Spatiotemporal Evolution and Obstacle Factor Analysis of Coupling Coordination Between Economic Resilience and Green, Low-Carbon Development in China

Abstract

To achieve economic resilience and green, low-carbon development are two goals of China’s high-quality economic development. This paper uses the entropy weight method and coupling coordination degree model to estimate the coupling coordination level of economic resilience and green, low-carbon development. Kernel density estimation, Moran index, Dagum Gini coefficient, Markov chain, and obstacle degree model are used to explore the spatiotemporal evolution characteristics and obstacle factors. The results are as follows. (1) The coupling coordination degree between China’s economic resilience and green, low-carbon development has increased overall. However, the eastern region has the highest, and the central region has the fastest growth. (2) The coupling coordination degree shows positive spatial autocorrelation, with most provinces exhibiting high–high or low–low aggregation characteristics. (3) The contribution of imbalance mainly comes from inter-regional differences, but the contribution of intra-regional differences to imbalance is increasing. (4) The spatio-temporal evolution pattern is generally better, and the probability of the coupling coordination degree maintaining the initial state is the largest. The neighborhood’s state affects the transition probability but does not affect that of high-level provinces. (5) Innovation capacity is the main obstacle to improving economic resilience, and per capita water resources are the main obstacle to green and low-carbon development. Finally, this paper puts forward suggestions for creating a good innovation environment, increasing R&D investment, promoting green technology progress, optimizing regional cooperation and resource allocation, and promoting industrial green transformation.

1. Introduction

In the past few decades, China has witnessed remarkable economic growth, characterized by rapid industrialization and urbanization, resulting in a significant economic take-off. However, this progress has been accompanied by a decline in environmental standards and green development efficiency [1].The traditional mode of economic growth and energy consumption structure, as well as the practice of exchanging the environment for economy, bring long-term problems such as environmental pressure and resource depletion to China [2,3]. These challenges threaten the sustainability of economic progress and could create barriers to future growth. Therefore, China’s economic growth must focus on green and low-carbon development. In addition to these environmental concerns, China’s economic trajectory has been shaped by external and internal challenges. The country has weathered significant global disruptions, such as the financial crisis, trade frictions, and COVID-19 [4]. Simultaneously, it has faced downward economic pressure caused by the replacement of old growth drivers with new ones [5]. The superposition of these factors has increased the uncertainty of economic operation, and if we cannot effectively address these difficulties, it will bring severe challenges to economic resilience. As China’s economy enters a high-quality development stage, economic growth should consider growth speed and quality to ensure sustainable economic growth and long-term stability. Especially in the context of globalization and increasing uncertainties, it is particularly important to promote the green and low-carbon transformation of economic development models and build a resilient economic growth model. The Recommendations of the Central Committee of the Communist Party of China for Formulating the 14th Five-Year Plan for Economic and Social Development and Long-Range Objectives through the Year 2035 not only points out accelerating green and low-carbon development and then promoting the comprehensive green transformation of economic and social development, but also elaborates on the complex and changing development environment and the challenges that China may face, and thus focuses on economic resilience and achieving stable and long-term economic development.

It has become increasingly urgent to address whether there is a synergy between achieving green, low-carbon development and enhancing economic resilience, as well as understanding how these two aspects influence each other. Existing studies have shown that the improvement of economic resilience can promote the reduction of carbon emissions and the green transformation of the energy industry, which is conducive to green and low-carbon development [6,7]. Conversely, long-term economic policy uncertainty may lead to a decrease in economic resilience and an increase in the level of carbon emissions, which is not conducive to environmental sustainability [8]. This indicates that strengthening economic resilience directly contributes to advancing green and low-carbon development. At the same time, environmental pollution can damage people’s health, affect the healthy development of the economy, and weaken economic resilience [9,10,11]. Furthermore, the high costs and challenges associated with pollution control result in long-term negative economic impacts, further reducing economic resilience [9]. Hence, promoting green and low-carbon development is an effective means to enhance economic resilience. Based on these insights, it is clear that economic resilience and green, low-carbon development are interconnected and mutually supportive. Therefore, focusing on the coupling mechanism between China’s economic resilience and green, low-carbon development is significant for promoting high-quality economic development.

Since the financial crisis, the uncertainty of socio-economic and environmental factors has increased, and economic resilience has been continuously emphasized. Scholars have explored economic resilience from multiple perspectives. Firstly, research focused on how economic systems recover from shocks, defining resilience as the ability and speed of an economic system to return to its initial equilibrium after disruptions [12,13]. This approach assumes a single equilibrium state that the system strives to regain. Secondly, some scholars have focused on enhancing the ability to resist shocks by identifying ways to increase risk resistance before the shock occurs. For example, He et al. [14] found that regions with diversified industrial structures can serve as autonomous stabilizers, dispersing risks and enhancing their ability to withstand economic shocks. Meanwhile, once the shock occurs and exceeds the threshold that the regional economy can withstand, the original structure or function of the economic system will make slight adjustments, leading to a new equilibrium [15]. Over time, economic resilience has been recognized as dynamic, and the original equilibrium state may lose its ability to cope with new shocks [16]. Therefore, the economic system must continuously adjust and “bounce forward” to ensure stable economic operation. However, a one-time shock may also permanently affect the economy [17], in which case the economic system needs to undergo a fundamental, structural transformation and seek new paths for economic growth to adapt to new challenges. These insights reveal that economic resilience is dynamic, multi-dimensional, and influenced by numerous factors [18]. Therefore, from an evolutionary perspective, this paper defines economic resilience as the ability of an economic system to buffer and resist shocks before they occur, rapidly recover after a shock, and dynamically adapt to external environments through structural transformation and innovation in the long term.

The current research on economic resilience mainly focuses on the measurement, spatio-temporal evolution, and influencing factors of economic resilience. In terms of measurement, some studies use the deviation of GDP or employment in a certain year from that in 2008 as a proxy variable for economic resilience [16,19,20]. Some scholars construct an indicator system to comprehensively investigate regional economic resilience and analyze its dynamic evolution and spatial distribution pattern [21,22]. Existing studies have found that China’s economic resilience is on the rise on the whole, there is a relatively stable spatial agglomeration phenomenon in space, and there are significant differences in economic resilience among different regions [22,23]. However, the northeastern region of China exhibits lower levels of economic resilience, with some areas, particularly outer suburbs, experiencing a worsening trend [24,25]. The impact of the COVID-19 epidemic has not changed the agglomeration characteristics and dependence of the spatial distribution of the overall economic resilience [26], but weakened the level of economic resilience of individual provinces and changed the spatial distribution of resilience within the province [27]. Meanwhile, the economic resilience of the tourism sector demonstrated stability and convergence fluctuations before and after the pandemic [28]. Additionally, studies have begun exploring the coupling of economic resilience with other domains, such as high-quality economic development and industrial agglomeration, further broadening the understanding of how resilience interacts with various socio-economic and environmental factors [19,21].

Scholars have extensively explored the factors influencing economic resilience, identifying several key determinants. Industrial structure is widely regarded as the most direct factor. A diversified industrial base enhances a region’s ability to resist shocks, while industrial specialization and agglomeration facilitate faster recovery [29,30]. As an internal driving force for industrial adjustment and technological upgrading, innovation can also enhance economic resilience. It is generally believed that regions with a high level of innovation are better able to resist crises and accelerate recovery [31], and regions with a more robust technical knowledge network structure are less affected by shocks [32]. As an innovative governance tool, digital technologies can maintain regional collaborative networks during disasters, promote economic recovery [12], improve the quality of the regional workforce and total factor productivity, and enhance resilience to risks [33]. In addition, the stability of local financial structures [34], the emergence of inclusive finance [35], the role of data as a factor of production [31,36], and the increase in labor and human capital [9,37,38] all have a positive impact on economic resilience. Government decision-making in economic activities will also affect upgrading production factors and optimizing economic structure, thus affecting economic resilience [39].

The concept of green and low-carbon development combines the “green economy” theory proposed by Pearce in 1989 and the concept of the “low carbon economy”, proposed by the United Kingdom in 2003. It is an important path to realize the coordinated development of economy, ecology and society. At present, the connotation and unified measurement standard of green and low-carbon development have not been found [40,41], but there is abundant research on green development or low-carbon development. Green development focuses on the coordination between the economic system and ecological system and believes that economic development should not be at the cost of sacrificing the environment and human health in exchange for economic growth and that development requires equal attention to economic growth and ecological improvement [42,43]. Regarding low-carbon development, Hu et al. [44] believe that low-carbon development needs to achieve a balance between economic development and carbon emission. Zhang et al. [45] believe that imposing carbon constraints on economic growth is an inherent requirement for coping with climate warming and achieving sustainable development. Lin and Li [46] pointed out that low-carbon development refers to achieving economic growth with less carbon emissions and emphasized the role of clean energy in low-carbon development. Thus, this paper believes that green and low-carbon development should transform the economic growth mode to harmonize economic progress with ecological protection, focus on energy conservation, emission reduction, and resource utilization and promote the decoupling of economic growth and carbon emissions, so as to cultivate a more sustainable and environmentally friendly economic system.

Scholars have constructed green development and low-carbon development index systems from various perspectives and studied their spatio-temporal evolution rules and influencing factors [21,45,47,48]. Among them, the digital economy can improve the level of green development and low-carbon development through industrial structure upgrading, green technology innovation, and environmental governance [45,49,50]. Smart city and low-carbon pilot policies positively affect green development [51,52]. In addition, factors such as industrial structure adjustment, human capital, environmental regulation, economic development level, and foreign investment also affect the efficiency of green development [53,54]. With the acceleration of the new urbanization process in China, scholars have paid more and more attention to the relationship between urbanization and low-carbon development. Zhang et al. [55] found an inverted U-shaped relationship between new urbanization and per capita carbon dioxide emissions in most cities. The decoupling of urbanization and carbon emissions can be achieved through economic growth, improving energy efficiency and improving the final energy consumption structure [56]. Based on the above research, some scholars have researched the influencing factors of green and low-carbon development. Existing studies have found that the digital economy [57], environmental regulation and taxation [58,59,60], smart city and low-carbon city pilot policies [51,61], and financial agglomeration [41] can achieve green and low-carbon development by promoting technological innovation, industrial adjustment, and green information sharing, while also producing positive spillover effects in surrounding areas.

