A Spatiotemporal Analysis of Ecological–Economic Coupling Coordination in the Chengdu–Chongqing Urban Agglomeration

Abstract

The quick and reliable quantification of the relationship between ecosystem and economic system is important in policymaking for sustainable urban agglomerations facing enormous pressure from high population density and development intensity. This is especially true in China, where urban agglomeration has been part of the country’s strategy for reform, modernization, and urbanization. This study applied the coupling coordination degree (CCD) model to assess the coupling coordination relationships between the ecosystem and economic system at the county level for the Chengdu–Chongqing agglomeration for the period of 2005–2019, and then, the local indicator of spatial association analysis (LISA) was used to illustrate the spatial distribution of CCDs further, hoping to capture the spatiotemporal dynamics of CCDs. The results found that (1) fringe counties and districts in the urban agglomeration were on the brink of ecological–economic disorder with low CCDs, (2) urbanized areas near Chongqing coordinated well with high CCDs, and (3) sound spatial governance and territorial planning may be better achieved by using the county-level unit than the city-level unit.

1. Introduction

Today, fast urbanization powered by technological advances and economic activities has drastically modified the earth’s ecosystems, including their structures, functions, and qualities [1,2,3,4]. However, urban livability heavily relies on healthy ecosystems and their services in and around cities [5,6,7,8,9,10]. Therefore, balancing socioeconomic development and ecosystem protection is essential, especially for urban agglomerations comprising large cities. Indeed, sustainable urban and regional development has been increasingly recognized as vital for quality of life around the world [11], and in this regard, China is no exception [12].

Over the past four decades of reform and opening, starting in 1978, China has been striving for sustainable urban development. In 2022, China had a population of 1.4 billion and an urbanization rate of 65%; moreover, it has been the world’s second-largest economy since 2010 [13]. Specifically, the gross domestic product (GDP) has increased from CNY 0.37 trillion in 1978 [14] to CNY 114.4 trillion in 2021 [15]. These tremendous achievements, especially those from large-scale human economic activities, also generated negative impacts on ecosystems, such as environmental pollution [16], loss of biodiversity [17,18,19], and land degradation [20,21,22]. These negative impacts, to varying extents, reduce the happiness that economic achievement brings to people. They also cause health problems and even lead to deaths [23,24,25].

The Chinese government recently reconsidered their development model and realized the need to change its course. Along with the acceptance of sustainable development concepts introduced in China in the late 1980s came the rise of the “scientific development idea” for rational and balanced development policies in the 1990s [26]. Then, in 2007, the Ecological Civilization model was officially proposed, introducing the concept of harmonious development between human beings and nature to achieve sustainable development [27,28]. Here, the central notion is that economic development and ecosystem protection must be coupled and coordinated. However, how do we know that the two have a harmonious state? Are there some quantitative analysis methods that can analytically help assess coupling and coordination?

To ensure ecological safety and arable land security, China has recently been implementing urban agglomeration strategies to optimize the spatial pattern of the national economic and social development [29,30]. It is envisaged that future economic activities and population aggregations will mainly occur in urban agglomerations or regions with large megacities. The strategic purpose is to limit large-scale industrialization and urbanization within certain regions as much as possible and reduce disturbances to ecosystems and loss of farmland. However, this will put more pressure on ecosystems within urban agglomerations. Overall, China is still a middle-income country, and economic development will continue to be the central task for a long time, especially in urban agglomerations.

In this study, we focus on the coupling and coordinated development between the ecosystems and the economic system in the Chengdu–Chongqing urban agglomeration. Three ecological and ten economic indicators were selected. The coupling coordination degree (CCD) model was applied to evaluate the relationship between the economy and ecosystems in 2005, 2010, 2015, and 2019 for 139 counties (or districts). We analyzed the spatiotemporal characteristics of CCDs in the urban agglomeration. It is anticipated that the research findings can provide decision support for policymakers to formulate better spatial development plans and governance policies. More specifically, this paper examines the CCDs for the Chengdu–Chongqing urban agglomeration at the county level, which is more conducive to policymaking and spatial governance in different zones. Section 1 of the paper provides the introduction, Section 2 provides a brief review of the relevant literature, Section 3 outlines the study area and associated databases, Section 4 details the methods, Section 5 summarizes the major results, Section 6 details a discussion, and Section 7 provides the conclusions.

