Spatio-Temporal Evolution and Action Path of Environmental Governance on Carbon Emissions: A Case Study of Urban Agglomerations in the Yellow River Basin

Abstract

Since the ecological protection and high-quality development of the Yellow River basin in China have become a major national strategy, reducing carbon emissions has become pivotal. Therefore, based on the relevant data of 53 cities from 2008 to 2021 in seven urban agglomerations in the Yellow River basin, this paper explores the overall situation and spatio-temporal evolution of environmental governance and carbon emissions in the urban agglomerations in the Yellow River basin using the entropy method, ArcGIS, slacks-based measurement models (SBM models), etc. Additionally, this paper quantitatively analyzes the pathways by which environmental governance affects carbon emissions in the urban agglomerations in the Yellow River basin. The results show that carbon emissions increased year on year from 2008 to 2021, the growth rate slowed down gradually and exhibited a downward trend, and the largest amount of carbon was emitted in 2019, at 3495 million tons. Before 2017, the growth rate of carbon emissions showed a trend of increasing year by year, with the largest increase rate being 11.17% in 2010. After that, the growth rate of carbon emissions continued to decrease and entered a stage of fluctuation. The growth rate of carbon emissions in 2020 was the lowest, reaching −5.66%. The environmental governance effect of urban agglomerations in the Yellow River basin exhibits a large gap; the regional difference is obvious, and the overall trend is rising. Environmental governance has a significant negative effect on carbon emissions in urban agglomerations in the Yellow River basin. The cross-terms of environmental governance, the energy consumption structure, industrial structure upgrading, green technological innovation, and foreign direct investment (FDI) have significant negative impacts on carbon emissions, while the indirect impacts on urban agglomerations have shown regional heterogeneity. The goal of reducing carbon emissions in urban agglomerations in the Yellow River basin is being realized gradually. Based on research conclusions, policy suggestions are put forward, hoping to provide ideas for environmental protection and high-quality development of urban agglomerations in the Yellow River basin.

1. Introduction

Global warming has become an urgent concern all over the world. According to the research of the World Meteorological Organization (WMO), the earth is now nearly 1 °C warmer than it was before industrialization began. On this trend, global temperatures will be 3 to 5 °C above pre-industrial levels by 2100. Twenty of the last twenty-two years have seen the highest temperatures on record, with 2015 to 2018 being the four warmest years. That may sound small, but the UN’s Intergovernmental Panel on Climate Change (IPCC) has pointed to catastrophic consequences if no intervention is taken: sea levels will rise, and some islands and coastal lowlands will be submerged; rising ocean temperatures and acidity; farming will face challenges. The cost of clean energy remains high, and countries are heavily dependent on coal and other fossil fuels to drive economic growth [1,2]. At the same time, the extensive economic growth mode of high energy consumption and high levels of pollution has made environmental problems increasingly severe. In 2021, China’s carbon emissions exceeded 11.9 billion tons, accounting for 33% of the global total. As the largest fossil energy consumer and greenhouse gas emitter in the world, China faces substantial pressure to engage in carbon emissions reduction. To cope with global climate change, China has actively participated in the carbon reduction campaign. China joined the Paris Agreement on Climate Change in 2016 and pledged to “peak carbon emissions by 2030 and strive to reach the peak as soon as possible, reduce carbon dioxide per unit of Gross Domestic Product (GDP) by more than 65% from 2005, achieve the peak carbon by 2030 and achieve carbon neutrality by 2060” [3,4]. China has set the “carbon peaking and carbon neutrality goals” of reducing carbon intensity and total carbon emissions. The Yellow River basin is an important industrial base and bears significant responsibility for carbon emissions reduction [5,6,7,8]. Carbon emissions in the Yellow River basin are important research topics in the Fourteenth Five Year Plan for National Economic and Social Development of the People’s Republic of China and the Outline of the Long-Range Goals for 2035. As excessive artificial and economic activities have disrupted the sustainable balance of the Yellow River basin, China’s economic development has shifted from a high-growth to a high-quality mode, along with people’s increasing demand for a better life. The question of how to reduce carbon emissions in the Yellow River basin has become an important part of promoting environmental protection and high-quality economic development in this region [9,10,11,12]. Reducing carbon emissions in the Yellow River basin is both an urgent and arduous task. Therefore, it is necessary to study whether environmental governance contributes to the realization of the “carbon peaking and carbon neutrality goals” in the Yellow River basin. Additionally, the question of how to influence the carbon emissions intensity of urban agglomerations in the Yellow River basin is of great significance for promoting the high-quality development of urban agglomerations in the region.

For a long time, scholars have been studying how to reduce carbon emissions by means of environmental governance. At present, environmental governance is the core means to solve the problem of carbon emissions and an important way to achieve social stability, economic development, and environmental protection. Many studies have discussed the link between environmental governance and carbon emissions. First, there is a “green paradox” relationship between environmental governance and carbon emission. The “green paradox” was proposed by Sinn, and it refers to the phenomenon whereby the implementation of policies and measures to limit environmental deterioration leads to the accelerated exploitation and use of fossil energy; the rapid increase in carbon emissions over a short period of time leads to environmental deterioration, which means that “good intentions do not always lead to good behaviors” [13,14,15,16]. Taking the Republic of Korea as an example, through the implementation effect of green development in the Republic of Korea and analyzing data indicators such as energy consumption and carbon emissions, it was found that the implementation of a green development strategy did not necessarily bring about environmental optimization, and that it even aggravated environmental pressure in some areas [17]. Through comparison of the carbon emissions efficiency and growth paths of the Yellow River basin and the Yangtze River basin, it was found that the carbon emissions efficiency was higher in areas with stronger environmental governance policies [18]. Those research findings fit with the theory of “green paradox”. Second, there is a “forced emissions reduction” relationship between environmental governance and carbon emissions. “Forced emissions reduction” means that strict government environmental policies significantly inhibit the production activities of pollution-intensive industries, reduce carbon emissions, and improve carbon emissions efficiency by reducing the use of fossil energy and improving the green technological innovations of enterprises. Carbon emissions reduction measures could be achieved through regional joint prevention and control, economic growth transformation, industrial structure optimization, and the strict implementation of differentiated carbon emissions reduction policies through research [19]. By studying the relationship between technological progress and carbon emissions promoted by the government, it was found that the government promotes the development of enterprises and the use of green and low-carbon technologies through the “innovation compensation effect”; after some time, the effect of green technologies gradually manifests and promotes carbon emissions reduction [20,21]. Third, environmental governance and carbon emissions coexist with the “green paradox” and “forced emissions reduction”. The purpose of environmental governance is to protect the environment. “Forced emissions reduction” and the “green paradox” may be the result of environmental governance affecting carbon emissions [22]. By analyzing the panel data from 2000 to 2010 in various provinces, it was found that the effect of environmental governance on carbon emissions reduction is not obvious, and only when the intensity of environmental governance reaches a certain stage can carbon emissions be inhibited [23]. By analyzing the impact of environmental policies and carbon emissions by constructing a panel threshold model and an intermediary effect model. The results showed that the higher the efficiency of environmental governance, the more obvious the promotion effect of upgrading the industrial structure on carbon emissions reduction [24,25]. By establishing a spatial auto-correlation panel lag model to test the impact and spatial effect of environmental governance on carbon emissions, it was found that the impact trend of environmental governance on carbon emissions showed an inverted “U” shape, with spatial correlation and regional heterogeneity shown by test data [26].

