Analysis of Synergistic Benefits between Carbon Emissions and Air Pollution Based on Remote Sensing Observations: A Case Study of the Central Henan Urban Agglomeration

Abstract

Reducing carbon emissions while controlling air pollution is a dual challenge for China. However, few studies have analyzed whether there is a synergy between the two. In view of this, this paper takes the urban agglomeration in Central Henan as an example, uses multi-source remote sensing and panel data from 2000 to 2022 and analyzes the spatiotemporal evolution patterns and synergistic benefits of air pollution and carbon emissions based on the spatial distribution direction analysis model, coupling coordination degree model and multi-scale geographic weighting model. The results indicate the following: (1) Carbon emissions show a growing trend, but the difference in the carbon emissions of different cities is relatively large, showing the characteristics of “one center and two zones” in space. Air pollution shows a trend of first increasing and then decreasing. (2) The synergistic benefits have been continuously enhanced, and the overall unbalanced state has gradually become coordinated. There is no obvious aggregation feature. (3) The impact of socioeconomic factors on the synergistic benefit is obviously stronger than that of natural ecological factors, among which the total energy consumption, population density and industrial structure are the leading factors of the synergistic benefit of carbon emissions and air pollution. This study offers valuable insights for green development, high-quality growth and collaborative environmental governance within the Central Henan urban agglomeration.

1. Introduction

As urbanization and industrialization accelerate, the urbanization rate of the permanent resident population in China grows, reaching 65.22% in 2022. Cities are not only important spaces for carrying a population, fostering groundbreaking activities and driving economic growth, but they are also significant contributors to carbon emissions and atmospheric pollutant emissions. The process of urbanization and urban expansion will greatly influence the atmospheric environment [1]. In 2022, the carbon emissions exceeded 11.48 billion tons, representing 28.35% of the global carbon emissions [2]. China has already outpaced the Americas, the European Union and other developed economies to rank as the world’s top carbon emitter. Meanwhile, 37.2% of the cities in China still failed to meet the air quality standards in 2022, showing that the air pollution problem is very serious. The Party’s 20th National Congress report pointed out that the coordinated promotion of carbon reduction, decreased pollution and the in-depth prevention and control of environmental pollution means that China’s carbon emission reduction and air pollution governance has entered a new phase.

The domain of carbon emissions has consistently garnered significant attention both domestically and internationally. In delving into the intricacies of carbon emission accounting, we discern that alterations in land use structure constitute a pivotal factor influencing carbon emissions [3,4]. Notably, building land carries a lot of human activities and serves as a notable carbon emitter. Cultivated land also exists as a carbon source. With the emergence of a vast amount of remote sensing data, the limitations in the time span and spatial coverage of carbon emission accounting have been overcome [5]. At the same time, methods such as the IPCC inventory method and input–output models are also commonly used in carbon emission accounting [6]. In analyzing the influencing factors, scholars have employed various methodologies such as the geographical detector [7,8], geographical weighted regression model [6,9], LMDI model [10], STIRPAT model [11] and spatial econometric model [12,13] to delve into the driving mechanisms behind carbon emissions. Empirical outcomes indicate that economic growth, populace size, urbanization, green technology innovation, energy consumption, foreign investment and other social and economic factors are correlated with carbon emissions [10,14]. Furthermore, noteworthy achievements have been made in the spatial pertinence study of carbon footprints [15], carbon transfer [16,17] and other related aspects.

In the relevant research of air pollution, the analysis of spatial–temporal variation patterns and influencing elements of air pollution has always been the focus of attention [18,19,20,21]. Spatial analysis [22], the spatial metrology model [23], geographical detector [24], EKC [25,26] and other methods are important means of research. With the development of computer technology, machine learning and deep learning methods have been employed to predict the levels of air pollutants [27]. Scholars analyze a vast amount of data related to air quality, including meteorological conditions, traffic volume, industrial emissions and more, in order to establish accurate prediction models. In addition to some conventional pollutants, atmospheric composite pollution has emerged as the primary type of urban pollution within China, especially the secondary pollution dominated by PM_2.5 and O₃ [28].

Despite significant progress made in the field of carbon emissions and air pollution research, the academic community still tends to focus narrowly on individual environmental issues, rarely considering them within the same framework. However, the dual strategic tasks currently faced by China’s ecological civilization construction—achieving a fundamental improvement in the ecological environment and promoting the achievement of carbon peaking and neutrality goals—are intertwined and mutually influential. Given the high degree of similarity in the formation mechanisms and sources of carbon emissions and air pollution (CEAP), adopting a coordinated approach is crucial. This not only helps reduce the costs of policy implementation but also effectively mitigates risks stemming from inappropriate policies, ultimately leading to broader environmental and societal benefits. The implementation of this coordinated strategy holds immense practical significance and profound historical impact in advancing China’s ecological civilization construction to a higher level. In this context, this research assumes the Central Henan urban agglomeration (CHUA) as the primary focus of the study, uses the spatial distribution direction analysis and the CCDM, and relies upon remote sensing data and panel data to investigate the spatio-temporal evolution patterns and synergistic benefits of CEAP. Based on the MGWR model, the influence mechanism of carbon emission and air pollution co-benefits was studied from the perspective of socioeconomic factors and natural factors, which provided a reference for green development, high-quality development and collaborative emission reduction in the CHUA. The structural framework of the paper is illustrated in Figure 1.

2. Data and Methods

2.1. Study Area

The administrative area of the Central Henan urban agglomeration covers 28.7 square kilometers, including 30 cities (Figure 2). The Central Henan urban agglomeration, situated in an area within a 500 km radius between Beijing, Wuhan, Jinan and Xi’an, serves as a central node connecting these significant cities. It is also the intersection region of the land bridge corridor and the Beijing–Guangzhou corridor in the national “two horizontal and three vertical” urbanization strategic layout, playing a vital role in the country’s strategic development planning. However, it also brings significant energy consumption to the CHUA, CEAP are increasing and the environmental situation is deteriorating. Clarifying the synergistic benefits of CEAP in the CHUA and realizing green, coordinated and high-quality development hold great strategic significance for promoting the development of the CHUA, boosting the growth of the central region, facilitating the establishment of new urbanization and broadening the scope for China’s economic progress.

2.2. Data

China Land Cover Dataset was constructed by Professor Huang of Wuhan University based on the Landsat data of 335,709 points on GEE, which was classified using a random forest classifier, and further improved the spatial–temporal consistency of the data set through spatial–temporal filtering and logical reasoning post-processing. The spatial resolution of the data set is 30 m, and it is divided into 9 divisions, encompassing cropland, forests, shrubs, grassland, barren, impervious, water, snow/ice and wetland. The PM_2.5 comes from the CHAP data set, which uses artificial intelligence technology to fill the space missing value of the satellite MODIS MAIAC AOD product with model data, and combines the ground-based observation, atmospheric reanalysis, emission inventory and other big data to produce the national non-gap ground PM_2.5 data from 2000 to 2022. The spatial resolution is 1 km, and the temporal resolution is a year. The data on CO, O₃ and SO₂ are derived from the MERRA-2 data set, and the temporal resolution is a month, and the spatial resolution is 0.5° × 0.625°. Details are shown in

Table 1. Indicators and data sources.

