Investigating into the Coupling and Coordination Relationship between Urban Resilience and Urbanization: A Case Study of Hunan Province, China

Abstract

In the context of accelerated urbanization, constructing resilient cities is an effective approach to tackling risks, such as extreme weather, and various urban challenges. The coupling and coordinated development of urbanization and urban resilience is a prominent embodiment of urban sustainable development and high-quality development capacity. In this study, Hunan Province, China, which is frequently affected by various disasters, is selected as a representative for examining the coupling and coordination relationship between urban resilience and urbanization level. The panel data are adopted to construct a dual-system evaluation framework integrating urban resilience and urbanization level based on the entropy weight-coefficient of variation (CV)-CRITIC method. The coupling coordination degree of this dual-system evaluation framework is calculated with the coupling model in physics and GM (1, 1) grey prediction model. Additionally, the spatial–temporal evolution characteristics of the coupling coordination degree are investigated and analyzed by ArcGIS and Geoda software. The following are indicated from the results: (1) The resilience of all cities is related to their geographical location and is characterized by a decrease from east to west; in addition, the resilience level of most cities presents a downward trend with time. (2) The urbanization level of most cities develops stably with time, but there is a growing gap in the urbanization level between regions. (3) There is a strong correlation between urban resilience and urbanization level in all cities; the unbalanced coupling and coordinated development emerge, specifically manifested by the polarization phenomenon. Eventually, a circle-difference spatial distribution pattern that starts from the central urban agglomeration and gradually decreases to the periphery is formed. (4) The prediction results of the coupling coordination degree suggest that there is an increasingly distinct polarization trend for the coupling and coordinated development between cities, and it is necessary to pay attention to those cities with a declined predicted value. (5) There is a significant positive spatial autocorrelation and agglomeration effects in the distribution of the coupling coordination degree of all cities, and the correlation is getting stronger with each passing year; the correlation mode is mainly characterized by homogeneity and supplemented by heterogeneity. Finally, several suggestions are proposed in this paper, in an attempt to lead the coordinated development of regions by novel urbanization and thus promote the sustainable development of cities. The methods and insights adopted in this study contribute to investigating the relationship between urban resilience and urbanization in China and other regions worldwide.

1. Introduction

The sustainable development of cities is closely related to human health and welfare [1]. It can be estimated that 68% of the population worldwide will live in urban areas by 2050, and the total urban population on the globe will increase by 2.5 billion, including 255 million from China [2]. From a global perspective, 80% of GDP is generated from cities, and 70% of greenhouse gases are emitted from cities, making cities increasingly one of the main battlefields of climate action [3]. In the context of increasing global climate change, the suddenness, abnormality, and complexity of natural disasters are also increasing; urban and rural disaster management is facing more complex and serious challenges; and the comprehensive prevention of natural disaster risks needs to be strengthened. The effectiveness of actions can be improved to the greatest extent by integrating climate change and sustainable development governance [4]. In the context of globalization, cities are closely connected with the whole world, which implies that cities could exert significant impacts on such global issues as sustainable development, global warming, and global health.

With the acceleration of urbanization, cities, as complex macrosystems, are constantly subject to various disturbances from internal and external uncertainties of natural or artificial hazards, and they are facing increasingly diverse risks [5,6,7,8]. For instance, the frequent occurrence of urban flooding, urban heat island effect, traffic congestion, and environmental pollution reveal the inadequacy of urban risk management. The interaction and accumulation of environmental, economic, and social problems in cities at different periods and stages have increased the vulnerability of cities. Faced with the phenomenon of increasing urban vulnerability, cities around the world should pay great attention to it and actively explore ways to improve urban resilience. In rural areas, due to insufficient policy support, insufficient technical strength, and unreasonable industrial structure, etc., their environmental and economic, and social development lag behind those of urban areas, which also adds to the difficulty of building urban resilience. The focus on speed rather than quality during traditional urbanization has made it more difficult for cities to resist public safety risks [9]. Urban resilience refers to the ability of cities to withstand disasters on their own and to recover quickly from them through the proper deployment of resources. In the long run, cities can learn from past disasters and improve their resilience to disasters. The theoretical framework of resilient cities includes five characteristics, robustness, rapidity, redundancy, resourcefulness, and adaptability, and four dimensions, technical, organizational, economic, and social. The construction of resilient cities can effectively eliminate the problem of urban risks and promote sustainable development [10]. Enhancing urban safety and resilience is a new trend in urban development. China has now formed an urban development pattern with central cities, urban agglomerations, and metropolitan areas as the mainstay, and the high concentration of population, industry, and infrastructure has intensified the risk of natural disasters in cities and towns, while the geographical differences in the ability to withstand disaster risks are becoming increasingly significant. As a powerful support for urban public safety, urban resilience has a two-way influence on the level of urbanization. The level of urban resilience determines that of urbanization to a certain extent. The improvement of urban resilience can actively and effectively respond to uncertain events during urbanization and provide a favorable development environment for urbanization. A high level of urbanization can enhance urban resilience, but it can also restrict the improvement of urban resilience. As one of the important provinces in the Yangtze River Basin, Hunan Province possesses special geomorphological and climatic conditions and continuously accelerating urbanization process, which aggravates the risk of mass disasters in the province. Therefore, it is an urgent demand for realizing the simultaneous advancement of urban resilience and urbanization construction.

In summary, a dual-system evaluation system incorporating “economy-society-infrastructure-ecology-community-organization” urban resilience and “population-land-economy-society” urbanization is established in this paper based on the entropy weight-coefficient of the variation-CRITIC method. Subsequently, the coupling coordination degree of this dual-system evaluation framework is calculated with the coupling and GM (1, 1) grey prediction model in physics. Moreover, the spatial–temporal evolution characteristics of the coupling coordination degree in this framework are subject to spatial correlation analysis with the assistance of ArcGIS and Geoda software, in an attempt to explore the coordination relationship between them. Finally, several suggestions are proposed according to the research results, with a view to providing a reference for promoting the coupling and coordinated development between urban resilience and urbanization and the sustainable and healthy development of cities in Hunan Province and other regions.

The structure of this paper is presented as follows. The concept of resilience, urban resilience and urbanization is introduced in the next section, followed by the main research efforts to explore the coupling and coordination relationship between urban resilience and urbanization, as well as the research background and objectives. Subsequently, the research methods, data collection, and data analysis methods used in this study are interpreted in detail, and the data analysis results and key issues are revealed and discussed. Finally, several policy suggestions are proposed to promote the sustainable development of cities.

2. Literature Review

Through a literature review of the three concepts of resilience, urban resilience, and urbanization, as well as a brief summary overview of the current status of research on the coupling and coordination of urban resilience and urbanization levels, this study is supported by strong theoretical support, and its research background and research significance will be more prominent.

2.1. Resilience

Resilience, originally implying a return to a pristine state, was introduced into systems ecology by an ecologist, Holling [11] in 1973, to elucidate the stability of ecosystems. Resilience is one of the significant attributes of the complex adaptive system, which stems from the need of society to cope with increasingly strong threats. It essentially refers to the ability of systems to absorb, adapt and recover from external stresses [12,13]. The concept of sustainable development can be traced back to the World Conservation Strategy jointly published by the International Union for Conservation of Nature (IUCN), the United Nations Environment Program (UNEP), and the World Wildlife Fund (WWF) in 1980. In 1987, the World Commission on Environment and Development (WCED) published the report Our Common Future, which formally expounded the concept of sustainable development systematically, which was defined as the “development that meets the needs of the present without compromising the ability of future generations to meet their own needs”. With the continuously expanded research field, resilience is often closely associated with such concepts as risk, vulnerability, and sustainability. Norris and Folke et al. [14,15,16] endowed it with a rich connotation.

