Study on the Coupling and Coordination between Urban Resilience and Low-Carbon Development of Central Plains Urban Agglomeration

Abstract

The synergistic improvement in urban resilience and low-carbon development level is significant for mitigating and adapting to climate change, achieving the ‘dual carbon goal’ and promoting sustainable urban development. By constructing a comprehensive evaluation index system of urban resilience and low-carbon development, this study quantitatively measures the level of urban resilience and low-carbon development of the Central Plains Urban Agglomeration (CPUA) from 2009 to 2021. Further, the coupling coordination degree model and geographical detector explore the spatial and temporal evolution pattern and driving factors of the coupling and coordination (CCD) of urban resilience and low-carbon development. The results show the following: (1) From 2009 to 2021, urban resilience shows a good momentum of continuous rise, and the spatial distribution pattern is ‘high in the northeast and low in southwest’. Low-carbon development is characterized by the development trend of ‘first decline and then rise’, forming a spatial distribution pattern of ‘high in the south and low in the northwest’. (2) The CCD also shows a rising development trend. The type of coupling and coordination is mainly reluctant coordination. (3) The CCD shows a significant spatial correlation, and the degree of spatial agglomeration shows a downward trend. (4) The level of economic development and the level of scientific and technological innovation are the main two driving forces for the spatial differentiation of the coordinated development of the two systems. In addition, the explanatory power of the interaction of various influencing factors was significantly enhanced. In a word, this study was helpful to clarify further the spatial interaction between urban resilience and low-carbon development and also to provide experience and reference for low-carbon resilience construction and high-quality development of other urban agglomerations in the world.

1. Introduction

Over the past 40 years of reform and opening up, China’s urban population has increased from 18% to 64%, experiencing the world’s largest urbanization process [1]. With the rapid growth of the urban population and economic development, it has also brought about a significant increase in energy consumption and carbon emissions. In 2019, carbon emissions reached 10.707 billion tons [2,3]. As the world’s largest carbon dioxide emitter and energy consumer, China is also one of the developing countries most significantly affected by climate change. China attaches great importance to and actively responds to the threats and challenges brought by climate change. China’s National Development and Reform Commission has carried out three batches of low-carbon pilot provinces and cities, explored emission reduction measures that can be widely promoted, and achieved positive results in low-carbon development. In 2021, China’s carbon emission intensity decreased by 26.4% compared with 2012 [4,5].

However, with the rapid development of urbanization, the increasing urban population and the resulting traffic congestion and resource shortage have brought tremendous pressure to the city [6]. At the same time, frequent natural disasters such as droughts, floods, and sudden infectious diseases caused by global climate change caused by greenhouse gas emissions have also made urban socio-economic development face severe threats and challenges [7]. In this context, how to ensure the regular operation of cities with a high concentration of population, economy, and resources and to improve the ability of cities to cope with risks and uncertainties is crucial. Building a green, low-carbon, and resilient city is gradually becoming the consensus of all countries worldwide. In 2013, the Rockefeller Foundation of the United States proposed the Global 100 Resilient Cities (100RC) project, which aims to help cities around the world become more resilient to risks in order to address the increasingly severe climate, social, and economic challenges [8]. In 2015, four of the Sustainable Development Goals (SDGs) proposed in the ‘Changing Our World—2030 Agenda for Sustainable Development’ adopted at the United Nations Summit were related to ‘Urban Resilience’, and World Climate Day 2021 also featured the theme ‘Resilient Cities for Climate Change’ [9]. To this end, China has successively proposed to carry out pilot projects such as sponge cities and climate-adaptive cities from the government level to improve the level of resilient development of cities and further proposed ‘opinions on promoting urban security development’ in 2018. Promoting the development of urban resilience has risen to a national macro-strategy. In 2020, in the ‘Proposal of the Central Committee of the Communist Party of China on the 14th Five-Year Plan for National Economic and Social Development and the 2035 Vision Goals’, it is clearly proposed to accelerate the promotion of low-carbon green development and actively build resilient cities.

In recent years, urban resilience has become a hot issue in academia and society. Resilience is originally a physical concept that is used to represent the ability of an object to return to its original state after being subjected to external forces. It was first introduced into the field of ecology by Canadian ecologist Holling and then widely used in various fields such as urban research and planning, disaster prevention, and mitigation. The concept of urban resilience has emerged [10]. At present, scholars in different fields have carried out a lot of research on urban resilience, mainly focusing on the analysis of connotation concepts [11,12], the construction of a resilience research framework [13,14], and the comprehensive evaluation of resilience level and dynamic simulation [15,16,17]. Zhao et al. defined urban resilience as the ability of cities to maintain or restore the normal operation of urban systems after external disturbances [18]. Ribeiro et al. proposed that the urban resilience assessment framework should comprehensively consider 11 characteristics, including redundancy, robustness, diversity, and innovation. They defined an urban resilience assessment model composed of five aspects: natural, economic, social, material, and institutional [19]. Tabibian et al. constructed an indicator system from six aspects of society, economy, and infrastructure to evaluate the level of urban resilience in Tehran [20]. Suárez et al. constructed an evaluation index system of urban resilience including five aspects—diversity, modularization, feedback closeness, social cohesion, and innovation level—and evaluated the urban resilience level of more than 50 capital cities in Spain. The results show that more than 50 cities have low urban resilience construction [21]. Some scholars have also discussed the interaction between urban resilience and urbanization level [22,23], economic development level [24,25], land development intensity [26,27], and tourism development [28]. The scale is mainly concentrated on macro-scales such as countries, urban agglomerations, and watersheds. Zhang et al. used 26 cities in the Yangtze River Delta Urban Agglomeration from 2006 to 2020 as research samples, constructed an evaluation index system for urban resilience and new urbanization, and deeply explored the interaction between urban resilience and new urbanization [29]. Cheng et al. used the comprehensive evaluation method, coupling coordination degree model, and obstacle degree model to explore the coupling and coordination relationship between urban resilience and tourism development level in the Yangtze River Delta Urban Agglomeration [30].