Indicators	Scale	Data Format	Data Source
CLCD	30 m	TIF	https://doi.org/10.5281/zenodo.8176941 (accessed on 9 January 2023)
PM2.5	1 km	NetCDF4	https://data.tpdc.ac.cn/zhhans/data/6168e75d-93ab-4e4a-b7ff-33152e49d0bf (accessed on 6 January 2023)
O3	0.5° × 0.625°	NetCDF4	https://disc.gsfc.nasa.gov (accessed on 10 January 2023)
CO
SO2

.

When exploring the mechanism of CEAP, it is crucial to consider not only social and economic factors, but natural factors should be introduced also (

Table 2. Social factors and natural factors.

Variable	Description	Source
PTI	The proportion of tertiary industry indicates industrial transformation	Statistical Bulletin of National Economic and Social Development China Urban Statistical Yearbook Yearbook of prefecture-level city
VSI	The output value of the secondary industry represents the industrial structure
POP	Population density
PGDP	Per capita GDP
OPEN	Total imports and exports represent the level of opening up
ECO	Total energy consumption
GCR	Urban green coverage rate
NDVI	Normalized vegetation index	https://doi.org/10.5067/MODIS/MOD13A3.006 (accessed on 6 January 2023)
TEM	Annual mean temperature	https://gis.ncdc.noaa.gov (accessed on 21 February 2023)
PRE	Average annual rainfall	Google Earth Engine

). With reference to a large number of studies and considering the availability of data, this paper selected industrial transformation (PTI), industrial structure (VSI), per capita GDP (PGDP), level of external openness (OPEN), population density (POP), urban green coverage (GCR), energy consumption (ECO), NDVI, temperature (TEM) and precipitation (PRE) as the driving factors. The socioeconomic data come from the Statistical Bulletin of National Economic and Social Development, the Statistical Yearbook of Chinese Cities and the yearbook of prefecture-level cities. The NDVI comes from the MOD13A3 data set released by the National Aeronautics and Space Administration. The TEM obtained from NCEI has been interpolated using the IDW method. The PRE comes from the CHIRPS Daily data set from the GEE platform, which combines satellite images with 0.05° resolution and in situ site data to form a global grid rainfall data set.

2.3. Methods

2.3.1. Carbon Emission Accounting

As early as the early 1970s, studies found that land use change was the main factor causing changes in the carbon dioxide levels. Numerous studies have demonstrated that construction land and cultivated land serve as carbon sources, while forest land and grassland mainly play the role of carbon sinks. The carbon emission estimation based on land use mainly includes two parts: cultivated land carbon emissions and building land carbon emissions. In this article, the total natural gas supply, total liquefied petroleum gas supply and total electricity consumption of the whole society are used to calculate the carbon emissions of construction land. These three data points are annual data of each city, mainly derived from the “China Urban Statistical Yearbook”. Therefore, the carbon emission estimation formula is as follows:

$$C_n=C_{plo}+C_{eco}$$

(1)

$$C_{plo}=D_{plo}\times A_i$$

(2)

$$C_{eco}=D_{nag}\times S_{nag}+D_{lpg}\times S_{lpg}+D_{ele}\times S_{ele}$$

(3)

According to the previous formula, $C_n$ is the carbon emissions of different cities; $C_{plo}$ stands for carbon emissions from cultivated land; ${\mathrm{C}}_{\mathrm{e}\mathrm{c}\mathrm{o}}$ is the carbon emissions of energy consumption; $A_i$ represents the area of cultivated land in different cities; $D_{plo}$ represents the carbon emission coefficient of cultivated land. According to existing studies, the carbon emission coefficient of cultivated land is 0.46 km/m²·y; $S_{nag}, S_{lpg} \mathrm{a}\mathrm{n}\mathrm{d} S_{ele}$, respectively, represent the total supply of natural gas, the total supply of liquefied petroleum gas and the electricity consumption of the whole society. $D_{nag}, D_{lpg} \mathrm{a}\mathrm{n}\mathrm{d} D_{ele}$ represent the coefficients of the total supply of natural gas, the total supply of liquefied petroleum gas and the total consumption of electricity in the whole society, which are, respectively. 21.622 tons, 3.1013 tons and 7.119 tons.

2.3.2. Spatial Distribution Direction Analysis

Spatial distribution direction analysis is used to describe the configuration and orientation of regional attributes throughout the spatial domain. Standard deviational ellipse is a classic tool for analyzing the directionality of the spatial distribution, which quantitatively explains the centrality, distribution and directive and spatial forms of the spatial distribution of economic factors from a global and spatial perspective. The main parameters include the center of gravity, azimuth, long axis, short axis, etc. The center of gravity and azimuth indicate the migration trajectory of the data, the long semi-axis signifies the directionality of the data distribution, and the short semi-axis indicates the extent of the data distribution. The greater disparity between the lengths of the long and short semi-axes, the more obvious the directionality of the data.

$$\begin{gathered} {\overline{X}}_w={\sum }_{i=1}^n{w}_i{x}_i/{\sum }_{i=1}^n{w}_i \\ {\overline{Y}}_w={\sum }_{i=1}^n{w}_i{y}_i/{\sum }_{i=1}^n{w}_i \end{gathered}$$

(4)

$$\tan \theta =\frac{\left({\sum }_{i=1}^n{w}_i^2{\tilde{x}}_i^2-{\sum }_{i=1}^n{w}_i^2{\tilde{y}}_i^2\right)+\sqrt{\left({\sum }_{i=1}^n{w}_i^2{\tilde{x}}_i^2-{\sum }_{i=1}^n{w}_i^2{\tilde{y}}_i^2\right)+4{\sum }_{i=1}^n{w}_i^2{\tilde{x}}_i^2{\tilde{y}}_i^2}}{2{\sum }_{i=1}^n{w}_i^2{\tilde{x}}_i{\tilde{y}}_i}$$

(5)

$$\begin{gathered} {\sigma }_x=\sqrt{{\sum }_{i=1}^n{\left(w_i{\tilde{x}}_i\mathit{cos}\theta -w_i{\tilde{y}}_i\mathit{sin}\theta \right)}^2/{\sum }_{i=1}^n{w}_i^2} \\ {\sigma }_y=\sqrt{{\sum }_{i=1}^n{\left(w_i{\tilde{x}}_i\sin \theta -w_i{\tilde{y}}_i\cos \theta \right)}^2/{\sum }_{i=1}^n{w}_i^2} \end{gathered}$$

(6)

In the above formula, $\left(x_i,y_i\right)$ represents the spatial coordinates of the variable; ${\overline{X}}_w, {\overline{Y}}_w$ represents the center of gravity coordinates; $w_i$ represents the space weight; $\theta$ represents the azimuth of the standard deviational ellipse; $\left({\tilde{x}}_i,{\tilde{y}}_i\right)$ represents the coordinate deviation from the variable to the center of gravity; ${\sigma }_x$ and ${\sigma }_y$ represent the standard deviations of the X- and Y-axes of the standard deviational ellipse, respectively.