In short, existing studies have made significant progress in quantifying economic resilience, green development, and low-carbon development, analyzing their dynamic evolution and influencing factors. These studies provide valuable insights for exploring the relationship between economic resilience and green and low-carbon development, offering a foundation for understanding their coupling and coordination. However, there are still some limitations in the current research. (1) Scholars have conducted a lot of research on green development and low-carbon development, respectively, but less research on the overall green and low-carbon development, and the evaluation system is not perfect. (2) The current research focuses on the relationship between some factors and economic resilience, green development, or low-carbon development. However, there is a lack of discussion on the overall relationship between economic resilience and green, low-carbon development, as well as research on the spatio-temporal evolution patterns after their coupling. Therefore, based on previous studies, this paper analyzes the dynamic interaction between economic resilience and green, low-carbon development, introduces kernel density estimation, Dagum Gini coefficient, Moran index, and Markov chain to explore the regional differences and dynamic evolution of the coupling coordination degree of these two, and introduces an obstacle degree model to analyze the main obstacle factors that hinder this coupling coordination level. This research provides both a theoretical framework and practical guidance for achieving coordinated development of economic resilience and green and low-carbon growth in the context of new challenges.

The contributions of this paper are as follows. (1) From the research perspective, it constructs a comprehensive evaluation index of economic resilience and green, low-carbon development. It integrates the two into the same research framework based on the system coupling perspective, analyzes their coupling coordination mechanism, and builds a coupling coordination degree model to describe their coupling coordination level to deepen the understanding of their coordination relationship. (2) In terms of research content, this paper uses kernel density estimation, Moran index, Dagum Gini coefficient, and Markov chain to comprehensively investigate the spatio-temporal evolution characteristics of the coupling coordination between economic resilience and green, low-carbon development, and analyzes the internal factors affecting the coupling coordination between the two through the obstacle degree model, thus enriching the research scope of economic resilience and green, low-carbon development.

2. Coupling Coordination Mechanism

As the goals and contents of high-quality development, economic resilience and green, low-carbon development have a close coupling mechanism. Strong economic resilience provides a strong foundation and a continuous driver for advancing green and low-carbon initiatives. Conversely, green and low-carbon development introduces new growth opportunities and avenues for enhancing economic resilience. Together, these two dimensions form a complementary and mutually reinforcing relationship, driving sustainable progress and innovation.

2.1. The Influence of Green and Low-Carbon Development on Economic Resilience

Green and low-carbon development is the direction of modern economic and social transformation. It emphasizes the coordination and unity of economic and ecological benefits. It pays attention to the long-term sustainable development of the economy, to provide new ideas for improving economic resilience. Green and low-carbon development will enhance economic resilience through industrial restructuring, technological innovation, human capital concentration, policy guidance, employment growth, and reduction in health costs. First of all, green industrial restructuring fosters the emergence of new green industries, enriches the industrial landscape, and compels traditional sectors to adopt cleaner, more environmentally friendly production practices. This shift encourages the transition of traditional industries toward technology-intensive models, driving the diversification and upscaling of industrial structures, which in turn enhances risk resistance. Second, green technology innovation accelerates the replacement of energy-intensive industries by clean industries [62], gradually weakens the dominant role of high-carbon industries in China’s economic development [63], reduces the dependence on traditional energy and the original growth path, and promotes sustainable growth [64]. Third, the concentration of human capital brought about by technological innovation can not only break information barriers and accelerate technological creation and spillover, which is conducive to the development of innovative activities [65], but also, along with industrial diversification and specialization, can disperse risks and accelerate economic recovery and adjustment [30]. Fourth, green and low-carbon policies can not only guide enterprises to green and low-carbon production and innovation and enhance their long-term sustainable development ability [66], but also optimize capital allocation, guide social capital to flow into green and low-carbon fields, help industrial transformation, and thus enhance economic resilience [67]. Fifth, advancements in clean energy and related innovations diversify energy options, enhance energy efficiency, and promote resource recovery and reuse. These measures stabilize energy costs, reduce production expenses, boost enterprise competitiveness, and improve the economy’s capacity to withstand shocks. Sixth, green and low-carbon industries will create new jobs and make up for job losses in traditional industries, driving income growth, which will not only change people’s consumption concept, improve consumption structure, release consumption potential, expand demand for green products, enhance market vitality, and boost economic growth, but also promote savings growth, alleviate panic in crisis, and ensure stable economic operation. Finally, green and low-carbon development mitigates pollution and environmental damage, improving ecological quality and reducing health issues caused by environmental degradation. This translates into lower medical costs, enhanced labor productivity, and strengthened economic resilience.

2.2. The Influence of Economic Resilience on Green and Low-Carbon Development

The improvement of economic resilience not only enhances the ability to cope with economic fluctuations and external shocks but also produces positive feedback on green and low-carbon development. However, macroeconomic risks and vulnerabilities in the low-carbon transition may affect the progress of green and low-carbon development [68], so promoting economic resilience is critical. Economic resilience promotes green and low-carbon development by attracting public and private investment, enhancing forecasting capabilities, facilitating policy implementation, optimizing energy mix and resource allocation, and influencing citizens’ behavior. First of all, regions with strong economic resilience can better withstand external shocks, maintain economic stability, and provide a stable external environment for green and low-carbon development, which can boost investor confidence, make it easier to attract public and private investment in the financial green and low-carbon sector, and promote the innovation and application of green and low-carbon products and technologies. Secondly, resilient regions are forward-looking, can reasonably predict the future changes in green and low-carbon development, and can plan to deal with possible challenges [9]. In particular, the development of digital technology reduces the information barriers between different sectors and industries, reduces the mismatch between supply and demand [69], provides a predictable environment for enterprises and investors and helps enterprises and investors make long-term plans, enhancing investors’ ability to cope with future market volatility. Thirdly, a resilient economy has greater adaptability, is better able to respond to policy changes, and has a stronger ability to execute. In promoting green and low-carbon development, a series of policy measures are needed to support, such as carbon tax, carbon trading, and environmental protection regulations. At the same time, strong adaptability can reduce resistance to the implementation of green and low-carbon policies, contribute to the smooth implementation and execution of these policies, and thus promote the realization of green and low-carbon goals. Fourthly, economic resilience is not only the ability to cope with shocks but also implies factors to promote sustainable development. Economic resilience promotes sustainable development by diversifying economic structures and reducing reliance on single natural resources. This diversification supports energy mix optimization, increases the share of renewable energy, and improves resource allocation efficiency. Such flexibility and efficiency enable resources to be channeled effectively into green and low-carbon industries. Finally, improvements in economic resilience often lead to higher incomes and greater environmental awareness among citizens. This drives increased demand for green and low-carbon products and services, expanding the green consumer market. The resulting market growth creates further incentives for innovation and the development of green industries, fostering a virtuous cycle of sustainable economic and environmental progress.

3. Materials and Methods

3.1. Index System

Economic resilience refers to the ability of an economy to respond to and adjust to external shocks, quickly recover, and achieve sustainable development. Therefore, multiple aspects should be considered in its evaluation. For example, Lin et al. [70] believe that economic strength, industrial structure, and innovation ability should be considered in selecting economic resilience indicators. Based on the methods adopted by Ma and Huang [21] and Gu and Liu [71], this paper selects indicators directly related to economic resilience from the three dimensions of resistance, resilience, and innovation transformation capacity, to construct an economic resilience indicator system containing 13 subdivided indicators.

The selection of resistance indicators primarily considers an economy’s buffering and recovery capabilities when facing external shocks. The economic development level directly reflects the foundation of an economy’s ability to cope with external shocks. The unemployment level is an important indicator of labor market stability, with lower unemployment levels indicating more excellent economic stability. The urban–rural income gap reflects social stability, as income inequality can exacerbate social tensions and hinder the ability to respond to shocks. A reasonable industrial structure enhances economic diversity and flexibility, reducing reliance on a single industry or resource. In the event of an external shock, a diversified industrial structure can mitigate the negative impacts of the shock through the stable development of other industries.

The selection of resilience indicators mainly considers whether an economy can quickly recover after a shock, how it adjusts, and how it adapts to a new environment. Consumption capacity and household wealth reflect the basic needs and wealth accumulation necessary for social recovery. Strong consumption capacity can expand domestic demand and continuously adapt to market changes. A higher level of household wealth can help people maintain their quality of life during economic difficulties, alleviating economic pressure and facilitating a quicker recovery of economic demand. Population growth and road density reflect the potential for social resource allocation and market vitality. Population growth means increased labor resources and market demand; younger labor forces have higher adaptability, and increased market demand aids economic recovery. A higher road density promotes the flow of resources and economic connectivity between regions, influencing recovery capacity. The GDP growth rate directly reflects the speed and momentum of economic expansion. When a shock hits an economy, a recovery in GDP growth indicates that the economy is recovering rapidly.

The selection of innovation and transformation capacity indicators mainly considers structural transformation, technological upgrades, and innovation. Industrial upgrading can promote the application of new technologies and the emergence of new industries, enhancing the economy’s resilience and competitiveness. The intensity of R&D expenditure directly influences the speed of innovation and technological progress, helping to optimize the industrial structure. Expanding the education scale enhances human capital levels and labor market adaptability, aiding in rapid economic recovery when facing shocks. Innovation capacity provides new economic growth paths, contributing to sustained economic development and reducing reliance on traditional growth models. The index system of economic resilience is shown in

Table 1. Index system of economic resilience.

