Study on the Coupling and Coordination Relationship Between Urban Living Environment and Economic Development

Abstract

The coordinated development of urban human settlements and the economy is a crucial indicator for assessing regional development and is essential for achieving sustainable high-quality development. Therefore, urban planning and management need to introduce scientific concepts to achieve a win–win situation for both the economy and the environment. (1) Background: Since the reform and opening up of China’s economy, it has undergone rapid development and urbanization. However, the improvement of human settlements has not kept pace. Some regions pursue economic development while neglecting environmental construction. To achieve a win–win situation for both the economy and the environment, urban planning and management need to incorporate scientific concepts. (2) Methods: This paper adopts a literature analysis method to construct a coupling coordination model and evaluate the level of coupling coordination between urban human settlements and economic development in 31 provinces and cities in China from 2011 to 2021. (3) Results: The level of coupling coordination has gradually increased year by year, but there are significant regional differences, with the East outperforming the West. Spatial analysis reveals a positive spatial correlation, indicating that provinces with similar development levels tend to cluster together. (4) Conclusions: The degree of economic outwardness, industrial structure, and residents’ income and consumption system are the main internal obstacles, while economic strength, urbanization, technological innovation, and human capital are positive external factors.

1. Introduction

The urban human settlement environment and economic development are the core of urban ecology and development. They are interdependent and influence each other, and jointly shape the present and future of the city. The quality of the living environment directly affects the living standard and happiness of the residents, and a good environment can attract talent and capital and promote economic prosperity. At the same time, economic development provides material support for the improvement of human settlements and solves environmental problems. However, during the pursuit of economic growth it is easy to ignore environmental protection, resulting in pollution and resource consumption, affecting sustainable development. Therefore, the government, enterprises, and the public need to work together to achieve a harmonious coexistence between the living environment and the economy through scientific planning, environmental protection technology, and public participation. We will use modern information technology to improve the efficiency of urban management, promote the rational allocation of resources, and jointly build a beautiful, prosperous, and sustainable future city.

Since the two concepts of human settlement environment and economic development were put forward, they have attracted extensive academic attention and become the focus of research and exploration by many scholars. Whether focusing on human settlements as a research topic, economic development, or exploring the interaction and correlation between the two, relevant research has achieved rich results. Especially in the study of the coupling and coordination state between the urban human settlement environment and economic development and the factors affecting their coordinated development, previous research results have provided important theoretical and practical support.

In 1898, E. Howard published his work titled “Tomorrow: A Peaceful Path to Real Reform” and proposed the groundbreaking concept of “social city”. This concept not only promotes an in-depth discussion on regional planning, urban and rural structure, and urban systems but also introduces the innovative concept of “Garden City” and “City”. Howard advocates the creation of a rural city that combines the convenience of the city with the tranquility of the country, in order to significantly improve the quality of the living environment of urban residents [1]. The “regional concept” advocated by P. Geddes emphasizes the importance of the development potential and boundaries of the city and its surrounding geographical environment [2]. L. Mumford was deeply influenced by P. Geddes’ theory and advocated the people-oriented urban planning method. Its core belief is to promote a harmonious coexistence between man and nature, aiming to create a natural and livable environment [3]. Eliel Saarinen advocated the construction of places suitable for human settlement on the basis of proximity to nature, and the structure of settlement places should be able to disperse the overly compact urban population [4]. Clernaec Perry proposed to divide the boundary of residential areas through urban roads, and the construction of residential areas is called the “neighborhood unit” theory, to ensure a comfortable and quiet living environment [5]. E.W. Burgess proposed that cities should be built with the same point as the center of the circle, and the construction should be spread around, which is called the “concentric circle” model [6]. Hoty proposed selective urban construction, called the sector model [7]. Based on the above two views, Harrsi proposed that multiple urban centers should be built and rational zoning planning of the city should be carried out [8].

With the rapid progress of science and technology and the diversification of human activities, international scholars have begun to discuss the changes in human settlements from various perspectives. For example, in 1993, Amanatidis studied the impact of human activities on human settlements and found that urbanization destroyed the balance of surface energy [9]. Talen argued in 2006 that social diversity is conducive to the rational allocation of resources [10]. In 2011, Clos pointed out that rapid economic growth and population surge had a significant impact on the living environment and living conditions [11]. During this period, the discipline of human settlements not only made breakthroughs in theoretical innovation but also accumulated rich results in empirical research, which promoted the discipline to develop in a more mature and accurate direction. The study of human settlements in China started around 1990, and after many years of effort, it has obtained rich research results and accumulated experience. The progress of society has brought attention to ecological environmental protection and the comprehensive and coordinated development of society and further promoted the discipline of a human settlement environment to integrate the knowledge of architecture, ecology, economics, and other fields and develop into a comprehensive interdisciplinary discipline. Wu Liangyong put forward the concept of “science of human settlement environment” in combination with the theory of Dausayadis and national conditions [12]. From the perspective of geography, Li Xueming emphasizes subsystem interaction, geographical influence, and GIS application [13,14,15]. Huang Zhiming analyzed the current situation of human settlements after the reform and opening up in China and discussed future trends [16]. Tian Guangyan emphasized the need to comprehensively consider the needs of residents, social development, and ecological environment to improve the quality of human settlements [17].

In the evaluation of the urban human settlement environment, Li Wangming et al. built a multidimensional evaluation system containing 29 indicators from eight dimensions, including housing, neighborhood, community space, greening, service, ecological environment, protection of scenic spots, and emergency service capabilities [18]. Ning Yuemin and Zha Zhiqiang focused on the three areas of ecological environment quality, living conditions, infrastructure, and public service facilities, then, built a human settlement environment evaluation system for the metropolis through 19 subdivision indicators, and conducted a specific assessment of Shanghai [19]. Aiming at the expansion of urbanization since the reform and opening up in China, Zhu Yingming built an index system to evaluate the status quo of urban development and the level of modernization [20]. As an interdisciplinary field, human settlements interact deeply with economic development. Wang Changzheng proposed a coordinated assessment model of the economy and environment [21]. Feng Qunke quantitatively studied the relationship between human settlements and housing prices in the Yangtze River Delta and found a strong positive correlation between them [22]. Lu Chunyang developed a coordinated development degree model and found that the coordination between the urban economy and human settlements in Chongqing has been increasing year by year [23]. Wu Bin constructed an index system to analyze the coordination between the economy and human settlements in Shenzhen through principal component analysis and a dynamic coordination degree model [24]. Zhang Zhengyong built an index system based on multiple dimensions and found that the coordination between the economy and human settlements in Urumqi showed an inverted U-shaped trend [25]. Li Shuangjiang analyzed the coordinated development of the economy and human settlements in Shijiazhuang and concluded that they were in the stage of coordinated development [26]. Chen Yukun constructed the urban human settlement environment and economic evaluation system of Wuhu and found through principal component analysis that the improvement of the human settlement environment contributed a lot to economic development, but economic development had little effect on the improvement of the human settlement environment [27]. Wang Xu and Zhao Guochao constructed an evaluation system for the coordinated development of urban and rural human settlements and economy, studied the coordination degree of 30 provinces by using the coupled coordination degree model, and revealed the spatial distribution and evolution law [28].

