Geographical Detection Analysis and Spatiotemporal Disparity Characteristics of the Coupling Coordination Development between Urbanization and the Eco-Environment

Abstract

The study of the coupling coordinated relationship between urbanization and the eco-environment is important to promote regional high-quality development. This paper measured the coupling coordination degree between urbanization and the eco-environment from 2008 to 2020, using the coupling coordination model, depicted its spatiotemporal characteristics with ArcGIS, and explored its driving factors and impacts of the interaction between pairwise factors on it with geo-detection methods. The results show that the coupling coordination level becomes increasingly improved, but there exist widening spatial disparities, and in 2020, its spatial distribution presents characteristics of “decreasing from north to south” and “increasing from west to east”. According to their evolutionary characteristics of coupling coordination categories, cities can be classified into four types: “transition from basic misalignment to primary coordination”, “transition from primary coordination to intermediate coordination”, “transition from primary coordination to senior coordination”, “no hierarchical transition”. Currently, driving factors of industrial structure, opening to the outside world and technological innovation significantly affect the coupling coordination degree, and the interaction between pairwise factors is enhanced. Policies should be implemented in categories to improve the coupling coordination level. Regional cooperation, exchanges, and interconnection of resources and factors should be strengthened to jointly build a coordinated regional development pattern.

1. Introduction

The rapid development of urbanization has promoted the rapid rise in China’s economy. By the end of 2019, the urbanization rate of China’s permanent resident population reached 60.6%, exceeding 60% for the first time, and reached 64.7% by the end of 2021. Urbanization brings about economic and social prosperity, but also produces a series of environmental pollution problems. In March 2021, the document “The 14th Five-Year Plan and the Outline of the 2035 Vision goals for the National Economic and Social Development of the People’s Republic of China” clearly proposed to accelerate the citizenship process of migrant population from agricultural to nonagricultural sector, perfect the spatial layout of urbanization and comprehensively improve the quality of cities, to promote the implementation of the new-type urbanization strategy. On this basis, it is of great theoretical and practical significance to explore the coupling coordination relationship between regional urbanization and the eco-environment, and a way to promote the formation of a benign interaction and coordinated development relationship between the two, to realize the complementary advantages and collaborative interconnections between regions, and regional sustainable development.

2. Literature Review

In order to promote the coordinated and sustainable development of the economic system and natural ecosystem, scholars at home and abroad have conducted persistent, in-depth research and attained an abundance of research results. Works such as the book Silent Spring [1] and the research report “The Limits to Growth” [2] were the first collection of research results that studied eco-environmental issues. The Environmental Kuznets Curve (EKC) reveals that with the improvement of the urban economic development level, its eco-environmental quality presents an inverted U-shaped evolutionary pattern [3], which lays a foundation for the related research on urbanization and the eco-environment. Early studies have shown that there is an interaction mechanism between urbanization development and eco-environmental changes [4,5], and the coordinated development between the two can be realized through reasonable planning [6]. The construction of a theoretical framework including six basic laws such as the coupling fission law, the dynamic hierarchy law, and the interactive coupling effect between urbanization and the eco-environment in supercities agglomeration regions [7], provides theoretical guidance for the study on the coupling coordination relationship between urbanization and the eco-environment in China. Since then, national and foreign researchers have conducted extensive research on the interactive relationship between urbanization and the eco-environment from various aspects such as interdisciplinary, multi-perspectives and multiple regions. The research theme has been continuously refined, and the research process can be summarized into three stages: (1) the theoretical exploration stage—focuses on exploring the interactive coupling law of urbanization and the eco-environment; (2) the evaluation stage—focuses on evaluating the coupling coordination level and analyzing its spatiotemporal features; (3) the mathematical analysis stage—focuses on analyzing the driving factors of the coupling coordination level by utilizing models and statistical methods. In China, current research focuses primarily on exploring the interaction mechanism between urbanization and the eco-environment, and the factors affecting coupling coordination between them. The research scope is mainly focused on central cities [8,9], with plentiful papers on the Yangtze River Economic Belt [10,11,12], and some on the provincial level [13,14] and urban agglomeration level [15,16]. The discipline’s perspective mainly focuses on ecological economics, geography, spatial economics, multidisciplinary synthesis, etc. [17,18]. Research methods include the coupling coordination degree model, the DEA (data envelopment analysis) method, the comprehensive evaluation model, the regression model analysis, etc.

The interaction between urbanization and the eco-environment is a dynamic evolutionary relationship of coercion, dynamic coupling, and spiral development from low-level coordination to high-level coupling [19,20], and this dynamic coupling relationship generally goes through four stages: low-level coordination, opposition, friction, and high-level coordination [5]. Combined with the law of “Environmental Kuznets Curve” [3], it is known that when urbanization is at a low level of development, the eco-environment can purify the negative impact brought on by urbanization and provide sufficient natural resources for urban development, and the relationship between them is defined as low-level coordination. When the negative impact of urbanization on the eco-environment exceeds the threshold of the ecosystem carrying capacity, the relationship between them is antagonistic. Under the condition of human intervention (human-made pollution deterioration, policy intervention, etc.), the relationship between them is running-in. When the level of urbanization development reaches a certain level, its negative effect on the eco-environment begins to gradually decrease and its positive effect gradually increases. Under human intervention, eco-environmental purification capacity improves. Gradually, the relationship between them evolves from antagonism to friction and finally achieves a high-degree coupling coordination.

Urbanization is a comprehensive development system consisting of various dimensions of population, economic, society and space, while the eco-environment is a comprehensive development system comprising two dimensions of eco-environmental pressure and eco-environmental protection. The dynamic process of the coupling coordination between urbanization and the eco-environment is expressed at the micro level as the dynamic interaction process between the subsystem elements of the two systems, that is, the dynamic interaction process between “population urbanization, spatial urbanization, economic urbanization, and social urbanization” and “eco-environmental pressure and eco-environmental protection” (see Figure 1). Population urbanization is the process of migration from rural households to urban areas. The significant increase in the urban population boosts eco-environmental pressure, while environmental protection policies and behaviors improve eco-environmental quality. Spatial urbanization is the external manifestation of urbanization in geographical space (spatial proliferation of infrastructure such as transportation, spatial expansion of urban areas, etc.). With the proportion of urban construction area rising, land destruction is becoming increasingly serious, and eco-environmental pressure is increasing, but the rational usage and planning of urban land can effectively reduce eco-environmental damage (water pollution, air pollution, etc.) [6] to relieve eco-environmental pressure. Economic urbanization is a process in which small, medium and large towns absorb the surrounding resources and elements through the “siphon effect” and form industrial clusters to achieve rapid economic development. The overloading of ecological resources by economic activities and the large amount of pollutants produced by them increase eco-environmental pressure and weaken the eco-environmental purification capacity. However, the improvement in resource utilization efficiency and the pollution control effect brought on by environmental regulation policies and technological progress eases eco-environmental pressure and improves eco-environmental quality. Social urbanization is a process of increasing and improving the quantity and quality of public services such as education, medical care, health and culture in cities and towns. Activities of resource utilization, production and living involved in this process increase eco-environmental pressure, while the scale effect of resource utilization, the improvement of population quality, and the social progress reduce eco-environmental pressure. Eco-environmental pressure and eco-environmental protection regulate and adjust the urbanization development at the levels of population, space, economy, and society, with the former being a negative regulator, i.e., restraining and hindering, and the latter being a positive regulator, i.e., promoting and optimizing. When the eco-environmental pressure exceeds the threshold, it will hinder the flow of population to the cities, restrict the spatial expansion of cities, limit the speed and quality of economic development in the cities, thus curbing the improvement of population quality and social progress; on the other hand, eco-environmental protection will optimize the living environment of cities, attract the flow of population to towns, reduce resistance to spatial expansion, and promote economic prosperity.