In general, the concept of resilience, which originates from an ecological perspective, has evolved from “single equilibrium (engineering resilience)—multiple equilibria (ecological resilience)—complex adaptive systems (adaptive cycles)”, from “equilibrium” to “adaptation” and from “ecosystem” to “social-ecological system”. The concept of resilience varies with the change in research objects and fields, and the focus and core connotation of each stage are not consistent. The extension, ambiguity, dynamism, and co-evolution of the concept of resilience make it difficult to apply it in practice, so it is especially important to clarify the concept and connotation of resilience to deepen its quantitative and practical research.

2.2. Urban Resilience

Under the background of increasing uncertain disturbance factors, such as climate change, policy changes and man-made disasters, the construction of resilient cities provides a novel insight for cities to respond to uncertain impacts from the perspective of development. In essence, it is to actively explore adaptive adjustment methods and approaches for unknown risks faced by cities. Local Governments for Sustainability (ICLEI) introduced the concept of “resilience” into the field of urban construction and disaster prevention; Rockefeller Foundation put forward 100 resilient city schemes, which set off a research upsurge on urban resilience [17,18]. Urban resilience is mainly investigated from four aspects, namely, human and environmental impacts, theoretical frameworks, evaluation, and simulation [19,20], with a process characteristic. The interdependence and promotion of resistance, recovery, and adaptation make the urban system a stable and dynamic evolution state [21,22]. By analyzing and reviewing the theoretical literature, Masnavi et al. [23] determined the basis for studying urban resilience. Meerow et al. [24] proposed six basic concepts related to urban resilience, which promote the perfection of urban resilience theory. Erling et al. [25] put forward a model based on three moral requirements to meet human needs, ensure social equity and respect environmental constraints, which provided an explanation for global sustainable development. The research framework proposed by Chen et al. [26] can be employed to understand the effectiveness of COVID-19 control in different countries, and it would enhance the urban resilience and sustainability related to health. Borekci et al. [27] expanded the research on organizational resilience from the perspective of multi-case design and extended the concept of resilience to the dimension of sustainability. Rod et al. [28] explored the methods to integrate critical infrastructure resilience into the existing security practice. Payne et al. [29] confirmed that community resilience can be quantified and decomposed into dimensions of resilience under the research background of two different regions. Oliver et al. [30] pointed out that the function of the ecosystem is threatened by the acceleration of environmental degradation, and they emphasized the importance of ecological resilience construction. Martin et al. [31] explained and summarized the concept of regional economic resilience and some related problems. Due to the fact that there are many subsystems in cities, such as ecology, infrastructure, and community, multiple aspects are considered in the research on urban resilience.

Overall, the perspective of urban resilience research is no longer limited to the study of ecosystems but has expanded to a comprehensive study focusing on physical space carriers, social capital management, and institutional development. The study of urban resilience as a whole has begun to bear fruit at the macro level, but at the meso and micro levels, it is somewhat lacking and needs further refinement. Although scholars have basically outlined the basic framework of urban resilience, they have not yet clarified the complex relationship between each element and the influencing factors of the framework, and further theoretical research is needed to improve the practical applicability of the urban resilience framework. Resilient city-related research has become increasingly mature, and relevant organizations and practice processes (such as the Resilience Alliance and the “Global 100 Resilient Cities” project) are also being improved, but there is less research on the resilient city methodology for different urban development stages and contexts, which needs to be further explored.

2.3. Urbanization

Urbanization achieves a fundamental transformation of the economy, social structure, and way of life and production through the concentration of factors of production, such as population, capital, information, and land, in cities. Generally, urbanization is a process of converting the agricultural population into non-agricultural population, agricultural territory into non-agricultural territory, and agricultural activities into non-agricultural activities. Reasonable urbanization can effectively promote the sustainable development of cities [32,33]. Guan et al. [9,34] suggested that urbanization is an inevitable requirement for promoting social progress, and traditional land-centered urbanization is typical of “incomplete urbanization” and “low-quality urbanization”. Zhang et al. [34] introduced the concept of “decoupling” in the environmental field and established a comprehensive index system on urbanization quality, which systematically evaluated the relationship between the level and quality of urbanization. Shi et al. [35] constructed the evaluation index system of urbanization coordination level based on the quality and scale of urbanization; and analyzed relevant spatial correlation, spatial difference, and spatial pattern evolution characteristics. He et al. [36] maintained that the accumulating pressure on the environment caused by urbanization is the key issue during urban development, and they verified the relationship between urbanization and ecological environment with a coupling and coordination model. Xiao et al. [37] established a four-dimensional comprehensive evaluation system related to urbanization quality, and they revealed the spatial correlation of urbanization in China through exploratory spatial data analysis. Ma et al. [38] investigated the coordination between population urbanization and land urbanization and proposed a relevant development model. Xu et al. [39] constructed an index system for the comprehensive evaluation of three subsystems of urbanization (population, economy, and land urbanization) based on the theory of coordinated development, and they explored the spatial–temporal characteristics of overall coordination and paired coordination of population, land, and economic urbanization with an entropy method, coupling coordination degree model and spatial autocorrelation analysis. Niu et al. [40] established an index system incorporating population, land, and industry, and they constructed a coupling and coordination model. Finally, they evaluated the comprehensive development level and the coordination degree of urbanization at the county level. Urbanization is a multi-dimensional concept, including population, economy, society, and land. The high-quality development of urbanization can be promoted by exploring the coordinated relationship between urbanization and urban resilience.

2.4. The Coupling and Coordination Relationship between Urban Resilience and Urbanization

Population growth and migration, economic and social development, and land spatial expansion are the bridges between urbanization and urban resilience. As a product of urban development, urbanization inevitably has a strong interactive relationship with urban resilience [41,42]. Zhou et al. [43] constructed a comprehensive evaluation index system based on urban resilience and urbanization level, and analyzed the spatial–temporal variation characteristics and spatial distribution types of the coupling coordination degree of 26 cities with the assistance of a coupling coordination degree model and a spatial autocorrelation model. Bai et al. [44] analyzed the spatial differentiation characteristics of urban resilience and urbanization in Jilin Province, and they made a coupling analysis on these spatial differentiation characteristics. Wang et al. [45] constructed an evaluation system with respect to urban ecological resilience, employed a coupling coordination degree model to measure the coupling coordination degree between urbanization and ecological resilience in the Pearl River Delta, and made an in-depth exploration into relevant spatial-temporal characteristics. Li et al. [46] analyzed the spatial evolution characteristics of coupling and coordination between urbanization and resource and environmental carrying capacity with a coupling coordination degree model and spatial autocorrelation analysis methods. Gao et al. [47] analyzed the coupling coordination degree between urban resilience and urbanization quality with a coupling and coordination model, spatial self-analysis, and LISA time path. The regional coordinated development and sustainable development of cities can be promoted by exploring the coupling and coordination relationship between urban resilience and urbanization.

After consulting the relevant literature, it was found that there are fewer studies on the establishment of the urbanization level evaluation index system from the perspective of population, economy, land, and society [40,48,49,50]. The urban resilience evaluation index system is mostly based on the four aspects of population, economy, land and society [51,52,53,54], and the influence of communities and organizations are seldom considered [55,56,57]. Moreover, it is scarce to combine the three methods to empower the indexes of urban resilience and urbanization system [58,59]. There is a lack of in-depth research on the relationship between urban resilience and urbanization from the perspective of coupling and coordination. The coupling and coordination model is usually used to verify the relationship between systems [60,61,62]. Benefiting from its prediction accuracy, the GM (1, 1) grey prediction model can be applied to analyze a few and uncertain data [63]. In spatial econometrics, however, ignoring spatial effects may induce errors in estimation and analysis [64,65]. Spatial autocorrelation analysis can be employed to verify the spatial homogeneity and heterogeneity of data. Due to the fact that Hunan Province is one of the provinces suffering from serious disasters in China and there is a lack of research to explore the relationship between urban resilience and urbanization in Hunan Province from the perspective of coupling and coordination, this province is selected as the research object, which has high research value and practical guiding significance. Therefore, the coupling and coordination relationship between urban resilience and urbanization of all cities in Hunan Province was explored, and several suggestions are also proposed to promote the sustainable and coordinated development of various regions. The findings of this study provide a reference for the sustainable development of other regions in the world.

