Differences and Drivers of Urban Resilience in Eight Major Urban Agglomerations: Evidence from China

Abstract

This paper constructs an evaluation indicator system for urban resilience in China on four dimensions—economy, environment, society, and infrastructure. The evaluation indicator is used by the entropy weight method to measure the resilience levels of 138 cities in 8 urban agglomerations from 2005 to 2018. Using the Theil index and variance decomposition method, we explore the size and sources of urban resilience differences among the eight urban agglomerations from the dual perspectives of space and structure and employ geographic detectors to identify the driving factors behind their differences. The results show that although the overall resilience level of the eight urban agglomerations is not high, it shows an upward trend. The differences within the eight urban agglomerations are the main spatial sources of urban resilience differences and economic resilience is the main structural source of urban resilience differences. Moreover, economic resilience and social resilience have the greatest contribution and driving effect on the resilience differences of BTH, YRD, PRD, MYR, CC, GP, and HC urban agglomerations, but the difference in resilience of CP is mainly caused by the difference in infrastructure resilience. Compared with the single factor, the impact of the interaction of each factor is even greater.

1. Introduction

With rapid urbanization, the urban carrying capacity is declining, and environmental pollution and traffic congestion [1,2] caused by big city disease are worsening. Concurrently, sudden and frequent occurrences of natural disasters, such as rainstorms, floods, and super typhoons, also impact the urban carrying capacity. To cope with these uncertain and complex disasters and guard against them, it is imperative to enhance the processing capacity of the urban system. The government pointed out in the 14th Five Year Plan and in the long-term objectives for 2035 that China should adopt the new concepts and trends of urban development and build urban resilience. Improving urban resilience not only meets the new requirements of urban construction but also reflects the goal of pursuing urban quality and its connotations [3,4]. In terms of the construction experience of a resilient city, Fuzhou has continuously improved its ability to resist internal waterlogging and greatly improved the safety and resilience of the city through the two keys of pollution control and waterlogging control. As a representative of China’s high-density cities, Shanghai has significantly enhanced its ecological resilience by implementing the construction of green and resilient urban areas. Urban agglomeration is the main form of new urbanization [5], with the dual functions of inward aggregation and outward driving. It is an important platform to support the high-quality economic development of China and an important spatial carrier to promote regional coordinated development. Given their natural resource endowments, geographical locations, development history, and other factors, the urban resilience level of China’s eight urban agglomerations have obvious regional heterogeneity. This heterogeneity not only inhibits their main functions but also makes it difficult to achieve regional high-quality development. To this end, China urgently needs to promote the coordinated improvement of urban resilience development level in eight major urban agglomerations. To be specific, we need to explore how big the spatiotemporal differences are in the level of urban resilience among the eight urban agglomerations in China, what the sources of these differences are, and what are the mechanisms by which these differences are formed. The exploration of these problems helps to clarify the different sources and driving factors of the differences in resilience level of the eight urban agglomerations. The findings also have practical significance for improving urban resilience in the eight major urban agglomerations and optimizing the overall regional spatial pattern.

As uncertainties and risks in the external environment increased, resilience became a topic of widespread concern among scholars. The term resilience was first introduced in the field of ecology by Holling [3]. However, as urban development continues to face pressures and challenges, the concept of resilience is gradually being applied to urban research [5]. The existing research has constructed the theoretical analysis framework of urban elasticity, mainly including connotation characteristics, quantitative evaluation, spatial differentiation, and influencing factors [6,7,8].

Scholars redefined resilient cities by reviewing the recovery practices of cities after disasters [9,10,11]. It is believed that resilient cities must be able to withstand stress [12], and they also advocated those resilient cities must have the ability to absorb and adapt to extreme shocks without violent fluctuations and to quickly and successfully create a new socio-economic structure [13,14]. Although there is no uniform definition of urban resilience, in general, urban resilience is the capabilities of a city to absorb and defuse external shocks.

Concerning quantitative evaluation, some scholars used the network to evaluate the resilience of cities. They did so by examining the influence of the urban network structure to respond to shocks and restore and improve the characteristics of the original system, and by evaluating the structural resilience capacity of the urban network [15,16]. The other group of scholars constructed a comprehensive index evaluation system of different urban resilience elements and used subjective or objective weighting methods to measure the internal elasticity of urban system. For example, Feng et al. [17] constructed an urban resilience assessment framework covering ECP (“exposure”, “connectivity”, and “potential”) in Shenyang, and used the analytic hierarchy process to score the weights of the indicators. Orencio and Fujii [18] employed the analytic hierarchy process to evaluate the local disaster resilience index of coastal communities based on seven indicators, such as environment and natural resource management. Cutter et al. [19] constructed urban resilience covering society, economy, housing and infrastructure, system, and community and environment and used Cronbach’s alpha to measure the urban resilience level. Qasim et al. [20] assigned weights to physical, social, economic, and institutional indicators through subjective weighting methods, and then obtained the community resilience index. Suárez et al. [21] measured the urban resilience index by standardizing, weighting, and aggregating the spatial indicators of commercial diversity, land use diversity, and so on. However, urban resilience is affected not only by social, economic, and infrastructure factors but also by natural factors [22]. Ribeiro and Pena [14] aimed to propose a conceptual framework to understand the concept of urban resilience from physics, nature, economy, system, and society, and focused on the characteristics, challenges, and opportunities of urban resilience. Xun and Yuan [23] constructed an urban resilience evaluation index system from four dimensions: ecological environment, municipal facilities, economic, and social. Based on Xun and Yuan [23], Wei [24] added two dimensions of institutional and capital.

In terms of spatiotemporal differences analysis, due to the influence of regional resource endowments, development foundations and other resource subjects, there are still regional differences in the development of urban resilience, which directly affect the unbalanced development capacity of cities. So, the academic community has gradually realized the importance of research on the difference of urban resilience. Many scholars chosen Getis–Ord Gi and Theil index to test the development differences of urban toughness in China, and the study found that there are significant spatial differences in urban resilience in China. For example, Qin et al. [25] applied Getis–Ord Gi analysis and exploratory spatial data analysis methods to measure the resilience distribution characteristics of cities in China. Ma et al. [26] used ArcGIS to study the temporal and spatial evolution of the resilience characteristics of the GP urban agglomeration and further explored the influencing factors using grey correlation analysis. Liu et al. [27] utilized the Theil index method and the exploratory spatial data analysis method to evaluate and analyze the urban resilience differentiation pattern in time and space and the spatial Dubin model to investigate the degree of influence of factors, such as urbanization rate and administrative power.

As for the influencing factors of urban resilience, economic and social factors, such as economic [28], social [29], regime [30], resource allocation [31], openness [22], innovation ability [32], and natural factors, such as precipitation and average temperature [22], have been proved to have a significant impact on urban resilience. Martin [33] adopted a mathematical model to analyze the reasons for the temporal and spatial differences in economic resilience among British cities. Huang et al. [31] used the DEMATEL-ISM method to determine the key influencing factors of urban resilience and their influencing mechanism. The study found that policy system, innovation ability, robustness, rapidity, redundancy, and resourcefulness are the core factors affecting urban resilience. Chen et al. [22] found that economic, fiscal, market, urbanization, openness, and innovation are important factors affecting the spatial difference of urban resilience. These studies have explored in depth the driving factors of the differences in the resilience of cities in different regions, urban agglomerations, and scales, and put forward specific suggestions on how to promote the balanced improvement of urban resilience.