The urban human settlement environment and economic development are important development concepts in the new era, which are of great significance to enhance regional competitiveness and high-quality development. The improvement of urban human settlements directly improves residents’ quality of life and improves environmental quality through planning, management, and policies, thus improving residents’ living standards [29]. A good environment attracts investment and talent and promotes economic growth, while economic development provides material and financial support for the improvement of human settlements [30]. In this paper, 31 provinces and cities in China are taken as research objects to evaluate the coupling coordination between urban the human settlement environment and economic development level, and explore the internal obstacles and external influencing factors of the coupling coordination, to promote the regional high-quality development of urban environment and economy in China.

2. Theoretical Basis

The human settlement environment is a form of space-time existence in which human beings interact with their living environment [31]. In a narrow sense, it refers to the space of human settlement activities, which is an artificial environment built on the basis of the natural environment and a geographical space closely related to human living space. In a broader sense, it refers to the sum of all kinds of materiality and immateriality that constitute the conditions for the existence and development of the subject in a certain space around the subject. At present, the human settlement environment generally refers to a broad concept, which not only refers to the tangible space of human habitation and activities, but also includes various aspects of population, resources, environment, social policy, and economic development that run through it [32]. The city is the main activity place of residents, and the urban human settlement environment is a regional complex interwoven with nature and social economy, covering living conditions, urban environment, and basic public service facilities, as shown in Figure 1.

2.1. Human Settlements Theory

The theory of human settlements focuses on how to create a suitable environment for human living, working, and leisure through planning and design in the process of urbanization. The theory was proposed and developed with contributions from multiple disciplines, including urban planning, architecture, environmental psychology, sociology, etc., aimed at addressing the environmental problems brought about by rapid urbanization and the decline in the quality of living.

In 1955, after the establishment of the Journal of Human Settlement, human settlements continued to develop. At present, the human settlement environment has evolved into a comprehensive discipline, bringing together different professional knowledge such as urban planning and construction, environmental protection and governance, and resource development. The definition and connotation of human settlements vary greatly due to the differences in the researchers’ specialties. Ecologists regard human settlements as the most important part affecting economic and social progress. Experts engaged in the study of spatial morphology that focus on the human settlement environment should be synchronized with the economic and social development, to maintain the consistency of social input and output and the balance of physiological and psychological needs. Geographers take regional characteristics and geographic resource planning as the focus of the study of the human settlement environment, pointing out that the geographical conditions in which human lives have an irreplaceable impact on the development of society. According to the different geographical scope, the living environment can be divided into the following three levels: their own housing conditions and related daily life must supply (such as gas, water, etc.) the living environment; design a hygienic community environment around the house; and an urban environment encompassing the above infrastructure and including geographical location, climate, and traffic conditions. According to the different needs of residents, the human settlement environment can be divided into the narrow human settlement environment which only involves residents’ daily learning, work, and entertainment, and the broad human settlement environment that covers the geographical, economic, and human conditions on which economic and social development depends [33]. Based on the coordinated development relationship between the overall human settlement environment and economic development, this paper chooses the generalized human settlement environment as a topic of research, and comprehensively analyzes the coordinated development state between the national human settlement environment and economic development.

2.2. The Coupling Coordination Mechanism Between the Urban Human Settlement Environment and Economic Development

As shown in Figure 2, the relationship between the economic development system and the human settlement environment system is dialectical and unified, and the two can not only provide a basic guarantee for each other but also become obstacles limiting each other’s development. Therefore, a reasonable sorting out of the mechanism of action between the two can provide theoretical guidance for further realizing the coordinated development of the economic and human settlement environment. Economic development can exert positive effects on human settlements from two aspects: rapid economic development can increase fiscal revenue, provide a financial guarantee for urban construction, promote regional ecological environment protection and improve public services, and further promote the stable development of human settlements; at the same time, with the development of economy, residents’ income will continue to increase, their education and personal quality will also be improved simultaneously, their material life will be satisfied, their spiritual pursuit will be enhanced, and foreign exchanges will be gradually strengthened, so that residents will put forward higher requirements on the quality of human settlements, and further promote the development of human settlements quality [34]. However, with the continuous expansion of the economic scale, the use of natural resources will gradually increase, causing a certain degree of damage to the natural environment. Economic expansion will promote the development of urbanization and the explosion of population. A large amount of production and domestic waste will destroy the urban environment [35], while a limited living area will cause the deterioration of living conditions. Without the guidance of reasonable regulations and policies, it will hinder the construction of human settlements and reduce the development level. The human settlement environment is the guaranteed condition of economic development, which can control the healthy development of the economy. A high-quality living environment can improve the comfort of local residents, attract more domestic and foreign investment, promote the introduction of high-level talents, promote the development of high-tech industries, improve the local industrial layout, improve employment, promote economic transformation, and create a new high point for local economic development [36]. A poor living environment cannot provide a guarantee for the improvement of the economic level, and will cause the slow or even stagnation of the economy, affecting the development of the entire economy and society.

3. Study Design

3.1. Construction of Evaluation Index System

(1)

Indicators of urban human settlements

Based on the understanding of the connotation of urban human settlements, on the basis of referring to existing research, this paper summarizes and further selects comprehensive and representative indicators, the urban living environment is mainly composed of three parts: living conditions, urban environment, and infrastructure public services, and the comfort of people’s living environment is mainly reflected in the size of residential area and the size of urban public area activity area, which is mainly reflected by the per capita residential area and urban population density. The ecological environment is an important indicator of residents’ living comfort, the size of urban park green space directly affects urban living comfort, and the quality of urban waste disposal can also reflect the quality of life of residents. The degree of perfection of basic public service facilities represents the level of urban development, and the higher the level of urban development, the more perfect the basic public service facilities. The size of urban roads, the number of buses, and the number of hospital beds reflect to some extent the level of infrastructure in a region. Based on the above analysis, 11 indicators were finally formed as the relevant indicators for the evaluation of urban human settlements, as shown in

Table 1. Relevant indicators of urban human settlements evaluation.

Criterion Layer	Indicator Layer	Unit of Measurement	Code
Living environment	Residential floor area per capita	square meter	X1
	Urban population density	people/sq km	X2
Ecological environment	Green coverage of built-up areas	%	X3
	The area of green space in parks per capita	square meter	X4
	Household garbage removal and transportation	10,000 tons	X5
Infrastructure	Number of buses per 10,000 people	set	X6
	Urban road area per capita	square meter	X7
	Urban road area per capita	piece	X8
Social stability	Jobless rate	%	X9
Education	The number of college students per 10,000 people	person	X10
	There are public libraries per capita	book/person	X11

.

(2)

Relevant indicators for economic development evaluation

Similarly, economic development is embodied through four indicators: economic strength, industrial structure, economic extroversion, and residents’ income and consumption. Per capita GDP and per capita local fiscal revenue represent the economic strength and development level of the region. The weight of the H industry and the proportion of employees represent the industrial structure of the region. The total import and export volume and foreign exchange income from tourism represent the external development of the region’s economy and the level of foreign exchange earnings. The amount of per capita disposable income, the average salary of on-the-job workers, and the per capita consumption expenditure represent the per capita consumption capacity and income level of the region. Based on the above analysis, 11 relevant indicators of economic development evaluation are formed, as shown in

Table 2. Indicators related to economic development evaluation.

Criterion Layer	Indicator Layer	Unit	Code
Economic	GDP per capita	RMB/person	X12
	Per capita local fiscal revenue	RMB/person	X13
Industrial structure	The secondary sector accounts for a share of GDP	%	X14
	The tertiary sector accounts for a share of GDP	%	X15
	Proportion of employment in the secondary sector	%	X16
	Proportion of employment in the tertiary sector	%	X17
The degree of economic extroversion	Foreign exchange earnings from tourism	Million dollars	X18
	Total imports and exports	Million dollars	X19
Household income consumption	Disposable income per capita	Yuan	X20
	The average salary of on-the-job employees	Yuan	X21
	Consumption expenditure per capita	Yuan	X22

.