Existing studies on the impact of environmental governance on carbon emissions have laid a good foundation for this research; however, they still have some shortcomings. Many studies have investigated the impact of environmental governance on ecological performance in the Yellow River basin; however, few have studied the environment. The effect of environmental governance on the carbon emissions of urban agglomerations in the Yellow River basin is of little relevance to the realization of carbon emissions reduction in urban agglomerations in the Yellow River basin. Based on the panel data of prefecture-level cities in the Yellow River basin from 2008 to 2021, this paper uses the entropy method, ArcGIS, and the Gaussian mixture model (GMM) to explore the overall situation and spatio-temporal evolution trend of environmental governance and carbon emissions in the urban agglomerations of Yellow River basin. This paper empirically assesses the direct and indirect effects of environmental governance on carbon emissions in the urban agglomerations of the Yellow River basin. This paper verifies the relationship between environmental governance and the carbon emissions of urban agglomerations in the Yellow River basin, considers the heterogeneity of the effects of environmental governance for different urban agglomerations, and advances corresponding countermeasures and suggestions, providing theoretical support for the realization of the high-quality development of urban agglomerations in the Yellow River basin.

2. Data and Methods

2.1. Research Area

The study was conducted in the urban agglomerations in the Yellow River basin (98°05′–122°42′ E and 32°51′–42°43′ N) (Figure 1). According to the Fourteenth Five Year Plan for the National Economic and Social Development of the People’s Republic of China and the Outline of the Long-Range Goals for 2035, there are seven urban agglomerations in the Yellow River basin. Due to lack of data, Tianshui City, Qingyang City, the Linxia Hui Autonomous Prefecture, Haidong City, the Haibei Tibetan Autonomous Prefecture, the Hainan Tibetan Autonomous Prefecture, and the Huangnan Tibetan Autonomous Prefecture are not considered in this paper. There are obvious differences in the development of the urban agglomerations in the Yellow River basin, which mainly manifests as “unbalanced and inadequate” development; this reflects the major differences in the environmental governance of urban agglomerations in the Yellow River basin. The differences in environmental governance in the Yellow River basin are mainly caused by the imbalance in the distribution of natural resources and the differences in the industrial base, transportation infrastructure, and technical conditions, as well as the imbalance in economic and social development caused by fiscal revenue.

First, the entropy method and the input–output method were used to measure the environmental governance indexes and to comprehensively analyze the overall environmental governance situation in the seven urban agglomerations in the Yellow River basin. Second, the carbon emissions of the urban agglomerations in the Yellow River basin were measured using the carbon emissions coefficient method, and the spatio-temporal evolution of environmental governance and carbon emissions in urban agglomerations in the Yellow River basin was qualitatively analyzed using the ArcGIS natural break-point method. Finally, the Gaussian mixture model (GMM) was further used to quantitatively study the action path and effect of environmental governance on carbon emissions in the urban agglomeration of the Yellow River basin.

2.2. Data Sources

The data on environmental word frequency in government work reports, industrial wastewater discharge, industrial sulfur dioxide discharge, and industrial smoke (powder) dust discharge in the government work reports of the seven major cities in the Yellow River basin from 2008 to 2021 are all taken from the Statistical Yearbook and the China Urban Statistical Yearbook of the seven urban agglomerations in the Yellow River basin. Some data are missing. The interpolation method was used to complete the dataset. The gross domestic product of prefecture-level cities in the Yellow River basin urban agglomeration was calculated for 2008 constant prices. The average exchange rate of RMB against the USD is shown in the table to calculate the foreign direct investment of each urban agglomeration in the Yellow River basin in the current year (

Table 1. Average exchange rate of RMB against USD from 2008 to 2021.

Year	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021
Exchange rate	7.299	6.836	6.828	6.621	6.301	6.289	6.142	6.228	6.642	6.752	6.436	6.899	6.898	6.455

).

2.3. Data Analysis

2.3.1. Index Measurement of Environmental Governance Based on Pollutant Emissions and the Government’s Environmental Concerns

This paper used the data related to the frequency of environmental words in government work reports, industrial wastewater discharge, industrial sulfur dioxide discharge, and industrial smoke (powder) dust discharge in the work reports of the seven major city clusters in the Yellow River basin from 2008 to 2021. This paper used the entropy method and the input–output method to calculate environmental governance. Compared with other methods, the entropy method determines the index weight according to the variation degree of each index value, which is an objective weighting method and avoids the deviation brought by human factors.

1.

Data standardization: the standardized treatment method of positive indicators was adopted to obtain the index value ${P^{″}}_{ij}$ of environmental governance efficiency. The specific calculation process is shown in the formula, which shows the value of the $j$ environmental pollution index of the $i$ urban agglomeration of the seven urban agglomerations in the Yellow River basin:

$${P^{″}}_{ij}=\frac{X_{ij}-min\left(X_{ij}\right)}{max{X}_{ij}-min{X}_{ij}}\left(i=\mathrm{1,2},3,\cdots ,m;j=\mathrm{1,2},3,\cdots ,n\right).$$

(1)

2.

Calculation of specific gravity of pollutants: the specific process of calculating the $P_{ij}$ of $j$ environmental pollution index of urban agglomeration $i$ in the Yellow River basin or urban agglomeration $i$ is shown in the formula:

$$P_{ij}=\raisebox{1ex}{${P^{″}}_{ij}$}\!\left/ \!\raisebox{-1ex}{$\sum _{i=1}^m{P^{″}}_{ij}$}\right.\left(i=\mathrm{1,2},3,\cdots ,m;j=\mathrm{1,2},3,\cdots ,n\right).$$

(2)

3.

