Spatiotemporal Evolution and Influencing Factors of the Coupling Coordination of Urban Ecological Resilience and New Quality Productivity at the Provincial Scale in China

Abstract

Enhancing urban ecological resilience (UER) is important in promoting sustainable urban development, and developing new quality productivity (NQP) is an intrinsic requirement to promote industrial change and high-quality development. The coordinated development of UER and NQP can help realize the green transformation and upgrading of various industries. This study considered 30 provinces in China as research objects, quantified their UER from nature, economy, and society, and explored the essential connotation of NQP under the guidance of Marx’s productivity theory. The entropy weight-CRITIC method and TOPSIS model were used to comprehensively measure the development levels of the UER and NQP from 2011 to 2022, and their coupling coordination degree (CCD) of UER and NQP was measured by combining the coupling coordination degree model. Consequently, the Global Moran’s I index and Geographical and Temporal Weighted Regression (GTWR) model were used to explore the effects of different influencing factors on the CCD from the spatiotemporal variability perspective. The results indicated the following: (1) UER and NQP improved during the study period but with large differences between the regions. (2) The overall CCD evolved from a mild imbalance to primary coordination. The average CCD values ranged from low to high in the northeastern, western, central, and eastern regions. (3) The GTWR results showed that the levels of economic development, urbanization rate, and technological innovation contributed positively to the CCD, with the urbanization rate having the strongest positive effect. Foreign investment, environmental regulations, and industrial structure generally negatively inhibit the CCD.

1. Introduction

Maintaining national economic stability and achieving industrial revitalization are inevitable requirements for China within a complex international environment to presently achieve high-quality development [1]. However, the long-term isolated and extensive socioeconomic development model has led to the undesirable phenomenon of “homogeneous competition” among different regions in China. The ecological problems caused by the disorderly development of the energy industry negatively impact sustainable development [2,3]. The 2020 China Statistical Yearbook shows that China’s total carbon emissions account for 30% of the world’s total, and the domestic consumption of fossil fuels accounts for more than 80%; and industrial sources of wastewater emissions account for 97% of China’s total emissions. However, problems with high-carbon industrial structures and industrial pollution emissions remain severe. China has explicitly expressed that continuing to focus on protecting blue skies, clear waters, and clean land, as well as promoting the comprehensive green transformation of economic and social development, is necessary [4]. This strategy aims to enhance regional ecological resilience, optimize resource allocation, and build resilient cities with “high governance levels and strong ecological protection”.

Scientific and technological innovations play a considerable role in industrial innovation [5]. Although China’s technological innovation capacity has continuously improved in recent years, its productivity has developed significantly. However, as China’s economy shifts to a stage of high-quality development, the current level of productivity makes it difficult to meet the increasing needs of the people [6,7]. The 2020 Global Innovation Index report reveals that China’s scientific research investment is the second largest worldwide; however, the conversion rate of scientific and technological achievements is only 6%, and the proportion of scientific research funding for basic and applied research is only 6% and 11.3%, respectively; this gap remains substantial compared with that of developed countries. Therefore, solving the complex challenges of core technologies is difficult owing to insufficient development of basic sciences. Therefore, in 2023, China introduced the concept of new quality productivity, aiming to realize a new leap in social productivity under the leadership of digital technology [8]. New quality productivity represents higher productivity in new formats and models. Its rapid promotion in various industries reflects the organic unity of technological breakthroughs, industrial upgrades, and green transformation.

Many regions in China encounter difficult choices between the ecological environment and regional development in promoting Chinese modernization. For example, how can industrial innovation be carried out in the face of resource constraints? How can high-quality economic development be realized while ensuring ecological security? Therefore, harmonizing the relationship between the ecological environment and the new quality of productive forces under industrial transformation has become a key issue in China’s modernization and development [9,10]. Therefore, this study aimed to explore the intrinsic connection between urban ecological resilience (UER) and new quality productivity (NQP). Based on the data from 30 provinces in China from 2011 to 2022, the coupling coordination degree of the UER–NQP was calculated, the temporal and spatial differentiation characteristics and impact elements of the coordinated development of the two were comprehensively analyzed, and relevant suggestions were proposed.

2. Literature Review

2.1. Research Status

“Resilience” originates from the Latin word “resilio” and was first used in the field of physics. The concept of resilience has undergone progressive revision and development from engineering to ecological resilience and then to evolutionary resilience [11]. Engineering resilience emphasizes the capacity of the overall structure to recover stability under external shocks and assumes that the structure has a single equilibrium steady state [12]. In 1973, Holling first combined “resilience” with ecology to form ecological resilience, which refers to the rate at which a system returns to equilibrium after a disturbance, as well as its ability to self-repair and adapt to new environments. This emphasizes that the system is adaptive with multiple equilibrium states [13]. With the development of research on system evolution mechanisms, “resilience” has gradually recognized the uncertainty of complex systems. Evolutionary resilience abandons the pursuit of static balance and attributes resilience to inherent properties of the system. This emphasizes that the system can adapt to long-term changes in the external environment and achieve evolutionary development by adjusting its structure and changing its developmental path [14,15].