Regarding low-carbon development, the current research mainly focuses on the connotation and concept definition, level evaluation, driving factors, and realization path of low-carbon development. The concept of low-carbon development was first proposed in the white paper ‘The Future of Our Energy’ in 2003. Due to severe environmental pollution problems, the United Kingdom is the first country to carry out low-carbon development and construction. It defines low-carbon development as based on lower energy consumption and less environmental pollution, creating maximum economic benefits, and higher quality of life and employment opportunities [31]. Subsequently, Japan, Denmark, Canada, and other countries have also introduced a series of policies and laws and regulations to promote urban low-carbon transformation and sustainable development. In terms of the construction of the evaluation index system, Price et al. constructed a regional low-carbon development evaluation system based on the consumption of various sectors of the energy terminal and applied it to the measurement of the low-carbon development level at the provincial level in China [32]. Chen et al. believed that urban carbon emissions are mainly composed of construction, production, transportation, and other departments. Through the decoupling index, the macro comprehensive evaluation of urban low-carbon development can be carried out, and the carbon emissions in the process of Shanghai’s economic development are quantitatively and empirically analyzed [33]. Regarding the driving factors and realization paths of low-carbon development, scholars in related fields have also carried out a lot of research. The research shows that industrial structure, urbanization level, scientific and technological innovation level, and energy structure are the main influencing factors affecting low-carbon development. Li et al. discussed the impact of urban morphology and socio-economic factors on carbon dioxide emissions and concluded that economic growth, population growth, and economic openness will lead to an increase in CO₂ emissions [34]. Hossein conducted an in-depth study on the influencing factors of carbon emissions in 30 provinces of Iran from 2009 to 2014 based on the spatial Dubin model and found that the level of urbanization and the level of economic development are the main factors affecting the improvement in low-carbon development level [35].

Although relevant scholars have carried out a lot of research in the field of urban resilience and low-carbon development and have achieved rich results, there are still some shortcomings. From the perspective of research, the existing research pays more attention to the measurement of a single system of urban resilience and low-carbon development, while there are few studies on the spatial and temporal evolution characteristics of urban resilience and low-carbon development from the two-dimensional perspective of time and space. In the study of the interaction between urban resilience and other systems, it focuses on the discussion of urban resilience and new urbanization, economic development level, and other systems. Few scholars have carried out relevant research on the coupling relationship between urban resilience and low-carbon development. As far as the research itself is concerned, most studies focus on the spatial and temporal evolution characteristics and spatial distribution types of the coupling and coordination relationship between urban resilience and other urban systems. At the same time, there are few studies on the influencing factors of the coupling and coordination relationship. The purpose of this study is to quantify the spatial and temporal evolution characteristics of urban resilience and low-carbon development in the Central Plains Urban Agglomeration, as well as the interaction relationship between the coupling and coordination of the two systems, and to explore the key driving factors that affect the synergistic improvement in urban resilience and low-carbon development, so as to form a new model of sustainable development that can be promoted and replicated. It provides a new perspective for the research of scholars in related fields. It provides an essential reference for the construction of low-carbon resilient cities in China and other urban agglomerations around the world.

Based on this situation, this paper constructs an evaluation index system of urban resilience and low-carbon development, comprehensively uses the entropy method, coupling coordination degree model, geographic detector, and other methods to analyze and comprehensively evaluate the coupling and coordination relationship between urban resilience and low-carbon development level of the Central Plains Urban Agglomeration from 2009 to 2021, and deeply explores its driving factors to build a low-carbon resilient city for the Central Plains Urban Agglomeration, improve the city’s ability to resist risks, achieve benign and sustainable development of the city, and improve the scientific basis.

2. Overview of the Study Area and Data Sources

2.1. Overview of the Study Area

The Central Plains Urban Agglomeration is located in the central region of China. The regional scope is mainly based on the ‘Central Plains Urban Agglomeration Development Plan’ issued by China’s National Development and Reform Commission in 2016. In addition to 18 cities in Henan Province, it also includes 12 cities in Shanxi, Shandong, Hebei, and Anhui provinces (see Figure 1). By the end of 2020, the land area of the CPUA was 287,000 km², the population reached 191 million people, the urbanization rate exceeded 56%, and the total GDP was 8.1 trillion RMB, accounting for about 8% of China [36]. Among the seven national urban agglomerations in China, the economic strength of the CPUA ranked second, only behind the Beijing–Tianjin–Hebei Urban Agglomeration. The Central Plains Urban Agglomeration is the fourth growth pole of China’s economic development and only lags behind the Beijing–Tianjin–Hebei Urban Agglomeration, the Yangtze River Delta Urban Agglomeration, and the Pearl River Delta Urban Agglomeration [37]. It is indisputable that the CPUA plays an important role in accelerating a new round of Mid-China Rising and the ecological protection and high-quality development strategy of the Yellow River Basin.

2.2. Data Sources

The data used in this study are mainly divided into two parts, namely, carbon emission data and socio-economic data. The carbon emission data are derived from the China Carbon Accounting Database (CEADs). The data are obtained by Shan et al., who used the particle swarm optimization–backpropagation algorithm to connect and fit the two sets of nighttime light data of DMSP/OLS and NPP/VIRRS. It is verified by energy accounting, and the fitting effect is as high as 0.988, which is highly reliable and significantly better than similar data [38]. The socio-economic data are mainly derived from the ‘China City Statistical Yearbook’, ‘Henan Statistical Yearbook’, ‘Shanxi Statistical Yearbook’, ‘Shandong Statistical Yearbook’, ‘Hebei Statistical Yearbook’, and ‘Anhui Statistical Yearbook’ from 2009 to 2021. The national economic and social development bulletins of various cities and individual missing data are supplemented according to the interpolation method.

3. Evaluation System Construction and Research Methods

3.1. Evaluation System Construction

Based on the previous research results [16,39,40,41,42,43,44,45,46], indexes were chosen by following the principles of scientificity and operability and also by combining the actual situation of the Central Plains Urban Agglomeration. From the four aspects of ecological resilience, economic resilience, social resilience, and infrastructure resilience, a total of 20 indicators were selected to measure urban resilience. At the same time, from the four dimensions of carbon emission level, energy consumption, industrial structure, and low-carbon policy, nine indicators such as total emissions, per capita energy consumption, and the proportion of environmental pollution control investment in GDP are selected to measure the low-carbon development level of each region. The specific evaluation indicators are shown in

Table 1. Urban resilience and low-carbon development evaluation system.

Target Layer	Element Layer	Index Layer
Urban resilience	Ecological resilience	Good air-quality grade days
		Green coverage rate of built-up area
		Per capita green area
		Centralized sewage treatment rate
		Harmless treatment rate of domestic waste
	Economic resilience	Growth rate of fixed assets investment
		The actual use of foreign investment
		Gross industrial output value above scale
		Gross National Product per capita
		Per capita disposable income of urban residents
	Social resilience	Number of college students per 10,000 people
		Number of beds in health care institutions per 10,000 people
		Registered unemployment rate at year-end
		Basic pension insurance coverage
		Total retail sales of social consumer goods
	Infrastructure resilience	Water consumption per capita
		Electricity consumption per capita
		Drainage pipe length
		Per capita urban road area
		Number of mobile phone users
Low-carbon development	Carbon emission level	Total carbon emissions
		Carbon emission intensity
		Carbon emissions per capita
	Energy consumption	Energy consumption per unit GDP
		Energy consumption per capita
	Industrial structure	The proportion of the output value of the secondary industry
		The proportion of tertiary industry output value
	Low-carbon policy	The proportion of investment in environmental pollution control in GDP
		R&D internal expenditure

.

