Assessment of Coupling Synergy Level Between Regional Development and Ecological Environment: A Case Study of Chengdu-Chongqing Dual-City Economic Circle, China

Abstract

The increasing tensions between urban development and ecological environments in China, as evidenced by the country’s rapidly globalising and urbanising society, pose significant challenges to the implementation of sustainable development strategies. In order to evaluate the coupled coordination degree (CCD) between regional development (RD) and ecological carrying capacity (ECC) within the Chengdu-Chongqing Twin Cities Economic Circle, this study proposes a comprehensive CCD evaluation framework. This framework includes indicator analysis, quantitative measurement of coupling coordination, identification of coupling types, and evaluation of coordination impacts. The results indicate that regional development is on a growth trajectory while ecological carrying capacity lags behind. The coupling coordination has shifted from a state of ’basically uncoordinated’ to ’basically coordinated’, exhibiting a decreasing gradient in spatial distribution, with core cities demonstrating leadership. The study thus provides a set of optimised strategies tailored to cities at different stages of development, offering a theoretical foundation and decision-making framework for promoting quality development within the Chengdu-Chongqing Twin Cities Economic Circle.

1. Introduction

As China’s urbanisation accelerates, the contradiction between economic development and ecological protection is becoming increasingly evident, particularly within the Chengdu-Chongqing twin-city economic circle, a nationally significant urban agglomeration. The Chengdu-Chongqing twin-city economic circle plan constitutes a pivotal national policy aimed at fostering high-quality economic development in western China [1]. The Chengdu-Chongqing area, as the core of economic development in western China, has experienced significant ecological pressure while promoting economic growth through rapid urbanisation and industrialisation. The reconciliation of economic development with ecological carrying capacity has become a core issue in the sustainable development strategy of the region and the world. This issue is of concern not only to the long-term stable growth of the regional economy but also to global ecological security and sustainable resource use. It is, therefore, the objective of this study to explore the coupling and coordination between economic development and ecological carrying capacity in the Chengdu-Chongqing Twin Cities Economic Circle. This is of great theoretical significance and provides a practical reference for other rapidly urbanising regions around the world.

Significant progress has been made in the relationship between economic development and ecological conservation, with a focus on the themes of ecological carrying capacity (ECC), regional development (RD) and sustainable urbanisation. For instance, Wang et al. [2] investigated the spatio-temporal interplay between urban development and ecological quality, underscoring the significance of sustainable practices in harmonising urban expansion and ecological preservation. Liang and Zhao [3] utilised the super-efficient SBM model to analyse the coupled coordination between economic development and environmental sustainability across various Chinese regions, thereby exposing the disparity between economic growth and ecological protection. The imbalance between economic growth and ecological protection was revealed. Furthermore, Wu et al. [4] investigated the coupled relationship between regional development and ecosystems through a multi-scale analysis approach, highlighting the importance of a multi-scale perspective in assessing the synergistic effects of urbanisation and ecological health.

Fan et al. [5] studied the coupled relationship between socio-economic development and the ecological environment, proposing a framework for assessing the coordinated development of the two and exploring the policy implications. Sui et al. [6] focused on the analysis of the coupled coordination of resources, economy, and ecology in the Yellow River Basin and emphasised the necessity of integrating resource management and economic and ecological policies. Lei et al. [7] particularly analysed the coupled coordination of urbanisation and the ecological environment in the Chengdu-Chongqing urban cluster, revealing the challenges posed by rapid economic expansion. Lei et al. [7] analysed the coupled relationship between urbanisation and the ecological environment in Chengdu-Chongqing urban agglomeration, revealing the challenges brought by rapid economic expansion. In contrast, Zou et al. [8] studied the coupled relationship between urbanisation and ecological environment with spatial and temporal heterogeneity in Shaanxi Province and proposed a comprehensive strategy to cope with regional differences. Zhai et al. [9] explored the coupled relationship between urbanisation and ecosystem service values in the Beijing-Tianjin-Hebei urban agglomeration, concluding that urbanisation frequently exerts a detrimental effect on ecosystem services. However, the synergistic effect can be enhanced through effective planning and ecological restoration. Yang et al. [10] evaluated the coupled and coordinated relationship between geo-ecological environments and urbanisation in the Chongqing region, emphasising the role of regional policies in mitigating the negative impacts. Zhang et al. [11] analysed the coupled relationship between the regional economy, tourism and ecological environment in western China and indicated the dual role of tourism in economic development and ecological pressure.

Wang et al. [12] investigated the coupled and coordinated relationship between ecological resilience and new urbanisation in the Chengdu-Chongqing Twin Cities Economic Circle and emphasised the importance of integrating ecological resilience and urbanisation practices for sustainable development. He et al. [13] explored the coupled and coordinated relationship between water resources, socio-economic development and the ecological environment in China and proposed a model for assessing the interdependence of these factors. In addition, Wang et al. [14] studied the relationship between urbanisation and the ecological environment in the Beijing-Tianjin-Hebei region and pointed out that although urbanisation promotes economic growth, it often leads to environmental degradation. Zhang et al. [15] utilised geographically weighted regression (GWR) to analyse the spatial and temporal variability in the coupled and coordinated relationship between economic development and the water environment in different Chinese provinces, thereby providing a novel perspective for understanding regional differences. Zhang [16] investigated the spatial and temporal changes in the coupled and coordinated relationship between urban socio-economic development and the ecological environment in different regions, emphasising the synergistic evolution of socio-economic and environmental factors.