3. Materials and Methods

3.1. Methods

3.1.1. The Entropy Weight-Coefficient of Variation-CRITIC Method

The entropy weight method determines the weights through the information entropy of indicators, and then makes certain corrections to the entropy weight according to each index so as to obtain a more objective index weight. The coefficient of variation method uses the degree of variation of indicators to calculate the weights, which eliminates the effect of different units or averages on the comparison of the degree of variation of two or more indicators. The CRITIC method measures the weights according to the conflict and contrast intensity among evaluation indicators, and it takes into account both the magnitude of indicator variability and the correlation between indicators, using the objective properties of the data itself for scientific evaluation. The combination of the three methods can reflect the importance of indicators more precisely.

Supposing there are $m$ evaluation objects and $n$ evaluation indicators, and $X_{ij}$ is expressed as the original data of the $jth$ indicator of the $ith$ evaluation object. The negative variables of the indicators are first transformed into positive variables, and then the indicator data are dimensionless.

$$\mathrm{Positive}\mathrm{indicators}\text{:} x_{ij}=\frac{X_{ij}-X_{min}}{X_{max}-X_{min}}$$

(1)

$$\mathrm{Negative}\mathrm{indicators}\text{:} x_{ij}=\frac{X_{max}-X_{ij}}{X_{max}-X_{min}}$$

(2)

In the formula, $i=1,\dots ,m;j,k=1,\dots ,n$.

For empowerment using the entropy weight method [66,67], calculate the combined weight, information entropy and weight of the $jth$ indicator of the $ith$ evaluation object. The formula is as follows.

$$P_{ij}=\frac{x_{ij}}{{\sum }_{i=1}^m{x}_{ij}}$$

(3)

$$E_j=-{\left(lnm\right)}^{-1}{\sum }_{i=1}^m{P}_{ij}ln{P}_{ij}$$

(4)

$$w_{1j}=\frac{1-E_j}{n-{{\displaystyle \sum }}_{j=1}^n{E}_j}$$

(5)

For assignment using the coefficient of variation method [68,69], calculate the mean ${\overline{x}}_j$, standard deviation ${\mathrm{S}}_{1j}$ and coefficient of variation $V_j$ of the measurement degree $x_{ij}$, and then seek the weight of the $jth$ index. The formula is as follows.

$${\mathrm{S}}_{1j}=\sqrt{\frac{{{\displaystyle \sum }}_{i=1}^m{\left(x_{ij}-{\overline{x}}_j\right)}^2}{m}}$$

(6)

$$V_j=\frac{S_{1j}}{{\overline{x}}_j}$$

(7)

$$w_{2j}=\frac{V_j}{{{\displaystyle \sum }}_{j=1}^n{V}_j}$$

(8)

For empowerment using the CRITIC method [70], to eliminate the effects of the magnitude and the positive and negative signs, the standard deviation coefficient is used instead of the standard deviation, and the correlation coefficient is treated by taking the absolute value [71]. Calculate the amount of information and weight of the $jth$ indicator. The formula is as follows.

$${\mathrm{C}}_j=\frac{S_{2j}}{{\overline{x}}_j}{\sum }_{j=1}^n\left(1-\left|r_{jk}\right|\right)$$

(9)

$${\mathrm{S}}_{2j}=\sqrt{\frac{{{\displaystyle \sum }}_{i=1}^m{\left(x_{ij}-{\overline{x}}_j\right)}^2}{m-1}}$$

(10)

$$w_{3j}=\frac{C_j}{{{\displaystyle \sum }}_{j=1}^n{C}_j}$$

(11)

In the formula, $\left|r_{jk}\right|$ is the absolute value of the correlation coefficient between the $jth$ indicator and the $kth$ indicator.

The formula for calculating the combination weight of the $jth$ indicator is as follows.

$$w_j=\alpha w_{1j}+\beta w_{2j}+\gamma w_{3j}\left(\alpha =\beta =\gamma =\frac{1}{3}\right)$$

(12)

3.1.2. Construction of the Dual-System Evaluation Index System

Based on the above understanding of the basic concepts of urban resilience and urbanization, as well as the literature combing of both, we already have a very clear perception of urban resilience, urbanization, and the connection between the two for a more in-depth study. Regarding the construction of the dual-system evaluation index system of urban resilience and urbanization, this study upheld the principles of data scientificity and accessibility and collected and screened the evaluation index systems of urban resilience and urbanization at home and abroad, as shown in

Table 1. Domestic and international urban resilience index system collation.

Guideline Layer	Index Layer	Literature Sources
Economic resilience	Income; Gross domestic product per capita; Gross Regional Product; Actual utilization of foreign direct investment amount; Annual output value of tertiary industry; Disposable personal income (DPI) of permanent residents; Average salary of employees; Total fixed assets investment, etc.	Xun et al. [53]; Chen et al. [72]; Yang et al. [73]; Assarkhaniki et al. [74]; Zhu et al. [75], etc.
Social resilience	Social capital; Population density; Educational status; Number of doctors in the public health system; Health technicians per 10,000 people; Pupil-to-teacher ratio at public schools; Per capita post and telecommunications business volume; Number of doctors per 10,000 population, etc.	Zhou et al. [43]; Xun et al. [53]; Chen et al. [72]; Assarkhaniki et al. [74]; Zhu et al. [75]; Qasim et al. [76]; Chen et al. [54], etc.
Infrastructure resilience	Length of water supply pipeline; Number of health institutions; Number of beds in hospitals and health centers; Number of mobile phone users, Communication network coverage; Percentage of infrastructure area per district (transportation, parks, electricity, water services (ha)); Average number of internet, television, radio, telephone, and telecommunications broadcasters per household; Per capita domestic water consumption; Drainage pipe density; Number of vehicles per 10,000 people; Integrated urban water supply production capacity, etc.	Bai et al. [52]; Xun et al. [53]; Chen et al. [54]; Assarkhaniki et al. [74]; Qasim et al. [76]; Liu et al. [77], etc.
Ecological Resilience	Green area per capita; Daily capacity of urban wastewater treatment; energy consumption per 10,000 GDP; Harmless treatment rate of urban domestic garbage; Industrial wastewater discharge per unit GDP; Industrial smoke and dust emissions per unit of GDP; Integrated energy consumption; Per capita park green space; Green space coverage of built-up areas, etc.	Bai et al. [52]; Xun et al. [53]; Chen et al. [72]; Yang et al. [73]; Liu et al. [77], etc.
Community Resilience	Number of registered volunteers/voluntary organizations; Social groups and organizations; Proportion of public service expenditure; Volunteer Activities; Percentage of population involved in Red Cross volunteer activities; Red Cross training workshop participants per 10,000 persons; Number of education programs on DRR and disaster preparedness per each local community by local government per year; Proportion of community service area, etc.	Assarkhaniki et al. [74]; Yang et al. [78], etc.
Organizational resilience	Health insurance; Number of participants in unemployment insurance; Number of Participants in basic medical insurance; Expenditures budgeted by local governments; Basic urban medical coverage, etc.	Xun et al. [53]; Qasim et al. [76]; Chen et al. [54]; Da et al. [79], etc.

and

Table 2. Collection of urbanization index systems at home and abroad.