To sum up, the above studies provide an essential theoretical basis and methodological guidance for this paper. However, they also have their limitations. The existing studies only decompose the sources of China’s urban resilience differences from a spatial perspective, do not analyze the sources of differences from a structural perspective, and do not discuss the formation mechanism of urban resilience differences. To expand on the scope of the existing literature, we construct a comprehensive evaluation index system for urban resilience, measure urban resilience based on the entropy weight method on 29 basic indicators, and use the Theil index and variance decomposition methods to identify the differences of the eight urban agglomerations, and its spatial and structural sources. Using geographic detectors to examine the impact of driving factors on the resilience differences among the eight urban agglomerations.

To our knowledge, this paper provides four primary contributions to the literature in terms of identifying differences and drivers in urban resilience. First, this paper constructs urban resilience evaluation indicators from the four dimensions of economy, society, environment, and infrastructure, and scientifically measure the urban resilience development level of eight urban agglomerations accordingly. This can reveal the current status and improvement potential of urban resilience development in the eight urban agglomerations. Second, the Theil index method is used to measure the spatial differences in the resilience of the eight urban agglomerations in China. The overall difference is divided into intraregional and interregional differences, and then the sources and contribution rates of spatial differences in urban resilience of the eight urban agglomerations are revealed. In this way, it can provide a basis for exploring differentiated countermeasures for the coordinated improvement of urban people’s livelihood. Third, we employ the variance decomposition method to examine the sources of urban resilience differences from a structural perspective, which decomposes urban resilience differences into the differences of economic, social, environmental, and infrastructure resilience. This can reveal key areas for synergistically improving the resilience of the eight urban agglomerations. Fourth, this paper applies a geographic detector to examine the drivers of urban resilience differences in the eight urban agglomerations. It is conducive to revealing the key influencing factors of the differences in urban resilience development among the eight urban agglomerations, thereby providing reference experience for exploring an effective collaborative improvement path for urban resilience.

The remainder of this paper is organized as follows. Section 2 introduces the construction of the index system, research methods, and data processing. In Section 3, the comprehensive index of urban resilience and the values of each dimension of economic resilience, social resilience, ecological resilience, and infrastructure resilience are calculated by entropy method; the temporal and spatial differentiation characteristics of the resilience levels of the eight urban agglomerations are measured and analyzed using the Theil index method. The structural sources of the differences in resilience of the eight urban agglomerations are measured using the variance decomposition method. The driving factors of the differences in resilience of the eight urban agglomerations are identified using geographic detectors. The conclusions and implications of the paper are presented in Section 4.

2. Materials and Methods

2.1. Study Area

The goals of this study were to explore the urban resilience of eight urban agglomerations in Figure 1—Beijing Tianjin Hebei (BTH), the Yangtze River Delta (YRD), the Pearl River Delta (PRD), the middle reaches of the Yangtze River (MYR), Chengdu Chongqing (CC), the Central Plains (CP), Guanzhong Plain (GP), and Harbin Changchun (HC) (Figure 1). There are two reasons for choosing these eight urban agglomerations. First, these eight urban agglomerations are distributed in different regions, of which BTH, YRD and PRD urban agglomeration are located in eastern China, MYR and CP urban agglomeration are located in central China, the CC and GP urban agglomeration are located in western China, and HC urban agglomeration are located in northeast China. Second, these eight urban agglomerations have great differences in economic development, geographical location, and climate conditions.

2.2. Construction of Index System

A complex and comprehensive problem, urban resilience covers many aspects related to urban development. Thus, it is important to accurately grasp the connotations of urban resilience. Based on the existing studies, we defined urban resilience as the ability of a city to deal with external disturbances and unknown shocks. Based on the studies cited in the literature review, we constructed a comprehensive evaluation index system of urban resilience from 29 basic indicators in 4 sectors—the economy, the environment, society, and infrastructure. The specific index is shown in Table 1.

Economic resilience reflects the ability of the economic system to resist external shocks [34]. We select eight indicators to reflect a city’s economic resilience level from the aspects of economic development level, industrial structure, opening-up level, savings level, and government financial expenditure.

Social resilience refers to the ability of the social system itself to adjust, recover, and adapt to external uncertainties and disturbances [35]. A city with a high level of resilience should have a strong ability to deal with external crises. Based on this, the level of urban social resilience is measured by seven indicators, including employment, education, medical care, and social management.

Ecological resilience refers to the sustainable development ability of urban ecosystem to cope with climate change, environmental pollution, urbanization, and other issues [36]. For this reason, we have selected seven indicators to reflect the ecological resilience of the urban system from the aspects of urban greening level, pollution emission intensity, and waste utilization level.

Infrastructure resilience emphasizes that the urban infrastructure system can resist and absorb the impact when the disaster occurs and recover quickly to ensure the normal operation of urban functions [37]. Therefore, we select seven indicators from public transport, road construction, pipe network density, and other aspects to reflect the resilience level of a city’s infrastructure.

2.3. Method

2.3.1. Entropy Weight Method

The entropy weight method can effectively bypass the problem of error results caused by subjective factors [38,39]. Based on the degree of dispersion of an index, the entropy value of each index was calculated by using the fuzzy evaluation matrix and the output information entropy. The index weight was modified based on the entropy value to calculate the comprehensive index. If the degree of dispersion is larger, it indicates that the influence of comprehensive evaluation score is larger.

First, the range standardization method was used to standardize the positive and negative indicators, as shown in Formulas (1) and (2):

(1)$\mathrm{positive} \mathrm{indicator}: y_{ij}=\frac{X_{ij}-min{X}_j}{max{X}_j-min{X}_j}$

(2)$\mathrm{negative} \mathrm{indicator}: y_{ij}=\frac{max{X}_j-X_{ij}}{max{X}_j-min{X}_j}$

where $y_{ij}$ is the standardized index value, $X_{ij}$ is the initial value of the j-th indicator in the i-th city, and $min{X}_j$ and $max{X}_j$ are the minimum and maximum values of the j-th indicator.

Second, normalize:

(3)$P_{ij}=y_{ij}/{{\displaystyle \sum }}_{i=1}^n{y}_{ij}\left(n=1, 2, ... 138\right)$

Finally, we calculated the information entropy $e_j$ and difference coefficient $d_j$ as follows:

(4)$e_j=-\frac{1}{ln{n}_i}{{\displaystyle \sum }}_{i=1}^n{P}_{ij}ln{P}_{ij}$

(5)$d_j=1-e_j$

Furthermore, the weight of index j can be obtained:

(6)$w_j=d_{ij}/{{\displaystyle \sum }}_{j=1}^m{d}_{ij}\left(m=1, 2, ..., 29\right)$

Thus, the comprehensive index of urban resilience can be calculated:

(7)$U={{\displaystyle \sum }}_{i=1}^m{y}_{ij}{w}_j$

where $n$ is the number of cities, and $m$ is the number of indicators.

