The Temporal and Spatial Evolution and Influencing Factors of the Coupling Coordination Degree Between the Promotion of the “Dual Carbon” Targets and Stable Economic Growth in China

Abstract

Coordinating the relationship between “dual carbon” targets and stable economic growth is crucial for promoting high-quality development in China. This study utilizes the coupling coordination model, kernel density estimation, and spatial econometric models to explore the temporal and spatial evolution characteristics and influencing factors of the coupling coordination degree between the promotion of the “dual carbon” targets and stable economic growth in 287 Chinese cities from 2011 to 2021. The results indicate that, in terms of temporal evolution, the promotion of China’s “dual carbon” targets increases yearly, while stable economic growth follows a “year-on-year increase—short-term decline—sustained recovery” pattern with the coupling coordination degree fluctuating upward. Regarding spatial evolution, the coupling coordination degree between the promotion of the “dual carbon” targets and stable economic growth in China presents a “higher in the east, lower in the west” spatial pattern, with varying gradient effects and polarization across the country and its regions. Influencing factors include government intervention, environmental regulations, energy efficiency, financial development, and R&D investment intensity. These findings provide scientific insights for addressing the mutual constraints between “dual carbon” targets and stable economic growth.

1. Introduction

Ecosystem sustainability is important for social and economic development. Accelerated global warming has led to frequent natural disasters and biodiversity destruction, severely impacting economic development. The primary cause is rising carbon emissions. As a major carbon emitter, China announced its “dual carbon” targets at the 75th United Nations General Assembly in September 2020: achieving peak carbon emissions by 2030 and carbon neutrality by 2060. Since then, the “dual carbon” targets have officially become a major development strategy in China. In March 2021, the ninth meeting of the Financial and Economic Commission of the Communist Party of China Central Committee studied the basic ideas and main measures to achieve the “dual carbon” targets. In October 2022, the report of the 20th National Congress further emphasized the need to actively and steadily promote the “dual carbon” goals, promote green development, and promote the harmonious coexistence of humanity and nature. It can be seen that the “dual carbon” goals are China’s solemn commitment to the world, which will also bring long-term benefits to human society.

At the same time, China’s economic development is currently in the “new normal” stage, characterized by a complex and challenging external environment and internal economic pressures. Maintaining stable economic growth is crucial. In 2014, at the opening ceremony of the Asia-Pacific Economic Cooperation Chief Executive Officer Summit, President Xi systematically elaborated on the concept of the “new normal” of China’s economy for the first time and pointed out that stable growth is a key starting point for entering the “new normal” stage. The report on the work of the government for 2023 once again emphasized the importance of stable growth for a good start in comprehensively building a modern socialist country.

However, there is a dilemma between the “dual carbon” goals and stable economic growth. Although the proposal of the “dual carbon” goals not only shows China’s great-power responsibility in promoting the construction of a community of human destiny but also highlights China’s carbon reduction action planning and route, the “dual carbon” goals and stable economic growth face the superposing challenges of energy structure adjustment and industrial structure transformation, involving economic and social systemic change with multiple goals and multiple constraints [1].

First, in order to secure China’s voice in global climate and economic development talks, China must unswervingly promote a high-level open economic growth model; therefore, in addition to replacing the huge fossil energy consumption stock, China’s future low-carbon transition also needs to meet the incremental energy demand brought about by economic growth. However, limited by resource endowment characterized by “poor oil, less gas, and relatively rich coal”, China’s energy structure will still be dominated by coal in the future for a period of time; the pressure of power demand growth is mainly borne by coal power, and the main energy status of coal is difficult to change in a short time. Under the existing energy structure, economic growth is closely related to fossil energy consumption, which will inevitably lead to a continuous increase in carbon emissions, thus restricting the realization of the “dual carbon” goals on schedule. Secondly, as the largest developing country, China’s industrial structure is widely distributed from the low end to the high end of the value chain, and traditional high-energy-consuming industries and strategic emerging industries coexist [1], which leads to certain difficulties in the transformation of an industrialized society dominated by traditional fuels such as coal. If the traditional high-energy industries adopt a “one-size-fits-all” carbon reduction policy, it may have a negative impact on stable economic growth. Early efforts to advance China’s “dual carbon” goals involved rigid measures such as project approvals, power cuts, direct production restrictions, and shutdowns, which imposed severe supply-side constraints and jeopardized steady economic growth. Finally, non-fossil energy such as nuclear energy, solar energy, wind energy, and bioenergy are clean and renewable, so increasing the proportion of non-fossil energy is an important way to promote the “dual carbon” goals. However, the current cost of clean energy is higher than that of traditional fossil fuels, and the development of clean technologies also requires new infrastructure and increased investment in research and development. The cost effects of these measures could have implications for stable economic growth; therefore, coordinating the relationship between the “dual carbon” targets and stable economic growth is a key problem that needs to be solved urgently. The current academic community, in general, has noticed this relationship but has not yet answered the question of how to coordinate it.

In order to answer this urgent question, this paper measures the coupling coordination degree of the promotion of China’s “dual carbon” goals and stable economic growth and analyzes the spatiotemporal evolution characteristics and influencing factors of the coupling coordination degree, which is conducive to alleviating the contradiction between environmental protection and economic development, and helps achieve the coordination of “dual carbon” goals promotion and stable economic growth in China. It also provides a useful reference for a global balance between greenhouse gas mitigation and economic development.

The remainder of this article is structured as follows: Section 2 reviews the relevant literature, explores the research gaps, and reveals the contributions of this study. Section 3 theoretically expounds on the coupling and coordination relationship between the promotion of “dual carbon” goals and stable economic growth. Section 4 establishes the evaluation index system and introduces the research methods and data sources. Section 5 illustrates the results, and Section 6 elaborates on the discussion and conclusions.

