Investigating into the Coupling and Coordination Relationship ...

Citation: Xiong, Y.; Li, C.; Zou, M.;

Xu, Q. Investigating into the

Coupling and Coordination

Relationship between Urban

Resilience and Urbanization: A Case

Study of Hunan Province, China.

Sustainability 2022, 14, 5889.

https://doi.org/10.3390/su14105889

Academic Editors: Yao Shen, Liyan

Xu, Stephen Law and Tao Yang

Received: 8 April 2022

Accepted: 10 May 2022

Published: 12 May 2022

Publisher’s Note: MDPI stays neutral

with regard to jurisdictional claims in

published maps and institutional affil-

iations.

Copyright: © 2022 by the authors.

Licensee MDPI, Basel, Switzerland.

This article is an open access article

distributed under the terms and

conditions of the Creative Commons

Attribution (CC BY) license (https://

creativecommons.org/licenses/by/

4.0/).

sustainability

Article

Investigating into the Coupling and Coordination Relationshipbetween Urban Resilience and Urbanization: A Case Study ofHunan Province, ChinaYanni Xiong, Changyou Li * , Mengzhi Zou and Qian Xu

School of Civil Engineering, Central South University, Changsha 410075, China; [email protected] (Y.X.);[email protected] (M.Z.); [email protected] (Q.X.)* Correspondence: [email protected]; Tel.: +86-139-0731-1012

Abstract: In the context of accelerated urbanization, constructing resilient cities is an effectiveapproach to tackling risks, such as extreme weather, and various urban challenges. The coupling andcoordinated development of urbanization and urban resilience is a prominent embodiment of urbansustainable 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 examiningthe coupling and coordination relationship between urban resilience and urbanization level. Thepanel data are adopted to construct a dual-system evaluation framework integrating urban resilienceand 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 thecoupling model in physics and GM (1, 1) grey prediction model. Additionally, the spatial–temporalevolution characteristics of the coupling coordination degree are investigated and analyzed by ArcGISand Geoda software. The following are indicated from the results: (1) The resilience of all citiesis related to their geographical location and is characterized by a decrease from east to west; inaddition, the resilience level of most cities presents a downward trend with time. (2) The urbanizationlevel of most cities develops stably with time, but there is a growing gap in the urbanization levelbetween regions. (3) There is a strong correlation between urban resilience and urbanization level inall cities; the unbalanced coupling and coordinated development emerge, specifically manifested bythe polarization phenomenon. Eventually, a circle-difference spatial distribution pattern that startsfrom the central urban agglomeration and gradually decreases to the periphery is formed. (4) Theprediction results of the coupling coordination degree suggest that there is an increasingly distinctpolarization trend for the coupling and coordinated development between cities, and it is necessaryto pay attention to those cities with a declined predicted value. (5) There is a significant positivespatial autocorrelation and agglomeration effects in the distribution of the coupling coordinationdegree of all cities, and the correlation is getting stronger with each passing year; the correlationmode is mainly characterized by homogeneity and supplemented by heterogeneity. Finally, severalsuggestions are proposed in this paper, in an attempt to lead the coordinated development of regionsby novel urbanization and thus promote the sustainable development of cities. The methods andinsights adopted in this study contribute to investigating the relationship between urban resilienceand urbanization in China and other regions worldwide.

Keywords: urban resilience; urbanization; sustainable development; coupling and coordinationrelationship; spatio-temporal dynamics; ESDA; grey prediction model; urban systems

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 by2050, and the total urban population on the globe will increase by 2.5 billion, including

Sustainability 2022, 14, 5889. https://doi.org/10.3390/su14105889 https://www.mdpi.com/journal/sustainability

https://doi.org/10.3390/su14105889

https://creativecommons.org/

https://creativecommons.org/licenses/by/4.0/

https://creativecommons.org/licenses/by/4.0/

https://www.mdpi.com/journal/sustainability

https://www.mdpi.com

https://orcid.org/0000-0002-9119-6429

https://doi.org/10.3390/su14105889

https://www.mdpi.com/journal/sustainability

https://www.mdpi.com/article/10.3390/su14105889?type=check_update&version=3

Sustainability 2022, 14, 5889 2 of 26

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 themain 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 climatechange and sustainable development governance [4]. In the context of globalization,cities are closely connected with the whole world, which implies that cities could exertsignificant impacts on such global issues as sustainable development, global warming, andglobal health.

With the acceleration of urbanization, cities, as complex macrosystems, are constantlysubject to various disturbances from internal and external uncertainties of natural orartificial hazards, and they are facing increasingly diverse risks [5–8]. For instance, thefrequent occurrence of urban flooding, urban heat island effect, traffic congestion, andenvironmental pollution reveal the inadequacy of urban risk management. The interactionand accumulation of environmental, economic, and social problems in cities at differentperiods and stages have increased the vulnerability of cities. Faced with the phenomenonof increasing urban vulnerability, cities around the world should pay great attention to itand actively explore ways to improve urban resilience. In rural areas, due to insufficientpolicy support, insufficient technical strength, and unreasonable industrial structure, etc.,their environmental and economic, and social development lag behind those of urbanareas, which also adds to the difficulty of building urban resilience. The focus on speedrather than quality during traditional urbanization has made it more difficult for citiesto resist public safety risks [9]. Urban resilience refers to the ability of cities to withstanddisasters on their own and to recover quickly from them through the proper deployment ofresources. In the long run, cities can learn from past disasters and improve their resilienceto 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 caneffectively eliminate the problem of urban risks and promote sustainable development [10].Enhancing urban safety and resilience is a new trend in urban development. China hasnow formed an urban development pattern with central cities, urban agglomerations, andmetropolitan 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 thegeographical differences in the ability to withstand disaster risks are becoming increasinglysignificant. 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 thatof urbanization to a certain extent. The improvement of urban resilience can activelyand effectively respond to uncertain events during urbanization and provide a favorabledevelopment environment for urbanization. A high level of urbanization can enhanceurban resilience, but it can also restrict the improvement of urban resilience. As one ofthe important provinces in the Yangtze River Basin, Hunan Province possesses specialgeomorphological and climatic conditions and continuously accelerating urbanizationprocess, which aggravates the risk of mass disasters in the province. Therefore, it isan urgent demand for realizing the simultaneous advancement of urban resilience andurbanization 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 degreeof this dual-system evaluation framework is calculated with the coupling and GM (1, 1)grey prediction model in physics. Moreover, the spatial–temporal evolution characteristicsof 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 explorethe coordination relationship between them. Finally, several suggestions are proposedaccording to the research results, with a view to providing a reference for promoting thecoupling and coordinated development between urban resilience and urbanization and thesustainable and healthy development of cities in Hunan Province and other regions.

The structure of this paper is presented as follows. The concept of resilience, urbanresilience and urbanization is introduced in the next section, followed by the main researchefforts to explore the coupling and coordination relationship between urban resilience andurbanization, as well as the research background and objectives. Subsequently, the researchmethods, data collection, and data analysis methods used in this study are interpreted indetail, 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, andurbanization, as well as a brief summary overview of the current status of research onthe coupling and coordination of urban resilience and urbanization levels, this studyis supported by strong theoretical support, and its research background and researchsignificance will be more prominent.

2.1. Resilience

Resilience, originally implying a return to a pristine state, was introduced into systemsecology 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 stemsfrom the need of society to cope with increasingly strong threats. It essentially refers tothe ability of systems to absorb, adapt and recover from external stresses [12,13]. Theconcept of sustainable development can be traced back to the World Conservation Strategyjointly published by the International Union for Conservation of Nature (IUCN), the UnitedNations Environment Program (UNEP), and the World Wildlife Fund (WWF) in 1980.In 1987, the World Commission on Environment and Development (WCED) publishedthe report Our Common Future, which formally expounded the concept of sustainabledevelopment systematically, which was defined as the “development that meets the needsof the present without compromising the ability of future generations to meet their ownneeds”. With the continuously expanded research field, resilience is often closely associatedwith such concepts as risk, vulnerability, and sustainability. Norris and Folke et al. [14–16]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 (eco-logical resilience)—complex adaptive systems (adaptive cycles)”, from “equilibrium” to“adaptation” and from “ecosystem” to “social-ecological system”. The concept of resiliencevaries with the change in research objects and fields, and the focus and core connotationof each stage are not consistent. The extension, ambiguity, dynamism, and co-evolutionof the concept of resilience make it difficult to apply it in practice, so it is especially im-portant to clarify the concept and connotation of resilience to deepen its quantitative andpractical research.

2.2. Urban Resilience

Under the background of increasing uncertain disturbance factors, such as climatechange, policy changes and man-made disasters, the construction of resilient cities providesa novel insight for cities to respond to uncertain impacts from the perspective of develop-ment. In essence, it is to actively explore adaptive adjustment methods and approaches forunknown risks faced by cities. Local Governments for Sustainability (ICLEI) introducedthe 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 fouraspects, namely, human and environmental impacts, theoretical frameworks, evaluation,and simulation [19,20], with a process characteristic. The interdependence and promotionof resistance, recovery, and adaptation make the urban system a stable and dynamic evolu-tion 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 basicconcepts related to urban resilience, which promote the perfection of urban resiliencetheory. Erling et al. [25] put forward a model based on three moral requirements to meethuman needs, ensure social equity and respect environmental constraints, which providedan explanation for global sustainable development. The research framework proposed byChen et al. [26] can be employed to understand the effectiveness of COVID-19 control indifferent countries, and it would enhance the urban resilience and sustainability relatedto health. Borekci et al. [27] expanded the research on organizational resilience from theperspective of multi-case design and extended the concept of resilience to the dimensionof sustainability. Rod et al. [28] explored the methods to integrate critical infrastructureresilience into the existing security practice. Payne et al. [29] confirmed that community re-silience can be quantified and decomposed into dimensions of resilience under the researchbackground of two different regions. Oliver et al. [30] pointed out that the function of theecosystem is threatened by the acceleration of environmental degradation, and they em-phasized the importance of ecological resilience construction. Martin et al. [31] explainedand 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, andcommunity, multiple aspects are considered in the research on urban resilience.