2. Literature Review

Human beings have noticed the relationship between economic activities and ecosystems for centuries. The ancient Chinese admonished the world: “fishing without drying up the pond, hunting without burning the forest” [31]. In the 1960s, scholars started examining the connections between the economy and the environment [32,33,34]. These connections included inputs from the environment to industries, whose outputs fed back into the environment as waste products. Human beings must take action to slow down or eliminate these bad connections. In the 1980s, Brundtland [11] proposed the concept of sustainable development. Later, scholars gradually deepened their understanding of the relationship between the economy and the environment. Grossman and Krueger [35] found a bad first relationship between economic growth and environmental indicators and then gradually improved it. This phenomenon can be seen in the Environment Kuznets Curve (EKC) [36], which set the foundation for the relationship between the economy and the environment from both theoretical and practical perspectives, found by Cato [37]. Rees [38] thought that there was an unavoidable conflict between economic and environmental protection, when beyond a certain point. Furthermore, an economy is a subsystem fully contained, growing, and dependent on a non-growing ecosphere. Rudi et al. then asks for the necessity and possibility to combine socio-economic development and environmental conservation policies in sub-Saharan Africa [39]. As for the methods to analyze the relation between two systems, Lorah and Southwick [40] applied the geographic information system (GIS) approaches to calculate the proportion of protected lands occurring within 50 miles of the center of each western county in the U.S. to analyze the relationship between environment protection and economic development. Liu et al. built a multiple linear regression model to study the tradeoff between ecological protection and economic growth in 296 counties of China [41]. No matter what scholars hold on the relationship between economic development and ecological protection, a rapid and quantitative method is needed to evaluate the impact of the two-pronged policy on sustainable development. To quantitatively analyze the degree and quality of the relationship between the two systems, we use the CCD model.

The CCD model was developed based on the coupling concept, originally from physics. Then, the Polish economist Oskar Lange first introduced it in socialist economy process management [42]. Liu [43] proposed the concept of coupling the planning mechanism and the market mechanism in the process of the socialist economy in China. Due to the particularity of China’s economy, resources, and environmental management system, the concept of coupling has been gradually accepted by the Chinese geography community and developed under the guidance of sustainable development. Scholars focused on the research on the coupled development of the economy and ecological conservation [44,45,46,47,48,49,50,51,52], which aligns with the need to coordinate the conflict between rapid economic growth and ecological conservation after China’s reform and opening up. The coupling degree reflects the extent of interactions between systems, and the CCD reveals the coordination quality of the coupled systems. The CCD model is often used to assess the coupling coordination level between two, three, or more subsystems in a certain region. Therefore, we can use CCD to represent the quality of sustainable development.

Since 2000, studies on various CCD models applied to the coupling and coordinated development of economic growth and ecological conservation have become popular, including that of Wang et al. on the coupling analysis of resources–environment and population–economic development in western Jilin Province [53]. Xing et al. combined CCD and system dynamics (SD) models to assess the coupling coordination degree in three dimensions, i.e., economy, resource, and environment, in Wuhan City, China [54]. Tang [55] integrated the CCD information entropy weight approaches to analyze the coordination between tourism and the environment for Heilongjiang Province. Shi et al. conducted an empirical study in tropical and subtropical regions of China to measure the coupling coordination and spatiotemporal heterogeneity between economic development and ecological environment [56]. Lai et al. calculated CCDs for the ecology, economy, and tourism in China from 2003 to 2017 [57]. Peng et al. analyzed the coupling coordination between environmental protection and economic development and showed that environmental protection would promote economic development [58]. Fei et al. applied the CCD model to measure coupling coordination and spatiotemporal changes for the regional economy, ecological environment, and island tourism for eleven island districts in eastern China’s Zhejiang Province during 2008–2018 [59]. Cui et al. integrated SD and CCD models to analyze the coupling coordination between the social economy and water environment in Kunming City [60]. Fan et al. assessed coupling coordinated development between the social economy and the ecological environment for provincial capital cities of China [61]. Li et al. evaluated the coordination development among economic, social, and environmental subsystems for nine central cities in China [62]. Hou et al. evaluated the economy, ecological environment, and health system of China from 2009 to 2016 through the perspective of green production [63]. Zhou and Cao [64] assessed oil, economy, and environment in western China. Tomal [65] evaluated CCD in six dimensions: economy, demography, housing, society, infrastructure, and environment, for all municipalities over the period 2003–2019. Liu et al. investigated the spatiotemporal dynamic evolution between social economy and water environmental quality in the Nansi Lake catchment of China [66]. Liu et al. explored coupling coordination and spatiotemporal heterogeneity between economic development and the ecological environment of thirty-six cities along the Yellow River Basin [67]. Zhang et al. examined spatiotemporal changes in CCD between economic development and water environment at the provincial level in China [68]. Ji et al. performed a spatiotemporal and multiscale analysis of the CCD between economic development and eco-environmental quality in China from 2001 to 2020 [69]. Finally, Wan et al. analyzed the CCD of the economy and ecological environment of the Chengdu–Chongqing urban agglomeration at the prefecture-level city scale [70], while ours is at a county level.

In China, the coupling concept and coordinated development research have been popular ever since they were applied in economics research. With the acceleration of industrialization and urbanization, the contradiction between economic development and ecological environmental protection has also become popular. However, the complexity of coupling and its measure call for more studies, especially for economic and ecological coordination in large urbanized regions.