(3)

Coupling and coordination evaluation index system of urban human settlements and economic development

According to the above selected evaluation indexes related to urban human settlements and economic development, the coupling and coordination evaluation index system of urban human settlements and economic development is constructed, as shown in

Table 3. Evaluation index system of coupling and coordination between urban human settlements and economic development.

System Layer	Criterion Layer	Indicator Layer	Indicator Direction
Urban human settlements	S1: Living environment	X1: Residential floor area per capita	+
		X2: Urban population density	-
	S2: Ecological environment	X3: Green coverage of built-up areas	+
		X4: The area of green space in parks per capita	+
		X5: Household garbage removal and transportation	+
	S3: Infrastructure	X6: Number of buses per 10,000 people	+
		X7: Urban road area per capita	+
		X8: Urban road area per capita	+
	S4: Social stability	X9: Jobless rate	-
	S5: Education	X10: The number of college students per 10,000 people	+
		X11: There are public libraries per capita	+
Economic development	S6: Economic	X12: GDP per capita	+
		X13: Per capita local fiscal revenue	+
	S7: Industrial structure	X14: The secondary sector accounts for a share of GDP	+
		X15: The tertiary sector accounts for a share of GDP	+
		X16: Proportion of employment in the secondary sector	+
		X17: Proportion of employment in the tertiary sector	+
	S8: The degree of economic extroversion	X18: Foreign exchange earnings from tourism	+
		X19: Total imports and exports	+
	S9: Household income consumption	X20: Disposable income per capita	+
		X21: The average salary of on-the-job employees	+
		X22: Consumption expenditure per capita	+

.

3.2. Model Building

When the system is in a benign coupling state, the systems are interdependent and mutually promoting, and the coupling coordination is used to represent the benign interaction between two or more systems, and the magnitude of the benign coupling degree represents the coordination between the two systems. Urban human settlements and economic development is a complex system composed of a variety of elements, the various elements within the system affect each other and interact, in the process of the development of the overall system, some elements gradually occupy a dominant position, under the effect of these leading factors, the overall system through benign coupling to the state of orderly development. The coupling and coordination of regional urban human settlements and economic development refers to the virtuous cycle process formed by the interaction and mutual influence between urban human settlements and economic development systems in 31 provinces, municipalities, and autonomous regions across the country. The coordinated development of the two systems is represented by the coupling degree and the coupling coordination degree, and the coupling coordination degree model of the interaction between the two systems of urban human settlements and economic development is constructed.

Before calculating the coupling coordination degree, it is necessary to select an appropriate method to calculate the index weight, and synthesize the index information through the appropriate evaluation function to obtain the evaluation index of each system, and then construct the coupling degree model and the coupling coordination degree model.

(1)

Metric weight calculation

In this paper, the objective weighting method is chosen to determine the index weight. The entropy method is an accurate and practical method to determine the weight according to the amount of information provided by the observation value of the index, which can scientifically and reasonably reflect the contribution of each index to the evaluation goal. In view of the fact that the evaluation indicators in this study are all derived from authoritative statistical data and have sufficient objective data support, the entropy method is used to determine the index weight.

The first step is to standardize the data. Firstly, the initial index matrix $={{(X}_{ij})}_{m\times n}$ was constructed with $m$ research objects and $n$ evaluation indicators, and the data should be standardized in order to eliminate the influence and achieve data comparability due to the differences in the dimension, magnitude, and positive and negative orientation of each index. In this paper, the initial data are dimensionless by range standardization:

$$Z_{ij}={\displaystyle \frac{X_{ij}-min\left\{X_{ij}\right\}}{max\left\{X_{ij}\right\}-min\left\{X_{ij}\right\}}},$$

(1)

$$Z_{ij}={\displaystyle \frac{max\left\{X_{ij}\right\}-X_{ij}}{max\left\{X_{ij}\right\}-min\left\{X_{ij}\right\}}},$$

(2)

where $Z_{ij}$ is the normalized data matrix and $X_{ij}$ data are the original data matrix. $max\left\{X_{ij}\right\}$ and $min\left\{X_{ij}\right\}$ represent the maximum and minimum values of the indicator $j$, respectively. When the indicator is a positive indicator, that is, the higher the indicator value, the higher the level, Formula (1) is used for data processing, and when the indicator is a negative indicator, Formula (2) is used for data processing, and the maximum value of the normalized matrix value is one and the minimum value is zero.

The second step uses the entropy method to calculate the index weights. Firstly, the contribution of the indicator $j$ in region $i$ is calculated during the sample period:

$$P_{ij}={\displaystyle \frac{Z_{ij}}{{\sum }_{i=1}^n{Z}_{ij}}}(\mathrm{i}=1,2,\dots \mathrm{n}; \mathrm{j}=1,2,\dots \mathrm{m}),$$

(3)

Calculate the entropy of the indicator $j$:

$$E_j=-k{\sum }_i^m{P}_{ij}ln{P}_{ij}$$

(4)

where $k=1/lnm$ is a constant.

Calculate the entropy redundancy of the indicator $j$:

$$d_j=1-E_j$$

(5)

Calculate the weight of indicator $j$:

$$W_j={\displaystyle \frac{d_j}{{\sum }_{i=1}^n{d}_j}}$$

(6)

The third step is to calculate the comprehensive evaluation index. After the index is weighted, the linear weighting method is used to calculate the comprehensive evaluation value. Let $x_i$(i = 1,2,3,…k) and $y_i$ (i = 1,2,3,…l) be the evaluation indexes of urban human settlements and economic development, respectively, and construct a comprehensive evaluation function model of the two systems:

$$F\left(x\right)={\sum }_{i=1}^k{a}_j\times x_i,$$

(7)

$$G\left(y\right)={\sum }_{i=1}^l{b}_j\times y_i,$$

(8)

In the formula, $F\left(x\right)$ and $G\left(y\right)$ represent the comprehensive evaluation values of urban human settlements and economic development, $a_j$ and $b_j$ are the weights of each index of the urban human settlements and economic development system, and $x_i$ and $y_i$ represent the standardized values of each index in the system.

(2)

Coupling degree model

The coupling degree model should be selected before constructing the coupling coordination degree model. Coupling is a measure of how well a system or feature interacts with each other. The greater the degree of coupling, the stronger the interaction between the systems. The coupling function is expressed as:

$$C_n=n,$$

(9)

where $n$ is the adjustment coefficient, $u_i$ (i = 1,2,3,…, m) is the evaluation value of each system.

In this paper, the coupling degree values of the two systems of urban human settlements and economic development are calculated, so that $u_1$ and $u_2$ are $f\left(x\right)$ and $g\left(y\right)$, respectively, and $n$ is two, and the coupling degree model of the two systems is constructed:

$$C={\left\{{\displaystyle \frac{f\left(x\right)g\left(y\right)}{{\left[f\left(x\right)+g\left(y\right)\right]}^2/4}}\right\}}^{{\displaystyle \frac{1}{2}}},$$

(10)

When $C$ = 1, it indicates that the two systems are in the best coupling mode, and the coordination between the two systems is better. When $C$ = 0, it indicates that there is no mutual relationship between the various elements in the system, and the coordination between the systems is relatively poor. Drawing on existing research results and referring to the actual situation of this study, the coupling degree of urban human settlements and economic development system is divided into four categories according to the numerical size, and the division is shown in

Table 4. Coupling types.