Calculation of entropy and its coefficient of difference: entropy is measured based on the ratio between actual entropy and maximum entropy, and its value ranges from 0 to 1. The entropy value $e_j$ of the pollutant index $j$ is shown in the formula. The difference coefficient of information entropy is also called “information entropy redundancy”. The variation coefficient $g_j$ of the entropy of the$j$ pollutant index is shown in the formula. The larger the $g_j$, the higher the importance of the fourth pollutant index in the evaluation:

$$e_j=-\frac{1}{Inm}{\sum }_{i=1}^m{P}_{ij}{InP}_{ij}\left(i=\mathrm{1,2},3,\cdots ,m;j=\mathrm{1,2},3,\cdots ,n\right),$$

(3)

$$g_j=1-e_j\left(j=\mathrm{1,2},3,\cdots ,n\right).$$

(4)

4.

Determination of index weight: the weight $W_j$ of the pollutant index $j$ of seven urban agglomerations in the Yellow River basin in the comprehensive evaluation was calculated as follows:

$$W_j=\raisebox{1ex}{$g_j$}\!\left/ \!\raisebox{-1ex}{$\sum _{j=1}^n{g}_j$}\right.\left(j=\mathrm{1,2},3,\cdots ,n\right).$$

(5)

5.

Construction of an environmental governance index: the environmental governance index ${{ER}_1}_i$ can be calculated with the following formula, representing the environmental governance index of urban agglomeration $i$ in the Yellow River basin:

$${{ER}_1}_i={\sum }_{j=1}^n\frac{f_i-W_j{P}_{ij}}{f_i}\left(i=\mathrm{1,2},3,\cdots ,m;j=\mathrm{1,2},3,\cdots ,n\right).$$

(6)

2.3.2. Carbon Emissions Coefficient Method to Measure Carbon Emissions

Considering the resource distribution of urban agglomerations in the Yellow River basin, this paper selects eight energy sources, including coal, to represent the mainstream energy consumption of urban agglomerations in the Yellow River basin according to the energy consumption data in the China Statistical Yearbook of Energy and China Carbon Accounting Database. This paper uses the calculation method of the Intergovernmental Panel on Climate Change (IPCC) to estimate carbon emissions. The specific calculation method is as follows:

$$EC={\sum }_{i-1}^n{C}_i={\sum }_{i-1}^n{E}_i\times {NCV}_i\times {CEF}_i\times \raisebox{1ex}{$44$}\!\left/ \!\raisebox{-1ex}{$12$}\right.,$$

(7)

$\mathrm{w}\mathrm{h}\mathrm{e}\mathrm{r}\mathrm{e}EC$ represents carbon dioxide emissions, $E_i$ represents the consumption of various energy sources, ${NCV}_i$ represents the average low-calorific value of $i$ energy source, and ${CEF}_i$ represents the carbon emissions coefficient of $i$ energy source (

Table 2. Energies’ standard load coefficients and carbon emissions coefficient.

Indexes	Raw Coal	Coke	Crude Oil	Gasoline	Kerosene	Diesel Oil	Fuel Oil	Natural Gas
Standard conversion factor	$0.7143t·t^{-1}$	$0.9714t·t^{-1}$	$1.4286t·t^{-1}$	$1.4714t·t^{-1}$	$1.4714t·t^{-1}$	$1.4574t·t^{-1}$	$1.4286t·t^{-1}$	$1.33\times {10}^{-3}t·m^{-3}$
Carbon dioxide emission coefficient	1.900	2.860	3.020	2.925	3.018	3.096	3.171	2.162

).

2.3.3. Construction of the Measurement Model

Based on the panel data of the seven major cities in the Yellow River basin from 2008 to 2021, the GMM was used for quantitative testing and analysis. Since the direct impact of environmental governance on the carbon emissions of urban agglomerations in the Yellow River basin is not a simple linear relationship, the squared term of environmental governance was introduced to investigate the potential nonlinear impact on carbon emissions of urban agglomerations in the Yellow River basin through a preliminary empirical test. In addition, there may be a lag effect on carbon emissions in the Yellow River basin urban agglomeration, which can be controlled after the introduction. The following econometric model was constructed to measure the direct effects of environmental governance on carbon emissions:

$${InC}_{i,t}={\beta }_0+{\beta }_1{InC}_{i,t-1}+{\beta }_2{ER}_{i,t}+{\beta }_3{ER}_{i,t}^2+{\delta X}_{i,t}+{\alpha }_i+{\epsilon }_{i,t},$$

(8)

where $i$ and $t$ represent city clusters and the year, respectively. ${InC}_{i,t}$ represents the logarithm of the carbon emissions intensity of the whole urban agglomeration in the Yellow River basin and each urban agglomeration. ${ER}_{i,t}$ stands for the environmental governance index; meanwhile, ${\beta }_1$ is the lag multiplier, representing the influence of the carbon emissions level of the previous period on the current period. ${\alpha }_i$ represents the non-observed effect of urban agglomerations, reflecting the persistent differences among urban agglomerations. ${\epsilon }_{i,t}$ represents a particular heterogeneous effect, assuming a normal distribution; $X$ represents other control variables, including the energy consumption structure, the upgrading of the industrial structure, green technological innovation, foreign direct investment (FDI), and per capita income.

In addition, to analyze the indirect impact of environmental governance on carbon emissions, this paper introduces the cross-terms of environmental governance, the energy consumption structure, the industrial structure level, green technological innovation, and FDI to explore the mechanism and intensity of action of different approaches on carbon emissions.

The specific model is as follows:

$$\begin{gathered} {InC}_{i,t}={\gamma }_0+{\gamma }_{1}In{C}_{i,t-1}+{\gamma }_{i}ER\times {Enter}_{i,t}+{\gamma }_{2}ER\times {Indu}_{i,t}+{\gamma }_{3}ER\times {Tech}_{i,t}+ \\ {\gamma }_{4}ER\times {FDI}_{i,t}+\delta X_{i,t}+{\alpha }_i+{\epsilon }_{i,t}, \end{gathered}$$

(9)

where $ER\times {Enter}_{i,t}$ represents the intersection of environmental governance and the energy consumption structure in year $i$ of urban agglomeration $t$; $ER\times {Indu}_{i,t}$ represents the intersection of environmental governance and industrial structure upgrading; $ER\times {Tech}_{i,t}$ represents the intersection of environmental governance and green technological innovation; $ER\times {FDI}_{i,t}$ represents the intersection of environmental governance and FDI; X represents the other control variables, including per capita income.