2.3.3. Entropy Weight Method

In this paper, PM_2.5, CO, O₃ and SO₂ are taken as the four indicators of air pollution, and the importance of different indicators of air pollution varies, so the weight of each indicator needs to be determined. Within the scope of this investigation, the entropy weight method is utilized to determine the weights of variables to build air pollution indicators. The entropy weight method serves as an objective approach to determine the weights of indicators by evaluating the variation values among different variables to avoid errors caused by human factors. Since the dimensions of each variable are different, the data are first normalized to eliminate the impact of dimension, then the proportion of a certain value under a variable is evaluated, the entropy and difference coefficients of different variables are calculated, and the weight of the variable is finally determined. The calculation methods are shown in Formulas (7)–(10). The weights of PM_2.5, CO, O₃ and SO₂ are 0.2, 0.33, 0.24 and 0.23, respectively.

$$X_{ij}=\frac{X_{ij}-\min \left(X_j\right)}{\max \left(X_j\right)-\min \left(X_j\right)}$$

(7)

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

(8)

$$F_j=-\frac{1}{\ln m}\times \sum _{i=1}^m{P}_{ij}\ln P_{ij}$$

(9)

$$D_j=1-F_j$$

(10)

$$W_j=D_j/\sum _{j=1}^n{D}_j$$

(11)

In the above formula, $j$ represents the variables PM_2.5, CO, O₃ and SO₂; $i$ represents the individual data points of the j-term variable; $X_{ij}$ is data normalization processing for four variables; $P_{ij}$ is the proportion of the $i$-th value of the $j$ variable; $F_j$ represents the entropy of the $j$ variable; $D_j$ represents the difference coefficient of the $j$ variable;${ W}_j$ represents the weight of the $j$ variable.

2.3.4. Coupling Coordination Degree Model

Carbon emissions and air pollution have the same root and origin. For example, energy consumption and transportation can bring carbon emissions and air pollution at the same time. They are two subsystems under the same system, which interact and are interrelated. Coupling denotes the interaction between two or more systems or forms of motion, resulting in mutual influence and interdependence, as shown in Formula (12):

$$C=\frac{2\sqrt{u_1{u}_2}}{u_1+u_2}$$

(12)

where $C$ is the coupling degree, $C\in \left[\mathrm{0,1}\right]; u_1$ and $u_2$ stand for carbon emissions and air pollution, respectively. The greater the degree of coupling, the stronger the effect between carbon emissions and air pollution.

Since the coupling degree cannot show whether carbon emissions and air pollution promote or restrict each other, different degrees better present the synergistic benefits of CEAP, the CCDM is employed to reflect the coordination degree of CEAP, and the coordination differences between different regions are analyzed. The computational approach utilized by the model is below:

$$T=\alpha u_1+\beta u_2$$

(13)

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

(14)

Here, $T$ denotes the comprehensive coordination index; $\alpha ,\beta$ are the contribution degrees of CEAP. The present study believes that carbon emissions and air pollution are equally important; $\alpha =\beta =0.5$. $D$ stands for coupling coordination, $D\in \left[\mathrm{0,1}\right]$. The closer D is to 1, the more coordinated carbon emissions and air pollution are and the stronger the synergistic effect is. The closer D is to 0, the opposite is true. According to the degree of coupling coordination, the coordination levels of CEAP are divided as presented in

Table 3. Classification of coupling coordination degree of carbon emissions and air pollution.

D	Coordination Level		D	Coordination Level
(0.0–0.1)	1	Extreme incoordination	[0.5–0.6)	6	Barely coordinated
[0.1–0.2)	2	Serious incoordination	[0.6–0.7)	7	Primary coordination
[0.2–0.3)	3	Moderate incoordination	[0.7–0.8)	8	Intermediate coordination
[0.3–0.4)	4	Mild incoordination	[0.8–0.9)	9	Sound coordination
[0.4–0.5)	5	Proximity incoordination	[0.9–1.0)	10	Excellent coordination

.

2.3.5. Multi-Scale Geographical Weighted Regression Model

In the traditional regression analysis, the association among independent and dependent variables remains stable in the whole study area, ignoring the spatial heterogeneity and non-stationarity of the geographical world. In 1996, Professor A. Stewart Fotheringham [29] proposed the Geographically Weighted Regression (GWR) model, a locally linear regression technique based on spatial variation modeling. The GWR model generates a unique local regression equation for each point, facilitating the identification of influencing factors specific to the research object at a given scale. This approach not only enhances the understanding of local spatial relationships but also provides insights into the spatial heterogeneity of variables, thus offering a comprehensive analysis of the data. However, the GWR only considers that each independent variable operates on the same spatial scale, and the spatial scale here refers to the bandwidth; that is, all independent variables have the same bandwidth, ignoring the difference in the spatial scale effects of independent variables. The multi-scale geographic weighting model (MGWR) [30] overcomes the defect of the same bandwidth and makes each independent variable have a different bandwidth, which can better reflect the spatial heterogeneity of independent variables. The calculation formula of the MGWR is expressed as follows:

$$y_i={\beta }_0\left(u_i,v_i\right)+\sum _{j=1}^p{\beta }_{bwj}\left(u_i,v_i\right)x_{ij}+{\epsilon }_i$$

(15)

Here, $\left(u_i,v_i\right)$ is the spatial position of variable$i$; ${\beta }_0\left(u_i,v_i\right)$is the constant of variable $i$; ${\beta }_{bwj}\left(u_i,v_i\right)$represents the $j\mathrm{t}\mathrm{h}$ regression coefficient of variable $i$ under bandwidth ${\beta }_{bwj}$; $x_{ij}$ is the $j\mathrm{t}\mathrm{h}$ independent variable of $i$; ${\epsilon }_i$ is the random error of variable $i$.

3. Results and Analysis

3.1. Spatial–Temporal Analysis of Carbon Emissions

3.1.1. Temporal Characteristics of Carbon Emissions

The changes in the total carbon emissions of the CHUA over time from 2000 to 2022 are shown in Figure 3. In general, the carbon emissions of the CHUA exhibited a fluctuating upward trend during the 2000–2022 period. Specifically, there are three stages in the rise of carbon emissions: In the first stage, carbon emissions grew slowly from 919.66 million tons in 2000 to 1010.14 million tons in 2008, an increase of 9.84%. In the second stage, there was a trend of initially increasing but subsequently decreasing, during the 2009–2015 period. The turning point was 2013, when carbon emissions reached 1031.72 million tons. From 2013 to 2015, carbon emissions continued to decrease by 2.74 percent. This is because during the “Twelfth Five-Year Plan” period, the State proposed to substantially decrease the energy consumption intensity and carbon emissions by adjusting the industrial structure and energy structure, improving the energy efficiency and other means, and it has seen results since the beginning of 2013. In the third stage, carbon emissions increased rapidly from 2016 to 2019. Carbon emissions in 2017 reached 1197.14 million tons, an increase of 191.16 million tons from 2016. Since 2018, the carbon emissions of the CHUA have surpassed 120 million tons, reaching a staggering 1387.06 million tons in 2022. These significant shifts are intricately linked to the “Development Plan of the Central Henan Urban Agglomeration”, which was endorsed by the State Council in December 2016. Following its approval, the CHUA has experienced rapid growth. In 2022 alone, its Gross Domestic Product amounted to CNY 9370.89 billion, accounting for 7.78% of the national total. The primary, secondary and tertiary industries account for 10%, 42% and 48%, respectively, in the GDP of the Central Henan urban agglomeration. This rapid economic expansion and industrial structure has led to a sharp rise in carbon emissions.