Target Level	Standardized Layer	Indicator Layer	Specific Indicators	Nature of the Indicator
Economy Resilience	Resistance	Level of economic development a1	GDP per capita	+
		Unemployment level a2	Unemployment rate	−
		Rural–urban income gap a3	Income of urban residents/income of rural residents	−
		Rational industrial structure a4	Value added of tertiary sector as a share of GDP	+
	Resilience	Consumption capacity a5	Total retail sales of consumer goods/GDP	+
		Wealth of the population a6	Per capita disposable income	+
		Population growth a7	Population at end of year/beginning of year -1	+
		Road density a8	Miles of urban roads/area of administrative district	+
		GDP growth rate a9	(Current GDPPrevious GDP)/Previous GDP	+
	Innovation and Transformation	Advanced industrialization a10	Value added of tertiary industry/value added of secondary industry	+
		R&D funding intensity a11	Internal expenditure on R&D funds/GDP	+
		Scale of education a12	Average number of students enrolled in higher education per 100,000 population	+
		Innovative capacity a13	Number of patents granted	+

“+” means that an increase in this indicator can enhance economic resilience, and “−” means that an increase in this indicator can weaken economic resilience.

.

Green and low-carbon development requires balancing economic growth, ecological protection, and carbon emissions. Based on the studies of scholars such as Yuan et al. [72], Lu, Shao, Wang and Dong [40], and Song et al. [73], this paper selects reasonable indicators from the two dimensions of green development and low-carbon development to build a green and low-carbon development indicator system.

Regarding selecting green development indicators, this paper primarily considers aspects such as recycling, pollution control, and ecological protection. The amount of industrial solid waste utilization reflects the recycling and reuse of industrial waste, demonstrating the efficient use of resources in green development. The daily sewage treatment capacity directly impacts the sustainable use of water resources, reduces water pollution, and protects aquatic ecosystems, key measures for achieving green development. The non-hazardous domestic waste disposal rate reflects the proportion of waste processed non-harmfully, reducing waste pollution and enhancing resource recycling which are important goals of green development. The greening coverage of built-up areas reflects the quality of the urban ecological environment. A high green coverage rate improves the livability of cities and promotes sustainable environmental development. The number of nature reserves reflects a region’s commitment to ecological and biodiversity protection. Increasing the number of reserves helps protect ecosystems, maintain ecological balance, and promote green development. Water resources are one of the foundations of green development. Per capita water resources reflect a region’s abundance of water resources and serve as an important indicator for evaluating regional green development and ecological carrying capacity. Adequate water resources promote green development.

In selecting low-carbon development indicators, this paper focuses on key aspects such as energy utilization efficiency, carbon emissions, and carbon absorption. Energy consumption per unit of GDP serves as a crucial measure of energy efficiency in economic activities. The lower the energy consumption per unit of GDP, the more efficient the energy use in the economic growth, reducing carbon emissions intensity and contributing to low-carbon development. Reducing per capita carbon emissions is one of the core goals of low-carbon development. The lower the per capita carbon emissions, the greater the progress a region has made in controlling greenhouse gas emissions. Transportation and agriculture are major sources of carbon emissions. An increase in public transportation vehicles per ten thousand people is an important means to reduce urban carbon emissions. In contrast, an increase in private vehicle ownership raises carbon emissions. The excessive use of agricultural fertilizers leads to greenhouse gas emissions, so reducing fertilizer use contributes to low-carbon development. Carbon capture capacity reflects a region’s or industry’s ability to reduce carbon emissions, and an increase in forest coverage is significant for carbon dioxide absorption. The index system of green and low-carbon development is shown in

Table 2. Index system of green and low-carbon development.

Target Level	Standardized Layer	Indicator Layer	Specific Indicators	Nature of the Indicator
Green and Low-carbon Development	Green Development	Comprehensive utilization of general industrial solid waste b1	Comprehensive utilization of general industrial solid waste	+
		Average daily sewage treatment capacity b2	Sewage treatment capacity/365	+
		Non-hazardous domestic waste disposal rate b3	Amount of non-hazardous domestic waste treated/Amount of domestic waste generated	+
		Greening coverage of built-up areas b4	Area covered by greening in built-up areas/Area of built-up areas	+
		Number of state-level nature reserves b5	Amount of state-level nature reserves	+
		Water resources per capita b6	Total water resources/Number of inhabitants	+
	Low-carbon Development	Energy consumption per 10,000 GDP b7	Total energy consumption/GDP	−
		Carbon emissions per capita b8	Carbon emissions/Number of inhabitants	−
		Public transportation vehicles per 10,000 population b9	Public transportation vehicles per 10,000 population	+
		Private vehicle ownership b10	Private vehicle ownership	−
		Agricultural fertilizer application b11	Agricultural fertilizer application rates	−
		Carbon capture capacity b12	Probability of forest cover	+

“+” means that the increase of this indicator can promote green and low-carbon development, “−” means that the increase of this indicator will inhibit green and low-carbon development.

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3.2. Data Sources

The sample for this study is panel data from 30 provinces in China (excluding Tibet and Hong Kong, Macao, and Taiwan) from 2012 to 2021. The data are mainly from the China Statistical Yearbook, Provincial Statistical Yearbook, China Economic Network statistical database, and EPS data platform, and the carbon emissions are from the CEADS database. Due to the long survey period, some indicators, such as total energy consumption, number of State-level nature reserves, are missing in some provinces in a specific year. Therefore, the linear interpolation method is adopted in this paper. Among them, the number of state-level nature reserves is rounded up after the interpolation method.

3.3. Research Methods

3.3.1. Entropy Weight Method

The entropy weight method is an objective weighting method based on entropy value, which can reduce the influence of subjective factors on calculation results and objectively reflect the difference and importance of different indicators. Based on the practice of Zou et al. [74], this paper uses the entropy weight method to calculate the comprehensive evaluation index of economic resilience and green and low-carbon development. The calculation formula is as follows.

First, the extreme value method is used to standardize the raw data to eliminate the differences in units and dimensions of each indicator.

Positive indicators:

$$y_{ij}={\displaystyle \frac{x_{ij}-x_{imin}}{x_{imax}-x_{imin}}}$$

(1)

Negative indicator:

$$y_{ij}={\displaystyle \frac{x_{imax}-x_{ij}}{x_{imax}-x_{imin}}}$$

(2)

where $y_{ij}$ is the index value after standardized processing of the original data, $x_{ij}$ is the original data of the $j$ indicator of individual $i$, and $x_{imax}$ and $x_{imin}$ are the maximum and minimum values of the original data of individual $i$.

Secondly, the information entropy and weight of each index are calculated.

$$Y_{ij}={\displaystyle \frac{y_{ij}}{\sum _{i=1}^n{y}_{ij}}}$$

(3)

$$e_j=-In{\displaystyle \frac{1}{n}}\sum _{i=1}^n{Y}_{ij}In{Y}_{ij}$$

(4)

$$d_j=1-e_j$$

(5)

$$w_j={\displaystyle \frac{d_j}{\sum _{j=1}^m{d}_j}}$$

(6)

Among them, $Y_{ij}$ is the feature weight, which is used to calculate the proportion of a single indicator of each individual in the whole indicator. $e_j$ is the information entropy of each indicator, $d_j$ is the difference coefficient of each indicator, which is used to judge the degree of dispersion and importance of each indicator, and $w_j$ is the weight of each indicator.

Finally, the comprehensive score $S_i$ of the index is calculated.

$$S_i=\sum _{j=1}^m\left(w_j{x}_{ij}\right)$$

(7)

3.3.2. Coupling Coordination Degree Model

Drawing on the practice of Yi et al. [75], this paper uses the coupling coordination degree model to evaluate and quantify the coupling relationship between economic resilience and green and low-carbon development as well as the coordination of their development. The specific formula is as follows:

$$C={\displaystyle \frac{2\sqrt{U_1{U}_2}}{U_1+U_2}}$$

(8)

$$T=\alpha U_1+\beta U_2$$

(9)

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

(10)

$C$ is the coupling degree, which measures the degree of mutual influence and interdependence of two systems. $D$ is the coupling coordination degree, and the value range is [0,1], which can measure whether the development of the two systems based on coupling is coordinated. $U_1$ represents the comprehensive evaluation index of economic resilience, $U_2$ represents the comprehensive evaluation index of green and low-carbon development, and $T$ is the comprehensive evaluation index of both. $\alpha$ and $\beta$ are the coefficients reflecting the importance of economic resilience and green and low-carbon development, which are considered equally important in this paper, so $\alpha =\beta =0.5$ is set. Based on the practice of Xu and Zhao [76], this paper divides the evaluation criteria of the coupling coordination degree between economic resilience and green and low-carbon development into 10 levels, as shown in

Table 3. Evaluation criteria of coupling coordination degree.

Coupling Coordination Degree D-Value Interval	Level of Coordination	Evaluation Criteria	Coupling Coordination Degree D-Value Interval	Level of Coordination	Evaluation Criteria
[0,0.1)	1	Extreme disorder	[0.5,0.6)	6	Sue for harmonization.
[0.1,0.2)	2	Severe disorder	[0.6,0.7)	7	Primary coordination
[0.2,0.3)	3	Moderate disorder	[0.7,0.8)	8	Intermediate level coordination
[0.3,0.4)	4	Mild disorder	[0.8,0.9)	9	Good coordination
[0.4,0.5)	5	Endangerment disorder	[0.9,1]	10	Quality coordination

.