2. Literature Review

2.1. Studies on the “Dual Carbon” Goals

Research on the “dual carbon” goals has mainly focused on carbon emissions, especially the measurement of carbon emissions and their influencing factors. With respect to the measurement research on the current situation of carbon emissions, Kaya et al. calculated the carbon intensity of primary energy, final energy, and converted energy in China, France, India, Japan, and the United States between 1960 and 1991, and found that China and India had high carbon intensities because of their high dependence on coal and traditional energy, and France’s carbon intensity fell sharply in the 1980s as it introduced nuclear power [2]. Mielnik et al. used carbon emissions per unit of energy as a carbon index and found that industrialized countries are “decarbonizing”, but developing countries are “carbonizing” [3]. Subsequently, many scholars adopted various methods to measure the carbon emissions of various countries in the world. For example, some scholars compared production-based, consumption-based, and technology-adjusted carbon emissions for 44 countries and country groups for the period from 2000 to 2014 and found that emissions in 20 European Union countries and the US decreased over the period [4]. Scholars examined whether CO₂ emissions per capita converged across 22 European countries between 1971 and 2006 [5]. By using the meta-frontier non-radial directional distance function measurement, scholars found that the carbon emission performance of the Clean Development Mechanism host country appeared to be lower than that of the investment countries [6]. It can be seen that the global academic community’s measurement research on carbon emissions is relatively mature.

The STIRPAT (Stochastic Impacts by Regression on Population, Affluence, and Technology) model, spatial measurement model, and least square method have been widely used to study the influencing factors of carbon emissions. Liu et al. used the STIRPAT model to analyze the influencing factors of carbon emissions in 30 provinces in China and found that population, per capita GDP, urbanization rate, and innovation input were the main factors in reducing carbon emissions, and the optimization and upgrading of industrial structure, energy intensity, energy structure, and transportation structure had a mitigating effect on carbon emissions [7]. Shao et al. used the spatial Durbin model to comprehensively investigate the direct and indirect effects of multi-dimensional factors reflecting economic restructuring and green technology progress on carbon emission performance [8]. Dong and Zhou used a spatial econometric model to find that the digital economy has an impact on local carbon emissions in the Yellow River Basin that first increases and then decreases and has a negative spatial spillover effect on neighboring carbon emissions [9]. In addition, some scholars have used the least square method and other measurement methods to study the positive effects of renewable energy, technological innovation, energy efficiency, and environmental regulations on carbon emission reduction in many countries [10,11,12,13,14]. By combing the relevant literature, it is found that a variety of research methods have been applied to study the influencing factors of carbon emissions. Among them, the spatial econometric model can not only explore many influencing factors of carbon emissions but also find out whether these influencing factors have spatial spillover effects.

2.2. Studies on Stable Economic Growth

Research on stable economic growth mainly focuses on its conceptual definition and influencing factors. In terms of the conceptual definition, various studies have enriched the theoretical research on stable economic growth from different perspectives. For instance, while Chao suggests that economic growth stability is a key aspect of economic growth quality [15], Liu and Che argue that severe deflation affects economic growth stability [16]. Zhao views stable economic growth as having minimal fluctuations in growth rates [17]. Regarding influencing factors, existing research indicates that the output efficiency of fixed asset investment efficiency and the alignment of human capital with technology application positively impact stable economic growth [18,19], whereas human capital mismatches can lead to insufficient innovation, thereby hindering stable economic growth [20].

2.3. Studies on the Relationship Between the “Dual Carbon” Goals and Stable Economic Growth

Research on the relationship between the “dual carbon” goals and stable economic growth originates from studies on the correlation, decoupling, and coupling coordination relationships between carbon emissions and economic growth. Correlation studies mainly present two views: the “inverted U-shaped relationship” and the “other shapes theory”. The “inverted U-shaped relationship” stems from the environmental Kuznets curve (EKC) hypothesis, and numerous scholars have confirmed the existence of the EKC for carbon emissions, which indicates an initial positive and subsequent negative correlation between carbon emissions and economic growth [21,22,23,24]. The “other shapes theory” posits that the relationship between carbon emissions and economic growth does not necessarily follow the EKC but rather is more similar to certain long-term relationships, such as “~-shaped” [25], “V-shaped” [26], or “positive U-shaped” [27]. In a study of the decoupling relationship between carbon emissions and economic growth, Wang et al. compared the decoupling of carbon emissions in China and the United States [28] and found that China mainly experienced expansionary coupling and weak decoupling, while the United States mainly experienced weak and strong decoupling. Wang and Wang also found that the United States had been experiencing economic growth compatible with carbon reduction since 2007 [29]. At the same time, many scholars have carried out research on the decoupling relationship of Chinese provinces and cities [30,31]. Additionally, extensive research has been conducted on the coupling coordination relationship between the two, with some scholars analyzing this relationship from perspectives such as agricultural carbon emissions, tourism carbon emissions, and high-quality economic development [32,33,34].

2.4. Contributions

Overall, research on the “dual carbon” goals and stable economic growth has steadily expanded. In contrast to the existing literature, this study makes three potential contributions. First, while the relevant studies on the “dual carbon” targets mainly focus on the measurement and influencing factors of carbon emissions, achieving the “dual carbon” goals also requires the consideration of carbon absorption. This study constructs an evaluation index system that includes both dimensions, which offer a more comprehensive measurement of progress toward the “dual carbon” goals and scientific decision-making support. Second, existing research on stable economic growth mainly emphasizes the stability of economic growth. However, this study evaluates both growth and stability, thus enriching the theoretical research and evaluation frameworks. Third, although existing research has thoroughly explored the relationship between carbon emissions and economic growth, research on the coupling coordination between advancement toward “dual carbon” goals and stable economic growth is limited, and the research on the influencing factors of the coupling coordination degree needs to be further explored. This study not only measures the coupling coordination degree between China’s “dual carbon” targets and stable economic growth but also explores the direct and indirect effects of government intervention, environmental regulations, energy efficiency, financial development level, and R&D (research and development) investment intensity on the coupling coordination degree, further supplementing the research on the relationship between “dual carbon” targets and economic growth.

This study uses a sample of 287 Chinese cities from 2011 to 2021 to construct an evaluation index system for the advancement toward the “dual carbon” goals and stable economic growth. It analyzes the spatiotemporal evolution of coupling coordination using the coupling coordination model and kernel density estimation and explores the influencing factors through spatial econometric models. This provides a reference for harmonizing the relationship between the “dual carbon” goals and stable economic growth, thus contributing to environmental and economic win–win outcomes and promoting high-quality development.

3. Theoretical Analysis

3.1. Characteristics of Coupled Coordination of the Promotion of “Dual Carbon” Goals and Stable Economic Growth

The “dual carbon” goals, encompassing “carbon peaking” and “carbon neutrality”, represent a major strategic decision for China. “Carbon peaking” refers to total carbon dioxide emissions reaching their peak and then steadily declining, while “carbon neutrality” involves offsetting the carbon dioxide emissions produced by enterprises, organizations, or individuals over a certain period through methods such as afforestation and energy-saving initiatives to achieve a relative “zero emission”. The active and steady pursuit of these goals is essential for fostering harmony between humans and nature and achieving the vision of a beautiful China.