According the above literature study, there is an abundance of research on the coupling coordination relationship between urbanization and the eco-environment. The corresponding theoretical framework including the six basic laws of analyzing the coupling coordination relationship between urbanization and the eco-environment [7], has been constructed and a research paradigm combining the theoretical and empirical research has been formed in China, but there are still the following deficiencies: (1) there are relatively few studies on the Pearl River–West River Economic Belt, and it is necessary and important to supplement and perfect relevant regional research; (2) previous studies mainly select the driving factor variables from the aspect of urbanization development, lacking variables concerning eco-environment development; this paper chooses indices of “per capita park green area” and “per capita industrial sulfur dioxide emissions” as one internal driving factor and one external driving factor, respectively, considering the actual development situation of the research area and the essential influence of eco-environmental endowments and pressures; (3) the analysis method of the driving factors of coupling coordinated development between urbanization and the eco-environment needs to be extended, and this paper uses geographical detector methods in geography to analyze this. Based on these, taking the Pearl River–West River Economic Belt (hereinafter referred to as the Economic Belt) as the research area, this paper measured the urbanization development level and eco-environment development quality with the comprehensive evaluation model, and the coupling coordinated degree between the two with the coupling coordination degree model, explored the spatiotemporal evolutionary features of the coupling coordinated degree by exploiting spatial analysis tools, such as ArcGIS, and analyzed the driving factors with geographical detector methods (the Geo-Detector program is free to download from the following URL: http://www.geodetector.cn/, accessed on 2 February 2023), to provide a policy reference for achieving high-level coupling coordination between urbanization and the eco-environment in the basin economic belt.

3. Overview of Study Area

The Pearl River–West River Economic Belt is located within the Pearl River–West River Basin, and its climate classification is Cfb (C: warm temperature; f: fully humid; b: warm summer) according to the “Köppen–Geiger climate classification” [21]. It covers four cities in Guangdong Province, including Guangzhou, Foshan, Zhaoqing, and Yunfu, and seven cities in the Guangxi Zhuang Autonomous Region including Nanning, Liuzhou, Wuzhou, Guigang, Baise, Laibin, and Chongzuo, with an area of 165,000 square kilometers. The number of total permanent residents was 62.566 million at the end of 2020 (an increase of 19.68% from 2013). With excellent natural endowments and superior shipping conditions, the Pearl River–West River Economic Belt has a better industrial base, better eco-environmental protection, and huge development potential. On the basis of the “Reply of the State Council on the Outline of the Reform and Development Plan for the Pearl River Delta Region (2008–2020) (State Letter No.129, 2008)” and other documents, the “Development Plan of the Pearl River-West River Economic Belt (2014–2020)” was born and approved by the State Council, and since then the development of the Pearl River–West River Economic Belt has risen as a national strategy. The Pearl River–West River Economic Belt connects eastern developed regions and western underdeveloped regions, traverses Guangdong and Guangxi, and links Yunnan and Guizhou at the top and Hong Kong and Macao at the bottom. It is not only the strategic hinterland for the transformation and development of the Pearl River Delta region and an important passage to the sea in southwest China, but the forefront zone for opening up and cooperating with Hong Kong, Macao and ASEAN (Association of Southeast Asian Nations), with an important strategic position in the coordinated development of national regions and opening up to the outside world. Taking the 11 cities in the Economic Belt as research objects, this paper explored the spatiotemporal evolutionary characteristics and the driving factors of the coupling coordinated development between urbanization and the eco-environment, concluding a realistic path to improve the coupling coordinated development level.

4. Index System Construction, Data Sources and Empirical Process

4.1. Index System Construction and Data Sources

This paper constructs an evaluation index system with the coupling coordination level between urbanization and the eco-environment as the target layer, with urbanization and the eco-environment as two subsystems (

Table 1. Secondary indicators and their weight results of urbanization and the eco-environment in the Pearl River–West River Economic Belt.

Target Layer	Subsystem	Primary Indicators Layer	Secondary Index Layer (Unit)	Attributes	Wj
Coupling coordination degree between urbanization and the eco-environment	Urbanization subsystem	Population urbanization	Urban population proportion (%)	$+$	0.055
			Natural population growth rate (‰)	$-$	0.010
		Spatial urbanization	Per capita built-up area (km2/10,000 persons)	$+$	0.088
			Per capita urban road area(m2/person)	$+$	0.024
			Urban-rural income gap index (--)	$-$	0.014
			Urban-rural consumption gap index (--)	$-$	0.006
		Economic urbanization	Per capita regional GDP (CNY/person)	$+$	0.079
			Per capita total investment in fixed assets (CNY/person)	$+$	0.048
			Per capita local fiscal revenue (CNY/person)	$+$	0.066
			Per capita R&D research funds (CNY/person)	$+$	0.217
			Proportion of the tertiary industry’s output value (%)	$+$	0.039
		Social urbanization	Urban water popularization rate (%)	$+$	0.003
			Urban gas popularization rate (%)	$+$	0.012
			Proportion of urban basic medical insurance population (%)	$+$	0.099
			Per capita number of public library books (volume/person)	$+$	0.055
			Per capita education funds (CNY/person)	$+$	0.043
			Number of public transport vehicles operated per 10,000 people (car/10,000 persons)	$+$	0.126
			Urban unemployment rate (%)	$-$	0.015
	Eco-environmental subsystem	Eco-environmental pressure	Per capita industrial wastewater discharge (103 kg/person)	$-$	0.005
			Per capita industrial sulfur dioxide emissions (m3/person)	$-$	0.006
			Per capita production of industrial solid waste (103 kg/person)	$-$	0.008
			Energy consumption per unit of regional GDP (103 kg of standard coal/10,000 CNY)	$-$	0.020
		Eco-environmental protection	Per capita park green area (m2/person)	$+$	0.059
			Per capita water resources (m3/person)	$+$	0.083
			Forest coverage rate (%)	$+$	0.036
			Per capita arable land area (m2/person)	$+$	0.126
			Urban sewage daily treatment capacity (107 kg/day)	$+$	0.370
			Harmless disposal rate of household garbage (%)	$+$	0.008
			Comprehensive utilization rate of industrial solid waste (%)	$+$	0.025
			Industrial pollution abatement investment proportion of GDP (%)	$+$	0.253

Note: The “$W_j$” represents the weight of j-th indicator.