2.3.2. Theil Index and Its Decomposition

The Theil entropy standard or Theil index is widely used by scholars in studies on regional differences. Through the calculation of the Theil index, we can intuitively see the size of regional differences. The smaller the Theil index, the smaller the difference [40,41]. The Theil index can also be decomposed into intra-regional differences and inter-regional differences to investigate the main spatial sources of overall differences [42]. In this paper, we used the Theil index method to investigate the spatial differences and sources of urban resilience of eight urban agglomerations in China. The specific formulas are as follows:

(8)$T=\frac{1}{n}{{\displaystyle \sum }}_{i=1}^n\frac{y_i}{\overline{y}}log\left(\frac{y_i}{\overline{y}}\right)$

(9)$T_k=\frac{1}{n}{\displaystyle \sum }_{i=1}^n\frac{y_i{}^k}{{\overline{y}}^k}\log (\frac{y_i{}^k}{{\overline{y}}^k})$

(10)$T_w={\displaystyle \sum }_{k=1}^m\left(\frac{n_k}{n}\frac{{\overline{y}}_k}{\overline{y}}\right)T_k$

(11)$T_b={{\displaystyle \sum }}_{k=1}^m\frac{n_k}{n}\left(\frac{{\overline{y}}_k}{\overline{y}}\right)\ln n(\frac{{\overline{y}}_k}{\overline{y}})$

In the above formulas, $T$ is the Theil index of the resilience of the eight urban agglomerations, indicating the overall difference. $T_w$ is the intracluster Theil index of eight urban agglomerations, indicating the size of intracluster differences. $T_b$ is the Theil index among the eight urban agglomerations, indicating the difference between the agglomerations. $T_{k }$ is the Theil index of each sub-region, indicating the difference in resilience among cities within the eight urban agglomerations, where $y_i$ represents the resilience level of the i-th urban agglomeration, $\overline{y }$ is the average level of the resilience of the urban agglomeration, $n$ is the total number of cities in the sample, $\frac{y_i{}^k}{{\overline{y}}^k}$ is the resilience level in urban agglomeration $k$, and $n_k$ is the total number of cities in urban agglomeration$k$. The ratio of intragroup and total Theil index is the intragroup contribution rate $\left(T_b/T\right)$. The ratio of intergroup to total Theil index is the intergroup contribution rate $\left(T_w/T\right)$. In addition, the contribution rate of each sub-region in the group is defined as $\left(n_k/n\right)\times \left({\overline{y}}_k/\overline{y}\right)\times \left(T_k/T\right)$.

2.3.3. Variance Decomposition

The comprehensive urban resilience index ($I$) = economic resilience index ($I_1$) + social resilience index ($I_2$) + environmental resilience index ($I_3$) + infrastructure resilience index ($I_4$). Thus, the differential structure of urban resilience is derived from economic, social, environmental, and infrastructural resilience. To investigate the impact of each dimension on urban resilience difference from the perspective of structure, we decomposed its comprehensive value by the variance decomposition method and the specific calculation is as follows:

(12)$var(I)=cov(I, I_1+I_2+I_3+I_4)={{\displaystyle \sum }}_{i=1}^{4}cov(I+I_i)$

By dividing$var(I)$ on both sides, we can get:

(13)$\frac{cov(I, I_1)}{var(I)}+\frac{cov(I, I_2)}{var(I)}+\frac{cov(I, I_3)}{var(I)}+\frac{cov(I, I_4)}{var(I)}=1$

The contribution rate of the resilience of each dimension to the difference of urban resilience is $C_i=cov(I, I_i)/var(I)$, where $var$ represents variance and $cov$ represents covariance.

2.3.4. Geographic Detector

Geographic detector is a statistical method used to detect spatial heterogeneity and its driving forces. It has the advantages of collinearity immunity between drivers and fewer constraints [43]. Single factor detector was used to measure the explanatory power of every single factor to identify the main driving factors (and their significance) of the spatial differentiation of urban resilience in the eight urban agglomerations. Interaction detection is to identify the interaction between factors affecting different urban resilience levels, that is, when evaluating the paired effects of each factor, whether it will enhance or weaken the explanatory power of urban resilience levels. The detection results not only cover the explanatory power ($q$ value) of each independent variable to the dependent variable but also calculate the degree of interaction between factors. The specific calculation is as follows:

(14)$q=1-\frac{1}{n{\sigma }^2}{{\displaystyle \sum }}_{h=1}^L{n}_h{\sigma }_h{}^2$

where $q$ is the explanatory power of the driving factor on the spatial differentiation of urban resilience, and the $q$ value is between 0 and 1. $h=1,2,\dots L; L$ is the number of strata. $n$ is the total number of cities and $n_h$ is the number of cities in $h$ strata. ${\sigma }^2$ and ${\sigma }_h^2$ are the discrete variance of the urban resilience of the whole and $h$ strata.

2.4. Data

The data used in this paper are collected from China Statistical Yearbook, China Urban Statistical Yearbook, Regional Statistical Yearbook, Government Gazette, etc. The data of relevant indicators are calculated, such as the per capita actual utilization of foreign capital, per capita fiscal expenditure, and the number of public management and social organization personnel per 10,000 people. In order to eliminate the impact of price changes and have comparability, this paper uses 2005 as the base period to deflator prices.

3. Results

3.1. Spatiotemporal Differentiation Characteristics of Urban Resilience

3.1.1. Measurement Results of Urban Resilience Levels

Figure 2 describes the changes in the overall resilience and sub dimensional mean of the eight urban agglomerations. From the perspective of the mean, the average resilience of China’s eight urban agglomerations is 0.1413. It increased from 0.112 in 2005 to 0.167 in 2018. This shows that although the urban resilience of China’s eight urban agglomerations is not high, its cities’ ability to cope with natural disasters are improving. From the perspective of the sub-dimension, the average value of economic resilience ranked first and grew the fastest with an average value of 0.045 and an average annual growth rate of 4.93%. This indicates that the urban economic system’s ability to cope with external shocks is increasing as is its role in regulating and controlling urban resilience. The average values of social resilience, infrastructure resilience, and environmental resilience were 0.037, 0.032, and 0.029, respectively, showing a slow upward trend during the sample period with growth rates of 3.09%, 1.44%, and 2.70%, respectively.

Table 2 reports the urban resilience levels of the eight urban agglomerations from 2005 to 2018. The resilience values of all urban agglomerations showed an upward trend, but there were significant differences among urban agglomerations. The urban resilience levels of the Pearl River Delta, Yangtze River Delta and Beijing–Tianjin–Hebei urban agglomerations are significantly higher than other urban agglomerations with average values of 0.303, 0.190, and 0.170, respectively, higher than the overall mean (0.1491). The urban agglomerations in MYR, CP, HC, GP, and CC urban agglomerations have relatively low resilience levels with average values of 0.117, 0.115, 0.107, 0.099, and 0.093, respectively, which are lower than the overall mean. In addition, the average resilience level of cities in the PRD urban agglomeration is more than three times that of the CC urban agglomeration. This means that the resilience levels of the eight urban agglomerations vary greatly, and there are obvious regional disequilibrium characteristics.

In terms of spatial distribution characteristics, the spatial distribution of urban resilience in China’s urban agglomerations mainly shows the following two characteristics. First, there is a phenomenon of “point-to-surface” diffusion. In 2005, there were few cities with relatively high urban resilience in China. In 2018, the overall resilience of Chinese cities has improved significantly, and cities with relatively high economic resilience have formed a planar distribution. Second, the hierarchical differentiation of urban resilience development in urban agglomerations is increasingly solidified. Although the resilience of Chinese cities has improved rapidly during the sample period, cities with high resilience are still mainly concentrated in the PRD and the YRD urban agglomerations. The reason for these results may be that, as the level of marketization improves, the mobility of resources within the PRD and the YRD urban agglomerations has increased significantly, and areas with high levels of urban resilience have spillover effects on surrounding low-level areas. This effectively promotes the improvement of the resilience strength of surrounding low-level areas, so that cities with high levels of resilience can form agglomeration distribution. In addition, from a regional perspective, the resilience level of the urban agglomeration in the eastern region is higher than that of the urban agglomeration in the central and western regions.