As a complex system with a non-single steady state, the resilience of cities is consistent with the cognitive paradigm of evolutionary resilience. Urban resilience is the ability of cities to adapt and cope with uncertain risk shocks, as well as to quickly restore equilibrium through self-organization and learning. Its connotations include different resilience dimensions, such as ecological, industrial, and social [16,17]. Therefore, UER comprises urban and ecological resilience and embodies ecological resilience in urban spaces. Recent studies on UER focus on urban planning and ecological risk prevention and control [18,19]. From the perspective of urban planning, the research paradigms of “pattern-process”, “scale-density-morphology”, and “scale-density-morphology-function” are established [20,21,22]. They analyze the connotation of UER for national spatial planning by constructing a composite model that meets the internal needs and external response capacity of the ecosystem, or exploring a series of ecological sources that may affect UER, such as forest area, habitat quality, and hydrological conditions [23,24,25]. These studies have made useful explorations of UER but have ignored the impact of human activities and social changes. In terms of ecological risk prevention and control, scholars have focused on exploring the impact of natural disasters on urban ecosystems and analyzing the changes in UER under scenarios such as rainstorms, waterlogging, and flash floods [26,27]. In addition, some scholars have focused on measuring the potential shock risk of social production activities, economic development, urbanization, and other factors on UER, and analyzing the social security capacity and urban development potential of cities in the event of short-term pressures or cumulative shocks, usually using TOPSIS, the entropy value method, the comprehensive evaluation method, and so on [28,29,30]. It can be seen that existing research on ecological resilience within cities focuses on analyzing the changes in the ecosystem itself, or only measures its level of recovery after resisting external interference, which makes it difficult to fully explain the essential connotation of UER. Therefore, considering the combined implications of ecological and human social activity factors is necessary to comprehensively evaluate the development status and innovative development capabilities of UER.

Productivity is an important factor in promoting social progress and is an endogenous driving force for achieving modernization [31]. Traditional productivity has the disadvantages of high resource and energy consumption and high ecological environmental pollution and is essentially “black productivity” [32]. The NQP unifies the ecologicalization of productivity and ecological productivity, which is essentially “green productivity”. Marx’s productivity theory posits that science and technology are important parts of productivity and spiritual products of society’s progress [33]. The process of transforming science and technology into material productivity involves three elements: laborers, materials, and objects. The difference in the degree and level of integration of science and technology with these three elements distinguishes traditional productivity from NQP. Therefore, the understanding of NQP mainly includes the following aspects: (1) Regarding connotative definitions, NQP is centered on scientific and technological innovation and is a key technological breakthrough. Lin Li and colleagues believe that NQP is an advanced productivity characterized by new technology, new economy and new business forms [34]. NQP can not only reshape the factors of productivity in an ecological way, but also realize the transformation from traditional production factor input to scientific and technological input [35,36]. (2) From the perspective of essential characteristics, NQP, based on innovation-driven development, presents the characteristics of science and technology fully empowering workers, labor materials, and labor objects. As far as workers are concerned, workers under traditional productivity are mainly ordinary workers, while workers matching NQP are technical intellectual workers with scientific ecological civilization concepts and practical abilities. Baharin et al. [37] affirmed the contribution of high-quality workers to production efficiency in a questionnaire survey based in Indonesia. Therefore, new-quality workers have a deeper understanding of production activities under the harmonious coexistence of man and nature, that is, to reasonably utilize and transform nature while meeting production efficiency and quality [38,39]. Regarding labor, NQP abandons low-efficiency primary production tools and uses digital intelligence methods to save energy, reduce emissions, improve efficiency, and simultaneously achieve economic and ecological benefits [40,41]. For labor, NQP uses renewable or non-material resources as the labor object, which can reduce pollution and promote the efficient use of resources [42]. (3) Regarding social needs, the NQP reconfigures the factors of production and achieves a good fit between laborers, labor materials, and labor objects under green development and ecological sustainability goals. It is an important support for social development as well as an inevitable requirement for liberating and developing social productivity [43,44]. Recent studies have confirmed the advancement of the NQP in the context of high-quality development and clarified the key development direction of ecological priority [45,46]. However, recent research studies are limited to theory and lack empirical analysis to discuss the logical relationship between relevant indicators, thus failing to measure the level of NQP development.

In terms of research content, research on UER focuses on exploring the impact of ecological elements and the potential impact of social production activities on UER, whereas research on NQP only analyzes the advantages of green and sustainable development from a theoretical perspective but does not analyze the relationship between NQP and the ecosystem in depth. Regarding research methodology, there has been no systematic quantitative analysis of the intrinsic connection between NQP and UER and no exploration of potential impact factors at the geospatial level. Owing to the different basic conditions and development directions of each area, regional differences exist in NQP and UER. This study analyzed the following questions: Are there large differences in NQPs and UERs in different regions, and can NQPs and UERs achieve coordinated development? What are the factors influencing this? Explaining these questions helps us understand the connotations of NQP and UER, as well as the significance of their coordinated development. Therefore, the contributions of this study are as follows: (1) Breaking through the shortcomings of previous studies that only analyzed a single system, the relationship between UER and NQP is explored in terms of coupling and coordination, focusing on the interaction between the two systems. (2) Combining qualitative and quantitative analysis, NQP is transformed from a theoretical concept to a quantifiable index, and its development level in each province is measured. The CCD methodology provides empirical evidence to facilitate the coordinated evolution of UER and NQP. (3) The research gap on the interaction between UER and NQP is filled, and a theoretical foundation for building a more harmonious “social-ecological” system is laid.

2.2. Mechanism Analysis

Coupling coordination refers to the interaction between systems or within a system, which influences, restricts, and finally forms a benign, interactive, coordinated development relationship [47]. Studying the CCD between the UER and NQP can help us understand whether there is a conflict between social production force changes and eco-environmental protection in different regions. The intensity of the CCD can be considered as the degree of constraint and synergy between different factors.