Stable economic growth reflects the “shift in growth speed” in China’s new normal economic context. Defined as “reasonable quantitative growth” ensuring “effective qualitative improvement”, it is key to high-quality economic development. It refers to sustained, steady economic expansion over time [15], encompassing both stability and growth, with neither being dispensable. In macroeconomics, economic growth signifies an increase in output—a fundamental driver of human welfare. Conversely, economic stability involves minimizing economic fluctuations. Neglecting stability can lead to growth, while overlooking growth in pursuit of economic stability may result in stagnation. Therefore, economic development must balance both stability and growth.

The “dual carbon” goals introduce new requirements for economic development driven by both internal and external environmental factors. Moreover, stable economic growth reflects adjustments in growth rates that align with the stage of economic and social development. These aspects form a mutually influencing and synergistically advancing coupled coordination relationship. The coordination between advancing the “dual carbon” goals and achieving stable economic growth is bidirectional: the goals drive economic structural transformation, while stable growth adapts to these structural adjustments. Essentially, it is a dynamic relationship where the environmental and economic subsystems mutually influence and promote each other.

3.2. Coupled Coordination Mechanism of Advancing the “Dual Carbon” Goals and Stable Economic Growth

Advancing the “dual carbon” goals and achieving stable economic growth is a dialectical process, with internal elements of both systems existing in a contradictory yet unified relationship. The realization of the “dual carbon” goals cannot entirely disregard stable economic growth, nor can stable economic growth be achieved at the expense of significant carbon emissions. The two subsystems—environmental and economic—must coexist harmoniously, integrate organically, and remain inseparable. This study constructs an analytical framework for the coupled coordination mechanism of advancing the “dual carbon” goals and stable economic growth, as shown in Figure 1, based on their interaction.

3.3. Advancement Toward the “Dual Carbon” Goals Provides Foundational Support for Stable Economic Growth

The “dual carbon” goals influence stable economic growth through five key aspects: incentivizing innovation, promoting low-carbon development, improving the ecological environment, empowering carbon assets, and optimizing the developmental foundation.

• Incentivizing innovation: According to the Porter hypothesis, environmental regulation can stimulate enterprises to innovate, thereby enhancing productivity and profitability. Driven by the “dual carbon” goals, environmental regulations can spur advancements in low-carbon technologies, guide industrial transformation and upgrading, and create new employment opportunities. This leads to an “innovation compensation effect”, shifting the economy from a low-level equilibrium to a Pareto improvement equilibrium that supports stable economic growth.
• Promoting low-carbon development: Advancement toward the “dual carbon” goals facilitates the transformation of the traditional economic development model, shifting from “end-of-pipe treatment” to “source prevention”. This drives the transformation of economic development momentum, creates a sustainable path for green, high-quality development, and supports stable economic growth.
• Improving the ecological environment: The “dual carbon” goals reduce carbon emissions, increase carbon sinks, and improve the living environment for workers. This benefits workers’ health and quality of life, boosts labor productivity, and injects sustained vitality into stable economic growth.
• Empowering carbon assets: The “two mountains” theory, positing “lucid waters and lush mountains are invaluable assets”, provides a new perspective on the value transformation of carbon emissions, resulting in the formation of carbon assets. Carbon emission rights and carbon sink assets are indispensable aspects of the ecological product value realization mechanism, possessing the function of ecological wealth appreciation. This facilitates the conversion of ecological resources into ecological products, promotes capital accumulation, and provides new strength for stable economic growth.
• Optimizing the developmental foundation: The climate environment is the foundation for stable economic growth, directly influencing economic and societal development, as well as the trajectory of human civilization. The sharp increase in carbon emissions has led to rising global temperatures, triggering natural disasters that severely threaten economic and societal stability, making them a significant threat to stable economic growth. Advancement toward the “dual carbon” goals creates favorable natural environmental conditions and provides sufficient production factors to support stable economic growth, thereby ensuring the foundation for economic development.

3.4. Stable Economic Growth Is a Precondition for Achieving the “Dual Carbon” Goals

Stable economic growth influences the advancement toward the “dual carbon” goals through four aspects: financial support, technical support, economic structural transformation, and the enhancement of people’s quality of life.

• Financial support: Stable economic growth provides the financial resources necessary for achieving the “dual carbon” goals. Developing clean energy, supporting low-energy infrastructure, and researching low-carbon technologies require significant investment. Such investment results from stable economic growth reaching a specific stage, thereby offering the necessary funds for carbon reduction activities.
• Technical support: Stable economic growth provides the technical support required for the “dual carbon” goals. Achieving stable economic growth implies an improvement in production technology. According to endogenous growth theory, technological progress significantly enhances resource utilization efficiency and reduces natural resource consumption [35], contributing to the formation of a “low input, high output” economic model that reduces carbon emissions.
• Economic structural transformation: Stable economic growth represents a more sustainable model of economic development, supporting the transformation of development modes and the optimization of industrial structures. The structural changes resulting from this transformation improve resource utilization and pollution control efficiency, thereby promoting the realization of the “dual carbon” goals.
• Meeting people’s needs for a better life: As economic stability progresses, living standards improve, and public demand for a better ecological environment increases. Increased awareness of ecological protection drives people to pursue and adopt low-carbon, healthy lifestyles. To meet the public’s growing demand for ecological services, the government should increase the supply of carbon reduction policies, thereby accelerating the realization of the “dual carbon” goals.

4. Materials and Methods

4.1. Index System

4.1.1. The Index System of the “Double Carbon” Targets Promotion Level

The achievement of the “dual carbon” goals is influenced by both carbon emissions and carbon sinks. This study selects indicators from these dimensions, as presented in

Table 1. Evaluation index system for the level of “dual carbon” advancement.

Target Layer	Criterion Layer	Index Layer	Representational Meaning	Attribute
The level of progress toward the “two-carbon” goal	Carbon emission	Carbon intensity	Carbon emissions per unit of economic output	−
		Energy intensity	Energy consumed per unit of economic output	−
		Proportion of coal consumption	Major fossil energy consumption	−
	Carbon uptake	Garden green area	Garden green space carbon sink resources	+
		Park green area	Park green space carbon sink resources	+
		Green coverage ratio	Greening level	+

Note: The calculation of carbon emissions follows the method used by Ren et al. [37], which estimates carbon emissions based on the consumption of three types of energy: natural gas, liquefied petroleum gas, and total electricity.