Coupling Degree	Coupling Type	Features
$0\le \mathrm{C}\le 0.3$	Low-level coupling	Low system correlation
$0.3\le \mathrm{C}\le 0.5$	Antagonistic coupling	There are mutual constraints between systems
$0.5\le \mathrm{C}\le 0.8$	Run-in coupling	Benign inter-system interactions
$0.8\le \mathrm{C}\le 1$	High-level of coupling	High-quality interaction

.

(3)

Coupling coordination degree model

The coupling degree can only show the interaction between the systems, but cannot show the state of the overall coordinated development, and cannot reflect whether the systems are at a high level of mutual promotion or a low level of mutual constraints. Analyzing the level of coupling coordination development of the two systems only through the coupling degree may lead to the inconsistency between the actual analysis results and the real situation, for example, when the development of each system is at a low level and the gap between the evaluation values is small, there will be a false high phenomenon in the coupling degree calculation, and a high coupling degree is still obtained. The coupling coordination degree can well compensate for this phenomenon in the coupling degree calculation. The coupling coordination degree is a physical quantity that further measures the overall coordinated development level of the system on the basis of measuring the interaction and mutual influence degree of each system, and its specific model is as follows:

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

(11)

$$T=\partial f\left(x\right)+\beta g\left(y\right),$$

(12)

where $D$ is the coupling coordination degree; $T$ is the comprehensive coordination index between the two systems, which reflects the overall efficiency or level between the two systems; $f\left(x\right)$ represents the comprehensive evaluation value of urban human settlements, and $g\left(y\right)$ represents the comprehensive evaluation value of economic development; $\partial$ and $\beta$ are the weights of each system in the overall coordinated development, and the sum of the two is one.

At present, there is no unified definition of the weights of $\partial$ and $\beta$, and generally speaking, when the influence and contribution of each system to the overall system development are equal or the differences in each system cannot be accurately compared, they can be considered equally important. The study believes that in the process of national economic development, urban human settlements and economic development play an equally important role, jointly promoting the high-quality and sustainable development of the economic and social level, so the two are given equal weight, that is, $\partial$ is equal to $\beta$ is equal to 0.5 (

Table 5. Coupling coordination types.

Coordination Type	Coupling Coordination	Grade Type
Disorder classes	$0<\mathrm{D}<0.2$	Severe dysregulation
	$0.2\le \mathrm{D}<0.4$	Moderate dysregulation
Transition classes	$0.4\le \mathrm{D}<0.6$	Barely coordinated
Coordination classes	$0.6\le \mathrm{D}<0.8$	Good coordination
	$0.8\le \mathrm{D}<1$	High-quality coordination

).

(4)

Research objects and data sources

The purpose of this paper is to study the level and coordination between urban human settlements and economic development in China. Considering that the relevant research data in Taiwan, Hong Kong, and Macao are difficult to obtain or lack seriously, this paper finally selected 31 provinces, municipalities and autonomous regions in China as the research object, and took 2011–2021 as the research period for comprehensive measurement. The data of each evaluation index are derived from the China Statistical Yearbook, China Environment Statistical Yearbook, China Land and Resources Statistical Yearbook, China City Statistical Yearbook, and the Statistical Bulletin of National Economic and Social Development of various provinces, municipalities and autonomous regions. For the missing data in individual years, the mean and regression substitution methods are mainly used.

4. Empirical Analysis

4.1. Analysis of Coupling Coordination Level Between Urban Human Settlement Environment and Economic Development

(1)

Evaluation results of the coupling degree and coordination degree of the urban human settlement environment and economic development

Based on the panel data of 31 provinces and cities from 2011 to 2021, the coupling degree and coupling coordination degree of the two systems are obtained by Formulas (1)–(10). The coupling degree calculation results are shown in

Table 6. Coupling degree between the urban human settlement environment and economic development in various provinces and cities from 2011 to 2021.

Province	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Beijing	0.988	0.994	0.995	0.996	0.998	0.999	0.999	1.000	1.000	1.000	0.995
Tianjin	0.958	0.977	0.985	0.995	0.998	0.999	0.996	0.997	0.998	0.986	0.989
Hebei	0.799	0.822	0.838	0.852	0.862	0.872	0.887	0.896	0.900	0.903	0.911
Shanxi	0.851	0.877	0.885	0.895	0.897	0.889	0.909	0.929	0.922	0.918	0.931
Nei Monggol	0.877	0.890	0.897	0.902	0.916	0.921	0.916	0.933	0.942	0.937	0.950
Liaoning	0.903	0.919	0.930	0.909	0.888	0.903	0.915	0.927	0.928	0.923	0.924
Jilin	0.792	0.832	0.858	0.859	0.869	0.872	0.885	0.884	0.875	0.863	0.867
Heilongjing	0.820	0.844	0.859	0.873	0.871	0.870	0.883	0.893	0.891	0.868	0.877
Shanghai	0.944	0.998	1.000	0.999	0.998	0.995	0.995	0.993	0.992	0.992	0.988
Jiangsu	0.988	0.989	0.981	0.982	0.983	0.980	0.984	0.989	0.988	0.989	0.995
Zhejiang	0.971	0.973	0.979	0.979	0.982	0.972	0.978	0.981	0.982	0.988	0.996
Anhui	0.840	0.864	0.876	0.884	0.901	0.913	0.917	0.930	0.934	0.936	0.950
Fujian	0.960	0.963	0.973	0.976	0.981	0.987	0.989	0.972	0.972	0.979	0.984
Jiangxi	0.742	0.783	0.819	0.852	0.876	0.875	0.868	0.879	0.872	0.875	0.870
Shandong	0.903	0.910	0.920	0.929	0.923	0.927	0.933	0.940	0.941	0.945	0.962
Henan	0.835	0.849	0.864	0.875	0.888	0.887	0.885	0.898	0.902	0.902	0.914
Hubei	0.815	0.834	0.853	0.868	0.885	0.891	0.912	0.925	0.930	0.919	0.941
Hunan	0.844	0.865	0.884	0.891	0.897	0.906	0.922	0.914	0.918	0.914	0.921
Guangdong	0.999	0.997	0.993	0.994	0.995	0.996	0.996	0.997	0.998	0.997	0.994
Guangxi	0.784	0.816	0.841	0.854	0.874	0.881	0.882	0.894	0.906	0.905	0.911
Hainan	0.759	0.767	0.836	0.856	0.879	0.888	0.897	0.924	0.931	0.935	0.945
Chongqing	0.879	0.889	0.897	0.919	0.927	0.935	0.938	0.941	0.940	0.962	0.962
Sichuan	0.819	0.839	0.859	0.880	0.877	0.900	0.905	0.914	0.915	0.922	0.937
Guizhou	0.845	0.831	0.840	0.832	0.849	0.847	0.861	0.874	0.867	0.875	0.900
Yunnan	0.872	0.901	0.905	0.902	0.927	0.920	0.937	0.953	0.959	0.966	0.971
Xizang	0.805	0.865	0.890	0.883	0.892	0.952	0.952	0.971	0.962	0.952	0.955
Shaanxi	0.863	0.865	0.883	0.891	0.898	0.901	0.923	0.937	0.944	0.951	0.965
Gansu	0.713	0.738	0.750	0.768	0.803	0.818	0.829	0.841	0.839	0.842	0.851
Qinghai	0.788	0.816	0.854	0.863	0.887	0.880	0.882	0.898	0.889	0.887	0.899
Ningxia	0.762	0.783	0.790	0.791	0.810	0.826	0.839	0.854	0.864	0.874	0.899
Xinjiang	0.804	0.846	0.877	0.880	0.864	0.857	0.879	0.891	0.888	0.885	0.899

.