3. Study Area and Associated Databases

3.1. Study Area

The Chengdu–Chongqing urban agglomeration is located in southwestern China, bordering Hunan and Hubei provinces in the east, the Qinghai-Tibet Plateau in the west [71], the Yunnan-Guizhou Plateau in the south, and the Shaanxi-Gansu region in the north. The geographic extent is between ${101}^{\circ }{56}^{\prime }{5}^{″}$–${108}^{\circ }{54}^{\prime }{18}^{″}$ E and ${27}^{\circ }{39}^{\prime }{46}^{″}$–${32}^{\circ }{19}^{\prime }{22}^{″}$ N. Mountains and rivers connect the urban agglomeration and are important ecological barriers in the upper reaches of the Yangtze River. The topography is complex and mainly composed of mountains and basins. This region is an intermediate transition area between the Qinghai-Tibet Plateau and the middle and lower reaches of the Yangtze River. The highest altitude in the area is 5693 m, and the lowest is 46 m. The climate belongs to the subtropical monsoon climate [72], with hot and rainy summers and warm and humid winters. In 2020, most areas in the region had an annual average wind speed of less than 2.0 m/s, and a tiny number of areas had an annual average wind speed of more than 4 m/s. Most areas have an annual average temperature of about 18 °C; some southern areas have an annual average temperature of about 19 °C, and very few western fringe areas have an annual average temperature of about 0 °C, mainly located in mountainous areas. Most areas have an annual average precipitation of about 1000–1500 mm, some areas in the Chengdu megacity have an annual average precipitation of about 800–1000 mm, and some western fringe areas have an annual average precipitation of above 2000 mm, mainly located in Ya’an City. [73]. Affected by the natural geographical environment and geological conditions, soil erosion is common and natural disasters frequently occur [74,75]. The area of moderate and above soil erosion accounted for 11% of the total area. In 2020, the regional forest coverage rate was 41.9%. From 2016 to 2020, the cumulative reduction of carbon dioxide emissions per unit of GDP was by more than 19.5%. During the “13th Five-Year Plan” period, the annual average concentration of fine particulate matter (PM2.5) dropped by 30% [76]. These natural and geographical conditions have a profound impact on the population and industrial spatial patterns within the region.

The study area contains the vast majority of the Chengdu–Chongqing urban agglomeration, comprising 15 cities in Sichuan and 26 districts or counties in Chongqing. They are Chengdu, Zigong, Luzhou, Deyang, Mianyang (except Beichuan and Pingwu counties), Suining, Neijiang, Leshan, Nanchong, Meishan, Yibin, Guang’an, Dazhou (except Wanyuan City), Ya’an (except Tianquan and Baoxing counties), and Ziyang. The Chongqing part includes Wanzhou, Fuling, Yuzhong, Dadukou, Jiangbei, Shapingba, Jiulongpo, Nan’an, Beibei, Yubei, Banan, Changshou, Jiangjin, Hechuan, Yongchuan, Nanchuan, Qijiang, Dazu, Bishan, Tongliang, Tongnan, Rongchang, Liangping, Fengdu, Dianjiang, and Zhongxian. There are 139 county-level units in the region, which has a total area of about 179,400 km² (Figure 1).

By the end of 2022, the total GDP of the urban agglomerations was CNY 7.8 trillion, which accounts for 6.4% of the whole country and 30.2% of the western region in China. The added value of the primary industry in the area was CNY 646.955 billion, accounting for 7.3% of the national total; the secondary industry’s added value was CNY 2989.058 billion, accounting for 6.2% of the national total; and the added value of the tertiary industry was CNY 4.122786 billion, accounting for 6.5% of the national total. Finally, the total retail sales of consumer goods were CNY 3446.014 billion, accounting for 7.8% of the national total [77], with a permanent population of nearly 98 million accounting for 6.91% of the national total [78]. The urbanization level has reached about 66% [79]. In 2020, the proportion of non-fossil energy consumption reached 33%, much higher than the national average. Because the region is in a phase of rapid development of industrialization and urbanization, the spatial pattern reconstruction of population and industry has led to a mismatch between economic growth and ecological environment carrying capacity. Therefore, studying the coupling coordinated development of the ecological–economic systems in this region is the foundation for proposing measures to solve these problems and support the sustainable development of the urban agglomeration.

3.2. Data

Ecological data were obtained from the China land cover dataset (CLCD) product released by Yang and Huang [80], including land use data in 2005, 2010, 2015, and 2019, with a spatial resolution of 30 m. The NDVI data came from the 1 km annual NDVI dataset of China of Resource and Environment Science and Data Center, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences [81], and NASA EOSDIS Land Processes DAAC [82]. DEM came from ALOS World 3D 30-meter DEM. V3.2 [83]. The administrative division data came from the 1:1000,000 public version of the basic geographic information dataset [84]. Economic data were derived from the Sichuan Statistical Yearbook [85] and the Chongqing Statistical Yearbook [86].

All the georeferenced images and vector data were projected to UTM 48N and WGS84. The statistical data were standardized in processing and linked to the county-level vector data by the administrative code. This allowed us to visualize and analyze the economic data using ArcGIS Desktop 10.3, a GIS software produced by Esri Company and authorized to be used by Chengdu University of Technology.