. For carbon emissions, following Zhang and Lei’s research and the Kaya identity decomposition, the indicators include carbon emission intensity, energy intensity, and the proportion of coal consumption [36]. In the carbon absorption dimension, green spaces act as carbon sink resources by absorbing carbon dioxide through photosynthesis, which is essential for “carbon neutrality”. In this study, to ensure data availability and comprehensiveness, parkland areas, urban green space areas, and greening coverage rates were selected for the carbon-reducing dimension indicator system.

4.1.2. Index System of Stable Economic Growth

The measurement methods for stable economic growth primarily include the traditional economic cycle method, economic growth rate, and economic growth stability index. The traditional economic cycle method assesses stability by analyzing economic peaks and troughs but is limited by its susceptibility to exceptional years. Relying solely on changes in the economic growth rate or the stability index can provide a biased perspective if based on a single indicator. Considering that stable economic growth encompasses both stability and growth, this study constructs an indicator evaluation system for stable economic growth from two dimensions: economic growth and economic stability (see

Table 2. Evaluation index system for the level of economic stability.

Target Layer	Criterion Layer	Index Layer	Representational Meaning	Attribute
Stable economic growth	Stability	Economic Growth Stability Index	The degree of deviation between the actual economic growth rate and the potential economic growth rate	+
	Growth	GDP	Economic growth level	+
		Increasing rate of GDP	Rate of economic growth	+

). Economic stability is measured by the degree of deviation between the actual and potential economic growth rates, represented by the economic growth stability index [38]. Economic growth is measured by significant increases in total economic output, represented by GDP and the GDP growth rate.

4.2. Methods

4.2.1. Coupling Coordination Degree Model

The coupling coordination degree model formula for the promotion of “dual carbon” goals and stable economic growth is as follows:

$$C_{it}=2\sqrt{U_{1,it}{U}_{2,it}}/\left(U_{1,it}+U_{2,it}\right)$$

(1)

$$T_{it}=a{U}_{1,it}+b{U}_{2,it}$$

(2)

$$D_{it}=\sqrt{C_{it}{T}_{it}}$$

(3)

Here, i and t represent the city and year, respectively. U₁ and U₂ represent the levels of advancement toward “dual carbon” goals and stable economic growth, respectively, calculated using the entropy method. C is the coupling degree, reflecting the interaction between the two subsystems. T is the comprehensive coordination index of the two subsystems. D is the coupling coordination degree between the advancement toward the “dual carbon” goals and stable economic growth, with a value range between 0 and 1. ɑ and b represent the importance of the two subsystems. Considering that both subsystems—the environment and the economy—are equally significant for achieving high-quality development, this study assigns ɑ = b = 1/2.

4.2.2. Kernel Density Estimation

The kernel density estimation method reflects the dynamic evolution pattern of variables through characteristics such as the distribution position and shape of the density curve; therefore, this study uses the kernel density estimation method to depict the dynamic distribution characteristics of the coupling coordination degree between advancement toward the “dual carbon” goals and stable economic growth. The specific formula is as follows:

$$f(D)={\displaystyle \frac{1}{nh}}{\displaystyle \sum _{i=1}^{n}K\left(\frac{D_i-d}{h}\right)}$$

(4)

Here, f(D) is the density function of the index to be estimated, K(·) is the Gaussian kernel function, h represents the bandwidth (precision), and d is the mean value of the coupling coordination degree. The characteristics of the kernel density curve, such as the centroid position, peak height, number of peaks, and tailing of the curve, can fully reflect the evolutionary trend of the coupling coordination degree between advancement toward the “dual carbon” goals and stable economic growth.

4.2.3. Spatial Metrology Model

If the observed values of the geographical units exhibit spatial correlation, a spatial econometric model should be used to explore the influencing factors of the coupling coordination degree between advancement toward the “dual carbon” goals and stable economic growth. Given the dual-dimensional characteristics of the coupling coordination between the “dual carbon” goals and stable economic growth, their coordinated development is influenced by multiple factors. These include the degree of government intervention, environmental regulations, energy efficiency, financial development level, and R&D investment intensity, all of which impact both the realization of the “dual carbon” goals [39,40,41,42] and economic development [43,44,45,46]; therefore, the following spatial econometric model is constructed:

$$D_{it}={\alpha }_0+\rho W{D}_{it}+a_1{X}_{it}+a_{2}W{X}_{it}+{\tau }_i+{\mu }_t+{\epsilon }_{it}$$

(5)

$${\epsilon }_{\mathrm{it}}=\lambda {\mathrm{m}}_{\mathrm{i}}{\epsilon }_{\mathrm{t}}+{\upsilon }_{\mathrm{it}}$$

(6)

Here, ρ is the spatial autoregressive coefficient. X_it denotes the influencing factors, which include the degree of government intervention (int): the ratio of general public budget expenditure to GDP; environmental regulation (env): an environmental regulation index calculated based on the intensity of various pollutant emissions [47]; energy efficiency (pro): the ratio of GDP to total energy consumption; the level of financial development (dev): the ratio of the year-end loan balance of financial institutions to GDP; R&D investment intensity (res): the ratio of science and technology expenditure to fiscal expenditure. λ is the spatial error autocorrelation coefficient. v_it represents the random disturbance term. W denotes the spatial weight matrix, with this study primarily using the economic matrix. The meanings of the other variables are consistent with those mentioned earlier.

4.3. Data Source

This study focuses on 287 cities in China from 2011 to 2021. The digital inclusive finance index is sourced from the Digital Finance Research Center of Peking University. Other data sources include the China City Statistical Yearbook, China Energy Statistical Yearbook, and China Electric Power Yearbook, along with various provincial and municipal statistical yearbooks, the EPS database, and the China Research Data Service Platform (CNRDS). Missing data were addressed using interpolation and the average growth rate method. All variables measured in monetary terms were deflated, with 2010 as the base period.