The impact of ecological environment on NQP is as follows: (1) Supply of natural resources. The resource-related and environmental stress brought about by traditional productivity limits ecological sustainability. A high level of UER means that the ecosystem has good resistance to internal and external shocks, good adjustment and recovery capabilities, can provide stable natural resources for social production activities, guide the progress of social productivity, and is also a necessary condition for the high-quality development of various industries [48,49]. (2) Social effect. Low UER levels mean a sharp conflict between humans and nature, which will not only harm human health, but also cause social poverty and discrimination, thereby exacerbating social instability, having a serious negative impact on social productivity, and hindering economic development. The improvement of the ecological environment can provide strong support for innovation and diversified development in various industries and promote the development of NQP [50]. (3) Technological innovation. Traditional production methods are characterized by high pollution and emissions, which lead to severe eco-environmental problems in some regions. In addition, insufficient government supervision leads to a vicious cycle of environmental pollution, which drives society’s demand for environmental protection technology and green innovation and facilitates NQP development [51].

The effects of NQP on the ecological environment are as follows: (1) Scale effect. Socioeconomic effects reach a level at which scale effects require more resource inputs and economic outputs, which are not beneficial to the ecological environment. NQP, characterized by new energy, digitalization, and high efficiency, can encourage industries to make low-carbon transformations, have positive scale impacts on ecosystems, and inject new vitality into economic growth [52]. (2) Structural effect. Developing the NQP aims to improve resource utilization, upgrade the industrial structure, and optimize environmental governance capabilities. Most studies conclude that the ecological implications of industrial structures cannot be ignored [53,54]. NQP’s advantages in energy efficiency, energy conservation, and emission reduction provide support for ecological restoration and optimization of UER. (3) Technological effect. China’s economic structure has recently shifted from being resource- to technology-intensive. Productivity improvements can drive technological updates, promote the application of green technology and clean production, reduce energy consumption and emissions in production activities, and help maintain the balance and stability of the eco-environment [55].

Based on the above analysis, it can be seen that there are interactive influences and close intrinsic links between UER and NQP. Therefore, a systematic, quantitative analysis of NQP and UER and the introduction of CCD and GTWR models can clarify the coordinated development status of NQP and UER and the influencing factors.

3. Data and Methods

3.1. Study Area

Owing to the lack of key indicator data in some areas, the 30 provincial administrative regions involved in this study did not include Hong Kong, Macao, Taiwan, or the Tibet Autonomous Region. China is divided into four major regions: eastern, central, western, and northeastern, according to the country’s geographic division.

3.2. Data Source

This paper was based on research undertaken at the provincial scale, which needs to ensure the availability and reliability of data. The indicators used in this study were panel data derived from the 2012–2023 national and provincial statistical yearbooks, provincial environmental statistical yearbooks, and the CSMAR database. NDVI and DEM elevation data were obtained from RESDC (http://www.resdc.cn) (accessed on 19 August 2024). The degree of soil erosion was determined using the RUSLE model [56]. In addition, to ensure the integrity of the data structure, linear interpolation was used to fill in the missing values.

3.3. Index System

No consensus exists in the academic community regarding the UER index system. By combining recent studies, this study proposed a “nature–economy–society” composite system, which includes a total of 14 indicators [57,58] (

Table 1. UER and NQP index systems.

	Subsystem	Index	Attribute	Weight
UER	Nature	Urban green coverage rate (%) [59]	+	0.0423
		NDVI [59,60]	+	0.0317
		Industrial “three wastes” emissions (t) [61]	−	0.1364
		Sewage treatment rate (%) [61]	+	0.1023
		Soil erosion degree (t km−2 a−1) [62]	−	0.0714
	Economy	GDP per capita (yuan) growth rate (%) [61,63]	+	0.1306
		Output of the tertiary sector as a percentage of GDP (%) [64]	+	0.1492
		Completed investment in industrial pollution control (billion yuan) [65]	−	0.0667
		Energy consumption per unit of GDP (t standard coal/10,000 yuan) [66]	−	0.0104
	Society	Degree of population urbanization (persons/km2) [66]	+	0.0135
		Comprehensive utilization rate of industrial solid waste (%) [67]	+	0.0949
		Natural population growth rate (%) [68]	+	0.0107
		Investment in infrastructure construction (100 million yuan) [65]	+	0.1079
		Urban sewage treatment rate (%) [66]	+	0.0320
NQP	New quality laborers	Regional investment in science (100 million yuan) [69]	+	0.0549
		Regional investment in education (100 million yuan) [34]	+	0.0494
		Number of persons enrolled in university (people) [70]	+	0.1099
		Number of R&D personnel (people) [71]	+	0.1386
	New quality labor materials	Number of Internet users per 100 people (units) [72]	+	0.0675
		Telecom business volume per capita (units) [70]	+	0.1028
		Digital inclusive finance index [69]	+	0.0704
		Total number of digital patents (units) [72]	+	0.0649
		Digital economy index [73]	+	0.0361
	New quality labor objects	Ratio of the sum of emerging industry income to GDP (%) [69]	+	0.0382
		Total renewable energy electricity consumption (billion kWh) [69]	+	0.0794
		Robot installation density (units/square meter) [71]	+	0.0363
		Ratio of R&D investment to GDP (%) [74]	+	0.0844
		Ratio of green patent applications to total patents (%) [8]	+	0.0673

). Similarly, based on Marx’s theory of the 3 elements of productivity, this study proposed an NQP index system that includes three subsystems: new quality laborers, new quality labor materials, and new quality labor objects, with a total of 14 indicators (Table 1).

3.4. Research Methods

3.4.1. Entropy Weight-CRITIC Method

The entropy weight-CRITIC method was used to comprehensively determine the indicator weights of the UER and NQP. The entropy weight method (EWM) uses the indicator information utility value to determine the indicator weight, whereas the CRITIC method (CM) measures the objective weight based on the comparison strength and conflict of the indicator [75]. Combining the two methods can avoid the limitation of large deviations in the weight calculation using a single method, and the result is more reasonable. The steps were as follows:

(1) Standardization of indicators:

$$\mathrm{Positive} \mathrm{indicators}: Q_{ij}={\displaystyle \frac{q_{ij}-q_{\min }}{q_{\max }-q_{\min }}}$$

(1)

$$\mathrm{Negative} \mathrm{indicators}: Q_{ij}={\displaystyle \frac{q_{\max }-q_{ij}}{q_{\max }-q_{\min }}}$$

(2)

where q_ij is the initial indicator value, Q_ij is the standardized value of the indicator, Q_ij ∈ [0,1], and q_max and q_min are the maximum and minimum values of the indicator, respectively.