Ecological resilience is closely related to the sustainable and healthy development of the city. The green coverage rate of the built-up area and the per capita park green area are important manifestations of the urban ecological environment. The larger the area of urban green space, the better the purification effect on the atmosphere, the more days of air compliance, and the more stable the ecosystem. The higher the centralized treatment rate of sewage and the harmless treatment rate of domestic garbage, the smaller the pressure of garbage and sewage produced by production and life on the urban ecosystem, and the higher the bearing capacity of the urban ecological environment [39,40].

Economic resilience refers to the ability of cities to cope with various pressures and disturbances to maintain sustainable development. Per capita GDP is an important indicator to measure the overall level of economic development in a region, and it is the most intuitive manifestation of urban economic development. For the Central Plains Urban Agglomeration, a special region where resource-based cities are in the majority and the secondary industry occupies an important position in economic development, the growth of industrial output value and fixed asset investment above the designated size is the basic support for stable economic development [41,42]. Foreign direct investment is an important source of funds for urban development, which can effectively enhance economic vitality, while per capita disposable income is the embodiment of residents’ confidence and consumption ability, thus driving economic development.

The degree of perfection of urban infrastructure construction directly affects the city’s ability and level of resistance to disturbances. The per capita urban road area fully reflects the city’s traffic development level and the city’s mobility and ease ability in the event of risk. The number of mobile network users reflects the degree of perfection of the urban communication system, which is closely related to the emergency response capacity of the city in the event of risk. Per capita water consumption and per capita electricity consumption can show the pressure of residents’ water and electricity consumption on urban infrastructure [16]. The greater per capita water consumption and per capita electricity consumption, the greater the pressure of residents’ demand on municipal facilities. When the urban system is impacted, the urban infrastructure is more likely to collapse.

Social resilience reflects the security capacity and development potential of cities in the face of short-term or cumulative shocks. The number of college students per 10,000 people and the number of beds in medical and health institutions per 10,000 people are important manifestations of regional education level and medical level. The improvement in the popularity of higher education can effectively improve residents’ ability to cope with risks and urban innovation ability. The improvement in urban innovation ability can effectively reduce the possibility of security risks. The number of beds in medical and health institutions can make the wounded receive more effective and timely treatment in the event of disasters. The unemployment rate and basic pension coverage rate play an important role in the stability of social order [43,44].

Carbon emissions and energy consumption can directly reflect the energy consumption of the city. Generally speaking, the greater the value, the greater the pressure on carbon emission reduction and the lower the level of low-carbon development. The government’s policy has a direct guiding role in urban low-carbon development and is an important factor affecting the development of the low-carbon economy. The investment expenditure on environmental protection and the expenditure on science and technology are important manifestations of the government’s low-carbon policy [45,46].

3.2. Research Methods

3.2.1. Entropy Method

The entropy method is a comprehensive evaluation method of objective weighting. The weight of each index is determined by the amount of information provided by the original data of each index, which can effectively avoid the subjectivity and randomness caused by subjective factors so that the final evaluation results are more accurate and scientific [47]. Therefore, this paper uses the entropy method to comprehensively evaluate the level of urban resilience and low-carbon development. The main calculation steps are as follows:

• The standardization of data processing:

  $$\mathrm{positive}\mathrm{indexes}:X_{ij}^{\prime }=\frac{x_{ij}-\min x_{ij}}{\max x_{ij}-\min x_{ij}}$$

  (1)

  $$\mathrm{negative}\mathrm{indicators}:X_{ij}^{\prime }=\frac{\max x_{ij}-x_{ij}}{\max x_{ij}-\min x_{ij}}$$

  (2)
• Calculate the proportion of each index with the following formula:

  $$P_{ij}=X_{ij}^{\prime }/{\displaystyle \sum _{i=1}^m{X}_{ij}^{\prime }}$$

  (3)
• Calculate the information entropy of each index with the following formula:

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

  (4)
• Calculate the weight of each index as follows:

  $${\omega }_j=\left(1-e_j\right)/{\displaystyle \sum _{j=1}^n\left(1-e_j\right)}$$

  (5)
• Calculate the comprehensive evaluation value as follows:

  $$U_{ij}={\displaystyle \sum _{i=1}^n{\omega }_j\times x_{ij}}$$

  (6)

3.2.2. Coupling Coordination Degree Model

Coupling refers to the phenomenon that two or more systems interact with each other. The coupling degree mainly reflects the strength of the interaction between the systems, and the coordination degree mainly reflects the relationship between the systems or between the elements [48,49]. Since the coupling degree model can only analyze the degree of interaction between the two systems, it cannot reflect the level of coordinated development of the two systems. Therefore, the coupling coordination degree model is introduced to determine the degree of coordinated development between urban resilience and low-carbon development in the Central Plains Urban Agglomeration. The main calculation formulas are as follows:

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

(7)

$$T=\alpha f\left(x\right)+\beta g\left(x\right)$$

(8)

$$C=2\times \sqrt{\frac{f\left(x\right)\times g\left(x\right)}{{\left(f\left(x\right)+g\left(x\right)\right)}^2}}$$

(9)

In the formulas, D is the coupling coordination degree, C is the coupling degree, and T is the comprehensive coordination index. f(x) and g(x) are the comprehensive scores of urban resilience and low-carbon development level, respectively, and α and β are the relative importance of the two systems. This paper argues that the importance and influence of the two subsystems are the same, so the values of α and β are both 0.5, and the coupling coordination degree of urban resilience and low-carbon development is divided into 10 categories (see

Table 2. Classification standard division of coordination degree.

Coordination Degree	Coordination Type	Coordination Degree	Coordination Type
0.00~0.09	Extreme disorder state	0.50~0.59	Barely coordinated state
0.10~0.19	Severe disorder state	0.60~0.69	Primary coordinated state
0.20~0.29	Moderate disorder state	0.70~0.79	Intermediate coordinated state
0.30~0.39	Mild disorder state	0.80~0.89	Good coordinated state
0.40~0.49	On the verge of disorder state	0.90~1.00	Excellent coordinated state

).

