1. Introduction
Natural disasters have been acknowledged as substantial constraints within the framework of social development [
1,
2]. Earthquakes, as highly destructive and sudden natural disasters, potentially trigger secondary hazards such as landslides, mudslides, collapses, and tsunamis, posing severe threats to human life and property safety [
3,
4,
5]. China is located between the Pacific Ring of Fire and the Eurasian Seismic Belt, making it one of the regions with high seismic activity globally. Over the past 20 years, China has experienced numerous earthquakes [
6,
7,
8]. While the intensity and frequency of these earthquakes have not shown significant variations, the human and economic losses incurred have increased substantially. Among these, the Wenchuan earthquake stands as the most impactful and destructive seismic event in China, triggering over 15,000 geological disasters. It resulted in the deaths of 69,227 people and direct economic losses amounting to 852.3 billion RMB [
9,
10]. However, the awareness of natural disaster risks among most individuals and groups remains lagging. Earthquake disasters were once considered low-frequency events but have gradually become a dominant topic in international disaster discussions as their impact has increased [
11].
In natural disaster events, accurately predicting the damage with the technology currently available to humans is extremely challenging [
12]. Traditional natural disaster risk assessments rely on disaster data and have not yet incorporated social, economic, and demographic dimensions, rendering them insufficient for disaster prevention and control needs. The term “vulnerability” refers to the likelihood of individuals, groups, or systems being harmed when exposed to risks, and it is currently applied across various disciplinary fields. According to Adger’s perspective [
13], vulnerability measures the extent of potential losses due to a lack of coping capacity when exposed to disaster risks. The concept of social vulnerability is derived from the broader notion of vulnerability. Wongbusarakum et al. [
14] suggest that social vulnerability is a function of exposure, sensitivity, and coping capacity.
Social vulnerability refers to the influence of individual or group characteristics and their environmental context on their ability to anticipate, respond to, and recover from natural disasters [
15]. Li et al. [
16] argue that the key factors influencing social vulnerability are the interplay among disaster intensity, household characteristics, and capital endowments, with variations in economic status and livelihood strategies leading to significant differences in social vulnerability. Research on social vulnerability can aid local governments in devising policies to prevent and reduce natural disaster risks [
17,
18], thereby enhancing the resilience of areas affected by such disasters. Vulnerable social groups are regarded as manifestations of societal structural differentiation and inequality, with their vulnerability heightened by factors such as age, gender, economic status, and education level, placing them at greater risk during disasters [
19]. Furthermore, understanding social vulnerability is crucial for disaster risk reduction, as it helps identify the most severely affected areas and vulnerable social groups [
20], facilitating the formulation of targeted policies that improve regional recovery capabilities and strengthen the agency and risk perception of these groups [
21,
22].
Previous literature on earthquake disaster risk assessment has primarily focused on the factors inducing earthquakes and the mechanisms of earthquake formation [
23], with relatively few studies integrating social, economic, and demographic dimensions. Additionally, there is a scarcity of research on specific disaster-prone regions. Hou et al. [
24] employed a super-efficiency DEA (data envelopment analysis) model to calculate the social vulnerability index to geological disasters for various provinces in China, identifying the distribution patterns of social vulnerability. They found that most regions fell into high–high or low–low clustering zones, with an inverse relationship between economic level and social vulnerability. Zhang et al. [
25] conducted a social vulnerability assessment of earthquakes across different regions of India, discovering that cities in northeastern India exhibited high to very high social vulnerability. Jena et al. [
26] constructed a disaster progression-based earthquake social vulnerability evaluation model using rough sets and applied it in Sichuan, revealing that cities in northeastern and eastern Sichuan had the highest social vulnerability, with Guangyuan being the most vulnerable. Shadmaan et al. [
27] assessed the social vulnerability to earthquakes in Chittagong, Bangladesh, finding that 55.34% of the area was classified as high vulnerability or above. Guo et al. [
28] utilized a rough hierarchical analysis method to evaluate the social vulnerability of Hanzhong, identifying building aging and density, age and household structure, vulnerable groups, and socioeconomic status as the primary factors influencing vulnerability.