). The urbanization subsystem consists of four first-level indicators of population, space, economy and society, and corresponding second-level indicators. The eco-environment subsystem consists of two first-level indicators of eco-environment pressure and eco-environment protection, and corresponding second-level indicators.

Selection of the secondary indices of the urbanization subsystem. It is a typical feature of population urbanization that the agricultural population moves into cities and gradually transforms into a non-agricultural population. Indicators of the urban population proportion (with the permanent resident population as the statistical standard) and the natural population growth rate, were selected to reflect its development. Spatial urbanization is the external embodiment of the urbanization process in regional space, including urban construction land use, infrastructures such as transportation, urban–rural income and consumption differences, etc., thus indices of per capita built-up area, per capita urban road area, urban–rural income gap index and consumption gap index were chosen to measure its development. Economic urbanization is a process in which urban infrastructure sharing and industrial agglomeration bring about increased output, reduced unit production cost, and the rapid development of the urban economy; therefore, GDP, per capita total investment in fixed assets, per capita local fiscal revenue, per capita R&D research fund investment and the tertiary industry output ratio of GDP were selected to measure the development situation of the urban economy. Social urbanization reflects the quality of urbanization in social civilization; therefore, from three aspects of culture, education, and social security, urban water popularization rate, urban gas popularization rate, proportion of urban basic medical insurance, per capita number of public library books, per capita educational funds, number of public transport vehicles in operation per 10,000 people, and urban unemployment rate, were chosen to measure its development level.

The secondary indices of the eco-environment subsystem were then selected. Eco-environmental quality depends on two factors: eco-environment pressure and eco-environmental protection. Eco-environmental pressure refers to the negative impacts of waste liquid, waste gas, solid waste and other pollutants brought about by industrialization on the eco-environment, which is measured by indices such as per capita industrial wastewater discharge, per capita industrial sulfur dioxide discharge, per capita industrial solid waste production, energy consumption per unit of GDP, etc. Eco-environmental protection refers to the improvement of eco-environmental quality brought about by environmental planning, design, and protection behavior in the process of urbanization, which is measured by indices of per capita park green area, per capita water resources, forest coverage rate, per capita arable land area, urban sewage daily treatment capacity, harmless disposal rate of household garbage, comprehensive utilization rate of industrial solid waste, industrial pollution abatement investment proportion of GDP, etc.

Considering the planning period of the documents, “Reply of the State Council on the Outline of the Reform and Development Plan for the Pearl River Delta Region (2008–2020) (State Letter No.129, 2008)” and the “Development Plan of the Pearl River–West River Economic Belt (2014–2020)”, and in order to explore the spatiotemporal evolutionary features of the coupling coordination between the two systems, this paper selects data from 2008 to 2020, which comes from the EPS (economy prediction system) data platform, as well as the municipal statistical yearbooks, statistical bulletins, etc.

4.2. Empirical Process

4.2.1. Data Processing

Missing data value processing. The interpolation method is an important method of discrete function approximation, which can be used to estimate the approximation of the function at other points through the value of the function at a finite number of points. To ensure data continuity and statistical validity, the interpolation method was used to supplement missing data values of some indicators.

Data standardization processing. In view of the advantages of the extreme difference method, this method can not only transform benefit and cost indices into positive convergence indices, but it can also eliminate dimensional differences between indicators, thus it is commonly used for data standardization processing, as follows:

$$\mathrm{positive\; indicators:} X_{tij}=(x_{tij}-\min x_j)/(\max x_j-\min x_j)$$

$$\mathrm{negative\; indicators:} X_{tij}=(\max x_j-x_{tij})/(\max x_j-\min x_j)$$

where $x_{tij}$ represents the value of the j-th indicator of the i-th city in the t-th year (t = 1, i, 2,3,..., 13; i = 1,2,3,..., 11; j = 1,2,3,..., 30), $\max x_j$ represents the maximum value of the j-th indicator of all cities in all years, and $\min x_j$ represents the minimum value of the j-th indicator of all cities in all years. $X_{tij}$ is the standardized $x_{tij}$, called the evaluation coefficient.

4.2.2. Entropy Method to Calculate Indicator Weights

The concept of entropy was originally derived from the measurement of thermal energy in physics, similar to the coefficient of variation method. Both methods are used to calculate the indicator weights through the indicator dispersion degree. The greater the dispersion degree an index has, the smaller its information entropy will be, and the greater amount of information it will provide, suggesting the greater influence the index will have on the comprehensive evaluation, and the greater its weight will be. To compare the evaluation values in different years, the improved entropy method [22] was used to calculate weights, as follows:

First, $X_{tij}$ the evaluation coefficient of the index, is converted into the evaluation value by calculating $p_{tij}$, the proportion of the standardized value of the j-th index in the i-th city in the t-th year, to the sum of the j-th standardized index values of all cities in all years:

$$p_{tij}=\frac{X_{tij}}{{\displaystyle {\sum }_{t=1}^m{\displaystyle {\sum }_{i=1}^n{X}_{tij}}}}$$

(1)

where $n$ is the number of regions and $m$ is the number of years, where $n$ = 11 and $m$ = 13.

Second, the entropy value $e_j$ of the j-th indicator is calculated:

$$e_j=-\frac{1}{\ln (nm)}{\displaystyle {\sum }_{t=1}^m{\displaystyle {\sum }_{i=1}^n{p}_{tij}\ln (}}{p}_{tij})$$

(2)

where $e_j\ge 0$. Considering that the natural number 0 in the result of the index standardization treatment will cause the inability to calculate $\ln (p_{tij})$, its entropy value $e_j$ is therefore assigned to 0, when the value of $X_{tij}$ is 0.