3.2.3. Exploring Spatial Data Analysis

(1)

Global spatial autocorrelation

This paper uses the global Moran index to explore the spatial correlation of the coupling coordination degree between urban resilience and low-carbon development in the Central Plains Urban Agglomeration from 2009 to 2021. The value of the global Moran’s index is between [−1, 1]. When the value is greater than 0, it shows a positive correlation, indicating that the coupling and coordination evolution of urban resilience and low-carbon development is consistent with the adjacent unit. When it is less than 0, it shows a negative correlation, indicating that it is opposite to the changing trend of the adjacent unit. When it is close to 0, it indicates that it tends to be randomly distributed [50]. The calculation method is as follows:

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

(10)

In the formula, I is the Moran’s I index, n represents the number of cities in the study area, and X_i and X_j represent the coupling coordination degree of the i and j cities, respectively, which is the average value of the coupling coordination degree of each city. $W_{ij}$ is the spatial weight matrix, which is generated by Geoda1.18 to describe the spatial proximity relationship of each city unit. Queen connection is used in this paper.

(2)

Local spatial autocorrelation

The global Moran’s index can reflect whether the degree of coordinated development between cities is related to the spatial distribution, but it cannot reflect the specific location of agglomeration and clarify the specific agglomeration types of each region. Therefore, this paper introduces local spatial autocorrelation to analyze the correlation degree of coordinated development between each city and its surrounding cities, identifies the spatial dependence and consistency of its coordinated development state, and reveals the agglomeration type of coordinated development degree between regions. The calculation formula is as follows:

$$Ii=\frac{n\left(x_i-\overline{x}\right){\displaystyle \sum _{i=1}^n{w}_{ij}\left(x_i-\overline{x}\right)}}{{\displaystyle \sum _{i=1}^n{\left(x_i-\overline{x}\right)}^2}}$$

(11)

In this formula, when I_i is positive, it shows that the agglomeration types between adjacent regions are high–high aggregation (H-H) or low–low aggregation (L-L). When I_i is negative, the agglomeration types are high–low aggregation (H-L) or low–high aggregation (L-H). The region with a high (low) degree of coordinated development is surrounded by regions with a low (high) degree of coordinated development.

3.2.4. Geographical Detector

A geodetector is a statistical method for analyzing the spatial heterogeneity of geographical elements and the driving effect of factors. It was proposed by Wang Jinfeng in 2010 and is widely used in social, ecological, economic, and other fields, including ecological detection, interactive detection, risk detection, and factor detection functions [51]. This paper uses factor detection to analyze the influencing factors of the coupling and coordinated development of urban resilience and low-carbon development level in the Central Plains Urban Agglomeration. The specific formula is as follows:

$$q=1-\frac{1}{N{\sigma }^2}{\displaystyle \sum _{h-1}^L{N}_h}{\sigma }_h^2$$

(12)

In the formula, q is the explanatory power of each influencing factor to the coupling coordination degree of urban resilience and low-carbon development level, and the range is [0, 1]. The larger the q value is, the stronger the explanatory power of the factor is. h is the stratification of factors, N and N_h are the number of samples of all and layer h, respectively, ${\sigma }^2$ and ${\sigma }_h^2$ are the variance of all samples and the variance of layer h, respectively, and L is the number of selected indicators.

4. Analysis of Results

4.1. Spatio-Temporal Evolution of Urban Resilience and Low-Carbon Development

4.1.1. Temporal Variation Characteristics

According to Formula (6), the average values of urban resilience and low-carbon development of cities in the Central Plains Urban Agglomeration from 2009 to 2021 are calculated respectively. The specific results are shown in Figure 2.

From 2009 to 2021, the urban resilience of the Central Plains Urban Agglomeration showed a steady upward trend, and the growth rate has slowed down in recent years. From 2009 to 2019, the development level of urban resilience increased rapidly, from 0.163 in 2009 to 0.294, an increase of 80.36%, but the overall development level was still low, with great room for improvement. The development gap between cities has been widening, and the regional differences are obvious. The range has increased from 0.3 in 2009 to 0.54. Except for Zhengzhou, the capital of Henan Province, the overall development level of other cities is not much different. Zhengzhou ranked first over the years, Luoyang and Handan ranked second and third, respectively, but there is still a big gap with Zhengzhou.

The level of low-carbon development shows a trend of declining first and then rising slowly. From 2009 to 2012, the level of low-carbon development in each city declined slightly, from 0.323 to 0.297. Since 2012, the level of low-carbon development in various cities has begun to rise steadily. After 2017, the growth rate has been accelerating, rising to 0.446 by 2021. The range between cities also reflects the stage-type characteristics of first decreasing, then rising and then decreasing, from 0.435 in 2009 to 0.139 in 2015, and finally rising to 0.344 in 2021. However, from the overall perspective of the whole research period, the development gap between cities has decreased. In terms of low-carbon development, Bengbu, Suzhou, and other cities in Anhui have always been in the first echelon within the urban agglomeration, which is inseparable from Anhui’s long-term emphasis on environmental pollution control and investment in science and technology. The development momentum of Zhengzhou, Xingtai, and other cities is good, and the level of low-carbon development has been significantly improved. Both have been upgraded from the middle reaches to the second and third places in 30 cities.

4.1.2. Spatial Distribution Pattern

In order to more intuitively analyze the spatial pattern and evolution characteristics of urban resilience and low-carbon development in the Central Plains Urban Agglomeration, the cross-sectional data of 2009, 2013, 2017, and 2021 were selected and visualized based on Arcgis10.8 (see Figure 3 and Figure 4).

On the whole, the urban resilience of the Central Plains Urban Agglomeration has significant spatial differences, showing a spatial distribution pattern centered on Zhengzhou, the capital of Henan Province that is high in the northeast and low in the southwest. In 2009, the areas with low urban resilience were mainly distributed in Suzhou, Bengbu, and Fuyang in Anhui Province. In 2013, the scope of low resilience areas in the southeast expanded significantly, and Shangqiu, Zhoukou, Xinyang, and other cities in southern Henan adjacent to Anhui also belonged to the depressions in the whole urban agglomeration. In 2017, the overall resilience of Xinxiang, Anyang, Kaifeng, and other regions in the northeast improved significantly, while Suzhou, Bozhou, and other cities in Anhui Province have improved their resilience, but their level is still at the bottom of the whole urban agglomeration. In 2021, the cities in the northeastern part of Henan showed a completely opposite development state, and the range of high-toughness regions expanded to the west. The resilience of Handan and Xingtai in Hebei rose rapidly, while the resilience of Hebi and Puyang declined, ranking 28th and 30th among the 30 cities, respectively.