3.3.3. Kernel Density Estimation

Kernel density estimation is a non-parametric method used to estimate the probability density function of random variables. By constructing the probability density function and using a smooth and continuous density curve, it can not only reveal the static distribution characteristics of the coupling coordination degree in a single year but also capture the gradual change in the coupling coordination degree in a region and provide more explanatory information in the spatial distribution. In addition, kernel density estimation is effective in identifying trends and changes between different regions, offering a clear view of both global and local variations across the entire spatial-temporal range. The formula for estimating nuclear density is:

$$f_h\left(x\right)={\displaystyle \frac{1}{nh}}\sum _{i=1}^{n}K\left({\displaystyle \frac{x-x_i}{h}}\right)$$

(11)

Among them, $f_h\left(x\right)$ is the estimated probability density function at $x$, $n$ is the number of samples, h is the bandwidth, which affects the smoothness of the estimation, and $K\left(·\right)$ is the kernel function. This paper uses the Gaussian kernel function for reference, from Chen et al. (2023) [77], to explore the dynamic evolution trend of the coupling coordination degree between economic resilience and green and low-carbon development. The specific form is Formula (12).

$$K\left(x\right)={\displaystyle \frac{1}{\sqrt{2\pi }}}\exp \left(-{\displaystyle \frac{x^2}{2}}\right)$$

(12)

3.3.4. Moran Index

The Moran index is mainly used to analyze the spatial autocorrelation of spatial data and understand whether a phenomenon or variable shows a trend of aggregation or dispersion in space. To reveal the spatial agglomeration characteristics of the coupling coordination degree, this paper refers to the research of Chengjing et al. [78]. It uses the global and local Moreland indices for spatial correlation analysis. The global Moran index helps to identify the spatial agglomeration and its distribution characteristics in the study area, and its value range is [−1,1], where a positive value indicates the existence of positive spatial autocorrelation, and the absolute value approaching 1 indicates stronger autocorrelation. The local Moran index looks at whether there is a spatial correlation between each geographical unit and the surrounding units. The Moran index formula is as follows:

$$Global mora{n}^{\prime }s I={\displaystyle \frac{\sum _{i=1}^n\sum _{j=1}^n{w}_{ij}\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)}{s^2\sum _{i=1}^n\sum _{j=1}^n{w}_{ij}}}$$

(13)

$$Local mora{n}^{\prime }s I={\displaystyle \frac{\left(x_i-\overline{x}\right)\sum _{j=1}^n{w}_{ij}\left(x_j-\overline{x}\right)}{s^2}}$$

(14)

$Global mora{n}^{\prime }s I$ represents the global moran index, $Local mora{n}^{\prime }s I$ represents the local Moran index, $x_i$ and $x_j$ represent the attribute values of the random variable $x$ on geographical units $i$ and $j$, $w_{ij}$ is an element of the spatial weight matrix, and $s^2$ is the sample variance.

3.3.5. Dagum Gini Coefficient

The Dagum Gini coefficient is mainly used to quantify inter-regional differences and reflect the unbalanced distribution among regions. To analyze the sources of inter-regional coupling coordination gaps and incoordination, this paper refers to the practice of Dong et al. [79]. The Dagum Gini coefficient is employed to decompose the research samples, allowing us to determine whether the inter-regional gap arises from internal differences, inter-regional differences, or differences resulting from overlapping factors. The specific formula is as follows:

$$G={\displaystyle \frac{\sum _{j=1}^k\sum _{h=1}^k\sum _{i=1}^{nj}\sum _{r=1}^{nh}\left|y_{ji}-y_{hr}\right|}{2\mu n^2}}$$

(15)

$$G_{jj}={\displaystyle \frac{\sum _{i=1}^{nj}\sum _{r=1}^{nh}\left|y_{ji}-y_{hr}\right|}{2\overline{Y_j}{n}^2}}$$

(16)

$$G_{jh}={\displaystyle \frac{\sum _{i=1}^{nj}\sum _{r=1}^{nh}\left|y_{ji}-y_{hr}\right|}{n_j{n}_h\left(\overline{Y_j}+\overline{Y_h}\right)}}$$

(17)

$$G_w=\sum _{j=1}^k{G}_{jj}{p}_j{s}_j$$

(18)

$$G_{nb}=\sum _{j=2}^k\sum _{h=1}^{j-1}{G}_{jh}\left(p_j{s}_h+p_h{s}_j\right)D_{jh}$$

(19)

$$G_t=\sum _{j=2}^k\sum _{h=1}^{j-1}{G}_{jh}\left(p_j{s}_h+p_h{s}_j\right)\left(1-D_{jh}\right)$$

(20)

$G$ is the overall Gini coefficient, $G_{jj}$ is the Gini coefficient within region $j$, and $G_{jh}$ is the Gini coefficient between region $j$ and region $h$. The core of the Dagum Gini coefficient lies in its decomposition ability. The overall Gini coefficient $G$ can be decomposed into $G_w$ within the region, $G_{nb}$ between the regions, and $G_t$ of super-variable density, which enables a more detailed analysis of the sources of inequality.

3.3.6. Markov Chain

The Markov chain is a random process, and its state transition is memoryless; that is, the probability distribution of the next state is only affected by the current state, but not by the previous state. The advantage of the Markov chain is that it is able to analyze the transition characteristics of the system state. By providing a Markov transition probability matrix, the transition probability of the system from one state to another is analyzed, so as to reveal the dynamic evolution characteristics of the coupling coordination degree under different regions or different time nodes. Referring to the practice of Chen et al. [77], this paper uses the traditional and spatial Markov transition probability matrices to analyze the dynamic evolution trend of the coupling coordination degree between economic resilience and green and low-carbon development.

$$P\left\{X_{\left(t\right)}=j|X_{\left(t-1\right)}=i,X_{\left(t-2\right)}=i_{t-2},\dots X_{\left(0\right)}=i_0\right\}=P\left\{X_{\left(t\right)}=j|X_{\left(t-1\right)}=i\right\}$$

(21)

Equation (21) illustrates the memoryless property of Markov transfer.

3.3.7. Obstacle Degree Model

In order to improve the coupling coordination level between China’s economic resilience and green and low-carbon development, this paper draws on the practice of Gao [80]. It uses the obstacle degree model to identify the obstacle factors that hinder the improvement of the coupling coordination degree. The calculation formula is as follows:

$$F_j=1-y_j$$

(22)

$$D_j=w_j$$

(23)

$$O_j={\displaystyle \frac{D_j\times F_j}{\sum _{j=1}^n{D}_j\times F_j}}\times 100\%$$

(24)

In the formula, $F_j$ is the deviation degree of the index, $y_j$ is the value after standardization of the $j$ index, $D_j$ is the factor contribution degree, $w_j$ is the weight of the $j$ index, and $O_j$ is the obstacle degree.

4. Results and Discussion

4.1. Result Analysis of Economic Resilience and Green and Low-Carbon Development

4.1.1. Result Analysis of Economic Resilience

Figure 1 illustrates the spatio-temporal evolution of economic resilience in China. From 2012 to 2021, the resilience index increased from 0.18 to 0.318, reflecting a 76.26% growth rate. This notable upward trend signifies China’s enhanced capacity to withstand external shocks. This is due to the policy adjustment under the underlying principle of pursuing progress while ensuring stability to promote China’s industrial adjustment and high-tech industry development, as well as the labor flow and human capital concentration brought by new urbanization, which promotes an increase in income and employment and the spillover and creation of knowledge and promotes the optimization of economic structure. After 2020, the comprehensive index of economic resilience has been dramatically improved, mainly due to China’s adherence to the policy of “stability on the six fronts and security in the six areas” during COVID-19, which stabilized the overall economic situation and helped economic recovery. Coupled with the proposal of the dual circulation development pattern, China is committed to opening up the blocking points that hinder the domestic cycle, expanding the advantages of China’s huge domestic market, making the economy still achieve growth in the case of sluggish foreign trade, promote economic recovery, and improve economic resilience.

At the regional level, economic resilience is rising in all regions. The eastern region has the highest level of resilience. It far exceeds the national average, the central region has surpassed the Northeast region since 2014, and the western region has always maintained the lowest level of economic resilience. Regarding growth rate, the eastern, central, western, and northeast regions were 77.8%, 97.44%, 66.91%, and 58.57%, respectively. The growth rate of the central region was much higher than the national average, the growth rate of the eastern region was slightly higher than the national average, and the growth rate of the northeast region was the lowest. This is because the eastern region has a more developed economy, a high-end industrial structure, and good conditions for innovation. The western region faces the problems of a single industry and brain drain, so the level of economic resilience is low, and improvement is slow. The central region has rapidly grown by undertaking industrial transfer from the eastern region and attracting talents from the western region. The northeast region is dominated by heavy industry and has a single industrial structure. Although the supply-side structural reform has improved this situation, it has not fully realized the industrial transformation of the Northeast region, resulting in a limited improvement in its economic resilience and being surpassed by the central region.

4.1.2. Result Analysis of Green and Low-Carbon Development

The spatio-temporal evolution trend of green and low-carbon development is shown in Figure 2. China’s green and low-carbon development level increased from 0.284 in 2012 to 0.32 in 2021, with a growth rate of 12.75%, and the overall trend is fluctuating upward. Still, compared with economic resilience, the increase is limited. Despite advancements in emerging industries, such as new energy vehicles and IT, intensified urbanization, and clean energy technology, resource- and energy-intensive industries continue to dominate. The energy consumption structure remains largely unchanged, hindering more substantial progress in green and low-carbon development. Since 2015, supply-side structural reform has eliminated backward and excess capacity, accelerated the green adjustment of industrial structure and optimization of factor structure, improved development quality, and led to a significant jump in the level of green and low-carbon development.

At the regional level, all areas show a fluctuating but upward trend in green and low-carbon development. The northeast region leads with the highest levels, followed by central China, both surpassing the national average. However, the differences in development levels across regions are relatively small. The growth rates of the eastern, central, western, and northeastern regions were 9.09%, 13.72%, 12.31%, and 24.34%, respectively, with no significant difference between the growth rates of other regions and the national average except for the northeast region. Northeast China is rich in forest resources and has the highest green and low-carbon development level. However, as a heavy industry base, the green and low-carbon development level has a large space to improve. With the closure of high-energy-consuming enterprises in northeast China, the elimination of old production capacity, and the increase in research and development of green and low-carbon technologies, green and low-carbon development has been promoted. The eastern region has a more developed economy, dense population, and frequent economic activities. Hence, the energy demand is large, and pollution control is difficult, which inhibits the promotion of green and low-carbon development levels. In the process of resuming work and production in 2020, large-scale infrastructure projects and the neglect of environmental protection requirements by enterprises placed additional environmental pressure on the eastern region. This led to a slight decline in its green and low-carbon development level, allowing the western region to surpass it temporarily. In contrast, the western region has shown steady progress in green and low-carbon development, supported by its abundant clean energy resources and environmental protection initiatives. However, as the eastern region’s economy recovered and environmental protection supervision was strengthened, its green and low-carbon development levels began to rebound.

4.2. Result Analysis of Coupling Coordination Degree

According to Formula (10), the coupling coordination degree of economic resilience and green and low-carbon development of the country, regions, and provinces is calculated, as shown in

Table 4. Coupling coordination degree of economic resilience and green and low-carbon development.