Then, the difference coefficient $d_j$, specifically the entropy value redundancy, is calculated to reflect the difference between the indicators:

$$d_j=1-e_j$$

(3)

Finally, the difference coefficient is normalized to obtain the indicators weights of urbanization sub-system and eco-environment sub-system in the cities:

$$W_{Uj}=\frac{d_j}{{\displaystyle {\sum }_{j=1}^{18}{d}_j}}; W_{Ej}=\frac{d_j}{{\displaystyle {\sum }_{j=19}^{30}{d}_j}}$$

(4)

where $W_{Uj}$ and $W_{Ej}$ are the weights of the secondary indicators of urbanization and eco-environment subsystem, respectively (the weight results can be seen in Table 1).

4.2.3. Measurement of the Urbanization and Eco-Environment Development Level

After obtaining the weights of the secondary indicators of the urbanization and eco-environment subsystems through the above steps, the development level of urbanization and the eco-environment of the 11 cities in the Pearl River–West River Economic Belt are calculated according to the following model:

$$U{(X)}_{it}={\displaystyle {\sum }_{j=1}^{18}{W}_{Uj}{X}_{tij}}; E{(X)}_{it}={\displaystyle {\sum }_{j=19}^{30}{W}_{Ej}{X}_{tij}}$$

(5)

where $U{(X)}_{it}$ and $E{(X)}_{it}$ are the development level of urbanization and the eco-environment of the i-th city in the t-th year, respectively (the results can be seen in

Table 2. Scores and rankings of the primary indicator development levels of urbanization and the eco-environment of the 11 cities in the Pearl River–West River Economic Belt in 2020.

City	Urbanization								Eco-Environment
	Population Urbanization		Spatial Urbanization		Economic Urbanization		Social Urbanization		Eco-Environment Pressure		Eco-Environment Protection
	S	R	S	R	S	R	S	R	S	R	S	R
N	0.040	4	0.062	3	0.165	4	0.129	3	0.039	2	0.236	5
L1	0.043	3	0.086	2	0.374	1	0.107	4	0.032	9	0.261	4
W	0.033	5	0.052	7	0.094	5	0.080	7	0.039	1	0.167	10
G1	0.024	8	0.054	5	0.064	10	0.081	6	0.036	6	0.176	8
B	0.021	10	0.047	8	0.069	9	0.072	9	0.023	11	0.211	7
L2	0.026	6	0.052	6	0.062	11	0.077	8	0.024	10	0.220	6
C	0.022	9	0.043	9	0.093	6	0.059	11	0.036	7	0.269	3
G2	0.054	2	0.093	1	0.226	2	0.262	1	0.038	3	0.653	1
F	0.061	1	0.041	10	0.200	3	0.188	2	0.038	4	0.175	9
Z	0.025	7	0.060	4	0.089	7	0.097	5	0.037	5	0.337	2
Y	0.021	11	0.032	11	0.070	8	0.067	10	0.035	8	0.163	11

Note: “S” represents the evaluation score, and “R” represents ranking of the evaluation scores. N, L1, W, G1, B, L2, C, G2, F, Z, and Y represent the cities Nanning, Liuzhou, Wuzhou, Gguigang, Baise, Laibin, Chongzuo, Guangzhou, Foshan, Zhaoqing, and Yunfu, respectively.

and

Table 3. Development level of urbanization and the eco-environment of the 11 cities in the Pearl River–West River Economic Belt from 2008 to 2020.

Year	Subsystem	Cities in the Pearl River-West River Economic Belt
		N	L1	W	G1	B	L2	C	G2	F	Z	Y
2008	U(X)	0.185	0.171	0.075	0.065	0.060	0.068	0.074	0.471	0.297	0.121	0.066
	E(X)	0.183	0.371	0.219	0.188	0.213	0.222	0.321	0.236	0.154	0.235	0.202
2009	U(X)	0.211	0.194	0.095	0.079	0.076	0.087	0.097	0.481	0.341	0.145	0.084
	E(X)	0.190	0.288	0.199	0.169	0.196	0.210	0.280	0.309	0.140	0.227	0.193
2010	U(X)	0.239	0.213	0.112	0.087	0.075	0.093	0.110	0.509	0.360	0.160	0.091
	E(X)	0.221	0.265	0.216	0.161	0.208	0.227	0.280	0.625	0.156	0.223	0.184
2011	U(X)	0.264	0.243	0.137	0.102	0.093	0.129	0.114	0.526	0.402	0.178	0.103
	E(X)	0.268	0.247	0.173	0.154	0.181	0.253	0.234	0.398	0.160	0.229	0.173
2012	U(X)	0.289	0.288	0.151	0.117	0.099	0.130	0.129	0.559	0.431	0.261	0.202
	E(X)	0.262	0.281	0.177	0.168	0.221	0.271	0.297	0.413	0.160	0.237	0.170
2013	U(X)	0.298	0.280	0.147	0.116	0.114	0.136	0.126	0.569	0.417	0.195	0.126
	E(X)	0.255	0.262	0.189	0.171	0.191	0.252	0.287	0.409	0.168	0.218	0.171
2014	U(X)	0.327	0.310	0.184	0.129	0.122	0.163	0.149	0.587	0.468	0.292	0.227
	E(X)	0.254	0.271	0.190	0.172	0.226	0.236	0.300	0.414	0.173	0.224	0.165
2015	U(X)	0.327	0.326	0.180	0.136	0.132	0.155	0.150	0.621	0.474	0.231	0.143
	E(X)	0.269	0.301	0.206	0.202	0.223	0.253	0.290	0.456	0.177	0.242	0.170
2016	U(X)	0.351	0.343	0.189	0.148	0.148	0.167	0.157	0.627	0.488	0.234	0.148
	E(X)	0.252	0.279	0.213	0.182	0.204	0.250	0.268	0.474	0.182	0.226	0.195
2017	U(X)	0.376	0.385	0.201	0.162	0.170	0.178	0.176	0.651	0.513	0.239	0.151
	E(X)	0.247	0.288	0.203	0.170	0.253	0.254	0.292	0.520	0.193	0.223	0.190
2018	U(X)	0.401	0.391	0.220	0.180	0.179	0.180	0.183	0.671	0.538	0.270	0.159
	E(X)	0.235	0.267	0.260	0.192	0.243	0.238	0.299	0.542	0.190	0.249	0.195
2019	U(X)	0.433	0.407	0.234	0.196	0.197	0.192	0.204	0.652	0.545	0.253	0.174
	E(X)	0.249	0.291	0.215	0.203	0.230	0.228	0.322	0.614	0.196	0.311	0.196
2020	U(X)	0.396	0.610	0.259	0.224	0.210	0.217	0.217	0.634	0.490	0.272	0.190
	E(X)	0.275	0.293	0.206	0.212	0.235	0.244	0.305	0.691	0.213	0.374	0.198

). $X_{tij}$ is the evaluation coefficient.