It can be seen from Figure 4 that the spatial difference of low-carbon development in the Central Plains Urban Agglomeration is also obvious. The overall distribution shows the distribution characteristics of ‘high in the south and low in the north, high in the surrounding areas and low in the center’, which is significantly different from the spatial distribution characteristics of urban resilience. In 2009, the areas with higher levels of low-carbon development were concentrated in the cities of Bozhou, Suzhou, and Bengbu in the southwest, while the areas with lower levels of development were mainly distributed in the northwest, with Changzhi, Jincheng, Handan, Jiaozuo, and other heavy industrial cities as the most typical. In 2013, the high-value area of low-carbon development level spread and expanded to the southeast, forming a high-value zone of low-carbon development in the southern part of the Central Plains Urban Agglomeration. The low-carbon development in the northwest was relatively backward. The scope of the region has converged; Liaocheng, Xingtai, and other regions have developed well and improved rapidly, while Shangqiu and Huaibei cities in the east have declined, showing an opposite development trend. In 2017, with the continuous improvement in the low-carbon development level of Zhengzhou, Kaifeng, Luoyang, and other cities in the central region, a high-value area surrounded by Xuchang, Zhoukou, and Pingdingshan was gradually formed in the central and southern part of the Central Plains Urban Agglomeration. The low-level development areas, in addition to the three cities surrounding, are mainly distributed in Jincheng, Jiyuan, Jiaozuo, and other cities at the junction of Shanxi and Henan provinces in the northwest, as well as Puyang and Heze in the east. In 2021, the high-value ring distribution in the central and southern regions is becoming more and more obvious. After more than 10 years of continuous development, the low-carbon development level of Xingtai, Liaocheng, and other cities in the northeast has significantly improved compared with 2009, becoming a new high-low carbon development level gathering area. Jincheng, Jiyuan, and other cities in the border area of Shanxi and Henan provinces have not been able to get rid of the situation of low-carbon development level, and the gap with other regions has been widening.

4.2. The Spatial and Temporal Evolution of the Coupling Coordination

4.2.1. Temporal Variation Characteristics

According to the comprehensive evaluation value of urban resilience and low-carbon development, combined with Formulas (7) and (9), the coupling coordination degree of the two systems is calculated. From the perspective of time, from 2009 to 2021, the coordinated development of urban resilience and low-carbon development in the Central Plains Urban Agglomeration showed a sustained and steady upward trend. From 2009 to 2015, the coupling coordination degree grew rapidly, with the average value increasing from 0.396 to 0.48, an increase of 21.21%. From 2015 to 2020, the growth rate slowed down compared with the previous period, only rising by 0.04 in five years. In 2020, the previous good development momentum was restored, and the average value of the coupling coordination degree rose to 0.546. Among them, Heze, Suzhou, Bengbu, and Fuyang have obviously increased, all exceeding 50%. From the perspective of coordination degree classification, the types of coupling coordination are mostly on the verge of imbalance and reluctant coordination and gradually develop from on the verge of imbalance to reluctant coordination, but the overall level of coordinated development is still low, and the coordination of development between the two systems still needs to be further improved.

4.2.2. Spatial Differentiation Characteristics

From a spatial point of view, the coupling and coordinated development of urban resilience and low-carbon development in the Central Plains Urban Agglomeration has been significantly improved, and the number of coordinated cities has been increasing, showing a distribution pattern of high center and low periphery. The polarization of Zhengzhou and Luoyang is obvious, but the spatial differences in various regions have been decreasing in recent years (see Figure 5).

In 2009, the mild disorder state and on the verge of disorder state were the main types, and the proportion of these cities belonging to the two types was as high as 96.7%. There was no serious disordered city. The areas on the verge of disorder were mainly located in the northwest of the urban agglomeration, while the areas with mild disorder were concentrated in the southwest, showing a spatial distribution pattern of ‘low in the southwest and high in the northeast’.

In 2013, the coordinated development of urban resilience and low-carbon development in the Central Plains Urban Agglomeration showed a good momentum of development. The distribution area of mild disorder in the southwest was significantly higher than that in 2009, and all of them developed upward to the verge of a disorder state. The proportion of this type of area reached 86.7%, which became the dominant type of coordinated development. The scope of the area in the state of imbalance was greatly reduced. And the spatial differences between regions were also significantly reduced.

In 2017, the distribution range of cities on the verge of disorder state shrunk significantly, and the proportion of this type of city dropped from 86.7% to 63%, but it still dominated. This type of city is mainly distributed in Jincheng, Jiyuan, Sanmenxia, and other cities at the junction of the two provinces of Shanxi and Henan in the northeast, as well as in the southeast of Henan. A barely coordinated state area formed a ‘northeast-southwest’ zonal distribution area in the central part of Henan Province, showing a spatial distribution of ‘high center and low around’.

In 2021, the gap between the coordinated development of various regions continued to decrease, and the vast majority of regions were in a barely coordinated state. This type became the new dominant type of coordinated development in the Central Plains Urban Agglomeration. Zhengzhou entered an excellent coordinated state, and it was also the only city to achieve high-quality coordination. The distribution range of the areas on the verge of imbalance shrunk significantly, especially in the central and southern regions. Although the border area of Henan and Anhui in the southeast has developed well in recent years, it is still the key area for the coordinated development of urban resilience and low-carbon development in the future.

4.3. The Spatial Clustering Characteristics and Evolution of the Coupling Coordination

In order to further explore the spatial agglomeration characteristics of urban resilience and low-carbon development in the Central Plains Urban Agglomeration, this paper uses geoda1.18 to construct the queen weight matrix, so as to calculate the global Moran’s index of the coupling coordination degree of the Central Plains Urban Agglomeration from 2009 to 2021 (see

Table 3. The global Moran’s I index of coupling coordination of Central Plains Urban Agglomeration (2009–2021).

Year	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Morans I	0.179	0.232	0.171	0.173	0.105	0.081	0.111	0.095	0.154	0.115	0.114	0.079	0.103
Z-Variance	2.804	3.222	2.804	2.685	2.051	1.744	2.077	1.757	2.547	1.986	2.324	1.731	2.155
p-value	0.005	0.001	0.005	0.007	0.040	0.081	0.038	0.079	0.011	0.047	0.020	0.084	0.069

).

From

Table 4. Index system of impact factors of coupling and coordination relationship between urban resilience and low-carbon development.

