Research on the Spatio-temporal Distribution Characteristics and Cluster Analysis of Carbon Emissions in Chinese Cities

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Understanding the spatio-temporal evolution and clustering characteristics of urban carbon emissions are the prerequisite for realizing carbon emission reduction policies and implementation. In this paper, 296 municipal units in China have taken as the study area. This researches have used the Moran's index and K-means clustering to study the spatio-temporal distribution characteristics and clustering characteristics of urban carbon emissions. The STIRPAT model has used to analyze the influencing factors of urban carbon emissions with different clustering characteristics. The results showed that: ①During 2005 to 2020, there was a growing trend of urban carbon emission in China, and there was a big gap between the high and low values of urban carbon emission, with most of the cities concentrating in the middle value. The same was true for the per capita carbon emission. ② The overall distribution of urban carbon emissions in China has characterized by high in the east and low in the west, with east and north China being the main carbon emission regions.③ The carbon emissions of Chinese cities have obvious spatial differences and significant spatial correlation characteristics. Based on the cluster analysis of urban carbon emissions, 296 cities have classified into low-carbon demonstration cities, low-carbon development cities, resource-dependent cities and energy-consuming cities. Four types of cities were put forward corresponding low-carbon development suggestions combined with influencing factors. Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Carbon dioxide (CO 2 ) plays a critical role in global warming, and climate change driven by rising CO 2 emissions has become a major focus of international concern [1–3] . The Intergovernmental Panel on Climate Change’s Special Report on Global Warming of 1.5 ℃ underscores that limiting temperature rise to 1.5 ℃ requires global CO 2 emissions to decrease by approximately 45% by 2030 relative to 2010 levels, with net-zero emissions to be achieved around 2050 [4–5] . In 2020, the Chinese government announced its dual goals of achieving carbon peak and carbon neutrality. In November 2021, it released the “Opinions on Fully, Accurately, and Comprehensively Implementing the New Development Philosophy to Achieve Carbon Peak and Carbon Neutrality” and the “Action Plan for Carbon Peak and Carbon Neutrality before 2030,” which outline strategic measures to promote energy efficiency, carbon reduction, and emission control [6] . At the current stage of rapid urbanization, cities continue to concentrate population, economic activity, infrastructure, and other key resources, leading to an intensification of urban carbon emissions [7] . In China, urban areas generate over 70% of national CO 2 emissions while consuming 65% of the country’s total energy to produce 85% of its GDP [8–10] . Therefore, urban carbon mitigation is pivotal to achieving national and global emission reduction targets. Cities vary significantly in terms of development status, population size, economic structure, and resource endowment, leading to substantial heterogeneity in urban carbon emissions. To effectively design and implement targeted carbon and emission reduction strategies, it is essential to understand the spatiotemporal distribution patterns of carbon emissions across cities nationwide. Wang et al. (2018, 2021) employed spatial autocorrelation analysis, spatial Markov transition matrices, and dynamic spatial panel data models to investigate the evolutionary trajectories of urban carbon emissions [11–12] . Wang et al. (2019) applied the super-efficiency SBM-DEA model integrated with the Malmquist index to measure and analyze spatial disparities in carbon emission efficiency and environmental efficiency among 14 cities (prefectures) in Hunan Province from 2010 to 2016 [13] . Yu et al. (2020) utilized the Tapio decoupling model to examine the relationship between economic growth and total carbon emissions [14] . Guo et al. (2021) adopted the Monte Carlo method to classify and analyze carbon peak trajectories for 286 sample cities in China [15] . Cheng et al. (2023) combined the Super-SBM and STIRPAT models to explore the spatiotemporal evolution of carbon emission performance across 30 provincial-level regions in China [16] . Existing studies have examined the spatial distribution of carbon emissions in China and its regions by integrating various methods and models to identify the factors influencing emissions. However, due to differences in development stages, population sizes, resource endowments, and other contextual factors, the drivers of carbon emissions vary significantly across cities. Therefore, it is crucial to analyze the characteristics of urban carbon emissions and identify the key influencing factors specific to different city types. This study selects 296 Chinese cities as the research sample, employing Moran’s I index and K-means clustering to investigate the spatiotemporal patterns and spatial agglomeration of urban carbon emissions. Furthermore, the STIRPAT model is applied to examine the determinants of carbon emissions across distinct clustering categories. The findings aim to provide policy insights for local governments in formulating strategies related to carbon emission reduction, energy structure optimization, and industrial transformation.. materials and methods Current data Carbon dioxide emissions data are sourced from the China Cities Greenhouse Gas Inventory (CCG) urban area dataset. A total of 296 cities' carbon emissions data were selected to align with the spatial boundaries of administrative divisions (Table 1 ). Raw data on population, GDP, tertiary industry output, energy consumption, and related indicators are obtained from the China Statistical Yearbook. Geospatial data on China’s administrative divisions and built-up area extents are provided by the Resource and Environmental Sciences Data Center of the Chinese Academy of Sciences (CRESD). Table 1 Data Overview and Source in the periods of 2005 to 2020 Specific data Source Periods Population 《China Statistical Yearbook》 2005 ~ 2020 GDP 《China Statistical Yearbook》 2005 ~ 2020 Carbon emissions 《China Urban Greenhouse Gas Working Group》 2005 ~ 2020 Industrial structure 《China Statistical Yearbook》 2005 ~ 2020 Energy consumption 《China Statistical Yearbook》 2005 ~ 2020 Urban built-up area Resource and Environmental Science and Data Center of Chinese Academy of Sciences 2005 ~ 2020 Analysis method Moran's I index is a well-established spatial statistical method that measures the degree of spatial autocorrelation by evaluating the correlation between attribute values across spatial units, thereby revealing underlying spatial patterns. In this study, Moran's I index is employed to examine the spatial correlation and clustering characteristics of carbon emissions across Chinese cities, providing insight into the extent and distribution of regional emission disparities.It is of great as follows: $$\:Z\left(I\right)=\frac{I-E\left(I\right)}{\sqrt{V\left(I\right)}}$$ 1 in which \(\:Z\left(I\right)\) is the Moran's I index in the range [-1, 1]; \(\:E\left(I\right)\) is the expected value of Moran's I index; and \(\:V\left(I\right)\) is the variance of Moran's I index. Cluster analysis is a statistical methodology used to classify research objects into relatively homogeneous groups based on their characteristics. In this study, the K-means clustering algorithm is employed, which begins by randomly selecting K data points as initial cluster centroids. Each remaining data point is then assigned to the nearest centroid based on Euclidean distance. Subsequently, the centroids are recalculated as the mean of all points within each cluster, and this process iterates until either the maximum number of iterations is reached or the change in centroid positions falls below a predefined threshold. This approach is applied to categorize cities according to their carbon emission patterns, enabling a more systematic analysis of regional emission heterogeneity. The formula is calculated as follows: $$\:\text{W}\text{S}\text{S}=\sum\:_{l}^{k}\sum\:_{C\left(i\right)=l}{‖{x}_{i}-\stackrel{-}{{x}_{l}}‖}^{2}$$ 2 in which \(\:k\) is the number of categories, \(\:C\left(i\right)\) is the sample aggregate of the lth category, \(\:\stackrel{-}{{x}_{l}}={（{\stackrel{-}{x}}_{1l}，{\stackrel{-}{x}}_{2l}\dots\:，{\stackrel{-}{x}}_{ml}）}^{T}\) is the mean or the center of the \(\:l\) category, and it is the clustering index dimension. 2.2.3 STIRPAT model The STIRPAT model is an extended stochastic framework for assessing environmental impacts, widely used to examine how factors such as population, affluence, and technological level influence environmental pressures. By capturing the complex mechanisms through which population dynamics, economic growth, and energy consumption affect the environment, the model provides a robust analytical basis for identifying the key drivers of carbon emissions in Chinese cities. Therefore, this study employs the STIRPAT model to quantitatively analyze the socioeconomic and technological determinants of urban carbon emissions.The formula is: $$\:I\text{n}\text{y}=In\alpha\:+{\beta\:}_{1}In{x}_{1}+{\beta\:}_{2}In{x}_{2}+{\beta\:}_{3}In{x}_{3}+{\beta\:}_{4}In{x}_{4}+Ine$$ 3 in which \(\:Iny\) represents the carbon emission; \(\:{Inx}_{1}\) represents the urbanization rate, the data comes from China Statistical Yearbook; \(\:{Inx}_{2}\) represents the per capita GDP, per capita GDP = GDP/population; \(\:{Inx}_{3}\) represents the index of the advanced industrial structure; \(\:{Inx}_{4}\) represents the energy intensity, the energy intensity is numerically equal to the ratio of the energy consumption to the GDP; \(\:\:In\alpha\:\) represents the constant term; Ine represents the error term. represents energy intensity, which is numerically equal to the ratio of energy consumption to GDP; \(\:In\alpha\:\) represents the constant term; \(\:Ine\) represents the error term. These indicators are log-transformed to obtain the multiple linear regression equation. Result The changes of carbon emissions during 2005 to 2020 This study focuses on a comprehensive dataset of 296 cities in China. Total carbon emissions amounted to 659×10 6 tons in 2005, with an average of 22.27 million tons per city. By 2010, total emissions had risen to 957×10 6 tons, and the city-level average increased to 32.33 million tons. In 2015, total emissions reached 1137×10 6 tons, with an average of 38.41 million tons per city. By 2020, total emissions reached 1187×10 6 tons, and the average emission per city was 40.09 million tons. Overall, carbon emissions exhibited a significant upward trend from 2005 to 2020, with a cumulative increase of approximately 80% over the 15-year period. Based on the 2020 carbon emissions data for 296 cities in China, the histograms of both total and per capita emissions approximately follow a normal distribution, with fewer cities exhibiting extremely low or high emission levels. The range between the highest and lowest values is substantial, indicating significant inter-city variation (Fig. 1 ). The histogram of total emissions exhibits a slight left skew (i.e., tail extending toward lower values), with the mean emission level at 40.09 million tons and the median at 42.11 million tons—values that are closely aligned, suggesting a relatively symmetric underlying distribution despite minor deviations. The number of cities with extremely low or high emission levels was relatively small, while the range between the highest and lowest values was substantial. The largest group consisted of 64 cities with emissions between 10 and 20 million tons, followed by 54 cities emitting between 20 and 30 million tons. There were 17 cities with emissions exceeding 100 million tons of carbon dioxide, and 37 cities with emissions below 10 million tons. In comparison, the