Currently, research on social vulnerability mainly focuses on national, provincial, municipal, and urban-rural levels, with a lack of studies on specific disaster-prone areas [
29,
30,
31]. There has been no consideration of the social vulnerability to disasters in earthquake fault zone regions. Therefore, this paper selects counties and districts near earthquake fault zones for an assessment of earthquake social vulnerability. The study area is concentrated in mountainous regions, mostly located on the Western Sichuan Plateau. The residents here are more susceptible to natural disasters due to poor natural conditions, low educational levels, inadequate social security, and delayed information perception [
32]. As part of the Qinghai–Tibet Plateau and Hengduan Mountains, the Western Sichuan Plateau is frequently disturbed by earthquakes, particularly those near the Longmenshan Fault Zone [
33].
Extensive research on social vulnerability has been conducted in the context of various natural disasters, including floods, landslides, and tsunamis. Studies have shown that enhancing the identification of natural disaster risks can effectively reduce social vulnerability and mitigate the impact on social development [
34,
35]. Floods, being one of the most common natural disasters, severely affect vulnerable populations [
36]. Factors such as demographic characteristics, socioeconomic status, and health conditions are major contributors to social vulnerability in flood disasters [
37]. Assessing social vulnerability can effectively gauge regional disaster risks, allocate resources based on the level of risk, and minimize losses to the greatest extent possible. Additionally, social vulnerability assessment has extended to fields such as climate change, land use, and ecological environments.
In recent years, climate change and improper land use by humans have triggered numerous extreme events, causing significant impacts on the ecological environment, such as sea level rise, extreme heat waves, and agricultural droughts. These issues pose serious threats to sustainable human development and hinder global economic progress and rural revitalization in various countries [
38,
39,
40]. Scholars have conducted in-depth research on social vulnerability related to climate change, developing a relatively comprehensive research framework [
41,
42,
43,
44,
45]. Debortoli et al. [
46], addressing the issue of discrepancies in climate change vulnerability assessment methods and the lack of comprehensive evaluation approaches, transformed the new conceptual theory of vulnerability into an expandable quantitative method. Their research found that prolonged high-temperature heatwaves significantly affect the daily life, travel, and health of urban residents in mountainous areas. Due to high social exposure, urban residents exhibit high heat vulnerability, with significant differences between urban centers and suburbs. Urban centers display high physical heat vulnerability, while suburbs exhibit high social heat vulnerability [
47]. Coastal cities, being highly sensitive to climate change, can benefit from social vulnerability studies that provide insights into patterns of internal vulnerability, thereby enhancing urban adaptability to climate change [
48]. Changes in land use impact social-ecological systems, increasing social vulnerability in the face of climate change. Integrating social vulnerability into ecological vulnerability can improve adaptability to climate change [
49]. Rural areas, primarily dependent on agricultural production, face challenges from climate change that affect agricultural planting and production, posing a threat to social food security. Vulnerability research can provide valuable insights for agricultural development [
40,
50].
The assessment of social vulnerability is considered crucial for understanding natural disaster risks and effectively enhancing coping capacity [
30]. However, there is still debate regarding the methods of social vulnerability assessment. Currently, the research framework for vulnerability is primarily based on the social vulnerability index (SoVI) proposed by Cutter et al. [
51], which measures vulnerability using Principal Component Analysis (PCA) to construct a social vulnerability evaluation model and calculate the SoVI. This method has been widely adopted by scholars.
Therefore, this study focuses on the social vulnerability of earthquake disaster zones, selecting 16 counties near earthquake fault zones as research subjects, all of which have experienced high-magnitude earthquakes. The study constructs an earthquake social vulnerability indicator system based on three aspects: exposure, sensitivity, and coping capacity, using 26 indicators. The weights of the indicators are determined using the entropy weight method and the CRITIC method. Subsequently, the improved TOPSIS method is employed to calculate the SoVI, and ArcGIS is used to map the spatial distribution of the SoVI. Finally, the social vulnerability levels of these cities after experiencing earthquake disasters are analyzed and discussed.
2. Study Area
In earthquake disasters, buildings and people located on fault lines typically sustain greater damage [
52]. Earthquakes in China are primarily distributed along 23 seismic belts, including the Taiwan region, Southwest region, Northwest region, North China region, and Southeast coastal region. This study focuses on the Sichuan Province of China, specifically the Xianshuihe Fault, Longmenshan Fault, Songgang Fault, and Tazang Fault.