Overall, the perspective of urban resilience research is no longer limited to the study ofecosystems but has expanded to a comprehensive study focusing on physical space carriers,social capital management, and institutional development. The study of urban resilience asa whole has begun to bear fruit at the macro level, but at the meso and micro levels, it issomewhat lacking and needs further refinement. Although scholars have basically outlinedthe basic framework of urban resilience, they have not yet clarified the complex relationshipbetween each element and the influencing factors of the framework, and further theoreticalresearch is needed to improve the practical applicability of the urban resilience framework.Resilient city-related research has become increasingly mature, and relevant organizationsand 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 methodologyfor 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 aspopulation, capital, information, and land, in cities. Generally, urbanization is a processof converting the agricultural population into non-agricultural population, agriculturalterritory into non-agricultural territory, and agricultural activities into non-agriculturalactivities. Reasonable urbanization can effectively promote the sustainable development ofcities [32,33]. Guan et al. [9,34] suggested that urbanization is an inevitable requirementfor promoting social progress, and traditional land-centered urbanization is typical of“incomplete urbanization” and “low-quality urbanization”. Zhang et al. [34] introducedthe concept of “decoupling” in the environmental field and established a comprehensiveindex system on urbanization quality, which systematically evaluated the relationshipbetween the level and quality of urbanization. Shi et al. [35] constructed the evaluationindex system of urbanization coordination level based on the quality and scale of urban-ization; and analyzed relevant spatial correlation, spatial difference, and spatial patternevolution characteristics. He et al. [36] maintained that the accumulating pressure on theenvironment caused by urbanization is the key issue during urban development, and theyverified the relationship between urbanization and ecological environment with a coupling


and coordination model. Xiao et al. [37] established a four-dimensional comprehensiveevaluation system related to urbanization quality, and they revealed the spatial correla-tion of urbanization in China through exploratory spatial data analysis. Ma et al. [38]investigated the coordination between population urbanization and land urbanization andproposed a relevant development model. Xu et al. [39] constructed an index system for thecomprehensive evaluation of three subsystems of urbanization (population, economy, andland urbanization) based on the theory of coordinated development, and they explored thespatial–temporal characteristics of overall coordination and paired coordination of popu-lation, land, and economic urbanization with an entropy method, coupling coordinationdegree model and spatial autocorrelation analysis. Niu et al. [40] established an indexsystem incorporating population, land, and industry, and they constructed a couplingand coordination model. Finally, they evaluated the comprehensive development leveland the coordination degree of urbanization at the county level. Urbanization is a multi-dimensional concept, including population, economy, society, and land. The high-qualitydevelopment of urbanization can be promoted by exploring the coordinated relationshipbetween 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 spatialexpansion are the bridges between urbanization and urban resilience. As a product ofurban development, urbanization inevitably has a strong interactive relationship withurban resilience [41,42]. Zhou et al. [43] constructed a comprehensive evaluation indexsystem based on urban resilience and urbanization level, and analyzed the spatial–temporalvariation characteristics and spatial distribution types of the coupling coordination degreeof 26 cities with the assistance of a coupling coordination degree model and a spatial auto-correlation model. Bai et al. [44] analyzed the spatial differentiation characteristics of urbanresilience and urbanization in Jilin Province, and they made a coupling analysis on thesespatial differentiation characteristics. Wang et al. [45] constructed an evaluation systemwith respect to urban ecological resilience, employed a coupling coordination degree modelto measure the coupling coordination degree between urbanization and ecological resiliencein the Pearl River Delta, and made an in-depth exploration into relevant spatial-temporalcharacteristics. Li et al. [46] analyzed the spatial evolution characteristics of coupling andcoordination between urbanization and resource and environmental carrying capacitywith a coupling coordination degree model and spatial autocorrelation analysis methods.Gao et al. [47] analyzed the coupling coordination degree between urban resilience andurbanization quality with a coupling and coordination model, spatial self-analysis, andLISA time path. The regional coordinated development and sustainable development ofcities can be promoted by exploring the coupling and coordination relationship betweenurban resilience and urbanization.

After consulting the relevant literature, it was found that there are fewer studies onthe establishment of the urbanization level evaluation index system from the perspective ofpopulation, economy, land, and society [40,48–50]. The urban resilience evaluation indexsystem is mostly based on the four aspects of population, economy, land and society [51–54],and the influence of communities and organizations are seldom considered [55–57]. More-over, it is scarce to combine the three methods to empower the indexes of urban resilienceand urbanization system [58,59]. There is a lack of in-depth research on the relationship be-tween urban resilience and urbanization from the perspective of coupling and coordination.The coupling and coordination model is usually used to verify the relationship betweensystems [60–62]. Benefiting from its prediction accuracy, the GM (1, 1) grey predictionmodel 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 andheterogeneity of data. Due to the fact that Hunan Province is one of the provinces sufferingfrom 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 ofcoupling and coordination, this province is selected as the research object, which has highresearch value and practical guiding significance. Therefore, the coupling and coordinationrelationship between urban resilience and urbanization of all cities in Hunan Provincewas explored, and several suggestions are also proposed to promote the sustainable andcoordinated development of various regions. The findings of this study provide a referencefor the sustainable development of other regions in the world.

3. Materials and Methods3.1. Methods3.1.1. The Entropy Weight-Coefficient of Variation-CRITIC Method

The entropy weight method determines the weights through the information entropyof indicators, and then makes certain corrections to the entropy weight according to eachindex so as to obtain a more objective index weight. The coefficient of variation method usesthe degree of variation of indicators to calculate the weights, which eliminates the effectof different units or averages on the comparison of the degree of variation of two or moreindicators. The CRITIC method measures the weights according to the conflict and contrastintensity among evaluation indicators, and it takes into account both the magnitude ofindicator variability and the correlation between indicators, using the objective propertiesof the data itself for scientific evaluation. The combination of the three methods can reflectthe importance of indicators more precisely.

Supposing there are m evaluation objects and n evaluation indicators, and Xij isexpressed as the original data of the jth indicator of the ith evaluation object. The nega-tive variables of the indicators are first transformed into positive variables, and then theindicator data are dimensionless.

Positive indicators: xij =Xij − Xmin

Xmax − Xmin(1)

Negative indicators: xij =Xmax − Xij

Xmax − Xmin(2)

In the formula, i = 1, . . . , m; j, k = 1, . . . , 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.

Pij =xij

∑mi=1 xij

(3)

Ej = −(lnm)−1 ∑mi=1 PijlnPij (4)

w1j =1− Ej

n−∑nj=1 Ej

(5)

For assignment using the coefficient of variation method [68,69], calculate the mean xj,standard deviation S1j and coefficient of variation Vj of the measurement degree xij, andthen seek the weight of the jth index. The formula is as follows.

S1j =

√∑m

i=1(xij − xj

)2

m(6)

Vj =S1j

xj(7)

w2j =Vj

∑nj=1 Vj

(8)


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

Cj =S2jxj

∑nj=1 (1− |rjk | ) (9)

S2j =

√∑m

i=1(xij − xj

)2

m− 1(10)

w3j =Cj

∑nj=1 Cj

(11)

In the formula, |rjk| is the absolute value of the correlation coefficient between the jthindicator and the kth indicator.

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

wj = αw1j + βw2j + γw3j

(α = β = γ =

13

)(12)

3.1.2. Construction of the Dual-System Evaluation Index System

Based on the above understanding of the basic concepts of urban resilience andurbanization, as well as the literature combing of both, we already have a very clearperception of urban resilience, urbanization, and the connection between the two for a morein-depth study. Regarding the construction of the dual-system evaluation index system ofurban resilience and urbanization, this study upheld the principles of data scientificity andaccessibility and collected and screened the evaluation index systems of urban resilienceand urbanization at home and abroad, as shown in Tables 1 and 2.

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 RegionalProduct; Actual utilization of foreign direct investment

amount; Annual output value of tertiary industry; Disposablepersonal 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 publicschools; 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 healthinstitutions; Number of beds in hospitals and health

centers; Number of mobile phone users, Communicationnetwork 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.


Table 1. Cont.


Ecological Resilience

Green area per capita; Daily capacity of urbanwastewater 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; Integratedenergy 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 ofpopulation involved in Red Cross volunteer activities; RedCross training workshop participants per 10,000 persons;

Number of education programs on DRR and disasterpreparedness 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 inunemployment insurance; Number of Participants in basic

medical insurance; Expenditures budgeted by localgovernments; Basic urban medical coverage, etc.

Xun et al. [53]; Qasim et al. [76];Chen et al. [54];

Da et al. [79], etc.

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


Population urbanizationShare of urban population; Percentage of employment in thetertiary 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 urbanizationShare of secondary industry output in GDP; The proportion oftertiary 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 systemsor forms of motion under the action of themselves and the outside world. This paperconstructs a dual-system coupled model of urban resilience and urbanization to measurethe coordinated development between the two. The formula [84,85] is as follows.