(2) The EWM used to calculate the weights is as follows:

$$p_{ij}={\displaystyle \frac{Q_{ij}}{{\displaystyle {\sum }_{i=1}^m{Q}_{ij}}}}$$

(3)

$$e_j=-{\displaystyle \frac{1}{\ln m}}{\displaystyle {\sum }_{i=1}^m{p}_{ij}\ln p_{ij}}$$

(4)

$${\omega }_1={\displaystyle \frac{1-e_j}{{\displaystyle {\sum }_{j=1}^n(1-e_j)}}}$$

(5)

(3) The CM used to calculate the weights is as follows:

$${\gamma }_j=\sqrt{{\displaystyle \frac{{\displaystyle {\sum }_{i=1}^m{(Q_{ij}-{\overline{Q}}_j)}^2}}{n-1}}}$$

(6)

$$c_j={\gamma }_j{\displaystyle {\sum }_{i=1}^m(1-|r_{ij}\left|\right)}$$

(7)

$${\omega }_2={\displaystyle \frac{c_j}{{\displaystyle {\sum }_{j=1}^n{c}_j}}}$$

(8)

where m is the number of provinces, n is the number of indicators, i = 1,...,m, j = 1,...,n, p_ij represents the indicator ratio, e_j is the indicator information entropy value, and ω₁ is the indicator weight obtained by the entropy weight method. $\overline{Q_j}$ represents the mean of each indicator, γ_j represents the standard deviation of indicator j, c_j represents the indicator information volume, r_ij is the correlation coefficient between the i-th indicator and the j-th indicator, and ω₂ is the indicator weight obtained by the CM.

Assuming that the two weighting methods have the same importance, the comprehensive weights of each index are as follows:

ω_j = 0.5ω₁ + 0.5ω₂

(9)

3.4.2. TOPSIS

The TOPSIS model (TM) can capitalize on the information in the dataset itself, thus more accurately reflecting the gaps between many different scenarios and finally obtaining results with a high degree of credibility [76]. We used the TM to calculate the levels of UER and NQP by measuring the degree to which each indicator deviated from or approached the positive and negative optimal resolutions.

(1) The weighted standardization matrix was determined as follows:

OD_ij = ω_j × y_ij

(10)

(2) The weighted Euclidean distances $S_j^{+}$ and $S_j^{-}$ between each evaluation object and the optimal and worst resolutions were derived as follows:

$$S_i^{+}=\sqrt{{\displaystyle {\sum }_{j=1}^n{\left(o{d}_j^{+}-o{d}_{ij}\right)}^2}}, S_i^{-}=\sqrt{{\displaystyle {\sum }_{j=1}^n{\left(o{d}_j^{-}-o{d}_{ij}\right)}^2}}$$

(11)

(3) The proximity CL of each object to the optimal resolution was calculated as follows:

$$C{L}_i={\displaystyle \frac{S_i^{-}}{S_i^{+}+S_i^{-}}}$$

(12)

where $o{d}_j^{+}$ and $o{d}_j^{-}$ are the distances from the j-th indicator to the optimal and worst targets, respectively, and od_ij is the weighted standardized value of the indicator. The larger the CL_i, the higher the UER and NQP levels, and vice versa.

3.4.3. Coupling Coordination Degree Model

Coupling degree (CD) means the degree of mutual influence between different systems, that is, the correlation. The greater the CD, the stronger the correlation. The coupling coordination degree (CCD) indicates the degree of system coordination [77].

$$C={\left[{\displaystyle \frac{U_1{U}_2}{{((U_1+U_2)/2)}^2}}\right]}^{{\displaystyle \frac{1}{2}}}$$

(13)

T = aU₁ + bU₂

(14)

D = (C × T)^1/2

(15)

where C is the CD and U₁ and U₂ are the UER and NQP levels, respectively, calculated from the TOPSIS model above. T is the degree of coordination. Assuming equal contributions from both systems, that is, a = b = 0.5, D is the CCD, and the CCD value range is [0, 1] and is divided into ten class intervals [3].

3.4.4. Global Spatial Autocorrelation

The global autocorrelation model measures the spatial correlation of the CCD. Moran’s I was used to quantify the spatial agglomeration effect. A positive value indicates that the variable is in a state of agglomeration distribution, a negative value indicates that the variable is in a state of dispersed distribution, and a value close to 0 indicates that the variable is in a state of random distribution [78], calculated as follows:

$$Global Moran’s I={\displaystyle \frac{m{\displaystyle {\sum }_{i=1}^m{\displaystyle {\sum }_{k=1}^m{W}_{ik}\left(D_i-\overline{D}\right)\left(D_k-\overline{D}\right)}}}{{\displaystyle {\sum }_{i=1}^m{\displaystyle {\sum }_{k=1}^m{W}_{ik}{\displaystyle {\sum }_{i=1}^m{\left(D_i-\overline{D}\right)}^2}}}}}$$

(16)

where D_i and D_k are the CCD levels of provinces i and province k, $\overline{D}$ is the mean CCD, and W_ik is the spatial weight matrix.