Guideline Layer	Index Layer	Literature Sources
Population urbanization	Share of urban population; Percentage of employment in the tertiary sector; Share of the non-farm population; Non-farm population size; Downtown Population Density, etc.	Cui et al. [80]; Jia et al. [81], etc.
Land urbanization	Urban housing area per capita; Area of the built-up area; Built-up area per capita; Urban road area per capita, etc.	Cui et al. [80]; Jia et al. [81], etc.
Economic urbanization	Share of secondary industry output in GDP; The proportion of tertiary industry output in GDP; Gross regional product per capita; Local fixed asset investment, etc.	Cao et al. [48]; Jia et al. [81]; Wang et al. [82], etc.
Social urbanization	Total social retail consumer goods per capita, Public financial expenditure, etc.	Cui et al. [80]; Chen et al. [83], etc.

.

3.1.3. Coupling Degree and Coupling Coordination Degree Model

Coupling refers to the close cooperation and interaction between different systems or forms of motion under the action of themselves and the outside world. This paper constructs a dual-system coupled model of urban resilience and urbanization to measure the coordinated development between the two. The formula [84,85] is as follows.

$$\mathrm{C}={\left\{\frac{\left(U_1\times U_2\right)}{{\left(\frac{U_1+U_2}{2}\right)}^2}\right\}}^{1/2}$$

(13)

$$\mathrm{D}=\sqrt{C\times T}$$

(14)

$$\mathrm{T}=\mathsf{\sigma }{U}_1+\phi U_2\left(\sigma =\phi =\frac{1}{2}\right)$$

(15)

In the formula, $U_1$ and $U_2$ are the combined evaluation indices of urban resilience and urbanization level, respectively. C indicates the coupling degree, and its magnitude ranges from 0 to 1. The closer the magnitude of C is to 1, the stronger the inter-system correlation is. D denotes the coupling coordination degree, and the magnitude of its value is positively correlated with the degree of coordination. T is the comprehensive coordination index, and it is generally guaranteed that $T\in \left(0,1\right)$, to ensure that $D\in \left(0,1\right)$ [80].

Based on the research of Wang et al. [86,87,88] and then reformulated after making revisions, the grades of D are classified (

Table 3. Grading criteria of coupling coordination degree.

$\mathbf{D}$	Grades of Coupling Coordination Degree	${\mathit{U}}_1>{\mathit{U}}_2$	${\mathit{U}}_1<{\mathit{U}}_2$
$0.8\le \mathrm{D}\le 1.0$	Good coordination	Retarded urbanization	Retarded urban resilience
$0.6\le \mathrm{D}<0.8$	Moderate coordination	Retarded urbanization	Retarded urban resilience
$0.5\le \mathrm{D}<0.6$	Low coordination	Retarded urbanization	Retarded urban resilience
$0.4\le \mathrm{D}<0.5$	Near incoordination	Retarded urbanization	Retarded urban resilience
$0.2\le \mathrm{D}<0.4$	Moderate incoordination	Retarded urbanization	Retarded urban resilience
$0.0\le \mathrm{D}<0.2$	Severe incoordination	Retarded urbanization	Retarded urban resilience

). When $U_1=U_2$, urban resilience and urbanization level are of the type of synergistic development under the same rank.

3.1.4. GM (1, 1) Grey Prediction Model

Gray forecasting is the prediction of systems that contain both known and uncertain information, in other words, the prediction of gray processes that vary within a certain range and are related to time series. Although the phenomena shown in the gray process are random and haphazard, they are, after all, ordered and bounded, so that the obtained data set possesses potential laws. After the random original time series is accumulated, the law of the new time series formed can be approximated by the solution of the first-order linear differential equation.

The time series and the new sequence obtained after accumulation are expressed as $X^{\left(0\right)}=\left\{X^{\left(0\right)}\left(i\right),i=1,\cdots n\right\}$ and, $X^{\left(1\right)}=\left\{X^{\left(1\right)}\left(i\right),i=1,\cdots n\right\}$ respectively. The differential equation of the GM (1, 1) grey prediction model is as follows.

$$\frac{d{X}^{\left(1\right)}}{dt}+a{X}^{\left(1\right)}=\mu$$

(16)

In the formula, a is the developmental gray number, μ is the endogenous control gray number.

Supposing the parameter vector to be estimated as $\text{â}=\left[\begin{array}{c}a\\ \mu \end{array}\right]$, the equation is obtained from the least squares method as follows.

$$\text{â}={\left(B^{T}B\right)}^{-1}{B}^T{Y}_n$$

(17)

In the formula,

$$B=\left[\begin{array}{c}\begin{array}{cc}-\frac{1}{2}\left[X^{\left(1\right)}\left(1\right)+X^{\left(1\right)}\left(2\right)\right]& 1\end{array}\\ \begin{array}{cc}-\frac{1}{2}\left[X^{\left(1\right)}\left(2\right)+X^{\left(1\right)}\left(3\right)\right]& 1\end{array}\\ \begin{array}{cc}⋮ & ⋮\end{array}\\ \begin{array}{cc}-\frac{1}{2}\left[X^{\left(1\right)}\left(n-1\right)+X^{\left(1\right)}\left(n\right)\right]& 1\end{array}\end{array}\right]\mathrm{,}{Y}_n=\left[\begin{array}{c}\begin{array}{c}{X}^{\left(0\right)}\left(2\right)\\ X^{\left(0\right)}\left(3\right)\end{array}\\ ⋮\\ X^{\left(0\right)}\left(n\right)\end{array}\right]$$

The prediction model equation is as follows.

$${\widehat{X}}^{\left(1\right)}\left(k\right)=\left[X^{\left(0\right)}\left(1\right)-\frac{\mu }{a}\right]e^{-a\left(k-1\right)}+\frac{\mu }{a}$$

(18)

Calculating the small error probability P and the variance ratio C to determine whether the GM (1, 1) grey prediction model can be used for prediction [89]. The judgment standard is shown in

Table 4. Grading criteria for accuracy of gray prediction models.

Accuracy Grade		Good	Qualified	Barely Qualified	Unqualified
Standard	P	>0.95	>0.80	>0.70	≤0.70
	C	<0.35	<0.60	<0.65	≥0.65

.

3.1.5. Exploratory Spatial Data Analysis (ESDA)

Exploratory spatial data analysis (ESDA) is based on spatial correlation measures to explain the mechanism of interaction between observed objects by visually describing the spatial distribution pattern of things or phenomena. In this study, the ESDA method is used to analyze the overall spatial correlation and spatial agglomeration of the coupled coordination degree of urban resilience and urbanization.

Global spatial autocorrelation refers to the spatial characteristic description of an attribute in the study area, revealing the overall correlation and spatial agglomeration of the observed objects. Moran’s index and Geary’s coefficient are generally used to measure the degree of spatial autocorrelation. In this study, the global Moran’ I was chosen for analysis with the following equation [90].

$${\mathrm{Moran}}^{\prime }\mathrm{s}I=\frac{n{{\displaystyle \sum }}_{i=1}^n{{\displaystyle \sum }}_{j=1}^n{W}_{ij}\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)}{{{\displaystyle \sum }}_{i=1}^n{{\displaystyle \sum }}_{j=1}^n{W}_{ij}{{\displaystyle \sum }}_{i=1}^n{\left(x_i-\overline{x}\right)}^2}=\frac{n{{\displaystyle \sum }}_{i=1}^n{{\displaystyle \sum }}_{j=1}^n{W}_{ij}\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)}{S^2{{\displaystyle \sum }}_{i=1}^n{{\displaystyle \sum }}_{j=1}^n{W}_{ij}}$$

(19)

$$S^2={\mathit{\sum }}_{i=1}^n\left(x_i-\overline{x}\right)/n$$

(20)

In the formula, $n$ is the total number of territorial units in the study area, $x_i\left(x_j\right)$ is the value of $x$ variable of territorial unit $i\left(j\right)$; $W_{ij}$ is the binary adjacency matrix, where according to the common boundary rule, if region $i$ is adjacent to region $j$ then $W_{ij}=1$, otherwise $W_{ij}=0$. The value range of the global Moran index is from −1 to 1. A positive (negative) value of the global Moran index reflects a positive (negative) correlation, and zero indicates no correlation [89].