4.2.4. Calculating the Coupling Coordination Degree

Drawing on the previous research results in the form and property of the coupled coordination degree model [23], this paper uses the following model to calculate the coupling degree between urbanization and the eco-environment in the Economic Belt:

$$C_{it}=2\cdot \frac{\sqrt{U{(X)}_{it}E{(X)}_{it}}}{U{(X)}_{it}+E{(X)}_{it}}$$

(6)

where $C_{it}$ is the coupling degree between urbanization and the eco-environment, and its value ranges from 0 to 1. The larger $C_{it}$ is, the stronger the interaction between them.

Then, the following model is used to calculate the coordination degree between urbanization and the eco-environment:

$$T_{it}=\alpha \cdot U{(X)}_{it}+\beta \cdot E{(X)}_{it}$$

(7)

where $T_{it}$ is the coordination degree coefficient (also known as the coordination index) between urbanization and the eco-environment of the i-th city in the t-th year. $\alpha$ and $\beta$, respectively, indicate the contribution degree of urbanization and the eco-environment to the city’s development. Here, the contribution degree to the city’s development is thought to be the same, thus taking $\alpha =\beta =0.5$.

Different from the coupling degree C and the coordination degree T, which can only, respectively, reflect the interaction degree and coordination degree unilaterally between urbanization and the eco-environment, the coupling coordination degree D can better avoid the error caused by relying solely on the coupling degree to judge, and can eliminate the dynamics and imbalance between the systems. The following model was used to calculate the coupling coordination degree:

$$D_{it}=\sqrt{C_{it}\cdot T_{it}}$$

(8)

where $D_{it}$ is the coupling coordination degree between urbanization and the eco-environment of the i-th city in the t-th year, and its value ranges from 0 to 1, measuring the coupling coordination level between the two (

Table 4. The coupling coordination degree between urbanization and the eco-environment of the 11 cities in the Economic Belt from 2008 to 2020.

Year	N	L1	W	G1	B	L2	C	G2	F	Z	Y	N
2008	0.429	0.501	0.357	0.333	0.336	0.351	0.393	0.578	0.463	0.411	0.340	0.408
2009	0.448	0.486	0.371	0.340	0.349	0.367	0.406	0.621	0.467	0.426	0.357	0.422
2010	0.479	0.487	0.394	0.344	0.353	0.381	0.419	0.751	0.487	0.434	0.360	0.444
2011	0.515	0.495	0.392	0.354	0.360	0.425	0.404	0.677	0.503	0.449	0.365	0.449
2012	0.525	0.533	0.404	0.374	0.385	0.433	0.443	0.693	0.513	0.499	0.431	0.476
2013	0.525	0.521	0.408	0.375	0.384	0.430	0.436	0.694	0.515	0.454	0.383	0.466
2014	0.537	0.538	0.432	0.386	0.408	0.443	0.460	0.702	0.533	0.506	0.440	0.489
2015	0.545	0.560	0.439	0.407	0.414	0.445	0.457	0.729	0.538	0.486	0.395	0.492
2016	0.545	0.556	0.448	0.405	0.417	0.452	0.453	0.738	0.546	0.480	0.412	0.496
2017	0.552	0.577	0.450	0.407	0.455	0.461	0.476	0.763	0.561	0.480	0.412	0.508
2018	0.554	0.568	0.489	0.431	0.457	0.455	0.483	0.777	0.566	0.509	0.420	0.519
2019	0.573	0.587	0.474	0.446	0.461	0.458	0.506	0.795	0.572	0.530	0.430	0.530
2020	0.574	0.650	0.481	0.467	0.471	0.480	0.507	0.813	0.568	0.565	0.440	0.547

).

The interactive relationship between urbanization and the eco-environment develops from antagonism to coordination, then from coordination to antagonism, and then to coordination, showing a dynamic development characteristic of a cyclical and spiral rise. According to relevant research results [24,25] and the measurement results of the coupling coordination degree, the coupling coordination degree can be divided into five categories (

Table 5. Category classification and development stage changes of the coupling coordination degree.

Coupling Coordination Degree	Category	Sub-Category	Category Feature	Relative Category	Change of the Coupling Coordination Degree
					2008	2014	2020
0.8–1	Coordination development	Advanced coordination	U	Ia
			E	Ib
			O	Ic			G2
0.6–0.8	Transformation development	Intermediate coordination	U	IIa
			E	IIb		G2	L1
			O	IIc
0.5–0.6		Primary coordination	U	IIIa	L1		Z
			E	IIIb	G	F	N, F
			O	IIIc		N, L1, Z	C
0.3–0.5	Uncoordinated development	Basic misalignment	U	IVa	W, G1, B, L2, C, Z, Y	B, C
			E	IVb	F
			O	IVc	N	W, G1, L2, Y	W, G1, B, L2, Y
0–0.3		Severe misalignment	U	VIa
			E	VIb
			O	VIc

Note: when U(x) − E(x) < −0.1, urbanization development lags behind the eco-environment, when U(x) − E(x) > 0.1, eco-environment development lags behind urbanization, and when −0.1 ≤ U(x) − E(x) ≤ 0.1, the two realize synergetic development, represented, respectively, by the letters U, E, O.

).

4.2.5. Geographic Detector

The Geo-detector is a set of statistical tools for driving force and factor analysis, and geographical spatial specificity analysis, which consists of four detectors, including a differentiation and factor detector, interaction detector, risk zone detector and ecological detector. Compared with other statistical analysis methods, its advantages include: (1) not requiring the model to meet the IID hypothesis (independent and identically distributed hypothesis) of classical statistical analysis, and can analyze both numerical and type data; (2) it can detect the influence degree of the interaction between two factors on the dependent variable [26]. In this paper, the first two Geo-detectors were used to probe the driving factors of spatial differentiation and the two factors’ interaction effects of the coupling coordination level between urbanization and the eco-environment in 2008, 2014 and 2020. The detection results are measured by q value, set as follows:

$$q=1-\frac{{\displaystyle {\sum }_h^L{N}_h{\sigma }_h^2}}{N{\sigma }^2}$$

(9)

In the above formula, $h$(=1, 2, ..., L) is a zone of independent or dependent variables, embodied specifically as different urban areas on the study area map; $N_h$ and $N$ represent the number of units in layer $h$ and the full region, respectively; ${\sigma }_h^2$ and ${\sigma }^2$ represent the variance of the dependent variable in layer h and the full region, respectively. According to previous research results [27,28,29,30], the following indicators were selected as independent variables: population urbanization rate, per capita regional GDP, tertiary industry proportion accounted for by regional GDP, per capita R&D research fund investment, per capita park green area, industrial pollution abatement investment proportion of regional GDP, total export–import volume of regional GDP and per capita industrial sulfur dioxide emissions; the driving factors were classified into three categories of internal driving factors, government driving factors and external driving factors. The coupling coordination degree between urbanization and the eco-environment is the dependent variables (variables settings can be seen in

Table 6. Driving factor variable settings of the coupling coordination degree.