The Xianshuihe Fault Zone is a highly active seismic fault located in the southwestern region of China. Since the Holocene epoch of the Quaternary period, the Xianshuihe Fault Zone has primarily exhibited left-lateral strike-slip movement with a minor thrust component. The strike-slip rate is approximately 10 mm/year, and vertical deformation is within 2 mm/year [
53]. The Longmenshan Fault Zone, situated on the eastern edge of the Tibetan Plateau, forms the boundary between the Tibetan Plateau and the Sichuan Basin. It consists of three main faults: the Anxian-Guanxian Fault, the Beichuan-Yingxiu Fault, and the Wenchuan-Maowen Fault. The Songgang Fault Zone begins at Baisha in Songgang, Maerkang, and extends in a northwest-southeast direction, with a total length of approximately 73 km. The Minshan Fault Zone is located along a steep crustal thickness gradient between eastern and western China, starting from Gongga Ridge in the north and extending nearly north-south, with a total length of about 170 km.
The study focuses on the following regions located along the Xianshuihe Fault Zone: Luding County, Kangding City, Jiulong County, Yajiang County, Danba County, Daofu County, Luhuo County in Ganzi Prefecture, and Shimian County, Hanyuan County in Ya’an City. Additionally, it includes Lushan County, Baoxing County in Ya’an City, and Wenchuan County, Lixian County in Aba Prefecture along the Longmenshan Fault Zone. The study also covers Maerkang City in Aba Prefecture along the Songgang Fault Zone and Jiuzhaigou County in Aba Prefecture along the Minshan Fault Zone. These 16 cities have all experienced high-magnitude earthquakes, providing sufficient representativeness in terms of spatial distribution and disaster intensity. The specific locations of the study areas are shown in
Figure 1 and
Table A1.
3. Construction of the Indicator System and Research Methodology
As Sichuan Province is one of the regions in China with a high frequency of earthquakes, it is essential to study the fault zones distributed in the mountainous areas of Sichuan. First, this study collected data on 26 indicators from 16 counties near four earthquake fault zones in Sichuan, China, and constructs an indicator system based on the “exposure–sensitivity–adaptation capacity” framework proposed by Wilhelmi and Morss [
54]. Second, the entropy weight method and the CRITIC method were employed to determine the indicator weights, reducing the influence of subjective factors. Then, the improved TOPSIS method is used to calculate the comprehensive social vulnerability index (SoVI). Finally, ArcGIS was utilized to spatially map the SoVI of each county, and an analysis of the SoVI of counties near the fault zones was conducted. The research process is illustrated in
Figure 2.
3.1. Construction of the Indicator System and Data Sources
The data types covered in this paper primarily include economic, demographic, environmental, and social security dimensions. The selection of indicators was based on literature reviews, field research analysis, and considerations of mountainous area characteristics. The indicator system was constructed from three aspects: exposure, sensitivity, and coping capacity. ‘Exposure’ refers to the extent to which the affected body comes into contact with earthquake disasters; ‘sensitivity’ indicates the likelihood of the affected body being damaged by earthquake disasters; ‘coping capacity’ refers to the ability to effectively manage and respond to disasters when an earthquake occurs. The main data sources included the Seventh National Population Census, County Statistical Yearbooks, National Economic Bulletins, and official government websites. Specific details are presented in
Table 1.
The determination of the number of emergency shelters is based on the ‘Special Planning Compilation Guide for Emergency Shelters’, jointly issued by China’s Ministry of Emergency Management and the Ministry of Natural Resources. According to the guide, emergency shelters can utilize public facilities such as community and township offices, schools, cultural and sports venues, and parks. Additionally, the selection of these 26 indicators is well-founded. The specific reasons for choosing these indicators are detailed in
Table 2.
3.2. Determination of Indicator Weights
The entropy weight method accurately determines the information entropy and corresponding weights of various indicators. This method calculates weights by assessing the degree of dispersion or coefficient of variation of each indicator, effectively avoiding the subjective biases inherent in traditional weight determination methods. This enhances the accuracy and reliability of decision-making. Characterized by its objectivity and scientific approach, this method provides an efficient weighting mechanism for multi-attribute decision-making.