Actually, many countries are facing the same serious challenges in deteriorated ecology & environment during the process of urban development [17]. This is particularly pertinent in the context of rapid population growth, which has resulted in substantial challenges for the ecological environment, giving rise to increasingly severe environmental pollution and a gradual imbalance between social progress, economic development, and environmental protection [18]. As human beings gradually comprehend the interaction between economic activities and the environment, particularly when a substantial number of pollutants resulting from economic activities inflict grave detrimental impacts on the environment, the interaction mechanism between economic development and the ecological environment has been further investigated by researchers [19,20]. In preliminary studies, economists Grossman and Krueger established that a mutually reinforcing relationship exists between environmental pollution and economic growth. The subsequent investigation by Grossman and Krueger established that restrictive rules between resource utilisation and environmental protection further strengthen this connection, providing a solid foundation for subsequent investigations into a harmonious development nexus between the economy and the environment [21].

Although extant studies have explored regional development and ecological carrying capacity to a certain extent, these studies are mostly one-dimensional analyses and lack the dynamic coordination analysis of the relationship between the two. This is particularly evident in the Chengdu-Chongqing Twin Cities Economic Circle, a region undergoing rapid urbanisation, where the extant literature has not yet explored in depth the coupling and coordination mechanism between economic development and ecological carrying capacity, nor their spatial and temporal evolution laws. Moreover, the prevailing static models in current research fail to adequately capture the dynamic interplay between regional development and ecological protection. To address this knowledge gap, this study adopts the ‘coupling coordination degree’ (CCD) framework, integrating spatial and temporal analyses to explore the interaction between economic development and ecological carrying capacity in the Chengdu-Chongqing region. This approach aims to provide a comprehensive and dynamic perspective on the coupled processes of economic development and ecological protection, thereby contributing to a more nuanced and informed understanding of the region’s urbanisation and environmental management.

The objective of this study is to evaluate the coupled coordination degree (CCD) between regional development and ecological carrying capacity in the Chengdu-Chongqing Twin Cities Economic Circle. In addition, the study will explore the spatial and temporal evolution patterns of the CCD and their implications for sustainable urban development. Specifically, the study will address the following research questions: (1) What is the coupling coordination degree between regional development and ecological carrying capacity in the Chengdu-Chongqing area? (2) How does this coordination differ between different cities in the Chengdu-Chongqing region? (3) What are the implications of this analysis for the formulation of sustainable urban development policies in the region? By addressing these questions, this study will provide a scientific foundation for the sustainable development of the Chengdu-Chongqing Twin Cities Economic Circle and will also offer a reference point for the ecological protection and economic development of other rapidly urbanising regions worldwide.

2. Materials and Methods

2.1. Data Sources

All data presented in this study were obtained from the China Urban Statistical Yearbook, a publication by the National Bureau of Statistics of China (NBS), as well as local statistical yearbooks for each city. In addition, the China Urban Statistical Yearbook, the Natural Resources and Environment Statistical Yearbook, the Sichuan Statistical Yearbook, and the bulletins of each city for the years 2010 to 2021 were also consulted. A small number of missing values were fitted and supplemented by linear interpolation.

2.2. Study Area

The Chengdu-Chongqing Twin Cities Economic Circle is located in western China and consists primarily of two significant urban agglomerations, namely Chengdu and Chongqing. It also extends to parts of Sichuan Province and Chongqing Municipality, as illustrated in Figure 1. Chengdu: Situated centrally within Sichuan Province, on the western periphery of the Sichuan Basin, with geographic coordinates of approximately 30°40′ north latitude and 104°04′ east longitude. As the capital of Sichuan Province, Chengdu is located on the upper reaches of the Yangtze River and is the core city of the Sichuan Basin. The city’s strategic location and well-developed transport infrastructure facilitate convenient connections with neighbouring provinces and regions. Chongqing: Chongqing is located in the southwestern part of China, with geographic coordinates of approximately 29°33′ north latitude and 106°34′ east longitude. Chongqing, a municipality directly under the central government, is located at the confluence of the Yangtze and Jialing Rivers, at the upper reaches of the Yangtze River, and is a transport and logistics hub in western China. The city’s strategic importance is further underscored by its proximity to Sichuan Province, a geographical feature that places it in a pivotal position in the region.

A combination of the GDPs of Chengdu and Chongqing, along with the economic data of the neighbouring regions of Sichuan and Chongqing, indicates that the total GDP of the Chengdu-Chongqing Greater Municipal Area will reach approximately RMB 5.0 trillion in 2023. This positions the Chengdu-Chongqing Greater Municipal Area as one of the most significant economic regions in western China. The total population of the Chengdu-Chongqing FTZ is approximately 53 million people. This substantial population signifies that the region functions not only as an economic centre but also as a significant consumer and labour market.

The Chengdu-Chongqing Twin Cities Economic Circle is committed to the further development of its infrastructure, the promotion of industrial collaboration, and the adoption of environmentally sustainable and low-carbon practices. These efforts are intended to ensure that the region maintains a prominent role in globalisation, intelligence, and digital transformation. The Chengdu-Chongqing economic circle is not only an economic engine in the West but is also expected to become an important demonstration area for China’s high-quality development. The present study has selected nine core cities (Chongqing, Chengdu, Zigong, Deyang, Mianyang, Leshan, Luzhou, Nanchong, and Dazhou) for in-depth research.