	Driving Factors Category	Variable Name	Variable Symbol	Variable Explanation
Dependent variable		Coupling coordination degree	D	Coupling coordination degree
Independent variable	Internal driving factors	Social development elements	Soc	Population urbanization rate
		Economic development elements	Eco	Per capita regional GDP
		Industrial structure elements	Ind	Proportion of the tertiary industry in regional GDP
		Technological innovation elements	TInn	Per capita R&D research funds investment
		Ecological resource endowment elements	ERE	Per capita park green area
	Government driving factor	Government behavior elements	Gov	Industrial pollution abatement investment proportion of regional GDP
	External driving factors	Elements of opening to the outside world	Open	Total export-import volume of regional GDP
		Ecological pressure elements	EnP	Per capita industrial sulfur dioxide emissions

). With the above variables, the geographic detections were conducted (

Table 7. Geographic detection results of the coupling coordination degree between urbanization and the eco-environment.

Differentiation and Factor Geographic Detection Results
Impact Factors	2008			2014			2020
	q Value	p Value	Ranking	q Value	p Value	Ranking	q Value	p Value	Ranking
Soc	0.99822	0	5	0.99999	0	1	0.99931	0	6
Eco	0.99769	0	6	0.99995	0	3	0.99962	0	5
Ind	0.99963	0	4	0.99818	0	6	0.99996	0	1
TInn	0.99993	0	1	0.99995	0	3	0.99992	0	3
ERE	0.99982	0	3	0.99986	0	5	0.99919	0	7
Gov	0.99993	0	1	0.99753	0	7	0.99969	0	4
Open	0.99622	0	8	0.99999	0	1	0.99996	0	1
EnP	0.99733	0	7	0.99523	0	8	0.99919	0	7
Interaction Geographic Detection Results
Year	Two Factors’ Interaction	Geographic Detection Results
2008	Fi ∩ Fj	Weaken						Enhance, nonlinear-
			Interact result: enhance, bi-
2014	Fi ∩ Fj	Weaken						Enhance, nonlinear-
			Interact result: enhance, bi-
2020	Fi ∩ Fj	Weaken						Enhance, nonlinear-
			Interact result: enhance, bi-

Note: F_i and F_j are, respectively, the i-th and j-th independent variables (factors), i, j = 1, 2, ..., 8, for example, F₁ = Soc, F₈ = EnP. The interaction geographical detection results showed a correlation coefficient of ${\rho }_{F_i{F}_j}>0.99$.

).

5. Empirical Results Analysis

5.1. Temporal Variation Features of the Coupling Coordinated Development between Urbanization and the Eco-Environment

5.1.1. Temporal Variation Features of the Coupling Coordination Level

Figure 2 shows that the five indicators of U, E, T, D, and C of the overall Economic Belt all present a gradual upward evolutionary trend, and change in ranges of 0.15–0.35, 0.20–0.30, 0.19–0.32, 0.40–0.55, and 0.89–1.00, respectively. From 2008 to 2020 the development levels of urbanization and the eco-environment, and the coordinated degree between the two in the Economic Belt, as a whole, are not high, but their coupling degree is very high, which confirms that there is a high degree of interrelation between urbanization and the eco-environment. From 2011 to 2013 the overall urbanization development level in the Economic Belt, was close to its eco-environment development level, exceeding the eco-environment development level after 2013, indicating that antagonism happened between the two during this period. According to the classification criteria in Table 5, the overall coupling relationship between urbanization and the eco-environment in the Economic Belt from 2008 to 2016 was in the uncoordinated development stage (basic misalignment), and from 2017 to 2020 was in the transformation development stage (primary coordination).

5.1.2. Evolutionary Features in the Relative Development Categories of the Urbanization and Eco-Environment Subsystems

In order to analyze the differential characteristics of the coupling coordination between urbanization and the eco-environment in different cities of the Economic Belt, according to the classification criteria in Table 5, this paper classifies the relative development categories of urbanization and the eco-environment of the 11 cities in the Economic Belt from 2008 to 2020 (

Table 8. Classification results of the relative development categories of the urbanization and eco-environment subsystems in the 11 cities of the Economic Belt from 2008 to 2020.

Year	N	L1	W	G1	B	L2	C	G2	F	Z	Y	The Economic Belt Overall
2008	O	U	U	U	U	U	U	E	E	U	U	O
2009	O	O	U	O	U	U	U	E	E	O	U	O
2010	O	O	U	O	U	U	U	U	E	O	O	O
2011	O	O	O	O	O	U	U	E	E	O	O	O
2012	O	O	O	O	U	U	U	E	E	O	O	O
2013	O	O	O	O	O	U	U	E	E	O	O	O
2014	O	O	O	O	U	O	U	E	E	O	O	O
2015	O	O	O	O	O	O	U	E	E	O	O	O
2016	O	O	O	O	O	O	U	E	E	O	O	O
2017	E	O	O	O	O	O	U	E	E	O	O	O
2018	E	E	O	O	O	O	U	E	E	O	O	O
2019	E	E	O	O	O	O	U	O	E	O	O	O
2020	E	E	O	O	O	O	O	O	E	U	O	O

Note: U means that urbanization development lags behind eco-environment development, E means the reverse, and O means that urbanization and eco-environment achieve synchronous development.

).

According to the evolutionary characteristics of urbanization and eco-environment development level of the 11 cities in the Economic Belt from 2008 to 2020, the 11 cities can be divided into three categories: the first category, cities mainly characterized by relatively lagging urbanization development, including Chongzuo city; the second category, cities with relatively lagging eco-environment development as the main feature, including two cities, Guangzhou and Foshan; the third category, cities mainly characterized by the synchronous development between urbanization and the eco-environment, including eight cities, Nanning, Liuzhou, Wuzhou, Guigang, Baise, Laibin, Zhaoqing and Yunfu. Among these cities, the eco-environment development in Foshan city from 2008 to 2020 has been in a relatively lagging state, which shows that factors related to the eco-environment have been restricting the coupling development between its urbanization and eco-environment; the urbanization development level in Chongzuo city from 2008 to 2019 has been relatively low, suggesting that factors about urbanization development have largely restrict the coupling development between its urbanization and eco-environment. In 2020, seven cities, Wuzhou, Guigang, Baise, Laibin, Chongzuo, Guangzhou and Yunfu, achieved coordinated and synchronized development between urbanization and the eco-environment; the urbanization development in Zhaoqing city lagged behind its eco-environment development; while the eco-environment development of three cities, Nanning, Liuzhou and Foshan, lagged behind their urbanization development.