Due to the presence of both positive and negative indicators within the indicator system, a standardized matrix is obtained by normalizing the original social vulnerability assessment matrix (1). The positive indicator data are processed using Formula (2) while the negative indicator data are processed using Formula (3). This results in the standardized social vulnerability assessment matrix (4).
In this context, Formula (4) represents the standardized assessment matrix. Zij is the standardized value of indicator j for i, where i = 1, 2, …, n and n is the number of entities being assessed, j = 1, 2, …, m, and m is the number of evaluation indicators.
We calculated
Pij according to Formula (5) based on the standardized matrix (4).
In Formula (5), Pij represents the proportion of the value of the i county under the j indicator compared to the total value of that indicator.
According to Formula (6), when calculating the entropy value
Ej for indicator
j, if
Pij = 0, then
Pij ln(
Pij) = 0.
In Formulat (6), n in lnn represents the number of research objects.
According to Formula (7), we calculated the coefficient of variation
Gj.
We calculated the weight
Gj according to Formula (8). The results of the weights calculated using the entropy weight method are shown in
Table 3.
In Formula (8), K1j represents the weight calculated for the j indicator using the entropy weight method.
3.3. CRITIC
The CRITIC method, serving as an objective weighting approach, determines the objective weights of indicators by analyzing the contrast strength and conflict among them. This method leverages the differences and complementarities between indicators to ensure that the allocation of weights is both scientific and reasonable. By assessing the uniqueness and conflict of the indicators’ impact on decision-making, the CRITIC method provides a quantifiable and objective way to determine weights, enhancing the effectiveness and credibility of multi-attribute decision analysis.
Contrast intensity describes the level of variation in the same indicator across different research subjects. A higher standard deviation in an indicator indicates greater volatility in the data and more significant differences in values between research subjects. Consequently, this greater variability suggests that the indicator possesses higher informational value in distinguishing between different research subjects, thereby warranting an increased weight in the overall evaluation system.
Conflict refers to the correlation between evaluation indicators. If two indicators exhibit a strong positive correlation, their conflict is lower, and consequently, their weights are also lower. Conversely, if they display a negative correlation, their conflict is higher, leading to higher weights for these indicators.
First, we calculated the contrast intensity using Formula (9), based on the standardized matrix (4).
In Formula (9), i = 1, 2, …, n, where n represents the number of research objects. Sj denotes the standard deviation of the j indicator.
Conflict is measured by the correlation coefficient
rij between indicators. If two indicators are strongly correlated, approaching 1, it indicates lower conflict. Conversely, if the correlation between two indicators is weaker, approaching −1, it indicates higher conflict. This is calculated using Formula (10).
In Formula (10), rij represents the correlation coefficient between evaluation indicators i and j.
Secondly, the information content of each indicator is calculated by multiplying the contrast intensity and conflict. The greater the information content, the more significant the role of the indicator in the evaluation. This is calculated using Formula (11).
In Formula (11), Cj represents the amount of information for the j indicator.
Finally, based on the calculated information content of each indicator, we used Formula (12) to calculate the objective weights for each indicator. The results of the indicator weights calculated using the CRITIC method are shown in
Table 4.
In Formula (12), K represents the objective weight of the j indicator calculated using the CRITIC method.
This study employs both the CRITIC method and the entropy weight method to determine the objective weights of indicators. The CRITIC method allocates weights based on the contrast intensity and conflict between indicators, while the entropy weight method assesses indicator weights using the principle of information entropy to measure the degree of dispersion among indicators in the dataset. To fully leverage the advantages of both methods and enhance the reliability of SoVI, this study considers both methods equally important. Consequently, we combine the weights according to Formula (13), aiming to strengthen the overall evaluation efficacy and practical value of the model by integrating the benefits of both objective weighting approaches. The results are shown in
Table 5.
In Formula (13), Wj represents the combined weight result for the j indicator, with e taking a value of 0.5.