histogram of per capita emissions was more concentrated—excluding outliers—and exhibited less dispersion than that of total emissions. The mean per capita emission was 12 tons, with a median of 11 tons, indicating a close alignment between the two measures and suggesting a more symmetric distribution. The spatio-temporal distribution of carbon emissions from 2005 to 2020 China's urban carbon emissions and China's topography have shown an opposite distribution, generally showing the characteristics of high east and low west (Fig. 2 ). Taking 2020 as an example (Fig. 1 d), the top 10 cities in the country in terms of total carbon emissions were Tangshan, Shanghai, Ordos, Suzhou, Chongqing, Tianjin, Binzhou, Handan, Yulin, and Yinchuan in order. These cities had high fossil energy consumption and relatively large urban populations. Most of them were industrialized cities, mainly concentrated in the eastern region. The cities with total emissions ranking in the bottom 10 in the country were Lhasa, Zhangjiajie, Ankang, Suining, Ziyang, Chandu, Shannan, Rikaze, Nagqu and Linzhi. These cities had low fossil energy consumption, sparse populations, and large forest carbon sinks. They were mainly concentrated in the southwest. The spatial distribution of per capita carbon emissions in cities have differed significantly from the spatial distribution of the total, as shown in Fig. 3 . Cities with high per capita emissions were mainly located in northern China, especially in the coal-producing provinces in the north. While, those with low per capita emissions were mainly located in the south, especially in Yunnan Province and the Tibet Autonomous Region in the southwestern part of the country. According to the division of China's seven major geographical regions, statistics on the share of carbon emissions in different geographic subregions were provided to reveal regional differences (the data excludes Hong Kong, Macao, and Taiwan). From 2005 to 2020, carbon emissions in East China accounted for more than 30% of the national total (Fig. 4 ), showing a slight upward trend. It was the main source of China's carbon emissions, the key distribution area of the country's power, smelting, and other high-carbon industries. North China's carbon emissions accounted for about 20% of the country's total. It was the key area of coal production and consumption in China and the second largest carbon emission area in China. Southwest, Northeast, and Central China of the proportion of carbon emissions have shown a decreasing trend. The total carbon emissions in Northeast China accounted for 11.12% of the total carbon emissions in China in 2005, and decreased to 9.12% in 2020. It mainly due to the decline of heavy industry, the promotion of ecological environment restoration and industrial structure adjustment. The carbon emissions in the Northwest and South China showed an upward trend of about 10%. With the construction of the Northwest Development Strategy and the Silk Road Economic Belt, the Northwest region has seen an increase in the number of development opportunities and economic investment in recent years, so the total amount of carbon emission has increased from 2005 to 2020. From 2005 to 2020, China's urban per capita carbon emissions have showed an upward trend, growing from 6.41t/person in 2005 to 12.20t/person in 2020, with a growth rate as high as 190%. From 2005 to 2015, it has shown a significant rise, with a fast annual growth rate, and from 2015 to 2020, and per capita carbon emissions has shown a relatively slow growth. Discussion Cluster feature analysis Using the Moran's I index to analyze spatial differences, the results indicate an increase from 0.194227 in 2005 to 0.482311 in 2020, suggesting a positive correlation in the spatial distribution of carbon emissions across China. The Moran's I indices for urban carbon emissions in China were all greater than zero and passed the significance test at the 95% level, demonstrating significant spatial agglomeration effects (Fig. 5 ). In this figure, the X-axis represents the standardized values of urban carbon emissions, while the Y-axis depicts the spatially lagged values of urban carbon emissions. These can be classified into four categories: (1) high-high agglomeration type (HH), located in the first quadrant; (2) low-high agglomeration type (LH), situated in the second quadrant; (3) low-low agglomeration type (LL), found in the third quadrant; and (4) high-low agglomeration type (HL), positioned in the fourth quadrant. K-means clustering was employed to assess urban carbon emissions in 2020. The clustering indices comprised both static indicators (such as population, GDP per capita, proportion of value-added from the secondary industry, energy consumption relative to gross regional product, proportion of built-up area, and carbon emissions per capita) and dynamic indicators (including population growth rate, GDP growth rate, growth rate of built-up area, and growth rate of carbon emissions). The Monte Carlo model was utilized to randomly select initial points in order to verify the stability of the clustering results. In conjunction with the local Moran's I scatter plot analysis, the final clustering outcomes revealed four distinct categories of cities: low-carbon demonstration type, low-carbon development type, resource-dependent type, and traditional energy consumption type. Influencing factors analysis The STIRPAT model, an expandable framework for environmental impact assessment, has been employed to analyze various impact factors. It carried out the related research on the four independent variables that are the urbanization rate, the per capita GDP, the index of industrial structure advancement and the energy intensity. Following logarithmic transformation, the STIRPAT model is formulated as a multivariate linear equation with urbanization rate ( \(\:{Inx}_{1}\) ), per capita GDP ( \(\:{Inx}_{2}\) ), industrial structure advanced index ( \(\:{Inx}_{3}\) ), and energy intensity ( \(\:{Inx}_{4}\) ) as independent variables. Carbon dioxide emissions ( \(\:Iny\) ) is designated as the dependent variable, while \(\:In\) α represents the constant term and \(\:Ine\) as the error term. Consequently, by incorporating these four indicators—urbanization rate, per capita GDP, industrial structure advancement index, and energy intensity—into the STIRPAT model, we derive a comprehensive equation. The results are presented in Table 2 ; based on Eq. ( 3 ), this reflects the benchmark regression analysis of panel data pertaining to low-carbon demonstration cities as an illustrative example F(4, 150) = 611.194;P = 0.000༛ R 2 = 0.233;R 2 (within) = 0.953 * P < 0.05 ** P < 0.01 From Table 2 , it can be seen that the model R 2 (within)=0.953, which indicates that \(\:{Inx}_{1}\) , \(\:{Inx}_{2}\) , \(\:{Inx}_{3}\) and \(\:{Inx}_{4}\) can explain 95.3% of the changes of \(\:Iny\) . The model passes the F-test, which indicates that at least one of \(\:{Inx}_{1}\) , \(\:{Inx}_{2}\) , \(\:{Inx}_{3}\) and \(\:{\:Inx}_{4}\) will have an effect on \(\:Iny\) , and the model formula is: \(\:Iny=15.164+0.311In{x}_{1}+0.495In{x}_{2}-1.256In{x}_{3}+0.077In{x}_{4}\) . Among them, the significance of \(\:{Inx}_{1}\) is 0.05 (t = 3.594, P = 0.0270, which indicates that \(\:{Inx}_{1}\) will have a significant positive influence on \(\:Iny\) . The significance of \(\:{Inx}_{2}\) is 0.01 (t = 11.228, P = 0.0000, which indicates that \(\:{Inx}_{2}\) will have a significant positive influence on \(\:Iny\) . The significance of \(\:{Inx}_{3}\) is 0.01 (t=-6.159, P = 0.000< 0.01), and the value of regression coefficient is -1.256 < 0, so \(\:{Inx}_{3}\) will have a significant negative influence on \(\:Iny\) . The significance of \(\:{Inx}_{4}\) is 0.05 (t = 2.031, P = 0.015 0, so \(\:{Inx}_{4}\) will have a significant positive influence on \(\:Iny\) . ①Low-carbon demonstration cities. They are mainly clustered in developed cities along the eastern coast, represented by Beijing, Tianjin, Shanghai, Nanjing, Hangzhou, Guangzhou, Xiamen and etc. These cities have high population growth rate, high GDP growth rate, relatively high carbon emissions. However, the per capita carbon emissions are at a low level, and the economic volume is large and in steady growth. From the formula of STIRPAT model, it can be calculated to see that urbanization rate, per capita GDP and energy intensity will promote the increase of carbon emissions, showing an increasing relationship. The elasticity coefficient of the index of advanced industrial structure is negative, indicating that the more the industrial structure develops in the direction of advanced development, the lower the carbon emissions, and the relationship between the two shows a decreasing relationship. The results of the regression of carbon emission influencing factors are shown in Table 2 . The standardized coefficients of urbanization rate, per capita GDP, industrial structure advanced index and energy intensity are 0.311, 0.495, -1.256 and 0.077 respectively, which indicate that the influence of each index on carbon emission is in descending order, industrial structure advanced, per capita GDP, urbanization rate and energy intensity. The industrial structure of low-carbon demonstration cities has been transformed into a low-carbon structure, with the industrial structure mostly dominated by the tertiary industry and a net inflow of population. In the future development process, optimizing urban planning is an effective means of low carbon emission [17–18]. The total carbon emission of the city well be achieved through rational planning of the functions of the city. Reducing population flow and reducing traffic distance can achieve emission reduction. Table 2 Regression results of panel data on factors influencing carbon emissions in low-carbon demonstration cities Term Coef Std.Err t p 95%CI Intercept 15.164 0.436 29.381 0.001 ** 13.925 ~ 16.023 \(\:{\varvec{I}\varvec{n}\varvec{x}}_{1}\) 0.311 0.125 3.594 0.027 * 0.022 ~ 0.456 \(\:{\varvec{I}\varvec{n}\varvec{x}}_{2}\) 0.495 0.097 11.228 0.000 ** 0.327 ~ 0.548 \(\:{\varvec{I}\varvec{n}\varvec{x}}_{3}\) -1.256 0.225 -6.159 0.000 ** -2.266~-0.973 \(\:{\varvec{I}\varvec{n}\varvec{x}}_{4}\) 0.077 0.012 2.031 0.015 * 0.001 ~ 0.094 Note: * * and * represent significant model results at confidence levels of 5% and 10% ②Low-carbon development type cities. Mainly concentrated in southwest, central and south China, etc., with a wide distribution, the representative cities are Guiyang, Nanchang, Changsha, Kunming, Ganzhou, Xiangtan, Mianyang and so on. These cities have faster population growth, low per capita GDP, relatively fast growth in carbon emissions, relatively low per capita carbon emissions, better industrial structure, but relatively low level of economic development, a low carbon industry driven economic growth development model, and economic development to be further developed. The standardized coefficients of urbanization rate, per capita GDP, industrial structure advanced index and energy intensity are 0.145, 0.238, -0.477 and 0.192 respectively by using the STIRPAT model. The results are shown in Table 3 , indicating that the impacts of each index on carbon emissions are industrial structure advanced, per capita GDP, energy intensity and urbanization rate in the order of largest to smallest. These cities are in the early stage of industrialization and urbanization, and carbon emissions will still rise and increase rapidly in the time frame of achieving carbon peak by 2030. Innovative low-carbon industrial technologies should be introduced in the future. Market-oriented low-carbon mechanisms should be established and the development of strategic emerging industries should be encouraged [19–21 . Urban construction should also focus on the application of low-carbon urban planning and building energy-saving retrofit technologies. City construction should focus on low-carbon urban planning and the application of building energy-saving retrofit technologies. Table 3 Regression Results of Panel Data on