2.3. Research Framework

As demonstrated in Figure 2. The present study first constructs the RD and ECC indicator system based on the basic characteristics of the Chengdu-Chongqing Twin Cities Economic Circle and the existing data. Subsequently, the weighting, comprehensive development level (CDL), and coupling coordination degree (CCD) are calculated. The study then employs these metrics to reveal the spatial and temporal distribution characteristics of RD and ECC coupling coordination level and coordination type in the Chengdu-Chongqing Twin Cities Economic Circle.

2.4. Method

2.4.1. Construction of the Indicator System

The present paper takes the Chengdu-Chongqing Twin Cities Economic Circle as the object of study. It follows the principles of goal orientation, systematicity, scientificity, feasibility, and difficulty of data collection. It also combines the resource and environmental status quo of the Chengdu-Chongqing Twin Cities Economic Circle. The result is the construction of the Chengdu-Chongqing Twin Cities Economic Circle Regional Development Evaluation Indicator System and the Ecological Carrying Capacity Evaluation Indicator System in terms of ecology, environment, economy and society.

In the present paper, the variables (indicators) selected encompass the two systems of regional development and ecological carrying capacity, comprising five subsystems of regional development (each containing nine indicators) and four subsystems of ecological carrying capacity (a total of fifteen indicators), as shown in

Table 1. Regional Development and Ecological Environment Indicator System and Weights of Chengdu-Chongqing Twin Cities Economic Circle.

Objective Level	Criterion Layer	Index	Unit	Characteristic	Weight
Regional development	Efficacy	GDP per capita	Chinese yuan renminbi (CNY)	positive	0.130666
	Coherence	Urbanisation rate	%	positive	0.129252
		Rationalisation of industrial structure	—	positive	0.1184
	Innovativeness	Total R&D expenditure	10,000 CNY	positive	0.13003
		Patent grant rate per 10,000 inhabitants	Number of cases/10,000 persons	positive	0.098406
	Stability	Dependence on foreign trade	%	positive	0.042095
		Level of investment in social security	10,000 CNY	positive	0.125356
	Shareability	Ratio of urban to rural per capita income	—	negative	0.113673
		Number of S&T personnel per 10,000 population	Persons per 10,000 persons	positive	0.112122
Ecological carrying capacity	Ecological and environmental support	Total water resources	$\mathrm{10,000}{\mathrm{m}}^3$	positive	0.038375
		Area under cultivation	${\mathrm{H}\mathrm{m}}^2$	positive	0.083122
		Green area per capita	${\mathrm{m}}^2/\mathrm{p}\mathrm{e}\mathrm{r}\mathrm{s}\mathrm{o}\mathrm{n}$	positive	0.017417
		Green cover of built-up areas	%	positive	0.040858
	Ecological pressure	Industrial waste water emissions	$\mathrm{10,000}\mathrm{t}\mathrm{o}\mathrm{n}\mathrm{n}\mathrm{e}\mathrm{s}$	negative	0.096634
		Industrial dust emissions	$\mathrm{10,000}\mathrm{t}\mathrm{o}\mathrm{n}\mathrm{n}\mathrm{e}\mathrm{s}$	negative	0.098977
		Fertiliser use	$\mathrm{10,000}\mathrm{t}\mathrm{o}\mathrm{n}\mathrm{n}\mathrm{e}\mathrm{s}$	negative	0.059751
	Ecological environment use efficiency	Water use per unit of GDP	${\mathrm{m}}^3/\mathrm{10,000}\mathrm{C}\mathrm{N}\mathrm{Y}$	negative	0.054507
		Energy use per unit of GDP	$\mathrm{T}\mathrm{o}\mathrm{n}\mathrm{n}\mathrm{e}\mathrm{s}\mathrm{s}\mathrm{t}\mathrm{a}\mathrm{n}\mathrm{d}\mathrm{a}\mathrm{r}\mathrm{d}\mathrm{c}\mathrm{o}\mathrm{a}\mathrm{l}/\mathrm{10,000}\mathrm{C}\mathrm{N}\mathrm{Y}$	negative	0.090085
		Sulphur dioxide emissions per unit of GDP	Tonnes/billions	negative	0.096764
		Carbon dioxide emissions per unit of GDP	Tonnes/billions	negative	0.090085
	Environmental governance capacity	Industrial dust removal	Tonnes	negative	0.023067
		Harmless treatment rate of household waste	%	negative	0.077183
		Centralised treatment rate of sewage treatment plants	%	negative	0.03804
		Total recovery rate of general industrial solid waste	%	negative	0.095137

. In order to ensure the accuracy and operability of variable measurements, the following key steps were followed in designing the variables:

(1)

Data availability and consistency: firstly, all variables were selected to be available within the Chengdu-Chongqing Twin Cities Economic Circle with consistent statistical quality. It was ensured that the statistical methods of these indicators were consistent across provinces and cities, especially in the yearbooks of different years, to ensure the comparability and consistency of the data. The process of operationalising the variables relies mainly on the provincial and municipal statistical yearbooks and relevant government reports.

(2)

Consistency with the connotations of regional development and ecological carrying capacity: when selecting variables, it is essential to ensure that they are consistent with the connotations of the two systems of regional development (RD) and ecological carrying capacity (ECC). According to the theoretical framework, it is necessary to ensure that each indicator can reasonably reflect the regional economic, social, environmental, and resource conditions and can quantify its contribution or impact on regional development.

(3)

Widely recognised and comparable: the selected indicators have not only received wide attention in the academic community but also have strong comparability. In particular, indicators that have been verified as valid in previous studies ensure the wide applicability and internationality of the indicator system.