5.1.3. Evolutionary Characteristics of the Coupling Coordinated Categories

Table 5 presents the category division results of the coupling coordination between urbanization and the eco-environment in the 11 cities of the Economic Belt in 2008, 2014 and 2020. As seen in the table, the coupling coordination level between urbanization and the eco-environment in the 11 cities gradually improves, and the coupling coordination subcategory of most cities has an obvious promotion or change; however, five cities, Wuzhou, Guigang, Baise, Laibin, Yunfu, have been in the basic misalignment, whose difference lies in the dynamic characteristics of their relative category fluctuating between IVa, IVb and IVc. In line with the results shown in Table 8 most cities achieved the synchronous development between urbanization and the eco-environment in 2020, including Guangzhou city (senior coordination), Chongzuo city (primary coordination) and five cities, Wuzhou, Guigang, Baise, Laibin and Yunfu (basic misalignment).

5.2. Spatial Evolutionary Characteristics of the Coupling Coordinated Development between Urbanization and the Eco-Environment

This paper adopts ArcGIS to depict the spatial evolutionary characteristics of the urbanization development level, eco-environment development level and the coupling coordination level between the two in 11 cities of the Economic Belt in 2008, 2014 and 2020, and also uses OriginPro8.1 to draw a three-dimensional space diagram of the coupling coordination degree between urbanization and the eco-environment in 2020 to further depict the spatial distribution characteristics.

5.2.1. Spatial Evolutionary Characteristics of the Urbanization Development Level

In order to compare and analyze the changes in the urbanization development level of different city in 2008, 2014 and 2020, according to the maximum (0.6714) and minimum values (0.0596) of the urbanization development level, the urbanization development level was evenly divided into four levels with the interval value of 0.1530, and the division ranges were, respectively: 0.0596–0.2130, 0.2131–0.3660, 0.3661–0.5190, and 0.5191–0.6720. As shown in Figure 3, the urbanization development level of all cities in the Economic Belt shows a development trend of continuous improvement, and there are very obvious changes in its spatial distribution pattern: in 2008, the urbanization development level of cities, Guangzhou and Foshan, is relatively high, and the urbanization development level in other cities is relatively low (under 0.2130) with little difference between these cities; However, in 2020 the spatial distribution of the urbanization development level presents significantly different characteristics. There is an increasing gap between partial cities from 2008 to 2020. Combined with the measurement results in Table 3, it can be found that the urbanization development level of most cities in the Economic Belt improved from 2008 to 2020, and the speed of improvement varied among cities. Among them, the urbanization development level in Liuzhou city improved the most, followed by cities, Nanning and Foshan; the urbanization development level of five cities, Wuzhou, Guigang, Laibin, Chongzuo and Zhaoqing, certainly improved, while that of Baise and Yunfu, did not significantly increase.

5.2.2. Spatial Evolutionary Characteristics of the Eco-Environment Development Level

In order to compare and analyze the evolution of the eco-environmental development quality of each city in 2008, 2014 and 2020, according to the maximum (0.6911) and minimum values (0.1399) obtained in the measurement results, the eco-environmental development quality was evenly divided into four classes with the interval value of 0.1530, and division ranges were, respectively: 0.1399–0.2780, 0.2781–0.4160, 0.4161–0.5540, and 0.5541–0.6920. Figure 4 depicts the spatial evolutionary characteristics of the eco-environmental development quality in the 11 cities of the Economic Belt in 2008, 2014 and 2020. Combined with the measurement results in Table 3, it can be found that the eco-environmental development level of most cities in the Economic Belt always remained at a relatively constant level, and only a few cities had hierarchical leaps. Among them, the eco-environmental development level of Guangzhou city improved the most, followed by Zhaoqing City; Liuzhou city decreased slightly; Chongzuo city kept at a relatively constant level, suggesting that it had a stable eco-environment and a comparative advantage in natural resource endowment.

5.2.3. Spatial Evolutionary and Distribution Characteristics of the Coupling Coordination Level

In order to compare and analyze the coupling coordination level between urbanization and the eco-environment in different cities in 2008, 2014 and 2020, this paper divided them into four categories (no city was in the severe misalignment category) according to the classification standard in Table 5, and the numerical ranges were, respectively: 0.0000–0.5000, 0.5001–0.6000, 0.6001–0.8000, and 0.8001–1.0000. Figure 5 depicts the spatial evolutionary characteristics of the coupling coordination level between urbanization and the eco-environment in the 11 cities of the Economic Belt in 2008, 2014 and 2020. According to the calculation results in Table 4, it can be seen that although the coupling coordination level between urbanization and the eco-environment in cities of the Economic Belt increased year by year, the gap between cities gradually expanding. Figure 5 also indicates a development trend that the spatial differences of the coupling coordination level is gradually widening. Although the coupling coordination degree of five cities, Wuzhou, Guigang, Baise, Laibin and Yunfu, constantly improved, their coupling coordination hierarchy has not shifted and has maintained at the basic misalignment sub-category. In six cities, Nanning, Liuzhou, Chongzuo, Guangzhou, Foshan and Zhaoqing, the hierarchy of the coupling coordination degree transitioned to a higher one. Among them, Guangzhou city had three hierarchical leaps, from primary coordination to intermediate coordination and then to advanced coordination; four cities of Nanning, Chongzuo, Foshan and Zhaoqing transitioned from basic misalignment to primary coordination, and Liuzhou city transitioned from primary coordination to intermediate coordination.

Figure 6 portrays the spatial disparities and changing trend characteristics on four orientations of the coupling coordination level between urbanization and the eco-environment in the Economic Belt in 2020. It can be seen that in 2020, the spatial distribution of the coupling coordination level between urbanization and the eco-environment in the Economic Belt is different on both directions of north–south and east–west, showing a distribution characteristic that the coupling coordination level decreases “from south to north” and increases “from west to east”.

5.3. Driving Factors and Interaction Analysis of the Coupling Coordination Level between Urbanization and the Eco-Environment

5.3.1. Driving Factor Analysis

From the differentiation and factor geo-detection results (seen in Table 7), it can be found that in 2008, 2014 and 2020, the influence values (q value, whose values range from 0 to 1, and the larger the q value for a factor is, the greater the influence of that factor on the dependent variable will be) of different driving factors on the coupling coordination level between urbanization and the eco-environment in the Economic Belt are all above 0.99 (all p values are 0), suggesting that the above factors significantly affect the coupling coordination level between urbanization and the eco-environment. The ranking results in Table 7 show that the key influencing factors (take the top three as a comparison) of the coupling coordination degree between urbanization and the eco-environment are different in different years. In 2008, the key influencing factors include: technological innovation elements, ecological resource endowment elements and government behavior elements. In 2014, the key influencing factors include: social development elements, elements of opening to the outside world, economic development elements and technological innovation elements. In 2020, the key influencing factors include industrial structure elements, elements of opening to the outside world and technological innovation elements. It can be seen that technological innovation elements have always been a very important factor affecting the coupling coordinated development between urbanization and the eco-environment, while the importance of elements of opening to the outside world is becoming increasingly prominent for the coupling coordinated development between the two factors.