C =

(U1 ×U2)(U1+U2

2

)2

1/2

(13)

D =√

C× T (14)

T = σU1 + ϕU2

(σ = ϕ =

12

)(15)

In the formula, U1 and U2 are the combined evaluation indices of urban resilience andurbanization level, respectively. C indicates the coupling degree, and its magnitude rangesfrom 0 to 1. The closer the magnitude of C is to 1, the stronger the inter-system correlationis. 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, andit is generally guaranteed that T ∈ (0, 1), to ensure that D ∈ (0, 1) [80].

Based on the research of Wang et al. [86–88] and then reformulated after makingrevisions, the grades of D are classified (Table 3). When U1 = U2, urban resilience andurbanization level are of the type of synergistic development under the same rank.

Table 3. Grading criteria of coupling coordination degree.

D Grades of Coupling Coordination Degree U1 > U2 U1 < U2

0.8 ≤ D ≤ 1.0 Good coordination Retarded urbanization Retarded urban resilience

0.6 ≤ D < 0.8 Moderate coordination Retarded urbanization Retarded urban resilience

0.5 ≤ D < 0.6 Low coordination Retarded urbanization Retarded urban resilience

0.4 ≤ D < 0.5 Near incoordination Retarded urbanization Retarded urban resilience

0.2 ≤ D < 0.4 Moderate incoordination Retarded urbanization Retarded urban resilience

0.0 ≤ D < 0.2 Severe incoordination Retarded urbanization Retarded urban resilience

3.1.4. GM (1, 1) Grey Prediction Model

Gray forecasting is the prediction of systems that contain both known and uncertaininformation, in other words, the prediction of gray processes that vary within a certainrange and are related to time series. Although the phenomena shown in the gray processare random and haphazard, they are, after all, ordered and bounded, so that the obtaineddata set possesses potential laws. After the random original time series is accumulated, thelaw of the new time series formed can be approximated by the solution of the first-orderlinear differential equation.

The time series and the new sequence obtained after accumulation are expressed asX(0) =

{X(0)(i), i = 1, · · · n

}and, X(1) =

{X(1)(i), i = 1, · · · n

}respectively. The differen-

tial equation of the GM (1, 1) grey prediction model is as follows.

dX(1)

dt+ aX(1) = µ (16)

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

Supposing the parameter vector to be estimated as â =

[aµ

], the equation is obtained

from the least squares method as follows.

â =(

BT B)−1

BTYn (17)

In the formula,

B =

− 1

2

[X(1)(1) + X(1)(2)

]1

− 12

[X(1)(2) + X(1)(3)

]1

......

− 12

[X(1)(n− 1) + X(1)(n)

]1

, Yn =

X(0)(2)X(0)(3)

...X(0)(n)

The prediction model equation is as follows.

X̂(1)(k) =[

X(0)(1)− µ

a

]e−a(k−1) +

µ

a(18)


Calculating the small error probability P and the variance ratio C to determine whetherthe GM (1, 1) grey prediction model can be used for prediction [89]. The judgment standardis shown in Table 4.

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

Accuracy Grade Good Qualified Barely Qualified Unqualified

StandardP >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 toexplain the mechanism of interaction between observed objects by visually describing thespatial distribution pattern of things or phenomena. In this study, the ESDA method isused to analyze the overall spatial correlation and spatial agglomeration of the coupledcoordination degree of urban resilience and urbanization.

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

Moran′s I =n ∑n

i=1 ∑nj=1 Wij(xi − x)

(xj − x

)∑n

i=1 ∑nj=1 Wij ∑n

i=1(xi − x)2 =n ∑n

i=1 ∑nj=1 Wij(xi − x)

(xj − x

)S2 ∑n

i=1 ∑nj=1 Wij

(19)

S2 = ∑ni=1(xi − x)/n (20)

In the formula, n is the total number of territorial units in the study area, xi(

xj)

isthe value of x variable of territorial unit i(j); Wij is the binary adjacency matrix, whereaccording to the common boundary rule, if region i is adjacent to region j then Wij = 1,otherwise Wij = 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, andzero indicates no correlation [89].

Local spatial autocorrelation refers to the degree of similarity between cities withmeasured attributes and neighboring cities. The Moran scatter plot visualizes the observedvalues. 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. LISAanalysis is a measure of the degree of similarity or dissimilarity between the attributes ofspatial units and the surrounding units and can reflect the degree of local spatial agglomer-ation in more detail.

3.2. Study Area

Hunan Province, with 14 prefectural-level administrative regions, lies between latitude24◦38′ and 30◦08′ north and longitude 108◦47′ and 114◦15′ east, with a horseshoe-shapedtopography surrounded by mountains on three sides and opening towards the north,straddling the Yangtze and Pearl River systems. The typical characteristics of the selectedresearch subjects are as follows: (1) The results of the seventh population census of HunanProvince show that the urbanization rate of Hunan Province has increased to 58.76%, witha growth rate exceeding the Chinese average, but the overall level is still lower than theaverage level of urbanization in China. (2) At the end of 2021, the province’s residentpopulation was 66.22 million, ranking seventh in China. (3) Hunan Province’s GDP haslong been among the top in China, but the per capita GDP ranks at the middle level in thecountry, 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, unbalanceddevelopment level of prefecture-level cities, and mismatch between urbanization level andindustrialization level, accompanied by geological disasters such as heavy rainfall, flood,drought, freezing and high temperature, as well as problems in the urban development pro-cess, such as traffic congestion, environmental pollution, and severe employment situation.

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

Sustainability 2022, 14, x FOR PEER REVIEW 11 of 28

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.

Figure 1. Distribution map of cities in Hunan Province. The big red circle refers to The “3 + 5” RingChangsha-Zhuzhou-Xiangtan City Cluster. The small red circle refers to the Changsha-Zhuzhou-Xiangtan Metropolitan Area. The three black bullets refer to the three central cities of Changsha,Yueyang and Hengyang respectively.

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 stategovernments of Hunan Province, and the statistical bulletin of national economic and socialdevelopment. Some indicator data were replaced by arithmetic or weighted averages,missing data were completed by the difference method, and some data were obtainedby queries from the EPSDATA data platform (https://www.epsnet.com.cn accessed on1 December 2021).

https://www.epsnet.com.cn


4. Results4.1. Construction of the Dual-System Evaluation Index System in Hunan Province

Based on panel data of Hunan Province, with strict adherence to the scientificity anddata availability, 28 secondary indexes from six dimensions, such as economic resilience,were adopted to construct the urban resilience level evaluation system, and 9 secondaryindexes from such four aspects as population urbanization were selected to establish anurbanization level evaluation system. Using Equations (5), (8), (11) and (12), the weights ofeach indicator were calculated, as shown in Tables 5 and 6.

Table 5. Comprehensive urban resilience evaluation and weight system.

Target Layer GuidelineLayer Weight Index Layer

Weight Property

(+/−)w1j w2j w3j wj

UrbanResilience

Level

Economicresilience

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 disposableincome 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 ofemployees in employment 0.0490 0.0461 0.0390 0.0477 +

Socialresilience

0.1373

Population density 0.0339 0.0319 0.0321 0.0326 +

Per capita post andtelecommunications business volume 0.0457 0.0456 0.0383 0.0432 +

Per capita number of teachers inprimary and secondary schools 0.0235 0.0266 0.0393 0.0298 +

Per capita health technicians 0.0301 0.0337 0.0312 0.0317 +

Infrastructureresilience 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 watersupply 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 +

Ecologicalresilience 0.1383

Growth rate of energyconsumption 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 inthe 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 environmentalprotection industry units 0.0304 0.0320 0.0337 0.0320 +

CommunityResilience 0.1348

Proportion of public service expenditure 0.0274 0.0286 0.0392 0.0317 +

Proportion of public administration andsocial organizations 0.0137 0.0192 0.0170 0.0166 +

Number of community service centers 0.1067 0.0837 0.0691 0.0865 +

Organizationalresilience

0.1439

Local general public budget revenue 0.0651 0.0599 0.0489 0.0580 +

Number of urban and rural residentsparticipating in basic medical insurance 0.0430 0.0414 0.0321 0.0388 +

Number of people participating inunemployment insurance Proportion 0.0386 0.0402 0.0624 0.0471 +


Table 6. Comprehensive urbanization level evaluation and weight system.

Target Layer GuidelineLayer Weight Index Layer

Weight Property

(+/−)w1j w2j w3j wj

UrbanizationLevel

Populationurbanization

0.2545

Proportion of employed population insecondary 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 unitat the end of the year 0.0751 0.0926 0.0909 0.0862 +

Landurbanization 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 +

Economicurbanization 0.3046

The proportion of output value fromsecondary and tertiary industries 0.0649 0.0736 0.0806 0.0730 +

Investment in real estate development 0.2675 0.2311 0.1964 0.2316 +

Socialurbanization

0.2025

Total retail sales of socialconsumer 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 eachcity were calculated. Figures 2 and 3 were created to show the results more clearly.


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.

Figure 2. Changes in the comprehensive urban resilience level of all cities in Hunan Province.

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.

Chan

gsha

Zhuzh

ou

Xia

ngtan

Hen

gyan

g

Shaoya

ng

Yuey

ang

Chan

gde

Zhan

gjia

jie

Yiy

ang

Chen

zhou

Yon

gzhou

Huai

hua

Lou

di

Xia

ngxi

Auto

nomou

s

Prefe

cture

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Th

e C

om

pre

hen

sive U

rban

Resi

lien

ce L

evel

City

2010

2014

2019

Figure 2. Changes in the comprehensive urban resilience level of all cities in Hunan Province.