3.4. Improved TOPSIS Method
In the calculation of SoVI, the TOPSIS method allows for the comprehensive evaluation of multiple objectives, calculating the closeness to the highest level of social vulnerability based on the distance from each objective to the ideal and the worst solutions. Furthermore, to minimize subjective biases in the assessment of social vulnerability, this study utilizes the weights calculated according to Formula (13) to perform a combined weighted treatment on the standardized social vulnerability assessment matrix (4), thereby generating the weighted standardized matrix of social vulnerability (14).
In this context, V represents the weighted standardized assessment matrix. Vij is the weighted standardized value of indicator j for county i, where i = 1, 2, …, m and m is the number of entities being assessed, j = 1, 2, …, n, and n is the number of evaluation indicators.
V+ represents the maximum value of indicator
j across the
i evaluation entities, referred to as the positive ideal solution, which is the optimal solution for each indicator. Conversely,
V− represents the minimum value of indicator
j across the
i evaluation entities, referred to as the negative ideal solution, which is the least desirable solution for each indicator. The formula for the positive ideal solution is given as (15), and the formula for the negative ideal solution is given as (16).
This paper employs the Euclidean distance formula for calculations. The distances to the positive and negative ideal solutions are computed using Formulas (17) and (18).
Di+ represents the distance from indicator
j to
V+, and
Di− represents the distance from indicator
j to
V−.
S
i represents the extent to which the social vulnerability of city
i approaches the maximum level, with values ranging from 0 to 1. A value of S
i closer to 0 indicates that the city’s social vulnerability is further from the maximum level, implying lower social vulnerability. Conversely, a value of S
i closer to 1 indicates that the city’s social vulnerability is nearer to the maximum level, implying higher social vulnerability. The specific calculation of S
i is as per Equation (19).
When conducting research and analysis based on the ‘exposure–sensitivity–coping capacity’ framework, calculations for exposure, sensitivity, and coping capacity are made separately based on Equation (19). These values are then summed to calculate the SoVI for each city, as detailed in Equation (20).
In Equation (20), SiA represents the exposure score for the i city; SiB represents the sensitivity score for the i city; SiC represents the coping capacity score for the i city. WA is the weight of exposure, WB is the weight of sensitivity, and WC is the weight of coping capacity.
5. Discussion
In previous studies, research on social vulnerability to disasters has predominantly focused on micro-level entities such as nations, provinces, and urban-rural distinctions. The selection of study regions has been excessively broad, resulting in a lack of specificity [
25,
28,
59]. To explore the differences in social vulnerability among cities in mountainous earthquake disasters, as well as the key influencing factors, this study selects 16 cities located near the seismic fault zones in the mountainous areas of Sichuan, which have all experienced high-magnitude earthquakes. Most of these cities have undergone post-disaster reconstruction, with Hanyuan notably undergoing relocation-based reconstruction. Hence, these cities have a certain representativeness in the research area, making it necessary to conduct assessments of social vulnerability to earthquake disasters. In terms of research methods, the entropy–CRITIC method is used to determine the weights of indicators, and the modified TOPSIS method is applied to compute SoVI. Furthermore, ArcGIS is utilized to create spatial maps of social vulnerability.
The issue of social vulnerability to earthquake disasters in mountainous cities is significant and is characterized by a high–high clustering pattern. Only Kangding, Baoxing, Wenchuan, and Danba exhibit lower social vulnerability, while the remaining cities display higher social vulnerability. The primary reason for the high social vulnerability in most mountainous cities is due to weak coping capacity. The average vulnerability scores for exposure, sensitivity, and coping capacity are 0.384, 0.439, and 0.586, respectively. Coping capacity tends to have a relatively higher average vulnerability score compared to exposure and sensitivity. Current research finds that despite having experienced severe natural disasters, the coping capacity of most residents or governments remains low when confronted with natural disasters again [
60,
61].Additionally, high illiteracy rates among residents, large variations in altitude, low per capita disposable income, low local fiscal revenue, and inadequate development of disaster prevention and mitigation infrastructure are key factors contributing to the insufficient coping capacity of mountainous cities [
62]. Due to high illiteracy rates, the government encounters numerous challenges in disseminating disaster knowledge, conducting drills, and training residents in risk perception and emergency response skills [
63]. Low per capita disposable income and local fiscal revenues prevent residents from acquiring necessary disaster relief supplies, complicate post-disaster housing reconstruction, and increase living costs. The inadequate development of disaster prevention and mitigation infrastructure indirectly increases residents’ vulnerability during disasters, complicating rescue efforts [
64]. Therefore, in mountainous cities, significant altitude differences and relatively low road network density contribute to complex terrain and poor road conditions, which may hinder rescue teams from reaching disaster areas promptly. When rescue operations are delayed, secondary disasters triggered by earthquakes, such as landslides and mudflows, can exacerbate the damage in disaster-stricken areas and cause secondary harm to residents.