It can be seen that the coupling degree between urban human settlements and economic development in China is at a relatively high level. Among them, Beijing, Tianjin, Shanghai, Jiangsu, Zhejiang, Fujian, Shandong, and Guangdong provinces have a strong interaction relationship between urban human settlements and economic development, and the coupling score of the two systems has been maintained at a high value of 0.9, and the coupling value reaches 1 in some years, achieving an orderly state. The coupling degree evaluation scores of Shanxi, Inner Mongolia, Liaoning, Heilongjiang, Anhui, Henan, Hubei, Hunan, Chongqing, Sichuan, Guizhou, Yunnan, Tibet, Shaanxi, and Xinjiang in the past 11 years are all greater than 0.8. The coupling development of urban human settlements and economic development in these regions is at a high level, and the system is closely linked. In most other provinces and cities, the coupling level between the urban human settlement environment and economic development is maintained above 0.7. Eight provinces, Hebei, Guangxi, Hainan, Jilin, Gansu, Qinghai, Ningxia, and Jiangxi, had coupling values lower than 0.8 in 2011. With the continuous improvement of system interaction, their coupling values were all higher than 0.8 by the end of the study, entering the stage of high-level coupling and gradually showing high-quality interaction between the two systems.

The country is further divided into three regions: eastern, central, and western regions for comparison, and the average coupling degree between urban human settlements and economic development systems of each province in the region is taken, respectively, to measure the coupling level of the two systems in each region, as shown in Figure 3.

On the whole, the coupling degree of each region showed a trend of fluctuating growth, and gradually advanced from the benign development of the system to the deep interaction, with a high degree of order. Among them, the coupling state of the eastern, central, and western regions and the national urban human settlement environment and economic development system will reach the highest level in 2021. The eastern region is the region with the most active economic capital and the most abundant resources, and the coupling development degree of the two systems leads the country, but with the migration of time, the advantage gap between the eastern region and the central region, the western region and the country first expands and then narrates. The coupling rate of the two systems in the western region is relatively large, from slightly lagging behind the three regions and the national average level in the early stage of the study to exceeding the central region in the later stage, the system coupling state is good, and the gap with the national average level is becoming smaller and smaller.

The coupling degree can only reflect the degree of correlation between the system and the strength of the function, and more reflects the interaction between the two. Therefore, it is necessary to further analyze the level of coupling coordination degree to explore the coordinated development of China’s inter-regional human settlements environment and economy. The score results of coupling coordination degree are shown in

Table 7. The coupling coordination degree between the urban human settlement environment and economic development in each province during 2011–2021.

Province	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Beijing	0.654	0.660	0.677	0.686	0.694	0.708	0.730	0.751	0.763	0.742	0.762
Tianjin	0.514	0.534	0.555	0.564	0.572	0.580	0.590	0.567	0.588	0.593	0.620
Hebei	0.349	0.365	0.379	0.390	0.401	0.414	0.433	0.450	0.467	0.475	0.498
Shanxi	0.339	0.365	0.383	0.382	0.384	0.393	0.414	0.435	0.452	0.460	0.495
Nei Monggol	0.392	0.416	0.440	0.457	0.466	0.478	0.482	0.493	0.509	0.513	0.537
Liaoning	0.455	0.482	0.498	0.491	0.478	0.484	0.496	0.510	0.517	0.522	0.546
Jilin	0.351	0.375	0.391	0.407	0.413	0.430	0.434	0.450	0.459	0.470	0.491
Heilongjiang	0.336	0.350	0.360	0.366	0.361	0.373	0.387	0.404	0.422	0.433	0.462
Shanghai	0.647	0.645	0.646	0.658	0.679	0.697	0.720	0.741	0.751	0.758	0.795
Jiangsu	0.565	0.587	0.581	0.603	0.618	0.630	0.654	0.674	0.685	0.695	0.729
Zhejiang	0.528	0.546	0.562	0.582	0.603	0.597	0.619	0.637	0.657	0.659	0.687
Anhui	0.343	0.374	0.394	0.412	0.429	0.445	0.469	0.491	0.507	0.526	0.552
Fujian	0.435	0.465	0.482	0.502	0.513	0.526	0.551	0.547	0.570	0.570	0.602
Jiangxi	0.299	0.326	0.342	0.357	0.372	0.384	0.406	0.425	0.448	0.458	0.492
Shandong	0.465	0.489	0.504	0.512	0.530	0.542	0.559	0.576	0.590	0.601	0.634
Henan	0.299	0.332	0.350	0.366	0.383	0.398	0.422	0.443	0.460	0.471	0.491
Hubei	0.362	0.388	0.410	0.434	0.458	0.473	0.485	0.504	0.521	0.518	0.553
Hunan	0.328	0.346	0.361	0.383	0.402	0.419	0.435	0.466	0.494	0.504	0.526
Guangdong	0.624	0.647	0.661	0.676	0.688	0.698	0.722	0.747	0.760	0.757	0.785
Guangxi	0.314	0.342	0.363	0.379	0.402	0.416	0.439	0.458	0.480	0.488	0.507
Hainan	0.358	0.390	0.396	0.414	0.428	0.436	0.455	0.468	0.486	0.491	0.519
Chongqing	0.393	0.427	0.442	0.463	0.474	0.482	0.500	0.517	0.533	0.529	0.564
Sichuan	0.338	0.369	0.388	0.403	0.422	0.431	0.452	0.478	0.504	0.512	0.533
Guizhou	0.257	0.298	0.328	0.355	0.373	0.390	0.406	0.422	0.441	0.449	0.460
Yunnan	0.308	0.332	0.366	0.384	0.395	0.415	0.433	0.453	0.476	0.488	0.512
Xizang	0.291	0.309	0.323	0.354	0.403	0.382	0.404	0.415	0.430	0.446	0.465
Shaanxi	0.363	0.391	0.407	0.424	0.443	0.449	0.465	0.487	0.498	0.504	0.531
Gansu	0.276	0.302	0.331	0.348	0.359	0.372	0.384	0.403	0.415	0.424	0.444
Qinghai	0.320	0.341	0.357	0.377	0.385	0.396	0.411	0.428	0.448	0.464	0.479
Ningxia	0.360	0.383	0.407	0.429	0.441	0.450	0.464	0.477	0.483	0.488	0.500
Xinjiang	0.340	0.366	0.389	0.407	0.427	0.435	0.453	0.468	0.475	0.473	0.493

.

On the whole, the degree of coupling coordination between the urban human settlement environment and economic development is far lower than the degree of coupling between them. It shows that although the interaction between the urban human settlement environment and economic development is relatively close, the coordinated development of the two systems is not good. In order to more intuitively compare the trend and gap of the coupling degree and coordination degree of the two systems in the country, as shown in Figure 4. The figure shows that the coordination degree between the urban human settlement environment and economic development increases evenly from 2011 to 2021, and the difference between the coupling degree and the coupling degree decreases slightly. However, the mean value of the coupling coordination degree between the two systems in China in 2021 is still below 0.6, and the overall state is still barely coordinated according to the divided coupling coordination level.

(2)

Analysis of time-series changes in the coupling coordination level between urban human settlement environment and economic development

(1). The sequential changes in the coupling and coordination between urban human settlements and economic development in various provinces and cities.