Figure 4 shows the changes in the carbon emissions of 30 cities in the CHUA in 2000, 2005, 2010, 2015 and 2022, and the carbon emissions of each city show an increasing trend. From 2000 to 2010, the carbon emissions of most cities increased steadily. However, there are also some cities with relatively large changes in carbon emissions. For example, Zhengzhou’s carbon emissions in 2010 were 47.38 million tons, an increase of 42.33% compared with 2005. During the “12th Five-Year Plan” period from 2010 to 2015, the State clearly put forward the requirements of carbon emission reduction, which achieved initial results in some cities, such as Luoyang, Shangqiu and Xingtai, of which Luoyang’s carbon emissions have dropped by 11.82 million tons. Obviously, with the improvement in the level of economic development, the carbon emissions of various cities have always been rising, and in 2022, there were nine cities with carbon emissions of more than 60 million tons, including Liaocheng, Handan, Yuncheng, Heze, Nanyang, Xinyang, Zhengzhou Luoyang and Zhumadian, of which the carbon emissions of Liaocheng increased to 79.31 million tons. Many of these cities possess a relatively developed industrial system, especially in traditional industries such as steel, coal, coking and building materials, which tend to be accompanied by high carbon emissions. For instance, the steel industry in Handan is the primary pillar of its industrial economy, and cities like Nanyang and Luoyang also have relatively developed industrial systems. Additionally, cities like Heze, Nanyang and Zhumadian are more developed in agriculture and animal husbandry, which generate a certain amount of carbon emissions.

3.1.2. Spatial Distribution Characteristics of Carbon Emissions

The carbon emissions of the CHUA show obvious spatial heterogeneity. As economic growth and energy consumption patterns vary among cities, the differences in the carbon emissions of each city continue to increase. As evident from Figure 5, the spatial heterogeneity scale of carbon emissions increased significantly in 2022 compared to 2000. Specifically, the high-carbon emission high-value area of the CHUA presents the spatial distribution characteristics of “one center and two belts”; that is, Handan–Xingtai have a center distribution, and Zhoukou–Shangqiu–Heze and Nanyang–Luoyang–Zhengzhou have belt distributions starting from Xinyang–Zhumadian. Since 2000, the carbon emission scales of Xinyang and Zhumadian have been relatively large. Over time, the carbon emission scales of Handan, Xingtai, Zhengzhou, Nanyang, Zhoukou and other places have begun to increase, while the carbon emission scales of Luohe, Hebi, Huaibei and Jiyuan have not changed significantly. However, despite the changes in carbon emissions among various cities, the spatial pattern of carbon emissions in the CHUA has not undergone fundamental changes. This phenomenon is closely related to the resources, energy endowments and development plans of different cities. Taking Handan and Xingtai as examples, these two cities possess abundant coal resources and have a high concentration of energy-intensive industries. This industrial structure has led to a concentrated distribution of carbon emissions in the region, becoming the main driving force for the growth of carbon emissions. On the other hand, Xinyang and Zhumadian, with vast territories, still rely heavily on agriculture and industry as the main forces driving local economic development. According to data, in 2022, the output value of the primary industry in Xinyang and Zhumadian reached CNY 59.36 billion and CNY 57.45 billion, respectively, while the output value of the secondary industry reached CNY 111.71 billion and CNY 128.05 billion, respectively. This development model, which is dominated by agriculture and industry, has not only promoted economic growth but also exacerbated the growth of carbon emissions.

To gain insights into the centroid migration trajectory of carbon emissions within the CHUA, the standard deviational ellipse was employed to visualize the centerfold migration trajectory of carbon emissions in 2000, 2005, 2010, 2015 and 2022 (Figure 6). A comprehensive compilation of various parameters associated with the standard deviational ellipse is presented in

Table 4. Carbon emission change trajectory from 2000 to 2022.

Year	Area/km2	Center X/m	Center Y/m	XStdDist/m	YStdDist/m	Rotation/°
2000	144,170.29	35,640,838.89	3,885,894.52	228,872.90	200,518.37	135.85
2005	143,967.64	35,638,010.68	387,176.86	228,140.41	200,879.40	134.56
2010	141,590.43	35,632,590.88	3,887,078.27	224,277.20	200,965.43	130.77
2015	141,123.51	35,633,990.62	3,887,929.02	224,281.73	200,298.67	134.32
2022	143,782.93	35,633,990.62	3,899,381.79	222,214.73	205,971.38	138.76

. The carbon emission center of the CHUA from 2000 to 2022 showed a characteristic of “moving from southeast to northwest”, with a migration distance of 22.55 km. The minimum migration distance was from 2010 to 2015, while the most pronounced shift in the trajectory of carbon emissions occurred between 2015 and 2022, with migration distances of 1.64 km and 12.37 km, respectively. The area of the standard deviational ellipse of carbon emissions from 2000 to 2022 showed a downward trend followed by an upward trend. Specifically, the area of the standard deviational ellipse of carbon emissions decreased by 3046.78 km² from 2000 to 2015 and increased by 2662.42 km² from 2015 to 2022, with a growth rate of 1.85%. The trajectory of the centroid migration and the alterations in the ellipse’s area were in alignment with the findings from the spatial and temporal evolution analysis of carbon emissions in the CHUA. As time passed, cities in the western region had a faster increase in carbon emissions, resulting in a shift towards the northwest region in the overall carbon emission distribution within the CHUA. The length of the major axis of the standard deviational ellipse of carbon emissions continues to decrease, whereas the length of the minor axis increases. The standard deviation along the X-axis decreases annually, falling by 2.91% between 2000 and 2022. The standard deviation along the Y-axis increases annually, rising from 200,518.37 m in 2000 to 205,971.38 m in 2022, indicating a downward trend in the direction of carbon emissions in the CHUA, while the carbon emissions of individual cities continue to increase.

3.2. Spatial–Temporal Analysis of Air Pollution

3.2.1. Temporal Characteristics of Air Pollution

In this paper, four important air pollutants (PM_2.5, CO, O₃ and SO₂) were selected to characterize air pollution in the CHUA. Figure 7 shows the change trend of air pollution in the CHUA from 2000 to 2022.

During the study, the concentration of PM_2.5 exhibited an initial upward trend, followed by a subsequent decline. Since 2006, there have been some cities with PM_2.5 concentrations exceeding 90 μg·m⁻³, such as Xingtai, with PM_2.5 concentrations of 97.91 μg·m⁻³ and 98.23 μg·m⁻³ in 2006 and 2007, respectively. In 2013, one third of the cities had the highest PM_2.5 concentration of 100 μg·m⁻³, with Hebi and Xingtai having concentrations as high as 110.29 μg·m⁻³ and 112.02 μg·m⁻³. Over the past few years, China has undergone rapid industrialization and urbanization, leading to increased energy consumption and pollutant emissions due to the construction of numerous factories and urban expansion. Particularly, the combustion of fossil fuels such as coal has been the primary cause of the rise in PM_2.5 concentrations. From 2013 to 2019, the concentration of PM_2.5 in each city of the CHUA continued to decline, ranging between 43.68 μg·m⁻³ and 61.7 μg·m⁻³, which benefited from the promulgation and implementation of policies such as the Action Plan for Air Pollution Prevention and Control and the Three-year Action Plan for Winning the Blue-Sky Defense. Good results have been achieved in the control of PM_2.5. By 2022, Hebi stood out as the only city with a PM_2.5 concentration surpassing 50 μg·m⁻³, while the majority of cities in the Central Plains urban agglomeration maintained concentrations ranging between 40 μg·m⁻³ and 50 μg·m⁻³. Notably, the region experienced a marked improvement in PM_2.5 pollution levels. This positive trend can be attributed not only to the profound influence of the COVID-19 pandemic’s profound impact on daily life and industrial activities, but also to China’s consistent and sustained commitment to particulate matter prevention and control measures.