Independent Variable Type	Independent Variable Name	Symbol
Economic development	Gross Domestic Product	X1
	Savings balance of financial institutions at year-end	X2
Public services	Number of health technicians per 1000 population	X3
	Per capita green area	X4
	Road network density	X5
Industrial structure	Total energy consumption	X6
	The proportion of tertiary industry added value	X7
Technological innovation	The amount of patent authorization	X8
	Total R&D personnel	X9

, it can be seen that the Moran index of the coupling coordination degree of urban resilience and low-carbon development in the Central Plains Urban Agglomeration from 2009 to 2021 is greater than 0, with a range of 0.081~0.232, which passes the 90% significance level test throughout the study period. This shows that the coupling coordination degree of the Central Plains Urban Agglomeration shows a positive spatial correlation and has a significant spatial agglomeration effect in the spatial distribution; that is, the coupling coordination degree shows that the high value areas are adjacent to each other and the low value areas are adjacent to each other. However, the Moran’s index value shows a downward trend, from 0.179 in 2009 to 0.103 in 2021. This shows that although the spatial agglomeration effect still exists, the spatial agglomeration intensity of the coupling coordination degree of urban resilience and low-carbon development in the Central Plains Urban Agglomeration is constantly weakening.

Since the global Moran’s index can only reflect the spatial agglomeration characteristics of the coupling coordination degree between urban resilience and low-carbon development of the Central Plains Urban Agglomeration, the local spatial autocorrelation model is introduced to explore further the specific types of spatial agglomeration distribution of coupling coordination degree. The local spatial autocorrelation analysis was carried out on the cross-sectional data of 2009, 2013, 2017, and 2021, and the Central Plains Urban Agglomeration was divided into five spatial agglomeration types. The results are shown in Figure 6.

(1)

High–high agglomeration area

It shows that urban resilience and low-carbon development are in a sound stage of development, and this type of city and its neighboring areas are highly coupled and coordinated. In 2009, in the central part of the Central Plains Urban Agglomeration, a high–high agglomeration area composed of Zhengzhou, Luoyang, Xinxiang, and Jiaozuo was formed. These four cities were also the most economically developed areas of the Central Plains Urban Agglomeration. The level of urban resilience and low-carbon development was in a leading position within the urban agglomeration, and the development of the two systems was more harmonious. With the continuous development of time, Xinxiang and Luoyang successively withdrew from this type of agglomeration area in 2013 and 2017, and the range of high–high agglomeration decreased significantly, leaving only Zhengzhou and Jiaozuo. In 2021, Zhengzhou also withdrew from the high–high agglomeration; only Jiaozuo still belongs to this type.

(2)

Low–high agglomeration area

It means that a low coupling coordinated city is adjacent to a high coupling coordinated city. Affected by their own development models, cities of this type are difficult to radiate and driven by the highly coupled cities adjacent to them. In 2009 and 2013, only Pingdingshan City in the southwest belonged to this type. Although it is adjacent to Luoyang, Zhengzhou, and other cities with the highest degree of coordinated development, the coordinated development level of Pingdingshan’s urban resilience and low-carbon development was never high. In 2017, Kaifeng also turned into a low–high agglomeration type, and Pingdingshan City continued to maintain its original state. In 2021, there was no significant change in this type of city.

(3)

Low–low agglomeration area

It indicates that a city and its surrounding cities belong to low coupling level aggregation. This type of urban resilience and low-carbon development level are at a lower level of development. In 2009, this type of city was mainly in Shangqiu City, and in the Central Plains Urban Agglomeration in the Anhui part, the number reached five. In 2013, the distribution of such cities continued to expand in the southeast. Zhoukou and Fuyang, together with the original cities of Suzhou and Bozhou, formed a larger low–low agglomeration area. In 2017, the low–low agglomeration area showed a trend of gradual contraction. Only Huaibei, Bengbu, and Bozhou in Anhui still belonged to this type, and Zhoukou, Fuyang, and Suzhou withdrew from the low–low type area. In 2021, although the number of cities belonging to this type of area remained unchanged, its distribution range changed. Bozhou withdrew from low–low agglomeration, but Suzhou re-entered low-low agglomeration. Such cities not only lag behind in the construction of urban resilience but also face great challenges in low-carbon development.

(4)

High–low agglomeration area

It indicates that the adjacent city of a high coupling coordination city is in a low coupling coordination state. This type of city is characterized by the low degree of coordination between the two systems of urban resilience and low-carbon development. In the process of urban development, too much attention is paid to the development of a single system, while ignoring the interaction and interaction promotion before the two systems. This type of city only appeared in Handan, Liaocheng, and Heze in the northeast of 2021, and there was no distribution of this type of agglomeration in other years, showing no obvious regularity.

5. Analysis of the Impact Factors

The coordinated development of urban resilience and low-carbon development is affected by many factors. Based on the previous research results [52,53,54,55], combined with the actual situation of the current development of the Central Plains Urban Agglomeration, this paper follows the principles of scientificity and accessibility of indicators, and comprehensively considers the weight of each index. Nine indicators are selected from four aspects, economic development, public service, industrial structure, and scientific and technological innovation, to construct an index system (see Table 4). The influencing factors of the spatial differentiation pattern of coordinated development of urban resilience and low-carbon development are detected by factor detection through geographic detectors. The impact degree and interaction results of each factor are shown in

Table 5. Detection results of impact factors.

Year	X1	X2	X3	X4	X5	X6	X7	X8	X9
2009	0.50	0.43	0.56	0.29	0.23	0.38	0.50	0.46	0.42
2013	0.61	0.35	0.27	0.42	0.33	0.41	0.13	0.50	0.74
2017	0.71	0.35	0.58	0.35	0.25	0.46	0.30	0.46	0.70
2021	0.57	0.39	0.41	0.49	0.29	0.76	0.32	0.53	0.62

and

Table 6. Detection results of interaction of influencing factors.

2009		2013		2017		2021
Interaction Factor	q-Value	Interaction Factor	q-Value	Interaction Factor	q-Value	Interaction Factor	q-Value
X3∩X4	0.994	X4∩X8	0.999	X7∩X9	0.995	X4∩X5	0.999
X2∩X9	0.988	X3∩X5	0.998	X6∩X7	0.970	X6∩X9	0.983
X2∩X4	0.985	X2∩X5	0.989	X2∩X6	0.967	X7∩X9	0.983
X3∩X1	0.983	X1∩X9	0.987	X1∩X5	0.966	X3∩X6	0.981
X4∩X6	0.982	X8∩X9	0.972	X3∩X7	0.964	X1∩X3	0.978
X4∩X8	0.981	X7∩X9	0.970	X3∩X1	0.964	X1∩X5	0.976

.