Table 7 shows that from 2011 to 2021, the coupling coordination level of the urban human settlement environment and economic development in various provinces and cities in China has increased steadily, but the growth rate is different. The range of coupling coordination degree decreased from 0.396 in 2011 to 0.351 in 2021, narrowing the regional gap. Anhui had a low initial value but the fastest growth rate, rising from 19 to 13, driven significantly by the economy. Gansu has been regressing for a long time, mainly due to the low development level of the urban human settlement environment. Beijing took the lead at the beginning and was overtaken by Shanghai later, but the coupling coordination value from 2011 to 2019 still ranked first.

According to the classification standard of the coupling coordination, in 2011, 21 provinces in China were moderately disordered, 6 provinces were barely coordinated, and 3 provinces were well coordinated. By 2021, the proportion of well-coordinated provinces will rise to 25.8%, there will be no moderately dysfunctional provinces, 21 provinces had coupling coordination degrees greater than 0.4, and the remaining provinces were barely coordinated. Gansu from the moderate imbalance to the edge of barely coordinated, Liaoning has always been barely coordinated. By 2021, 19 provinces will be barely coordinated and 8 provinces will be well coordinated. Figure 5 shows that the average coupling degree and coordination degree of all provinces and cities from 2011 to 2021 are similar, and they are in a barely coordinated state. The coordinated development of Beijing, Shanghai and Guangdong is leading, followed by Zhejiang and Jiangsu, and the imbalance is moderate in Hebei, Jiangxi, Guizhou, Tibet, and Gansu. In general, from 2011 to 2021, the coupling degree and coordination degree of provinces and cities are similar, with an average of 0.4–0.6, barely coordinated. The coordination degree of Beijing, Shanghai, and Guangdong took the lead, with an average value greater than 0.7, indicating good coordination. Zhejiang and Jiangsu followed, with an average value greater than 0.6. The mean value of Heilongjiang, Jiangxi, Guizhou, Tibet, and Gansu was greater than 0.4, with moderate imbalance.

(2). The sequential change in the coupling coordination between the overall urban human settlement environment and economic development (Figure 6).

From the perspective of the coupling coordination level of the whole country and the three major regions, the mean value of the coupling coordination between urban human settlements and economic development nationwide increased from 0.39 to 0.56, from about 0.51 to 0.65 in the eastern region, from 0.33 to 0.51 in the central region, and from 0.33 to 0.5 in the western region. In the central and western regions, the growth rate of the coupled coordination mean is similar, from the moderate imbalance to the barely coordinated stage, showing a steady and gradual growth trend. Among them, the coupling coordination value of the two systems in the country broke through 0.4 in 2012, entering the stage of reluctant coordination, and the central region achieved a leap in the type of coordinated development in 2013. The eastern region has always been the “leader” in the coupling and coordination level of urban human settlements and economic development. As early as 2011, the eastern region achieved a reluctant coordination between the two systems, and finally broke through 0.6 in 2018 to enter a good coordination stage, indicating that the urban human settlements and economic development in the eastern region are at a relatively mature development level compared with the whole country and the other two regions, and have maintained a certain development speed and are in a far-ahead position. The level of coupling coordination in the central and western regions is relatively similar, and has always been below the national average level, but it also maintains a stable growth trend. In 2015, the average of the coupling coordination degree in the central and western regions was greater than 0.4, and the coupling coordination degree in the central and western regions has successfully moved from a moderate imbalance to a barely coordinated transition state, and the coupling coordination relationship between urban human settlements and economic development has become increasingly close.

(3)

Analysis of the spatial pattern of the coupling coordination level between the urban human settlement environment and economic development

In order to further explore the development and evolution characteristics of the coupling coordination level between the urban human settlement environment and economic development in 31 provinces and cities in China, and directly and clearly demonstrate their spatial evolution, the ArcGIS10.2 software is used according to the coupling coordination D-value data and classification standards of three time sections in 2011, 2016, and 2021. A coordinated spatial distribution map of urban human settlements and economic development in 31 provinces of China was drawn.

According to Figure 7, Figure 8 and Figure 9, the coupling and coordinated development of the urban human settlement environment and economic development in 31 provinces (municipalities and districts) has obvious regional imbalance, which crosses the three development levels of moderate imbalance, barely coordinated and well coordinated. In the three selected time nodes, the provinces in the coordinated state are concentrated in the eastern region, and most of them belong to the three major urban agglomerations of Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta.

In 2011, the coordination between the urban human settlement environment and economic development system in most provinces and cities in China was in a stage of moderate imbalance, and only six provinces, Liaoning, Shandong, Tianjin, Jiangsu, Zhejiang, and Fujian, were barely coordinated, all located in the eastern region. In 2016, the eastern regions of Beijing, Jiangsu, Shanghai, and Guangdong were in a good state of coordination. In the central region, except for Shanxi, Heilongjiang, Jiangxi, and Henan, which are still in the stage of moderate imbalance, other regions have entered the stage of reluctant coordination. The four provinces that have jumped to a good coordination level are mainly Beijing, Jiangsu, Shanghai, and Guangdong. In the western region, except for Sichuan, Chongqing, Yunnan, Gansu, and Qinghai, which are in a moderate state of imbalance, the rest of the region has risen to a barely coordinated state. By 2021, Tianjin, Shandong, Zhejiang, and Fujian will be added to the well-coordinated state, all of which are located in the eastern region, indicating that the coordination degree between urban human settlements and economic development in the eastern region of China is relatively high, the development process is relatively fast, and the diffusion effect of the well-coupled and coordinated development of the two systems is relatively significant. The central region and the western region have entered the process of barely coordinated development, among which Heilongjiang and Gansu have entered barely coordinated development from the disordered state in 2018. The provinces with unbalanced coupling between the urban human settlement environment and economic development are mainly distributed in the central and western regions. Until 2021, the development gap between the central and western regions will gradually narrow and the development will be relatively stable. In contrast, there is still a large gap with the eastern region. From the spatial evolution and distribution of the coupling and coordination development types of provinces and cities, it is obvious that the coupling and coordination of regional urban human settlements and economic development in China show a gradient a decreasing trend from East to West, and the overall spatial distribution pattern is “high in the east and low in the west”.

4.2. Spatial Correlation Analysis of the Coupling Coordination Level Between the Urban Human Settlement Environment and Economic Development

It can be roughly observed from the spatio-temporal evolution analysis that the regions adjacent to the provinces with a higher level of coupling coordination between urban human settlement environment and economic development also have a good development trend, and there is a high and low clustering situation in space. In order to further study this feature, exploratory spatial data analysis (ESDA) is used to reveal its spatial interaction mechanism.