(4)

The indicator system has been developed in close integration with the actual development situation, with particular attention paid to the reality of the Chengdu-Chongqing Twin Cities Economic Circle in order to ensure its alignment with actual development needs. In the selection of variables, the local economic, social and environmental characteristics have been combined as much as possible, with the objective of ensuring that the indicators not only reflect the abstract concepts in the theory but also the specific development status of the region.

2.4.2. Entropy Method

The data were normalised prior to analysis; then, the weights were subsequently calculated using the entropy method with the following formula and steps:

$$P_{ij}={\displaystyle \frac{Y_{ij}}{\sum _{i=1}^n{Y}_{ij}}}$$

(1)

$$e_j=-{\displaystyle \frac{1}{ln\left(n\right)}}{\sum }_{i=1}^n{p}_{ij}\times ln\left(p_{ij}\right)$$

(2)

$$W_j={\displaystyle \frac{1-e_j}{{\sum }_{j=1}^{m}1-e_j}}$$

(3)

where $P_{ij}$ is the weight of the index value of the year $i$ in the calculation of the j-th index, $e_j$ is the entropy value of the index $j$, and $W_j$ is the weight of the index $j$. In the above formula, $n$ represents the number of years and $m$ represents the number of indexes.

2.4.3. Coupled Coordination Degree Model

The calculation formulas are as follows:

$$U_1(U_2)={\sum }_{j=1}^m{W}_j\times Y_{ij}$$

(4)

$$\mathrm{C}=\sqrt{{\displaystyle \frac{4({\mathrm{U}}_1·{\mathrm{U}}_2)}{{({\mathrm{U}}_1+{\mathrm{U}}_2)}^2}}}$$

(5)

$$\mathrm{T}=\mathsf{\alpha }{\mathrm{U}}_1+\mathsf{\beta }{\mathrm{U}}_2$$

(6)

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

(7)

In this study, $U_1$ and $U_2$ are used to represent the comprehensive development level index of the RD system and ECC system, respectively. $Y_{ij}$ is used to represent the standardised value of each index and $W_j$ is used to represent its weight. The value of $U_1$ and $U_2$ ranges from 0 to 1, with higher scores indicating higher development levels for both the RD and ECC systems. $\mathrm{T}$ denotes the comprehensive evaluation index between RD and EE systems. In Formula (6), $\mathsf{\alpha }$ and $\mathsf{\beta }$ ($\mathsf{\alpha }+\mathsf{\beta }$ = 1) represent the contribution coefficients of the RD system and EE system, respectively. In this study, $\mathsf{\alpha }=0.5$ and $\mathsf{\beta }=0.5$ were set. The value range of the coupling coordination degree $\mathrm{C}$ and the coupling degree $\mathrm{D}$ is $[0,1]$. In addition, the coupling coordination degree is classified into five levels and three types according to the values of coupling coordination degree in order to reflect the coupling coordination development level of the two systems in contrast.

2.4.4. Geographically and Temporally Weighted Regression Model

The specific formula is as follows:

$$Y_{it}={\beta }_0(u_i,v_i,t_i)+{\sum }_{k=1}^p{\beta }_k(u_i,v_i,t_i)X_{ik}+{\epsilon }_i$$

(8)

In this study, the dependent variable is defined as $Y_{it}$, representing the value of the city $i$ in period $t$. The number of explanatory variables for the city $i$ is denoted as $k$. The spatio-temporal coordinate of the $i$-$\mathrm{t}\mathrm{h}$ sample city is represented by $(u_i,v_i,t_i)$, while $X_{ik}$ denotes the value of the $k$-th explanatory variable for city i. The regression coefficient of the k-th explanatory variable for the city $\mathrm{i}$ as a function of spatio-temporal coordinates is represented by ${\beta }_k(u_i,v_i,t_i)$. Finally, the residual is defined as ${\epsilon }_i$ [22].

2.4.5. Classification of Coupled Coordination Degree

In this study, the entropy value method was utilised to ascertain the weights of the indicators in the RD and ECC systems. The entropy method, a commonly employed objective allocation technique, has been extensively utilised in diverse domains, including socio-economics, engineering and technology. Initially, the data underwent a process of normalisation prior to analysis, and subsequently, the weights were determined through the utilisation of the entropy method [23,24]. In the second step, the comprehensive development level (CDL) and CCD between the two systems were measured using the CCD model. In the third step, the regions were divided into coupling and coordination degrees using the $U_1$ and $U_2$, the coupling degree C value, the comprehensive evaluation index T value, and the coupling coordination degree D value calculated by the CDL and CCD. This was performed in order to comparatively reflect the coordination level of these two systems [25,26] (

Table 2. Classification of coupled coordination degree between Regional Development and Ecological Environment.

Coupling Coordinated Development Types	D *	Degree of Coupling Coordination
Coordinated development	(0.80, 1.00]	Highly balanced
Transition coordinated development	(0.60, 0.80]	Moderately balanced
	(0.40, 0.60]	Basically balanced
Uncoordinated development	(0.20, 0.40]	Moderately imbalanced
	(0.00, 0.20]	Seriously imbalanced

* D = coupling coordination degree value.

).