5. Results

5.1. Promotion of “Dual Carbon” Goals and Stable Economic Growth Levels

The level of advancement toward “dual carbon” goals. The level of advancement toward “dual carbon” goals during the observation period is shown in Figure 2. Overall, the national level of progress in achieving the “dual carbon” goals has shown a year-by-year upward trend, rising from 0.118 in 2011 to 0.882 in 2021. In the early stages, China was in the process of industrialization and urbanization, with rapid economic growth heavily reliant on substantial inputs of natural and environmental resources. The resource conditions of “abundant coal, scarce gas, and limited oil” led to an energy structure primarily based on coal, placing China among the top carbon emitters in the world. With the continued promotion of low-carbon development concepts, China has actively optimized its economic structure, moving away from traditional economic growth models to reduce the significant carbon emissions associated with economic growth. Additionally, by increasing green areas and building green ecological barriers, China has enhanced its urban carbon sink capacity, accelerating progress toward the “dual carbon” goals.

By region, progress in achieving the “dual carbon” goals has improved across all four major regions. The calculation results for the four regions are obtained based on the mean value of index data for each region and then calculated using the entropy method. The eastern region leads in advancing these goals, while the central, western, and northeastern regions lag behind the national level. In 2011, the order from highest to lowest progress was as follows: eastern region (0.562) > northeastern region (0.197) > central region (0.149) > western region (0.084). By 2021, the order was as follows: eastern region (0.976) > central region (0.385) > western region (0.384) > northeastern region (0.380). The northeastern region’s progress has been the slowest, gradually falling behind the central and western regions over time.

The primary reason for the northeastern region’s slower progress is its heavy reliance on local resources such as oil and coal, with its economic structure being dominated by heavy industry. This dependence on traditional industries makes rapid structural changes challenging. Conversely, China has increasingly focused on promoting high-quality development in the central and western regions. Policy guidelines such as “Promoting High-Quality Development in the Central Region in the New Era” and “Advancing the Development of the Western Region to Form a New Pattern in the New Era” emphasize accelerating the development of green industries, energy conservation, emission reduction, and the development of energy-saving and environmental protection industries. Furthermore, the central and western regions benefit from abundant clean energy sources, such as solar and wind power, which helps reduce carbon emissions.

The level of stable economic growth. The trend in China’s economic stability growth during the observation period is illustrated in Figure 3. Overall, the level of economic stability growth shows a fluctuating upward trend, rising from 0.354 in 2011 to 0.853 in 2021. Specifically, the pattern is “annual increase—short-term decline—continued recovery”. Rapid growth occurs from 2011 to 2019, a short-term decline from 2019 to 2020 and a subsequent recovery from 2020 to 2021. The decline in economic growth stability from 2019 to 2020 can be attributed to significant external shocks from a global public health event. Subsequently, the Chinese government adopted economic stability as a long-term strategic decision to address the complex and volatile domestic and international situations.

From a regional perspective, the level of stable economic growth in China is uneven among regions, with the eastern region in a leading position, while the gap between the central, western, and northeastern regions and the eastern region is gradually widening. Although the public health events in 2020 will reduce the stable economic growth level of major regions in the short term, all regions have stable growth potential, and the stable economic growth level will recover significantly in 2021. The stable economic growth level in 2011 was in the order of east (0.513) > central (0.293) > west (0.271) > northeast (0.239) from high to low. In 2021, the level of stable economic growth in the eastern region (0.889) > central region (0.431) > western region (0.369) > northeastern region (0.344) was in descending order. The rate of stable economic growth in the eastern region was significantly higher than that of the central, western, and northeast regions. This is directly related to the strong foundation of economic development, the optimization of industrial structure, and the higher degree of openness and innovation in the eastern region.

5.2. Temporal Evolution Characteristics of the Coupling Coordination Between the Promotion of “Dual Carbon” Goals and Stable Economic Growth

During the observation period, the coupling coordination degree between China’s advancement toward “dual carbon” goals and economic stability growth, as well as that of the four major regions, is shown in Figure 4. The coupling coordination degree at the national level displays a trend of fluctuating increase, rising from 0.452 in 2011 to 0.931 in 2021, indicating that China has maintained steady economic operation while advancing toward the “dual carbon” goals. Although there was a slight decline in 2020 due to fluctuations in economic growth stability, the overall coupling coordination degree between the “dual carbon” goals and economic stability growth increased. This demonstrates the success of the “seeking progress while maintaining stability” approach, with positive and steady advancement toward the “dual carbon” goals.

By region, the coupling coordination degree between advancement toward the “dual carbon” goals and stable economic growth has improved across all four major regions. In 2011, the regional coupling coordination degrees were, in descending order, the eastern region (0.733) > northeastern region (0.466) > central region (0.457) > western region (0.389). By 2021, the order was as follows: eastern region (0.965) > central region (0.638) > western region (0.614) > northeastern region (0.601). The eastern region consistently held the leading position in coupling coordination degree, which is directly related to its strong economic foundation, optimized industrial structure, and high level of openness and innovation. Meanwhile, the northeastern region’s ranking dropped from second to fourth, indicating slow growth and a lack of momentum in its coupling coordination degree. This is primarily because the northeastern region is a key base for coal, steel, machinery, energy, and chemicals in China, and its insufficient industrial transformation has led to prominent carbon emission issues. Consequently, the potential for stable economic growth is yet to be fully realized, and the coupling coordination degree between advancement toward the “dual carbon” goals and stable economic growth has lagged.

5.3. Spatial Evolution Characteristics of the Coupling and Coordination of the Promotion of the “Dual Carbon” Goals and Stable Economic Growth

Spatial distribution pattern. Using ArcGIS 10.8, the coupling coordination degree between advancement toward the “dual carbon” goals and stable economic growth in 2011, 2014, 2017, and 2021 was classified into four levels: low coordination (0.000–0.300), moderate coordination (0.301–0.500), high coordination (0.501–0.800), and extreme coordination (0.801–1.000). The spatial visualization results are shown in Figure 5.