Factors Influencing Carbon Emissions in Low-carbon Development Cities Term Coef Std.Err t p 95%CI Intercept 10.617 0.407 25.682 0.001 ** 10.134 ~ 13.602 \(\:{\varvec{I}\varvec{n}\varvec{x}}_{1}\) 0.145 0.085 1.916 0.003 * 0.104 ~ 0.388 \(\:{\varvec{I}\varvec{n}\varvec{x}}_{2}\) 0.238 0.081 9.125 0.000 ** 0.124 ~ 0.391 \(\:{\varvec{I}\varvec{n}\varvec{x}}_{3}\) -0.477 0.311 -4.288 0.000 ** -0.315~-0.629 \(\:{\varvec{I}\varvec{n}\varvec{x}}_{4}\) 0.192 0.109 1.729 0.035 * 0.026 ~ 0.224 Note: * * and * represent significant model results at confidence levels of 5% and 10% (3) Resource-dependent cities. Mainly in the north and west of the relatively resource-concentrated cities, represented by Yinchuan, Yulin, Linzhi, Jiayuguan, Wuhai, Shizuishan and etc. The population are not large and GDP is relatively high. Most of cities are dominated by the secondary industry, and the population, GDP and carbon emissions show a slow growth trend. The regression results of carbon emission influencing factors are obtained, the standardized coefficients of urbanization rate, per capita GDP, industrial structure advanced index and energy intensity are 0.278, 0.314, -0.567, 0.493 respectively. The impact of each indicator on carbon emissions is energy intensity, industrial structure advancement, per capita GDP and urbanization rate in turn. Due to the long-term reliance on resource extraction and processing industries with high energy consumption and low output value, the low-carbon transformation of energy supply is in a grim situation, and the dominant industries in some cities have shrunk. Therefore, this type of cities should focus on improving the efficiency of resource use and building a diversified industrial system when carrying out the planning. Guiding the large-scale and intensive development of resources and improving the level of resource conservation and comprehensive utilization can reduce carbon emissions in the development. developing green mining, fostering and expanding successive alternative industries, and accelerating the development of modern service industry can also reduce carbon emissions [22–24] . It is expected that carbon emissions will still have a rising trend in the future. The industrial structure still has a large space for adjustment and transformation, trying to use low-carbon technology to transform and upgrade traditional industries, eliminate backward production capacity, green energy substitution, etc., which can appropriately reduce the city's carbon emissions Table 4: Regression Results of Panel Data on Factors Influencing Carbon Emissions in Resource Dependent Cities Term Coef Std.Err t p 95%CI I ntercept 11.973 0.546 26.537 0.002 ** 10.691~12.981 0.278 0.141 3.005 0.005 * 0.154~0.302 0.314 0.196 8.761 0.000 ** 0.237~0.386 -0.567 0.258 -4.845 0.001 ** -0.369~-0.657 0.493 0.187 2.114 0.053 * 0.148~0.524 Note: * * and * represent significant model results at confidence levels of 5% and 10% (4) Traditional energy-consuming cities. They are mainly concentrated in some heavy industrial development cities in the north, represented by Handan, Daqing, Anshan, Lianyungang, Baotou, Hohhot, Jixi and etc. The industries of these cities are mainly heavy industries, and their economic development relies on traditional energy-consuming industries, with relatively slow GDP growth and slow population growth. Due to the development of heavy industry, the carbon emissions are higher. The per capita carbon emissions of these cities are relatively high and the carbon emissions continue to grow. In the Table 5 , the standardized coefficients of urbanization rate, per capita GDP, industrial structure advanced index and energy intensity are 0.303, 0.295, -0.398, 0.412 respectively. The influence of each index on carbon emission is energy intensity, industrial structure advanced, urbanization rate, per capita GDP . The energy structure is one of the key factors affecting carbon emissions. The effective use of low-carbon industrial and recycling technologies, and the promotion of clean energy such as solar energy, wind energy, and hydroelectricity can reduce the carbon emissions. Government will guide the transformation of the industrial structure to low-carbon strategic emerging industries, such as high-end equipment manufacturing, new materials and modern services.Some cities have begun to vigorously develop new energy, focusing on "green power + Green hydrogen". Some cities have actively explored the cultivate "wind power + hydrogen storage" and "wind power + hydrogen storage" [25–27] . Table 5 Regression results of panel data on factors affecting carbon emissions in traditional energy-consumer cities Term Coef Std.Err t p 95%CI Intercept 13.128 0.529 27.358 0.001 ** 12.921 ~ 15.228 \(\:{\varvec{I}\varvec{n}\varvec{x}}_{1}\) 0.303 0.361 4.631 0.005 * 0.255 ~ 0.437 \(\:{\varvec{I}\varvec{n}\varvec{x}}_{2}\) 0.295 0.184 7.264 0.000 ** 0.216 ~ 0.409 \(\:{\varvec{I}\varvec{n}\varvec{x}}_{3}\) -0.398 0.305 -0.401 0.001 ** -0.264~-0.568 \(\:{\varvec{I}\varvec{n}\varvec{x}}_{4}\) 0.412 0.279 1.732 0.061 * 0.367 ~ 0.704 Note: ** and * represent model results that are significant at the 5% and 10% confidence levels Conclusion This paper have analyzes the spatial-temporal distribution characteristics of carbon emissions in 296 cities in China, and objectively reveals the spatial -temporal evolution characteristics of urban carbon emissions in China from 2005 to 2020. Using Moran's I index and K-mean clustering method to analyze the clustering of urban carbon emissions in 2020, and combining with the STIPAT model to explore the influencing factors of carbon emissions in different clustered cities, the results will provide certain references for the green and low-carbon development of the cities and the implementation of carbon emission reduction policies. From 2005 to 2020, China's urban carbon emissions have an obvious growth trend, and there is a large gap between the high and low values of urban carbon emissions. The average value of urban carbon emissions in 2020 was 40.09 million tons, and the median value was 42.11 million tons; the average value of per capita carbon emissions was 12 tons, and the median value was 11 tons, with the average value and median value being relatively close to each other. In 2020, the average urban carbon emissions was 4.09 million tons, with a median of 42.11 million tons; The average carbon emission per capita was 12 tons, and the median was 11 tons. The average carbon emissions per capita was close to the middle index. From 2005 to 2020, the average per capita carbon emissions of China's cities have showed an upward trend, growing from 6.41t/person in 2000 to 12.20t/person in 2020, with a growth rate as high as 190%. China's urban carbon emissions have obvious spatial differences and significant spatial correlation characteristics, presenting a distribution pattern of east > central > western. The overall distribution of urban carbon emissions in China was characterized by high in the east and low in the west, with east and north China being the main carbon emission regions in China, accounting for more than 50%. Moran's I index has increased from 0.194227 in 2005 to 0.482311 in 2020, which indicates that there is a positive correlation in the spatial distribution of China's carbon emissions. Moran's I index showed mainly of “low-low-low” and “low-low-high” types. Combined with the local Moran's I scatter diagram, the final clustering results were four types of cities: low-carbon demonstration type, low-carbon development type, resource-dependent type and traditional energy consumption type. The influencing factors of carbon emission in different types of cities are different. The STIRPAT model was used to analyze the influence of four indicators on carbon emissions in 2020. These four indicators were urbanization rate, per capita GDP, industrial structure upgrading index and energy intensity. ①Low-carbon demonstration-type cities were mainly concentrated in the eastern coastal area. Rational planning of urban functional areas and optimization of urban planning are effective means of low carbon emissions. ②Low-carbon development-type cities were mainly concentrated in Southwest, Central and South China. Developing low-carbon industrial technology and establishing a market-oriented low-carbon mechanism are effective ways to reduce carbon emissions. ③Resource-dependent cities were mainly scattered in the northern and western regions. Carbon emissions can be reduced by carrying out low-carbon technological transformation and upgrading traditional industries. ④Traditional energy-consuming cities were concentrated in some heavy industrial development areas in the north. Developing new energy, developing green and low-carbon industries and optimizing urban spatial layout can reduce carbon emissions. Declarations Author Contribution Ren and Wang collected and analyzed the data and wrote the manuscript; Song prepared Figure 1; Xiong prepared Figures 2 and 3; Yan reviewed the manuscript and the conclusion section. Data Availability Data is provided within supplementary information files. Funding Declaration This research was supported by the National Natural Science Foundation of China (Grant No. 42101473). Consent to Publish declaration Not applicable. Consent to Participate declaration Not applicable. Ethics declaration Not applicable. References Zheng X Q, Lu Y L, Yuan J J, et al. Drivers of change in China's energy-related CO2 emissions[J]. PNAS, 2020, 117(1): 29-36. Green F, Stern N. China's changing economy: implications for its carbon dioxide emissions[J]. Climate policy,2017,17(4):423-442. Guan D B, Klasen S, Hubacek K, et al. Determinants of stagnating carbon intensity in China. Nature Climate Change, 2014, 4(11): 1017-1023. Liu Z, Guan D B, Moore S, et al. Steps to China's carbon peak. Nature, 2015, 522(7556): 279-281. Xu G,Schwarz P,Yang H.Determining China's CO2 emissions peak with a dynamic nonlinear artificial neural network approach and with a dynamic nonlinear artificial neural network approach and scenario analysis [J].Energy Policy,2019,128:752-762. ZHUANG Guiyang，WEI Mingxin. Theory and pathway of city leadership in emission peak and carbon neutrality. China population, resources and environment, 2021,31(9):114-121. CAI Bofeng，CAO Libin，LEI Yu，et al. China’s carbon emission pathway under the carbon neutrality target.China population, resources and environment,2021,31(1):7 -14. FANG Chuanglin. China's Urban Agglomeration and Metropolitan Area Construction Under the New Development Pattern. Economic Geography, 2021,41(4):1-7. CHEN Zhanming, WU Shimei, MA Wenbo, et a1．Driving forces of carbon dioxide emission for China’s cities: empirical analysis based Off extended STIRPAT Model. China population，resources and environment, 2018, 28(10):45-54. WANG Yong, XU Ziyi, ZHANG Yaxin. Influencing factors and combined scenario prediction of carbon emission peaks in megacities in China: Based on Threshold-STIRPAT Model. Journal of Environmental Science,2019,39(12): 4284-4292. WANG Shaojian, Su Yongxian, Zhao Yabo. Regional inequality, spatial spillover effects and influencing factors of China's city-level energy-related carbon emissions. Acta Geographica Sinica, 2018,73(3): 414-428. MO Huibin, WANG Shaojian. Spatio-temporal Evolution and Spatial Effect Mechanism of Carbon Emission at County Level in the Yellow River Basin. Geographical Science,2021,41(8):1324-1335. WANG Zhaofeng, DU Yaoyao. Spatial-temporal Differences and Influencing Factors of Carbon Emission Efficiency in Hunan Province Based on SBM-DEA Model. Geographical Science, 2019, 39(5): 797-806. YU Xiang,CHEN Nan, LI Manqi. Research on carbon emission characteristics and reduction pathways of low-carbon pilotcities in China. China population, resourcesandenvironment, 2020,30(7):1-9. GUO Fang. WANG Can, ZHANG Shihui. Cluster Analysis of Carbon Emissions Peaking Trends in Chinese Cities. Chinese Journal of Environmental Management, 2021,13(1):40-48. CHENG Yu, ZHANG Yue, WANG Jingjing. Spatial-temporal evolution of provincial carbon emission performance and driving force of technological innovation in China. Geographical Science, 2023,43(2): 313-323 GU Zhangfeng, XU Lihua, MA Weiqi, et al. Spatio-temporal evolution of carbon emissions in metropolitan areas and its influencing factors:A case study of Zhejiang province. Journal of Natural Resources, 2022, 37(6): 1524-1539. JIANG Yunchen, ZHONG Sujuan, WANG Yi, et al. Spatio-temporal characteristics and influencing factors of carbon emission peak by province of China. Journal of Natural Resources, 2022,37(5):1289-1302. LIU Yuke, JIN Shengtian. Temporal and Spatial Evolution Characteristics and Influencing Factors of Energy Consumption Carbon Emissions in Six Provinces of Central China. Economic Geography,2019,39(1):182-191. Wang Y Q，Tan D M，Zhang J T，Meng N，Han B L，0uyang Z Y．