3. Results

3.1. Comprehensive Development Level of RD

The present study utilised the Regional Development Index (RDI) data of multiple cities in southwestern China from 2010 to 2021 to elucidate the region’s developmental trajectory and distinctive attributes. The data demonstrate that the regional development, encompassing the cities of Chengdu, Chongqing, Deyang and Leshan, has undergone three distinct development phases: the initial phase of slow growth (2010–2013), characterised by a gradual increase in the development index from 0.2 to 0.3; the subsequent phase of relative stability (2014–2016), during which the development of each city decelerated, yet maintained a steady increase; and the final phase of rapid development (2017–2021), marked by a substantial increase in the development index, ultimately reaching levels of 0.7–0.8, as shown in Figure 3.

It is noteworthy that the developmental trajectories of these cities exhibit notable convergence characteristics. The initial disparities in urban development become discernible in the early stages; however, as time progresses, particularly in the period following 2017, when there was a notable surge in development, these differences gradually diminish, leading to a convergence in development levels. This development pattern is indicative of the efficacy of the regional synergistic development strategy, and it demonstrates that national strategies, such as Western development, have been successful in promoting balanced regional development.

The development indexes of these cities have exhibited approximately threefold growth over the past decade or so, with a particularly pronounced increase in growth rate and acceleration in development pace after 2017. This phenomenon is indicative of the substantial achievements in the economic and social development of the Southwest region. Furthermore, it serves to illustrate the region’s favourable development and future potential under the national regional development strategy.

3.2. Comprehensive Development Level of ECC

The dynamics of the ecological carrying capacity index of ten cities in Southwest China between 2010 and 2021 reveal the region’s progress in achieving ecological sustainability, as shown in Figure 4. Chengdu’s ecological carrying capacity index was at a low level in 2010 but then exhibited a significant growth trend, especially in 2019 and 2021, when it reached its highest point. The ecological carrying capacity indices of other cities, including Dazhou, Deyang, Leshan, Luzhou, Mianyang, Nanchong, Yibin and Chongqing, demonstrate variable patterns of fluctuation. Dazhou, for instance, demonstrated a decline following a peak in 2016, while Deyang exhibited an uptick in 2021 after reaching its zenith in the same year. Leshan and Luzhou peak in 2019 and decline slightly in 2021. Mianyang, on the other hand, exhibited a gradual rise following a trough in 2014, reaching its peak in 2019 and experiencing a slight decline in 2021. Nanchong and Yibin also experienced a peak in 2019, followed by a decline in 2021. Chongqing’s index peaked in 2016 and then declined. It is noteworthy that Chengdu attained the highest ecological carrying capacity index in 2021, thus demonstrating its leadership in ecological protection and sustainable development. Conversely, Dazhou exhibited the highest index in 2016, which may be indicative of the city’s particular ecological efforts or achievements in that year. While the performance of cities varies from year to year, most cities demonstrate enhanced performance in 2019 and 2021, which may be attributable to certain common external factors or policies. Despite these fluctuations, the ecological carrying capacity index of most cities has exhibited an overall upward trend between 2010 and 2021, which may reflect these cities’ efforts in environmental protection and sustainable development.

3.3. Spatiotemporal Characteristics of the CCD Between the RD and ECC

From 2010 to 2021, the coupling degree of all cities exhibited an upward trend, indicating an enhancement in the coordination between low-carbon development and the ecological environment, as shown in Figure 5. In 2010, the coupling degree of all cities ranged from 0.35 to 0.45, indicating a state of “basic disorder”. By 2015, the degree of coupling had increased to over 0.60, with the majority of cities entering the state of “basic coordination”. In 2021, the coupling degree of all cities exceeds 0.8, indicating a high degree of coordination. Between 2010 and 2015, the coupling degree exhibited a gradual increase, while between 2015 and 2021, it experienced a notable acceleration, particularly in Chengdu, Yibin and Nanchong.

Spatially, the coupling degree of Chengdu, Yibin and Nanchong is consistently higher than that of other cities, indicating that these cities are better coordinated in terms of low-carbon development and ecological environment. In Chengdu, the coupling degree increased from 0.4479 in 2010 to 0.8931 in 2021, showing a continuous and significant improvement. Luzhou, despite initially having a lower starting point, demonstrates rapid progress, reaching 0.9414 in 2021, indicating a substantial enhancement in coordination. Dazhou, with the lowest initial starting point but steady growth, will attain 0.8948 in 2021, approaching the levels of the other cities. These findings suggest that cities are proactively enhancing the ecological environment while promoting low-carbon development, thereby achieving better-coordinated development. Nevertheless, there are also some regional differences that require further attention and optimisation.

3.4. Analysis of RD and ECC Coupling Coordination Types

As shown in Figure 6, the development of coupling coordination of cities in the study area has gone through three different development phases. The first phase (2010–2013) can be called the “rapid improvement period”, during which the coupling coordination degree of each city generally increased rapidly from the low level of 0.3–0.4 to the range of 0.5–0.6. This rapid improvement was probably due to the new urbanisation strategy implemented by the state at that time, as well as the in-depth promotion of the Western development strategy. In particular, Chengdu, as a regional centre city, showed a strong development advantage in this stage, and the speed and level of its coupling coordination degree was significantly higher than that of other cities. The second stage (2014–2017) can be referred to as the “steady development period”, in which the coupling coordination degree of cities is still improving, but the growth rate has slowed down significantly, showing a typical “S-shaped” development curve. The most important feature of this stage is that the gap between cities has started to narrow, indicating that the regional coordinated development strategy has started to play a role in promoting the synchronised development of neighbouring cities. The third stage (2018–2021) is characterised by “high convergence”, with the degree of coupling coordination of each city reaching a high level of 0.8 or more and the gap between cities further narrowing, indicating a good trend of regional coordinated development.