Overall, the coupling coordination degree between advancement toward the “dual carbon” goals and stable economic growth in China is primarily characterized by low coordination, with a spatial pattern of “higher in the east, lower in the west”. Specifically, it is characterized as follows: ① In 2011, approximately 82.578% of cities were in the low coordination category. The number of moderately coordinated cities accounted for 15.331%. The number of highly coordinated cities accounted for 2.091%, including only six cities—Beijing, Shanghai, Nanjing, Guangzhou, Shenzhen, and Chongqing. No cities were in the extreme coordination category. ② In 2014, approximately 78.049% of cities remained in the low coordination category. The number of moderately coordinated cities accounted for 19.512%. The number of highly coordinated cities accounted for 2.439%, with Tianjin being added. However, no cities were in the extreme coordination category. ③ In 2017, the proportion of cities in the low coordination category reduced to 73.519%. The number of moderately coordinated cities accounted for 22.648%. The number of highly coordinated cities accounted for 3.136%, with Hangzhou, Qingdao, Wuhan, and Chengdu being added. The number of extremely coordinated cities accounted for 0.697%, including Beijing and Shanghai. ④ By 2021, the proportion of cities in the low coordination category further decreased to 66.551%. The number of moderately coordinated cities accounted for 28.571%. The number of highly coordinated cities accounted for 3.833%, with Suzhou, Zhengzhou, and Xi’an being added. The number of extremely coordinated cities accounted for 1.045%, with Guangzhou being added.

Overall, from 2011 to 2021, the coupling coordination degree between advancement toward the “dual carbon” goals and stable economic growth in China has improved from 0.452 to 0.931, with an increase of 105.973%. Although low coordination remains dominant, the number of cities in the high and extreme coordination categories has gradually increased, primarily concentrated in the eastern region; however, there is still room for further improvement in the coupling coordination degree.

Dynamic evolution of spatial distribution. To further analyze the spatial distribution dynamics of the coupling coordination degree between advancement toward the “dual carbon” goals and economic stability, this study presents kernel density estimation plots for the coupling coordination degree at the national level and for the four major regions in 2011, 2014, 2017, and 2021, as shown in Figure 6.

Figure 6a illustrates the dynamic distribution of the coupling coordination degree between advancement toward the “dual carbon” goals and economic stability across the country. The rightward shift of the main peak in the evolution curve indicates a rising trend in the coupling coordination degree. At the same time, the main peak is skewed to the left with a noticeable right tail, suggesting that a significant proportion of cities nationwide have a relatively low coupling coordination degree. The shape of the curve shows a gradual decrease in the height of the main peak and an increase in its width. By 2021, a secondary peak will appear in the evolution curve, highlighting the increasing imbalance in coupling coordination. This indicates a growing disparity in the levels of “dual carbon” advancement and economic stability among cities, with polarization becoming evident. This phenomenon can be attributed to the high coupling coordination degrees in economically developed regions such as Beijing, Shanghai, Guangzhou, and Shenzhen, where the “Matthew effect” exacerbates the gaps between cities.

Figure 6b–e depict the dynamic distribution of the coupling coordination degree between the “dual carbon” goals and economic stability in the eastern, central, western, and northeastern regions, respectively. The rightward shift of the peaks in the evolution curves across all four regions indicates gradual improvement in the coupling coordination degree, though most cities still exhibit relatively low levels. The curves show that the height of the main peak in the eastern cities is gradually decreasing, while, in the western and northeastern cities, the height of the main peak declines with fluctuations, and the width of the peak increases. This suggests that the disparities in coupling coordination degrees among cities within these regions are widening with a significant gradient effect. Conversely, the height of the main peak in the central cities’ evolution curve is gradually increasing while its width is narrowing, indicating a reduction in the disparity in coupling coordination development among cities in the central region. Additionally, secondary peaks appear in the evolution curves of the eastern, central, and northeastern regions, with the secondary peak being more pronounced in the northeastern region, reflecting a more severe polarization phenomenon in this area.

5.4. Factors Influencing the Coupling and Coordination of the Promotion of the “Dual Carbon” Goals and Stable Economic Growth

Spatial correlation analysis. The calculation results of the Global Moran’s Index for the coupling coordination degree between advancement toward the “dual carbon” goals and economic stability from 2011 to 2021 are presented in

Table 3. Spatial correlation of the coupling coordination degree between the “dual carbon” goals and economic stability.

Year	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Moran’s I	0.253 ***	0.258 ***	0.266 ***	0.276 ***	0.277 ***	0.266 ***	0.281 ***	0.280 ***	0.282 ***	0.279 ***	0.278 ***
Z	10.030	10.237	10.557	10.936	10.975	10.565	11.152	11.084	11.179	11.066	11.035

Note: *** denotes p < 0.01.

. The results are significantly positive, indicating a spatially positive correlation in the coupling coordination degree between the “dual carbon” goals and economic stability. The Global Moran’s Index shows an upward trend with fluctuations during the study period, suggesting that the spatial clustering of the coupling coordination degree has strengthened over time. Therefore, the coupling coordination degree of Chinese cities has an obvious and gradually strengthened clustering trend; that is, the coupling coordination degree of neighboring cities is similar, which may be due to the mutual adoption of low-carbon technologies and mutual cooperation for economic development between neighboring cities.

Spatial measurement model test. Before using a spatial econometric model to explore the factors influencing the coupling coordination degree, Lagrange multiplier, Likelihood ratio, and Hausman tests need to be conducted to determine the appropriate econometric model. The calculation results of these tests are presented in

Table 4. Spatial measurement model test.

Test	Index	Statistic
Lagrange multiplier test	Lagrange multiplier-lag	54.809 ***
	Robust Lagrange multiplier-lag	11.086 ***
	Lagrange multiplier-error	54.136 ***
	Robust Lagrange multiplier-error	10.413 ***
Likelihood ratio test	Likelihood ratio-spatial Durbin model-spatial autoregression	12.270 **
	Likelihood ratio-spatial Durbin model-spatial error	20.000 **
Hausman test	Hausman	3418.280 ***

Note: ** denotes p < 0.05, *** denotes p < 0.01.

. All four indicators from the Lagrange multiplier test and both indicators from the Likelihood ratio test passed the significance tests, indicating that the spatial Durbin model is a more scientific choice. Additionally, the result of the Hausman test shows that the fixed-effects model is more scientific; therefore, a two-way fixed-effects spatial Durbin model was ultimately selected to analyze the factors influencing the coupling coordination between advancement toward the “dual carbon” goals and economic stability.

Analysis of model estimation results. The calculation results of the spatial econometric model analysis for influencing factors are presented in

Table 5. Estimation results of the two-way fixed-effects spatial Durbin model.