Provinces	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Hubei	0.467	0.486	0.498	0.505	0.526	0.533	0.542	0.554	0.564	0.584
Jiangxi	0.474	0.474	0.485	0.501	0.514	0.523	0.530	0.550	0.553	0.565
Shanxi	0.428	0.443	0.453	0.461	0.470	0.471	0.477	0.489	0.491	0.508
Anhui	0.447	0.462	0.472	0.492	0.503	0.509	0.524	0.529	0.547	0.563
Hunan	0.474	0.481	0.493	0.505	0.516	0.521	0.531	0.553	0.560	0.572
Henan	0.428	0.440	0.453	0.463	0.472	0.483	0.503	0.510	0.521	0.541
Central	0.453	0.464	0.476	0.488	0.500	0.507	0.518	0.531	0.539	0.555
Guangdong	0.554	0.567	0.575	0.597	0.615	0.633	0.663	0.679	0.704	0.731
Zhejiang	0.551	0.557	0.563	0.582	0.589	0.590	0.609	0.622	0.632	0.656
Hainan	0.471	0.491	0.489	0.475	0.505	0.506	0.520	0.516	0.519	0.540
Tianjin	0.463	0.471	0.475	0.474	0.485	0.486	0.478	0.481	0.485	0.502
Jiangsu	0.545	0.551	0.551	0.567	0.578	0.583	0.598	0.604	0.629	0.655
Beijing	0.566	0.573	0.588	0.592	0.605	0.615	0.611	0.617	0.618	0.624
Shandong	0.493	0.500	0.509	0.523	0.541	0.551	0.563	0.574	0.583	0.610
Fujian	0.506	0.515	0.515	0.527	0.551	0.549	0.562	0.578	0.575	0.589
Shanghai	0.478	0.488	0.500	0.509	0.519	0.527	0.525	0.534	0.542	0.556
Hebei	0.455	0.466	0.473	0.484	0.484	0.495	0.514	0.521	0.533	0.556
Eastern	0.508	0.518	0.524	0.533	0.547	0.553	0.564	0.573	0.582	0.602
Jilin	0.438	0.455	0.455	0.462	0.471	0.472	0.485	0.496	0.502	0.511
Liaoning	0.483	0.495	0.495	0.500	0.507	0.518	0.528	0.531	0.544	0.562
Heilongjiang	0.468	0.494	0.495	0.510	0.526	0.527	0.543	0.560	0.563	0.573
Northeast	0.463	0.481	0.482	0.491	0.501	0.506	0.518	0.529	0.536	0.549
Guangxi	0.463	0.476	0.485	0.497	0.504	0.517	0.521	0.531	0.537	0.542
Chongqing	0.445	0.455	0.466	0.470	0.481	0.489	0.492	0.499	0.507	0.524
Shaanxi	0.468	0.483	0.491	0.496	0.510	0.509	0.516	0.530	0.536	0.556
Ningxia	0.365	0.381	0.390	0.391	0.401	0.410	0.412	0.426	0.430	0.433
Yunnan	0.461	0.466	0.478	0.485	0.501	0.511	0.523	0.527	0.527	0.536
Qinghai	0.474	0.457	0.475	0.461	0.479	0.494	0.509	0.515	0.522	0.519
Inner Mongolia	0.440	0.459	0.469	0.476	0.477	0.477	0.484	0.494	0.497	0.523
Guizhou	0.417	0.420	0.430	0.448	0.454	0.466	0.479	0.488	0.501	0.504
Gansu	0.398	0.406	0.411	0.417	0.427	0.441	0.447	0.456	0.463	0.465
Xinjiang	0.413	0.432	0.429	0.433	0.451	0.459	0.463	0.471	0.466	0.466
Sichuan	0.480	0.488	0.500	0.503	0.519	0.533	0.552	0.559	0.569	0.585
Western	0.438	0.447	0.457	0.462	0.473	0.482	0.491	0.499	0.505	0.514
Nationwide	0.467	0.478	0.485	0.494	0.506	0.513	0.523	0.533	0.541	0.555

.

4.2.1. Analysis of Overall Coupling Coordination Level

As can be seen from Figure 3, the coupling coordination degree of the two systems at the national level has a steady upward trend, increasing from 0.467 in 2012 to 0.555 in 2021, representing a rise of 18.81%. Secondly, all regions have achieved an improvement in the coupling coordination level, but the coupling coordination degree of different regions also presents obvious heterogeneity. The eastern region rose from 0.508 to 0.602, reaching primary coordination, and the central, northeast, and western regions all barely achieved coordination. Numerically, the coupling coordination degree consistently ranked highest in the eastern region and lowest in the western region. The growth rate of the coupling coordination degree in the central region is the highest. The growth rate in the central and northeast regions is higher than that in the eastern region, while the growth rate in the western region is the lowest, indicating that the gap of the coupling coordination level between the central and northeast regions and the eastern region is narrowing, while the gap between the western region and the eastern region is expanding. This divergence stems from differences in economic resilience. The eastern region benefits from a diverse industrial structure, advanced high-tech industries, developed service sectors, and high innovation activity, making its economic resilience the strongest. In contrast, the western region faces challenges such as a brain drain, a limited industrial base, and an unfavorable innovation environment, leading to the lowest economic resilience. Due to the small difference in green and low-carbon levels between different regions, the coupling coordination level in the eastern region is the highest, and the coupling coordination degree in the western region is the lowest. The central region has improved its coupling coordination by absorbing industrial transfers from the eastern region. In contrast, the northeastern region has advanced through supply-side structural reforms, eliminating outdated practices, and fostering green technology innovation. These efforts have facilitated industrial restructuring and contributed to the rise in their coupling coordination degrees.

4.2.2. Analysis of Coupling Coordination Level by Province

As shown in Table 4, the degree of coupling coordination between economic resilience and green, low-carbon development has increased across all provinces from 2012 to 2021. Most provinces achieved a grade improvement during this period, except for Fujian and Xinjiang. Guangdong and Shandong notably advanced by two levels, with Guangdong achieving intermediate coordination by 2020. By 2021, the number of provinces classified as having primary coordination rose to five, while only three provinces remained on the verge of dysregulation, and none were categorized as having mild dysregulation, indicating significant overall progress. In terms of the number of provinces exceeding the national average level, 16 provinces will exceed the national average level in 2012 and 2021. Still, the composition of provinces has changed: 80% of provinces in the eastern region exceed the national average level, but in its composition, Hebei has replaced Hainan. The proportion of internal provinces in the central region increased from 50% to 66.67%. The share of internal provinces in the western region fell from 27.27 percent to 18.18 percent. In 2021, the top five coupling and coordination degree provinces were Guangdong, Zhejiang, Jiangsu, Beijing and Shandong, with indexes of 0.731, 0.656, 0.655, 0.624, and 0.61, respectively. All provinces had relatively advanced economy and attached great importance to ecological environment and development quality. The top five provinces for growth rates are Guangdong, Henan, Anhui, Hubei, and Shandong, with 31.96%, 26.37%, 25.87%, 24.89%, and 23.74%, respectively. Provinces in the central region rely on their location advantages to achieve high-quality development. At the same time, Guangdong and Shandong continuously break through the development bottleneck with their talents, industrial structure, and market advantages. These provinces pursue stable economic growth while achieving green and low-carbon development.

4.3. Dynamic Evolution Trend

To better illustrate the absolute differences in the coupling coordination levels between economic resilience and green, low-carbon development across various regions in China, this study employs kernel density estimation (via Formula (11)) for each region and examines the dynamic evolution characteristics in selected years: 2012, 2015, 2018, and 2021.

4.3.1. Dynamic Evolution Trend of Overall Distribution

Figure 4 shows the distribution and dynamic evolution trend of the coupling coordination degree between China’s economic resilience and green and low-carbon development. In terms of distribution location, from 2012 to 2021, the location and distribution interval of the kernel density curve moved to the right, indicating that the overall coupling coordination level increased. In terms of distribution pattern, the height of the main peak decreased, and the width increased, indicating that the difference in coupling coordination degree between provinces increased. In terms of distribution extensibility, the curve’s sides became more elongated and smoother, with a noticeable right tail, suggesting a more dispersed distribution and widening gaps between provinces with higher and lower coupling coordination levels. Although the overall level is improved, there is a significant difference in the speed of improvement among different provinces, with some provinces making rapid progress and more provinces maintaining a low level. In terms of polarization characteristics, the change from bimodal to multi-modal indicates that the coupling coordination level becomes complex and diverse among provinces, possibly due to the different implementation standards of policies and industrial structure adjustment degrees in different provinces.

4.3.2. Dynamic Evolution Trend of Regional Distribution

Figure 5 shows the distribution and dynamic evolution trend of the coupling coordination degree in the four regions. In terms of distribution position, the position and distribution interval of kernel density curves in the four regions moved to the right, indicating that the coupling coordination degree increased year by year. In terms of distribution pattern, the main peaks in the eastern and western regions decreased compared with the beginning of the period. The main peaks in the central region also showed a downward trend after increasing at the beginning of the period. The width of the main peaks in the three regions increased, indicating an expanding trend of internal differences. Compared with the beginning of the period, the height of the main peak in northeast China increased, and the width decreased slightly, indicating that the regional absolute difference gradually decreased. Regarding distribution ductility, left-sided trailing appeared in the central region in the later period, indicating that the development gradually became unbalanced, and the coupling coordination degree of some provinces was significantly lower than that of other provinces. In other regions, there was no obvious trailing phenomenon, and the curve in the eastern region slowed down, indicating that the internal coupling coordination level was diversified. In contrast, the curve shape in the western and northeastern regions remained basically unchanged, indicating that the gap in the coupling coordination degree of the internal provinces continued to exist. In terms of polarization characteristics, the eastern region changes from double peaks to non-obvious multi-peaks, indicating that the internal multi-polarization trend is present. The transition from double peak to single peak in the central region indicates that the coupling coordination degree gradually converges. This may be due to the centralized distribution of coupling coordination degree caused by the similar economic development levels and development ideas in the internal provinces. The change in “double peak-single peak-double peak” in western China may be due to the temporary convergence of coupling coordination degree brought by policy guidance, but the polarization characteristics are obvious due to the differences in internal provinces. The northeast region consistently maintained a single peak, indicating uniform development trends across its provinces.