3.1.2. Spatial Differences in Urban Resilience

Table 3 shows the overall, intragroup, and intergroup differences in the urban resilience. In general, the overall difference in the resilience of the eight urban agglomerations is between 0.156 and 0.187 with a mean of 0.171, which indicates that the urban resilience levels of the eight urban agglomerations have significant spatial disequilibrium characteristics. In terms of the evolution trend, the overall difference in urban resilience decreased from 0.175 in 2005 to 0.166 in 2018, with an average annual decrease of 0.41%. The reason may be that with the increasing emphasis on the construction of resilient cities, all cities have increased their investment in urban resilience construction, thereby reducing the overall difference in urban resilience among urban agglomerations. Overall, the intragroup and intergroup resilience differences among the eight urban agglomerations show characteristics of first rising and then falling. The average difference within the urban agglomerations is 0.090, which is greater than the average difference between the urban agglomerations by 0.081. From the perspective of intragroup differences, the PRD urban agglomeration has the largest intergroup differences in urban resilience, while CP urban agglomeration has the smallest intergroup differences. The intragroup differences of urban agglomerations in the YRD, the PRD, and GP show an obvious downward trend with an average annual decline of 1.76%, 3.80%, and 0.66%, respectively. BTH, MYR, CC, HC, and the CP urban agglomerations showed an upward trend in varying degrees. The intergroup difference of the CP urban agglomeration grew the fastest, while BTH urban agglomeration grew the slowest, with an average annual growth rate of 3.41% and 0.38%, respectively.

3.1.3. Sources of Spatial Differences in Urban Resilience

Table 4 shows the main sources of spatial differences in the urban resilience of the eight urban agglomerations. Concerning the horizontal value, the intergroup difference of each urban agglomeration is the main spatial source of the urban resilience difference among the eight urban agglomerations with an average contribution rate of 52.56% and the average contribution rate of intergroup difference is 47.44%. Among the sources of intragroup differences, the PRD urban agglomeration contributes the most with an average contribution of 15.06%. The intragroup differences of the MYR, BTH, CP, CC, GP, and HC urban agglomerations show a downward trend and an average difference of 7.36%, 5.19%, 5.00%, 3.45%, 3.11%, and 2.09%, respectively.

As for the development trend, the contribution of intragroup differences within urban resilience differences increased from 53.14% in 2005 to 54.30% in 2014. The intragroup differences reached a maximum value in the sample period, followed by a decrease. The changing trend of the contribution of intergroup differences to urban resilience differences is the opposite of the changing trends within the group. In addition, the contribution of intragroup differences in the PRD and YRD urban agglomerations decreased rapidly with an average annual decline of 3.46% and 1.45%, respectively. The intragroup difference contributions of the BTH, MYR, CC, GP, HC, and CP urban agglomeration show an upward trend in varying degrees. Among them, CP urban agglomeration rose the fastest, while BTH urban agglomeration rose the slowest.

3.2. Structural Sources of Differences in Urban Resilience

3.2.1. Dynamic Evolution Trend

Table 5 shows the structural sources of the differences in overall resilience among the eight urban agglomerations. From the horizontal value, economic resilience is the main structural source of the overall resilience difference of the eight urban agglomerations with an average contribution rate of 47.94%. The contribution of environmental resilience, social resilience, and infrastructure resilience to the difference in urban resilience is relatively small, and the average contribution rates are 23.14%, 18.21%, and 10.72%, respectively. With regard to dynamic trends, the contribution rate of economic resilience to the difference of urban resilience shows an upward trend from 46.87% in 2005 to 49.01% in 2018. The contribution rate of environmental resilience is increasing, from 21.73% in 2005 to 26.74% in 2008, but then decreasing. Social resilience first decreased from 18.95% in 2005 to 12.64% in 2011, reaching the minimum value in the sample period, and increasing year by year. Infrastructure resilience showed a rapid downward trend during the sample period with an average annual decline of 2.77%.

3.2.2. Regional Heterogeneity

Figure 3 shows the structural sources of urban resilience differences among the eight urban agglomerations. The main structural source of the urban resilience difference among the BTH, YRD, and CC urban agglomerations is economic resilience with average contribution rates of 60.40%, 46.34%, and 36.74%, respectively. The contribution rate of economic resilience to the above three urban agglomerations shows an upward then downward trend. Next is social resilience, whose average contribution rates are 25.21%, 25.64%, and 28.47%, respectively. The difference in social resilience in BTH urban agglomeration shows a fluctuating downward trend, while the difference in social resilience in the YRD and CC urban agglomerations show a fluctuating upward trend. The contribution rate of environmental resilience and infrastructure resilience to the resilience difference of BTH and YRD urban agglomerations is small with an average of 10.44%, 3.96%, 16.27%, and 11.75%, respectively. For CC urban agglomeration, the contribution rate of infrastructure resilience is slightly higher than that of environmental resilience with an average contribution rate of 19.21% and 15.58%, respectively.

The main structural source of urban resilience difference in the PRD urban agglomeration is economic resilience with an average contribution rate of 45.41%. The second is environmental resilience, with an average contribution rate of 33.51%, showing a trend of first increasing from 32.44% in 2005 to 40.29% in 2008, and then decreasing. Social resilience and infrastructure resilience contribute little to it, and the average contribution rates are 11.64% and 9.44%, respectively. The difference in the resilience of urban agglomerations in MYR comes from social resilience with an average contribution rate of 38.62%. Next, is economic resilience, with an average contribution rate of 31.66% and an average annual increase of 2.44% during the sample period. The contributions of environmental resilience and infrastructure resilience to the difference of urban resilience among the urban agglomerations in MYR is similar with average contribution rates of 14.72% and 15.00%, respectively.

The difference in urban resilience in GP urban agglomeration comes from social resilience and environmental resilience. Their contribution rates are relatively similar with average contribution rates of 30.21% and 29.96%, respectively. Next is infrastructure resilience with an average contribution rate of 21.33%, showing an inverted U-shaped change trend. Economic resilience makes the least contribution to it with an average contribution rate of 18.51%, a fluctuating upward trend and an average annual increase of 12.63%. The main structural source of the urban resilience difference of HC urban agglomeration changed from social resilience to economic resilience with average contribution rates of 31.88% and 30.00%, respectively. Next, is environmental resilience, with an average contribution rate of 22.43%, which increases and then decreases. The contribution of infrastructure resilience is the smallest with an average contribution rate of 15.69%, and no significant fluctuation during the sample period. The main structural source of the difference in urban resilience of the CP urban agglomeration is infrastructure resilience, with an average contribution rate of 32.38%. It fluctuated and increased from 2005 to 2013, and then decreased slightly. Next, is environmental resilience with an average contribution rate of 27.06%, which increased and then decreased. The average contribution rates of social resilience and economic resilience are 22.52% and 18.04%, respectively, showing frequent fluctuations.

3.3. Drivers of Spatial Differences in Urban Resilience

3.3.1. Single Factor Analysis

The single factor that drives the force detection of the resilience difference in the eight urban agglomerations was identified by geographic detectors (Table 6). X_1, X_2, X₃, and X₄ represents economic resilience, social resilience, environmental resilience, and infrastructure resilience, respectively. The economic and environmental resilience has a substantial driving effect on the spatial difference of the overall resilience of the eight urban agglomerations with action intensities between 0.824 and 0.800, respectively. Social resilience and its action intensity were 0.773. The driving effect of infrastructure resilience was the smallest with an action intensity of 0.453.

From a subregional perspective, economic resilience and social resilience are the biggest drivers of spatial differences in urban resilience in BTH urban agglomeration, 0.925 and 0.885, respectively. The impact of environmental resilience and infrastructure resilience on the spatial differences in urban resilience of the BTH urban agglomeration is not significant. The main driving factor of the spatial difference of urban agglomeration resilience in YRD is social resilience followed by economic resilience with an intensity of 0.770 and 0.896, respectively. The driving force of environmental resilience and infrastructure resilience was small with an action strength of 0.733 and 0.701. The driving forces of economic, social, environmental, and infrastructure resilience on the spatial difference of the overall resilience of the PRD urban agglomeration increased successively with action intensities of 0.884, 0.916, 0.934, and 0.954, respectively. The main driving factors of the spatial difference of urban resilience in MYR and CC urban agglomerations are social resilience with an action intensity of 0.963 and 0.935, respectively. The driving force of infrastructure resilience on the difference of urban resilience in MYR urban agglomeration is the smallest with an action intensity of only 0.494.