From 2000 to 2022, the O₃ column concentration presents a zigzag distribution. As can be seen from Figure 7d, Handan, Xingtai, Liaocheng and Puyang have higher O₃ column concentrations, while Fuyang, Nanyang and Xinyang have lower O₃ column concentrations. Among them, the O₃ column concentration in 2010 and 2015 was relatively high, and only two cities, Nanyang and Xinyang, had O₃ column concentrations less than 300 DU, and Xingtai had an O₃ column concentration of more than 320 DU. It is likely that Handan, Xingtai, Liaocheng and Puyang may have more industrial enterprises and traffic vehicles, resulting in greater emissions of O₃ precursors such as NOx and VOCs (volatile organic compounds) in these areas, which favor the formation of O₃. In contrast, Fuyang, Nanyang and Xinyang may have relatively fewer industries, leading to less emissions of O₃ precursors.

On the whole, the concentration of SO₂ in the CHUA showed an increasing trend. At the same time, Jiaozuo, Jiyuan, Zhengzhou and Xinyang were the cities with the highest concentrations of SO₂, while Sanmenxia was the city with the lowest concentration of SO₂, ranging from 13.39 μg·m⁻³ to 29.71 μg·m⁻³. This is because cities such as Jiaozuo, Jiyuan, Zhengzhou and Xinyang have well-developed industries, especially the rapid development of industries such as coal, power and chemicals, which may have led to significant SO₂ emissions. From 2000 to 2009, the concentration of SO₂ increased rapidly from 13.7 μg·m⁻³–31.97 μg·m⁻³ to 27.86 μg·m⁻³–61.27 μg·m⁻³. The change in the SO₂ concentration from 2010 to 2022 was rather tortuous. In 2011, the SO₂ concentrations in various cities were the highest they had been in the past two decades, and there were nine cities with SO₂ concentrations exceeding 50 μg·m⁻³, among which Jiaozuo’s SO₂ concentration reached 65.6 μg·m⁻³, which was the city with the highest SO₂ concentration. To further reduce SO₂ emissions, it is necessary to adopt more effective measures, such as optimizing energy structure, strengthening industrial pollution control and improving vehicle emission standards.

The variation in the CO concentration is different among the cities in the CHUA. The CO concentrations in Handan, Anyang and Hebi were higher, the CO concentrations continued to increase from 2000 to 2009, and the concentrations in 2009 were 1.97 mg·m⁻³, 1.62 mg·m⁻³ and 1.48 mg·m⁻³, respectively, while the concentrations did not change much after 2010. The concentrations of CO in Sanmenxia, Xinyang, Luoyang and Nanyang were always at low levels, ranging from 0.18 mg·m⁻³ to 0.4 mg·m⁻³. In addition, the concentrations of CO in Huaibei and Bozhou increased suddenly in 2012 and 2014, and the concentration distribution was 1.35 mg·m⁻³, 1.16 mg·m⁻³, 1.2 mg·m⁻³ and 1.18 mg·m⁻³. It is possible that these two cities experienced rapid industrial development or changes in the energy structure during these specific years, leading to a sudden increase in CO emissions. Alternatively, they may have encountered unfavorable climate conditions during certain periods, which made it difficult for pollutants to disperse.

3.2.2. Spatial Distribution Characteristics of Air Pollution

The spatial distribution characteristics of the four kinds of air pollutants are shown in Figure 8. From 2000 to 2015, the distribution of PM_2.5 showed obvious aggregation characteristics, and the cities along Xingtai, Handan and Zhengzhou had high concentrations of PM_2.5. As an international big city, Zhengzhou’s high-density population, dense traffic network and developed economic conditions will make the PM_2.5 concentration continue to rise. Handan and Xingtai, as resource-based heavy industry cities, will inevitably bring more pollution, accelerate industrial transformation and upgrading, eliminate backward production capacity and develop more high-end industries, which will be important reasons for Handan and Xingtai to reduce their PM_2.5 concentrations. By 2022, the PM_2.5 concentration within the CHUA had noticeably improved, as evidenced by the fact that only Hebi exceeded the threshold of 50 μg·m⁻³, while the remaining cities boasted low PM_2.5 levels.

The O₃ column concentration showed a significant step distribution, increasing from south to north. In 2000 and 2005, only Handan, Xingtai and Liaocheng had O₃ column concentrations exceeding the standard value (300 DU). In 2010 and 2015, most cities in the northern part of the CHUA had higher O₃ column concentrations. After several years of consistent improvement, in 2022, none of the cities within the CHUA recorded O₃ column concentrations surpassing 320 DU. The regions with elevated concentrations remained concentrated predominantly in the central and northern areas, while the southern region exhibited O₃ column concentrations that fell within the range of 290 DU to 300 DU. This phenomenon should be related to the urbanization process in different regions, meteorological conditions, precursor emissions and the policy measures implemented in various cities.

The distribution of SO₂ concentrations reveals pronounced clustering patterns, with notably higher levels observed in Zhengzhou and its adjoining cities. In contrast, lower concentrations are prevalent in the southwestern and southern regions. A notable aspect, as evident from Figure 8, is that despite the implementation of numerous national air pollution prevention and control measures, the SO₂ levels have not demonstrated a discernible decline. It is worth mentioning that only in 2022 did Shangqiu, Huaibei and neighboring cities witness a reduction in their SO₂ concentrations. Henceforth, it is imperative to accord greater attention to SO₂ in our ongoing efforts to mitigate atmospheric pollution.

During the period from 2000 to 2015, the CHAU exhibited distinct spatial distribution characteristics in terms of CO. The high-value areas of CO were primarily concentrated in Handan, Anyang and their surrounding cities. These regions, due to factors such as industrial development, dense traffic and energy utilization structures, experienced relatively high CO emissions. In contrast, the southwestern region emerged as an area with relatively low CO concentrations within the CHUA. This could be attributed to the regional industrial structure, traffic conditions and the implementation of environmental protection measures. However, by 2022, the spatial distribution pattern of CO had undergone significant changes. The high-value areas originally concentrated around Handan and Anyang began to spread towards the southeast and northwest, forming a bipolar distribution characteristic of “southeast–northwest”. This shift may have been influenced by various factors such as regional economic and social development disparities, climate change and the enforcement of environmental protection policies. Notably, cities in the western region experienced an upward trend in CO concentrations during this period. This could be attributed to the increasing traffic and industrial activities in the western region driven by economic growth and urbanization, leading to an increase in CO emissions. Conversely, cities in the southeastern region witnessed a decline in CO concentrations, potentially linked to the strengthened efforts in air pollution control and the optimization of energy structures in recent years.