The impact of urbanization on carbon emissions：Analysis of panel data from 158 cities in China．Acta Ecologica Sinica, 2020, 40(21): 7897-7907. PAN Jinghu, ZHAO Xuanru. Spatial difference simulation of China's carbon emissions using spatial regression models. Journal of Environmental Science, 2018,38(7):2894-2901. MA Li, WANG Jingxu, ZHANG Didi, et al. Developing FFCO2 emission inventory with high spatio-temporal resolution: Methodology and prospects. Acta Geographica Sinica ,2022,77(3):650-664. ZHANG Zhenlong, HOU Yanzhen, SUN Honghao. Calculation of carbon emissions and the difference of low-carbon development efficiency on city territorial space, Journal of Natural Resources, 2023,38(6):1461-1481. DENG Jixiang, LIU Xiao, WANG Zheng. Characteristics Analysis and Factor Decomposition Based on the Regional Difference Changes in China's CO2 Emission Journal of Natural Resources, 2014, 29(2): 189-200. XI Mingyue, CHEN Xuegang, WU Shengli. A review of carbon emissions from urban energy consumption in China. Environmental Protection Science, 2024, 50(3): 20-27. Yuan Y, Sun X T. Exploring the relationship between urbanization, industrial structure, energy consumption, economic growth and CO2 emissions: an empirical study based on the heterogeneity of inter-provincial income levels in China [J]. Climate Change Research, 2020, 16 (6): 738-747 Li Y Y，Zhang S．SpaIio-temporal evolution of urban carbon emission intensity spatiotemporal heterogeneity of influencing factors in China. China Environmental Science,2023,43(6)：3244-3254. Additional Declarations No competing interests reported. Supplementary Files 1.CarbonEmissionDataofPrefecturelevelCitiesinChinafrom1997to2020InterpolationMethod.xlsx 3.EnergyConsumptionDataofVariousCities20002022.xlsx 2.Permanentresidentpopulationandeconomicdatafrom2000to2022.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7730947","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":533733440,"identity":"3316167d-c8da-4bb5-b39d-d6a8422ed3f0","order_by":0,"name":"Huiru 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14:44:49","extension":"html","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":94354,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7730947/v1/1dbfa69b372e29c3a8e8bc34.html"},{"id":94456670,"identity":"e3b89076-5724-4a29-b58d-953de0c1d480","added_by":"auto","created_at":"2025-10-27 14:44:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":255350,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHistogram of Carbon Emissions in Chinese Cities (Left: Total Carbon Emissions; Right: Per Capita Carbon Emissions)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7730947/v1/cfc8e7276d31dd9975b58f34.png"},{"id":94456318,"identity":"2ea8326a-b930-4712-9ef6-3dd665fddf9b","added_by":"auto","created_at":"2025-10-27 14:44:26","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":110110,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCarbon Emission Distribution Map of Major Cities in China (a. in 2005; b. in 2010; in 2015; d. in 2020)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7730947/v1/b6bcf1616365e6082de00c3e.jpeg"},{"id":94456200,"identity":"7ef011e9-dd4e-4a3a-a3e7-ef9f24ae52c3","added_by":"auto","created_at":"2025-10-27 14:44:20","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":119046,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistribution of Per Capita Carbon Emissions of Major Cities in China (a. in 2005; b. in 2010; in 2015; d. in 2020)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7730947/v1/b560a6547c440b76a059fe19.jpeg"},{"id":94456701,"identity":"117546c1-f208-40dd-9a1f-ab8b2d15aeba","added_by":"auto","created_at":"2025-10-27 14:45:01","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":122909,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChanges in the proportion of total regional carbon emissions and per capita carbon emissions in China from 2000 to 2020\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7730947/v1/21fddd6d416ebc21c5fe1038.jpeg"},{"id":94456519,"identity":"7f4756c7-dd12-4d75-b011-4f2dfd23819e","added_by":"auto","created_at":"2025-10-27 14:44:37","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":25928,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMoran's I scatter plot of carbon emissions in Chinese cities from 2000 to 2020 (a: in 2005; b: in 2010; c: in 2015; d: in 2020)\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-7730947/v1/08c11437697d1e00b755b21d.jpeg"},{"id":98420979,"identity":"462364de-b9df-47d4-a123-2da896cc5c24","added_by":"auto","created_at":"2025-12-17 16:21:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1650088,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7730947/v1/62cf785d-91b4-4aea-9547-72d4ce9e8eaf.pdf"},{"id":94456465,"identity":"f3b85de3-9422-43f7-9592-0b5469cbdda0","added_by":"auto","created_at":"2025-10-27 14:44:30","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":306900,"visible":true,"origin":"","legend":"","description":"","filename":"1.CarbonEmissionDataofPrefecturelevelCitiesinChinafrom1997to2020InterpolationMethod.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7730947/v1/d4fe73a608158ae83644f432.xlsx"},{"id":94456609,"identity":"d95f5f76-14b2-4fe8-859b-8525ffd950da","added_by":"auto","created_at":"2025-10-27 14:44:48","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":244578,"visible":true,"origin":"","legend":"","description":"","filename":"3.EnergyConsumptionDataofVariousCities20002022.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7730947/v1/2bd4f5829d9a7a9618894531.xlsx"},{"id":94456322,"identity":"8192f839-140c-46ee-94cb-bc73999996f7","added_by":"auto","created_at":"2025-10-27 14:44:27","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1363174,"visible":true,"origin":"","legend":"","description":"","filename":"2.Permanentresidentpopulationandeconomicdatafrom2000to2022.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7730947/v1/7948cd331508d46e6203134f.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Research on the Spatio-temporal Distribution Characteristics and Cluster Analysis of Carbon Emissions in Chinese Cities","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCarbon dioxide (CO\u003csub\u003e2\u003c/sub\u003e) plays a critical role in global warming, and climate change driven by rising CO\u003csub\u003e2\u003c/sub\u003e emissions has become a major focus of international concern\u003csup\u003e[1\u0026ndash;3]\u003c/sup\u003e. The Intergovernmental Panel on Climate Change\u0026rsquo;s Special Report on Global Warming of 1.5 ℃ underscores that limiting temperature rise to 1.5 ℃ requires global CO\u003csub\u003e2\u003c/sub\u003e emissions to decrease by approximately 45% by 2030 relative to 2010 levels, with net-zero emissions to be achieved around 2050 \u003csup\u003e[4\u0026ndash;5]\u003c/sup\u003e. In 2020, the Chinese government announced its dual goals of achieving carbon peak and carbon neutrality. In November 2021, it released the \u0026ldquo;Opinions on Fully, Accurately, and Comprehensively Implementing the New Development Philosophy to Achieve Carbon Peak and Carbon Neutrality\u0026rdquo; and the \u0026ldquo;Action Plan for Carbon Peak and Carbon Neutrality before 2030,\u0026rdquo; which outline strategic measures to promote energy efficiency, carbon reduction, and emission control \u003csup\u003e[6]\u003c/sup\u003e. At the current stage of rapid urbanization, cities continue to concentrate population, economic activity, infrastructure, and other key resources, leading to an intensification of urban carbon emissions \u003csup\u003e[7]\u003c/sup\u003e. In China, urban areas generate over 70% of national CO\u003csub\u003e2\u003c/sub\u003e emissions while consuming 65% of the country\u0026rsquo;s total energy to produce 85% of its GDP \u003csup\u003e[8\u0026ndash;10]\u003c/sup\u003e. Therefore, urban carbon mitigation is pivotal to achieving national and global emission reduction targets.\u003c/p\u003e\u003cp\u003eCities vary significantly in terms of development status, population size, economic structure, and resource endowment, leading to substantial heterogeneity in urban carbon emissions. To effectively design and implement targeted carbon and emission reduction strategies, it is essential to understand the spatiotemporal distribution patterns of carbon emissions across cities nationwide. Wang et al. (2018, 2021) employed spatial autocorrelation analysis, spatial Markov transition matrices, and dynamic spatial panel data models to investigate the evolutionary trajectories of urban carbon emissions \u003csup\u003e[11\u0026ndash;12]\u003c/sup\u003e. Wang et al. (2019) applied the super-efficiency SBM-DEA model integrated with the Malmquist index to measure and analyze spatial disparities in carbon emission efficiency and environmental efficiency among 14 cities (prefectures) in Hunan Province from 2010 to 2016 \u003csup\u003e[13]\u003c/sup\u003e. Yu et al. (2020) utilized the Tapio decoupling model to examine the relationship between economic growth and total carbon emissions \u003csup\u003e[14]\u003c/sup\u003e. Guo et al. (2021) adopted the Monte Carlo method to classify and analyze carbon peak trajectories for 286 sample cities in China \u003csup\u003e[15]\u003c/sup\u003e. Cheng et al. (2023) combined the Super-SBM and STIRPAT models to explore the spatiotemporal evolution of carbon emission performance across 30 provincial-level regions in China \u003csup\u003e[16]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eExisting studies have examined the spatial distribution of carbon emissions in China and its regions by integrating various methods and models to identify the factors influencing emissions. However, due to differences in development stages, population sizes, resource endowments, and other contextual factors, the drivers of carbon emissions vary significantly across cities. Therefore, it is crucial to analyze the characteristics of urban carbon emissions and identify the key influencing factors specific to different city types. This study selects 296 Chinese cities as the research sample, employing Moran\u0026rsquo;s I index and K-means clustering to investigate the spatiotemporal patterns and spatial agglomeration of urban carbon emissions. Furthermore, the STIRPAT model is applied to examine the determinants of carbon emissions across distinct clustering categories. The findings aim to provide policy insights for local governments in formulating strategies related to carbon emission reduction, energy structure optimization, and industrial transformation..\u003c/p\u003e"},{"header":"materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eCurrent data\u003c/h2\u003e\u003cp\u003eCarbon dioxide emissions data are sourced from the China Cities Greenhouse Gas Inventory (CCG) urban area dataset. A total of 296 cities' carbon emissions data were selected to align with the spatial boundaries of administrative divisions (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Raw data on population, GDP, tertiary industry output, energy consumption, and related indicators are obtained from the China Statistical Yearbook. Geospatial data on China\u0026rsquo;s administrative divisions and built-up area extents are provided by the Resource and Environmental Sciences Data Center of the Chinese Academy of Sciences (CRESD).