3.4.5. GTWR Model

The geographical and temporal weighted regression model (GTWR) considers the instability of space and time and overcomes the limitation of the geographically weighted regression model (GWR), which only considers spatial effects and fully reflects the temporal changes of data [79]. It reveals the heterogeneity of variables in time and space and explains the spatiotemporal relationship between other variables and dependent variables [80]. The GTWR calculation formula is as follows:

$$CC{D}_i={\eta }_0(u_i,v_i,t_i)+{\displaystyle {\sum }_{k=1}^n{\eta }_k(u_i,v_i,t_i)X_{ik}+{\epsilon }_i}$$

(17)

where CCD_i represents the CCD level of the i-th province, η₀ is the constant term, and u_i, v_i, and t_i are the spatiotemporal geographic coordinates of the i-th province, respectively. η_k is the regression coefficient of the k-th influencing factor of the CCD in province i, n is the number of spatial locations, X_ik is the value of the k-th influencing factor of the CCD in province i, and ε_i is the random error term of province i.

4. Results

4.1. Development Level of UER and NQP

The EWM-CM and TOPSIS methods were used to comprehensively calculate the UER and NQP levels in 30 provinces (cities) in China. The results were analyzed separately, as shown in Figure 1.

Regarding regions, the UER levels of various regions showed an upward trend. The eastern region had the highest UER levels. The UER levels in the central and western regions had similar characteristics and were slightly lower than average, whereas the northeastern region had the lowest. Among them, the eastern region has the benefit of being close to rivers and the sea, focusing on ecological, environmental governance, and sustainable economic development. Most eastern provinces had high UER. The central and western regions are limited by multiple factors, such as location accessibility and the eastern siphon effect, and their UER levels were relatively low. The economy of the northeastern region is relatively retrospective, the industrial structure is relatively singular, and the pollution and environmental damage caused by traditional industries make it difficult to promote ecological environmental protection measures.

From the development trend, the national NQP level increased from 0.232 in 2011 to 0.411 in 2022. The NQP in the eastern region has increased steadily annually since 2014. At the forefront of the reform, the eastern region has a strong foundation for policies, and most industries have achieved high-quality transformation. The NQP of the central region was slightly higher than average, second only to the eastern region. NQP showed lower growth rates in the western and northeastern regions. Although their NQP values have increased, slow industrial transformations and weak productivity improvement capabilities have hindered further breakthroughs in NQP.

4.2. CCD Level

The overall CCD maintained a significant positive growth trend (Figure 2), increasing from moderate imbalance (0.289) in 2011 to primary coordination (0.669) in 2022, and the level of integration of the two subsystems, UER and NQP, continued, deepened, and evolved towards coordinated development. The development of CCD experienced moderate and mild disorder in the early stage (2011–2013), was on the threshold of disorder in the middle stage (2014–2017), barely coordinated in the middle and late stages (2018–2019), and primarily coordinated in the late stage (2020–2022).

As shown in Figure 3a, the CCD distributions of UER and NQP for each year were relatively concentrated, with a single peak distribution. Over time, the peak value for each year moved to the right, indicating that the national CCD had an upward trend. The CCD levels in Beijing, Guangdong, Zhejiang, Jiangsu, Shanghai, Fujian, Shandong, Hainan, Jiangxi, Yunnan, and other regions were greater than 0.5, indicating an evident advantage. The provinces with CCD levels in the range of 0.3–0.4 were mostly distributed in the western and northeastern regions, such as Jilin, Liaoning, and Xinjiang (Figure 3b).

Based on the CCD calculation results, we analyzed the spatial evolution characteristics and selected three representative years, 2011, 2016, and 2022, for ArcGIS 10.8 visualization (Figure 4). Its characteristics are as follows:

(1) The average CCD showed the spatial characteristics of the eastern > central > western > northeastern regions, showing a “center-edge” distribution pattern with Beijing, Zhejiang, and Guangdong as the core and Fujian, Jiangsu, Anhui, Shanghai, and Shandong as important strategic support points. In 2011, none of the provinces reached a coordinated state; from 2011 to 2016, most provinces achieved a level change from a disordered state to coordination, forming a momentum of contiguous agglomeration development, and most of them were in the eastern provinces. By 2022, the CCD coordinated area had continuously expanded; Beijing, Zhejiang, and Guangdong had entered a good coordination range, and Hubei, Shandong, Jiangsu, and Shanghai were in intermediate coordination.

(2) During the study period, the CCD of each province increased to varying degrees, forming a benign situation with overall improvement and partial excellence. However, the problem of unbalanced development between different regions remains. In 2011, severe disorder provinces were three, moderate disorder provinces were eleven, and mild disorder provinces were sixteen. By 2022, the proportion of coordinated areas reached 90%, and the uncoordinated areas were Xinjiang, Qinghai, and Jilin. These three provinces have a relatively weak economic foundation, fragile ecological environment, and lack of infrastructure, public resources, and other prerequisites, showing the characteristics of a “coordinated development trap”.

4.3. Influencing Factors

4.3.1. Spatial Correlation

To accurately examine whether there was a spatial correlation between the CCD levels of the UER and NQP from 2011 to 2022, the spatial weight matrix of economic-geographical distance was selected to conduct the spatial autocorrelation test (

Table 2. Moran’s I of CCD.

Year	Moran’s I	Year	Moran’s I
2011	0.197 ***	2017	0.196 ***
2012	0.203 ***	2018	0.217 ***
2013	0.211 ***	2019	0.223 ***
2014	0.170 **	2020	0.228 ***
2015	0.168 **	2021	0.230 ***
2016	0.189 ***	2022	0.235 ***

Note: **, and *** indicate significance at the 0.05, and 0.01 levels, respectively.