5.1. Single Factor Detection Results

According to the single factor detection results (see Table 5), it can be seen that in 2009, the coupling coordination degree of urban resilience and low-carbon development was mainly affected by factors such as regional GDP, the number of health technicians per thousand people, and the proportion of the added value of the tertiary industry. Among them, the number of health technicians per thousand people was the strongest, reaching 0.56, followed by the proportion of regional GDP and the added value of the tertiary industry, both of which were 0.5. In 2013, the explanatory power of factors such as the number of patent authorizations and the total number of R&D personnel continued to increase, especially the total number of R&D personnel. The explanatory power of the total number of R&D personnel increased most significantly, reaching 0.74, replacing the proportion of the added value of the tertiary industry, and together with the regional GDP became the main influencing factors of the spatial differentiation of the coordination degree. The influence of public service factors such as per capita park green space and road network density increased, but the explanatory power was still low, and the overall change was not significant. In 2017, the explanatory power of regional GDP and total R&D personnel still ranked first and second among various factors. The explanatory power of economic development level to the spatial differentiation of coordinated development reached its peak, and the explanatory power of total energy consumption was also significantly more robust than that in 2009. In 2021, the explanatory power of total energy consumption continued to increase, replacing the regional GDP and the number of R&D personnel as the new main influencing factor. The explanatory power of the two elements was 0.76 and 0.62, respectively, ranking among the top explanatory powers of each factor. The improvement in scientific and technological innovation levels has continuously injected impetus into the coordinated development of urban resilience and low-carbon development levels.

With the passage of time, although the explanatory power of the level of economic development is still maintained at a high level, the influence of industrial structure and scientific and technological innovation on coordinated development gradually exceeds the level of economic development and has become the main driving factor affecting the coordinated development of the two systems in the Central Plains Urban Agglomeration. As the first productive force of evolution, science and technology not only promote the continuous improvement in urban resilience but also dramatically improve the promotion and wide application of low-carbon green technologies in cities. The industrial transformation of traditional resource-based cities represented by Jincheng, Jiyuan, and Jiao in the urban agglomeration has not achieved remarkable results and still stays in the stage of high energy consumption, high emission, and high pollution. Bengbu, Luoyang, and other cities continue to promote the development of high-tech industries and green industries, energy utilization efficiency continues to improve, and the level of sustainable development has been significantly improved. This two-level development direction also makes the industrial structure more and more influential in explaining the spatial differentiation of the coordinated development of urban resilience and low-carbon development.

5.2. Significant Interaction Results

The detection results of factor interaction in 2009, 2013, 2017, and 2021 were sorted respectively, and the combination of factors with interaction influence in the top six was organized to obtain Table 6. From the factor interaction detection results (see Table 6), it can be seen that after the interaction of different factors, the explanatory power is significantly enhanced compared with the single factor. The interaction results show different degrees of nonlinear enhancement or two-factor enhancement, and there is no independent and weakened situation. This shows that the spatial pattern of the coupling coordination degree distribution of urban resilience and low-carbon development in the Central Plains Urban Agglomeration results from the joint action of economic development level, public service, industrial structure, and scientific and technological innovation.

From the results of factor interaction in 2009, it can be seen that although the single factor explanatory power of the savings balance of financial institutions at the end of the year was general, the explanatory power of X2∩X9 and X2∩X8 was significantly enhanced, with q values of 0.996 and 0.985, respectively. The explanatory power of spatial differentiation of coupling coordination exceeded 98%, and X3∩X4 was among the top three influencing factors of interactive explanatory power this year. This shows that the level of economic development plays a vital role in the coordinated development of early urban resilience and low-carbon development. In 2013, the combination of the top three influencing factors became X₄∩X₈, X₃∩X₅, X₂∩X₅, and q values also exceeded 0.98. The reciprocal of the single factor explanatory power ranking was the three factors of public service, especially the road network density and the per capita park green space area. After the factor interaction, the explanatory power was significantly improved, and the type of action was mainly double factor enhancement. The interaction between public service and other factors and the interaction between public service factors gradually dominated at this stage. That was largely because there was a significant gap in the allocation of medical resources and the construction of basic public facilities among various regions in the Central Plains Urban Agglomeration. However, as the Central Plains Urban Agglomeration continued to promote basic public service resources to the grassroots, rural and remote areas, and difficult people, the imbalance of medical resources and infrastructure allocation within the urban agglomeration was alleviated to a certain extent. The explanatory power of the interaction between public service factors and other factors continued to decline in the following years. In 2017, the q value after the interaction of X7∩X9 became the first factor interaction combination this year, reaching 0.995. The interaction between the total energy consumption and the proportion of the added value of the tertiary industry also reached 0.970. The interaction between industrial structure and technological innovation gradually replaced the interaction combination of economic development level and other factors and became the main influencing factor of the spatial differentiation of the coordinated development of urban resilience and low-carbon development in the Central Plains Urban Agglomeration. In 2021, X₄∩X₅ was the most explanatory factor combination, with a q value of 0.999, and the q values of X₇∩X₉ and X₆∩X₉ were 0.983, ranking second. The explanatory power of the interaction combination of scientific and technological innovation and industrial structure remains at the forefront, which fully shows that the influence of industrial structure and scientific and technical innovation ability in the process of coordinated improvement in urban resilience and low-carbon development level of the Central Plains Urban Agglomeration is increasing. In recent years, the implementation of the strategy of strengthening the capital of Henan Province has made the economic development level and scientific and technological differences between Zhengzhou and other regions in the urban agglomeration continue to widen, resulting in its impact on the spatial heterogeneity of the coupling and coordination level of urban resilience and low-carbon development.

6. Discussion

6.1. Coupling Coordination Analysis between Urban Resilience and Low-Carbon Development

Achieving the coupling and coordinated development of urban resilience and low-carbon development is of great significance for improving the ability of cities to adapt to climate change and resist risks and is also the key to achieving high-quality sustainable development. Therefore, this study uses the entropy method, coupling coordination degree model, and spatial autocorrelation model to analyze the temporal and spatial evolution characteristics of the coupling and coordination relationship between the two systems and uses the geodetector model to analyze the key driving factors affecting the coupling and coordination development of the two systems.