3. Results

3.1. Analysis of the Overall Environmental Governance Situation and Carbon Emissions in Urban Agglomerations in the Yellow River Basin

3.1.1. Analysis of the Overall Status of Environmental Governance

The environmental governance indexes of the Yellow River basin urban agglomerations calculated using the entropy method and the input–output method showed significant differences. Based on the above measurement of environmental governance and the analysis of various indicators in the Yellow River basin, the overall environmental governance indexes of the Yellow River basin and the major urban agglomerations were obtained (Figure 2). The overall environmental governance of the urban agglomerations in the Yellow River basin has steadily improved, with a downward trend from 2008 to 2012 and an upward trend after 2012. There was a significant increase in 2016, surpassing the highest level in 2008. The environmental governance indexes of the Shandong Peninsula urban agglomeration, the Central Plain urban agglomeration, and the Guanzhong Plain urban agglomeration remained the top three for an extended period. The environmental governance indexes of the Jinzhong urban agglomeration, the Hu–Bao–Er–Yu urban agglomeration, and the Lanzhou–Xining urban agglomeration decreased first and then increased. The environmental governance indexes of the urban agglomeration of Ningxia along the Yellow River increased steadily, and the environmental governance indexes of all urban agglomerations showed an overall upward trend.

3.1.2. Analysis of the Overall Situation of Carbon Emissions

From 2008 to 2021, the total carbon emissions of various energy sources in the urban agglomerations of the Yellow River basin in China increased year on year, and the increase rate decreased, exhibiting a downward trend. In 2019, the total carbon emissions reached 3.5 billion tons, which indicates that the social and economic development of China is bound to be accompanied by the consumption of fossil energy. With the development of society and the economy, carbon emissions are unavoidable (Figure 3). Overall, the carbon emissions of the seven cities in the Yellow River basin in 2008 were 2.18 billion tons; by 2021, compared to 2008, carbon emissions increased by 1.1 billion tons, an increase of 50%. In terms of the growth rate over the years, the maximum growth rate reached 11.17% in 2010 and 8.17% in 2011. After that, the growth rate slowed down year by year. In 2018, carbon emissions showed reverse growth, with a growth rate of −1.05%. On the whole, the total carbon emissions of urban agglomerations in the Yellow River basin are in a gradually decreasing trend, and the growth rate of carbon emissions is gradually lower than 0.

The direct comparison of carbon emissions between the Yellow River basinand the seven major urban agglomerations shows that the carbon emissions of the urban agglomerations of the Yellow River basin exhibit an overall stationary trend and tend to be stable after 2016 (Figure 4). Regarding the carbon emissions of urban agglomerations, the carbon emissions of the Shandong Peninsula urban agglomeration, the Central Plain urban agglomeration, and the Hu–Bao–Er–Yu urban agglomeration have been in the top three for some time, but the carbon emissions of the Shandong Peninsula urban agglomeration show a downward trend after reaching their peak in 2016. The carbon emissions of the Central Plain urban agglomeration are in a stable and fluctuating stage, and the carbon emissions of the Hu–Bao–Er–Yu urban agglomeration are constantly rising. Carbon emissions in 2021 will triple those of 2008, making it the city cluster with the fastest growth in carbon emissions. In terms of carbon emissions, the places with the lowest levels are the Lanzhou–Xining urban agglomeration, Ningxia along the Yellow River, and the Jinzhong urban agglomeration. The carbon emissions of the Lanzhou–Xining urban agglomeration showed a trend of first rising and then declining, and they reached their highest point in 2014. The carbon emissions of the Ningxia urban agglomeration along the Yellow River continuously increased, but the increase rate was small, while the Jinzhong urban agglomeration showed an overall trend of rising fluctuation. In terms of carbon emissions, the Shandong Peninsula urban agglomeration had the highest carbon emissions, the Hu–Bao–Er–Yu urban agglomeration ranked second, and the Central Plain urban agglomeration ranked third, followed by the Guanzhong Plain urban agglomeration, the Jinzhong urban agglomeration, the Ningxia urban agglomeration along the Yellow River, and the Lanzhou–Xining urban agglomeration.

3.2. Spatio-Temporal Evolution of Environmental Governance and Carbon Emissions in Urban Agglomerations in the Yellow River Basin

3.2.1. Evolution of the Internal Patterns of Environmental Governance

Based on the environmental governance indexes of each component city of the seven major urban agglomerations in the Yellow River basin, the years 2008, 2014, and 2021 were selected as the representative years, and the environmental governance indexes were divided into three types (the high environmental governance index, the medium environmental governance index, and the low environmental governance index) through the natural breakpoint (Figure 5).

From the types of environmental governance indexes, (1) the number of cities with high environmental governance indexes fluctuates continuously and tends to cluster towards the central cities of urban agglomerations. (2) The number of cities with a low index of environmental governance decreased and dispersed in the early stage of the study and was concentrated in heavy industrial areas. (3) In 2008, cities with a low index of environmental governance were mainly concentrated in the Central Plain urban agglomeration, the Lanzhou–Xining urban agglomeration, and the Guanzhong Plain urban agglomeration; in 2014, cities with a low index of environmental governance were mainly concentrated in the Shandong Peninsula urban agglomeration and the Hu–Bao–Er–Yu urban agglomeration; in 2021, cities with a low index of environmental governance gradually became cities with high indexes and medium indexes of environmental governance.

On the whole, the environmental governance indexes of urban agglomerations in the Yellow River basin show trends of decreasing first and then increasing. The distribution of high-index and medium-index cities is concentrated, and the number of cities is large, which indicates that the environmental governance indexes of urban agglomerations in the Yellow River basin are constantly improving. The number of cities with low indexes of environmental governance is decreasing; these cities are mainly concentrated in the indexes of the Jinzhong urban agglomeration and the Central Plain urban agglomeration.

3.2.2. Evolution of the Internal Pattern of Carbon Emissions

The carbon emissions of urban agglomerations in the Yellow River basin were calculated according to the carbon emissions coefficient method. The years 2008, 2014, and 2021 were selected as representative years to compare and analyze the spatial evolution of the carbon emissions of seven urban agglomerations in the Yellow River basin during the study period (Figure 6).