). The global Moran’s I was greater than zero each year, fluctuated from 2011 to 2016, and then steadily increased, indicating a continued decrease in CCD differences and an increase in agglomeration between provinces. In general, with the progress of regional economy, ecology, and social productivity, the CCD of neighboring provinces shows spatial agglomeration characteristics, and the UER and NQP show a coordinated development trend. A study on the region along the Yangtze River Economic Belt pointed out that, over time, ecological resilience will show a higher degree of spatial agglomeration distribution trend [81].

4.3.2. GTWR Model Test

We used GTWR to study the spatial distribution characteristics of the CCD and selected six variables to explore the main factors affecting the CCD [47,82]. Among these, the CCD was the explained variable. The explanatory variables were economic development (GDPR), foreign investment (OPEN), urbanization rate (UBR), industrial structure (IS), technological innovation (TI), and environmental regulation (ER) (

Table 3. Influencing factors of CCD.

Variable	Meaning	Description of the Variable
GDPR	Economic development	GDP per capita (10,000 yuan)
OPEN	Foreign investment	Annual foreign investment (100 million yuan)
UBR	Urbanization rate	Proportion of urban resident population to total population (%)
IS	Industrial structure	Secondary industry output value as a percentage of GDP (%)
TI	Technological innovation	R&D expenditure (100 million yuan)
ER	Environmental regulation	Environmental protection investment as a percentage of GDP (%)

).

(1) GDPR represents GDP per capita. The higher the value, the higher the per capita income level, and the stronger the economic support for UER and the material support for the solid development of NQP [83].

(2) OPEN represents the annual foreign investment of each province. This indicator can reflect the degree of openness and economic changes in a region. Research has confirmed that opening up to the outside world can stimulate a country’s innovation capabilities and improve productivity levels [84]. These advantages may have a positive impact on the development of UER and NQP.

(3) UBR represents the urban proportion of the population in the region. Generally, an increase in the urbanization rate will accelerate the development of social productivity and form significant economies of scale, which is conducive to the development of NQP [82]. However, the pollution emissions caused by population agglomeration may be harmful to the ecological environment.

(4) IS is the proportion of the output value of the secondary industry in the region to GDP. High industrialization is a double-edged sword for regional development. In the short term, it will enable the regional economy to grow. However, in the long run, the damage of industrialization to the ecosystem cannot be ignored [1].

(5) TI represents regional research and development (R&D) expenditure. Technological innovation can promote productivity renewal and play a positive role in the high-quality development of NQP; at the same time, technological innovation will promote green transformation and achieve low-carbon development, which is beneficial to UER [10].

(6) ER is the proportion of annual environmental protection investment in GDP, representing the intensity of government environmental governance. As a regulatory body, the government can provide institutional guarantees for the coordinated development of UER and NQP [44].

The multicollinearity of the independent variables affects the regression results. The test obtained the variance inflation factor (VIF) of each variable as less than 10, and the tolerance was greater than 0.1, indicating no multicollinearity (

Table 4. VIF test results.

Variable	GDPR	OPEN	UBR	IS	TI	ER
VIF	5.065	3.385	1.950	2.011	1.374	0.844

). The GTWR plug-in of ArcGIS 10.2 software was used to automatically set the bandwidth and time–space distance parameter ratio to 1 and perform spatiotemporal geographic weighted regression on the influencing factors. The AICc and goodness of fit R² were selected as confidence evaluation indicators of the model, and the fitting results are listed in

Table 5. Comparison of parameters of each model.

Model	GTWR	GWR	OLS
R2	0.9977	0.9962	0.9433
AICc	−671.599	−587.069	13.1508

. The AICc of the GTWR model was −671.599, which was 84.53 and 684.75 lower than those of the GWR and OLS models, respectively, and R² was 0.9977, which was higher than those of the GWR and OLS models. Therefore, the GTWR model has a higher accuracy [85].

4.3.3. Temporal Changes in Influencing Factors

The GTWR model was used to calculate the annual average regression coefficients of the different influencing factor variables for each year during the study period, and a box plot was drawn based on the changes in the coefficients (Figure 5). The coefficients of the two variables of economic development (GDPR) and urbanization rate (UBR) were positive, and the regression coefficient mean of UBR was the largest, indicating that UBR is the most critical factor affecting CCD. Foreign investment (OPEN) and environmental regulation (ER) negatively impacted the CCD but weakened annually in the later stages, reducing the degree of dispersion. Although the regression coefficients of technological innovation (TI) in each year were positive, the values were small. The impact of industrial structure (IS) on CCD turned from positive to negative, and the negative impact increased annually.

4.3.4. Spatiotemporal Characteristics of Influencing Factors

We used ArcGIS10.2 to show the distribution state of the mean regression coefficients of each factor during the study period (Figure 6). The GDPR positively contributed to the CCD, which is distributed in a zonal manner and showed a downward trend from the southeastern coast to the northwestern inland area. The impact of OPEN on CCD was mainly negative, and only a few areas had a positive effect, which was higher in the western and northeastern, lower in the central, and lowest in the eastern coastal provinces; UBR considerably positively affected CCD. A decreasing trend was observed from the central and western regions to the eastern coastal areas. IS negatively impacted CCD. The regression coefficient values showed a decreasing distribution trend from the northwestern and eastern areas to the central areas, and there was an obvious spatial agglomeration in the low-value areas. The regression coefficients of TI and CCD were positively correlated. High-value areas are mainly located in the central provinces, whereas low- and medium-value areas are distributed throughout the country. ER significantly negatively impacted CCD. Provinces with high negative values were located in the southeastern coastal provinces. The western region was less negatively affected. Medium values were located in the northeastern and central parts of the country.

5. Discussion

Based on the results in Section 4, the UER, NQP, CCD, and their influencing factors were further interpreted.