5.3.2. Interaction Analysis

The interaction geo-detection method can identify the interactions between different driving factors, and also evaluate whether the interpretation strength of the dependent variable is enhanced or weakened when the pairwise factors interact together [26]. According to the geographical detection results in Table 7, there exist interactions between two different driving factors in 2008, 2014 and 2020, and the interaction categories were all two-factor-enhanced types (the red part in Table 7), that is, the interaction between two factors significantly enhanced the interpretation of the coupling coordination level. This indicates that the interaction among the driving factors explains the dependent variable more strongly than the single driving factor explains the dependent variable. Therefore, the implementation of comprehensive policies is a realistic path to improve the coupling coordination level between urbanization and the eco-environment.

6. Discussion

6.1. Conclusions

This paper measured the development level of urbanization and the eco-environment and the coupling coordination degree between the two in 11 cities of the Pearl River–West River Economic Belt from 2008 to 2020, analyzed their spatiotemporal evolutionary characteristics, and explored the driving factors affecting the coupling coordination degree and the interaction between the two factors by exploiting the geographical detection method. The main conclusions are as follows:

(1). The development level of urbanization and the eco-environment in the Economic Belt improved to varying degrees, but the spatial differences between cities are widening, and the unbalanced development problem is prominent. The urbanization development level is decreasing from the three central cities of Nanning, Liuzhou, and Guangzhou, to their surrounding areas. The eco-environmental development level has obvious hierarchical features, forming three development hierarchies, the cities in which include (Guangzhou), (Liuzhou, Chongzuo, Zhaoqing), (Nanning, Wuzhou, Guigang, Baise, Laibin, Foshan, and Yunfu), but there is a large gap between the different hierarchies.

(2). From 2008 to 2020, the coupling coordination level between urbanization and the eco-environment in the Economic Belt continuously improved, and the overall coupling coordination category presents the evolutionary characteristics of the transition from low- to high-level. According to these evolutionary characteristics, cities in the Economic Belt can be divided into four categories: ① cities from basic misalignment to primary coordination: Nanning, Chongzuo, Foshan and Zhaoqing; ② cities from primary to intermediate coordination: Liuzhou city; ③ cities from primary coordination to advanced coordination: Guangzhou city; ④ cities without hierarchical transition: Wuzhou, Guigang, Baise, Laibin and Yunfu.

(3). The spatial distribution disparities of the coupling coordination level in the Economic Belt are becoming increasingly prominent, and the development gap between cities is gradually widening. In 2020, the coupling coordination level between urbanization and the eco-environment in the 11 cities of the Economic Belt is higher in the east and north, and lower in the west and south, showing the spatial distribution characteristics of “decreasing from north to south” and “increasing from west to east”.

(4). All the driving factors have a significant impact on the coupling coordination degree, among which the industrial structure elements, the elements of opening to outside world and the technological innovation elements are currently important driving factors affecting the coupling coordinated development between urbanization and the eco-environment in the Economic Belt. The interaction between the pairwise driving factors is strong, and can enhance the interpretation of the coupling coordination degree. The comprehensive measures are conducive to improving the coupling coordination level.

6.2. Improvement Path of Coupling Coordination between Urbanizaiton and the Eco-Environment in the Economic Belt

In view of the above conclusions, the countermeasures are proposed as follows:

(1). Policies should be classified to be implemented according to the actual development situation of urbanization and the eco-environment of cities in different coupling coordination categories. For advanced-coordination cities (Guangzhou city), attention should be paid to environmental protection while steadily improving the urbanization development quality. For intermediate-coordination cities (Liuzhou city), efforts should be made to improve eco-environmental governance and protection while steadily promoting the urbanization construction process, so as to improve the quality of the eco-environmental development. For primary-coordination cities (Nanning, Chongzuo, Foshan and Zhaoqing), industrial transformation and green development should be realized as soon as possible, and the urbanization development level and eco-environmental development quality should be improved through technological innovation. For cities without hierarchical transition (Wuzhou, Guigang, Baise, Laibin, and Yunfu), they should start from their own resource endowment to develop characteristic industries, and constantly improve the development level of the urban economy and society on the basis of not affecting the eco-environmental quality.

(2). Regional information exchange and cooperation platform, and the economic development cooperation mechanism should be built to realize interconnection and connectivity between different cities in the aspects of public service equalization, resource and element sharing, etc., to narrow the regional gap. Meanwhile, full attention should be given to radiating and driving the role of two core cities, Nanning and Guangzhou, and a sub-core city, Liuzhou, to form a regionally coordinated and sustainable development pattern of “point with line, line with surface”.

(3). The extent of opening to the outside world should be expanded to improve the economic extroversion degree and the regional endowment advantages should be made full use of to develop the economy, improve the economic strength and attract domestic and foreign factors such as capital, talent, and technology to cities. At the same time, industry investment and industrial R&D funding should be increased, and the industrial structure should be optimized, to improve the technological innovation ability and capacity of transforming scientific research results into real productivity.

(4). Under the principle of “pollution control and source control”, the pollution emissions should be reduced and the pressure on the eco-environment should be decreased, to improve the carrying capacity of the ecosystem. The government should formulate environmental regulation policies, improve supervision and increase financial support for pollution abatement, and moreover, strengthen the construction of urban green space to optimize the ecological resource endowment structure. Enterprises should increase investment in the research and development of pollution abatement technologies to improve energy-usage efficiency and clean production capacity, reducing pollution emissions. Residents and non-profit institutions should raise the awareness of environmental protection, applying it in their production and life practice activities.

6.3. Study Limitation

This study reveals the spatiotemporal evolutionary characteristics of the urbanization development level, eco-environmental development quality and the coupling coordination between the two factors in the Pearl River–West River Economic Belt and explores the driving factors of the coupling coordination development between the two systems and its improvement path in the future. However, there are still some limitations. First of all, there exists certain limitations in the consideration and selection of driving factors in that not all driving factors are considered, compared and analyzed, limiting the generalization of some conclusions. Future research of relevant topic can test other driving factors. Secondly, the improvement path is proposed at the theoretical level. Policy formulation in different regions should combine with their endowment and development features and make flexible adjustments in accordance with the practical implementation of policies.