Regarding the types of carbon emissions levels, the following findings were deduced. (1) In 2008, cities with high levels of carbon emissions were concentrated in the northern part of the Shandong Peninsula urban agglomeration and the southern part of the Hu–Bao–Er–Yu urban agglomeration; in 2014, they were concentrated in the whole of the Hu–Bao–Er–Yu urban agglomeration and the northern part of the Shandong Peninsula urban agglomeration. (2) The number of cities with high levels of carbon emissions showed an upward trend; their distribution scope gradually expanded, and their spatial scope changed obviously, clearly spreading to western cities in the Yellow River basin. (3) From 2014 to 2021, the number of cities with medium–low-level carbon emissions showed an increasing trend, and some cities in the Shandong Peninsula urban agglomeration, the Central Plain urban agglomeration, and the Lanzhou–Xining urban agglomeration experienced a significant decrease.

In general, the carbon emissions of energy consumption in the urban agglomerations of the Yellow River basin showed a trend of first rising and then declining. Cities with high levels of water carbon emissions were concentrated in old industrial zones and metallurgical cities, while the number of cities with low-level carbon emissions showed a trend of first increasing and then decreasing; compared with existing studies, it was found that the quantitative study on carbon emissions of urban agglomerations in the Yellow River basin can better highlight the carbon emissions problems faced by cities in the Yellow River basin. Compared with the existing studies, the study on carbon emissions of urban agglomerations in the Yellow River basin is more targeted and realistic.

3.3. An Empirical Analysis of the Effects of Environmental Governance on Carbon Emissions in Urban Agglomerations in the Yellow River Basin

To avoid the phenomenon of pseudo-regression during the empirical process, it is necessary to conduct a stationarity test for each variable; the LLC test and the IPS test were used (

Table 3. Stability test results for the panel data.

	LLC Test	IPS Test	Conclusion
lnC	−7.320 (0.000)	−2.349 (0.000)	Stationary variable
ER1	−5.230	−2.269	Stationary variable
	(0.000)	(0.000)
ER2	−3.901	−1.802	Stationary variable
	(0.000)	(0.000)
Enter	−18.724	−11.268	Stationary variable
	(0.000)	(0.000)
lndu	−7.075 (0.000)	−1.935 (0.027)	Stationary variable
Tech	−5.425 (0.000)	−1.481 (0.069)	Stationary variable
FDI	−3.338 (0.000)	−1.775 (0.039)	Stationary variable
lnP	−3.394	−2.266	Stationary variable
	(0.000)	(0.011)

Note: The original hypothesis of the LLC test and IPS test is the existence of a unit root; that is, a variable is unstable. The p-value of the estimator is provided in parentheses.

).

The test results show that all variables pass the LLC test and IPS test, and all variables are stationary variables that can be used for the following analysis.

3.4. Direct Effects of Environmental Governance on Carbon Emissions in the Urban Agglomerations of the Yellow River Basin

3.4.1. The Level of Urban Agglomerations in the Yellow River Basin

The empirical results of the impact of environmental governance on carbon emissions in urban agglomerations in the Yellow River basin are shown in the table. In this paper, the first-order lag term of the explained variable is introduced into the econometric model as the explanatory variable, thus evolving into a dynamic panel model (

Table 4. Direct effects of environmental governance on carbon emissions in the urban agglomerations of the Yellow River basin.

	(1)	(2)
	lnC	lnC
lnCt−1	4.542 **	3.601
	(1.873)	(2.382)
ER1	−6.277 **	5.344 *
	(2.558)	(2.889)
ER12		−2.854 *
		(1.598)
Enter	−1.088 ***	−0.911 **
	(0.413)	(0.442)
lndu	−4.270 **	−3.535 *
	(1.781)	(1.903)
Tech	−1.994 **	−1.698 **
	(0.793)	(0.813)
FDI	1.187 **	0.927
	(0.530)	(0.594)
lnP	−4.065 **	−3.009
	(2.028)	(2.581)
Constant term	10.734 *	10.182
	(5.511)	(8.199)
AR (1) p-value	0.447	0.578
AR (2) p-value	0.961	0.913
Sargan test p-value	0.433	0.621

Note: standard error is given in brackets; *, **, and *** are significant at the level of 10%, 5%, and 1%, respectively; AR (1) and AR (2) represent the Arellano–Bond autocorrelation test of the first- and second-order difference residual sequences, respectively, while the Sargan test is the over-recognition test.

).

Table 4 shows the direct effect of environmental governance on carbon emissions in the urban agglomerations of the Yellow River basin. The model passes the AR test and the Sargan test. For the urban agglomerations of the Yellow River basin, the carbon emissions in the early stage have a significant impact on the carbon emissions in the current period. Environmental governance plays the role of “forced emissions reduction” in the urban agglomerations of the Yellow River basin, and there is no “green paradox” in the Yellow River basin urban agglomerations. In model (2), the first power coefficient of environmental governance is positive, and the second power coefficient has a significantly negative impact on the carbon emissions of the urban agglomerations of the Yellow River basin at the 10% level, indicating that there is a significant inverted U-shaped curve relationship between environmental governance and the carbon emissions of these urban agglomerations. Moreover, there is a threshold for the direct impact of environmental governance on such carbon emissions. When the environmental governance effect of the urban agglomerations in the Yellow River basin is less than the threshold value, the carbon emissions of these urban agglomerations increase along with the enhancement of the environmental governance effect, which is the “green paradox”. When the effect strength of environmental governance in the urban agglomerations of the Yellow River basin is greater than the threshold value, the inhibition effect of environmental governance on carbon emissions in this region occupies the upper part, achieving the expected effect of environmental governance.