Between 2010 and 2021, the coupling coordination degree of cities in the Sichuan and Chongqing regions shows an overall increasing trend, but there is a clear stratification in the level of development, as shown in Figure 7. Chongqing and Yibin are the most prominent performers with the highest level of coupling coordination (0.8–0.9), Mianyang and Nanchong are the next highest, while the provincial capitals of Chengdu and Dazhou are at a lower level (0.1–0.3). It is worth noting that most of the cities’ growth trends levelled off after 2015 and entered a period of relative stability, reflecting the fact that there is still an imbalance in the development of cities in the Sichuan-Chongqing region and that special attention needs to be paid in the future to the coordinated development of lower ranking cities such as Chengdu.

3.5. Factors Affecting CCD

3.5.1. The Spatiotemporal Characteristics of the Interaction Between RD and ECC

Firstly, five indicators of RD and four indicators of ECC were used as influencing factors. The significance and non-collinearity of these factors are shown in

Table 3. Summary of linear regression parameter estimates with RD system indicators and ECC system indicators as independent variables and CCD as dependent variables, respectively.

	B	Std Error	Beta	t	p	VIF	R2	Adjusted R2	F
Constant	0.725	-	-	-	-	-	0.9715	0.9551	p = 0.000 *** F = 59.5527
Ecological and environmental support	0.011	0.009	0.011	1.158	0.000 ***	1.372
Ecological pressure	−0.104	0.014	−0.104	−7.226	0.000 ***	3.252
Ecological environment use efficiency	−0.027	0.015	−0.027	−1.791	0.000 ***	3.572
Environmental governance capacity	−0.003	0.009	−0.003	−0.297	0.001	1.169
Efficacy	0.208	0.030	0.208	6.924	0.000 ***	13.033	0.9731	0.9507	p = 0.0001 F = 43.4075
Coherence	−0.022	0.013	−0.022	−1.758	0.000 ***	2.276
Innovativeness	−0.003	0.024	−0.003	−0.124	0.001	8.149
Stability	−0.079	0.029	−0.079	−2.705	0.000 ***	12.297
Shareability	0.006	0.009	0.006	0.623	0.001	1.199

Note: *** p < 0.001, B refers to the non-standardised regression coefficient, Beta refers to standardised regression coefficients, t refers to the process value of the t-test, VIF refers to the variance inflation factor, F refers to the statistic of the F-test, R² refers to the coefficient of determination.

. The study identified five drivers of RD system effectiveness, coherence, innovativeness, stability and shareability, as well as the four drivers of the ECC system: ecological and environmental support, ecological pressure, ecological environment use efficiency, and environmental governance capacity. These factors can be used as explanatory variables for further calculations in the GTWR model.

As demonstrated in

Table 4. GTWR model calculation results and parameters.

Dependent Variable	R2	Bandwidth	Sigma	AICc	Spatiotemporal Distance Ratio	Adjusted R2	Residual Squares
Ecological carrying capacity system	0.9837	0.1	0.5	−62.3487	0.4785	0.9810	0.0497
Regional development system	0.9822	0.1	0.5	−76.7986	0.6625	0.9806	0.0542

, the parameter outputs associated with the GTWR results are presented. The R² and adjusted R² for both explanatory variables are greater than 0.85, indicating that the model can effectively assess the effect relationship.

3.5.2. Impact of RDS Indicators on the Coupling Co-Ordination Degree (CCD)

As illustrated in Figure 8, the findings demonstrate the multi-dimensional impact of key RDS indicators on the Coupling Coordination Degree (CCD). The results reveal the hierarchy and complexity of the impact of these indicators on CCD through precise quantitative analysis. This finding is of significant importance for the formulation of urban planning and development strategies.

Total R&D expenditure (0.2348) emerges as the most significant driver of urban complexity, followed by the level of investment in social security (0.1851) and urbanisation rate (0.1281). The number of S&T personnel per 10,000 population (0.1228) also shows strong influence, highlighting the crucial role of scientific and technological talent in fostering urban innovation.

The ratio of urban to rural per capita income (0.0979) and GDP per capita (0.078) demonstrate a relatively lower impact, suggesting that while economic indicators are important, they are not the determining factors. Patents granted per 10,000 inhabitants (0.0664) and dependence on foreign trade (0.0608) have even smaller effects, indicating that their relationship with urban complexity may be more nuanced or context-specific. Rationalisation of industrial structure shows the least impact (0.0261), implying that industrial diversity may be less significant than knowledge intensity and innovation capacity.

3.5.3. Impacts of Ecological Carrying Capacity System Indicators on Coupling Coordination Degree (CCD)

As demonstrated in Figure 9, the findings illustrate the multi-dimensional impact of key RDS indicators on the Coupling Coordination Degree (CCD). The results reveal the hierarchy and complexity of the impact of these indicators on CCD through precise quantitative analysis, which makes this finding of significant importance for the formulation of urban planning and development strategies.

The findings indicate that total R&D expenditure (0.2348) is the most significant driver of urban complexity, followed by the level of investment in social security (0.1851) and the urbanisation rate (0.1281). The number of S&T personnel per 10,000 population (0.1228) also demonstrates a strong influence, highlighting the crucial role of scientific and technological talent in fostering urban innovation.

The ratio of urban to rural per capita income (0.0979) and GDP per capita (0.078) demonstrate a relatively lower impact, suggesting that while economic indicators are important, they are not the determining factors. Patents granted per 10,000 inhabitants (0.0664) and dependence on foreign trade (0.0608) have even smaller effects, suggesting that their relationship with urban complexity may be more nuanced or context-specific. Rationalisation of industrial structure shows the least impact (0.0261), implying that industrial diversity may be less significant than knowledge intensity and innovation capacity.