The driving force of environmental resilience on the difference of urban resilience in CC urban agglomeration was the smallest with an action intensity of only 0.698. Social resilience and infrastructure resilience are the main driving factors for the spatial difference of urban resilience in GP urban agglomeration with action intensities of 0.941 and 0.942, respectively. The action intensities of economic resilience and environmental resilience on the spatial difference of urban resilience were not significant. The economic resilience and infrastructure resilience of the HC urban agglomeration had a large driving force on the spatial difference of resilience, with an action intensity of 0.933 and 0.977, respectively. The driving force of social resilience was small with an action intensity of 0.888. Social, environmental, and infrastructure resilience had a significant impact on the spatial difference of the urban resilience of the CP urban agglomeration with an action intensity of more than 0.700.

3.3.2. Analysis of Interaction Factors

The interactive detection results of the drivers of spatial differences in the urban resilience of the eight urban agglomerations are shown in Table 7. These results have a two-factor enhancement relationship in which 46 pairs of driving factors have an interactive explanatory power greater than 0.900, accounting for more than 85% of all interactive factor pairs. The explanatory power of the interaction between economic resilience and environmental resilience and social resilience and environmental resilience in the PRD and HC urban agglomerations is 1.000. In addition, the explanatory power of each factor interaction of urban resilience in the eight urban agglomerations has an obvious spatial heterogeneity. The factor pair with the largest explanatory power of interactive factors is the interactive term of economic resilience and environmental resilience. In YRD urban agglomeration, the factor pair with the greatest explanatory power of interactive factors is social resilience and infrastructure resilience with an action intensity of 0.967.

In BTH, the PRD, GP, and CP urban agglomerations, the factor pairs with the largest explanatory power of interactive factors are economic resilience and environmental resilience with action intensities of 0.972, 1.000, 0.998, and 0.966, respectively. The most important interaction factors driving the urban agglomeration in MYR are social resilience and environmental resilience with an action intensity of 0.988. In CC urban agglomeration, the factors with the largest explanatory power of interactive factors are economic resilience and infrastructure resilience with an action intensity of 0.995.

4. Conclusions and Implications

In promoting the urban resilience improvement of urban agglomerations, we should not only focus on the improvement of “points”, but also pay attention to the balanced development of “surfaces”, so as to realize the comprehensive development of “coordination in improvement and improvement in coordination” of urban resilience in urban agglomerations. In this paper, the resilience levels of 138 cities in China’s eight urban agglomerations were measured by the entropy weight method from 2005 to 2018. Specifically, the difference in size and source of the urban resilience in China’s eight urban agglomerations was investigated from the perspective of space and structure using the Theil index and variance decomposition methods. The driving factors of the spatial differences in urban resilience were tested by using a geographic detector. The main conclusions are presented below.

First, the overall urban resilience level of the eight urban agglomerations in China is low, but the growth rate is discernable. During the sample period, the urban resilience level of the eight urban agglomerations shows significant spatial disequilibrium characteristics and the overall spatial difference shows a downward fluctuation trend. The difference within the urban agglomerations is the main source of the difference between the eight urban agglomerations, and the difference within the PRD urban agglomeration contributes the most. The contribution of intragroup differences in HC urban agglomeration is the smallest.

Second, of all the four dimensions, economic resilience is the main structural source of urban resilience differences, followed by environmental resilience. Social resilience and infrastructure resilience contribute relatively little to urban resilience differences. From a subregional perspective, the main structural source of the resilience development differences among BTH, the YRD, the PPRD, and the CC urban agglomerations is economic resilience. The main structural source of resilience development difference among the MYR and GP urban agglomerations derives mainly from social resilience. The main structural source of the resilience differences in HC urban agglomeration derives from social resilience and economic resilience. The main structural source of the resilience difference in CP urban agglomeration derives from infrastructure resilience.

Third, economic resilience and social resilience have a great independent driving effect on the spatial differences in the urban resilience of seven urban agglomerations—BTH, the YRD, the PRD, MYR, CC, GP, and HC. However, the differences in the resilience of CP urban agglomerations could be due to differences in infrastructure resilience.

Fourth, as for the factor combinations, each pair of two-factor combinations in the eight urban agglomerations enhances the impact on the spatial difference of urban resilience. The factor pairs with the largest explanatory power of interactive factors in BTH, the PRD, MYR, GP, and CP urban agglomerations are economic resilience and environmental resilience. The factor pairs with the largest explanatory power of interactive factors in YRD urban agglomerations are social resilience and infrastructure resilience. In CC urban agglomeration, the greatest explanatory factors of interactive factors are economic resilience and infrastructure resilience. In MYR and HC urban agglomerations, the greatest explanatory factors of interactive factors are social resilience and environmental resilience.

Based on the findings of this paper, we can propose some policy implications. First, China should focus on the problem of insufficient urban resilience development and tap into the potential of urban resilience. At present, the urban resilience of each urban agglomeration is still at a low level, and each urban agglomeration should focus on and concentrate on making up for the shortcomings of urban resilience. Specifically, the Beijing–Tianjin–Hebei and Yangtze River Delta urban agglomerations should increase investment in ecological technology innovation and use technology to improve their ecological resilience shortcomings. As the “leader” in economic development, the Pearl River Delta urban agglomeration should continue to play an exemplary role for other urban agglomerations. The urban agglomeration in MYR and CP should fully tap into its economic growth potential, optimize the industrial structure, enhance the contribution of technological innovation in promoting economic growth, and promote the construction of urban resilience. In the future, the Chengdu–Chongqing, GP, and Harbin–Chongqing urban agglomerations should continue to promote economic growth while increasing investment in environmental protection and infrastructure construction (Tang et al., 2021), thereby comprehensively promoting the improvement of urban resilience.

Second, accurately grasping the differences in urban resilience among urban agglomerations and implementing urban resilience improvement strategies tailored to local conditions is important. The resilience levels of different urban agglomerations vary significantly. When formulating urban resilience improvement strategies, the government should fully consider the development characteristics of different urban agglomerations and formulate differentiated urban resilience improvement strategies. Specifically, urban agglomerations with low resilience levels, such as Chengdu and Chongqing, should focus on their own shortcomings. Under the premise of relatively limited investment resources, they must increase the input of factors to accurately fill the shortcomings of urban resilience improvement. Urban agglomerations with high levels of resilience, such as Beijing–Tianjin–Hebei, should take advantage their own advantages to maintain a steady increase in their resilience.

Third, the structural sources and formation mechanism of urban resilience development differences should be accurately grasped to give impetus to the balanced development of urban resilience. The difference in economic resilience is the main driver for the difference in the development of resilience among the eight urban agglomerations. Each urban agglomeration should speed up the cultivation of strategic emerging industries by building a collaborative innovation network, breaking down barriers to factor flow, and enhancing the coordination of economic resilience among urban agglomerations. Meanwhile, it should be noted that the interactions between economic, social, ecological, and infrastructure resilience have a far greater impact on the differences in urban resilience development than their independent effects. Therefore, the favorable interactions among the various drivers should be fully exploited to promote the balanced development of urban resilience.

In addition, the limitations of this paper are as follows. Cultural resilience is not added to the urban resilience indicator system. However, cultural resilience has an important impact on the construction of resilient cities. Although this paper discusses the reasons for differences in urban resilience from a dual perspective of space and structure, it does not address whether differences in sociocultural and human behavior have an impact on spatial differences in urban resilience.