Variables	Coefficient	Variables	Coefficient
int	0.023 *** (0.006)	W×int	−0.002 (0.014)
env	0.018 *** (0.006)	W×env	0.033 ** (0.016)
pro	0.043 *** (0.006)	W×pro	0.023 (0.027)
dev	0.004 *** (0.001)	W×dev	0.003 (0.004)
res	0.282 *** (0.039)	W×res	0.264 ** (0.126)
ρ	0.304 *** (0.036)
City FE	Yes
Time FE	Yes
N	3157
R2	0.199
Log-likelihood	8155.227

Note: Numbers in parentheses indicate a standard error, ** denotes p < 0.05, *** denotes p < 0.01.

. The spatial autoregressive coefficient (ρ) is significantly positive, indicating that the coupling coordination degree between advancement toward the “dual carbon” goals and economic stability exhibits a significant positive spatial spillover effect. Specifically, an improvement in the local coupling coordination degree helps to promote the coupling coordination degree in neighboring areas.

Spatial effect decomposition. To provide a more intuitive analysis of the factors influencing the coupling coordination degree between advancement toward the “dual carbon” goals and economic stability, the spatial econometric results are further decomposed into direct and indirect effects. The decomposition results are presented in

Table 6. Decomposition of spatial effects.

Factors	Direct Effect	Indirect Effect
int	0.024 *** (0.006)	0.008 (0.019)
env	0.019 *** (0.006)	0.055 ** (0.024)
pro	0.044 *** (0.006)	0.049 (0.037)
dev	0.004 *** (0.001)	0.007 (0.005)
res	0.293 *** (0.038)	0.494 *** (0.176)

Note: Numbers in parentheses indicate a standard error, ** denotes p < 0.05, *** denotes p < 0.01.

.

5.4.1. Direct Effect

As presented in Table 6, the direct effect of government intervention is significantly positive, indicating that government intervention has a notable positive impact on the coupling coordination degree between advancement toward the “dual carbon” goals and economic stability. Local government interventions influence various aspects, such as economic development and industrial structure adjustment. In the process of advancing the “dual carbon” goals in China, the ecological indicator assessment and the dual-control indicator assessment of energy consumption by local governments have led to government interventions that exhibit a clear carbon reduction orientation. The objective is to achieve sustainable socio-economic development, which helps improve the coupling coordination degree between the “dual carbon” goals and economic stability.

As presented in Table 6, the direct effect of environmental regulation is significantly positive, meaning that environmental regulation can positively contribute to the enhancement of the coupling coordination degree between the “dual carbon” goals and economic stability. The higher the environmental regulation index, the more effort the government puts into environmental pollution control, implementing stricter environmental standards, thereby reducing the extent of environmental pollution. This progression toward sustainable development contributes to the coordinated development of advancing the “dual carbon” goals and economic stability.

As presented in Table 6, the direct effect of energy efficiency is significantly positive, indicating that energy efficiency has a significant positive impact on the coupling coordination degree between advancement toward the “dual carbon” goals and economic stability. Improving energy efficiency can reduce the energy consumed per unit of GDP, which not only helps decrease carbon emissions from energy consumption but also enhances the economic output per unit of energy consumed, thus promoting the coupling coordination between the “dual carbon” goals and economic stability.

As presented in Table 6, the direct effect of financial development is significantly positive, indicating that the level of financial development can positively contribute to the enhancement of the coupling coordination degree between the “dual carbon” goals and economic stability. Financial support not only promotes stable and healthy economic growth by boosting consumption and reducing income disparities but also provides funding for carbon reduction, supporting the development of clean energy and carbon reduction technologies. Therefore, financial development can promote the coupling coordination degree between the “dual carbon” goals and economic stability.

As presented in Table 6, the direct effect of R&D investment intensity is significantly positive, indicating that R&D investment intensity can increase the coupling coordination degree between advancement toward the “dual carbon” goals and economic stability. R&D investment intensity refers to the proportion of scientific and technological expenditure within the total fiscal expenditure, representing the level of technological investment and innovation input. Achieving the “dual carbon” goals requires technological innovation to empower low-carbon technologies, and stable economic development also relies on technological innovation to provide momentum. Therefore, increasing R&D investment intensity helps improve the coupling coordination degree between the “dual carbon” goals and economic stability.

5.4.2. Indirect Effect

According to the calculation results of the indirect effects in Table 6, the indirect effect of government intervention on the coupling coordination degree between advancement toward the “dual carbon” goals and economic stability is not significant. This is because the direct intervention measures taken by the government have a limited impact on the coupling coordination degree in neighboring areas. The indirect effect of environmental regulation is significantly positive, indicating that environmental regulation can promote the enhancement of the coupling coordination degree between the “dual carbon” goals and economic stability in neighboring areas. This is due to the spillover effect from regions with high environmental regulation indices to surrounding areas, where neighboring regions compete to improve their environment by adopting strict environmental standards. This is conducive to the coordinated development of the “dual carbon” goals and economic stability in neighboring areas.

The indirect effects of energy efficiency and financial development on the coupling coordination degree between the “dual carbon” goals and economic stability are not significant. This suggests that these factors have a limited impact on neighboring areas owing to regional barriers, such as market conditions and local protectionism, which restrict their spillover effects. Conversely, R&D investment intensity has a significantly positive indirect effect, indicating that it positively influences the coupling coordination degree in neighboring areas. This positive impact arises from the innovation spillover effect, where regions with high R&D investment share their innovative outcomes with neighboring areas, accelerating inter-regional collaborative innovation and enhancing coupling coordination.

6. Discussion and Conclusions

6.1. Discussion

The promotion level of the “dual carbon” goals in China shows a trend of increasing year by year. This conclusion is echoed by Wei et al.‘s finding that the average level of carbon performance in China has improved [48]. In contrast, the conclusion of this paper combines the two dimensions of carbon emission and carbon absorption, thus enriching the perspective of carbon emission reduction research. China’s stable economic growth level exhibits a pattern of “annual increase—short-term decline—continued recovery”. Yang and Zhang found that the overall quality of China’s economic development was in a state of gradual improvement from 1993 to 2018 [49], a conclusion consistent with the result found in this paper that “the level of stable economic growth will increase year by year before 2019”. The coupling coordination degree between advancement toward “dual carbon” goals and stable economic growth has generally increased, albeit with fluctuations. Zhang et al. used the entropy method and coupled coordination model to find that the coordination between economic growth and carbon emissions in 178 Chinese cities increased from 2011 to 2019 [30]. This view is similar to the conclusion of this paper.