3.4.2. The Level of Urban Agglomerations

Based on the empirical results for urban agglomerations, the environmental governance of the downstream Shandong Peninsula urban agglomeration has a significant impact on carbon emissions, which is significantly negative at the level of 1%, and other effects are also significant (

Table 5. Analysis of the direct effects of environmental governance on the carbon emissions of urban agglomerations.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	Central Plain Urban Agglomeration	Lanzhou–Xining Urban Agglomeration	Guanzhong Plain Urban Agglomeration	Hu–Bao–Er–Yu Urban Agglomeration	Ningxia Urban Agglomeration along the Yellow River	Shandong Peninsula Urban Agglomeration	Jinzhong Urban Agglomeration
lnCt−1	1.187 ***	0.104 ***	0.232 ***	0.169 ***	0.158 ***	0.803 ***	0.142 ***
	(0.255)	(0.036)	(1.087)	(0.051)	(0.064)	(0.199)	(0.057)
ER1	−0.199 *	−0.173 ***	−0.242 ***	−0.059	−0.199 *	−0.380 ***	−0.538 *
	(0.117)	(0.057)	(0.090)	(0.130)	(0.282)	(0.145)	(0.332)
Enter	0.000	0.000	−0.273 **	0.124	−0.238 **	−0.376 ***	0.000
	(0.086)	(0.000)	(0.117)	(0.134)	(0.115)	(0.144)	(0.000)
lndu	−0.109 *	0.000	−0.431 **	0.507	0.000	−0.336 **	0.000
	(0.061)	(0.000)	(0.210)	(0.745)	(0.000)	(0.165)	(0.000)
Tech	0.019	−0.265 ***	−0.108 *	0.000	−0.000	−0.227 ***	−0.233 **
	(0.075)	(0.091)	(0.061)	(0.000)	(0.088)	(0.064)	(0.113)
FDI	−0.125 **	−0.061	−0.403 *	0.000	0.000	−0.229 **	−0.118 **
	(0.059)	(0.237)	(0.231)	(0.000)	(0.000)	(0.109)	(0.054)
lnP	−0.136	0.336 ***	−0.463	0.312 *	0.287 ***	−0.313	0.412 ***
	(0.172)	(0.028)	(1.669)	(0.173)	(0.050)	(0.228)	(0.047)
Constant term	0.604	0.000	3.881	0.000	0.000	1.692	0.000
	(0.617)	(0.000)	(7.801)	(0.000)	(0.000)	(1.120)	(0.000)
AR (1) p-value	0.054	0.613	0.837	0.483	0.604	0.365	0.367
AR (2) p-value	0.210	0.692	0.384	0.115	0.410	0.263	0.190
Sargan test p-value	0.608	0.677	0.254	0.935	0.260	0.247	0.478

Notes: ***, **, and * indicate the significance of the 1%, 5%, and 10% levels, respectively.

). In the middle reaches of the Guanzhong Plain urban agglomeration, the Central Plain urban agglomeration, and the Jinzhong urban agglomeration, the impact of environmental governance on carbon emissions is significantly negative at the levels of 1%, 10%, and 10%, respectively, and other factors also have significant impacts. The impact of environmental governance on carbon emissions in the upstream Lanzhou–Xining urban agglomeration is significantly negative at the level of 1%; the impact of green innovation capabilities on carbon emissions is significantly negative. The impact of environmental governance on carbon emissions in the urban agglomeration of Ningxia along the Yellow River is significantly negative at the level of 10%; the impact of energy consumption structure is significantly negative at the level of 5%. The impact of environmental governance on carbon emissions in the Ningxia urban agglomeration along the Yellow River is not significant. At the urban agglomeration level, environmental governance has a direct effect on carbon emissions in the urban agglomerations of the Yellow River basin, and there is regional heterogeneity.

3.5. Indirect Effects of Environmental Governance on Carbon Emissions in the Urban Agglomerations of the Yellow River Basin

3.5.1. The Level of Urban Agglomerations in the Yellow River Basin

The table shows the indirect effects of environmental governance on carbon emissions (

Table 6. Indirect effects of environmental governance on carbon emissions in the urban agglomerations of the Yellow River basin.

	(1)	(2)	(3)	(4)
	lnC	lnC	lnC	lnC
lnCt−1	1.183 ***	1.253 ***	1.098 ***	1.124 ***
	(0.132)	(0.105)	(0.167)	(0.304)
ER1 × Enter	−0.150 **	0.031	−0.317 ***	−0.178
	(0.059)	(0.069)	(0.111)	(0.297)
ER1 × lndu		−0.154 **	−0.317	−0.195
		(0.090)	(0.212)	(0.213)
ER1 × Tech			−0.272 **	−0.183
			(0.119)	(0.204)
ER1 × FDI				−0.166 *
				(0.099)
lnP	−0.209	−0.235 **	−0.080	−0.052
	(0.131)	(0.104)	(0.101)	(0.247)
Constant term	0.729 *	0.774 **	0.585 **	0.288 *
	(0.437)	(0.382)	(0.281)	(0.153)
AR (1) p-value	0.025	0.034	0.071	0.043
AR (2) p-value	0.482	0.974	0.894	0.582
Sargan test p-value	0.545	0.519	0.702	0.487

Notes: ***, **, and * indicate the significance of the 1%, 5%, and 10% levels, respectively.

). In this paper, variables are gradually added for empirical analysis. It is estimated that model (1), model (2), model (3), and model (4) represent intersections of environmental governance, energy consumption structure, industrial structure upgrading, green technological innovation, and FDI in the urban agglomerations of the Yellow River basin. The main objective is to gradually observe the indirect effects of environmental governance on carbon emissions in the urban agglomerations of the Yellow River basin in four ways, to test the robustness of the results, and to estimate the fixed effects of the static panel model (1), model (2), model (3), and model (4).

Estimation model (1), model (2), model (3), and model (4) passed the AR test and the Sargan test, indicating that the selection of experimental instrumental variables was reasonable and the model could be effectively identified. The analysis of the direct effects of environmental governance on the carbon emissions of the urban agglomerations of the Yellow River basin is consistent, and the carbon emissions of the urban agglomerations in this region in the early stage have a significant positive impact on the carbon emissions of the current period. The cross-terms of environmental governance and the energy consumption structure in model (1), model (2), model (3), and model (4) are all significantly negative at the level of 5%. Environmental governance can curb the carbon emissions of urban agglomerations in the Yellow River basin by implementing a low-carbon energy consumption structure. Reducing carbon emissions at the level of urban agglomerations in the Yellow River basin by forcing the development of a low-carbon energy consumption structure of environmental governance is an arduous process, but its effects are gradually becoming evident. Under the constraints of environmental governance, the impact of industrial structure upgrading on the carbon emissions of the urban agglomerations in the Yellow River basin is significantly negative at the 5% level, which means that environmental governance has an indirect positive impact on reducing the carbon emissions of urban agglomerations in the Yellow River basin by upgrading the industrial structure. Under the constraints of environmental governance, the impact of green technological innovation on the carbon emissions of urban agglomerations in the Yellow River basin is significantly negative at the level of 5%. Government departments gradually attach importance to the research and development of environmental protection technology and energy-saving technology, providing opportunities for reducing the carbon emissions of urban agglomerations in the Yellow River basin. Under the constraints of environmental governance, the impact of FDI on the carbon emissions of urban agglomerations in the Yellow River basin is significantly negative at the level of 10%. Environmental governance reduces carbon dioxide emissions by inhibiting the development of highly polluting and energy-consuming enterprises in these urban agglomerations.