Author Contributions

Conceptualization, J.H. and M.D.; methodology, M.D.; software, J.H. and Z.S.; validation, J.H. and M.D.; formal analysis, J.H. and Z.S.; investigation, J.H. and Z.S.; resources, M.D.; data curation, J.H.; writing—original draft preparation, J.H. and Z.S.; writing—review and editing, J.H. and M.D.; visualization, M.D. and Z.S.; supervision, J.H. and M.D.; funding acquisition, J.H. and M.D. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The data presented in this study are openly available in National Bureau of Statistics, such as China Urban Statistical Yearbook (2005–2019).

Conflicts of Interest

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Figures and Tables

Figure 1. Location of the eight major urban agglomerations in China.

Figure 2. Change trend of urban resilience and mean value of fractal resilience.

Figure 3. Structural sources of differences in urban resilience development among the eight urban agglomerations. (a) Beijing Tianjin Hebei (BTH). (b) Yangtze River Delta (YRD). (c) Pearl River Delta (PRD). (d) Middle Reaches of the Yangtze River (MYR). (e) Chengdu Chongqing (CC). (f) Guanzhong (GP). (g) Harbin Changchun (HC). (h) Central Plains (CP).

Table 1

Comprehensive evaluation index system of the urban resilience of eight urban agglomerations.

Target Layer	Criterion Layer	Index Layer	Index Property
Urbanresilience	Economyresilience	GDP per capita (yuan).	+
		The proportion of tertiary industry in GDP (%).	+
		The proportion of science and technology expenditure in GDP (%).	+
		The actual amount of foreign capital utilized per capita (USD 10,000).	+
		The proportion of urban registered unemployed (%).	−
		Savings deposit per capita (yuan).	+
		The proportion of employees (%).	+
		Financial expenditure per capita (yuan).	+
	Societyresilience	Urban disposable income per capita (yuan).	+
		The proportion of employed persons in the tertiary industry (%).	+
		Average wage of on-the-job employees (yuan).	+
		Number of college students per 10,000 people.	+
		Number of beds per 10,000 people (piece).	+
		Urban population density (%).	+
		Public management and social organization personnel per 10,000 people.	+
	Environmentresilience	Industrial sulfur dioxide emission intensity (10,000 tons/yuan).	−
		Park green space area per capita (ha/10,000 people).	+
		Green area per capita (ha/10,000 people).	+
		The proportion of built-up area to urban area (km2).	+
		Greening coverage rate of built-up area (%).	+
		Domestic sewage treatment rate (%).	+
		Comprehensive utilization rate of general industrial solid waste (%).	+
	Infra-structureresilience	Road area per capita (m2).	+
		Density of drainage pipe (%).	+
		Gas penetration rate (%).	+
		Buses and trams per 10,000 people (unit).	+
		The proportion of land for residential facilities (%).	+
		Public health facilities per capita (piece).	+
		Harmless treatment rate of municipal solid waste (%).	+

Table 2

The level of urban resilience in the eight urban agglomerations.

Year	BTH	YRD	PRD	MYR	CC	GP	HC	CP
2005	0.140	0.161	0.253	0.096	0.077	0.087	0.097	0.091
2006	0.143	0.152	0.240	0.094	0.073	0.088	0.092	0.096
2007	0.147	0.162	0.263	0.100	0.078	0.090	0.100	0.101
2008	0.153	0.170	0.264	0.106	0.086	0.094	0.107	0.108
2009	0.151	0.164	0.263	0.100	0.081	0.089	0.095	0.102
2010	0.148	0.160	0.252	0.100	0.077	0.087	0.095	0.100
2011	0.163	0.184	0.299	0.114	0.089	0.095	0.100	0.115
2012	0.191	0.216	0.349	0.129	0.106	0.112	0.120	0.129
2013	0.168	0.190	0.305	0.113	0.093	0.098	0.106	0.117
2014	0.191	0.212	0.341	0.129	0.106	0.113	0.121	0.127
2015	0.200	0.218	0.354	0.132	0.106	0.109	0.120	0.128
2016	0.190	0.204	0.324	0.125	0.100	0.103	0.112	0.122
2017	0.201	0.228	0.351	0.146	0.112	0.112	0.121	0.137
2018	0.195	0.237	0.379	0.148	0.118	0.105	0.114	0.141
Mean	0.170	0.190	0.303	0.117	0.093	0.099	0.107	0.115

Table 3

Spatial differences in urban resilience of the eight urban agglomerations.

Year	BTH	YRD	PRD	MYR	CC	GP	HC	CP	OverallDifference	IntragroupDifference	IntergroupDifference
2005	0.100	0.086	0.216	0.064	0.053	0.114	0.049	0.038	0.175	0.093	0.082
2006	0.112	0.084	0.193	0.065	0.066	0.062	0.057	0.046	0.174	0.090	0.084
2007	0.099	0.082	0.185	0.070	0.066	0.091	0.063	0.051	0.181	0.091	0.090
2008	0.080	0.075	0.189	0.076	0.091	0.077	0.036	0.046	0.158	0.086	0.072
2009	0.091	0.084	0.180	0.074	0.109	0.082	0.065	0.057	0.187	0.095	0.092
2010	0.086	0.075	0.181	0.081	0.075	0.088	0.046	0.051	0.170	0.088	0.082
2011	0.097	0.074	0.175	0.085	0.076	0.085	0.068	0.047	0.175	0.090	0.086
2012	0.111	0.075	0.166	0.087	0.089	0.110	0.082	0.054	0.181	0.094	0.087
2013	0.107	0.075	0.170	0.086	0.093	0.112	0.074	0.063	0.180	0.096	0.084
2014	0.107	0.070	0.158	0.077	0.078	0.126	0.076	0.053	0.164	0.089	0.075
2015	0.098	0.065	0.159	0.078	0.094	0.115	0.071	0.060	0.164	0.088	0.075
2016	0.112	0.065	0.158	0.081	0.076	0.114	0.080	0.063	0.169	0.090	0.080
2017	0.109	0.061	0.143	0.076	0.075	0.096	0.070	0.067	0.156	0.084	0.072
2018	0.105	0.068	0.131	0.086	0.077	0.104	0.075	0.075	0.166	0.088	0.079

Table 4

The spatial source contribution of the overall resilience difference among the eight urban agglomerations.

Year	Intragroup Contribution									Intergroup Contribution
	BTH	YRD	PRD	MYR	CC	GP	HC	CP	Sum
2005	5.03%	12.89%	18.56%	5.78%	2.23%	3.72%	1.67%	3.25%	53.14%	46.86%
2006	5.82%	12.53%	16.89%	5.90%	2.63%	2.03%	1.82%	4.10%	51.72%	48.28%
2007	4.73%	11.84%	16.16%	6.06%	2.52%	2.72%	2.03%	4.39%	50.44%	49.56%
2008	4.33%	12.27%	17.19%	7.70%	4.31%	2.65%	1.36%	4.61%	54.42%	45.58%
2009	4.39%	11.82%	15.26%	6.21%	4.14%	2.37%	1.88%	4.71%	50.79%	49.21%
2010	4.52%	11.47%	15.90%	7.77%	2.99%	2.81%	1.56%	4.63%	51.65%	48.35%
2011	4.88%	10.99%	15.42%	7.77%	3.11%	2.57%	2.08%	4.22%	51.03%	48.97%
2012	5.36%	10.90%	14.16%	7.62%	3.67%	3.27%	2.47%	4.59%	52.03%	47.97%
2013	5.19%	11.02%	14.52%	7.41%	3.84%	3.36%	2.24%	5.56%	53.14%	46.86%
2014	5.72%	11.24%	14.06%	7.76%	3.69%	4.28%	2.59%	4.95%	54.30%	45.70%
2015	5.33%	10.34%	14.19%	7.89%	4.46%	3.77%	2.40%	5.61%	53.99%	46.01%
2016	5.99%	10.12%	13.73%	7.88%	3.36%	3.60%	2.54%	5.68%	52.90%	47.10%
2017	6.06%	10.19%	13.07%	8.44%	3.70%	3.20%	2.33%	6.69%	53.68%	46.32%
2018	5.31%	10.67%	11.74%	8.84%	3.69%	3.18%	2.24%	6.97%	52.64%	47.36%

Table 5

The structural source contribution of the overall resilience difference among the eight urban agglomerations.