4. Methods

4.1. Building Indicators System

This study integrates the evaluation of the quality of the ecosystem and the level of economic development from the ecological and economic systems to construct an evaluation index system for the coupling coordination degree in the study area (

Table 1. Indicators system and weights.

System	Subsystem	Indicators	Unit	Weights	Types
Ecosystem	BRI	CLA	km2	0.291	+
		WLA	km2
		GLA	km2
		WA	km2
		CSLA	km2
		ULA	km2
	VCI	NDVI	–	0.254	+
	WNDI	RL	km	0.225	+
		WA	km2
		AWR	m3
	LDI	SEI	–	0.280	−
Economic system	economic base	GDP	ten thousand yuan(¥)		+
		GDP per capita	Yuan(¥)		+
		GIP	ten thousand yuan(¥)		+
		FAI	ten thousand yuan(¥)		+
	consumption	TRSCG	ten thousand yuan(¥)	determined based on annual economic data	+
		AWSWJ	Yuan(¥)		+
		RPC	Yuan(¥)		+
	economic structure	PTI	%		+
		PSI	%		+
		UR	%		+

). Ecosystem quality is represented by the ecological index (EI), and economic development level is represented by the economic development index (EDI). The calculation of EI is mainly referenced from the Technical Criterion for Ecosystem Status Evaluation (HJ/T192–2006 and HJ192–2015) issued by the Ministry of Ecology and Environment of the People’s Republic of China [87,88]. There were four subsystems involved in the EI calculation, i.e., the biodiversity richness index (BRI), vegetation coverage index (VCI), water network denseness index (WNDI), and land degradation index (LDI). BRI included cropland areas (CLA), woodland areas (WLA), grassland areas (GLA), water body areas (WA), construction land areas (CSLA), unused land areas (ULA), and region areas (RA). VCI included normalized difference vegetation index (NDVI). WNDI included river lengths (RL), WA, and the amount of water resources (AWR). Finally, LDI included the soil erosion index (SEI). Three subsystems were involved in the EDI calculation, i.e., economic base, consumption, and economic structure. The economic base included four indicators: gross domestic product (GDP), GDP per capita, gross industrial production (GIP), and fixed asset investment (FAI). The consumption included three indicators: total retail sales of consumer goods (TRSCG), average wages of staff and workers on the job (AWSWJ), and revenue per capita (RPC). The economic structure included three indicators: the proportion of tertiary industry (PTI), the proportion of secondary industry (PSI), and the urbanization ratio (UR).

4.2. Ecological Index

The EI was calculated using Formula (1):

$$EI={\sum }_{i=1}^n{K}_i{B}_i$$

(1)

where $B_i$ is ecological status assessment indicators, $K_i$ indicates the corresponding weight given to the indicator, and n represents the number of indicators involved in the ecological status assessment.

After inserting the four variables—BRI, VCI, WNDI, and LDI—into Formula (1), and referencing the variables’ weights from YURIGULIKASIMU [89], Formula (2) is obtained:

$$EI=0.291\times BRI+0.254\times VCI+0.225\times WNDI+0.280\times (100-LDI)$$

(2)

The BRI can be calculated using Formula (3):

$$\begin{array}{cc}\hfill BRI=& A_{bio}\times \left(\right(0.11\times CLA+0.35\times WLA+0.21\times GLA+\hfill \\ \hfill & 0.28\times WA+0.04\times CSLA+0.01\times ULA\left)\right)/RA\hfill \end{array}$$

(3)

where $A_{bio}$ is the normalization coefficient for the biological richness index. $A_{bio}=100/A_{Max}$, $A_{Max}$ is the maximum value before normalization for an index.

The VCI can be calculated using Formula (4):

$$\begin{array}{c}\hfill VCI=NDV{I}_{mean}=A_{veg}\times \left(\frac{{\sum }_{i=1}^n{P}_i}{n}\right)\end{array}$$

(4)

where $A_{veg}$ is the normalization coefficient for the vegetation coverage index, $A_{veg}=100/A_{Max}$, where $A_{Max}$ is the maximum value before normalization for an index, P is the pixels’ mean NDVI from May to September, and n is the pixel number in the region.

The WNDI can be calculated using Formula (5):

$$\begin{array}{cc}\hfill WNDI=& A_{riv}\times \frac{RL}{RA}+A_{lak}\times \frac{WA}{RA}+A_{res}\times \frac{AWR}{RA}\hfill \end{array}$$

(5)

where $A_{riv}$ is the normalization coefficient for river lengths, $A_{lak}$ is the normalization coefficient for water area, $A_{res}$ is the normalization coefficient for the amount of water resources, $A_{riv,lak,res}=100/A_{Max}$, $A_{Max}$ is the maximum value before normalization for an indicator, $RL$ means river length, $WA$ means water area, and $AWR$ means amount of water resources.

The LDI can be calculated using Formula (6):

$$\begin{array}{cc}\hfill LDI=& A_{ero}\times \left(\right(0.05\times slight+0.15\times moderate+0.2\times strong+\hfill \\ \hfill & 0.25\times severe+0.35\times verysevere\left)\right)/RA\hfill \end{array}$$

(6)

where $A_{ero}$ is the normalization coefficient for the land degradation index, $A_{ero}=100/A_{Max}$, where $A_{Max}$ is the maximum value before normalization for an index, and $slight$, $moderate$, $strong$, $severe$, and $verysevere$ represent the soil erosion intensity (SEI) classification.