On the whole (Figure 2), there is a fluctuating trend in the urban resilience level ofall cities. The urban resilience level of seven cities, including Changsha, Zhuzhou, andYueyang, takes the leading position stably, and that of Changsha is far higher comparedwith other cities, with a significant polarization phenomenon. However, the urban resiliencelevel of Changsha, Yongzhou, and Xiangxi Autonomous Prefecture increases steadily, andthe 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 ofChangde and Yiyang is opposite to those three cities and increases slightly, while Huaihuashows a decrease–stabilization trend. Although the ability of some cities to resist and learnto adapt to risks has gradually increased, it is still required for these cities to strengthentheir own resilience construction so as to prevent the resilience level from falling back.

Sustainability 2022, 14, 5889 14 of 26Sustainability 2022, 14, x FOR PEER REVIEW 16 of 28

Figure 3. Changes in the comprehensive urbanization level of all cities in Hunan Province.

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.

Figure 4. Coupling and coordination of comprehensive urban resilience and urbanization in all cities

of Hunan Province.

Chan

gsha

Zhuzh

ou

Xia

ngtan

Hen

gyan

g

Shaoya

ng

Yuey

ang

Chan

gde

Zhan

gjia

jie

Yiy

ang

Chen

zhou

Yon

gzhou

Huai

hua

Lou

di

Xia

ngxi

Auto

nomou

s

Prefe

cture

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Th

e C

ou

pli

ng C

oord

inati

on

In

dex

City

C(2010)

C(2014)

C(2019)

T(2010)

T(2014)

T(2019)

D(2010)

D(2014)

D(2019)


4.3. Urbanization Level Analysis

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

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

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




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.

Figure 4. Coupling and coordination of comprehensive urban resilience and urbanization in all cities

of Hunan Province.

Chan

gsha

Zhuzh

ou

Xia

ngtan

Hen

gyan

g

Shaoya

ng

Yuey

ang

Chan

gde

Zhan

gjia

jie

Yiy

ang

Chen

zhou

Yon

gzhou

Huai

hua

Lou

di

Xia

ngxi

Auto

nomou

s

Prefe

cture

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

Th

e C

ou

pli

ng C

oord

inati

on

In

dex

City

C(2010)

C(2014)

C(2019)

T(2010)

T(2014)

T(2019)

D(2010)

D(2014)

D(2019)

Figure 4. Coupling and coordination of comprehensive urban resilience and urbanization in all citiesof Hunan Province.

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

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


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 [0.4030, 0.9170] , [0.3670, 0.9178] , and [0.3197, 0.9231] , respectively. Zhangjiajie, Xiangxi Autonomous Prefecture, and

Zhangjiajie have the lowest coupling coordination degree, respectively, in these three

years.

Figure 5. The temporal evolution of the coupling coordination degree for all cities in Hunan

Province.

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).

(a) 2010 (b) 2014

Figure 5. The temporal evolution of the coupling coordination degree for all cities in Hunan Province.



In an attempt to investigate the coupling and coordination relationship between urbanresilience and urbanization more comprehensively, the spatial distribution map of thecoupling coordination degree in each year is plotted with the assistance of ArcGIS 10.2software (Figure 6), followed by the summarization and classification (Table 7).


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 [0.4030, 0.9170] , [0.3670, 0.9178] , and [0.3197, 0.9231] , respectively. Zhangjiajie, Xiangxi Autonomous Prefecture, and

Zhangjiajie have the lowest coupling coordination degree, respectively, in these three

years.

Figure 5. The temporal evolution of the coupling coordination degree for all cities in Hunan

Province.


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).

(a) 2010 (b) 2014


(c) 2019

Figure 6. The spatial pattern of the coupling coordination degree for all cities in Hunan Province.

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

City 2010 2014 2019

D Grade 𝑼𝟏/𝑼𝟐 D Grade 𝑼𝟏/𝑼𝟐 D Grade 𝑼𝟏/𝑼𝟐

Changsha Good coordination 𝑈1 < 𝑈2 Good coordination 𝑈1 < 𝑈2 Good coordination 𝑈1 < 𝑈2

Zhuzhou Moderate coordination 𝑈1 > 𝑈2 Moderate coordination 𝑈1 > 𝑈2 Low coordination 𝑈1 > 𝑈2

Xiangtan Low coordination 𝑈1 > 𝑈2 Low coordination 𝑈1 > 𝑈2 Low coordination 𝑈1 > 𝑈2

Hengyang Low coordination 𝑈1 < 𝑈2 Low coordination 𝑈1 < 𝑈2 Low coordination 𝑈1 < 𝑈2

Shaoyang Near incoordination 𝑈1 > 𝑈2 Near incoordination 𝑈1 < 𝑈2 Near incoordination 𝑈1 < 𝑈2

Yueyang Low coordination 𝑈1 < 𝑈2 Low coordination 𝑈1 < 𝑈2 Low coordination 𝑈1 < 𝑈2

Changde Low coordination 𝑈1 > 𝑈2 Low coordination 𝑈1 > 𝑈2 Low coordination 𝑈1 > 𝑈2

Zhangjiajie Near incoordination 𝑈1 > 𝑈2 Moderate incoordination 𝑈1 > 𝑈2 Moderate incoordination 𝑈1 > 𝑈2

Yiyang Near incoordination 𝑈1 > 𝑈2 Near incoordination 𝑈1 > 𝑈2 Near incoordination 𝑈1 > 𝑈2

Chenzhou Low coordination 𝑈1 > 𝑈2 Low coordination 𝑈1 > 𝑈2 Low coordination 𝑈1 > 𝑈2

Yongzhou Near incoordination 𝑈1 > 𝑈2 Near incoordination 𝑈1 < 𝑈2 Low coordination 𝑈1 > 𝑈2

Huaihua Low coordination 𝑈1 < 𝑈2 Near incoordination 𝑈1 < 𝑈2 Near incoordination 𝑈1 < 𝑈2

Loudi Near incoordination 𝑈1 > 𝑈2 Near incoordination 𝑈1 > 𝑈2 Near incoordination 𝑈1 > 𝑈2

Xiangxi

Autonomous

Prefecture

Near incoordination 𝑈1 < 𝑈2 Moderate incoordination 𝑈1 > 𝑈2 Moderate incoordination 𝑈1 > 𝑈2

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

Figure 6. The spatial pattern of the coupling coordination degree for all cities in Hunan Province.


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

City2010 2014 2019

D Grade U1/U2 D Grade U1/U2 D Grade U1/U2

Changsha Good coordination U1 < U2 Good coordination U1 < U2 Good coordination U1 < U2

Zhuzhou Moderatecoordination U1 > U2

Moderatecoordination U1 > U2 Low coordination U1 > U2

Xiangtan Low coordination U1 > U2 Low coordination U1 > U2 Low coordination U1 > U2

Hengyang Low coordination U1 < U2 Low coordination U1 < U2 Low coordination U1 < U2

Shaoyang Nearincoordination U1 > U2

Nearincoordination U1 < U2


Yueyang Low coordination U1 < U2 Low coordination U1 < U2 Low coordination U1 < U2Changde Low coordination U1 > U2 Low coordination U1 > U2 Low coordination U1 > U2

Zhangjiajie Nearincoordination U1 > U2

Moderateincoordination U1 > U2


Yiyang Nearincoordination U1 > U2

Nearincoordination U1 > U2


Chenzhou Low coordination U1 > U2 Low coordination U1 > U2 Low coordination U1 > U2

Yongzhou Nearincoordination U1 > U2

Nearincoordination U1 < U2 Low coordination U1 > U2

Huaihua Low coordination U1 < U2Near

incoordination U1 < U2Near

incoordination U1 < U2

Loudi Nearincoordination U1 > U2



XiangxiAutonomous

Prefecture




As shown in Figure 6, the coupling coordination degree of all cities in this province ismostly 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 patternthat starts from the central urban agglomeration and gradually decreases to the periphery isformed. Although the urban resilience of Changsha is consistent with its urbanization, theconstruction of resilience is still required. Zhuzhou has transformed from moderate coordi-nation and retarded urbanization to low coordination and retarded urbanization, whichindicates that the gap between urbanization and the resilience level is gradually increasing.Thus, it is necessary to accelerate urbanization. Xiangtan, Changde, and Chenzhou havelow coordination and retarded urbanization; meanwhile, Hengyang and Yueyang have lowcoordination and retarded urban resilience. These five cities should focus on the develop-ment of retarded parts and gradually improve the coupling coordination degree. Shaoyanghas always been near incoordination and has transformed from retarded urbanizationto retarded urban resilience. It indicates that the reconciliation effect between resilienceand urbanization development is not good. Meanwhile, Yiyang and Loudi have alwaysbeen near incoordination and retarded urbanization. Thus, it is required for these threecities to take measures as soon as possible to improve the synergy between resilience andurbanization development to prevent the occurrence of incoordination. Zhangjiajie andXiangxi Autonomous Prefecture have transformed from near incoordination to moderateincoordination, with retarded urbanization. It indicates that urbanization shall be acceler-ated in these two cities to strive to achieve a coordinated state. Yongzhou has transformedfrom near incoordination to low coordination, with retarded urbanization, which indicatesthat urbanization shall be accelerated in this city. Meanwhile, Huaihua has a contrary state,with retarded urban resilience, which indicates that resilience construction shall be paidmore attention in the following period.