All 16 cities have experienced high-magnitude earthquakes, yet the issue of insufficient coping capacity during such disasters remains significant. Among these, the Yajiang area exhibits the weakest coping capacity for earthquake disasters, scoring 0.819, a result significantly higher than other regions. This situation is primarily attributed to the high illiteracy rate in Yajiang, resulting in poor risk perception among residents; the relatively low number of mobile phone users in Yajiang, which delays the reception of new information and further reduces the efficiency of emergency responses; and the shortage of health technicians, posing severe challenges to post-disaster medical rescue operations. These factors collectively contribute to the relative weakness of local rescue efforts in the Yajiang area after disasters. Concurrently, these are also the main factors affecting the coping capacity of cities in the Western Sichuan Plateau mountainous region. There is an urgent need for relevant departments and all sectors of society to enhance their focus and investment to improve the disaster response capabilities of the Western Sichuan Plateau mountainous area.
Hanyuan has demonstrated exceptional performance in earthquake disaster response, with a score of 0.286, the only region scoring below 0.3. This success is primarily attributed to the relocation and reconstruction strategy adopted after the Wenchuan earthquake. During this process, Hanyuan significantly increased investments in disaster prevention and mitigation infrastructure and conducted scientific and rational planning. Additionally, Hanyuan actively recruited professionals in disaster prevention and mitigation, providing strong human resource support to enhance disaster response capabilities. These measures have collectively established a solid foundation for Hanyuan in managing earthquake disasters. Furthermore, Shimian has also shown a high level of coping capacity. The presence of numerous large-scale industrial enterprises allows Shimian to provide timely disaster relief supplies and effectively address post-disaster employment issues for local residents, facilitating rapid economic recovery. After the Wenchuan earthquake, Wenchuan’s earthquake disaster response capabilities have significantly improved. The region has strengthened its infrastructure in transportation, water resources, energy, and telecommunications, and its booming tourism industry has provided a reliable source of income for local residents.
An aging population, the size of the agricultural population, and the number of children are the main factors affecting sensitivity. These groups are not only more vulnerable to harm during disasters but also exhibit relatively weaker resilience [
65]. Among the 16 mountainous cities, Hanyuan has the most significant issue with disaster sensitivity, scoring 0.806. This is primarily due to the higher proportion of elderly and children in Hanyuan, who are more susceptible to the impacts of earthquakes, leading to higher mortality rates among these groups [
66,
67]. Additionally, Hanyuan has a substantial agricultural population, and natural disasters significantly impact agriculture, destroying crops and causing severe economic losses in the agricultural sector. In contrast, Jiuzhaigou has the lowest vulnerability in terms of sensitivity due to its smaller agricultural population, a lower degree of population aging, and a higher proportion of secondary and tertiary industries.
The exposure level of mountainous cities has not formed a significant high–high clustering phenomenon. This is primarily due to the relatively low levels of population density, economic density, and road network density in most mountainous cities, leading to lower exposure risk when facing natural disasters. However, there is significant vulnerability in the ability of mountainous cities to cope with natural disasters. Consequently, even with lower exposure levels, natural disasters such as earthquakes can still result in considerable casualties and substantial economic losses in mountainous cities. Additionally, this study has not yet considered the issue of secondary disasters, such as landslides, collapses, and debris flows, which are easily triggered by earthquakes in mountainous cities. Secondary disasters can lead to repeated exposure problems, significantly increasing the sensitivity of cities to disasters and further testing their coping capacity.