Year	Economy	Society	Environment	Infrastructure
2005	46.87%	18.95%	21.73%	12.45%
2006	50.19%	16.25%	21.26%	12.30%
2007	49.98%	16.51%	23.26%	10.25%
2008	44.11%	17.98%	26.74%	11.17%
2009	51.74%	15.46%	22.47%	10.33%
2010	48.31%	15.71%	24.53%	11.45%
2011	52.58%	12.64%	24.09%	10.69%
2012	49.26%	17.32%	22.27%	11.15%
2013	47.21%	19.03%	22.92%	10.84%
2014	45.56%	19.54%	24.46%	10.44%
2015	44.47%	20.59%	24.45%	10.49%
2016	46.68%	20.63%	22.68%	10.01%
2017	45.13%	23.00%	22.06%	9.81%
2018	49.01%	21.28%	21.07%	8.64%

Table 6

The explanatory power of single factor in four dimensions.

Urban Agglomeration	X1	X2	X3	X4
Overall	0.824 ***	0.773 ***	0.800 ***	0.453 ***
BTH	0.925 **	0.885 **	0.516	0.207
YRD	0.770 ***	0.896 ***	0.733 ***	0.701 ***
PRD	0.884 *	0.916 **	0.934 **	0.954 **
MYR	0.855 ***	0.963 ***	0.678 **	0.494 *
CC	0.778 *	0.935 ***	0.698 *	0.884 **
GP	0.849	0.941 **	0.775	0.942 **
HC	0.933 ***	0.888 **	0.730	0.977 ***
CP	0.520	0.716 **	0.795 ***	0.710 ***

Note: *, **, *** are significant at the level of 10%, 5%, and 1%.

Table 7

Explanatory power of four-dimensional interaction factors.

Urban Agglomeration	${\mathit{X}}_1\cap {\mathit{X}}_2$	${\mathit{X}}_1\cap {\mathit{X}}_3$	${\mathit{X}}_1\cap {\mathit{X}}_4$	${\mathit{X}}_2\cap {\mathit{X}}_3$	${\mathit{X}}_2\cap {\mathit{X}}_4$	${\mathit{X}}_3\cap {\mathit{X}}_4$
Overall	0.905	0.944	0.929	0.905	0.820	0.862
BTH	0.967	0.972	0.948	0.957	0.946	0.576
YRD	0.928	0.953	0.886	0.936	0.967	0.913
PRD	0.986	1.000	0.987	0.973	0.987	0.978
MYR	0.976	0.987	0.973	0.988	0.977	0.744
CC	0.992	0.793	0.995	0.982	0.944	0.982
GP	0.990	0.998	0.983	0.989	0.990	0.992
HC	0.998	0.964	0.991	1.000	0.998	0.984
CP	0.780	0.966	0.964	0.943	0.949	0.830

References

1. Tang, J.; Heinimann, H.; Han, K.; Luo, H.; Zhong, B. Evaluating resilience in urban transportation systems for sustainability: A systems-based Bayesian network model. Transp. Res. Part C Emerg. Technol.; 2020; 121, 102840. [DOI: https://dx.doi.org/10.1016/j.trc.2020.102840]

2. Chen, J.; Liu, J.; Peng, Q.; Yin, Y. Resilience assessment of an urban rail transit network: A case study of Chengdu subway. Phys. A Stat. Mech. Appl.; 2022; 586, 126517. [DOI: https://dx.doi.org/10.1016/j.physa.2021.126517]

3. Holling, C.S. Resilience and stability of ecological systems. Annu. Rev. Ecol. Syst.; 1973; 4, pp. 1-23. [DOI: https://dx.doi.org/10.1146/annurev.es.04.110173.000245]

4. Bautista-Puig, N.; Benayas, J.; Mañana-Rodríguez, J.; Suárez, M.; Sanz-Casado, E. The role of urban resilience in research and its contribution to sustainability. Cities; 2022; 126, 103715. [DOI: https://dx.doi.org/10.1016/j.cities.2022.103715]

5. Bruneau, M.; Chang, S.E.; Eguchi, R.T.; Lee, G.C.; O’Rourke, T.D.; Reinhorn, A.M.; Shinozuka, M.; Tierney, K.; Wallace, W.A.; Von Winterfeldt, D. A framework to quantitatively assess and enhance the seismic resilience of communities. Earthq. Spectra; 2003; 19, pp. 733-752. [DOI: https://dx.doi.org/10.1193/1.1623497]

6. Tang, J.; Heinimann, H.; Khoja, L. Quantitative evaluation of consecutive resilience cycles in stock market performance: A systems-oriented approach. Phys. A Stat. Mech. Appl.; 2019; 532, 121794. [DOI: https://dx.doi.org/10.1016/j.physa.2019.121794]

7. Shamsuddin, S. Resilience resistance: The challenges and implications of urban resilience implementation. Cities; 2020; 103, 102763. [DOI: https://dx.doi.org/10.1016/j.cities.2020.102763]

8. Chelleri, L.; Baravikova, A. Understandings of urban resilience meanings and principles across Europe. Cities; 2021; 108, 102985. [DOI: https://dx.doi.org/10.1016/j.cities.2020.102985]

9. Godschalk, D.R. Urban hazard mitigation: Creating resilient cities. Nat. Hazards Rev.; 2003; 4, pp. 136-143. [DOI: https://dx.doi.org/10.1061/(ASCE)1527-6988(2003)4:3(136)]

10. Campanella, T.J. Urban resilience and the recovery of New Orleans. J. Am. Plan. Assoc.; 2006; 72, pp. 141-146. [DOI: https://dx.doi.org/10.1080/01944360608976734]

11. Fan, M.F. Disaster governance and community resilience: Reflections on Typhoon Morakot in Taiwan. J. Environ. Plan. Manag.; 2015; 58, pp. 24-38. [DOI: https://dx.doi.org/10.1080/09640568.2013.839444]

12. Meerow, S.; Newell, J.P.; Stults, M. Defining urban resilience: A review. Landsc. Urban Plan.; 2016; 147, pp. 38-49. [DOI: https://dx.doi.org/10.1016/j.landurbplan.2015.11.011]

13. Drobniak, A. Exploring the urban economic resilience concept. Reg. Mag.; 2012; 286, pp. 8-10. [DOI: https://dx.doi.org/10.1080/13673882.2012.10515114]

14. Ribeiro, P.J.G.; Gonçalves, L.A.P.J. Urban resilience: A conceptual framework. Sustain. Cities Soc.; 2019; 50, 101625. [DOI: https://dx.doi.org/10.1016/j.scs.2019.101625]

15. Bozza, A.; Asprone, D.; Manfredi, G. Developing an integrated framework to quantify resilience of urban systems against disasters. Nat. Hazards; 2015; 78, pp. 1729-1748. [DOI: https://dx.doi.org/10.1007/s11069-015-1798-3]

16. Cui, P.; Li, D. A SNA-based methodology for measuring the community resilience from the perspective of social capitals: Take Nanjing, China as an example. Sustain. Cities Soc.; 2020; 53, 101880. [DOI: https://dx.doi.org/10.1016/j.scs.2019.101880]