This paper analyzes the spatial characteristics of the coupling and coordination degree between China’s “dual carbon” goals and stable economic growth using ArcGIS 10.8 software and the kernel density method. It is found that the degree of coupling coordination in China has improved and primarily shows low coordination. The overall spatial pattern reveals “higher in the east, lower in the west” coordination. Shen et al. introduced the improved coupling coordination degree model and found that the average coordination degree of 30 provinces in China showed an upward evolution from 1995 to 2015, and the spatial distribution of China’s coupling coordination degree was characterized by the higher coordination degree in the eastern provinces [50]. This conclusion is similar to the conclusion of this paper, but the difference is that this paper combines the promotion of the “dual carbon” goals and stable economic growth to enrich the two subsystems of the coupling coordination degree.

This paper uses the spatial econometric model to explore the influencing factors of the coupling coordination degree between advancement toward the “dual carbon” goals and stable economic growth. The results show that government intervention, environmental regulation, energy efficiency, financial development level, and R&D investment intensity all positively influence the coupling coordination degree in their respective regions. Xie et al. analyzed the factors influencing the decoupling of China’s economic growth and energy CO₂ emissions and found that the strengthening of government intervention effectively promoted decoupling [51], which is consistent with the findings in this paper. In addition, many scholars explored the influencing factors of carbon emission decoupling by using the log-mean score difference index method and the production–theoretical decomposition analysis method. It is proposed that energy intensity, affluence, and population size are among the influencing factors of carbon emission decoupling [28,52,53,54]. Some scholars have also used the geographically weighted regression model to discuss the effects of resident income, population size, and secondary industry scale on the coupling coordination degree between carbon emission and economic development [55]. Further, this paper discusses the spatial spillover effect of influencing factors on the coupling coordination degree. It is found that environmental regulation and R&D investment intensity have positive spatial spillover effects, promoting the coupling coordination degree in neighboring areas.

The findings of this paper enrich the research in related fields to a certain extent, but there are some limitations. First, due to the limited availability of data from prefecture-level cities, the current index system cannot evaluate the coupling coordination degree between the promotion of the “dual carbon” goals and stable economic growth absolutely objectively. Second, the coupling coordination degree is a comprehensive system with many influencing factors; however, this paper only studies five aspects, including financial development, energy efficiency, and R&D investment intensity. Other factors affecting coupling coordination are not analyzed. Therefore, in future research, we should expand the perspective, continue to improve the evaluation index system, and explore more influencing factors.

6.2. Conclusions

In this study, after clarifying the coupling coordination mechanism between the advancement toward the “dual carbon” goals and stable economic growth, an evaluation index system is constructed for the levels of advancement toward the “dual carbon” goals and economic stability growth. Using a coupling coordination model, kernel density estimation, and spatial econometric models, this study explores the temporal and spatial evolution characteristics and influencing factors of the coupling coordination degree between advancement toward the “dual carbon” goals and stable economic growth in 287 prefecture-level-and-above cities in China from 2011 to 2021. The main conclusions are as follows:

• Regarding temporal evolution, the level of China’s advancement toward the “dual carbon” goals shows a trend of yearly increase. The level of economic stability growth exhibits a pattern of “annual increase—short-term decline—continued recovery”. The coupling coordination degree between advancement toward the “dual carbon” goals and stable economic growth has generally increased, albeit with fluctuations. All four major regions have shown improvements, with the eastern region significantly leading in progress, while the northeastern region has experienced slower growth.
• Spatially, the coupling coordination degree between advancement toward the “dual carbon” goals and stable economic growth in China primarily shows low coordination. The overall spatial pattern reveals “higher in the east, lower in the west” coordination. Both nationwide and within the four major regions, varying degrees of gradient effects and polarization phenomena are observed.
• A spatial correlation is observed for the coupling coordination degree between advancement toward the “dual carbon” goals and stable economic growth. Government intervention, environmental regulation, energy efficiency, financial development level and R&D investment intensity all positively influence the coupling coordination degree in their respective regions. Environmental regulation and R&D investment intensity have positive spatial spillover effects, promoting the coupling coordination degree in neighboring areas. However, the spatial spillover effects of government intervention, energy efficiency, and financial development level are less significant, with minimal impact on neighboring areas. Based on these findings, this study proposes the following policy recommendations.

First, it is crucial to address the phased and regional characteristics of the coupling coordination between the “dual carbon” goals and stable economic growth by adopting development strategies tailored to local conditions. In the highly coordinated eastern regions, efforts should be made to leverage their role as demonstration leaders in environmental and economic coordination. This includes creating national pilot zones and exploring priority development paths that simultaneously achieve both “dual carbon” and economic stability goals. For the central, western, and northeastern regions, the government should further enhance financial support and actively implement strategies such as Western Development, the Rise of Central China, and the Revitalization of Northeast China. These regions should explore win–win paths for low-carbon development that balance environmental protection with economic growth and narrow the gap with the eastern region regarding carbon reduction and economic stability.

Second, it is crucial to fully leverage the positive influence of factors such as government intervention, environmental regulation, energy efficiency, financial development level, and R&D investment intensity on the coupling coordination degree between the “dual carbon” goals and economic stability growth, thereby further promoting their coordinated development. Low-carbon-oriented government intervention should avoid a “one-size-fits-all” approach and consider stable economic growth. The environmental regulation system should be improved with a reasonable selection of regulatory tools and a moderate increase in regulation intensity. A strong push is warranted for clean energy development, thus reducing the reliance on fossil fuels, optimizing the energy consumption structure, and improving energy efficiency. Accelerating the marketization of the financial industry is essential for enhancing financial development, guiding bank credit toward environmentally friendly enterprises, and funding the research, application, and production of clean technologies. Increased R&D investment should foster inventions of low-carbon technologies, enhance innovation capabilities, and accelerate the transformation of China’s economic growth model.

Third, leveraging the spatial spillover effects of coupling coordination between the “dual carbon” goals and stable economic growth is crucial for strengthening inter-regional cooperation and enhancing endogenous interaction mechanisms among regions. In areas with low or moderate coordination, mechanisms for collaborative development should be actively established to avoid the formation of pollution-intensive zones due to low-quality clustering. Conversely, in regions with high or extreme coordination, breaking down regional barriers and fostering active collaboration can maximize the positive spillover effects. This approach presents an opportunity to significantly improve the coupling coordination degree, accelerate the construction of a robust industrial system, and promote carbon reduction while achieving economic stability and growth.