4.4. Spatial Correlation Analysis

4.4.1. Global Autocorrelation Analysis

In order to test whether there is spatial autocorrelation in the coupling coordination degree of economic resilience and green, low-carbon development, this paper uses the spatial adjacency matrix to calculate Moran’s I of the coupling coordination degree of the two systems.

Table 5. Global Moran index.

Year	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Moran’s I	0.233	0.216	0.192	0.245	0.257	0.241	0.228	0.232	0.226	0.227
p-value	0.015	0.023	0.039	0.011	0.008	0.012	0.016	0.014	0.016	0.016

shows that from 2012 to 2021, the Moran’s I index of the coupling coordination degree between China’s economic resilience and green, low-carbon development is greater than 0. It passes the test at the 5% significance level, indicating a significant positive spatial autocorrelation, and the coupling coordination degree level of neighboring provinces is usually similar. This is related to the similarity of resource endowment, infrastructure, and industrial structure of neighboring provinces, which leads to the geographical clustering of provinces with similar coupling coordination degrees and spatial spillover effects. In terms of the time trend, the Moran’s I index fluctuated obviously in the early stage but was relatively stable and showed a downward trend in the later stage, reflecting the weakening of spatial correlation, which differentiated development policies may cause. Specifically, the global Moran’s I index declined sharply from 2012 to 2014, reaching the lowest value of 0.192 in 2014. From 2014 to 2016, it rose rapidly to the highest value of 0.257 and fell again. This change reflects that under the new normal, provinces have different levels of economic development quality standards and policy implementation, and regional barriers continue to exist, so the spatial correlation is weakened.

4.4.2. Local Autocorrelation Analysis

The global autocorrelation analysis could not reveal the spatial agglomeration characteristics of the coupled coordination degree of provinces in China, so this paper further calculated the local Moran’s I index and drew the Moran scatter plot for 2012, 2015, 2018, and 2021, as shown in Figure 6. The scatter plot shows that most of the points are concentrated around the expected value line and distributed in the first and third quadrants, showing the characteristics of high–high or low–low clustering, indicating the existence of obvious positive spatial correlation and the phenomenon of two-level differentiation in regional development. High–high concentration is mainly concentrated in some eastern and central provinces, such as Shanghai, Jiangsu, Shandong, Zhejiang, Jiangxi, Hunan, Anhui, etc. Low–low concentration is concentrated in the western regions, such as Yunnan, Inner Mongolia, Shanxi, Xinjiang, Ningxia, Gansu, Guizhou, Qinghai, and so on. The eastern region has a diverse industrial structure and a good innovation environment, while the central region has realized industrial upgrading and green adjustment by undertaking industrial transfer, both of which contribute to improving the degree of coupling coordination. On the contrary, the western region relies on traditional industries and lacks innovation vitality, so the overall level is low. In addition, some provinces, such as Beijing and Sichuan, show the characteristics of high–low agglomeration, indicating that their coupling coordination degree is much higher than that of neighboring provinces, possibly because these provinces have a strong “siphon effect”. The inflow of surrounding human capital improves the coupling coordination level of the province but does not feed back to the neighboring provinces, or even weakens the coupling coordination level.

4.5. Analysis of Spatial Differences

In order to further analyze the regional differences in coupling coordination degree in China, the paper measured the overall, intra-group, and inter-group Gini coefficients of coupling coordination degree and the sources of the overall differences from 2012 to 2021 through the Dagum Gini Coefficient. The specific results are shown in

Table 6. Overall and within-group Gini coefficients from 2012 to 2021.

Year	Overall	Gini Coefficient Within Group
		Eastern	Central	Western	Northwest
2012	0.052	0.044	0.024	0.044	0.022
2013	0.049	0.042	0.021	0.04	0.018
2014	0.048	0.043	0.021	0.041	0.019
2015	0.051	0.048	0.02	0.042	0.022
2016	0.052	0.048	0.023	0.041	0.024
2017	0.051	0.05	0.024	0.041	0.024
2018	0.053	0.053	0.022	0.043	0.025
2019	0.053	0.056	0.025	0.041	0.027
2020	0.055	0.06	0.025	0.042	0.025
2021	0.058	0.06	0.024	0.046	0.025

,

Table 7. Inter-group Gini coefficients from 2012 to 2021.

Year	Inter-Group Gini Coefficient
	East –Central	East –West	East –Northeast	Central –West	Central –Northeast	West –Northeast
2012	0.049	0.060	0.046	0.038	0.024	0.041
2013	0.046	0.058	0.042	0.035	0.024	0.040
2014	0.044	0.057	0.044	0.036	0.021	0.039
2015	0.047	0.060	0.048	0.037	0.021	0.040
2016	0.048	0.060	0.049	0.038	0.024	0.040
2017	0.048	0.059	0.051	0.037	0.025	0.039
2018	0.050	0.062	0.053	0.039	0.024	0.042
2019	0.052	0.062	0.055	0.039	0.026	0.040
2020	0.054	0.065	0.057	0.041	0.026	0.042
2021	0.053	0.069	0.058	0.044	0.025	0.045

and

Table 8. Contribution rate of Gini coefficient during 2012–2021.

Year	Contribution/%
	Within a Group	Intergroup	Hypervariable Density
2012	23.241	65.719	11.04
2013	22.5	68.567	8.932
2014	23.703	65.423	10.874
2015	23.757	64.628	11.615
2016	23.487	64.249	12.264
2017	24.188	61.46	14.352
2018	24.539	60.26	15.201
2019	24.622	58.438	16.94
2020	24.699	58.739	16.562
2021	24.197	62.198	13.605

.

Table 6 shows that the overall Gini coefficient increases from 0.052 in 2012 to 0.058 in 2021, experiencing V-shaped fluctuations and showing a steady upward trend as a whole, indicating that the imbalance of coupling coordination degree persists and expands. The Belt and Road Initiative has brought development opportunities to the central and western regions and narrowed the gap with the eastern regions. However, economic growth pressures, regional development differences, and policy implementation differences eventually increase the overall Gini coefficient.

The variation trend of the Gini coefficient within the group was consistent with the overall Gini coefficient, and all four regions experienced a process of first decreasing and then increasing. The Gini coefficient in the eastern region increases the most due to the gap between initial endowments and development drivers. Due to the similarity of development level and strategy in the central region, the Gini coefficient within the group fluctuated but remained relatively stable as a whole. The Gini coefficient rose slightly in the western and northeastern regions, but in the western region, it was lower than the initial level until 2020 and suddenly rose in 2021 because the difference in economic structure increased the imbalance in the economic recovery process. In general, the intra-group Gini coefficient in the eastern region is always slightly higher than that in the western region, and the intra-group Gini coefficient in the central region and the Northeast region maintains the lowest level of inequality due to similar development levels and similar development strategies.

Table 7 shows that inter-group Gini coefficients increased, indicating that the differences in the level of coupling and coordination between economic resilience and green and low-carbon development among different regions have widened. Among them, the Gini coefficient of “east–northeast” increased by 0.012, with an increase of 26.09%, and the Gini coefficient of “middle–northeast” increased by the smallest, with only 0.001. The growth rates of “central–west,” “east–west” and “northeast–west” were 15.79%, 15%, and 9.76%, respectively, which were second only to the growth rate of “east–northeast” Gini coefficient, indicating that the degree of coupling and coordination between the western region and other regions was becoming more unequal. The western region was lagging in improving economic resilience and green and low-carbon development level. There is a significant difference in the coupling coordination degree between the eastern region and other regions, especially the “east–west” inter-group Gini coefficient is the largest. This is mainly because the eastern region is in a leading position in coping with economic fluctuations and promoting green technology due to its relatively advanced economy, superior innovation environment, perfect infrastructure, and advantages of high-tech industry.

Table 8 shows that the contribution rate of inter-group difference is much higher than that of intra-group difference and super-variable density during the study period, indicating that inter-group difference is the main source of the difference in the coupling coordination level of economic resilience and green, low-carbon development in the four regions. The contribution rate of intra-group difference was stable, rising slightly from 23.241% to 24.197%, being the highest in 2020. The contribution rate of difference between groups showed an N-type fluctuation, being the highest in 2013 and the lowest in 2019, with an overall decrease of 3.521%. On the contrary, the super variable density contribution rate increased from 11.04% to 13.605%, being the lowest in 2013 and the highest in 2019. In 2016-2017, the contribution of inter-group differences to the inequality of coupling coordination degree decreased significantly, while the contribution of super variable density increased. This may be because regional development strategies strengthened the linkages between different regions, leading to the increase in inequality contribution caused by regional overlap. The differences in the coupling coordination of China’s economic resilience and green, low-carbon development mainly come from the differences between different regions. However, the differences between groups are narrowing, while the differences within groups are increasing. Therefore, we should not only maintain the good situation of the decreasing differences between regions, but also take measures to prevent the continuous expansion of differences within groups from becoming a new cause affecting coordination.

4.6. Analysis of Dynamic Characteristics of Spatiotemporal Transfer

The above research can only simply describe the static characteristics of the spatiotemporal change in the coupling coordination degree of economic resilience and green, low-carbon development. Still, it cannot express its dynamic change trend. Therefore, the traditional and spatial Markov transfer probability matrix is constructed in this paper to deeply analyze the long-term evolution law of the coupling coordination degree of the two systems and explore the probability of upward or downward transfer of each province at the current stage. The coupling coordination degree was divided into low level, medium-low level, medium-high level, and high level using quartile points and was represented by k = 1,2,3,4, respectively. The specific results are shown in

Table 9. Markov transition probability matrix.