4.3. Economic Development Index

EDI can be calculated using Formula (7):

$$\begin{array}{c}\hfill EDI={\sum }_{i=1}^{10}{W}_i{C}_i\end{array}$$

(7)

where $W_i$ is the weight of the i-th indicator and $C_i$ represents the standardized value of the i-th indicator. The $W_i$ value was calculated based on annual economic data using the entropy method [90]. The first step in calculating the weights is to standardize the data of the ten indicators involved in the EDI calculation. All ten indicators had positive effects, and all of them were subject to positive data processing (Formula (8)):

$$\begin{array}{c}\hfill {\tilde{x}}_{ij}=\frac{X_{ij}-min{X}_{ij}}{max{X}_{ij}-min{X}_{ij}}\end{array}$$

(8)

where ${\tilde{x}}_{ij}$ is the standardization result, $X_{ij}$ represents the value of the j-th indicator of the i-th county-level unit, and $ma{x}_{ij}$ and $mi{n}_{ij}$ represent the maximum and minimum value of the i-th indicator in all objects, respectively.

Then, the entropy value of the j-th indicator ($e_j$) was calculated using Formulas (9) and (10):

$$\begin{array}{c}\hfill k=1/ln\left(n\right)\end{array}$$

(9)

$$\begin{array}{c}\hfill e_j=-k{\sum }_{i=1}^n{\tilde{x}}_{ij}ln{\tilde{x}}_{ij}\end{array}$$

(10)

Finally, the weight of the j-th indicator ($w_j$) was calculated using Formulas (11) and (12):

$$\begin{array}{c}\hfill g_j=1-e_j\end{array}$$

(11)

$$\begin{array}{c}\hfill w_j=g_j/{\sum }_{j=1}^p{g}_j\end{array}$$

(12)

4.4. Coupling Coordinated Degree Model

The CCD model can be written as Formula (13):

$$\begin{array}{cc}\hfill D& =\sqrt{C\times T}\hfill \end{array}$$

(13)

where D is the CCD value, C is the coupling degree, and T is the coordinated degree. C and T can be calculated using Formulas (14) and (15) [91], respectively:

$$\begin{array}{c}\hfill C=2\times {\left[\frac{U_1·U_2}{{\left(U_1+U_2\right)}^2}\right]}^{\frac{1}{2}}\end{array}$$

(14)

$$\begin{array}{c}\hfill T=Q_1{U}_1+Q_2{U}_2\end{array}$$

(15)

where $U_1$ is the ecosystem, $U_2$ is the economic system, and $Q_1$ and $Q_2$ represent the coefficients of the ecosystem quality and economic development level, respectively. In this formula, $Q_1+Q_2=1$ was specified. Referring to the relevant literature [92], combined with the actual state of the study area, $Q_1$ and $Q_2$ were determined to be 0.46 and 0.54, respectively.

As shown in

Table 2. Coupling coordinated degree classification.

CCD (D)	Coordination Level	Description
0 ≤ D < 0.1	1	extremely disordered
0.1 ≤ D < 0.2	2	severely disordered
0.2 ≤ D < 0.3	3	moderately disordered
0.3 ≤ D < 0.4	4	mildly disordered
0.4 ≤ D < 0.5	5	on the brink of disorder
0.5 ≤ D < 0.6	6	barely coordinated
0.6 ≤ D < 0.7	7	primary coordination
0.7 ≤ D < 0.8	8	moderately coordinated
0.8 ≤ D < 0.9	9	good coordination
0.9 ≤ D < 1.0	10	excellent coordination

, the coupling coordinated degree of the coupled systems is divided into ten types.

5. Results

5.1. Ecosystem Status Evaluation

The data for 2005, 2010, 2015, and 2019 were brought into the BRI, VCI, WNDI, and LDI calculation formulas and we obtained the results shown in Figure 2, Figure 3, Figure 4 and Figure 5. Then, the calculated results for the BRI, VCI, WNDI, and LDI in 2005, 2010, 2015, and 2019 were incorporated into Formula (2) and we obtained the results shown in Figure 6.

According to these figures and the results from the statistical analysis, the overall ecosystem quality of the study area shows that most counties and districts were at a “normal” level ($35<EI\le 55$) during the previous 15 years. This category comprised 118 counties and districts with an area of 150,169 km² in 2005, 111 with an area of 133,792 km² in 2010, 112 with an area of 141,000 km² in 2015, and 113 with an area of 146,129 km² in 2019, accounting for 83.78%, 74.64%, 78.66%, and 81.52% of the total area, respectively. In total, 16 counties and districts with an area of 28,661 km² were at a “good” level ($55<EI\le 75$) in 2005, 22 with an area of 44,971 km² in 2010, 20 with an area of 37,549 km² in 2015, and 18 with an area of 32,311 km² in 2019, accounting for 15.99%, 25.09%, 20.95%, and 18.03% of the total area, respectively. Finally, 5 counties and districts with an area of 422 km² were at a “bad” level in 2005, 6 with an area of 489 km² in 2010, 7 with an area of 703 km² in 2015, and 8 with an area of 812 km² in 2019, accounting for 0.24%, 0.27%, 0.39%, and 0.45% of the total area, respectively. There was no county or district at an “excellent” or “worse” level.