3.3. Synergies between Carbon Emissions and Air Pollution

3.3.1. Coupling Coordination Degree Timing Characteristics

From 2000 to 2022, the coupling coordination degree of CEAP in the CHUA exhibited an upward trend, indicating a continuous strengthening of their coordination levels (Figure 9).

In 2000 and 2005, the carbon emissions and air pollution of most cities experienced an uncoordinated state. During this period, most cities failed to effectively control carbon emissions and air pollution while pursuing economic growth, resulting in a low level of coordination between the two. In 2005, the coupling coordination degree of Anyang was the highest, which was 0.632, belonging to the primary coordination state. On the contrary, in 2005, Pingdingshan’s coupling coordination degree was extremely low, indicating a state of severe imbalance. This suggested that Pingdingshan faced significant challenges in promoting coordinated CEAP, necessitating the adoption of more proactive and effective measures for improvement.

In 2010, significant progress was achieved in the coordinated management of carbon emissions and air pollution in the CHUA. That year, 90% of the cities within the CHUA achieved a coordinated state in terms of carbon emissions and air pollution. Worth mentioning are the cities of Zhengzhou, Luoyang, Anyang, Hebi and Jiyuan, which attained good coordination with coupling coordination degrees of 0.835, 0.894, 0.894, 0.807 and 0.803, respectively. These results indicate their exceptional performance in managing carbon emissions and air pollution. Compared to previous years, the level of coordination in these cities has improved markedly. Notably, Luoyang’s coupling coordination degree increased by an impressive 140% during this period. The successful experience of Luoyang not only provides valuable lessons for other cities but also sets a model for promoting the synergistic governance of carbon emissions and air pollution across the entire CHUA and beyond.

In 2015, 80% of the cities in the CHUA were in a state of coordination, among which the coupling coordination degrees of Anyang and Jiyuan reached 0.902 and 0.927. The reason was that the implementation of the Action Plan for Air Pollution Prevention and Control in 2013 improved the synergies in CEAP. However, despite the positive progress made by most cities in coordinated governance, the performance of some cities was still unsatisfactory. For example, in 2015, the coupling coordination degree of Sanmenxia dropped to 0.166, indicating a severe imbalance. This suggests that Sanmenxia needs to take more proactive and effective measures to promote the synergistic effect of CEAP.

In 2022, a remarkable 87% of cities demonstrated a state of sync, illustrative of a widespread trend towards effective coordination. Within this group, six cities stood out as exemplars, boasting a coupling coordination degree that surpassed 0.8. Liaocheng, in particular, excelled with an exceptional coupling coordination degree of 0.98. This notable achievement represented a substantial leap forward, reflecting an improvement of 0.458 over its 2015 levels and firmly placing the city in a state of excellent coordination. Contrastingly, Bozhou found itself in the primary stages of the coordination, with a coupling coordination degree of 0.619. While this might seem modest in comparison, it is important to highlight the city’s impressive growth trajectory. Since 2015, the coupling coordination degree of Bozhou has increased by 77%. Meanwhile, Zhoukou also deserves recognition for its impressive turnaround. Its coupling coordination degree has increased by 75%, successfully transforming from being on the verge of coordination to achieving intermediate coordination. To elevate the regional coupling coordination to a higher level and achieve more harmonious and efficient development, we can provide strong support for high-quality regional development by rationally planning land use, adjusting urban layouts, promoting the application of clean energy and facilitating industrial upgrading.

3.3.2. Spatial Pattern of Coupling Coordination Degree

There is significant spatial heterogeneity in the coupling coordination degree of CEAP from 2000 to 2022, and the spatial characteristics of the coordination degree level are shown in Figure 10.

In 2000, the coupling degree between urban CEAP in the CHUA generally exhibited a state of imbalance. More alarmingly, a staggering 36% of cities were in a state of severe imbalance. Eleven cities, including Nanyang, Liaocheng and Yuncheng, were among them, indicating an urgent need to address their environmental issues. These cities face multiple challenges stemming from unreasonable industrial structures, leading to high carbon emissions, inefficient energy utilization resulting in resource waste and the inadequate implementation of environmental policies causing lags in environmental governance. It is imperative for these cities to take decisive measures to accelerate the transformation of their economic development mode, optimize their industrial structure, improve energy efficiency and strengthen the implementation of environmental policies to achieve coordinated management of carbon emissions and air pollution. Only by doing so can they gradually overcome the predicament of severe imbalance and embark on a green and sustainable development path.

In 2005, although some progress was achieved in the coordination, the overall picture remained concerning. While the number of cities experiencing serious and moderate incoordination had receded compared to prior years, a staggering 83% of cities still struggled with incoordination. Notably, Xinyang and Pingdingshan stood out with particularly serious incoordination, exhibiting coordination degrees of merely 0.134 and 0.135, respectively. This incoordination state will have a profound impact on the development of the city. However, there are also some cities that have made positive progress in coordination. Anyang, Nanyang, Sanmenxia, Jiyuan, Hebi and Jiaozuo were among the first cities to initiate coordination, with coordination degrees ranging from 0.505 to 0.632. Despite these positive developments, there is still considerable room for improvement. These cities need to continue their efforts, further refine their collaborative governance mechanisms, enhance the level of coordination and make greater contributions to the sustainable development of the CHUA.

In 2010, the coordination level between CEAP showed a general upward trend. Among them, 23% of cities achieved good coordination, with Anyang and Luoyang attaining a coupling coordination degree of 0.894, ranking first in the CHUA. This significant achievement was the result of active efforts made by these two cities in optimizing the industrial structure, improving their energy efficiency and implementing environmental protection measures. However, despite the overall improvement in coordination within the CHUA, some cities still performed poorly in coordinating carbon emissions and air pollution. For example, Handan, Yuncheng and Bozhou remained in a state of imbalance. These cities’ economic development mainly relied on extensive development models, with relatively homogenous industrial structures dominated by traditional industries with high energy consumption and emissions. Simultaneously, these cities lagged behind in industrial transformation, with slow progress in developing emerging and green industries, being unable to effectively compensate for the environmental impact of traditional industries. This development model not only caused severe environmental damage but also constrained the long-term healthy development of the cities.

In 2015, 24 cities were in a coordinated state, with Anyang and Jiyuan achieving high-quality coordination levels. However, this coordinated state was not a ubiquitous phenomenon across all cities in the CHUA. Taking Handan City as an example, it was in intermediate coordination in 2010 but transitioned to primary coordination by 2015. This indicates that despite making some progress in environmental protection and sustainable development, Handan still needs to continue its efforts. To further enhance its coordination level, Handan must conduct a more in-depth analysis of its existing issues and take targeted measures for improvement. Meanwhile, the coordination statuses of some cities experienced significant declines. During this period, four cities shifted to a state of imbalance: Changzhi, Zhoukou, Luohe and Sanmenxia. Among them, Changzhi, Zhoukou and Luohe were at intermediate levels of imbalance, while Sanmenxia’s imbalance was more severe, reaching a state of severe imbalance. These changes serve as a reminder that maintaining a coordinated level between carbon emissions and air pollution is not easy and requires continuous effort and exploration of suitable development paths by each city.

