impact Assess. Rev. 2007 27 6 505 521 10.1016/j.eiar.2006.12.005
Lee, Y. J. & Huang, C. M. Sustainability index for Taipei. Environ. Impact Assess. Rev. 27(6), 505–521 (2007).10.1016/j.eiar.2006.12.005
6. Li YH Westlund H Liu YS Why some rural areas decline while some others not: An overview of rural evolution in the world J. Rural Stud. 2019 68 135 143 10.1016/j.jrurstud.2019.03.003
Li, Y. H., Westlund, H. & Liu, Y. S. Why some rural areas decline while some others not: An overview of rural evolution in the world. J. Rural Stud. 68, 135–143 (2019).10.1016/j.jrurstud.2019.03.003
7. Cimellaro GP Reinhorn AM Bruneau M Framework for analytical quantification of disaster resilience Eng. Struct. 2010 32 11 3639 3649 10.1016/j.engstruct.2010.08.008
Cimellaro, G. P., Reinhorn, A. M. & Bruneau, M. Framework for analytical quantification of disaster resilience. Eng. Struct. 32(11), 3639–3649 (2010).10.1016/j.engstruct.2010.08.008
8. Folke C Resilience (republished) Ecol. Soc. 2016 21 4 44 10.5751/ES-09088-210444
Folke, C. Resilience (republished). Ecol. Soc. 21(4), 44 (2016).10.5751/ES-09088-210444
9. Hung HC Yang CY Chien CY Building resilience: Mainstreaming community participation into integrated assessment of resilience to climatic hazards in metropolitan land use management Land Use Policy 2016 50 3 48 58 10.1016/j.landusepol.2015.08.029
Hung, H. C. et al. Building resilience: Mainstreaming community participation into integrated assessment of resilience to climatic hazards in metropolitan land use management. Land Use Policy 50(3), 48–58 (2016).10.1016/j.landusepol.2015.08.029
10. Kontokosta CE Malik A The resilience to emergencies and disasters index: Applying big data to benchmark and validate neighborhood resilience capacity Sustain. Cities soc. 2018 36 272 285 10.1016/j.scs.2017.10.025
Kontokosta, C. E. & Malik, A. The resilience to emergencies and disasters index: Applying big data to benchmark and validate neighborhood resilience capacity. Sustain. Cities soc. 36, 272–285 (2018).10.1016/j.scs.2017.10.025
11. Adger WN Social and ecological resilience: Are they related? Prog. Hum. Geogr. 2000 24 3 347 364 10.1191/030913200701540465
Adger, W. N. Social and ecological resilience: Are they related?. Prog. Hum. Geogr. 24(3), 347–364 (2000).10.1191/030913200701540465
12. Boin A Comfort LK Demchak CC Comfort LK Boin A Demchak CC The rise of resilience Designing Resilience: Preparing for Extreme Events 2010 University of Pittsburgh Press 1 12
Boin, A., Comfort, L. K. & Demchak, C. C. The rise of resilience. In Designing Resilience: Preparing for Extreme Events (eds Comfort, L. K. et al.) 1–12 (University of Pittsburgh Press, 2010).
13. Boon HJ Palutikof JP Boulter SL Barnett J Rissik D Community resilience to disaster in four regional Australian Townships Applied Studies in Climate Adaptation 2015 Wiley 386 393
Boon, H. J. Community resilience to disaster in four regional Australian Townships. In Applied Studies in Climate Adaptation (eds Palutikof, J. P. et al.) 386–393 (Wiley, 2015).
14. Norris FH Stevens SP Pfefferbaum B Community resilience as a metaphor, theory, set of capacities, and strategy for disaster readiness Am. J. Community Psychol. 2008 41 1–2 127 150 10.1007/s10464-007-9156-6 18157631
Norris, F. H. et al. Community resilience as a metaphor, theory, set of capacities, and strategy for disaster readiness. Am. J. Community Psychol. 41(1–2), 127–150 (2008).18157631 10.1007/s10464-007-9156-6
15. Wilson GA Hu ZP Rahman S Community resilience in rural China: The case of Hu village, Sichuan province J. Rural Stud. 2018 60 130 140 10.1016/j.jrurstud.2018.03.016
Wilson, G. A., Hu, Z. P. & Rahman, S. Community resilience in rural China: The case of Hu village, Sichuan province. J. Rural Stud. 60, 130–140 (2018).10.1016/j.jrurstud.2018.03.016
16. Arbon P Developing a model and tool to measure community disaster resilience Aust. J. Emerg. Manag. 2014 29 4 12 16
Arbon, P. Developing a model and tool to measure community disaster resilience. Aust. J. Emerg. Manag. 29(4), 12–16 (2014).