5.2. Economic Development Level Evaluation

The EDI results of the study area in 2005, 2010, 2015, and 2019 were obtained after calculation using Formula (7) and geographic data visualization using ArcGIS. The results are presented in Figure 7.

As shown in Figure 7 and the analysis results from 2005, the Jiulongpo District in Chongqing had the highest level of economic development, and the Pingshan District in Yibin City ranked last. Regional economic development levels in 2010 were similar to those of 2005; however, the number of districts and counties with an economic level greater than 0.10 increased. In 2015, the top districts and counties were Yubei District, Shapingba District, and Jiulongpo District, all in Chongqing City. In 2019, the economic development level of the entire Chengdu–Chongqing urban agglomeration improved. Yubei District in Chongqing had the highest EDI, at 1, in 2019. The economic development levels of the study areas from 2005 to 2019 showed a spatial pattern of continuous improvement around the two core cities, Chongqing and Chengdu.

5.3. Coupling Coordinated Degree Analysis

The ecological–economic coupling degree reflects the coupling relationships, and the coordinated degree reflects the quality of the relationships between the ecosystem and the economic system in the study area. Excessive economic growth significantly reduces the ecosystem quality of the region, and a degraded ecosystem limits the long-term development of the economy, in turn. These limits result in mutual constraints and inhibition between the two systems. The ideal situation is to find a development model that can not only maintain good ecosystem quality, but also maintain a stable growth rate of the economy. Therefore, we need a quantitative and rapid method to assess the relationship and its quality between the two subsystems in a region. The CCD model was developed for this goal.

5.3.1. Ecological–Economic Systems Coupling Analysis

The coupling degree (C) was calculated using Formula (14), and the results are presented in Figure 8.

The value of the coupling degree ranges from 0 to 1. If the value is equal to 0, no coupling occurs. If the value is more than 0 and less than 0.3, the ecological–economic systems have a tendency to be coupled, but the overall level is low; when the coupling degree is between 0.3 and 0.5, the two systems have formed a weakly stable coupling system, in an antagonistic state, but the development level of a particular subsystem is still low. If economic development continues at a high growth rate, there will be tremendous pressure on the ecosystem and lead to severe ecological problems. A coupling degree in the range of 0.5 to 0.8 indicates that the two systems are already in a run-in state, forming a relatively stable coupling system and showing a nice trend in development. Meanwhile, economic growth has reached a certain level, and people are starting to pay attention to the ecosystem’s issues and take action to protect ecosystems. If the value exceeds 0.8, the coupling state of the two systems reaches a relatively strong coupling level, and both subsystems can develop to a higher level, showing a virtuous circle.

As seen in Figure 8, the coupling state of the study area is generally good in the early stages. Most of them are in the run-in stage and high-quality coupling state. More and more districts and counties are in an antagonistic state. From 2005 to 2010, the coupling state in the Guang’an District of Guang’an City deteriorated the most, from the initial high-quality coupling to the primary coupling state. This was because the region’s economic growth had risen rapidly in the previous five years, while ecosystem quality had shown a downward trend, and both systems had experienced uncoordinated development. During the 2010–2015 period, the coupling state of the central districts and counties in the study area continued to deteriorate. The main reason was that while the regional economic development level rose, the quality of the ecosystem had not improved. The situation improved partly in 2019, and only the Wuhou District in Chengdu City and Yanting County in Mianyang City showed a worsening trend. Overall, the coupling degrees of the districts and counties in Chongqing were generally good. Most of them maintained a high-quality coupling status. Some districts and counties in Sichuan Province experienced a decline in the coupling degrees of the ecological–economic systems due to their economic growth in the previous 15 years, and the overall state was the worst in 2015. In addition, the coupling degrees of the central urban area in Chengdu City were poor.

5.3.2. Coupling Coordinated Degree Analysis

Although the coupling degree can reflect the relationship between the ecosystem quality and the level of economic development to a certain extent, it cannot reflect the “synergy” effect of the subsystems on the whole. For this reason, the evaluation of the coupling and coordination of the ecological–economic systems also needs to consider the coupling and coordination degree. The spatial pattern of CCD in the study area is shown in Figure 9.