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

The GM (1, 1) grey prediction model is utilized to perform calculations by takingthe coupling coordination degree of all cities in the province from 2010 to 2019 as theoriginal sequence. The prediction results are in line with −a ≤ 0.3, C < 0.35, P > 95%,which indicates that this model can be used for prediction with high accuracy (Table 8).According to the grade of the coupling coordination degree, 0.2, 0.4, and 0.5 are the dividingvalues for severe incoordination, moderate incoordination, near incoordination, and lowcoordination. It can be seen from the predicted value that around 2029, Shaoyang willenter low coordination, Changde and Chenzhou will fall out of low coordination, XiangxiAutonomous Prefecture will get rid of moderate incoordination, and the coordinationstate of other cities will remain unchanged. Therefore, it is required to pay attention tothese 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 toactively take corresponding measures to prevent the occurrence of serious incoordination.

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

City

Year D Fitting Value GM (1, 1) Predicted Value a C P2010 2014 2019 2024 2029

Changsha 0.917 0.918 0.923 0.929 0.934 −0.006 0.001 1Zhuzhou 0.634 0.617 0.595 0.574 0.554 0.034 0.002 1Xiangtan 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 1Shaoyang 0.457 0.463 0.478 0.493 0.508 −0.031 0.002 1Yueyang 0.570 0.571 0.552 0.533 0.515 0.034 0.003 1Changde 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 1Yiyang 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 1Yongzhou 0.425 0.494 0.506 0.518 0.531 −0.024 0.000 1Huaihua 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 1Xiangxi Autonomous Prefecture 0.451 0.367 0.399 0.434 0.473 −0.084 0.003 1

4.4.4. Moran’s I and Lisa Cluster Map Analysis

1. Univariate global Moran’s I index analysis

The spatial weight matrix is established with ArcGIS software based on the Queencontiguity principle to analyze the global spatial correlation, as shown in Table 9.

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

IndexYear

2010 2014 2019

Moran’s I 0.146 0.169 0.173Z-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 thesignificance level test of 90% or more, which indicates that there are significant positivespatial autocorrelation characteristics and spatial agglomeration effects in the couplingcoordination 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 Moranscatter plot is drawn on the basis of the p-value test, as shown in Figure 7. (1) The regressionline and spatial unit are basically located in the first or third quadrants, and the I value ispositive and increases with each passing year. The coupling coordination pattern presentsa spatial binary state. It is manifested as a spatial distribution pattern, in which highercoupling coordination and lower coupling coordination units are adjacent. The spatialcorrelation of coupling coordination between regions is enhanced with each passing year.(2) Changsha, Zhuzhou, Xiangtan, Yueyang, and Chenzhou have higher degrees of couplingand coordination between urban resilience and urbanization level; meanwhile, West Hunanis always at a low level in terms of the coupling coordination degrees. Different locationsand economic bases induce differences in the development between different regions.(3) From the dispersion degree of distribution points in Moran scatter plot, it is morediscrete in the first quadrant and relatively concentrated in the second quadrant. The Moranscatter points in these two quadrants tend to converge, while those in the third quadranttend to disperse from convergence, which indicates that the coupling and coordinationgap between five cities led by Changsha is gradually narrowing, while that in West Hunanis increasing.


(a) 2010 (b) 2014 (c) 2019

Figure 7. Moran scatter plot of all cities in Hunan Province. Blue circles represent cities. The dashed

lines indicate the horizontal and vertical axes that divide the four quadrants. The solid line indicates

the fitted regression line.

The following are as listed in Table 10: (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.

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

Figure 7. Moran scatter plot of all cities in Hunan Province. Blue circles represent cities. The dashedlines indicate the horizontal and vertical axes that divide the four quadrants. The solid line indicatesthe fitted regression line.

The following are as listed in Table 10: (1) The high–high type area refers to the urbanresilience and urbanization level of a city being in a state of an agglomeration with a highercoupling coordination degree and favorable development, with a positive correlation. Thereare five cities in this type in 2010, including Changsha, Zhuzhou, Xiangtan, Yueyang, andChenzhou. Changsha departs from this type in 2014, and Changsha and Hengyang areclassified into this type in 2019. It indicates that the areas with a coupling coordination degreecan promote the development of adjacent areas. (2) The low–high type area refers to thata city with a lower coupling coordination degree is adjacent to that with a higher couplingcoordination degree. Yiyang and Loudi always belong to this type for the reason that they arelocated in the middle area between West Hunan and Changsha–Zhuzhou–Xiangtan urbanagglomeration. The development of these two cities is subject to adjacent cities with a highercoupling 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 lowercoupling coordination degree, and these cities have a lower urban resilience and urbanizationlevel. However, there is a significant spatial autocorrelation between urban resilience andurbanization 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 thesecities. (4) The high–low type area refers to that a city with a higher coupling coordinationdegree is adjacent to that with a lower coupling coordination degree, with a negative spatialcorrelation. There is only one city (Hengyang) in this type in 2010; Changsha and Changdeare classified into this type in 2014; and Changsha and Hengyang depart from this type in2019. These cities have a higher level of urbanization and resilience compared with adjacentcities. Thus, it is necessary to give full play to their radiation effect and realize the mutualpromotion and progress with the surrounding areas.

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, XiangxiAutonomous Prefecture

Shaoyang, Zhangjiajie,Yongzhou, Huaihua, Xiangxi

Autonomous Prefecture

Shaoyang, Zhangjiajie, Yongzhou,Huaihua, Xiangxi


HL Hengyang Changsha, Hengyang, Changde Changde

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


LL

Shaoyang, Changde,

Zhangjiajie, Yongzhou,

Huaihua, Xiangxi


Shaoyang, Zhangjiajie,

Yongzhou, Huaihua,

Xiangxi Autonomous

Prefecture

Shaoyang, Zhangjiajie,

Yongzhou, Huaihua,

Xiangxi Autonomous

Prefecture

HL Hengyang Changsha, Hengyang,

Changde Changde

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.

Figure 8. Cont.

Sustainability 2022, 14, 5889 21 of 26Sustainability 2022, 14, x FOR PEER REVIEW 23 of 28

Figure 8. LISA cluster distribution of the coupling coordination degree for all cities in Hunan

Province.

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

Figure 8. LISA cluster distribution of the coupling coordination degree for all cities in Hu-nan Province.

5. Discussion

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

On the one hand, the level of urban resilience determines the level of urbanizationto a certain extent. Cities’ safe and healthy development, as well as the advancement ofurbanization, are being hampered as a result of an increase in numerous sorts of uncertainrisks. Because of their good linkage emergency management capabilities, high-resiliencecities can effectively respond to unknown events, ensure the orderly exchange of infor-mation and energy inside and outside the city, and the normal functioning of various cityfunctions, as well as providing a good development environment for urbanization. Whenlow-resilience cities respond to a crisis, key development factors, such as population andfinancial resources, tend to flow to stable markets, causing the crisis’ scope to spread andjeopardizing other regions’ stable development. Then, once the crisis has passed, the city’slost 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 theimprovement of urban resilience. Urbanization has provided cities with several devel-opment prospects. Talent pooling, financial support, and social capital cooperation helpto improve the infrastructure construction, community service system and emergencymanagement system, etc. Interdepartmental synergy helps improve urban resilience. How-ever, due to the complexity of the urbanization development system, problems such asecological damage, infrastructure and housing quality failures, employment challenges,and transportation tensions emerge when urbanization progresses too quickly, impedingthe level of urban resilience.

Based on the definition of urban resilience, it is clear that urban resilience is aninherent property of urban systems and is not unique to a particular city. Promotinghigh-quality urbanization development is an important work that every city in the world isactively promoting. Therefore, the research methodology in this paper is equally applicableto the study of the coordinated relationship between urban resilience and urbanizationdevelopment in China and other regions of the world. The basis of this study is basedon objective and real data, and the research findings are not determined by local landmanagement and planning policies, but the findings can provide some reference for theformulation of relevant local policies in the future.


6. Conclusions

The findings of this study reveal the following facts. (1) As per the evaluation resultanalysis of the urban resilience level, except for Changsha, Yongzhou, and Xiangxi Au-tonomous Prefecture, whose urban resilience level increases steadily, there is a trend offluctuation and decrease in a certain range for other cities in the province. The resiliencelevel is related to geographical location, with the characteristic of a decrease from the eastto the west. Additionally, there is an obvious polarization phenomenon. (2) As per theevaluation result analysis of the urbanization level, the urbanization level of most cities isin a relatively stable development state; meanwhile, that of a few cities decreases, whichcauses the gradual expansion of the development gap between regions. (3) As per theresult analysis of the coupling coordination degree, all cities mainly present two states:“low coordination” and “near incoordination”, with a stable development state. There is astrong correlation between urbanization and urban resilience. However, the coupling andcoordinated development between regions is unbalanced, and there is an increasingly dis-tinct polarization trend for the coupling and coordinated development between cities. Mostcities are classified into the type of retarded urbanization. Eventually, a circle-differencespatial distribution pattern that starts from the central urban agglomeration and gradu-ally 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 couplingand coordinated development is stable with a slight increase trend, other cities presenta 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 spatialautocorrelation characteristics and agglomeration effects in the coupling and coordinateddevelopment between all cities in this province, and the correlation is increasing with eachpassing year. There is an obvious spatial binary state in the pattern of coupling and coor-dinated development, and the correlation mode is mainly characterized by homogeneityand supplemented by heterogeneity, which is gradually improving. Moreover, the numberof cities with agglomeration characteristics in the province is increasing. Therefore, it isnecessary to strengthen inter-regional exchanges and cooperation to achieve sustainableand 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 ur-

banization gap between the east and the west. On the one hand, it is required to ensurethe stable development of urbanization level in HH areas led by Changsha. On the otherhand, due to the fact that the urbanization level in West Hunan is at a low level in thewhole province, it is necessary to increase the input of resources and technology, encourageeconomic exchanges with neighboring cities, adjust the existing industrial structure andimprove the allocation of resources, thus promoting the overall economy and sustainabledevelopment level of the whole province. Benchmark cities can be selected to drive thedevelopment of surrounding areas, the new development philosophy shall be upheldin the future development, the absolute and relative differences in the development ofurbanization level shall be reduced in an orderly manner, and a new pattern of coordinatedurban and rural development shall be constructed.