17. Li YH Westlund H Zheng XY Bottom-up initiatives and revival in the face of rural decline: Case studies from China and Sweden J. Rural Stud. 2016 47 506 513 10.1016/j.jrurstud.2016.07.004
Li, Y. H. et al. Bottom-up initiatives and revival in the face of rural decline: Case studies from China and Sweden. J. Rural Stud. 47, 506–513 (2016).10.1016/j.jrurstud.2016.07.004
18. Brodsky AE Cattaneo LB A transconceptual model of empowerment and resilience: Divergence, convergence and interactions in kindred community concepts Am. J. Community Psychol. 2013 52 333 346 10.1007/s10464-013-9599-x 24057948
Brodsky, A. E. & Cattaneo, L. B. A transconceptual model of empowerment and resilience: Divergence, convergence and interactions in kindred community concepts. Am. J. Community Psychol. 52, 333–346 (2013).24057948 10.1007/s10464-013-9599-x
19. Brody SD Kang JE Bernhardt S Identifying factors influencing flood mitigation at the local level in Texas and Florida: The role of organizational capacity Nat. Hazards 2010 52 167 184 10.1007/s11069-009-9364-5
Brody, S. D., Kang, J. E. & Bernhardt, S. Identifying factors influencing flood mitigation at the local level in Texas and Florida: The role of organizational capacity. Nat. Hazards 52, 167–184 (2010).10.1007/s11069-009-9364-5
20. Sherrieb K Norris FH Galea S Measuring capacities for community resilience Soc. Indic. Res. 2010 99 2 227 247 10.1007/s11205-010-9576-9
Sherrieb, K., Norris, F. H. & Galea, S. Measuring capacities for community resilience. Soc. Indic. Res. 99(2), 227–247 (2010).10.1007/s11205-010-9576-9
21. Torgler B Building resilience: Social capital in post-disaster recovery J. Econ. Lit. 2013 5 2 576 578
Torgler, B. Building resilience: Social capital in post-disaster recovery. J. Econ. Lit. 5(2), 576–578 (2013).
22. Ungar M The social ecology of resilience: Addressing contextual and cultural ambiguity of a nascent construct Am. J. Orthopsychiatry 2011 81 1 1 17 10.1111/j.1939-0025.2010.01067.x 21219271
Ungar, M. The social ecology of resilience: Addressing contextual and cultural ambiguity of a nascent construct. Am. J. Orthopsychiatry 81(1), 1–17 (2011).21219271 10.1111/j.1939-0025.2010.01067.x
23. Waugh WL Kapucu N Hawkins C Rivera F Management capacity and rural community resilience Disaster Resiliency: Interdisciplinary Perspectives 2013 Routledge 291 307
Waugh, W. L. Management capacity and rural community resilience. In Disaster Resiliency: Interdisciplinary Perspectives (eds Kapucu, N. et al.) 291–307 (Routledge, 2013).
24. Madsen W O’Mullan C Perceptions of community resilience after natural disaster in a rural Australian township J. Community Psychol. 2016 44 3 277 292 10.1002/jcop.21764
Madsen, W. & O’Mullan, C. Perceptions of community resilience after natural disaster in a rural Australian township. J. Community Psychol. 44(3), 277–292 (2016).10.1002/jcop.21764
25. Westlund H Haas T Westlund H Urban-rural relations in the post-urban world In the Post-Urban World: Innovative Transformations in Global City Regions 2018 Routledge 70 81
Westlund, H. Urban-rural relations in the post-urban world. In In the Post-Urban World: Innovative Transformations in Global City Regions (eds Haas, T. & Westlund, H.) 70–81 (Routledge, 2018).
26. Li YH Huang HQ Song CY The nexus between urbanization and rural development in China: Evidence from panel data analysis Growth Change 2022 53 1037 1051 10.1111/grow.12535
Li, Y. H., Huang, H. Q. & Song, C. Y. The nexus between urbanization and rural development in China: Evidence from panel data analysis. Growth Change 53, 1037–1051 (2022).10.1111/grow.12535
27. Wei YM Jin JL Wang Q Wei YM Jin JL Wang Q Impacts of natural disasters and disasters risk management in China: The case of China’s experience in Wenchuan earthquake Resilience and Recovery in Asian Disasters 2015 Springer 287 307
Wei, Y. M., Jin, J. L. & Wang, Q. Impacts of natural disasters and disasters risk management in China: The case of China’s experience in Wenchuan earthquake. In Resilience and Recovery in Asian Disasters (eds Wei, Y. M. et al.) 287–307 (Springer, 2015).