As seen in Figure 9 and Figure 10, the overall CCD of the study area showed that most counties and districts were on the brink of disorder (CCD level 5) and barely coordinated (CCD level 6). A total of 64, 45, 44, and 45 counties and districts were on level 5, and their area proportions were 49.79%, 31.93%, 33.41%, and 30.29% in 2005, 2010, 2015, and 2019, respectively. The area proportions showed an overall downward trend. A total of 48, 53, 46, and 41 counties and districts were on level 6, and their area proportions were 34.18%, 43.21%, 30.04%, and 31.46% in 2005, 2010, 2015, and 2019, respectively. The area proportions showed an upward trend from 2005 to 2010, followed by a downward trend. Primary coordination (CCD level 7) followed the two. A total of 20, 25, 15, and 20 counties and districts were on level 7, and their area proportions were 14.02%, 14.76%, 10.37%, and 13.42% in 2005, 2010, 2015, and 2019, respectively. It is worth noting that the mildly disordered level (CCD level 4) had 3, 3, 17, and 23 counties and districts in 2005, 2010, 2015, and 2019, and their area proportions were 0.84%, 0.19%, 13.71%, and 17.02%, respectively. The number of counties and districts changed from 3 in 2010 to 17 in 2015 to 23 in 2019, and their area proportions changed from 0.19% to 13.71% to 17.02%, correspondingly. Moderately coordinated (CCD level 8) included 3, 10, 11, and 8 counties and districts in 2005, 2010, 2015, and 2019, and their area proportions were 1.16%, 8.53%, 9.04%, and 6.09%, respectively. In addition, no areas were in the worst (i.e., extremely disordered) or best (i.e., excellent coordination) levels.

To further illustrate the spatial distribution of CCD in the study area from 2005 to 2019, the local indicator of spatial association (LISA) maps for CCD were calculated. The results are shown in Figure 11. The LISA cluster maps show that the CCDs of Bishan, Jiangjin, Jiulongpo, Dadukou, Shapingba, Yubei, Changshou, Jiangbei, Yuzhong, Nan’an, and Banan districts in Chongqing City had a significant high–high spatial cluster, and Daying County in Suining City, Lezhi county in Ziyang City, and Xinwen county in Yibin City had significant low–low spatial clusters in 2005. In 2010, in addition to the districts in Chongqing City in 2005, Fuling, Nanchuan, Qijiang, and Beibei districts and Dianjiang County were added to the regions with a high–high spatial cluster. No counties and districts had a low–low spatial cluster this year. In 2015, Tongliang District in Chongqing City was added as high–high spatial cluster, and Pingshan County in Yibin City, Mabian and Muchuan Counties in Leshan City, Yucheng District in Ya’an City, and Daying County in Suining City had significant low–low clusters. In 2019, Yongchuan District of Chongqing City was added to the high–high cluster, and Dianjiang County of Chongqing City was removed. Chongzhou City and Wenjiang District in Chengdu City changed to a significant high–high cluster. Mabian County in Leshan City, Yucheng District in Ya’an City, and Hongya County in Meishan City had significant low–low clusters. From 2005 to 2019, the districts’ CCD in the main urban metropolitan area of Chongqing City always showed a significant high–high cluster pattern. However, there was no obvious regularity in the spatial clusters of CCDs of the counties and districts in Sichuan Province. Overall, the districts and counties in the main urban area of Chongqing City showed stronger regional synergy in the coupling coordination between ecosystem protection and economic growth.

6. Discussion

This paper focused on how CCD methods could be used to assess the coupling coordinated relationships between ecosystem and economic systems at the county level in urban agglomerations. Our motivation came from the ongoing territorial spatial planning in the study area, attempting to support the local governments’ spatial governance policymaking. The significance of our study on the CCDs of regional ecological–economic systems lies in the coupling coordinated assessment of the two systems for the upcoming ecological protection policies or economic growth policies to deal with possible ecological or economic risks arising from the implementation of emerging policies. Two indexes (EI and EDI) were used to calculate the CCDs and determine their levels in 2005, 2010, 2015, and 2019, as well as the spatiotemporal variation, which was analyzed.

However, when we calculated EI, we selected some indicators of the natural ecosystem, but did not select those related to environmental pollution due to the lack of data. Additionally, the indicators we chose can be calculated using remote sensing satellite image data and public data. In the future, the calculation results of remote sensing satellite data, such as sulfur dioxide, nitrogen dioxide, aerosol, and water environment quality, can be added to form a remote sensing technology system to quickly evaluate the ecosystem status in urban agglomerations.

We presented and analyzed the results from a spatiotemporal perspective. EI results showed that from 2005 to 2019, most counties and districts had a “normal” level of ecosystem quality, and the counties and districts with a “good” level of ecosystem quality were distributed in the fringes of the study area. They were mainly located in the southwest, south, southeast, and northeast. Counties with “bad” ecosystem quality levels accounted for a small percentage. There was no county or district at an “excellent” or “worse” level. EDI results showed that from 2005 to 2019, the economic development level had a spatial pattern of continuous improvement around the two core cities, Chongqing and Chengdu. The CCDs showed that from 2005 to 2019, most counties and districts in the urban agglomeration were on the brink of disorder and had barely coordinated levels. In addition, there were no areas labeled worst (i.e., extremely disordered) or best (i.e., excellent coordination). The LISAs of the CCDs were further calculated from 2005 to 2019. The results showed that the districts and counties in Chongqing City overall had stronger regional synergy in the coupling coordination of ecosystem protection and economic growth. The most likely reason for this pattern lies in the difference in terrain. The Chengdu metropolitan area is located in a relatively flat area of the basin, so it is easier to carry out concentrated and contiguous urban construction, and it seriously encroached on the surrounding cultivated land [93]. On the contrary, the Chongqing metropolitan area is located in a mountainous area, and decentralized construction makes it easy to preserve the natural ecosystem and farmland. Future research will continue to explore the factors related to the different spatial patterns of CCDs in the two core metropolitan areas of the Chengdu–Chongqing urban agglomeration so as to provide further policymaking support for the sustainable development of the urban agglomeration.