Local spatial autocorrelation refers to the degree of similarity between cities with measured attributes and neighboring cities. The Moran scatter plot visualizes the observed values. Classifying the study objects into four patterns of high–high agglomeration (HH), low–high agglomeration (LH), low-low agglomeration (LL) and high–low agglomeration (HL) can reveal the high–low agglomeration characteristics among the study units. LISA analysis is a measure of the degree of similarity or dissimilarity between the attributes of spatial units and the surrounding units and can reflect the degree of local spatial agglomeration in more detail.

3.2. Study Area

Hunan Province, with 14 prefectural-level administrative regions, lies between latitude 24°38′ and 30°08′ north and longitude 108°47′ and 114°15′ east, with a horseshoe-shaped topography surrounded by mountains on three sides and opening towards the north, straddling the Yangtze and Pearl River systems. The typical characteristics of the selected research subjects are as follows: (1) The results of the seventh population census of Hunan Province show that the urbanization rate of Hunan Province has increased to 58.76%, with a growth rate exceeding the Chinese average, but the overall level is still lower than the average level of urbanization in China. (2) At the end of 2021, the province’s resident population was 66.22 million, ranking seventh in China. (3) Hunan Province’s GDP has long been among the top in China, but the per capita GDP ranks at the middle level in the country, similar to China’s GDP position in the world. (4) In promoting urbanization in Hunan Province, there are phenomena such as low level of tertiary industry, unbalanced development level of prefecture-level cities, and mismatch between urbanization level and industrialization level, accompanied by geological disasters such as heavy rainfall, flood, drought, freezing and high temperature, as well as problems in the urban development process, such as traffic congestion, environmental pollution, and severe employment situation.

In order to optimize the spatial pattern of the province’s towns, Hunan Province has proposed a new town pattern of “one circle, one group, three belts, and multiple points” to promote the synergistic development of small, medium, and large towns. ChangZhuTan urban circle, which is the core of Hunan’s development. “3 + 5” city cluster, which is an extension of ChangZhuTan city circle, including Yueyang, Hengyang, Changde, Yiyang, Loudi, and other five cities, covering the central and northern regions of Hunan. There are three urban development belts, Beijing-Guangzhou, Shanghai-Kunming, and Chongqing-Changxia, supported by large traffic, through convenient traffic channels such as highways and high-speed railways, with developed areas driving underdeveloped areas, and finally forming a balanced development pattern. Changsha, the provincial capital, is the national central city, Hengyang and Yueyang are the two provincial sub-central cities, and the rest of the cities are used as auxiliary support to jointly promote the high-quality development of new urbanization in the province. The specific distribution is shown in Figure 1.

3.3. Data Sources

The research data were obtained from the Statistical Yearbook of Hunan Province, China Civil Affairs Statistical Yearbook, the official websites of the municipal and state governments of Hunan Province, and the statistical bulletin of national economic and social development. Some indicator data were replaced by arithmetic or weighted averages, missing data were completed by the difference method, and some data were obtained by queries from the EPSDATA data platform (https://www.epsnet.com.cn accessed on 1 December 2021).

4. Results

4.1. Construction of the Dual-System Evaluation Index System in Hunan Province

Based on panel data of Hunan Province, with strict adherence to the scientificity and data availability, 28 secondary indexes from six dimensions, such as economic resilience, were adopted to construct the urban resilience level evaluation system, and 9 secondary indexes from such four aspects as population urbanization were selected to establish an urbanization level evaluation system. Using Equations (5), (8), (11) and (12), the weights of each indicator were calculated, as shown in

Table 5. Comprehensive urban resilience evaluation and weight system.

Target Layer	Guideline Layer	Weight	Index Layer	Weight				Propert y (+/−)
				${\mathit{w}}_{1\mathit{j}}$	${\mathit{w}}_{2\mathit{j}}$	${\mathit{w}}_{3\mathit{j}}$	${\mathit{w}}_{\mathit{j}}$
Urban Resilience Level	Economic resilience	0.1895	Per capita GDP	0.0459	0.0434	0.0314	0.0402	+
			Growth rate of investment in fixed assets	0.0111	0.0155	0.0238	0.0168	+
			Per capita disposable income for all residents	0.0425	0.0394	0.0294	0.0371	+
			Actual utilization of foreign capital	0.0532	0.0540	0.0448	0.0507	+
			Average annual wage of employees in employment	0.0490	0.0461	0.0390	0.0477	+
	Social resilience	0.1373	Population density	0.0339	0.0319	0.0321	0.0326	+
			Per capita post and telecommunications business volume	0.0457	0.0456	0.0383	0.0432	+
			Per capita number of teachers in primary and secondary schools	0.0235	0.0266	0.0393	0.0298	+
			Per capita health technicians	0.0301	0.0337	0.0312	0.0317	+
	Infrastructure resilience	0.2562	Public transport vehicles	0.0351	0.0343	0.0425	0.0373	+
			Number of health institutions	0.0233	0.0258	0.0279	0.0257	+
			Number of mobile phone users	0.0327	0.0377	0.0309	0.0338	+
			Integrated urban water supply production capacity	0.0427	0.0426	0.0331	0.0395	+
			Drainage pipe density	0.0587	0.0476	0.0521	0.0528	+
			Per capita highway mileage	0.0179	0.0233	0.0237	0.0216	+
			Number of high-tech units	0.0516	0.0484	0.0365	0.0455	+
	Ecological resilience	0.1383	Growth rate of energy consumption per unit GDP	0.0183	0.0214	0.0302	0.0233	-
			Sewage treatment rate	0.0158	0.0207	0.0261	0.0209	+
			Per capita park green area	0.0233	0.0266	0.0410	0.0303	+
			Green space coverage rate in the built-up area	0.0117	0.0167	0.0241	0.0175	+
			Household waste disposal rate	0.0091	0.0137	0.0202	0.0143	+
			Number of environmental protection industry units	0.0304	0.0320	0.0337	0.0320	+
	Community Resilience	0.1348	Proportion of public service expenditure	0.0274	0.0286	0.0392	0.0317	+
			Proportion of public administration and social organizations	0.0137	0.0192	0.0170	0.0166	+
			Number of community service centers	0.1067	0.0837	0.0691	0.0865	+
	Organizational resilience	0.1439	Local general public budget revenue	0.0651	0.0599	0.0489	0.0580	+
			Number of urban and rural residents participating in basic medical insurance	0.0430	0.0414	0.0321	0.0388	+
			Number of people participating in unemployment insurance Proportion	0.0386	0.0402	0.0624	0.0471	+

and

Table 6. Comprehensive urbanization level evaluation and weight system.

Target Layer	Guideline Layer	Weight	Index Layer	Weight				Propert y (+/−)
				${\mathit{w}}_{1\mathit{j}}$	${\mathit{w}}_{2\mathit{j}}$	${\mathit{w}}_{3\mathit{j}}$	${\mathit{w}}_{\mathit{j}}$
Urbanization Level	Population urbanization	0.2545	Proportion of employed population in secondary and tertiary industries	0.0266	0.0409	0.0831	0.0502	+
			Urbanization rate	0.1338	0.1236	0.0968	0.1181	+
			Number of employees in the unit at the end of the year	0.0751	0.0926	0.0909	0.0862	+
	Land urbanization	0.2384	Urban living area per capita	0.1101	0.1046	0.1730	0.1292	+
			Urban construction land area	0.1198	0.1212	0.0866	0.1092	+
	Economic urbanization	0.3046	The proportion of output value from secondary and tertiary industries	0.0649	0.0736	0.0806	0.0730	+
			Investment in real estate development	0.2675	0.2311	0.1964	0.2316	+
	Social urbanization	0.2025	Total retail sales of social consumer goods per capita	0.1285	0.1180	0.1023	0.1163	+
			Public financial expenditure	0.0737	0.0944	0.0903	0.0862	+

.