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eData Overview and Source in the periods of 2005 to 2020\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSpecific data\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSource\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePeriods\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePopulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e《China Statistical Yearbook》\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2005\u0026thinsp;~\u0026thinsp;2020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGDP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e《China Statistical Yearbook》\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2005\u0026thinsp;~\u0026thinsp;2020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCarbon emissions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e《China Urban Greenhouse Gas Working Group》\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2005\u0026thinsp;~\u0026thinsp;2020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndustrial structure\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e《China Statistical Yearbook》\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2005\u0026thinsp;~\u0026thinsp;2020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnergy consumption\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e《China Statistical Yearbook》\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2005\u0026thinsp;~\u0026thinsp;2020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eUrban built-up area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eResource and Environmental Science and Data Center of Chinese Academy of Sciences\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2005\u0026thinsp;~\u0026thinsp;2020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAnalysis method\u003c/h3\u003e\n\u003cp\u003eMoran's I index is a well-established spatial statistical method that measures the degree of spatial autocorrelation by evaluating the correlation between attribute values across spatial units, thereby revealing underlying spatial patterns. In this study, Moran's I index is employed to examine the spatial correlation and clustering characteristics of carbon emissions across Chinese cities, providing insight into the extent and distribution of regional emission disparities.It is of great as follows:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:Z\\left(I\\right)=\\frac{I-E\\left(I\\right)}{\\sqrt{V\\left(I\\right)}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ein which \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Z\\left(I\\right)\\)\u003c/span\u003e\u003c/span\u003e is the Moran's I index in the range [-1, 1]; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:E\\left(I\\right)\\)\u003c/span\u003e\u003c/span\u003e is the expected value of Moran's I index; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:V\\left(I\\right)\\)\u003c/span\u003e\u003c/span\u003eis the variance of Moran's I index.\u003c/p\u003e\u003cp\u003eCluster analysis is a statistical methodology used to classify research objects into relatively homogeneous groups based on their characteristics. In this study, the K-means clustering algorithm is employed, which begins by randomly selecting K data points as initial cluster centroids. Each remaining data point is then assigned to the nearest centroid based on Euclidean distance. Subsequently, the centroids are recalculated as the mean of all points within each cluster, and this process iterates until either the maximum number of iterations is reached or the change in centroid positions falls below a predefined threshold. This approach is applied to categorize cities according to their carbon emission patterns, enabling a more systematic analysis of regional emission heterogeneity. The formula is calculated as follows:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:\\text{W}\\text{S}\\text{S}=\\sum\\:_{l}^{k}\\sum\\:_{C\\left(i\\right)=l}{‖{x}_{i}-\\stackrel{-}{{x}_{l}}‖}^{2}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ein which \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:k\\)\u003c/span\u003e\u003c/span\u003e is the number of categories, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:C\\left(i\\right)\\)\u003c/span\u003e\u003c/span\u003e is the sample aggregate of the lth category, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\stackrel{-}{{x}_{l}}={（{\\stackrel{-}{x}}_{1l}，{\\stackrel{-}{x}}_{2l}\\dots\\:，{\\stackrel{-}{x}}_{ml}）}^{T}\\)\u003c/span\u003e\u003c/span\u003eis the mean or the center of the \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:l\\)\u003c/span\u003e\u003c/span\u003e category, and it is the clustering index dimension.\u003c/p\u003e\u003cp\u003e2.2.3 STIRPAT model\u003c/p\u003e\u003cp\u003eThe STIRPAT model is an extended stochastic framework for assessing environmental impacts, widely used to examine how factors such as population, affluence, and technological level influence environmental pressures. By capturing the complex mechanisms through which population dynamics, economic growth, and energy consumption affect the environment, the model provides a robust analytical basis for identifying the key drivers of carbon emissions in Chinese cities. Therefore, this study employs the STIRPAT model to quantitatively analyze the socioeconomic and technological determinants of urban carbon emissions.The formula is:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:I\\text{n}\\text{y}=In\\alpha\\:+{\\beta\\:}_{1}In{x}_{1}+{\\beta\\:}_{2}In{x}_{2}+{\\beta\\:}_{3}In{x}_{3}+{\\beta\\:}_{4}In{x}_{4}+Ine$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ein which \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Iny\\)\u003c/span\u003e\u003c/span\u003e represents the carbon emission; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Inx}_{1}\\)\u003c/span\u003e\u003c/span\u003erepresents the urbanization rate, the data comes from China Statistical Yearbook; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Inx}_{2}\\)\u003c/span\u003e\u003c/span\u003e represents the per capita GDP, per capita GDP\u0026thinsp;=\u0026thinsp;GDP/population; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Inx}_{3}\\)\u003c/span\u003e\u003c/span\u003e represents the index of the advanced industrial structure; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Inx}_{4}\\)\u003c/span\u003e\u003c/span\u003e represents the energy intensity, the energy intensity is numerically equal to the ratio of the energy consumption to the GDP;\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:In\\alpha\\:\\)\u003c/span\u003e\u003c/span\u003e represents the constant term; Ine represents the error term. represents energy intensity, which is numerically equal to the ratio of energy consumption to GDP; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:In\\alpha\\:\\)\u003c/span\u003e\u003c/span\u003e represents the constant term; \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Ine\\)\u003c/span\u003e\u003c/span\u003e represents the error term. These indicators are log-transformed to obtain the multiple linear regression equation.\u003c/p\u003e"},{"header":"Result","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eThe changes of carbon emissions during 2005 to 2020\u003c/h2\u003e\u003cp\u003eThis study focuses on a comprehensive dataset of 296 cities in China. Total carbon emissions amounted to 659\u0026times;10\u003csup\u003e6\u003c/sup\u003e tons in 2005, with an average of 22.27\u0026nbsp;million tons per city. By 2010, total emissions had risen to 957\u0026times;10\u003csup\u003e6\u003c/sup\u003e tons, and the city-level average increased to 32.33\u0026nbsp;million tons. In 2015, total emissions reached 1137\u0026times;10\u003csup\u003e6\u003c/sup\u003e tons, with an average of 38.41\u0026nbsp;million tons per city. By 2020, total emissions reached 1187\u0026times;10\u003csup\u003e6\u003c/sup\u003e tons, and the average emission per city was 40.09\u0026nbsp;million tons. Overall, carbon emissions exhibited a significant upward trend from 2005 to 2020, with a cumulative increase of approximately 80% over the 15-year period.\u003c/p\u003e\u003cp\u003eBased on the 2020 carbon emissions data for 296 cities in China, the histograms of both total and per capita emissions approximately follow a normal distribution, with fewer cities exhibiting extremely low or high emission levels. The range between the highest and lowest values is substantial, indicating significant inter-city variation (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The histogram of total emissions exhibits a slight left skew (i.e., tail extending toward lower values), with the mean emission level at 40.09\u0026nbsp;million tons and the median at 42.11\u0026nbsp;million tons\u0026mdash;values that are closely aligned, suggesting a relatively symmetric underlying distribution despite minor deviations.\u003c/p\u003e\u003cp\u003eThe number of cities with extremely low or high emission levels was relatively small, while the range between the highest and lowest values was substantial. The largest group consisted of 64 cities with emissions between 10 and 20\u0026nbsp;million tons, followed by 54 cities emitting between 20 and 30\u0026nbsp;million tons. There were 17 cities with emissions exceeding 100\u0026nbsp;million tons of carbon dioxide, and 37 cities with emissions below 10\u0026nbsp;million tons. In comparison, the histogram of per capita emissions was more concentrated\u0026mdash;excluding outliers\u0026mdash;and exhibited less dispersion than that of total emissions. The mean per capita emission was 12 tons, with a median of 11 tons, indicating a close alignment between the two measures and suggesting a more symmetric distribution.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eThe spatio-temporal distribution of carbon emissions from 2005 to 2020\u003c/h3\u003e\n\u003cp\u003eChina's urban carbon emissions and China's topography have shown an opposite distribution, generally showing the characteristics of high east and low west (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Taking 2020 as an example (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed), the top 10 cities in the country in terms of total carbon emissions were Tangshan, Shanghai, Ordos, Suzhou, Chongqing, Tianjin, Binzhou, Handan, Yulin, and Yinchuan in order. These cities had high fossil energy consumption and relatively large urban populations. Most of them were industrialized cities, mainly concentrated in the eastern region. The cities with total emissions ranking in the bottom 10 in the country were Lhasa, Zhangjiajie, Ankang, Suining, Ziyang, Chandu, Shannan, Rikaze, Nagqu and Linzhi. These cities had low fossil energy consumption, sparse populations, and large forest carbon sinks. They were mainly concentrated in the southwest.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe spatial distribution of per capita carbon emissions in cities have differed significantly from the spatial distribution of the total, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Cities with high per capita emissions were mainly located in northern China, especially in the coal-producing provinces in the north. While, those with low per capita emissions were mainly located in the south, especially in Yunnan Province and the Tibet Autonomous Region in the southwestern part of the country.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAccording to the division of China's seven major geographical regions, statistics on the share of carbon emissions in different geographic subregions were provided to reveal regional differences (the data excludes Hong Kong, Macao, and Taiwan). From 2005 to 2020, carbon emissions in East China accounted for more than 30% of the national total (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), showing a slight upward trend. It was the main source of China's carbon emissions, the key distribution area of the country's power, smelting, and other high-carbon industries.