5.1. Analysis of UER and NQP Levels

The UER and NQP levels in various provinces in China have increased by degrees, but the differences are relatively evident. At the forefront of China’s modernization construction, the eastern region has brought positive feedback to the UER by relying on a low-carbon and intensive industrial transformation path [86]. The central region has taken over the transfer of downstream industries, has a weak economic foundation, and is prone to frequent natural disasters such as droughts and floods; therefore, the UER is improving slowly [87]. Significant differences were observed among the western provinces. The southwestern region has a superior ecological background and rich ecological resources, while the northwestern region has an extremely fragile natural ecology, which restricts the development of UER; Ying et al. [88] found similar evidence that high UER areas in western China are concentrated in the southwestern provinces. The northeastern region has recently actively protected forest resources and optimized ecological barriers. However, the low quality of fallow forests and the trend of grassland degradation have not been fundamentally reversed, and the problem of land and water competition between agriculture and wetlands remains serious. As an old industrial base, the regional industry has been actively reformed; however, it is difficult to fundamentally solve the problems of air and water pollution caused by the long-term development of heavy industry in the short term, which makes it difficult to improve the UER [89]. Regarding the NQP, the eastern region includes core economic development areas, which continue to attract persons with high-tech and high-quality competencies to domicile [90]. Its NQP level was the highest in China. As China’s economic hinterland, the advanced manufacturing industry in the central and western regions has developed vigorously in recent years; however, the proportion of traditional energy industries remains high, and the NQP level lags behind that of the eastern region. In recent years, the northeast region has vigorously developed tourism and high-quality agriculture, which has laid a certain foundation for the development of NQP. However, its scientific and technological innovation foundation is still relatively weak compared with other regions. It lacks high-quality human resources with digital technology and innovation capabilities and high-level technology research and development investment. In addition, the Northeast region lacks the ability to attract high-quality talents. A national survey by Hu et al. [91] shows that the net inflow of high-level talents in northeast China is lower than the average level of mainland China. Difficulties in attracting talents will lead to low levels of development of the tertiary industry and social and economic levels, making it difficult to cultivate new-quality workers who meet the development needs of the new era. In the short term, the competitiveness of the region’s NQP is relatively weak.

5.2. Spatial and Temporal Characteristics of CCD

The value of the CCD continues to increase, proving that the integration of UER and NQP has improved. This trend promotes the transformation of social productivity towards high quality and ecological sustainability [92]. From 2011 to 2014, most provinces had large environmental loads and resource consumption, and the CCD was below 0.4. From 2015 to 2018, the overall level of CCD improved but was in a critically coordinated state. From 2019 to 2022, the overall CCD level was 0.6 or above, and the UER and NQP of most provinces entered an orderly development state. As shown in Figure 4, most provinces south of the Yangtze River, led by Zhejiang, were close to a coordinated state in 2011. These provinces relied on intelligent and digital transformation to innovate traditional industries and actively carry out ecological restoration. Low-value areas, such as Gansu, Ningxia, and Qinghai, lacked scientific and technological innovation, and economic development remained mainly resource-driven. In 2016, the CCD levels of most provinces improved, but the gap between the regions widened. Most eastern coastal areas have moved to a coordinated state, but the central, western, and northeastern areas have low CCD values; most rely on traditional economic growth, and the ecological environment is not optimistic. In 2022, the CCD of all provinces has improved, the overall Moran’s I has improved compared with 2011, and the degree of aggregation has improved. However, the problem of unbalanced development still exists, and a distribution state of decreasing from southeast to northwest has gradually formed. The eastern region still maintains a leading position; the positive radiation effect of this region has enabled central regions such as Hubei, Shaanxi, Anhui, and Jiangxi to improve the quality and efficiency of traditional industries, and the CCD level has been significantly improved. However, remote provinces such as Xinjiang, Qinghai, and Jilin are still in an uncoordinated state. They should actively improve the industrial structure, increase the penetration rate of green and innovative industries into traditional industries, and be guided by ecologically sustainable development to achieve the dynamic coordination of UER and NQP [93].

5.3. Analysis of Influencing Factors

Figure 5 and Figure 6 show the impact of different factors on the CCD, showing evident spatial heterogeneity.

The fitting coefficient of the GDPR has increased annually, indicating that a good level of economic development provides capital support for industrial transformation and environmental governance and thus provides the possibility of strengthening social low-carbon environmental awareness and green transformation [57,83]. Low-value areas are mainly distributed in resource-based provinces in the central, western and northeastern regions, such as Xinjiang, Qinghai, Inner Mongolia, and Heilongjiang, where the economy is relatively weak. These regions have low levels of input in environmental governance, infrastructure construction, and technological innovation factors, and are more difficult to supply with material support than the eastern regions, so their positive effects on CCD are less obvious. Wang et al. [94] also reached similar conclusions.

The fitting coefficient of OPEN indicated that more provinces had negative impacts. For the less developed provinces in the west and northeast, the cost advantage is conducive to attracting foreign investment, positively affecting CCD, that is, the “pollution halo” effect. The entry of foreign capital suppresses the emission of pollutants through technology spillover effects and structural effects, and advanced technology and production processes are used to improve social productivity [46]. At the same time, the inflow of foreign capital will also stimulate the expansion of economic scale in economically challenged areas, thereby providing financial support for environmental governance. Eastern provinces have small technological gaps with foreign companies, making technology diffusion more difficult. The profit-seeking nature of foreign investment will aggravate environmental pollution problems and produce a “pollution haven” effect. Therefore, OPEN tended to strongly inhibit CCD development in the eastern regions [44].