(2) Establish the concept of resilience spatial planning and steadily improve the re-silience 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 citieswith retarded urban resilience, the concept of resilience should be fully integrated intourban construction, and the construction of urban infrastructure, economic, social andecological environment and safety monitoring and emergency response mechanism shouldbe improved and optimized so as to enhance public and community disaster risk awarenessand self-help and mutual aid ability, thus improving the urban risk prevention ability.

(3) Actively promote the radiation effect of highly coordinated cities and weakenthe polarization phenomenon. Under the condition of improving the benign interaction


between urban construction and urbanization, all cities shall also establish efficient com-munication channels, actively carry out comprehensive cooperation and exchanges withsurrounding areas and play the strategic role in regional coordinated development, form adual-core or multi-core circle structure development model in the province, and acceleratethe pace of urban construction.

(4) Promote the urban resilience and urbanization of regional cities simultaneouslyand promote the sustainable development of cities. Marketization, informationization andfinancial support are the three important factors that affect the urbanization level and thecoupling and coordinated development of urban resilience. The market mechanism andreform and innovation mechanism shall be further improved. There shall be coordinatedadvancement in the benign increase of urban population, efficient and moderate economicgrowth, harmony and progress of society, effective transformation of urban and rural landand construction of ecological environment. Cross-cooperation among various departmentscan improve the level of urbanization and urban resilience, promote the flow of devel-opment factors between regions, narrow regional gaps, improve the comprehensiveness,synergy and sustainability of urban development and enhance the overall developmentquality and livability of the whole province.

Author Contributions: Conceptualization, Q.X. and Y.X.; methodology, Y.X.; software, M.Z.; formalanalysis, Y.X.; investigation, Y.X. and M.Z.; data curation, Y.X.; writing—original draft preparation,Y.X.; writing—review and editing, C.L. All authors have read and agreed to the published version ofthe manuscript.

Funding: This research received no external funding.

Institutional Review Board Statement: Not applicable.

Informed Consent Statement: Informed consent was obtained from all subjects involved in the study.

Data Availability Statement: The case analysis data used to support the findings of this study areavailable from the corresponding author upon request.

Conflicts of Interest: The authors declare no conflict of interest.

References1. Tonne, C.; Adair, L.; Adlakha, D.; Anguelovski, I.; Belesova, K.; Berger, M.; Brelsford, C.; Dadvand, P.; Dimitrova, A.;

Giles-Corti, B.; et al. Defining pathways to healthy sustainable urban development. Environ. Int. 2021, 146, 106236. [CrossRef][PubMed]

2. World Urbanization Outlook. 2018 Edition Report Released. Shanghai Urban Plan. Rev. 2018, 3, 129.3. Eisenack, K.; Roggero, M. Many roads to Paris: Explaining urban climate action in 885 European cities. Glob. Environ. Chang.

2022, 72, 102439. [CrossRef]4. Nerini, F.F.; Sovacool, B.; Hughes, N.; Cozzi, L.; Cosgrave, E.; Howells, M.; Tavoni, M.; Tomei, J.; Zerriffi, H.; Milligan, B.

Connecting climate action with other Sustainable Development Goals. Nat. Sustain. 2019, 2, 674–680. [CrossRef]5. Fuchs, S.; Karagiorgos, K.; Kitikidou, K.; Maris, F.; Paparrizos, S.; Thaler, T. Flood risk perception and adaptation capacity:

A contribution to the socio-hydrology debate. Hydrol. Earth Syst. Sci. 2017, 21, 3183–3198. [CrossRef]6. Lu, X.; Cheng, Q.; Xu, Z.; Xu, Y.; Sun, C. Real-Time City-Scale Time-History Analysis and Its Application in Resilience-Oriented

Earthquake Emergency Responses. Appl. Sci. 2019, 9, 3497. [CrossRef]7. Gao, Z.; Wan, R.; Ye, Q.; Fan, W.; Guo, S.; Ulgiati, S.; Dong, X. Typhoon Disaster Risk Assessment Based on Emergy Theory:

A Case Study of Zhuhai City, Guangdong Province, China. Sustainability 2020, 12, 4212. [CrossRef]8. Zhang, X.; Chen, N.; Sheng, H.; Ip, C.; Yang, L.; Chen, Y.; Sang, Z.; Tadesse, T.; Lim, T.P.Y.; Rajabifard, A.; et al. Urban drought

challenge to 2030 sustainable development goals. Sci. Total Environ. 2019, 693, 133536. [CrossRef] [PubMed]9. Guan, X.; Wei, H.; Lu, S.; Dai, Q.; Su, H. Assessment on the urbanization strategy in China: Achievements, challenges and

reflections. Habitat Int. 2018, 71, 97–109. [CrossRef]10. El-Kholei, A.O. Are Arab cities prepared to face disaster risks? Challenges and opportunities. Alex. Eng. J. 2019, 58, 479–486.

[CrossRef]11. Holling, C.S. Resilience and Stability of Ecological Systems. Annu. Rev. Ecol. Syst. 1973, 4, 1–23. [CrossRef]12. Bozza, A.; Asprone, D.; Fabbrocino, F. Urban Resilience: A Civil Engineering Perspective. Sustainability 2017, 9, 103. [CrossRef]13. Barasa, E.; Mbau, R.; Gilson, L. What Is Resilience and How Can It Be Nurtured? A Systematic Review of Empirical Literature on

Organizational Resilience. Int. J. Health Policy 2018, 7, 491–503. [CrossRef] [PubMed]

http://doi.org/10.1016/j.envint.2020.106236

http://www.ncbi.nlm.nih.gov/pubmed/33161201

http://doi.org/10.1016/j.gloenvcha.2021.102439

http://doi.org/10.1038/s41893-019-0334-y

http://doi.org/10.5194/hess-21-3183-2017

http://doi.org/10.3390/app9173497

http://doi.org/10.3390/su12104212

http://doi.org/10.1016/j.scitotenv.2019.07.342


http://doi.org/10.1016/j.habitatint.2017.11.009

http://doi.org/10.1016/j.aej.2019.04.004

http://doi.org/10.1146/annurev.es.04.110173.000245

http://doi.org/10.3390/su9010103

http://doi.org/10.15171/ijhpm.2018.06



14. Norris, F.H.; Stevens, S.P.; Pfefferbaum, B.; Wyche, K.F.; Pfefferbaum, R.L. Community Resilience as a Metaphor, Theory, Set ofCapacities, and Strategy for Disaster Readiness. Am. J. Commun. Psychol. 2008, 41, 127–150. [CrossRef] [PubMed]

15. Folke, C.; Ca Rpenter, S.R.; Walker, B.; Scheffer, M.; Chapin, T.; Rockstrm, J. Resilience Thinking: Integrating Resilience,Adaptability and Transformability. Ecol. Soc. 2010, 15, 299–305. [CrossRef]

16. Barrett, C.B.; Constas, M.A. Toward a theory of resilience for international development applications. Proc. Natl. Acad. Sci. USA2014, 111, 14625–14630. [CrossRef]

17. Pizzo, B. Problematizing resilience: Implications for planning theory and practice. Cities 2015, 43, 133–140. [CrossRef]18. Leitner, H.; Sheppard, E.; Webber, S.; Colven, E. Globalizing urban resilience. Urban Geogr. 2018, 39, 1276–1284. [CrossRef]19. Wang, L.; Xue, X.; Zhang, Y.; Luo, X. Exploring the Emerging Evolution Trends of Urban Resilience Research by Scientometric

Analysis. Int. J. Environ. Res. Public Health 2018, 15, 2181. [CrossRef]20. Cardoso, M.A.; Brito, R.S.; Pereira, C.; Gonzalez, A.; Stevens, J.; Telhado, M.J. RAF Resilience Assessment Framework—A Tool to

Support Cities’ Action Planning. Sustainability 2020, 12, 2349. [CrossRef]21. Shamsuddin, S. Resilience resistance: The challenges and implications of urban resilience implementation. Cities 2020, 103, 102763.

[CrossRef] [PubMed]22. 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. [CrossRef] [PubMed]23. Masnavi, M.R.; Gharai, F.; Hajibandeh, M. Exploring urban resilience thinking for its application in urban planning: A review of

literature. Int. J. Environ. Sci. Technol. 2018, 16, 567–582. [CrossRef]24. Meerow, S.; Newell, J.P.; Stults, M. Defining urban resilience: A review. Landsc. Urban Plan 2016, 147, 38–49. [CrossRef]25. Erling, H.; Kristin, L.; David, B. The Imperatives of Sustainable Development. Sustain. Dev. 2016, 25, 213–226.26. Chen, J.; Guo, X.; Pan, H.; Zhong, S. What determines city’s resilience against epidemic outbreak: Evidence from China’s

COVID-19 experience. Sustain. Cities Soc. 2021, 70, 102892. [CrossRef]27. Yılmaz Börekçi, D.; Rofcanin, Y.; Heras, M.L.; Berber, A. Deconstructing organizational resilience: A multiple-case study. J. Manage.