Spatial Lag	t/t + 1	N	1	2	3	4
No Lag	1	72	0.764	0.236	0.000	0.000
	2	74	0.027	0.689	0.284	0.000
	3	65	0.000	0.000	0.815	0.185
	4	59	0.000	0.000	0.000	1.000
1	1	34	0.794	0.206	0.000	0.000
	2	19	0.105	0.737	0.158	0.000
	3	3	0.000	0.000	1.000	0.000
	4	2	0.000	0.000	0.000	1.000
2	1	29	0.862	0.138	0.000	0.000
	2	17	0.000	0.647	0.353	0.000
	3	18	0.000	0.000	0.889	0.111
	4	19	0.000	0.000	0.000	1.000
3	1	8	0.375	0.625	0.000	0.000
	2	27	0.000	0.630	0.370	0.000
	3	22	0.000	0.000	0.727	0.273
	4	18	0.000	0.000	0.000	1.000
4	1	1	0.000	1.000	0.000	0.000
	2	11	0.000	0.818	0.182	0.000
	3	22	0.000	0.000	0.818	0.182
	4	20	0.000	0.000	0.000	1.000

.

4.6.1. Spatio-Temporal Transition Path Based on Traditional Markov Transition Matrix

Table 9 shows that in the traditional Markov transfer probability matrix, the probability of the diagonal line is significantly higher than that of the non-diagonal line, the provinces with high and medium-high levels have the highest probability of maintaining the initial state, and the lowest probability of maintaining the initial state of the coupling coordination level is 68.9%, indicating that all provinces tend to keep the initial state of the coupling coordination degree. There is a phenomenon of “club convergence”. The probability of low-level provinces maintaining the initial state is higher than that of medium-low level provinces, indicating that low-level provinces are more susceptible to path dependence and have the phenomenon of “low-end locking.” From the perspective of each transfer path, the initial coupling coordination degree of each province affects the direction of future development. Except for the medium-low level provinces, the other provinces show upward transfer, and the probability of upward transfer is significantly higher than that of downward transfer, indicating the overall good development trend. The probability of distance from the diagonal line is 0, indicating that the evolution of the coupling coordination level follows the law of “gradual and continuous”, and cannot achieve “leap-forward” transfer. The downward transfer probability of lower-level provinces indicates that they have not completely gotten rid of the influence of the original path, resulting in the decline of the coupling coordination level. In contrast, the higher-level provinces explore a new development model, so as to maintain a good development.

4.6.2. Spatio-Temporal Transition Path Based on Spatial Markov Transition Matrix

The coupling coordination level of a province is affected not only by internal factors but also by the external environment. In this paper, the neighborhood state is regarded as the spatial lag condition, the spatial lag condition is associated with the traditional Markov transition probability matrix, and the spatial Markov transition probability matrix is constructed to explore the influence of the neighborhood state on the coupling coordination level.

On the whole, the state of the neighborhood significantly affects the stability and transition probability of the provincial coupling coordination level. When a province is in low, medium-low, medium-high, and high neighborhood states, the weighted average probabilities of maintaining the original coupling coordination level are 79.31%, 85.54%, 72.01%, and 87.03%, respectively, and the probabilities of transferring from low level to high level are 20.6%, 13.8%, 62.5%, and 100%. The probability changes compared with the case without considering the spatial lag condition. The state of the neighborhood has no effect on the provinces with high level, but in the case of the low-level neighborhood, the probability of the coupling coordination degree of the low-level province is increased to 79.4%, and it is more likely to appear “low-end locking”. In each neighborhood, the probability of the diagonal is greater than the probability of the non-diagonal; that is, the phenomenon of “club convergence” is also supported in the spatial dimension.

At the same time, different neighborhood states have different effects on the path transfer probability. In general, the higher the neighborhood level, the greater the probability of upward migration of provinces with the same coupling coordination level, such as P23/1(0.158) < P23/2(0.353) < P23/3(0.370). However, in the neighborhood of lower level, the probability of transfer from low level to medium-low level may be lower. This may be because the coupling coordination level of the neighborhood itself is not high, and the radiation-driving ability of other provinces is not strong, so it will not bring too much help to the low-level provinces. Compared with the results of the traditional Markov transition matrix, a lower neighborhood level will increase the probability of a downward transition, such as P21/1(0.105) > P21(0.027), and a higher neighborhood level will increase the probability of an upward transition, such as P23/3(0.37) > P23(0.284). And the improvement of the neighborhood level prevents the phenomenon of downward transfer. When the neighborhood reaches a high level, each province’s coupling coordination degree tends to be stable, and the probability of upward transfer is small. The reasons for this phenomenon are as follows. On the one hand, when the coupling coordination degree gap between neighboring provinces is too small, backward provinces may continue to follow their own development mode, resulting in a certain path dependence, bringing the convergence of low-end clubs. On the other hand, when the coupling coordination degree gap between neighboring provinces is too large, the development mode of high-level provinces may not adapt to the lower-level provinces, so the radiation drive and spatial spillover effect of high-level provinces on lower-level provinces will not be particularly obvious.

4.7. Obstacle Factor Analysis

The obstacles to the main factors of economic resilience and green and low-carbon development were calculated respectively and arranged according to time and region to explore the internal sources affecting the economic resilience and green and low-carbon development of the region. This paper lists the obstacles to the main factors of economic resilience and green and low-carbon development in 2012, 2015, 2018, and 2021, as shown in

Table 10. Obstacle degree of economic resilience factors.

Year	a1	a2	a3	a4	a5	a6	a7	a8	a9	a10	a11	a12	a13
2012	0.0951	0.0307	0.0156	0.0581	0.0125	0.1001	0.0062	0.0523	0.0115	0.1295	0.1162	0.0388	0.3334
2015	0.0926	0.0312	0.0136	0.0529	0.0096	0.0948	0.0080	0.0538	0.0159	0.1271	0.1186	0.0389	0.3429
2018	0.0867	0.0305	0.0137	0.0489	0.0109	0.0885	0.0094	0.0528	0.0140	0.1275	0.1244	0.0399	0.3530
2021	0.0812	0.0338	0.0112	0.0545	0.0159	0.0822	0.0112	0.0489	0.0125	0.1419	0.1283	0.0344	0.3438

,

Table 11. Obstacle degree of annual economic resilience factors by region.

Year	Region	a1	a2	a3	a4	a5	a6	a7	a8	a9	a10	a11	a12	a13
2012	Central	0.0963	0.0323	0.0130	0.0647	0.0122	0.1005	0.0081	0.0452	0.0111	0.1340	0.1207	0.0394	0.3226
2015	Central	0.0961	0.0304	0.0113	0.0582	0.0067	0.0983	0.0083	0.0428	0.0152	0.1340	0.1243	0.0393	0.3351
2018	Central	0.0922	0.0296	0.0113	0.0531	0.0070	0.0950	0.0092	0.0415	0.0121	0.1351	0.1274	0.0400	0.3466
2021	Central	0.0898	0.0257	0.0095	0.0584	0.0095	0.0924	0.0102	0.0359	0.0107	0.1474	0.1295	0.0305	0.3507
2012	East	0.0925	0.0270	0.0131	0.0533	0.0106	0.0992	0.0033	0.0620	0.0141	0.1320	0.1047	0.0355	0.3527
2015	East	0.0873	0.0303	0.0105	0.0491	0.0105	0.0901	0.0078	0.0650	0.0169	0.1285	0.1044	0.0370	0.3624
2018	East	0.0763	0.0310	0.0113	0.0464	0.0132	0.0787	0.0100	0.0641	0.0170	0.1299	0.1133	0.0394	0.3694
2021	East	0.0671	0.0393	0.0102	0.0537	0.0206	0.0684	0.0129	0.0687	0.0156	0.1543	0.1215	0.0385	0.3292
2012	Northeast	0.0966	0.0364	0.0079	0.0672	0.0160	0.0987	0.0114	0.0443	0.0115	0.1298	0.1151	0.0352	0.3298
2015	Northeast	0.0965	0.0378	0.0085	0.0589	0.0115	0.0942	0.0130	0.0401	0.0197	0.1244	0.1208	0.0346	0.3400
2018	Northeast	0.0952	0.0391	0.0082	0.0483	0.0116	0.0897	0.0141	0.0441	0.0161	0.1187	0.1292	0.0365	0.3492
2021	Northeast	0.0943	0.0383	0.0040	0.0528	0.0163	0.0866	0.0135	0.0389	0.0144	0.1277	0.1300	0.0231	0.3600
2012	West	0.0963	0.0317	0.0214	0.0563	0.0134	0.1010	0.0063	0.0496	0.0094	0.1248	0.1247	0.0425	0.3226
2015	West	0.0944	0.0307	0.0191	0.0518	0.0099	0.0974	0.0068	0.0532	0.0145	0.1229	0.1277	0.0414	0.3303
2018	West	0.0907	0.0281	0.0186	0.0491	0.0106	0.0935	0.0077	0.0511	0.0117	0.1236	0.1316	0.0413	0.3425
2021	West	0.0858	0.0320	0.0151	0.0536	0.0151	0.0880	0.0096	0.0408	0.0102	0.1316	0.1335	0.0358	0.3489

,

Table 12. Obstacle degree of green and low-carbon development factors.

Year	b1	b2	b3	b4	b5	b6	b7	b8	b9	b10	b11	b12
2012	0.1396	0.1950	0.0042	0.0253	0.1593	0.3080	0.0161	0.0021	0.0631	0.0031	0.0092	0.0750
2015	0.1435	0.1921	0.0020	0.0250	0.1522	0.3233	0.0127	0.0023	0.0604	0.0054	0.0098	0.0713
2018	0.1420	0.1914	0.0005	0.0225	0.1494	0.3280	0.0104	0.0026	0.0618	0.0083	0.0094	0.0736
2021	0.1412	0.1831	0.0001	0.0190	0.1502	0.3301	0.0086	0.0030	0.0707	0.0109	0.0088	0.0744

and

Table 13. Obstacle degree of annual green and low-carbon development factors by region.