The UBR fitting coefficient was positive, with high-value areas distributed in the western and northeastern regions. Promoting urbanization can promote urban-rural integration and lead to short-term economic growth; however, with the surge in the urban population, problems such as excessive urban carrying pressure are becoming increasingly prominent, and the positive effect of urbanization is weakening. Therefore, the fitting coefficients show an overall downward trend. For economically underdeveloped areas, new urbanization enables the effective circulation of production factors, stimulates economic development vitality through multiplier effects, drives industrial upgrading, and alleviates ecological pressure. The agglomeration effect of urbanization can optimize the allocation of technology, talent, land, and funds, providing necessary guarantees and support for the transition of the NQP [82]. The positive impact of urbanization in the eastern provinces was relatively low and even had an inhibitory effect. This is because there was an early influx of labor into Beijing and the Yangtze River Delta region. Although the advantage of cost can promote ecological restoration, the uncontrolled expansion of city land space at a later stage requires more natural resources, exacerbates pollution, and leads to “urban diseases”, which are not conducive to improving the CCD [81]. In the future, reasonable national land space planning will be needed to alleviate the resource and energy consumption caused by the highly urbanized population.

The negative impact of IS confirms that extensive industrial development will cause ecological pressure and inhibit the high-quality transformation of industries. In recent years, the positive western area has adopted high-energy-consuming and equipment-manufacturing industries to boost the economy. Its UER can be improved in a short period through technological applications and ecological compensation. However, combined with the contiguous low-value areas in the middle, attracting traditional industries to settle at low costs will accelerate mindless resource consumption and squeeze out high-tech industry resources [54].

The TI fitting coefficients were small, but all were positive. On the one hand, technological innovation can improve resource utilization, and on the other hand, it can monitor and track regional environmental pollution processes, effectively implement pollution control, and give new impetus to technological economic growth [95]. The central provinces have a high proportion of industries. Although productivity levels have improved in recent years, the core competitiveness of science and technology modernization industries remains behind that of the eastern region. Guiding the transformation of innovation source capacity through national technological innovation investments is urgently required. The technological foundations of the western and northeastern regions are thin, and heavy industrial pollution is more prevalent in the arrears. Fundamentally improving CCD by relying solely on technological innovation investments is impossible. Industrial transfer and technological progress in the Yangtze River Delta provinces have reduced environmental pollution, and the technological innovation foundation is good; therefore, TI has little effect on promoting CCD.

ER inhibition decreased continuously from east to west. A strong inhibitory effect was evident in the eastern provinces. Their strict environmental rules increase the cost burden on enterprises and reduce emissions and pollution, resulting in a “compliance cost effect”. However, the collection of pollution and resource taxes increases the production cost of enterprises, restricting technological innovation [55]. The inhibitory effect had less impact on the western and northeastern provinces. These regions have a “race to the bottom” phenomenon under environmental regulation; that is, they lower the environmental access threshold to win economic growth competition, weakening the intensity of environmental regulation. However, if the tool of environmental regulation is not effectively played for a long time, it will make cross-regional environmental governance difficult to carry out and even cause the “free-rider” phenomenon. Xie et al. [49] corroborated the negative effects of environmental regulations on industrial development.

6. Conclusions and Recommendations

This study explored the CCD level between the UER and NQP subsystems based on temporal and spatial dimensions and discussed the temporal and spatial impacts of different factors on CCD, which can provide theoretical support for different regions to achieve a more coordinated eco-environment and social productivity.

The study highlights the following: (1) From 2011 to 2022, the indices of the two subsystems showed an upward trend, but gaps remained between different regions. (2) The overall CCD increased steadily during the study period, evolving from mild imbalance to primary coordination. The CCD values showed spatial differentiation characteristics in the eastern, central, western, and northeastern regions, and the CCD of each province increased. (3) The GTWR results indicated that the intensity of the factors influencing the CCD varied in different regions: UBR > GDPR > TI > OPEN > IS > ER. UBR was the main factor affecting CCD.

Based on these results, recommendations were made to promote the development of UER–NQP.

(1) Each province should fully consider its own characteristics and formulate differentiated green transformation strategies to improve the UER level, and ecological construction should be strengthened and ecological protection implemented in areas with low UER values. At the same time, it is necessary to promote the rationalization of technology, system and industrial structure, reduce the negative impacts of new and old industries on the environment and economic system, and solve the fossil energy consumption dilemma, so as to create a good development condition for NQP. (2) Provinces in the CCD low-value zones (northeastern, central, and western) should break the original path dependence and path lock and develop new industries with low energy consumption and pollution, guided by the requirements of green and sustainable development, and focus on promoting new quality laborers, eliminating obstacles to the movement of new quality labor materials and strengthening the policy and financial support for new quality labor objects. (3) Increases in the threshold of access to environmental regulations in some regions will stimulate technological innovation in high-carbon and high-pollution industries and transform the effects of regulations from short-term damage to long-term benefits. Simultaneously, the radiation-driven role of high-level regions (eastern) will be used to explore the new direction of overall coordinated development according to local conditions.

The limitations of this study are as follows: (1) Research scale. This study uses provincial data, which may not be able to distinguish the development differences of different prefecture-level cities, and the results are relatively macro. In the future, a more detailed division can be made for the research area to analyze the situation at the city level, county level and specific urban agglomerations. Cross-regional comparisons at the micro-scale based on the findings of this paper can provide targeted suggestions for regional development. In addition, the measurement methods and application scenarios of the coupling coordination degree model can be explored in depth, and future research can combine the Geo-Detector method to analyze the interaction of different influencing factors in order to obtain more detailed and accurate results. (2) Research content. This study only focuses on the development of NQP. In the future, research on the empowerment of new quality productivity in specific fields (such as finance and industry) can be carried out to explore the benign interactive relationship between industrial resources and financial capital, provide endogenous motivation for high-tech enterprises in promoting strategic investment, market development, and scientific and technological innovation, and provide theoretical support for promoting the green transformation and high-quality development of traditional industries.