\u003c/p\u003e\u003cp\u003eNorth China's carbon emissions accounted for about 20% of the country's total. It was the key area of coal production and consumption in China and the second largest carbon emission area in China. Southwest, Northeast, and Central China of the proportion of carbon emissions have shown a decreasing trend. The total carbon emissions in Northeast China accounted for 11.12% of the total carbon emissions in China in 2005, and decreased to 9.12% in 2020. It mainly due to the decline of heavy industry, the promotion of ecological environment restoration and industrial structure adjustment.\u003c/p\u003e\u003cp\u003eThe carbon emissions in the Northwest and South China showed an upward trend of about 10%. With the construction of the Northwest Development Strategy and the Silk Road Economic Belt, the Northwest region has seen an increase in the number of development opportunities and economic investment in recent years, so the total amount of carbon emission has increased from 2005 to 2020.\u003c/p\u003e\u003cp\u003eFrom 2005 to 2020, China's urban per capita carbon emissions have showed an upward trend, growing from 6.41t/person in 2005 to 12.20t/person in 2020, with a growth rate as high as 190%. From 2005 to 2015, it has shown a significant rise, with a fast annual growth rate, and from 2015 to 2020, and per capita carbon emissions has shown a relatively slow growth.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eCluster feature analysis\u003c/h2\u003e\u003cp\u003eUsing the Moran's I index to analyze spatial differences, the results indicate an increase from 0.194227 in 2005 to 0.482311 in 2020, suggesting a positive correlation in the spatial distribution of carbon emissions across China. The Moran's I indices for urban carbon emissions in China were all greater than zero and passed the significance test at the 95% level, demonstrating significant spatial agglomeration effects (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In this figure, the X-axis represents the standardized values of urban carbon emissions, while the Y-axis depicts the spatially lagged values of urban carbon emissions. These can be classified into four categories: (1) high-high agglomeration type (HH), located in the first quadrant; (2) low-high agglomeration type (LH), situated in the second quadrant; (3) low-low agglomeration type (LL), found in the third quadrant; and (4) high-low agglomeration type (HL), positioned in the fourth quadrant.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eK-means clustering was employed to assess urban carbon emissions in 2020. The clustering indices comprised both static indicators (such as population, GDP per capita, proportion of value-added from the secondary industry, energy consumption relative to gross regional product, proportion of built-up area, and carbon emissions per capita) and dynamic indicators (including population growth rate, GDP growth rate, growth rate of built-up area, and growth rate of carbon emissions). The Monte Carlo model was utilized to randomly select initial points in order to verify the stability of the clustering results. In conjunction with the local Moran's I scatter plot analysis, the final clustering outcomes revealed four distinct categories of cities: low-carbon demonstration type, low-carbon development type, resource-dependent type, and traditional energy consumption type.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eInfluencing factors analysis\u003c/h3\u003e\n\u003cp\u003eThe STIRPAT model, an expandable framework for environmental impact assessment, has been employed to analyze various impact factors. It carried out the related research on the four independent variables that are the urbanization rate, the per capita GDP, the index of industrial structure advancement and the energy intensity.\u003c/p\u003e\u003cp\u003eFollowing logarithmic transformation, the STIRPAT model is formulated as a multivariate linear equation with urbanization rate (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Inx}_{1}\\)\u003c/span\u003e\u003c/span\u003e), per capita GDP (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Inx}_{2}\\)\u003c/span\u003e\u003c/span\u003e), industrial structure advanced index (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Inx}_{3}\\)\u003c/span\u003e\u003c/span\u003e), and energy intensity (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Inx}_{4}\\)\u003c/span\u003e\u003c/span\u003e) as independent variables. Carbon dioxide emissions (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Iny\\)\u003c/span\u003e\u003c/span\u003e) is designated as the dependent variable, while \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:In\\)\u003c/span\u003e\u003c/span\u003eα represents the constant term and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Ine\\)\u003c/span\u003e\u003c/span\u003e as the error term. Consequently, by incorporating these four indicators\u0026mdash;urbanization rate, per capita GDP, industrial structure advancement index, and energy intensity\u0026mdash;into the STIRPAT model, we derive a comprehensive equation. The results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e; based on Eq.\u0026nbsp;(\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), this reflects the benchmark regression analysis of panel data pertaining to low-carbon demonstration cities as an illustrative example\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eF(4, 150)\u0026thinsp;=\u0026thinsp;611.194;P\u0026thinsp;=\u0026thinsp;0.000༛\u003c/h2\u003e\u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.233;R\u003csup\u003e2\u003c/sup\u003e (within)\u0026thinsp;=\u0026thinsp;0.953\u003c/p\u003e\u003cp\u003e*\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 **\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/p\u003e\u003cp\u003eFrom Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, it can be seen that the model R\u003csup\u003e2\u003c/sup\u003e(within)=0.953, which indicates that \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Inx}_{1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Inx}_{2}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Inx}_{3}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Inx}_{4}\\)\u003c/span\u003e\u003c/span\u003e can explain 95.3% of the changes of\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Iny\\)\u003c/span\u003e\u003c/span\u003e. The model passes the F-test, which indicates that at least one of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Inx}_{1}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Inx}_{2}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Inx}_{3}\\)\u003c/span\u003e\u003c/span\u003e and\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:Inx}_{4}\\)\u003c/span\u003e\u003c/span\u003e will have an effect on \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Iny\\)\u003c/span\u003e\u003c/span\u003e, and the model formula is: \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Iny=15.164+0.311In{x}_{1}+0.495In{x}_{2}-1.256In{x}_{3}+0.077In{x}_{4}\\)\u003c/span\u003e\u003c/span\u003e. Among them, the significance of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Inx}_{1}\\)\u003c/span\u003e\u003c/span\u003eis 0.05 (t\u0026thinsp;=\u0026thinsp;3.594, P =\u0026thinsp;0.027\u0026lt;0.05), the value of regression coefficient is 0.311\u0026gt;0, which indicates that \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Inx}_{1}\\)\u003c/span\u003e\u003c/span\u003e will have a significant positive influence on \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Iny\\)\u003c/span\u003e\u003c/span\u003e. The significance of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Inx}_{2}\\)\u003c/span\u003e\u003c/span\u003eis 0.01 (t\u0026thinsp;=\u0026thinsp;11.228, P\u0026thinsp;=\u0026thinsp;0.000\u0026lt;0.01), and its regression coefficient value is 0.495\u0026gt;0, which indicates that \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Inx}_{2}\\)\u003c/span\u003e\u003c/span\u003e will have a significant positive influence on \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Iny\\)\u003c/span\u003e\u003c/span\u003e. The significance of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Inx}_{3}\\)\u003c/span\u003e\u003c/span\u003e is 0.01 (t=-6.159, P\u0026thinsp;=\u0026thinsp;0.000\u0026lt; 0.01), and the value of regression coefficient is -1.256\u0026thinsp;\u0026lt;\u0026thinsp;0, so \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Inx}_{3}\\)\u003c/span\u003e\u003c/span\u003e will have a significant negative influence on \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Iny\\)\u003c/span\u003e\u003c/span\u003e. The significance of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Inx}_{4}\\)\u003c/span\u003e\u003c/span\u003e is 0.05 (t\u0026thinsp;=\u0026thinsp;2.031, P =\u0026thinsp;0.015\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the value of its regression coefficient is 0.077\u0026thinsp;\u0026gt;\u0026thinsp;0, so \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{Inx}_{4}\\)\u003c/span\u003e\u003c/span\u003e will have a significant positive influence on \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:Iny\\)\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e①Low-carbon demonstration cities. They are mainly clustered in developed cities along the eastern coast, represented by Beijing, Tianjin, Shanghai, Nanjing, Hangzhou, Guangzhou, Xiamen and etc. These cities have high population growth rate, high GDP growth rate, relatively high carbon emissions. However, the per capita carbon emissions are at a low level, and the economic volume is large and in steady growth. From the formula of STIRPAT model, it can be calculated to see that urbanization rate, per capita GDP and energy intensity will promote the increase of carbon emissions, showing an increasing relationship. The elasticity coefficient of the index of advanced industrial structure is negative, indicating that the more the industrial structure develops in the direction of advanced development, the lower the carbon emissions, and the relationship between the two shows a decreasing relationship.\u003c/p\u003e\u003cp\u003eThe results of the regression of carbon emission influencing factors are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The standardized coefficients of urbanization rate, per capita GDP, industrial structure advanced index and energy intensity are 0.311, 0.495, -1.256 and 0.077 respectively, which indicate that the influence of each index on carbon emission is in descending order, industrial structure advanced, per capita GDP, urbanization rate and energy intensity. The industrial structure of low-carbon demonstration cities has been transformed into a low-carbon structure, with the industrial structure mostly dominated by the tertiary industry and a net inflow of population. In the future development process, optimizing urban planning is an effective means of low carbon emission [17\u0026ndash;18]. The total carbon emission of the city well be achieved through rational planning of the functions of the city. Reducing population flow and reducing traffic distance can achieve emission reduction.