The above results confirm that environmental governance can indirectly achieve carbon emissions reductions by influencing the energy consumption structure, upgrading the industrial structure, facilitating green technological innovation, and improving FDI in the urban agglomerations in the Yellow River basin.

3.5.2. The Level of Urban Agglomerations

It can be seen from the above results that, consistently with the direct effect results, the carbon emissions in the previous period have had a significant positive influence on the carbon emissions in the current period (

Table 7. Indirect effects of environmental governance on carbon emissions in urban agglomerations.

	(1)	(2)	(3)	(4)	(5)	(6)	(7)
	Central Plain Urban Agglomeration	Lanzhou–Xining Urban Agglomeration	Guanzhong Plain Urban Agglomeration	Hu–Bao–Er–Yu Urban Agglomeration	The Ningxia urban agglomeration along the Yellow River	Shandong Peninsula Urban Agglomeration	Jinzhong Urban Agglomeration
lnCt−1	0.870 ***	0.942 **	0.692 **	0.743 *	0.593 *	0.639 ***	0.622 **
	(0.230)	(0.435)	(0.293)	(0.417)	(0.341)	(0.185)	(0.309)
ER1 × Enter	−0.197 **	0.000	−0.591 **	0.043	0.000	−0.614 ***	0.000
	(0.080)	(0.000)	(0.351)	(0.550)	(0.000)	(0.226)	(0.000)
ER1 × lndu	−0.048	0.000	−0.120 **	0.148	−0.151 **	−0.202 **	−0.402 *
	(0.238)	(0.000)	(0.052)	(0.176)	(0.072)	(0.083)	(0.218)
ER1 × Tech	−0.106	−0.063	−0.003	0.000	−0.120	−0.227 **	−0.392 ***
	(0.236)	(0.112)	(0.308)	(0.000)	(0.097)	(0.301)	(0.282)
ER1 × FDI	−0.394 **	−0.121 **	−0.129	−0.202	0.000	−0.282 *	−0.209
	(0.406)	(0.053)	(0.200)	(0.222)	(0.000)	(0.165)	(0.161)
lnP	0.188	0.030	−0.128	0.459 ***	0.166	−0.043	0.377 ***
	(0.296)	(0.132)	(0.473)	(0.034)	(0.127)	(0.307)	(0.034)
Constant term	−0.620 *	0.000	0.511 *	0.000	0.000	0.463 *	0.000
	(0.375)	(0.000)	(0.372)	(0.000)	(0.000)	(0.248)	(0.000)
AR (1) p-value	0.049	0.465	0.313	0.184	0.048	0.131	0.525
AR (2) p-value	0.165	0.154	0.278	0.331	0.781	0.363	0.308
Sargan test p-value	0.800	0.643	0.271	0.773	0.231	0.114	0.447

Notes: ***, **, and * indicate the significance of the 1%, 5%, and 10% levels, respectively.

).

Under the influence of environmental governance, the impact of the energy consumption structure on carbon emissions in the Shandong Peninsula urban agglomeration, the Central Plain urban agglomeration, and the Guanzhong Plain urban agglomeration is significantly negative at the levels of 1%, 5%, and 5%, respectively, indicating that environmental governance policies in the Shandong Peninsula urban agglomeration, the Central Plain urban agglomeration, and the Guanzhong Plain urban agglomeration can affect energy consumption structure. The carbon emissions of urban agglomerations can be reduced by adjusting the energy consumption structure in the Yellow River basin. Although the influence of the energy consumption structure on carbon emissions is not significant, the coefficient is positive, indicating that there is significant resistance to reducing energy consumption methods dominated by coal in the Hu–Bao–Er–Yu urban agglomeration.

Under the influence of environmental governance, the upgrading of the industrial structure of the Shandong Peninsula urban agglomeration, the Guanzhong Plain urban agglomeration, and the Ningxia urban agglomeration along the Yellow River have a significantly negative impact on carbon emissions at the level of 5%, and the upgrading of the industrial structure of the Jinzhong urban agglomeration has a significantly negative impact on carbon emissions at the level of 10%. This indicates that the above urban agglomeration has made progress in promoting the development of the tertiary industry and optimizing the structure of the secondary industry. Under the effect of environmental governance policies, the enterprises with high levels of pollution and energy consumption are gradually reduced in number, which plays a positive role in carbon emissions reduction.

Under the influence of environmental governance, the impact of green innovation capabilities on the carbon emissions of the Jinzhong urban agglomeration is significantly negative at 1%, that on the Shandong Peninsula urban agglomeration is significantly negative at 5%, and that on other agglomerations is not significant but the coefficient is negative. This indicates that the Jinzhong urban agglomeration and the Shandong Peninsula urban agglomeration have gradually adjusted the significant carbon emissions caused by high levels of energy consumption and heavy industry enterprises and have gradually reduced carbon emissions through policy support for green technology research and development and popularization. Other agglomerations have initially shown similar effects.

Under the influence of environmental governance, the impact of FDI on carbon emissions in the Central Plain urban agglomeration and the Lanzhou–Xining urban agglomeration is significantly negative at 5%, indicating that Henan Province, the main body of the Central Plain urban agglomeration, has continuously optimized the structure of foreign enterprises after becoming an economic development zone. The Lanzhou–Xining urban agglomeration has absorbed foreign green technologies under the Belt and Road Initiative. FDI in the two urban agglomerations has a significant negative effect on carbon emissions. The impact of FDI on carbon emissions in the Shandong Peninsula urban agglomeration is significantly negative at the level of 10%, indicating that the Shandong Peninsula urban agglomeration constantly raises the requirements and standards for enterprises with high levels of pollution and energy consumption when introducing foreign investment, thus reducing carbon emissions.

The above results confirm that the environmental governance of urban agglomerations in the Yellow River basin can indirectly achieve reductions in carbon emissions by influencing the energy consumption structure, upgrading the industrial structure, facilitating green innovation abilities, and FDI, but there is regional heterogeneity among different urban agglomerations.