In 2022, three cities emerged as exemplars of excellent coordination, namely Handan, Liaocheng and Zhengzhou. Cities such as Xingtai and Heze also demonstrated a relatively high degree of coupling coordination, which enhances environmental quality, generates economic and social benefits for the cities and fosters a mutually beneficial outcome. However, we should also recognize that some cities still face challenges in coordinating carbon emissions and air pollution. Cities such as Hebi, Xinxiang and Jiyuan are on the verge of imbalance, indicating that they still face certain challenges and difficulties in carbon emission control and air pollution governance. These cities need to conduct in-depth analyses of the root causes of the problems and take practical and effective measures to address them, such as addressing unreasonable industrial structures, improving energy efficiency and strengthening the implementation of environmental protection measures. By doing so, they can achieve coordinated governance of carbon emissions and air pollution at an earlier data.

3.4. Analysis of Driving Factors of Synergistic Benefit

To further investigate the spatial differences and influencing mechanisms underlying the synergistic benefits of CEAP, this paper selected industrial transformation, industrial structure, per capita GDP, level of external openness, population density, urban green coverage, energy consumption, NDVI, temperature and precipitation as influencing factors. The MGWR model was employed to delve into the underlying mechanism of its impact on the synergy of benefits in the CHUA. The descriptive statistical results are shown in

Table 5. Regression coefficients describing the statistics.

Variable	The Central Henan Urban Agglomeration
	Min	Max	Mean	Median	Standard Deviation
PTI	−0.820	0.471	−0.322	−0.358	0.353
VSI	−1.553	1.467	0.385	0.569	0.777
POP	−0.292	0.649	0.063	0.004	0.235
PGDP	−0.410	2.018	0.511	0.325	0.545
GCR	−10.656	8.979	1.331	0.613	5.085
OPEN	−2.195	−0.048	−1.024	−1.054	0.844
ECO	−1.395	1.850	0.597	0.621	0.714
PRE	−0.118	0.061	−0.036	−0.046	0.060
TEM	0.072	0.117	0.093	0.090	0.016
NDVI	0.202	0.218	0.210	0.208	0.004

. There exists a notable spatial heterogeneity in the impact of PTI, VSI, POP, PGDP, GCR, OPEN, ECO and PRE on the co-benefits of CEAP in the CHUA. While the effects of TEM and NDVI on the synergy of CEAR did not show significant differences, the regression coefficients were around 0.093 and 0.210, respectively, and fluctuated in a small range. This indicates that the effects of TEM and NDVI are positive, and as the temperature rises and vegetation coverage increases, the synergy of CEAR may also improve accordingly.

Regression coefficients with spatial heterogeneity were visually analyzed (Figure 11). The PTI to the synergistic benefit of CEAP is negative (Figure 11a), mainly in the central and western regions, and the regression coefficient ranges from −0.820 to −0.238, indicating that industrial transformation has an inhibitory effect on the synergistic benefit. Specifically, the performances of Jiyuan, Luohe, Anyang, Hebi, Nanyang, Luoyang and Shangqiu are particularly prominent, with absolute values of regression coefficients greater than 0.670. In particular, the regression coefficient of Hebi reaches −0.820, indicating that whenever industrial transformation increases by 1%, the coupling coordination degree of Shangqiu decreases by 0.820%. This undoubtedly reminds us that we must be more cautious in promoting industrial transformation. Industrial transformation is a complex process that requires technological support such as clean energy technologies and energy-saving technologies. Firstly, the introduction of new technologies and equipment necessitates significant financial investment, often with a long return period. Secondly, during the adjustment of the industrial structure, it is necessary to consider the interconnectedness and dependence between different industries, as well as the regional differences between areas. All these factors can affect the synergistic benefits of reducing CEAP.

The impact of VSI on synergistic benefits varies in different cities (Figure 11b), not only in terms of direction, but also in magnitude. For Sanmenxia and Yuncheng, the impact of the industrial structure on synergistic benefits exhibits a significant negative correlation, with regression coefficients of −1.553 and −1.304, respectively. This means that as the industrial structure is adjusted, the coordinated governance effect of CEAP in these cities not only fails to improve, but actually shows a downward trend. This is because these cities are primarily dominated by traditional industrial structures, often centered around high-energy consumption and high-emission industries such as coal, oil, steel and cement. These industries generate significant amounts of carbon emissions and air pollution during their production processes, thereby affecting the realization of synergistic benefits. However, Huaibei, Bozhou and Suchou are different. The impact of the industrial structure on synergistic benefits exhibits a significant positive correlation, with regression coefficients ranging from 1.110 to 1.467. This indicates that changes in the industrial structure can promote the coordination of CEAP. This phenomenon may be related to the level of urban development. More developed cities with more advanced industrial structures and technological levels can take more effective measures to address environmental issues such as CEAP. In contrast, less developed cities may face greater challenges in environmental governance due to relatively backward industrial structures and limited technological levels.

The impact of the POP on the synergistic benefits of different cities exhibits significant differences (Figure 11c). The regression coefficients of Changzhi and Fuyang show a significant negative correlation, with correlation coefficients of −0.292 and −0.182, respectively. In these two cities, the increase in population density is often accompanied by a higher energy demand, and there is often an urban heat island effect that leads to poor air circulation and difficulty in dispersing pollutants. This, to a certain extent, inhibits the synergistic benefits of reducing carbon emissions and air pollution. However, there are also some cities that exhibit completely opposite effects on the impact of population density on the synergistic benefits. For example, the regression coefficients of Hebi, Xinxiang and Kaifeng are between 0.038 and 0.649, showing a significant positive correlation. This may be because while an increase in population size leads to an increase in direct and indirect energy consumption, on the other hand, the agglomeration effect resulting from the population concentration can improve technological levels to a certain extent, enhance resource utilization efficiency and promote the development of circular economy and green industries, thereby facilitating the synergistic benefits of reducing carbon emissions and air pollution.

Except Bozhou, the PGDP has a significant positive impact on the synergistic benefits of CEAP, and this positive promotion effect gradually increases from southwest to northeast (Figure 11d), with regression coefficients ranging from 0.237 to 2.020. As the level of economic development continues to improve, individuals are demanding newer, greener and higher-quality methods of economic progress. The extensive economic development method is gradually abandoned, and the government and enterprises prefer to adopt sustainable development strategies, focusing on the balance between economic growth and environmental protection. Through promoting clean energy, strengthening energy conservation and emission reduction and developing a circular economy and other measures, these regions have effectively controlled the growth rate of CEAP while maintaining rapid economic growth.

The contribution of GCR to the synergistic benefits of CEAR in different cities cannot be ignored (Figure 11e). For example, the regression coefficients of Changzhi, Liaocheng, Huaibei and other cities are between 4.280 and 8.979. This is because green vegetation can absorb substances such as CO₂ and PM2.5 in the air, inhibiting the synergistic benefits of carbon emissions and air pollution through the direct absorption and storage of carbon dioxide, the improvement of the urban heat island effect, the filtration and adsorption of air pollutants, and the provision of ecological services. However, the impact of GCR on other cities is completely different. For Xingtai and Kaifeng, the regression coefficients of GCR are −10.656 and −9.981, respectively, indicating a strong inhibitory effect. Due to the limited capacity of green vegetation to absorb pollution, pollution in most cities has already exceeded the upper limit for pollution reduction. It is far from enough to rely solely on expanding the green space to improve CEAP, and it may even have a counterproductive effect.