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegression results of panel data on factors influencing carbon emissions in low-carbon demonstration cities\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTerm\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoef\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd.Err\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIntercept\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e15.164\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.436\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e29.381\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e13.925\u0026thinsp;~\u0026thinsp;16.023\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{I}\\varvec{n}\\varvec{x}}_{1}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.594\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.027\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.022\u0026thinsp;~\u0026thinsp;0.456\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{I}\\varvec{n}\\varvec{x}}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.495\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.097\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e11.228\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.327\u0026thinsp;~\u0026thinsp;0.548\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{I}\\varvec{n}\\varvec{x}}_{3}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-1.256\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.225\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-6.159\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-2.266~-0.973\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{I}\\varvec{n}\\varvec{x}}_{4}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.015\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.001\u0026thinsp;~\u0026thinsp;0.094\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: * * and * represent significant model results at confidence levels of 5% and 10%\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e②Low-carbon development type cities. Mainly concentrated in southwest, central and south China, etc., with a wide distribution, the representative cities are Guiyang, Nanchang, Changsha, Kunming, Ganzhou, Xiangtan, Mianyang and so on. These cities have faster population growth, low per capita GDP, relatively fast growth in carbon emissions, relatively low per capita carbon emissions, better industrial structure, but relatively low level of economic development, a low carbon industry driven economic growth development model, and economic development to be further developed. The standardized coefficients of urbanization rate, per capita GDP, industrial structure advanced index and energy intensity are 0.145, 0.238, -0.477 and 0.192 respectively by using the STIRPAT model. The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, indicating that the impacts of each index on carbon emissions are industrial structure advanced, per capita GDP, energy intensity and urbanization rate in the order of largest to smallest.\u003c/p\u003e\u003cp\u003eThese cities are in the early stage of industrialization and urbanization, and carbon emissions will still rise and increase rapidly in the time frame of achieving carbon peak by 2030. Innovative low-carbon industrial technologies should be introduced in the future. Market-oriented low-carbon mechanisms should be established and the development of strategic emerging industries should be encouraged\u003csup\u003e[19\u0026ndash;21\u003c/sup\u003e. Urban construction should also focus on the application of low-carbon urban planning and building energy-saving retrofit technologies. City construction should focus on low-carbon urban planning and the application of building energy-saving retrofit technologies.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegression Results of Panel Data on Factors Influencing Carbon Emissions in Low-carbon Development Cities\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTerm\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoef\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd.Err\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIntercept\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10.617\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.407\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e25.682\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e10.134\u0026thinsp;~\u0026thinsp;13.602\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{I}\\varvec{n}\\varvec{x}}_{1}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.916\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.003\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.104\u0026thinsp;~\u0026thinsp;0.388\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{I}\\varvec{n}\\varvec{x}}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.124\u0026thinsp;~\u0026thinsp;0.391\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{I}\\varvec{n}\\varvec{x}}_{3}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.477\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.311\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-4.288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.315~-0.629\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{I}\\varvec{n}\\varvec{x}}_{4}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.192\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.109\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.729\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.035\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.026\u0026thinsp;~\u0026thinsp;0.224\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: * * and * represent significant model results at confidence levels of 5% and 10%\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e(3) Resource-dependent cities. Mainly in the north and west of the relatively resource-concentrated cities, represented by Yinchuan, Yulin, Linzhi, Jiayuguan, Wuhai, Shizuishan and etc. The population are not large and GDP is relatively high. Most of cities are dominated by the secondary industry, and the population, GDP and carbon emissions show a slow growth trend. The regression results of carbon emission influencing factors are obtained, the standardized coefficients of urbanization rate, per capita GDP, industrial structure advanced index and energy intensity are 0.278, 0.314, -0.567, 0.493 respectively. The impact of each indicator on carbon emissions is energy intensity, industrial structure advancement, per capita GDP and urbanization rate in turn.\u003c/p\u003e\u003cp\u003eDue to the long-term reliance on resource extraction and processing industries with high energy consumption and low output value, the low-carbon transformation of energy supply is in a grim situation, and the dominant industries in some cities have shrunk. Therefore, this type of cities should focus on improving the efficiency of resource use and building a diversified industrial system when carrying out the planning. Guiding the large-scale and intensive development of resources and improving the level of resource conservation and comprehensive utilization can reduce carbon emissions in the development. developing green mining, fostering and expanding successive alternative industries, and accelerating the development of modern service industry can also reduce carbon emissions\u003csup\u003e[22\u0026ndash;24]\u003c/sup\u003e. It is expected that carbon emissions will still have a rising trend in the future. The industrial structure still has a large space for adjustment and transformation, trying to use low-carbon technology to transform and upgrade traditional industries, eliminate backward production capacity, green energy substitution, etc., which can appropriately reduce the city's carbon emissions\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 4: Regression Results of Panel Data on Factors Influencing Carbon Emissions in Resource Dependent Cities\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1687%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTerm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoef\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.696%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd.Err\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9385%;\"\u003e\n \u003cp\u003e\u003cstrong\u003et\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3568%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5624%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1687%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eI\u003c/strong\u003e\u003cstrong\u003entercept\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2777%;\"\u003e\n \u003cp\u003e11.973\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.696%;\"\u003e\n \u003cp\u003e0.546\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9385%;\"\u003e\n \u003cp\u003e26.537\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3568%;\"\u003e\n \u003cp\u003e0.002\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5624%;\"\u003e\n \u003cp\u003e10.691~12.981\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1687%;\"\u003e\n \u003cp\u003e\u003cimg width=\"25\" height=\"16\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2777%;\"\u003e\n \u003cp\u003e0.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.696%;\"\u003e\n \u003cp\u003e0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9385%;\"\u003e\n \u003cp\u003e3.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3568%;\"\u003e\n \u003cp\u003e0.005\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5624%;\"\u003e\n \u003cp\u003e0.154~0.302\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1687%;\"\u003e\n \u003cp\u003e\u003cimg width=\"25\" height=\"16\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2777%;\"\u003e\n \u003cp\u003e0.314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.696%;\"\u003e\n \u003cp\u003e0.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9385%;\"\u003e\n \u003cp\u003e8.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3568%;\"\u003e\n \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5624%;\"\u003e\n \u003cp\u003e0.237~0.386\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1687%;\"\u003e\n \u003cp\u003e\u003cimg width=\"25\" height=\"16\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2777%;\"\u003e\n \u003cp\u003e-0.567\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.696%;\"\u003e\n \u003cp\u003e0.258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9385%;\"\u003e\n \u003cp\u003e-4.845\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3568%;\"\u003e\n \u003cp\u003e0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5624%;\"\u003e\n \u003cp\u003e-0.369~-0.657\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16.1687%;\"\u003e\n \u003cp\u003e\u003cimg width=\"25\" height=\"16\" src=\"data:image/png;base64,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\" alt=\"image\"\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 18.2777%;\"\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 16.696%;\"\u003e\n \u003cp\u003e0.187\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14.9385%;\"\u003e\n \u003cp\u003e2.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.3568%;\"\u003e\n \u003cp\u003e0.053\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20.5624%;\"\u003e\n \u003cp\u003e0.148~0.524\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: * * and * represent significant model results at confidence levels of 5% and 10%\u003c/p\u003e\u003cp\u003e(4) Traditional energy-consuming cities. They are mainly concentrated in some heavy industrial development cities in the north, represented by Handan, Daqing, Anshan, Lianyungang, Baotou, Hohhot, Jixi and etc. The industries of these cities are mainly heavy industries, and their economic development relies on traditional energy-consuming industries, with relatively slow GDP growth and slow population growth. Due to the development of heavy industry, the carbon emissions are higher. The per capita carbon emissions of these cities are relatively high and the carbon emissions continue to grow. In the Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, the standardized coefficients of urbanization rate, per capita GDP, industrial structure advanced index and energy intensity are 0.303, 0.295, -0.398, 0.412 respectively. The influence of each index on carbon emission is energy intensity, industrial structure advanced, urbanization rate, per capita GDP .