3.6. Robustness Test

To verify the robustness of the above conclusions, we used the emissions values of three major pollutants and calculated ER₂ using the entropy method instead of ER₁ to test the direct impact on carbon emissions. ER₂ is substituted into the formula (

Table 8. Robustness test.

	(1)	(2)
	lnC	lnC
lnCt−1	2.824 *	1.293 *
	(1.671)	(0.726)
ER2	−0.556 ***	3.008 *
	(0.213)	(1.749)
ER22		−0.329 *
		(0.179)
Enter	−0.335 **	0.047
	(0.167)	(0.177)
lndu	1.316 **	−0.387 *
	(0.559)	(0.235)
Tech	1.556 **	−0.451 **
	(0.628)	(0.195)
FDI	−0.218 **	−0.067
	(0.108)	(0.087)
lnP	−0.270	2.362
	(0.832)	(1.577)
Constant term	12.981 **	12.542 **
	(5.227)	(6.118)
AR (1) p-value	0.439	0.539
AR (2) p-value	0.795	0.564
Sargan test p-value	0.426	0.918

Note: standard error is given in brackets; *, **, and *** are significant at the levels of 10%, 5%, and 1%, respectively; AR (1) and AR (2) represent the Arellano–Bond autocorrelation test of the first- and second-order difference residual sequences, respectively, while the Sargan test is the over-recognition test.

). It is clear from the results that, consistently with the direct effect results, carbon emissions in the previous period have a significant positive impact on the carbon emissions in the current period. The estimated coefficient of the direct effect of the emissions of the three pollutants on carbon emissions is different from that of the direct impact of environmental governance on carbon emissions, but the direction of the effect is consistent with the empirical results presented above. This indicates that the conclusion of this paper has not changed substantially.

4. Discussions

This study examined the spatio-temporal characteristics of carbon emissions from the seven urban agglomerations in the Yellow River basins and the factors driving their distribution. Economic growth may produce serious environmental pollution problems, especially in relation to carbon emissions. Continuing to deepen environmental governance is fundamental to reducing carbon emissions. This is consistent with the research conclusions of scholars [27,28,29]. The situation of urban agglomerations in the Yellow River basin is different, which needs to be classified, which is the same conclusion as the current research on the Beijing–Tianjin–Hebei area and Shenzhen from scholars [30,31]. This paper believes that the extensive carbon emission model is no longer suitable for the Yellow River basins’ current stage of development, and it is necessary to reduce carbon emissions through environmental governance and explore green and sustainable growth modes.

First, strict environmental regulations will indirectly curb carbon emissions by optimizing industrial structure, accelerating industrial upgrading, promoting green technology research and development, and optimizing investment structure. According to different economic conditions, different resource endowments, and different industrial structures, cities in the Yellow River basin urban agglomerations should adopt regional differentiated, reasonable and effective environmental regulation policies, and means to promote the optimization of industrial structure. At the same time, the Yellow River basin urban agglomerations should avoid the situation of actively lowering environmental standards to attract investment and prevent the introduction of polluting industries or enterprises. Further, carbon emissions should be reduced by streamlining extensive investments.

Second, targeted monitoring, inspection, and punishment for violations of regulations in areas with high carbon emissions, such as the Hu–Bao–Er–Yu urban agglomeration, should be carried out to ensure the effective implementation of environmental regulations. For cities with low environmental regulation intensity, such as the Jinzhong urban agglomeration, the intensity of environmental regulation should be appropriately enhanced to gradually control and reduce carbon emissions.

Third, cities in the seven urban agglomerations in the Yellow River basin must attach importance to the construction of ecological civilization, establish a government assessment system combining economic development with energy conservation and emission reduction, and establish a regional coordination mechanism for environmental regulation among cities to form a joint pollution control pattern, gradually eliminate the influence of local government competition on this inhibition effect, so as to better curb carbon emissions in the urban agglomerations in the Yellow River basin.

Lastly, in targeted research, this paper has the following disadvantages: by reference to the research achievements of predecessors, this article constructs the measuring index system of urban environment governance in the Yellow River, but the system construction still has deficiencies and cannot fully reflect the theoretical mechanism. Some of the influencing factors are difficult to quantify, and some of the municipal level data are missing, which has an impact on the results.

5. Conclusions

This paper took carbon emissions of urban agglomerations in the Yellow River basin as the research object. Based on the panel data of prefecture-level cities of the seven major urban agglomerations in the Yellow River basin from 2008 to 2021, it analyzed the evolution characteristics of the spatio-temporal pattern of environmental regulation and carbon emissions and explored the overall situation and spatio-temporal evolution of environmental governance and carbon emissions in the urban agglomerations in the Yellow River basin using the entropy method, ArcGIS, SBM model, etc. Through the analysis of mathematical models, this paper found that environmental governance can promote a reduction in the carbon emissions of urban agglomerations in the Yellow River basin. Environmental governance can reduce the carbon emissions in this region and has a significant impact on urban agglomerations effectively. For each region, the impact of environmental governance on its carbon emissions varied.

From the path of action, environmental governance can affect the carbon emissions of the urban agglomerations of the Yellow River basin indirectly. The energy consumption structure, the industrial structure level, green technological innovation, FDI, per capita income, and other factors have different impacts on the region as a whole and on each part of the urban agglomerations in the Yellow River basin.

From the comparison of urban agglomerations, the intensity of environmental regulation shows obvious regional differences, among which the environmental regulation of the Lanzhou–Xining urban agglomeration is the most relaxed, and the environmental regulation of Shandong Peninsula urban agglomerations is the most stringent. On the whole, the environmental regulation intensity of downstream urban agglomerations is higher than that of downstream urban agglomerations. The carbon emissions of the Shandong Peninsula urban agglomeration increased first and then decreased, while the carbon emissions of the Hu–Bao–Er–Yu urban agglomeration remained at a high level.

From the perspective of spatial pattern, the intensity of environmental regulations increased from east to west gradually, and the differences between regions gradually increased. The distribution of high-level types of environmental regulation is concentrated, and the number of cities is the largest. The cities with low levels of environmental regulation are concentrated in the Guanzhong Plain urban agglomeration and the Jinzhong urban agglomeration. The high-level types of carbon emissions are concentrated in petroleum and metallurgical cities such as the Shandong Peninsula urban agglomeration and the Hu–Bao–Er–Yu urban agglomeration, while the number of low-level type cities with carbon emissions fluctuates and increases, mainly in the Central Plain urban agglomeration and the Lanzhou–Xining urban agglomeration, and the number is increasing continuously.