Year	Region	Region	b1	b2	b3	b4	b5	b6	b7	b8	b9	b10	b11	b12
2012	Central	central	0.1182	0.1978	0.0040	0.0235	0.1629	0.3198	0.0133	0.0018	0.0722	0.0027	0.0145	0.0693
2015	Central	central	0.1245	0.1968	0.0010	0.0226	0.1557	0.3288	0.0103	0.0026	0.0702	0.0053	0.0151	0.0672
2018	Central	central	0.1204	0.1959	0.0000	0.0192	0.1512	0.3458	0.0075	0.0029	0.0652	0.0088	0.0144	0.0686
2021	Central	central	0.1230	0.1845	0.0000	0.0146	0.1547	0.3434	0.0053	0.0037	0.0752	0.0119	0.0133	0.0703
2012	East	east	0.1423	0.1747	0.0020	0.0200	0.1788	0.3315	0.0097	0.0015	0.0547	0.0053	0.0082	0.0712
2015	East	east	0.1437	0.1696	0.0012	0.0195	0.1787	0.3432	0.0075	0.0015	0.0506	0.0084	0.0084	0.0676
2018	East	east	0.1373	0.1606	0.0001	0.0180	0.1811	0.3513	0.0057	0.0016	0.0556	0.0123	0.0078	0.0687
2021	East	east	0.1374	0.1484	0.0000	0.0169	0.1815	0.3549	0.0045	0.0017	0.0629	0.0155	0.0071	0.0692
2012	Northeast	northeast	0.1412	0.1997	0.0111	0.0301	0.1281	0.3267	0.0198	0.0027	0.0687	0.0023	0.0093	0.0603
2015	Northeast	northeast	0.1549	0.1904	0.0042	0.0301	0.1100	0.3517	0.0150	0.0025	0.0659	0.0040	0.0106	0.0609
2018	Northeast	northeast	0.1586	0.2026	0.0027	0.0303	0.0833	0.3572	0.0139	0.0028	0.0681	0.0061	0.0107	0.0637
2021	Northeast	northeast	0.1606	0.2035	0.0000	0.0252	0.0856	0.3518	0.0130	0.0033	0.0729	0.0080	0.0106	0.0654
2012	West	west	0.1484	0.2108	0.0043	0.0297	0.1480	0.2751	0.0224	0.0028	0.0643	0.0017	0.0072	0.0854
2015	West	west	0.1506	0.2106	0.0025	0.0299	0.1378	0.2945	0.0180	0.0028	0.0625	0.0032	0.0080	0.0797
2018	West	west	0.1537	0.2140	0.0006	0.0264	0.1376	0.2891	0.0154	0.0032	0.0639	0.0051	0.0078	0.0834
2021	West	west	0.1492	0.2084	0.0001	0.0215	0.1369	0.2944	0.0129	0.0038	0.0746	0.0069	0.0073	0.0839

.

Table 10 shows that the main barriers to economic resilience in the selected years are innovation capacity, advanced industrial structure, and intensity of R&D funding. Among them, the obstacle degree of innovation ability is significantly higher than other factors in the four years, indicating the profound impact of innovation on economic activities. Although the government has promoted the improvement of the innovation environment through policy guidance, the intellectual property rights protection system, the innovation incentive mechanism, and the industry–university–research cooperation system still need to be improved, which hinders the improvement of economic resilience, and the innovation atmosphere and innovation capacity of the whole society still have room for improvement. The barriers to advanced industrial structure increased year by year, from 12.95% to 14.19%, indicating that industrial transformation is facing challenges, especially because heavy and resource-intensive industries are our country’s pillar industries. In addition, the intensity of R&D funding also maintained steady growth, indicating that investment and support for R&D are still insufficient. Hence, increasing R&D funding, optimizing the use structure of R&D funding, and improving R&D efficiency is crucial to enhance economic resilience.

Table 11 shows that the main obstacle factors of economic resilience in the four regions are innovation ability, advanced industrial structure, and R&D funding intensity. Innovation ability has always been the main obstacle restricting the improvement of economic resilience. Except for the decline of the degree of obstacles in the eastern region, the other regions generally increased, which may be related to the advantages of the eastern region in terms of talent, capital, and technology, which are conducive to the expansion of innovative activities. In addition, the development of vaccines and medical equipment in the eastern region during the epidemic period is also an important reason for improving innovation ability. The second obstacle factor in the eastern and central region is the advanced industrial structure, and the third is the intensity of R&D funds, and the obstacle degree of both is on the rise, especially in the eastern region after the epidemic. The impact is more significant, possibly because the economic recovery after the COVID-19 increased dependence on the secondary industry, resulting in the limitation of the upgrading of the industrial structure. The second obstacle factor in the northeast region was the advanced industrial structure in the early stage and the R&D funding intensity in the later stage, indicating that the supply-side structural reform promoted the adjustment of industrial structure, but the investment in new technology R&D was insufficient. In 2012, the western region was mainly affected by the advanced industrial structure and after 2012, it was the intensity of R&D funding, indicating that the insufficient investment in R&D limited the improvement of economic resilience.

Table 12 shows that the main obstacles to green and low-carbon development nationwide are water resources per capita, sewage treatment capacity per day, and national nature reserves. Among them, the obstacle degree of per capita water resources is much higher than other factors, and the trend is rising, indicating that water resource stress puts pressure on green and low-carbon development. Water availability is essential for the sustainable operation of agriculture, industry, and energy production and is increasingly important for green and low-carbon development. The decrease in the obstacle degree of daily sewage treatment capacity reflects the positive effect of technological progress of sewage treatment and government support for sewage treatment and recycling on reducing environmental pollution and achieving green and low-carbon development. The decrease in obstacle degree in the number of national nature reserves indicates that these reserves have achieved positive results in maintaining biodiversity, enhancing carbon absorption and storage, promoting ecotourism, and promoting the economic structure’s green and low-carbon transformation.

Table 13 shows that per capita water resources are the primary obstacle factor for green and low-carbon development in the four major regions, and its obstacle degree is on the rise, indicating that the shortage of water resources is widespread and restricts the promotion of green and low-carbon development. The second obstacle in the eastern region is the number of national nature reserves. As an economic center with a dense population, high urbanization level, and tight land resources, the eastern region is more inclined to develop commercial and residential land, leading to conflicts in constructing protected areas. The third obstacle factor is the daily sewage treatment capacity. Still, its degree of difficulty is decreasing, indicating that the eastern region has made progress in improving the efficiency and recycling of water resources. The second obstacle factor in the central region is the daily sewage treatment capacity, and the third is the number of national nature reserves, which shows that the traditional agriculture and primary processing industry in this region have a great demand for water resources and insufficient treatment capacity. However, compared with the eastern region, the population and industrial activities in this region are less, and the land value is lower than that in the eastern region, so it has the advantages of nature reserves. The second obstacle factor in both the western and northeast regions is the daily sewage treatment capacity. The third is the comprehensive utilization of general industrial solid waste, which reflects their dependence on traditional industries to produce a large amount of sewage and solid waste discharge. However, the low level of technology limits the recycling capacity and hinders green and low-carbon development.

5. Conclusions and Suggestions

5.1. Conclusions

This paper constructs a comprehensive evaluation system of economic resilience and green, low-carbon development through the entropy weight method, measures the coupling coordination degree of the two systems in 30 provinces across the country, and analyzes their spatio-temporal evolution and obstacle factors. The results show that, first, the degree of coupling coordination between economic resilience and green and low-carbon development has increased as a whole. The coupling coordination degree in the eastern region is the highest, the growth rate in the central region is the highest, and the gap between the eastern and western regions is widening. Second, the difference in the level of coupling coordination in China has expanded, and the diversified characteristics of coupling and coordination development in various provinces are significant, with diversified development in the eastern region, polarization in the western region, and convergence in the central region. Third, provinces with similar coupling coordination degrees tend to cluster in space, but the agglomeration effect weakens with time, and most provinces show the characteristics of low–low agglomeration or high–high agglomeration. Fourth, the imbalance of China’s coupling coordination degree will always exist and continue to expand, mainly from inter-regions. Fifth, most provinces tend to maintain the current level of coupling coordination, which is prone to the phenomenon of “club convergence”, and low-level provinces are prone to path dependence, resulting in “low-end locking”. Sixth, the main obstacles to economic resilience are innovation ability, industrial structure upgrading, and R&D funding intensity. The main obstacles to green and low-carbon development are water resources per capita, daily sewage treatment capacity, and the number of national nature reserves.

5.2. Suggestions

Based on the above conclusions, this paper puts forward the following suggestions. First, focus on harmonizing efforts to enhance economic resilience while promoting green and low-carbon development. This includes fostering collaboration and deep integration between these two systems. Emphasize transitioning China’s development model toward green, low-carbon, digital, and intelligent pathways. Explore the potential of green and low-carbon industries to establish a modern industrial system and reduce resource dependency. Second, strengthen inter-regional cooperation and coordination, establish regional cooperation mechanisms, promote exchanges and cooperation between neighboring provinces in the field of economic resilience and green and low-carbon development, improve cooperation mechanisms between the eastern region and other regions, promote infrastructure construction, realize resource, technology, and information sharing, and guide industrial distribution to regions with strong resource and environmental carrying capacity and low ecological impact. Give play to the radiation-driving role of provinces with higher coupling coordination levels to provinces with lower coupling coordination levels. Third, development strategies tailored to the natural resources and economic conditions of different regions should be implemented, the allocation of internal factors should be optimized, the advantages of each region should be given full play, and investment in clean energy and innovation should be encouraged. For provinces with lower coupling coordination levels, more policy and financial support should be provided to help establish a green and low-carbon industrial system and promote industrial diversification. For provinces with a higher coupling coordination level, it is necessary to encourage them to increase investment in emerging industries such as circular economy, green energy, and low-carbon technology and promote continuous technological innovation. Fourth, the government should accurately grasp the obstacle factors to break through the bottleneck of improving the coupling coordination level. On the one hand, the government should create a good innovation environment, increase public R&D investment, improve innovation incentive mechanisms, attract talents and private investment, build a bridge of school–enterprise cooperation, and improve the efficiency of transforming scientific research results into productivity. On the other hand, strictly control and supervise the discharge of industrial pollution and urban sewage, implement water-saving and sewage treatment measures, such as promoting water-saving and recycling water technology, promoting the upgrading of sewage treatment equipment, optimizing water use efficiency, and reducing water resources waste, to improve economic resilience and green and low-carbon development level, and help the coupling coordination degree to improve.