(1)

Spatial correlation analysis method

Tobler’s first law of geography states that everything is correlated, revealing that things near each other are more correlated than things far away. Due to the influence of economic relations, geographical proximity, cultural origin, and other factors, there is a correlation between many variables that cannot be ignored, that is, spatial correlation. Through the spatial autocorrelation analysis, the spatial correlation can be effectively analyzed. Spatial autocorrelation can be divided into global and local spatial autocorrelation. Among them, the global spatial autocorrelation is mainly used to verify the spatial pattern of a certain element in the whole region and show its distribution in the global spatial sequence to analyze whether there is spatial correlation. Global Moran’s I Index, Geary’s C index and so on are often used to measure. Local spatial autocorrelation examines the degree of correlation between the geographical phenomenon or attribute value of a local unit and the same phenomenon of the local unit in the neighborhood, which is usually analyzed by using the local Moran’s index and presented by scatter plot and cluster plot. Usually, the global Moran’s I index is calculated first to confirm whether clusters or outliers occur in the research space. Since the global autocorrelation index can only explain the overall distribution and correlation status of a certain phenomenon within the research scope, the local autocorrelation index is introduced to measure the correlation status of the local space and further explain the specific region where clusters or outliers occur. Hence, locate the spatial distribution positions of clusters with similar properties. There are three kinds of spatial correlation: positive correlation, negative correlation, and random distribution. The spatial positive correlation means that the values of the variables become more and more similar as the spatial distance and location of the distribution shrink and cluster, and the observed values in different regions show a trend of convergence. Negative correlation is on the contrary, random distribution means that the distribution of variable values in space is completely random, and there is no regularity in high and low values.

(1). Moran’s I index of global space is used to measure the overall spatial agglomeration distribution trend of coupling and coordination between urban human settlements and economic development. The specific expression of Moran’s I index is:

$$\mathrm{M}\mathrm{o}\mathrm{r}\mathrm{a}\mathrm{n}’\mathrm{s} \mathrm{I}={\displaystyle \frac{{\sum }_{i=1}^n{\sum }_{j=1}^n{w}_{ij}\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)}{S^2{\sum }_{i=1}^n{\sum }_{j=1}^n{w}_{ij}}},$$

(13)

where $S^2{\displaystyle \frac{1}{n}}{\sum }_{i=1}^n{\left(x_i-\overline{x}\right)}^2$ represents the sample variance, n is the total number of samples, $x_i$ represents the attribute value of region $i$, $\overline{x}$ represents the average value, and $w_{ij}$ is the spatial weight matrix.

In this paper, the inverse distance weight matrix is selected to calculate the spherical geographical distance between province $i$ and province $j$ based on the latitude and longitude data of each province, and the weight matrix is constructed by taking the reciprocal of it, which is expressed as follows:

$$W_{ij}\left\{\begin{array}{c}{\displaystyle \frac{1}{d_{ij}}},wheni\ne j\\ 0,wheni=j\end{array}\right.,$$

(14)

The value range of the global Moran’s I index is [−1, 1]. When the significance level is given, if the global Moran’s I index value is greater than zero, it means that there is a spatial positive correlation between the coupling coordination level of the two systems. The value of Moran’s I index is less than zero, there is a spatial negative correlation between the development level of the two systems. When the value of the Moran’s I index is zero, it indicates that the spatial distribution is random and there is no spatial correlation. Generally, the Z-test is used to perform a statistical test on the global Moran index, and the formula is as follows:

$$Z={\displaystyle \frac{\mathrm{M}\mathrm{o}\mathrm{r}\mathrm{a}\mathrm{n}’\mathrm{s}\mathrm{I}-\mathrm{E}\left(\mathrm{I}\right)}{\sqrt{VAR\left(I\right)}}},$$

(15)

where $E={\displaystyle \frac{-1}{n-1}}$, $E$ is the mathematical expectation, and VAR is the variance.

(2). The formula for calculating local Moran’s I is as follows:

$$I_i={\displaystyle \frac{\left(x_i-\overline{x}\right)}{S^2}}{\sum }_{j=1}^n{W}_{ij}\left(x_i-\overline{x}\right)=Z_i{\sum }_{j=1}^n{W}_{ij}{Z}_j,$$

(16)

where $Z_i$ is the regional observed value after standardization; ${\sum }_{j=1}^n{W}_{ij}{Z}_j$ is the spatial lag value, that is, the weighted average of the neighborhood around the observed value.

(2)

The coupling and coordination spatial correlation characteristics of urban human settlements and economic development

According to the calculation result of the coupled coordination value of 31 provinces and cities in our country, the spatial autocorrelation index is calculated by Arc GIS and GeoDa 1.22 software platform tools, and the Moran scatter chart and Lisa agglomeration chart are drawn. The global and local spatial autocorrelation analysis of the coupling coordination degree between urban human settlement environment and economic development in China’s provinces and cities from 2011 to 2021 is carried out.

(1). Global spatial correlation analysis.

Firstly, the global spatial Moran Index is calculated, and the Moran’s I calculation results from 2011 to 2021 are shown in

Table 8. Global Moran’s I index of national coupling coordination degree from 2011 to 2021.

Year	I	z	p
2011	0.1376	1.8524	0.03
2012	0.1373	1.8255	0.03
2013	0.1299	2.1085	0.03
2014	0.1165	1.9532	0.04
2015	0.1058	1.9257	0.03
2016	0.1198	2.0799	0.02
2017	0.1231	2.1261	0.02
2018	0.1165	2.1788	0.01
2019	0.1262	1.9902	0.03
2020	0.1336	2.3195	0.01
2021	0.1535	2.0324	0.03

.

Through the global Moran index test, it is found that the values of the inverse distance weight matrix measure are consistent with reality, and the significance test is passed, indicating that the measure results are reliable. During the 2011–2021 study period, the Moran’s I value of the coupling coordination degree of the two systems of provinces and cities in China is greater than 0, and the P value is less than 0.05, that is, the possibility of randomly generating this clustering pattern is less than 5%. Through the 95% confidence test, the coupling coordination degree of urban human settlements and economic development in all provinces and cities in China has the characteristics of spatial positive correlation. There is a significant positive spatial interaction between adjacent units, that is, provinces with the same coupling and coordinated development level tend to be spatially clustered. According to the statistical results in Table 8, the value of the global Moran Index is in the range of 0.1–0.16. From the change trend of the value, the overall change range of the Moran’s I value of the coupling coordination degree of the two systems is small and tends to be stable from 2011 to 2021. It shows that the coupling and coordination of regional urban human settlements and economic development system in China presents a steady and gradual agglomeration development.

(2). Local spatial correlation analysis.

Local Moran’s I index was used to measure the correlation features between provinces and neighboring provinces, and was presented by Moran scatter plot and Lisa aggregation plot. Three research time nodes of 2011, 2016, and 2021 were selected to draw scatter plot and Lisa cluster plot, as shown in Figure 10.

The horizontal coordinate of the scatter plot represents the observed value $Z_i$ after standardization of the space unit itself, and the vertical coordinate represents the “lag” value of the space unit, that is, the index expected value $W_{Zi}$ of the adjacent unit determined by the matrix W after standardization. The first quadrant (H-H) indicates that not only the coupling coordination level in the region is higher, but also the coupling coordination level in the neighborhood is higher, showing a high-value cluster. The second quadrant (L-H) represents the situation where the low value is surrounded by the high value, that is, the coupling coordination level of itself is low but the coupling coordination level of the surrounding area is high. The third quadrant (L-L) is low-value agglomeration, that is, the coupling coordination values of itself and the surrounding areas are low. The research unit in the fourth quadrant (H-L) is characterized by low coupling coordination values in the local region but high coupling coordination values in the neighborhood, which is a situation where high values are surrounded by low values.

From the comparison between Figure 10 and Figure 11, it can be found that the provincial and municipal units located in the first quadrant (high-high type) and the second quadrant (low-high type) account for the majority. Compared to the third quadrant (low-low type) and the fourth quadrant (high-low type), this indicates that the aggregation areas of high values and low values are more significant, with less spatial difference. Although the Moran’s I scatter plot does not conduct a detailed test on the local Moran index for each area, the Lisa cluster map shows the spatial units that have passed the significance test at a given significance level, clarifying the positive correlation between the coupling coordination value in the urban human settlement environment and economic development space, and highlighting the spatial aggregation areas of high and low values.