Organ. 2021, 27, 422–441. [CrossRef]28. Rod, B.; Lange, D.; Theocharidou, M.; Pursiainen, C. From Risk Management to Resilience Management in Critical Infrastructure.

J. Manag. Eng. 2020, 36, 4020039. [CrossRef]29. Payne, P.R.; Kaye-Blake, W.H.; Kelsey, A.; Brown, M.; Niles, M.T. Measuring rural community resilience: Case studies in New

Zealand and Vermont, USA. Ecol. Soc. 2021, 26, 2. [CrossRef]30. Oliver, T.H.; Heard, M.S.; Isaac, N.J.B.; Roy, D.B.; Procter, D.; Eigenbrod, F.; Freckleton, R.; Hector, A.; Orme, C.D.L.;

Petchey, O.L.; et al. Biodiversity and Resilience of Ecosystem Functions. Trends Ecol. Evol. 2015, 30, 673–684. [CrossRef]31. Martin, R.; Sunley, P. On the notion of regional economic resilience: Conceptualization and explanation. J. Econ. Geogr. 2014,

15, 1–42. [CrossRef]32. Gu, C. Urbanization: Processes and driving forces. Sci. China Earth Sci. 2019, 62, 1351–1360. [CrossRef]33. Zhang, Y.; Su, Z.; Li, G.; Zhuo, Y.; Xu, Z. Spatial-Temporal Evolution of Sustainable Urbanization Development: A Perspective of

the Coupling Coordination Development Based on Population, Industry, and Built-Up Land Spatial Agglomeration. Sustainability2018, 10, 1766. [CrossRef]

34. Zhang, X.; Song, W.; Wang, J.; Wen, B.; Yang, D.; Jiang, S.; Wu, Y. Analysis on Decoupling between Urbanization Level andUrbanization Quality in China. Sustainability 2020, 12, 6835. [CrossRef]

35. Shi, Y.; Zhu, Q.; Xu, L.; Lu, Z.; Wu, Y.; Wang, X.; Yang, F.; Deng, J. Independent or Influential? Spatial-Temporal Features ofCoordination Level between Urbanization Quality and Urbanization Scale in China and Its Driving Mechanism. Int. J. Environ.Res. Public Health 2020, 17, 1587. [CrossRef] [PubMed]

36. He, J.; Wang, S.; Liu, Y.; Ma, H.; Liu, Q. Examining the relationship between urbanization and the eco-environment usinga coupling analysis: Case study of Shanghai, China. Ecol. Indic. 2017, 77, 185–193. [CrossRef]

37. Xiao, Y.; Song, Y.; Wu, X. How Far Has China’s Urbanization Gone? Sustainability 2018, 10, 2953. [CrossRef]38. Ma, L.; Cheng, W.; Qi, J. Coordinated evaluation and development model of oasis urbanization from the perspective of new

urbanization: A case study in Shandan County of Hexi Corridor, China. Sustain. Cities Soc. 2018, 39, 78–92. [CrossRef]39. Xu, D.; Hou, G. The Spatiotemporal Coupling Characteristics of Regional Urbanization and Its Influencing Factors: Taking the

Yangtze River Delta as an Example. Sustainability 2019, 11, 822. [CrossRef]40. Niu, J.; Du, H. Coordinated Development Evaluation of Population–Land–Industry in Counties of Western China: A Case Study

of Shaanxi Province. Sustainability 2021, 13, 1983. [CrossRef]41. Keen, M.; Connell, J. Regionalism and Resilience? Meeting Urban Challenges in Pacific Island States. Urban Policy Res. 2019,

37, 324–337. [CrossRef]42. Li, J.; Liu, Q.; Sang, Y. Several Issues about Urbanization and Urban Safety. Procedia Eng. 2012, 43, 615–621. [CrossRef]43. Zhou, Q.; Liu, D. Research on the Coupled and Coordinated Development of Urban Resilience and Urbanization Level in Yangtze

River Delta City Cluster. Res. Soil Water Conserv. 2020, 27, 286–292.44. Bai, L.; Feng, X.; Sun, R.; Zhao, H. Coupling Analysis of Urban Resilience Level and Urbanization Quality in Jilin Province.

Urban. Archit. 2018, 35, 19–23.45. Wang, S.; Cui, Z.; Lin, J.; Xie, J.; Su, K. The coupling relationship between urbanization and ecological resilience in the Pearl River

Delta. J. Geogr. Sci. 2022, 32, 44–64. [CrossRef]

http://doi.org/10.1007/s10464-007-9156-6


http://doi.org/10.5751/ES-03610-150420

http://doi.org/10.1073/pnas.1320880111

http://doi.org/10.1016/j.cities.2014.11.015

http://doi.org/10.1080/02723638.2018.1446870

http://doi.org/10.3390/ijerph15102181

http://doi.org/10.3390/su12062349

http://doi.org/10.1016/j.cities.2020.102763




http://doi.org/10.1007/s13762-018-1860-2

http://doi.org/10.1016/j.landurbplan.2015.11.011

http://doi.org/10.1016/j.scs.2021.102892

http://doi.org/10.1017/jmo.2018.72

http://doi.org/10.1061/(ASCE)ME.1943-5479.0000795

http://doi.org/10.5751/ES-12026-260102

http://doi.org/10.1016/j.tree.2015.08.009

http://doi.org/10.1093/jeg/lbu015

http://doi.org/10.1007/s11430-018-9359-y

http://doi.org/10.3390/su10061766

http://doi.org/10.3390/su12176835



http://doi.org/10.1016/j.ecolind.2017.01.017

http://doi.org/10.3390/su10082953

http://doi.org/10.1016/j.scs.2018.02.007

http://doi.org/10.3390/su11030822

http://doi.org/10.3390/su13041983

http://doi.org/10.1080/08111146.2019.1626710

http://doi.org/10.1016/j.proeng.2012.08.108

http://doi.org/10.1007/s11442-022-1935-3


46. Li, C.; Wu, Y.; Gao, B. Research on the Coupling and Coordination of Urbanization and Resource and Environmental CarryingCapacity in Central Yunnan Urban Cluster. Res. Soil Water Conserv. 2022, 29, 389–397.

47. Gao, Y.; Chen, W. Study on the coupling relationship between urban resilience and urbanization quality—A case study of 14 citiesof Liaoning Province in China. PLoS ONE 2021, 16, e244024. [CrossRef]

48. Cao, W.; Zhang, X.; Pan, Y.; Zhang, C. A study on the degree of coordinated development of population, land and economicurbanization in developed regions. China Popul. Resour. Environ. 2012, 22, 141–146.

49. He, S.; Shao, X. Spatial Agglomeration and Coupled Coordinated Development of Population-Land-Economic Urbanization inBeijing-Tianjin-Hebei Region. Econ. Geogr. 2018, 38, 95–102.

50. Qiu, M.; Yang, Z.; Zuo, Q.; Wu, Q.; Jiang, L.; Zhang, Z.; Zhang, J. Evaluation on the relevance of regional urbanization andecological security in the nine provinces along the Yellow River, China. Ecol. Indic. 2021, 132, 108346. [CrossRef]

51. Yang, X.; Li, Z.; Zhang, J.; Li, H. Spatial and Temporal Evaluation of Urban Resilience in a Sustainable Development Perspective.Urban Probl. 2021, 3, 29–37.

52. Bai, L.; Xiu, C.; Feng, X.; Mei, D.; Wei, Y. Comprehensive assessment of urban resilience in China and its spatial and temporalvariation characteristics. World Reg. Stud. 2019, 28, 77–87.

53. Xun, X.; Yuan, Y. Research on the urban resilience evaluation with hybrid multiple attribute TOPSIS method: An example inChina. Nat. Hazards 2020, 103, 557–577. [CrossRef] [PubMed]

54. Chen, X.; Quan, R. A spatiotemporal analysis of urban resilience to the COVID-19 pandemic in the Yangtze River Delta.Nat. Hazards 2021, 106, 829–854. [CrossRef]

55. Zhong, M.; Lin, K.; Tang, G.; Zhang, Q.; Chen, X. A Framework to Evaluate Community Resilience to Urban Floods: A CaseStudy in Three Communities. Sustainability 2020, 12, 1521. [CrossRef]

56. Lixin, Y.; Cheng, K.; Xiaoying, C.; Yueling, S.; Xiaoqing, C.; Ye, H. Analysis of social vulnerability of residential community tohazards in Tianjin, China. Nat. Hazards 2017, 87, 1223–1243. [CrossRef]

57. Limnios, E.A.M.; Mazzarol, T.; Ghadouani, A.; Schilizzi, S.G.M. The Resilience Architecture Framework: Four organizationalarchetypes. Eur. Manag. J. 2014, 32, 104–116. [CrossRef]

58. Geng, Y.; Zhang, H. Coordination assessment of environment and urbanization: Hunan case. Environ. Monit. Assess 2020,192, 1–18. [CrossRef]