4.2. Urban Resilience Level Analysis

Drawing on scholars’ studies, the urban resilience level and urbanization level of each city were calculated. Figure 2 and Figure 3 were created to show the results more clearly.

On the whole (Figure 2), there is a fluctuating trend in the urban resilience level of all cities. The urban resilience level of seven cities, including Changsha, Zhuzhou, and Yueyang, takes the leading position stably, and that of Changsha is far higher compared with other cities, with a significant polarization phenomenon. However, the urban resilience level of Changsha, Yongzhou, and Xiangxi Autonomous Prefecture increases steadily, and the resilience index of the majority of cities decreases. The resilience index of Zhuzhou, Xiangtan, and Yueyang continues to decrease, while the resilience index of Hengyang, Shaoyang, Zhangjiajie, and Loudi shows a decrease–increase trend. The resilience index of Changde and Yiyang is opposite to those three cities and increases slightly, while Huaihua shows a decrease–stabilization trend. Although the ability of some cities to resist and learn to adapt to risks has gradually increased, it is still required for these cities to strengthen their own resilience construction so as to prevent the resilience level from falling back.

4.3. Urbanization Level Analysis

As shown in Figure 3, Changsha, Zhuzhou, Xiangtan, Hengyang, Yueyang, and Chenzhou are the major cities with urbanization construction in this province, and their urbanization level ranks among the top in the whole province. Due to the mountainous and precipitous terrain, West Hunan has backward infrastructure construction and supporting facilities in the whole area. Additionally, the poor industrial structure optimization and serious outflow of talent induce the low urbanization level in this area. Among them, (1) the urbanization level of Zhangjiajie shows an increase–decrease trend, with a decrease degree larger than the increase degree, and finally drops to the lowest in the province; (2) the urbanization level of Yongzhou and Shaoyang is consistent with that of Zhangjiajie, with a decrease degree smaller than the increase degree, and both cities realize the overtaking of Xiangxi Autonomous Prefecture; (3) the development trend of urbanization level in Xiangxi Autonomous Prefecture is opposite to the former three, and the decrease degree is larger than the increase degree. The urbanization development of other cities in this province is relatively stable.

4.4. Analysis of the Coupling and Coordination Relationship between Urban Resilience and Urbanization

The coupling degree value (C value), the coordination degree value (T value), and the coupling coordination degree value (D value) are calculated by Equations (13)–(15). As shown in Figure 4, the C value between the urban resilience system and the urbanization system remains relatively stable on the whole, and there is little difference between all cities, except for the distinct fluctuations in the coupling degree value of Zhangjiajie. It suggests that there is a strong correlation between both systems in the province. Except for Changsha, the coordination degree of other cities is low, which results in the imbalance of coupling and coordinated development in the province.

4.4.1. The Temporal Evolution and Spatial Distribution Characteristics of the Coupling Coordination Degree

In terms of the time dimension (Figure 5), the average coupling coordination degree between the urban resilience and the urbanization level in the whole province decreases from 0.5359 to 0.5216, and there is a polarization phenomenon. The coupling coordination degree of Changsha, Shaoyang, Changde and Yongzhou increases slightly, while that of other cities decreases slightly with a fluctuating process. The coupling coordination degree in 2010, 2014 and 2019 is between $\left[\mathrm{0.4030},\mathrm{0.9170}\right]$, $\left[\mathrm{0.3670},\mathrm{0.9178}\right]$, and $\left[\mathrm{0.3197},\mathrm{0.9231}\right]$, respectively. Zhangjiajie, Xiangxi Autonomous Prefecture, and Zhangjiajie have the lowest coupling coordination degree, respectively, in these three years.

4.4.2. The Spatial Distribution Characteristics of the Coupling Coordination Degree

In an attempt to investigate the coupling and coordination relationship between urban resilience and urbanization more comprehensively, the spatial distribution map of the coupling coordination degree in each year is plotted with the assistance of ArcGIS 10.2 software (Figure 6), followed by the summarization and classification (

Table 7. Types of resilience and urbanization coupling coordination for all cities in Hunan Province.

City	2010		2014		2019
	D Grade	${\mathit{U}}_1/{\mathit{U}}_2$	D Grade	${\mathit{U}}_1/{\mathit{U}}_2$	D Grade	${\mathit{U}}_1/{\mathit{U}}_2$
Changsha	Good coordination	$U_1<U_2$	Good coordination	$U_1<U_2$	Good coordination	$U_1<U_2$
Zhuzhou	Moderate coordination	$U_1>U_2$	Moderate coordination	$U_1>U_2$	Low coordination	$U_1>U_2$
Xiangtan	Low coordination	$U_1>U_2$	Low coordination	$U_1>U_2$	Low coordination	$U_1>U_2$
Hengyang	Low coordination	$U_1<U_2$	Low coordination	$U_1<U_2$	Low coordination	$U_1<U_2$
Shaoyang	Near incoordination	$U_1>U_2$	Near incoordination	$U_1<U_2$	Near incoordination	$U_1<U_2$
Yueyang	Low coordination	$U_1<U_2$	Low coordination	$U_1<U_2$	Low coordination	$U_1<U_2$
Changde	Low coordination	$U_1>U_2$	Low coordination	$U_1>U_2$	Low coordination	$U_1>U_2$
Zhangjiajie	Near incoordination	$U_1>U_2$	Moderate incoordination	$U_1>U_2$	Moderate incoordination	$U_1>U_2$
Yiyang	Near incoordination	$U_1>U_2$	Near incoordination	$U_1>U_2$	Near incoordination	$U_1>U_2$
Chenzhou	Low coordination	$U_1>U_2$	Low coordination	$U_1>U_2$	Low coordination	$U_1>U_2$
Yongzhou	Near incoordination	$U_1>U_2$	Near incoordination	$U_1<U_2$	Low coordination	$U_1>U_2$
Huaihua	Low coordination	$U_1<U_2$	Near incoordination	$U_1<U_2$	Near incoordination	$U_1<U_2$
Loudi	Near incoordination	$U_1>U_2$	Near incoordination	$U_1>U_2$	Near incoordination	$U_1>U_2$
Xiangxi Autonomous Prefecture	Near incoordination	$U_1<U_2$	Moderate incoordination	$U_1>U_2$	Moderate incoordination	$U_1>U_2$

).

As shown in Figure 6, the coupling coordination degree of all cities in this province is mostly manifested in two forms, namely the “low coordination” and “near incoordination”. There is a polarization trend for the difference in the coupling coordination between regions, which is gradually expanding. Eventually, a circle-difference spatial distribution pattern that starts from the central urban agglomeration and gradually decreases to the periphery is formed. Although the urban resilience of Changsha is consistent with its urbanization, the construction of resilience is still required. Zhuzhou has transformed from moderate coordination and retarded urbanization to low coordination and retarded urbanization, which indicates that the gap between urbanization and the resilience level is gradually increasing. Thus, it is necessary to accelerate urbanization. Xiangtan, Changde, and Chenzhou have low coordination and retarded urbanization; meanwhile, Hengyang and Yueyang have low coordination and retarded urban resilience. These five cities should focus on the development of retarded parts and gradually improve the coupling coordination degree. Shaoyang has always been near incoordination and has transformed from retarded urbanization to retarded urban resilience. It indicates that the reconciliation effect between resilience and urbanization development is not good. Meanwhile, Yiyang and Loudi have always been near incoordination and retarded urbanization. Thus, it is required for these three cities to take measures as soon as possible to improve the synergy between resilience and urbanization development to prevent the occurrence of incoordination. Zhangjiajie and Xiangxi Autonomous Prefecture have transformed from near incoordination to moderate incoordination, with retarded urbanization. It indicates that urbanization shall be accelerated in these two cities to strive to achieve a coordinated state. Yongzhou has transformed from near incoordination to low coordination, with retarded urbanization, which indicates that urbanization shall be accelerated in this city. Meanwhile, Huaihua has a contrary state, with retarded urban resilience, which indicates that resilience construction shall be paid more attention in the following period.