3.5.4. Analysis of GTWR Coefficient of Regional Development and CCD

As demonstrated in Figure 10, the influence of regional development factors on the coupling coordination degree (CCD) in different cities over time can be observed. With regard to regional development factors, the fitted coefficients of GTWR in each city demonstrate diverse patterns of influence, and their trends are both convergent and diffuse among different cities. Chengdu showed a strong positive influence from 2010 to 2016, followed by a decline, indicating a diminishing impact over time. The findings of the study demonstrate that Nanchong exhibits a consistently increasing positive influence, suggesting a trend of convergence and diffusion. In contrast, Leshan and Yibin display a negative impact, following an inverted U-shaped pattern, where the negative effect initially decreases before rising again. Mianyang transitioned from a negative to a positive impact during the study period, while Luzhou maintained a predominantly positive influence, albeit with a gradual decline. Dazhou and Chongqing also show positive impacts, though these diminish over time. Regarding natural factors, Deyang and Chongqing primarily exhibit negative effects with relatively stable coefficients, while Dazhou consistently demonstrates a positive effect with minimal variation. The findings suggest that CCD is influenced by multiple factors, with spatial dependence evident in the early and middle stages of the study period. Over time, this dependence weakens, highlighting increasing heterogeneity in urban development patterns.

3.5.5. Analysis of GTWR Coefficients of Ecological Carrying Capacity and CCD

As demonstrated in Figure 11, the temporal heterogeneity of GTWR fit coefficients for ecological carrying capacity and CCD across cities in Sichuan and Chongqing is illustrated. It is evident that Chongqing, as a centrally administered municipality, exhibits a distinctive trend, commencing from a negative value (−0.1) in 2010, reaching a peak of approximately 0.35 in the period 2015–2016 and subsequently stabilising at a considerable level. This indicates the effectiveness of the ecological civilisation initiatives undertaken by the municipality. Chengdu, a regional central city, demonstrates a consistent increase from 0.1 in 2010 to 0.2 in 2021, suggesting a growing positive impact on CCD, which is consistent with the park city strategy implemented in this city. The neighbouring cities of Mianyang and Deyang demonstrate stable positive influences, fluctuating between 0.1 and 0.2, likely attributable to their developed industrial systems and environmental measures. Nanchong’s coefficient exhibits greater variability but demonstrates an upward trend, particularly post-2015, suggesting successful ecological protection efforts. Conversely, Dazhou’s coefficient persists in being negative, particularly from 2010 to 2014, likely attributable to environmental pressures stemming from resource exploitation and industrial transformation. Leshan and Yibin, situated along the Yangtze River, exhibit a modest range of 0 to 0.1, suggesting a moderate impact on CCD. Luzhou, conversely, demonstrates considerable volatility, reaching a peak in 2015–2016 before a decline, which may be attributed to industrial restructuring and environmental policies. Overall, coefficient dispersion was minimal prior to 2015, indicating consistent ecological impacts. However, post-2015, increased dispersion, particularly in central cities such as Chongqing and Chengdu, reflects regional differentiation as urban development progresses. This underscores the necessity for customised ecological policies, taking into account local development stages and environmental characteristics. Large cities should lead in ecological protection, resource cities like Dazhou must balance development and environmental protection, and smaller cities should adopt governance paths suited to their realities.

4. Discussion

4.1. Characteristics of Synergistic Development Between RD and ECC

The cities within the study area can be categorised into three development patterns based on their spatial differentiation characteristics. The first category consists of leading cities, exemplified by Chengdu, which consistently maintains a high coupling coordination degree due to its strong economic foundation, improved infrastructure, and high-quality public services. The second category is that of ‘catching-up cities’, an example of which is Mianyang. These cities initially possessed a lower coupling coordination base but have since made significant improvements through industrial transformation, urban function enhancement, and proactive measures to reduce the gap with leading cities. The third category is that of ‘post-haircut cities’, an example of which is Nanchong. These cities were initially underdeveloped but have achieved notable progress through industrial transfers, strengthened infrastructure, and other development measures.

The improvement in coupling coordination across these cities can be attributed to several factors. Firstly, national strategies, such as the new urbanisation and Western development strategies, played a crucial role by offering policy support and resource guarantees for regional development. Secondly, the establishment of regional cooperative development mechanisms has facilitated the flow of resources and collaboration among cities, promoting overall development. Thirdly, cities have optimised their own development strategies, focusing on industrial structure adjustments and innovation-driven growth to enhance their development potential.

The combined impact of these factors has resulted in synergistic urban development, with a substantial enhancement in the coupling coordination of cities within the study area. Even cities that were initially lagging behind have achieved high levels of coupling coordination by the conclusion of the study, indicating the efficacy of regional development strategies. This trend mirrors the effectiveness of the regional development framework, which fosters integrated and coordinated urban growth and provides a robust foundation for future regional integration. Despite the article encompassing data from 2010 to 2021, it is crucial to acknowledge the potential impact of the peak epidemic period (2020–2021) on the outcomes. A key limitation of the study is the possibility that the atypical nature of epidemics may have distorted some of the data, thereby influencing the accuracy of the conclusions drawn.