From the perspective of supporting spatial governance policymaking, the analysis unit is suitable set at the county level. The county-level analysis unit is more in line with the actual needs of supporting the policymaking of urban agglomeration economic development and ecological protection because the county government is not only the executor of the superior policies but also the maker of the county policies. In any case, it was much better than the existing studies that took Chongqing City as the analysis unit and prefecture-level cities in Sichuan Province as the analysis units. In future research, we plan to solve this problem with the technical method GIS. In addition, the social dimension needs to be added to the research, and the coupling coordinated development of the three subsystems of environment–economy–society can better support the sustainable development policymaking for urban agglomerations. If combined with scenario analysis methods and system dynamics (SD) models, we may be able to provide better decision support for the future sustainable development of urban agglomerations.

Nonetheless, this paper provides a case of how to use the CCD model to rapidly and quantitatively evaluate the relationship between the quality of the ecosystem and the level of economic development of urban agglomerations.

7. Conclusions

CCD can represent the quality of sustainable development. This is one of the studies that uses rapid and quantitative methods to evaluate whether the economic system and ecological system of Chengdu–Chongqing urban agglomeration are coupled and coordinated. The purpose is to better assist policy makers in monitoring the effects of economic or ecological protection policies in a timely manner according to the sptiotemporal changes of CCDs within the urban agglomeration in the context of spatial planning. The main contribution is to clarify the coupling and coordinated relationships of the ecological–economic systems and to present their spatiotemporal variations at the county level in Chengdu–Chongqing urban agglomeration. We calculated the EI, representing the ecosystem quality and its required parameters from 2005 to 2019, and the EDI, representing the level of economic development. Finally, we obtained the CCDs for four years. Their spatiotemporal variation characteristics were analyzed. Moreover, through LISA analysis, we found that high CCD levels were clustered in the main urban area of Chongqing City. These findings demonstrate that considerable ecological barriers, such as mountains and rivers, are the geographical basis for urban agglomerations to ensure the coupling and coordination of economic development and ecological protection. For the megacity of Chengdu and other cities located in the flat part of the urban agglomeration, reasonable urban growth boundaries (UGB) must be delineated in spatial planning to prevent urban sprawl from encroaching on forest land, farmland, and water. Alternatively, megacities and large cities should be planned and built in flat areas according to the group structure development mode. The industry category, nature, and scale of the layout should match the nature and scale of each group. In this way, UGBs or group structures play a role similar to ecological barriers. These findings further indicate that decentralized development is significant for maintaining the balance between economic development and ecological protection in urban agglomeration. For urban agglomerations, it is necessary to limit the excessive agglomeration of economic factors and population in central megacities, and balance megacities, large cities, medium-sized cities, small cities, and towns within urban agglomerations. Spatial planning needs to reasonably determine the scale and development orientation of each city based on the coupling and coordination between economic system and ecological system. In addition, from a theoretical perspective, it can be linked with relevant research on social–ecological systems, such as resilience, to better explore the development model of ecological civilization from multiple dimensions and achieve sustainable development of urban agglomerations.

The spatiotemporal variation of the CCDs between the economic system and the ecological system is related to the economic development stage of the central city and each county-level unit in the urban agglomeration and is also closely related to the terrain, climate, and industrial structure. This is a preliminary hypothesis based on the results of this study. Subsequent research will continue the idea of the Environmental Kuznets Curve and expand the time range. From the early stage of reform and opening up to the present, it will be divided into four stages, i.e., the early stage of industrialization, the middle stage of industrialization, the late stage of industrialization, and the post-industrialization, to explore the temporal and spatial changes of the coupling coordination degree. In this way, the general law of the relationship between economic development and ecological protection of Chengdu–Chongqing urban agglomeration can be obtained. The GWR model or geographic detectors will be used to analyze the spatial correlation between the CCDs and the terrain, climate, industry, and population in subsequent studies so as to find the main factors that affect the coupling coordination of the two. Furthermore, the mechanism of coupling and coordination between the two can be theoretically discussed.

The technical method system can be applied to other urban agglomerations or large spatial scale regions. The combined application of remote sensing, geographic information system technologies, and the CCD model has played an important role in the rapid quantitative calculation of CCDs. Compared with the traditional method, which relies purely on public statistical data, the scope and flexibility of data availability and analysis are clearly more extensive.