4.4.3. Prediction of the Coupling and Coordinated Development between Urban Resilience and Urbanization

The GM (1, 1) grey prediction model is utilized to perform calculations by taking the coupling coordination degree of all cities in the province from 2010 to 2019 as the original sequence. The prediction results are in line with $-\mathrm{a}\le 0.3,\mathrm{C}<0.35,\mathrm{P}>95\%$, which indicates that this model can be used for prediction with high accuracy (

Table 8. Fitting and prediction of the coupling coordination degree between urban resilience and urbanization in all cities in Hunan Province.

	Year	D Fitting Value			GM (1, 1) Predicted Value		a	C	P
City		2010	2014	2019	2024	2029
Changsha		0.917	0.918	0.923	0.929	0.934	−0.006	0.001	1
Zhuzhou		0.634	0.617	0.595	0.574	0.554	0.034	0.002	1
Xiangtan		0.576	0.567	0.563	0.560	0.556	0.007	0.001	1
Hengyang		0.580	0.538	0.539	0.541	0.542	−0.003	0.000	1
Shaoyang		0.457	0.463	0.478	0.493	0.508	−0.031	0.002	1
Yueyang		0.570	0.571	0.552	0.533	0.515	0.034	0.003	1
Changde		0.516	0.545	0.528	0.512	0.496	0.031	0.002	1
Zhangjiajie		0.403	0.395	0.319	0.258	0.208	0.213	0.015	1
Yiyang		0.446	0.444	0.449	0.454	0.459	−0.011	0.001	1
Chenzhou		0.553	0.572	0.540	0.509	0.480	0.056	0.005	1
Yongzhou		0.425	0.494	0.506	0.518	0.531	−0.024	0.000	1
Huaihua		0.514	0.482	0.469	0.456	0.443	0.028	0.001	1
Loudi		0.462	0.423	0.442	0.462	0.482	−0.044	0.002	1
Xiangxi Autonomous Prefecture		0.451	0.367	0.399	0.434	0.473	−0.084	0.003	1

). According to the grade of the coupling coordination degree, 0.2, 0.4, and 0.5 are the dividing values for severe incoordination, moderate incoordination, near incoordination, and low coordination. It can be seen from the predicted value that around 2029, Shaoyang will enter low coordination, Changde and Chenzhou will fall out of low coordination, Xiangxi Autonomous Prefecture will get rid of moderate incoordination, and the coordination state of other cities will remain unchanged. Therefore, it is required to pay attention to these cities with declining predicted values to prevent the occurrence of incoordination, especially Zhangjiajie, which is about to fall below 0.2. Thus, it is necessary for this city to actively take corresponding measures to prevent the occurrence of serious incoordination.

4.4.4. Moran’s I and Lisa Cluster Map Analysis

• Univariate global Moran’s I index analysis

The spatial weight matrix is established with ArcGIS software based on the Queen contiguity principle to analyze the global spatial correlation, as shown in

Table 9. Global Moran’s I of the Coupling Coordination Degree of All Cities in Hunan Province.

	Year	2010	2014	2019
Index
Moran’s I		0.146	0.169	0.173
Z-Variance		1.763	1.896	2.012
p-Value		0.078	0.058	0.044

.

The global Moran’s I of the coupling coordination degree is between 0.140 and 0.180, and the Z score increases with each passing year. The results of each year can pass the significance level test of 90% or more, which indicates that there are significant positive spatial autocorrelation characteristics and spatial agglomeration effects in the coupling coordination degree between all cities in Hunan Province.

2.

Univariate local Moran’s I index analysis

Geoda software is used to calculate the relevant spatial local indexes, and the Moran scatter plot is drawn on the basis of the p-value test, as shown in Figure 7. (1) The regression line and spatial unit are basically located in the first or third quadrants, and the I value is positive and increases with each passing year. The coupling coordination pattern presents a spatial binary state. It is manifested as a spatial distribution pattern, in which higher coupling coordination and lower coupling coordination units are adjacent. The spatial correlation of coupling coordination between regions is enhanced with each passing year. (2) Changsha, Zhuzhou, Xiangtan, Yueyang, and Chenzhou have higher degrees of coupling and coordination between urban resilience and urbanization level; meanwhile, West Hunan is always at a low level in terms of the coupling coordination degrees. Different locations and economic bases induce differences in the development between different regions. (3) From the dispersion degree of distribution points in Moran scatter plot, it is more discrete in the first quadrant and relatively concentrated in the second quadrant. The Moran scatter points in these two quadrants tend to converge, while those in the third quadrant tend to disperse from convergence, which indicates that the coupling and coordination gap between five cities led by Changsha is gradually narrowing, while that in West Hunan is increasing.

The following are as listed in

Table 10. Spatial distribution types of all cities in Hunan Province.

City Type	2010	2014	2019
HH	Changsha, Zhuzhou, Xiangtan, Yueyang, Chenzhou	Zhuzhou, Xiangtan, Yueyang, Chenzhou	Changsha, Zhuzhou, Xiangtan, Yueyang, Chenzhou, Hengyang
LH	Yiyang, Loudi	Yiyang, Loudi	Yiyang, Loudi
LL	Shaoyang, Changde, Zhangjiajie, Yongzhou, Huaihua, Xiangxi Autonomous Prefecture	Shaoyang, Zhangjiajie, Yongzhou, Huaihua, Xiangxi Autonomous Prefecture	Shaoyang, Zhangjiajie, Yongzhou, Huaihua, Xiangxi Autonomous Prefecture
HL	Hengyang	Changsha, Hengyang, Changde	Changde

: (1) The high–high type area refers to the urban resilience and urbanization level of a city being in a state of an agglomeration with a higher coupling coordination degree and favorable development, with a positive correlation. There are five cities in this type in 2010, including Changsha, Zhuzhou, Xiangtan, Yueyang, and Chenzhou. Changsha departs from this type in 2014, and Changsha and Hengyang are classified into this type in 2019. It indicates that the areas with a coupling coordination degree can promote the development of adjacent areas. (2) The low–high type area refers to that a city with a lower coupling coordination degree is adjacent to that with a higher coupling coordination degree. Yiyang and Loudi always belong to this type for the reason that they are located in the middle area between West Hunan and Changsha–Zhuzhou–Xiangtan urban agglomeration. The development of these two cities is subject to adjacent cities with a higher coupling coordination degree, and they are in the transitional development stage. (3) The low–low type area refers to that a city and its adjacent cities form an agglomeration with a lower coupling coordination degree, and these cities have a lower urban resilience and urbanization level. However, there is a significant spatial autocorrelation between urban resilience and urbanization level. There are six cities in this type in 2010, such as Shaoyang and Changde. Changde departs from this type in 2014. Inconspicuous location advantages and a low level of opening to the outside would restrict the coupling and coordinated development of these cities. (4) The high–low type area refers to that a city with a higher coupling coordination degree is adjacent to that with a lower coupling coordination degree, with a negative spatial correlation. There is only one city (Hengyang) in this type in 2010; Changsha and Changde are classified into this type in 2014; and Changsha and Hengyang depart from this type in 2019. These cities have a higher level of urbanization and resilience compared with adjacent cities. Thus, it is necessary to give full play to their radiation effect and realize the mutual promotion and progress with the surrounding areas.