As a new concept and new path for cities to cope with uncertain risks and climate change, urban resilience is gradually becoming a hot research field. This study innovatively introduces low-carbon development into the field of urban resilience research and provides a new research perspective and method for the study of urban resilience and low-carbon development. The results show that from 2009 to 2021, the level of urban resilience in the Central Plains Urban Agglomeration has continued to increase, and the growth rate has slowed down in recent years. Among them, Zhengzhou metropolitan area has the highest level of resilience, which is consistent with the research results of Liu [46] et al. However, compared with the evaluation results of Cheng [56], Mu [57], and other scholars on the Yangtze River Delta Urban Agglomeration, Beijing–Tianjin–Hebei Urban Agglomeration, and other regions, there is a big difference, indicating that there is still a big gap between the Central Plains Urban Agglomeration and the resilience level of the urban agglomeration in the developed areas of eastern China. The research of Zhou [58], Zhao [59], and other scholars shows that the low-carbon development level of the Central Plains Urban Agglomeration shows the spatial distribution pattern of ‘high in the southeast and low in the northwest’. This paper also confirms this result. During the study period, the coupling coordination degree between urban resilience and low-carbon development of the Central Plains Urban Agglomeration showed a sound momentum of continuous rise, from 0.39 to 0.48. Although this shows that urban resilience and low-carbon development have gradually achieved coordinated development, its coordination level is still at a low level. The next step still needs to continuously improve the resilience level of each system and formulate differentiated emission reduction targets according to the actual situation of each region, so as to achieve a higher level of coordination. In terms of the influencing factors of the spatial differentiation of the coupling and coordination of urban resilience and low-carbon development, GDP level, industrial structure, and other factors are the main driving factors of coordinated development, which is consistent with the previous research conclusions of Li [60], Jiang [61], and Cai [62]. Their research shows that the high coupling areas are mainly concentrated in the regional economic center and science and technology center, and the level of scientific and technological innovation and regional financial strength has a positive effect on the coupling coordination degree of the region.

6.2. Policy Implications

This research shows that the spatial difference between the coordinated development of urban resilience and low-carbon development in the Central Plains Urban Agglomeration is shrinking, but there are still significant gaps between regions, and the constraints and problems are also different. Each city should adjust measures to local conditions, identify its own biggest shortcomings in the coordinated development of low-carbon development and resilience construction, implement targeted optimization policies, and promote the coordinated and stable development of urban systems. With the growth level of the region, Zhengzhou and Luoyang should give full play to their leading role in urban resilience construction and low-carbon technology innovation, so as to promote the overall improvement in the low-carbon resilience level of urban agglomeration. For Anyang, Xingtai, Heze, and other cities in the northeast, resilience construction has made great progress in recent years, but the carbon lock-in effect brought by the long-term production mode, consumption mode, and urban construction mode not only makes it still maintain a high emission level but also makes it have a long way to go in environmental governance. In the southeastern part of the urban agglomeration, especially in Huaibei, Bengbu, and other cities at the junction of Henan and Anhui provinces, it has a good momentum in green development and low-carbon transformation, but the level of urban resilience is relatively slow. In the future, we should pay more attention to urban infrastructure construction and constantly enhance our own economic strength and comprehensive disaster prevention and resilience. The most prominent areas of the problem are Jiyuan, Jincheng, Changzhi, and other cities at the junction of Shanxi and Henan provinces. Urban resilience and low-carbon development are at a low stage of development. The industrial transformation has not achieved obvious results. Environmental pollution and energy dependence are very serious, facing the dual pressures of urban resilience improvement and the green and low-carbon transformation of industrial structure. These regions should fully learn from the development experience of other high-toughness cities, grasp the strategic opportunity of the comprehensive reform pilot area of high-quality development of ecological protection and resource-based economic transformation in the Yellow River Basin, and explore the low-carbon resilience construction road suitable for their own development in combination with their own actual situation.

6.3. Limitations

Of course, this study also inevitably has some limitations. In the construction of the evaluation index system, limited by the availability of data, this study has certain limitations in the selection of indicators, and some indicators have not been included in the index system. In future research, we will continuously enrich the data sources and further improve the evaluation index system, so as to achieve a more accurate measurement of urban resilience, low-carbon development and coordinated development level. In addition, in terms of research scale, this study only discusses the spatial and temporal evolution and influencing factors of the coupling and coordination between urban resilience and low-carbon development in the Central Plains Urban Agglomeration from the city scale. As a key unit to promote sustainable development, achieve dual carbon goals, and enhance resilience, it is of great significance to further explore the interaction and influencing factors of the coordinated development of urban resilience and low-carbon development from the county micro-scale, which is also the direction of the next step.

7. Conclusions

In this study, by constructing a comprehensive evaluation index system of urban resilience and low-carbon development, the entropy method is used to measure the development level of urban resilience and low-carbon development in the Central Plains Urban Agglomeration from 2009 to 2021. With the help of the coupling coordination degree model and the spatial autocorrelation model, the spatial and temporal evolution characteristics and spatial agglomeration characteristics of the coupling coordination degree of urban resilience and low-carbon development in the Central Plains Urban Agglomeration are discussed, and the geographical detector model is used to explore the key factors affecting the spatial differentiation of the coupling and coordinated development of the two systems. The main conclusions are as follows:

(1) From 2009 to 2021, the urban resilience level of the Central Plains Urban Agglomeration showed a good momentum of steady rise. Spatially, it showed a spatial distribution pattern with Zhengzhou, the capital of Henan Province in the central region, as the center that is high in the northeast and low in the southwest. The level of low-carbon development showed an evolution trend of decreasing first and then rising slowly. In terms of spatial distribution, it was characterized by ‘high in central and south and low in northwest’, but there was a distribution pattern of subsidence areas composed of ‘Pingdingshan, Xuchang, and Zhoukou’ in central Henan.

(2) From 2009 to 2021, the coupling coordination degree of urban resilience and low-carbon development in the Central Plains Urban Agglomeration also showed a rising development trend as a whole, but the growth rate slowed down. The polarization of Zhengzhou and Luoyang was severe, and the level of coordinated development in other regions was not much different. The coupling coordination type basically achieved a three-level leap from ‘mild disorder state—the verge of disorder state—barely coordinated state’.

(3) The coupling coordination degree of urban resilience and low-carbon development in the Central Plains Urban Agglomeration showed significant spatial correlation characteristics, and the spatial agglomeration degree of the overall coupling coordination degree showed a trend of fluctuating decline. The number of high–high agglomeration cities was decreasing, mainly distributed in Zhengzhou, Jiaozuo, and other cities in the middle. The distribution of low–low agglomeration cities also shrunk significantly compared with 2009 and was still concentrated in the border area of Henan and Anhui provinces in the southeast. The low–high agglomeration cities did not change much during the study period, mainly distributed in Kaifeng and Pingdingshan, while the high–low agglomeration only appeared in Handan, Liaocheng, and Heze in 2021, and no such agglomeration type appeared in other years.

(4) The spatial differentiation of the coupling coordination degree between urban resilience and low-carbon development was affected by many factors, and the explanatory power of each factor in different years was different. Among them, the level of economic development and the level of technological innovation were the main driving forces for the coordinated development of the two systems. The explanatory power of public service and economic development level weakened significantly after 2013, while scientific and technological innovation and energy structure have improved the explanatory power of spatial differentiation of coordinated development most significantly in recent years, gradually replacing economic development and public services as the new dominant factors.