The impact of OPEN on the synergistic benefits of CEAR showed a negative correlation, with an average regression coefficient of −1.024 (Figure 11f). This indicates that for every 1% increase in the level of opening up, the coordination of CEAP decreased by 0.133%. This is due to the fact that some regions prioritize economic development and opening up to the outside world, neglecting environmental protection factors. They undertake the transfer of high-energy-consuming, high-emission and highly polluting industries from other countries or regions and are unable to fully absorb and apply environmental protection technologies and clean energy technologies, thus inhibiting the synergistic benefits of CEAP.

Except Bengbu, the contribution of ECO to the synergistic benefits of CEAP in the CHUA is undoubtedly positive, with regression coefficients ranging between 0 and 1.850 (Figure 11g). This may be because the energy consumption of cities mainly comes from conventional energy sources like coal, whose combustion generates significant amounts of carbon dioxide and other atmospheric pollutants, leading to a simultaneous increase in CEAP. At the same time, the industrial structure of these cities is relatively traditional, with a relatively large proportion of high-energy-consuming, high-emitting and highly polluting industries. This is also an important reason for the positive contribution to the synergistic benefits of CEAP in terms of overall energy consumption.

The impact of PRE on the synergistic benefits of CEAR is mainly concentrated in the affected cities in the northeast, showing a significant negative correlation, with regression coefficients ranging from −0.118 to −0.1094 (Figure 11h). Specifically, rainfall may affect activities such as transportation and industrial production, ultimately resulting in elevated carbon emissions. During the process of rainfall, chemical substances such as nitrogen oxides and sulfur dioxide are released, forming fine particulate matter such as sulfuric acid mist and nitric acid mist, which leads to a deterioration of air quality. This affects the synergistic benefits of carbon emissions and air pollution.

4. Discussion

At present, studies on CEAP are concentrated either at the national and provincial levels [31,32] or at the level of developed urban agglomerations [33,34], while the CHUA has received relatively little attention. The Central Plains city group is the largest city group with the densest population and outstanding traffic location advantages, which also leads to the continuous increase of emissions and air pollution in the Central Plains city group. To achieve pollution mitigation and emission reduction is a major challenge for the green, coordinated and high-quality development of the CHUA. In this context, this paper studies the synergistic benefits between long-term CEAP. Taking into account the spatial heterogeneity of the CHUA, the MGWR is selected to identify the driving factors, ensuring greater scientific rigor and reliability in the research outcomes. The findings will facilitate the formulation of policies aimed at mitigating CEAP and achieving its synergistic benefits.

Throughout the duration of the study, the carbon emissions of the CHUA showed an increasing trend over time, showing spatial distribution characteristics of “one center and two zones”, which aligned with those reported in previous studies [35]. Air pollutant concentrations are rising. Over time, the coordination degree of CEAP in the CHUA continued to improve. This paper confirms that the contributions of VSI, POP, PGDP and GCR to CEAP vary greatly in different cities. NDVI and TEM have a positive contribution to the co-benefit of CEAP in Central Plains urban agglomerates, while the impacts of PTI, ECO, OPEN and PRE on the outside world are negative. The results serve as a valuable reference for emission reduction and air pollution prevention efforts.

It is worth noting that although CEAP are encompassed within the study’s ambit to supplement the research on urban pollution reduction and carbon reduction, there are still some areas that can be improved. Firstly, the carbon emission accounting based on land use is more complicated, and more accurate land use classification will make carbon emission accounting more accurate. Secondly, this paper studies the influencing factors of the synergistic benefits of CEAP from the perspectives of social economy and natural ecology. However, considering the timing of some data, data economy and policy regulation are not selected to influence carbon emissions and air pollution. Therefore, subsequent research can focus on digital economy and policy regulation separately.

5. Conclusions and Suggestions

5.1. Conclusions

Utilizing remote sensing data and panel data, this study analyzes the spatial–temporal evolution patterns of CEAP in the CHUA from 2000 to 2022, and explores the synergistic benefits of CEAP and their influencing mechanisms by combining the coupled coordination degree model and multi-scale geographical weighted regression. This paper provides the basis for realizing the synergistic effect of pollution reduction and carbon reduction in the CHUA. The key findings are as follows:

(a) During the 2000–2022 period, carbon emissions and concentrations of PM_2.5, O₃, SO₂ and CO in the CHUA continued to increase, with regional differences. From the spatial point of view, the carbon emissions of the CHUA present the spatial distribution characteristics of “one center and two belts”, and the carbon emission center of gravity presents the characteristics of “southeast to northwest”. The cities along the Xingtai–Handan–Zhengzhou route have a higher concentration of PM_2.5, and the O₃ column concentration presents a significant step distribution. SO₂ and CO have similar aggregation characteristics, and the low concentration areas are concentrated in the southwest and southern cities.

(b) The coupling coordination degree of CEAP of cities in the CHUA showed an upward trend, and the coordination degree of the two was continuously strengthened. By 2022, most cities reached a coordinated state with relatively high coupling coordination degrees. Spatially, there are significant differences in the coupling coordination degrees among cities, but there is no obvious clustering pattern.

(c) The influence of social and economic factors on the synergistic benefit of CEAP is stronger than that of natural ecological factors. Among them, ECO, VSI and OPEN have the largest contribution to the co-benefit of CEAP, followed by PGDP, POP and PTI. The impact of natural ecological factors cannot be ignored. The contributions of VSI, POP, PGDP and GCR to CEAP vary among cities. NDVI and TEM have positive contributions to the synergistic benefits of CEAP, while the impacts of PTI, OPEN, ECO and PRE on the outside world are negative.

5.2. Suggestions

Based on the content of the paper’s research, this article proposes some strategies. Firstly, considering the differences in urban carbon emissions, cities with smaller carbon emission scales (such as Jiyuan and Hebi) should vigorously develop low-carbon industries, such as ecological industries and tourism, to reduce overall carbon emissions. For cities with large carbon emission scales (such as Handan and Zhumadian), they should establish a diversified industrial system, develop “technological innovation” cities and expand the development space for emerging industries to promote the green transformation of industrial structures. Secondly, given the synergistic impact of industrial structures on carbon emissions and air pollution, cities should further optimize their industrial structures and increase the proportion of low-carbon and green industries. Taking into account the impacts of population density and industrial transformation, cities should rationally plan and distribute populations and industries, avoid the excessive concentration of populations and industries and reduce resource consumption and environmental pollution. In addition, it is recommended to establish a cross-regional environmental regulatory system, share emission reduction experiences and jointly formulate emission reduction policies. Policies related to carbon emission reduction and air pollution control should be formulated and improved, clarifying emission reduction targets and responsible entities. The environmental supervision of enterprises should be strengthened to reduce pollutant emissions. At the same time, economic measures such as taxation and financial subsidies should be utilized to guide enterprises to increase their investment in environmental protection and promote the development of green industries.