59. Lin, Y.; Peng, C.; Shu, J.; Zhai, W.; Cheng, J. Spatiotemporal characteristics and influencing factors of urban resilience efficiency inthe Yangtze River Economic Belt, China. Environ. Sci. Pollut. Res. 2022, 1–20. [CrossRef]

60. Zhang, F.; Sun, C.; An, Y.; Luo, Y.; Yang, Q.; Su, W.; Gao, L. Coupling coordination and obstacle factors between tourism and theecological environment in Chongqing, China: A multi-model comparison. Asia Pac. J. Tour. Res. 2021, 26, 811–828. [CrossRef]

61. Song, Q.; Zhou, N.; Liu, T.; Siehr, S.A.; Qi, Y. Investigation of a “coupling model” of coordination between low-carbon developmentand urbanization in China. Energ. Policy 2018, 121, 346–354. [CrossRef]

62. Kong, Y.; Liu, J. Sustainable port cities with coupling coordination and environmental efficiency. Ocean. Coast. Manag. 2021,205, 105534. [CrossRef]

63. Chen, Y.; Liu, H.; Hsieh, H. Time series interval forecast using GM (1,1) and NGBM (1, 1) models. Soft Comput. 2019, 23, 1541–1555.[CrossRef]

64. Fan, C.; Myint, S. A comparison of spatial autocorrelation indices and landscape metrics in measuring urban landscapefragmentation. Landsc. Urban Plan. 2014, 121, 117–128. [CrossRef]

65. Cheng, Z.; Li, L.; Liu, J. Industrial structure, technical progress and carbon intensity in China’s provinces. Renew. Sust. Energ. Rev.2018, 81, 2935–2946. [CrossRef]

66. Gorgij, A.D.; Kisi, O.; Moghaddam, A.A.; Taghipour, A. Groundwater quality ranking for drinking purposes, using the entropymethod and the spatial autocorrelation index. Environ. Earth Sci. 2017, 76, 1–9. [CrossRef]

67. Delgado, A.; Romero, I. Environmental conflict analysis using an integrated grey clustering and entropy-weight method: A casestudy of a mining project in Peru. Environ. Model. Softw. 2016, 77, 108–121. [CrossRef]

68. Chen, P. On the Diversity-Based Weighting Method for Risk Assessment and Decision-Making about Natural Hazards. Entropy2019, 21, 269. [CrossRef]

69. Li, Q.; Meng, X.X.; Liu, Y.B.; Pang, L.F. Risk Assessment of Floor Water Inrush Using Entropy Weight and Variation CoefficientModel. Geotech. Geol. Eng. 2018, 37, 1493–1501. [CrossRef]

70. Fan, W.; Xu, Z.; Wu, B.; He, Y.; Zhang, Z. Structural multi-objective topology optimization and application based on the criteriaimportance through intercriteria correlation method. Eng. Optimiz. 2021, 1–17. [CrossRef]

71. Fu, W.; Zhu, L. Research on the Evaluation of High Quality Development of Manufacturing Industry from the Perspective ofYangtze River Delta Integration—TOPSIS Evaluation Model Based on Improved CRITIC-Entropy Method Combined Weights.J. Ind. Technol. Econ. 2020, 39, 145–152.

72. Chen, Y.; Su, X.; Zhou, Q. Study on the Spatiotemporal Evolution and Influencing Factors of Urban Resilience in the Yellow RiverBasin. Int. J. Environ. Res. Public Health 2021, 18, 10231. [CrossRef] [PubMed]

73. Yang, Y.; Fang, Y.; Xu, Y.; Zhang, Y. Assessment of urban resilience based on the transformation of resourcebased cities: A casestudy of Panzhihua, China. Ecol. Soc. 2021, 26, 20. [CrossRef]

74. Assarkhaniki, Z.; Rajabifard, A.; Sabri, S. The conceptualisation of resilience dimensions and comprehensive quantification of theassociated indicators: A systematic approach. Int. J. Disaster Risk Reduct. 2020, 51, 101840. [CrossRef]

http://doi.org/10.1371/journal.pone.0244024

http://doi.org/10.1016/j.ecolind.2021.108346

http://doi.org/10.1007/s11069-020-04000-0


http://doi.org/10.1007/s11069-020-04493-9

http://doi.org/10.3390/su12041521

http://doi.org/10.1007/s11069-017-2781-y

http://doi.org/10.1016/j.emj.2012.11.007

http://doi.org/10.1007/s10661-020-08598-3

http://doi.org/10.1007/s11356-021-18235-2

http://doi.org/10.1080/10941665.2021.1925715

http://doi.org/10.1016/j.enpol.2018.05.037

http://doi.org/10.1016/j.ocecoaman.2021.105534

http://doi.org/10.1007/s00500-017-2876-0

http://doi.org/10.1016/j.landurbplan.2013.10.002

http://doi.org/10.1016/j.rser.2017.06.103

http://doi.org/10.1007/s12665-017-6589-6

http://doi.org/10.1016/j.envsoft.2015.12.011

http://doi.org/10.3390/e21030269

http://doi.org/10.1007/s10706-018-0702-9

http://doi.org/10.1080/0305215X.2021.1901087



http://doi.org/10.5751/ES-12280-260220

http://doi.org/10.1016/j.ijdrr.2020.101840


75. Zhu, J.; Sun, H. Spatial and Temporal Evolution of Urban Resilience and Influencing Factors in Three Major Urban Agglomerationsin China. Soft Sci. 2020, 34, 72–79.

76. Qasim, S.; Qasim, M.; PrasadShrestha, R.; NawazKhand, A.; Tun, K.; Ashraf, M. Community resilience to flood hazards in KhyberPukhthunkhwa province of Pakistan. Int. J. Disaster Risk Reduct. 2016, 18, 100–106. [CrossRef]

77. Liu, X.; Li, S.; Xu, X.; Luo, J. Integrated natural disasters urban resilience evaluation: The case of China. Nat. Hazards 2021,107, 2105–2122. [CrossRef]

78. Yang, B.; Li, G.; Liu, Q. Analysis of social resilience evaluation of international communities based on DPSRC model—An exampleof 16 international communities in Xiaobei, Guangzhou. Areal Res. Dev. 2020, 39, 70–75.

79. Da, K.; Li, M. Evaluation of Urban Resilience from the Perspective of Emergency Management-Based on Panel Data of 14 Cities inLiaoning Province. J. Shenyang Jianzhu Univ. (Soc. Sci.) 2020, 22, 595–603.

80. Cui, M. Coupling and Coordination between Urbanization and Ecological Environment in Nine Cities of Central Plains CityCluster. Econ. Geogr. 2015, 35, 72–78.

81. Jia, Q.; Yun, Y. Measurement of Urbanization Quality and Analysis of Regional Differences in Beijing-Tianjin-Hebei MetropolitanArea. J. Arid. Land Resour. Environ. 2015, 29, 8–12.

82. Wang, S.; Cui, Z.; Lin, J.; Xie, J.; Su, K. Coupling and Coordination Research of Urbanization and Ecological Resilience in the PearlRiver Delta Region. Acta Geogr. Sin. 2021, 76, 973–991.

83. Chen, M.; Lu, D.; Zha, L. The comprehensive evaluation of China’s urbanization and effects on resources and environment.J. Geogr. Sci. 2010, 20, 17–30. [CrossRef]

84. Yang, C.; Zeng, W.; Yang, X. Coupling coordination evaluation and sustainable development pattern of geo-ecological environmentand urbanization in Chongqing municipality, China. Sustain. Cities Soc. 2020, 61, 102271. [CrossRef]

85. Zhao, L.; Li, L.; Wu, Y. Research on the Coupling Coordination of a Sea–Land System Based on an Integrated Approach and NewEvaluation Index System: A Case Study in Hainan Province, China. Sustainability 2017, 9, 859. [CrossRef]

86. Wang, Y.; Ding, Z.; Yu, M.; Shang, Z.; Song, X.; Chang, X. Quantitative Analysis of the Coordination Relationship between ModernService Industry and Urbanization Based on Coupling Model-Case Study of Changshu City, Jiangsu Province. Geogr. Res. 2015,34, 97–108.

87. Wang, Q.; Tang, F. Spatial and temporal differentiation of the coupled and coordinated development of ecological-economic-socialsystems in the Dongting Lake area. Econ. Geogr. 2015, 35, 161–167.

88. Weng, G.; Li, L. Coupling and coordination degree and spatial correlation analysis of the integrated development of tourism andcultural industries in China. Econ. Geogr. 2016, 36, 178–185.

89. Jiang, Z.; Zhu, G. Gray prediction model GM (1,1) and its application in traffic volume forecasting. J. Wuhan Univ. Technol. (Transp.Sci. Eng.) 2004, 28, 305–307.

90. Dong, M.; Zou, B.; Pu, Q.; Wan, N.; Yang, L.; Luo, Y. Spatial pattern evolution and casual analysis of county level economy inChangsha-Zhuzhou-Xiangtan urban agglomeration, China. Chin. Geogr. Sci. 2014, 24, 620–630. [CrossRef]

http://doi.org/10.1016/j.ijdrr.2016.03.009

http://doi.org/10.1007/s11069-020-04478-8

http://doi.org/10.1007/s11442-010-0017-0

http://doi.org/10.1016/j.scs.2020.102271

http://doi.org/10.3390/su9050859

http://doi.org/10.1007/s11769-014-0685-2

Investigating into the Coupling and Coordination Relationship ...

Documents

Transcript of Investigating into the Coupling and Coordination Relationship ...