5. Conclusions and Suggestions

5.1. Main Conclusions

The purpose of this paper is to study the coupling and coordination of urban human settlements and economic development in provincial areas of China, and further explore the factors affecting the coordination level of the two. Based on the coupling coordination degree model, this paper measures the coordination level of urban human settlement environment and economic development in 31 provinces and cities in China from 2011 to 2021, analyzes the spatio-temporal evolution, and uses the spatial autocorrelation method to analyze their spatial correlation characteristics. Due to objective reasons, data from Hong Kong, Macau, and Taiwan cannot be obtained, which leads to limitations in the data. Finally, the obstacle degree model and spatial econometric model are comprehensively used to conduct an empirical analysis of the internal and external environmental factors of the system, based on which countermeasures and suggestions are proposed to promote the overall coordinated development. The main research conclusions are as follows:

From the perspective of the spatio-temporal evolution of the coupling coordination level between urban human settlement environment and economic development, the coupling coordination level between the urban human settlement environment and economic development of all provinces and cities in China shows a stable development trend from 2011 to 2021, and has crossed the three stages of moderate imbalance, barely coordination and good coordination as a whole. Among them, the coupling and coordinated development of the two systems in Beijing, Shanghai, and Guangdong are in the forefront, while the average value of Heilongjiang, Jiangxi, Guizhou, Tibet, and Gansu is below 0.4, which is in a moderate imbalance state and lagging behind compared with other provinces. The eastern region has always been the “leader” of the urban human settlement environment and economic development level, and the gap with the central and western regions has gradually narrowed over time. The coupled and coordinated development level of the central and western regions is relatively similar, and has always been below the national average level, but also maintained a stable growth trend. The coupling and harmonious development of urban human settlements and economic development in China are mostly concentrated in Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta. In spatial distribution, the coupling coordination of the two systems decreases from East to West, forming a stepped pattern of “high in the east and low in the west”.

From the perspective of the coupling coordination spatial correlation between the urban human settlement environment and economic development, the coupling coordination degree of the urban human settlement environment and economic development in China has the characteristics of spatial positive correlation, and the provinces with the same coupling coordination development level tend to spatial agglomeration distribution. The global Moran’s I value has a small variation range and tends to be stable. Locally, the provinces with “low-high” type and “high-high” type are more than those with “high-low” type and “low-low” type, and the spatial difference is small. In addition to the expansion of high-value agglomeration, the low-value agglomeration area in western China has been further enlarged, and Qinghai province has been added. On the whole, the coupling coordination value of urban human settlements and economic development in each province has formed a two-level differentiation in the country, the eastern region is relatively high, while the central and western regions are relatively low, and the phenomenon of high-value accumulation and low-value collapse is distinct.

From the perspective of factors influencing the coupling coordination degree, within the system, the degree of economic extroversion, industrial structure, and residents’ income and consumption system have obvious impediments to coordinated development. Specific to the index level, the impediments of the total import and export, foreign exchange income from tourism, the proportion of employment in the tertiary industry, and per capita local fiscal revenue are at the forefront. In the external environment, economic strength, urbanization, scientific and technological innovation, and human capital have a positive effect on the coupling and coordination of regional urban human settlement environment and economic development, and have a two-way enhancement and promotion effect on the coupling and coordination development of urban human settlement environment and economic development.

From the results of the regional coupling and coordination level differences, coastal areas and some central regions have maintained a high level of coupling and coordination for a long time, while regions such as Tibet and Xinjiang have lower levels of coupling. The reasons can be attributed to the coastal areas and some central regions having a high level of urbanization, convenient transportation, focus on technological innovation and attracting talent and capital, and are key areas for economic development. In contrast, the western regions have harsh terrain, with Tibet being mostly highland mountains and Xinjiang having vast desert areas. The adverse terrain conditions lead to inconvenient transportation, slow economic development, which in turn affects their level of coupling and coordination.

Furthermore, by linking the results with existing theories and frameworks of urban development and regional economics, it is found that Beijing, Shanghai, and other cities lead in terms of coupling coordination levels. These areas are economically developed, have well-established infrastructure, and strong technological innovation capabilities, which contribute to a high level of urban resilience. In contrast, regions such as Tibet and Xinjiang have lower levels of coupling coordination and poorer urban resilience. From the perspective of economic base theory, in areas with a weak economic base, the development of the non-basic sector is restricted, which affects the overall level of coupling coordination. The high level of coupling coordination in regions with a strong economic base is due to their robust basic sectors, which create a significant amount of external demand, promote rapid economic growth, and drive the development of the non-basic sector.

5.2. Countermeasures and Suggestions

Based on the research conclusions drawn in this paper, the following policy recommendations are given:

(1). Transform government functions and provide institutional guarantees. Establish a policy and legal system to protect the ecological environment, accelerate the construction of environmental protection laws and regulations, strengthen publicity and education, strengthen government departments’ understanding of environmental protection and preventive measures, and prevent and crack down on environmental damage. Establish stricter industrial emission standards, promote green building standards, and strengthen measures for the protection of natural resources. The government needs to transform its functions, optimize the market mechanism, release the vitality of the market, incorporate environmental protection into the market mechanism, use market means to regulate, improve the property rights trading system, and encourage investment in environmental protection. The government can encourage companies to invest in environmental protection technology and equipment by offering tax relief and subsidies. We will improve the environment for innovation and entrepreneurship, establish and improve public service platforms, guide social innovation, strengthen cooperation between universities and enterprises, and promote the industrialization of innovation results. We will strengthen infrastructure construction, build modern cities, increase investment in public services such as transportation and medical care, and improve urban living conditions. Promote the construction of smart cities, and use information technology to improve urban management efficiency and service levels.

(2). Give play to the main role of enterprises and inject vitality into development.

Enterprises play a key role in the synergy between urban residential environment and economic development, not only providing employment and diversification services, but also economic driving force. Encourage companies to establish environmental management systems and regularly publish social responsibility reports, openly and transparently demonstrating their efforts in environmental protection. Stimulating corporate dominance is critical to balancing the two, and improving environmental innovation capabilities is especially critical. Companies need to go beyond economic interests, cultivate social responsibility, introduce environmentally friendly technologies, and innovate to solve environmental problems. At the same time, enterprises need to increase investment in scientific and technological innovation, enhance the ability of independent innovation, and pay attention to technology integration and re-innovation. The primary industry ensures food security and promotes modern agriculture, optimize the structure of the secondary industry, and improve the efficiency of resources and energy, allowing the tertiary industry to strengthen employment and promote healthy development.

(3). Focus on the needs of residents and improve the quality of life. The urban living environment and economic development are related to family well-being; the core is to meet the needs of residents. Improving people’s living standards is the key. By enhancing consumption power and optimizing consumption structure, economic growth will be driven, demand will be alleviated, and environmental improvement and economic prosperity will be promoted. Optimize community planning, increase green space area, and improve living environments. Strengthen the construction of public service facilities such as education and medical care to enhance service levels. We will improve the real estate market, optimize public supporting facilities, encourage innovation in products and services, foster consumption hotspots, develop consumer credit, and enrich people’s lives. Strengthen the social security system, increase financial support, build a multi-level security network, raise assistance standards, support charitable causes, create a harmonious urban environment, and promote sustainable economic development. Support non-profit organizations and social enterprise development, providing more public welfare services, such as environmental volunteer activities, community elderly care services, etc.