17. Feng, X.; Xiu, C.; Li, J.; Zhong, Y. Measuring the Evolution of Urban Resilience Based on the Exposure–Connectedness–Potential (ECP) Approach: A Case Study of Shenyang City, China. Land; 2021; 10, 1305. [DOI: https://dx.doi.org/10.3390/land10121305]

18. Orencio, P.M.; Fujii, M. A localized disaster-resilience index to assess coastal communities based on an analytic hierarchy process (AHP). Int. J. Disaster Risk Reduct.; 2013; 3, pp. 62-75. [DOI: https://dx.doi.org/10.1016/j.ijdrr.2012.11.006]

19. Cutter, S.L.; Ash, K.D.; Emrich, C.T. The geographies of community disaster resilience. Glob. Environ. Chang.; 2014; 29, pp. 65-77. [DOI: https://dx.doi.org/10.1016/j.gloenvcha.2014.08.005]

20. Qasim, S.; Qasim, M.; Shrestha, R.P.; Khan, A.N.; Tun, K.; Ashraf, M. Community resilience to flood hazards in Khyber Pukhthunkhwa province of Pakistan. Int. J. Disaster Risk Reduct.; 2016; 18, pp. 100-106. [DOI: https://dx.doi.org/10.1016/j.ijdrr.2016.03.009]

21. Suárez, M.; Gómez-Baggethun, E.; Benayas, J.; Tilbury, D. Towards an urban resilience Index: A case study in 50 Spanish cities. Sustainability; 2016; 8, 774. [DOI: https://dx.doi.org/10.3390/su8080774]

22. Chen, Y.; Su, X.; Zhou, Q. Study on the spatiotemporal evolution and influencing factors of urban resilience in the yellow river basin. Int. J. Environ. Res. Public Health; 2021; 18, 10231. [DOI: https://dx.doi.org/10.3390/ijerph181910231] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34639530]

23. Xun, X.; Yuan, Y. Research on the urban resilience evaluation with hybrid multiple attribute TOPSIS method: An example in China. Nat. Hazards; 2020; 103, pp. 557-577. [DOI: https://dx.doi.org/10.1007/s11069-020-04000-0]

24. Wei, R. Research on Urban Resilience Evaluation Model and Its Application in China. Open J. Soc. Sci.; 2020; 8, pp. 407-416. [DOI: https://dx.doi.org/10.4236/jss.2020.83035]

25. Qin, W.; Lin, A.; Fang, J.; Wang, L.; Li, M. Spatial and temporal evolution of community resilience to natural hazards in the coastal areas of China. Nat. Hazards; 2017; 89, pp. 331-349. [DOI: https://dx.doi.org/10.1007/s11069-017-2967-3]

26. Ma, F.; Wang, Z.; Sun, Q.; Yuen, K.F.; Zhang, Y.; Xue, H.; Zhao, S. Spatial–Temporal Evolution of Urban Resilience and Its Influencing Factors: Evidence from the Guanzhong Plain Urban Agglomeration. Sustainability; 2020; 12, 2593. [DOI: https://dx.doi.org/10.3390/su12072593]

27. Liu, L.; Luo, Y.; Pei, J.; Wang, H.; Li, J.; Li, Y. Temporal and spatial differentiation in urban resilience and its influencing factors in Henan Province. Sustainability; 2021; 13, 12460. [DOI: https://dx.doi.org/10.3390/su132212460]

28. Saja, A.M.A.; Teo, M.; Goonetilleke, A.; Ziyath, A.M. An inclusive and adaptive framework for measuring social resilience to disasters. Int. J. Disaster Risk Reduct.; 2018; 28, pp. 862-873. [DOI: https://dx.doi.org/10.1016/j.ijdrr.2018.02.004]

29. Martin, R.; Sunley, P. On the notion of regional economic resilience: Conceptualization and explanation. J. Econ. Geogr.; 2015; 15, pp. 1-42. [DOI: https://dx.doi.org/10.1093/jeg/lbu015]

30. Cutter, S.L.; Barnes, L.; Berry, M.; Burton, C.; Evans, E.; Tate, E.; Webb, J. A place-based model for understanding community resilience to natural disasters. Glob. Environ. Chang.; 2008; 18, pp. 598-606. [DOI: https://dx.doi.org/10.1016/j.gloenvcha.2008.07.013]

31. Huang, G.; Li, D.; Zhu, X.; Zhu, J. Influencing factors and their influencing mechanisms on urban resilience in China. Sustain. Cities Soc.; 2021; 74, 103210. [DOI: https://dx.doi.org/10.1016/j.scs.2021.103210]

32. Chen, Y.; Zhu, M.; Zhou, Q.; Qiao, Y. Research on Spatiotemporal Differentiation and Influence Mechanism of Urban Resilience in China Based on MGWR Model. Int. J. Environ. Res. Public Health; 2021; 18, 1056. [DOI: https://dx.doi.org/10.3390/ijerph18031056]

33. James, S.; Ron, M. The economic resilience of regions: Towards an evolutionary approach. Camb. J. Reg. Econ. Soc.; 2010; 3, pp. 27-43.

34. Christian, R.; Thomas, S. Towards social resilience: A quantitative and qualitative survey on citizens’ perception of social media in emergencies in Europe. Technol. Forecast. Soc. Chang.; 2017; 121, pp. 168-180.

35. Xiao, W.; Lv, X.; Zhao, Y.; Sun, H.; Li, J. Ecological resilience assessment of an arid coal mining area using index of entropy and linear weighted analysis: A case study of Shendong Coalfield, China. Ecol. Indic.; 2020; 109, 105843. [DOI: https://dx.doi.org/10.1016/j.ecolind.2019.105843]

36. Ranasinghe, U.; Jefferies, M.; Davis, P.; Pillay, M. Resilience Engineering Indicators and Safety Management: A Systematic Review. Saf. Health Work; 2020; 11, pp. 127-135. [DOI: https://dx.doi.org/10.1016/j.shaw.2020.03.009]

37. Martin, R. Regional economic resilience, hysteresis and recessionary shocks. J. Econ. Geogr.; 2012; 12, pp. 1-32. [DOI: https://dx.doi.org/10.1093/jeg/lbr019]

38. Srdjevic, B.; Medeiros, Y.D.P.; Faria, A.S. An objective multi-criteria evaluation of water management scenarios. Water Resour. Manag.; 2004; 18, pp. 35-54. [DOI: https://dx.doi.org/10.1023/B:WARM.0000015348.88832.52]

39. Huang, J.; Zhong, P.S. Spatial Difference and Dynamic Evolution of the Development Level of New Urbanization in Henan Province. J. Xinyang Norm. Univ.; 2022; 42.

40. Clarke-Sather, A.; Qu, J.; Wang, Q.; Zeng, J.; Li, Y. Carbon inequality at the sub-national scale: A case study of provincial-level inequality in CO₂ emissions in China 1997–2007. Energy Policy; 2011; 39, pp. 5420-5428. [DOI: https://dx.doi.org/10.1016/j.enpol.2011.05.021]

41. Wu, M.Q.; Huang, J. The spatial inequality and convergence of innovation ability in China. J. Xinyang Norm. Univ.; 2022; 35, pp. 72-78.

42. Bourguignon, F. Decomposable income inequality measures. Econom. J. Econom. Soc.; 1979; 47, pp. 901-920. [DOI: https://dx.doi.org/10.2307/1914138]

43. Wang, J.F.; Hu, Y. Environmental health risk detection with GeogDetector. Environ. Model. Softw.; 2012; 20, pp. 114-115. [DOI: https://dx.doi.org/10.1016/j.envsoft.2012.01.015]