28. Kapucu N Hawkins CV Rivera FI Disaster preparedness and resilience for rural communities Risk Hazards Crisis Public Policy 2013 4 4 215 233 10.1002/rhc3.12043
Kapucu, N., Hawkins, C. V. & Rivera, F. I. Disaster preparedness and resilience for rural communities. Risk Hazards Crisis Public Policy 4(4), 215–233 (2013).10.1002/rhc3.12043
29. Hong B Bonczak BJ Gupta A Measuring inequality in community resilience to natural disasters using large-scale mobility data Nat. Commun. 2021 12 1 1870 10.1038/s41467-021-22160-w 33767142
Hong, B. et al. Measuring inequality in community resilience to natural disasters using large-scale mobility data. Nat. Commun. 12(1), 1870 (2021).33767142 10.1038/s41467-021-22160-w
30. Su HZ Zhao XY Wang LC How rural community resilience evolves after a disaster? A case study of the eastern Qinghai-Tibetan Plateau, China Appl. Geogr. 2024 165 103238 10.1016/j.apgeog.2024.103238
Su, H. Z. et al. How rural community resilience evolves after a disaster? A case study of the eastern Qinghai-Tibetan Plateau, China. Appl. Geogr. 165, 103238 (2024).10.1016/j.apgeog.2024.103238
31. Berkes F Ross H Community resilience: Toward an integrated approach Soc. Nat. Resour. 2013 26 1 5 20 10.1080/08941920.2012.736605
Berkes, F. & Ross, H. Community resilience: Toward an integrated approach. Soc. Nat. Resour. 26(1), 5–20 (2013).10.1080/08941920.2012.736605
32. Pfefferbaum RL Neas BR Pfefferbaum B The communities advancing resilience toolkit (CART): Development of a survey instrument to assess community resilience Int. J. Emerg. Mental Health 2013 15 1 15 29
Pfefferbaum, R. L. et al. The communities advancing resilience toolkit (CART): Development of a survey instrument to assess community resilience. Int. J. Emerg. Mental Health 15(1), 15–29 (2013).
33. Yang B Feldman MW Li SZ The status of perceived community resilience in transitional rural society: An empirical study from central China J. Rural Stud. 2020 80 427 438 10.1016/j.jrurstud.2020.10.020
Yang, B., Feldman, M. W. & Li, S. Z. The status of perceived community resilience in transitional rural society: An empirical study from central China. J. Rural Stud. 80, 427–438 (2020).10.1016/j.jrurstud.2020.10.020
34. Imperiale AJ Vanclay F Experiencing local community resilience in action: Learning from post-disaster communities J. Rural Stud. 2016 47 204 219 10.1016/j.jrurstud.2016.08.002
Imperiale, A. J. & Vanclay, F. Experiencing local community resilience in action: Learning from post-disaster communities. J. Rural Stud. 47, 204–219 (2016).10.1016/j.jrurstud.2016.08.002
35. Imperiale AJ Vanclay F Conceptualizing community resilience and the social dimensions of risk to overcome barriers to disaster risk reduction and sustainable development Sustain. Dev. 2021 29 5 891 905 10.1002/sd.2182
Imperiale, A. J. & Vanclay, F. Conceptualizing community resilience and the social dimensions of risk to overcome barriers to disaster risk reduction and sustainable development. Sustain. Dev. 29(5), 891–905 (2021).10.1002/sd.2182
36. Sim T Han Z Guo C Disaster preparedness, perceived community resilience, and place of rural villages in northwest China Nat. Hazards 2021 108 907 923 10.1007/s11069-021-04712-x
Sim, T. et al. Disaster preparedness, perceived community resilience, and place of rural villages in northwest China. Nat. Hazards 108, 907–923 (2021).10.1007/s11069-021-04712-x
37. Sovacool BK Axsen J Sorrell S Promoting novelty, rigor, and style in energy social science: Towards codes of practice for appropriate methods and research design Energy Res. Soc. Sci. 2018 45 12 42 10.1016/j.erss.2018.07.007
Sovacool, B. K., Axsen, J. & Sorrell, S. Promoting novelty, rigor, and style in energy social science: Towards codes of practice for appropriate methods and research design. Energy Res. Soc. Sci. 45, 12–42 (2018).10.1016/j.erss.2018.07.007
38. Caferra R Colasante A D’Adamo I Interacting locally, acting globally: Trust and proximity in social networks for the development of energy communities Sci. Rep. 2023 13 16636 10.1038/s41598-023-43608-7 37789005
Caferra, R. et al. Interacting locally, acting globally: Trust and proximity in social networks for the development of energy communities. Sci. Rep. 13, 16636 (2023).37789005 10.1038/s41598-023-43608-7