For the fact that Moran scatter plot cannot be used to judge the degree of autocorrelation of each agglomeration and whether it is statistically significant, the LISA cluster map drawn by ArcGIS is used to perform the verification, in an attempt to visualize the local spatial autocorrelation and spatial heterogeneity. As shown in Figure 8, the agglomeration area with a coupling and coordination spatial correlation is characterized by the differentiation between the east and the west, mainly concentrating in Zhuzhou, Xiangtan, Changde, Huaihua, and Xiangxi Autonomous Prefecture, and all of them have reached the criteria of 95% confidence level. Zhuzhou and Xiangtan are highly positively correlated with the coupling coordination degree of surrounding areas, and Xiangtan departs from this type in 2014. The areas with a low value and positive correlation are located in West Hunan, and the number is increasing with each passing year. There is an obvious leap phenomenon in Changde in 2019 (from the non-significant agglomeration to the area with a high value and negative correlation), which suggests that the coupling and coordinated development of this city accelerates and surpasses the adjacent areas. Other cities fail to pass the significance test, which indicates that there is less influence and contact between these cities and adjacent areas, and they are in a relatively isolated development state.

5. Discussion

The accelerated urbanization process has exposed cities to increasing uncertainties and unknown risks. Urban resilience is the ability of cities to prevent and recover from internal “urban diseases” and external natural disasters. The degree of urban governance can be improved by resilient city building. Urban resilience building and urbanization building are carried out simultaneously, and the two influence each other.

On the one hand, the level of urban resilience determines the level of urbanization to a certain extent. Cities’ safe and healthy development, as well as the advancement of urbanization, are being hampered as a result of an increase in numerous sorts of uncertain risks. Because of their good linkage emergency management capabilities, high-resilience cities can effectively respond to unknown events, ensure the orderly exchange of information and energy inside and outside the city, and the normal functioning of various city functions, as well as providing a good development environment for urbanization. When low-resilience cities respond to a crisis, key development factors, such as population and financial resources, tend to flow to stable markets, causing the crisis’ scope to spread and jeopardizing other regions’ stable development. Then, once the crisis has passed, the city’s lost development factors must be reabsorbed, resulting in a lower level of urbanization.

On the other hand, high urbanization levels can both contribute to and constrain the improvement of urban resilience. Urbanization has provided cities with several development prospects. Talent pooling, financial support, and social capital cooperation help to improve the infrastructure construction, community service system and emergency management system, etc. Interdepartmental synergy helps improve urban resilience. However, due to the complexity of the urbanization development system, problems such as ecological damage, infrastructure and housing quality failures, employment challenges, and transportation tensions emerge when urbanization progresses too quickly, impeding the level of urban resilience.

Based on the definition of urban resilience, it is clear that urban resilience is an inherent property of urban systems and is not unique to a particular city. Promoting high-quality urbanization development is an important work that every city in the world is actively promoting. Therefore, the research methodology in this paper is equally applicable to the study of the coordinated relationship between urban resilience and urbanization development in China and other regions of the world. The basis of this study is based on objective and real data, and the research findings are not determined by local land management and planning policies, but the findings can provide some reference for the formulation of relevant local policies in the future.

6. Conclusions

The findings of this study reveal the following facts. (1) As per the evaluation result analysis of the urban resilience level, except for Changsha, Yongzhou, and Xiangxi Autonomous Prefecture, whose urban resilience level increases steadily, there is a trend of fluctuation and decrease in a certain range for other cities in the province. The resilience level is related to geographical location, with the characteristic of a decrease from the east to the west. Additionally, there is an obvious polarization phenomenon. (2) As per the evaluation result analysis of the urbanization level, the urbanization level of most cities is in a relatively stable development state; meanwhile, that of a few cities decreases, which causes the gradual expansion of the development gap between regions. (3) As per the result analysis of the coupling coordination degree, all cities mainly present two states: “low coordination” and “near incoordination”, with a stable development state. There is a strong correlation between urbanization and urban resilience. However, the coupling and coordinated development between regions is unbalanced, and there is an increasingly distinct polarization trend for the coupling and coordinated development between cities. Most cities are classified into the type of retarded urbanization. Eventually, a circle-difference spatial distribution pattern that starts from the central urban agglomeration and gradually decreases to the periphery is formed. (4) As per the result analysis of the GM (1, 1) grey model, except for seven cities, such as Changsha and Hengyang, whose coupling and coordinated development is stable with a slight increase trend, other cities present a downward trend. The incoordination in Zhangjiajie is becoming increasingly serious. Therefore, it is required for this city to focus on accelerating the urbanization process. (5) As per the result analysis of spatial autocorrelation, there are significant positive spatial autocorrelation characteristics and agglomeration effects in the coupling and coordinated development between all cities in this province, and the correlation is increasing with each passing year. There is an obvious spatial binary state in the pattern of coupling and coordinated development, and the correlation mode is mainly characterized by homogeneity and supplemented by heterogeneity, which is gradually improving. Moreover, the number of cities with agglomeration characteristics in the province is increasing. Therefore, it is necessary to strengthen inter-regional exchanges and cooperation to achieve sustainable and coordinated development.

Based on the above findings, the following policy suggestions can be proposed.

(1) Promote the rapid transformation of underdeveloped cities and narrow the urbanization gap between the east and the west. On the one hand, it is required to ensure the stable development of urbanization level in HH areas led by Changsha. On the other hand, due to the fact that the urbanization level in West Hunan is at a low level in the whole province, it is necessary to increase the input of resources and technology, encourage economic exchanges with neighboring cities, adjust the existing industrial structure and improve the allocation of resources, thus promoting the overall economy and sustainable development level of the whole province. Benchmark cities can be selected to drive the development of surrounding areas, the new development philosophy shall be upheld in the future development, the absolute and relative differences in the development of urbanization level shall be reduced in an orderly manner, and a new pattern of coordinated urban and rural development shall be constructed.

(2) Establish the concept of resilience spatial planning and steadily improve the resilience level. The spatial pattern of urban resilience in the province shall be optimized, and the high-quality development of urbanization shall be promoted. Especially for cities with retarded urban resilience, the concept of resilience should be fully integrated into urban construction, and the construction of urban infrastructure, economic, social and ecological environment and safety monitoring and emergency response mechanism should be improved and optimized so as to enhance public and community disaster risk awareness and self-help and mutual aid ability, thus improving the urban risk prevention ability.

(3) Actively promote the radiation effect of highly coordinated cities and weaken the polarization phenomenon. Under the condition of improving the benign interaction between urban construction and urbanization, all cities shall also establish efficient communication channels, actively carry out comprehensive cooperation and exchanges with surrounding areas and play the strategic role in regional coordinated development, form a dual-core or multi-core circle structure development model in the province, and accelerate the pace of urban construction.

(4) Promote the urban resilience and urbanization of regional cities simultaneously and promote the sustainable development of cities. Marketization, informationization and financial support are the three important factors that affect the urbanization level and the coupling and coordinated development of urban resilience. The market mechanism and reform and innovation mechanism shall be further improved. There shall be coordinated advancement in the benign increase of urban population, efficient and moderate economic growth, harmony and progress of society, effective transformation of urban and rural land and construction of ecological environment. Cross-cooperation among various departments can improve the level of urbanization and urban resilience, promote the flow of development factors between regions, narrow regional gaps, improve the comprehensiveness, synergy and sustainability of urban development and enhance the overall development quality and livability of the whole province.