4.2. Implications Based on Spatiotemporal Characteristics of Coupled Coordinated Development

The analysis of urban data reveals a coupling and coordinated development (CCD) relationship between ecological carrying capacity (ECC) and urban regional development (RD), with significant imbalances across Chinese cities. While RD has shown steady growth, ECC has lagged behind, particularly in urban agglomerations like EE. This suggests the potential for better coupling and coordination between the two systems in the future, with a focus on reinforcing ECC as a policy priority. Energy efficiency improvements, especially in terrestrial, aquatic environments and natural ecology, are essential to address weaker links in the energy efficiency system. Socio-economic factors like ‘total water resources’ and ‘cultivated land’ positively impact CCD in the upstream region but negatively in the middle and lower regions, showing an inverted ‘U’ shaped pattern. The ‘greening coverage of built-up areas’ transitioned from a positive to a negative impact, indicating the necessity for land-use reforms driven by social progress and the evolving demands of residents. Environmental pressure indicators such as ‘sulphur dioxide emissions per unit of GDP’ and ‘carbon dioxide emissions per unit of GDP’ exert a negative impact, particularly in downstream regions where heightened environmental pressure hinders CCD. Technological reforms in developed regions are imperative as enterprises endeavour to meet policy objectives. Technological innovation is crucial to economic growth, especially in the Chengdu-Chongqing Twin Cities Economic Circle, which emphasises ecological protection and collaborative pollution management. The impact of natural factors, such as ‘green space per capita’ and ‘energy consumption per unit of GDP’, exhibits variations across regions, reflecting differences in regional ecology and resource utilisation. The measures outlined in the Chengdu-Chongqing plan are designed to enhance ecological environments and promote CCD. The ‘rate of non-hazardous treatment of domestic waste’ demonstrates a positive and stable impact on CCD, exhibiting minimal variation in coefficient value.

The ecological environment carrying capacity indicator demonstrates a decreasing then increasing trend over time, accompanied by an increase in urban development heterogeneity. This diversification fosters more expansive development opportunities, necessitating the formulation of region-specific strategies by policymakers to ensure balanced and coordinated urban development. Nonetheless, it is acknowledged that a delay in the effect of these policies may be in evidence. The present findings are subject to the limitation that the effect of the policies may not be significant in the short term; further observation is required to ascertain the long-term impact.

5. Conclusions and Policy Recommendations

The synergistic evolution of regional development (RD) and ecological civilisation construction (ECC) is a crucial pathway for achieving the United Nations Sustainable Development Goals (SDGs) and the vision of shared prosperity. In this study, the urban cluster of the Chengdu-Chongqing Regional Economic Circle serves as a case study, where an innovative theoretical framework is constructed to comprehensively analyse the spatio-temporal coupling mechanism of RD and ECC and its developmental trends. Methodologically, the study advances beyond conventional single evaluation methods by integrating the CRITIC method with the entropy value method, establishing a dual empowerment mechanism. Additionally, the coupled coordination degree (CCD) model is applied to offer a multidimensional analysis of the dynamic evolution of regional development and ecological civilisation construction from 2010 to 2021. The study’s findings and their implications are as follows:

(1)

Regional Development Evolution: The RD of urban agglomerations during the study period exhibits evolutionary characteristics of “low level—steady improvement—convergence development”. This development pattern supports the hypothesis of “β-convergence” in regional development theory, signifying the effectiveness of coordinated regional development strategies in narrowing the development gap between cities. This reinforces the idea that regional development strategies have achieved significant success in promoting convergence and reducing disparities.

(2)

Coupling and Coordination of RD and ECC: The coupling and coordination between RD and ECC demonstrate distinct stage characteristics, transitioning from “slightly dysfunctional” to “primary coordination”. This shift reflects the success of the “ecological priority and green development” strategy within the new development concept, highlighting its role in fostering the harmonious relationship between regional development and ecological civilisation. This transition provides further empirical evidence for the positive impact of high-quality development strategies in achieving a balance between development and environmental sustainability.

(3)

Spatial Structure and Coordination: The study reveals a spatial structure of “decreasing circles” centred around the core city. This spatial heterogeneity highlights the interdependence between regional development and environmental protection, with the core city playing a pivotal role in the coordinated development of surrounding areas. This spatial pattern underscores the radiating effect of central cities, thereby establishing a theoretical basis for the integrated development of urban agglomerations and emphasising the importance of coordinated spatial planning to achieve sustainable urban growth.

Through in-depth analysis, it has been demonstrated that the coupling coordination between RD and ECC is influenced by multiple dimensions such as location conditions, economic foundation, and geographical environment. These factors exhibit significant spatial differentiation, thereby enriching the theory of regionally coordinated development and providing scientific support for the formulation of differentiated regional policies.

Based on these findings, the study makes the following policy recommendations:

(1)

Fostering Innovation-Driven, High-Quality Development: It is imperative to augment investment in scientific and technological innovation, nurture green and low-carbon industries, optimise land resource allocation, and enhance urban ecological resilience. These measures will facilitate a novel paradigm of innovation-driven, high-quality development, thereby contributing to the sustainability of regional growth and ecological protection.

(2)

Optimising Urban Spatial Layout: It is paramount to fortify the pole-core function of central cities whilst concurrently cultivating small and medium-sized cities with distinctive characteristics. The establishment of a networked urban development system is imperative to promote balanced growth and foster regional integration.

(3)

Improving Factor Market Allocation: Optimising resource allocation efficiency, adjusting the industrial structure, and guiding the orderly flow of factors is recommended. By leveraging comparative advantages, this approach can help maximise the potential for high-quality, coordinated regional development.