\u003c/p\u003e\u003cp\u003eThe energy structure is one of the key factors affecting carbon emissions. The effective use of low-carbon industrial and recycling technologies, and the promotion of clean energy such as solar energy, wind energy, and hydroelectricity can reduce the carbon emissions. Government will guide the transformation of the industrial structure to low-carbon strategic emerging industries, such as high-end equipment manufacturing, new materials and modern services.Some cities have begun to vigorously develop new energy, focusing on \"green power\u0026thinsp;+\u0026thinsp;Green hydrogen\". Some cities have actively explored the cultivate \"wind power\u0026thinsp;+\u0026thinsp;hydrogen storage\" and \"wind power\u0026thinsp;+\u0026thinsp;hydrogen storage\"\u003csup\u003e[25\u0026ndash;27]\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRegression results of panel data on factors affecting carbon emissions in traditional energy-consumer cities\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTerm\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCoef\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eStd.Err\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003et\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e95%CI\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIntercept\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e13.128\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.529\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e27.358\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e12.921\u0026thinsp;~\u0026thinsp;15.228\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{I}\\varvec{n}\\varvec{x}}_{1}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.361\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.631\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.005\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.255\u0026thinsp;~\u0026thinsp;0.437\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{I}\\varvec{n}\\varvec{x}}_{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.295\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e7.264\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.216\u0026thinsp;~\u0026thinsp;0.409\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{I}\\varvec{n}\\varvec{x}}_{3}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.305\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e-0.401\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e-0.264~-0.568\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{I}\\varvec{n}\\varvec{x}}_{4}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.412\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.732\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.061\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.367\u0026thinsp;~\u0026thinsp;0.704\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: ** and * represent model results that are significant at the 5% and 10% confidence levels\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis paper have analyzes the spatial-temporal distribution characteristics of carbon emissions in 296 cities in China, and objectively reveals the spatial -temporal evolution characteristics of urban carbon emissions in China from 2005 to 2020. Using Moran's I index and K-mean clustering method to analyze the clustering of urban carbon emissions in 2020, and combining with the STIPAT model to explore the influencing factors of carbon emissions in different clustered cities, the results will provide certain references for the green and low-carbon development of the cities and the implementation of carbon emission reduction policies.\u003c/p\u003e\u003cp\u003eFrom 2005 to 2020, China's urban carbon emissions have an obvious growth trend, and there is a large gap between the high and low values of urban carbon emissions. The average value of urban carbon emissions in 2020 was 40.09\u0026nbsp;million tons, and the median value was 42.11\u0026nbsp;million tons; the average value of per capita carbon emissions was 12 tons, and the median value was 11 tons, with the average value and median value being relatively close to each other. In 2020, the average urban carbon emissions was 4.09\u0026nbsp;million tons, with a median of 42.11\u0026nbsp;million tons; The average carbon emission per capita was 12 tons, and the median was 11 tons. The average carbon emissions per capita was close to the middle index. From 2005 to 2020, the average per capita carbon emissions of China's cities have showed an upward trend, growing from 6.41t/person in 2000 to 12.20t/person in 2020, with a growth rate as high as 190%.\u003c/p\u003e\u003cp\u003eChina's urban carbon emissions have obvious spatial differences and significant spatial correlation characteristics, presenting a distribution pattern of east\u0026thinsp;\u0026gt;\u0026thinsp;central\u0026thinsp;\u0026gt;\u0026thinsp;western. The overall distribution of urban carbon emissions in China was characterized by high in the east and low in the west, with east and north China being the main carbon emission regions in China, accounting for more than 50%. Moran's I index has increased from 0.194227 in 2005 to 0.482311 in 2020, which indicates that there is a positive correlation in the spatial distribution of China's carbon emissions. Moran's I index showed mainly of \u0026ldquo;low-low-low\u0026rdquo; and \u0026ldquo;low-low-high\u0026rdquo; types. Combined with the local Moran's I scatter diagram, the final clustering results were four types of cities: low-carbon demonstration type, low-carbon development type, resource-dependent type and traditional energy consumption type.\u003c/p\u003e\u003cp\u003eThe influencing factors of carbon emission in different types of cities are different. The STIRPAT model was used to analyze the influence of four indicators on carbon emissions in 2020. These four indicators were urbanization rate, per capita GDP, industrial structure upgrading index and energy intensity. ①Low-carbon demonstration-type cities were mainly concentrated in the eastern coastal area. Rational planning of urban functional areas and optimization of urban planning are effective means of low carbon emissions. ②Low-carbon development-type cities were mainly concentrated in Southwest, Central and South China. Developing low-carbon industrial technology and establishing a market-oriented low-carbon mechanism are effective ways to reduce carbon emissions. ③Resource-dependent cities were mainly scattered in the northern and western regions. Carbon emissions can be reduced by carrying out low-carbon technological transformation and upgrading traditional industries. ④Traditional energy-consuming cities were concentrated in some heavy industrial development areas in the north. Developing new energy, developing green and low-carbon industries and optimizing urban spatial layout can reduce carbon emissions.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eRen and Wang collected and analyzed the data and wrote the manuscript; Song prepared Figure 1; Xiong prepared Figures 2 and 3; Yan reviewed the manuscript and the conclusion section.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within supplementary information files.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eFunding Declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the National Natural Science Foundation of China (Grant No. 42101473).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declaration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZheng X Q, Lu Y L, Yuan J J, et al. 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Journal of Environmental Science,2019,39(12): 4284-4292.\u003c/li\u003e\n\u003cli\u003eWANG Shaojian, Su Yongxian, Zhao Yabo. Regional inequality, spatial spillover effects and influencing factors of China\u0026apos;s city-level energy-related carbon emissions. Acta Geographica Sinica, 2018,73(3): 414-428.\u003c/li\u003e\n\u003cli\u003eMO Huibin, WANG Shaojian. Spatio-temporal Evolution and Spatial Effect Mechanism of Carbon Emission at County Level in the Yellow River Basin. Geographical Science,2021,41(8):1324-1335.\u003c/li\u003e\n\u003cli\u003eWANG Zhaofeng, DU Yaoyao. Spatial-temporal Differences and Influencing Factors of Carbon Emission Efficiency in Hunan Province Based on SBM-DEA Model. Geographical Science, 2019, 39(5): 797-806.\u003c/li\u003e\n\u003cli\u003eYU Xiang,CHEN Nan, LI Manqi. Research on carbon emission characteristics and reduction pathways of low-carbon pilotcities in China. 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Acta Geographica Sinica ,2022,77(3):650-664.\u003c/li\u003e\n\u003cli\u003eZHANG Zhenlong, HOU Yanzhen, SUN Honghao. Calculation of carbon emissions and the difference of low-carbon development efficiency on city territorial space, Journal of Natural Resources, 2023,38(6):1461-1481.\u003c/li\u003e\n\u003cli\u003eDENG Jixiang, LIU Xiao, WANG Zheng. Characteristics Analysis and Factor Decomposition Based on the Regional Difference Changes in China\u0026apos;s CO2 Emission Journal of Natural Resources, 2014, 29(2): 189-200.\u003c/li\u003e\n\u003cli\u003eXI Mingyue, CHEN Xuegang, WU Shengli. A review of carbon emissions from urban energy consumption in China. Environmental Protection Science, 2024, 50(3): 20-27.\u003c/li\u003e\n\u003cli\u003eYuan Y, Sun X T. Exploring the relationship between urbanization, industrial structure, energy consumption, economic growth and CO2 emissions: an empirical study based on the heterogeneity of inter-provincial income levels in China [J]. Climate Change Research, 2020, 16 (6): 738-747\u003c/li\u003e\n\u003cli\u003eLi Y Y，Zhang S．SpaIio-temporal evolution of urban carbon emission intensity spatiotemporal heterogeneity of influencing factors in China. China Environmental Science,2023,43(6)：3244-3254.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7730947/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7730947/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCities are the most important source of greenhouse gas emissions, and urban carbon emission reduction has become an important measure of global response to climate change. Understanding the spatio-temporal evolution and clustering characteristics of urban carbon emissions are the prerequisite for realizing carbon emission reduction policies and implementation. In this paper, 296 municipal units in China have taken as the study area. This researches have used the Moran's index and K-means clustering to study the spatio-temporal distribution characteristics and clustering characteristics of urban carbon emissions. The STIRPAT model has used to analyze the influencing factors of urban carbon emissions with different clustering characteristics. The results showed that: ①During 2005 to 2020, there was a growing trend of urban carbon emission in China, and there was a big gap between the high and low values of urban carbon emission, with most of the cities concentrating in the middle value. The same was true for the per capita carbon emission. ② The overall distribution of urban carbon emissions in China has characterized by high in the east and low in the west, with east and north China being the main carbon emission regions.③ The carbon emissions of Chinese cities have obvious spatial differences and significant spatial correlation characteristics. Based on the cluster analysis of urban carbon emissions, 296 cities have classified into low-carbon demonstration cities, low-carbon development cities, resource-dependent cities and energy-consuming cities. Four types of cities were put forward corresponding low-carbon development suggestions combined with influencing factors.\u003c/p\u003e","manuscriptTitle":"Research on the Spatio-temporal Distribution Characteristics and Cluster Analysis of Carbon Emissions in Chinese Cities","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-27 11:40:05","doi":"10.21203/rs.3.rs-7730947/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"38236061-fa83-4b2f-aa45-0942368f4aa2","owner":[],"postedDate":"October 27th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-10T16:01:29+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-27 11:40:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7730947","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7730947","identity":"rs-7730947","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}