Therefore, future research needs to further explore the impacts of chain reactions between natural disasters on social vulnerability. The focus should be on the influence on disaster early warning and the demand changes for disaster mitigation infrastructure, as well as residents’ awareness of and response capabilities to disaster chains, particularly paying attention to highly sensitive groups such as the elderly, children, and agricultural populations. This will enable the creation of more effective disaster response strategies to address these vulnerabilities, enhance disaster response capabilities, and reduce the negative impacts of natural disasters.
6. Conclusions
To investigate the social vulnerability of 16 mountainous cities following seismic disasters, this study established a social vulnerability indicator system for mountainous urban areas. Using the entropy weight–CRITIC method to improve the TOPSIS approach, we conducted a quantitative evaluation of social vulnerability and generated spatial distribution maps of social vulnerability using ArcGIS. The social vulnerability index (SoVI) was employed to rank and classify the social vulnerability levels of these cities and to perform spatial analysis. Additionally, the study analyzed key factors influencing social vulnerability from the perspectives of exposure, sensitivity, and coping capacity, providing valuable insights for local governments in formulating disaster response policies. The specific conclusions are detailed below.
The social vulnerability of the 16 mountainous cities exhibits significant variability, with Hanyuan showing the most pronounced social vulnerability, making it potentially more sensitive to seismic disasters. In contrast, Baoxing has the lowest social vulnerability, demonstrating strong adaptability and recovery capacity. Most cities display high social vulnerability with evident spatial clustering. Specifically, Luhuo, Daofu, and Yajiang are characterized by high–high clustering; Jiulong, Shimian, Hanyuan, and Luding also exhibit high–high clustering, while Kangding, Baoxing, and Wenchuan show low–low clustering. High–high clustering can amplify disaster risks, necessitating that disaster response policies be based on the social vulnerability distribution map, with a particular focus on geological disaster risk management in high–high clustering areas.
Due to the generally low population and economic densities in mountainous cities, there is no significant spatial clustering in terms of exposure; however, there are still certain regional differences. The higher proportions of elderly, children, and agricultural populations in these areas directly contribute to their increased sensitivity to seismic disasters. The vulnerability in coping capacity is particularly pronounced in these cities, with evident high–high clustering. Key factors influencing the coping capacity to seismic disasters include illiteracy rate, per capita disposable income, local fiscal revenue, and elevation differences. Additionally, some cities exhibit significant differences across these three dimensions. Luhuo, Danba, Daofu, and Lixian have high disaster exposure but show high vulnerability in coping capacity. Kangding demonstrates low sensitivity to disasters but exhibits high vulnerability in both exposure and coping capacity. Hanyuan shows high vulnerability in both exposure and sensitivity yet has the lowest vulnerability in coping capacity. Understanding these differences can assist the government in rational planning and allocation of social resources, enhancing emergency management capabilities and ultimately reducing social vulnerability.
Coping capacity is a critical factor influencing the social vulnerability of mountainous cities to earthquake disasters. All 16 mountainous cities are located near seismic fault zones and have experienced high-magnitude earthquakes. However, except for Hanyuan, Shimian, and Wenchuan, the remaining cities exhibit high vulnerability in terms of coping capacity. This is closely related to the socioeconomic fragility of mountainous cities and the weak risk awareness among residents. Therefore, the government needs to implement tailor-made policies to encourage the development of the tourism economy, thereby increasing residents’ incomes and strengthening socioeconomic resilience. For example, after the Wenchuan earthquake, Wenchuan successfully integrated tourism with the agricultural economy around rural revitalization, achieving significant results and ranking 14th among the 16 mountainous cities in terms of social vulnerability. Additionally, due to the higher illiteracy rate in mountainous areas, the government should also enhance disaster risk awareness among residents and conduct public education and disaster preparedness drills for earthquakes and other geological disasters.
This study has not yet considered the issue that earthquakes in mountainous areas are prone to trigger secondary disasters such as landslides, collapses, and mudflows. These secondary disasters can lead to recurrent incidents, increase the exposure of individuals, groups, or buildings, and significantly heighten urban sensitivity to disasters, causing secondary harm to human society and further testing urban response capabilities. Therefore, incorporating the chain reactions of disasters into the study of social vulnerability is a key issue that needs to be addressed.