Based on the extended STIRPAT model, this paper examines social and economic factors regarding PM_2.5 concentration intensity in 255 Chinese cities from 2007 to 2016, and includes quantile regressions to analyze the different effects of these factors among cities of various sizes. The results indicate that: (1) during 2007–2016, urban PM_2.5 concentration exhibited declining trends in fluctuations concerning the development of the urban economy, accompanied by uncertainty under different city types; (2) population size has a significant effect on propelling PM_2.5 concentration; (3) the effect of structure reformation on PM_2.5 concentration is evident among cities with different populations and levels of economic development; and (4) foreign investment and scientific technology can significantly reduce PM_2.5 emission concentration in cities. Accordingly, local governments not only endeavor to further control population size, but should implement a recycling economy, and devise a viable urban industrial structure. The city governance policies for PM_2.5 concentration reduction require re-classification according to different population scales. Cities with large populations (i.e., over 10 million) should consider reducing their energy consumption. Medium population-sized cities (between 1 million and 10 million) should indeed implement effective population (density) control policies, while cities with small populations (less than 1 million) should focus on promoting sustainable urban development to stop environmental pollution from secondary industry sources.

Based on the Spatial Durbin Model (SDM), this study evaluates the spatial spillover effect of PM_2.5 concentration in Beijing–Tianjin–Hebei (BTH) and its surrounding areas from 2000 to 2016, analyzes its main influencing factors and verifies the Environmental Kuznets Curve (EKC). In addition, Social Network Analysis (SNA) is used to measure the regional air pollution transmission network. The results are as follows: (1) A significant inverted U-shaped EKC with spatial spillover effect between the sampled 48 cities was verified. (2) Industrial structure had both local and spillover effects on air pollution with a U-shaped curve; technological progress exerted a negative spillover effect on air pollution, while traffic evidenced positive local and spillover effects; meteorological conditions showed different impacts on air pollution. (3) Heze, Tianjin, Xingtai, Shijiazhuang and Liaocheng are the top five cities in the centrality of the air pollution correlation network, indicating air pollution in these cities have significant impacts on other cities within the network; while Sanmenxia, Weihai, Yuncheng, Langfang and Zhumadian are the bottom five cities, which indicates that the air pollution of these cities has the least correlation with other cities. The policy suggestions for 48 cities involve: building up a regional joint prevention and control mechanism, enhancing the supervision of cities located in the centrality of the air pollution correlation network, accelerating high-tech and service-oriented industrialization, encouraging technological innovation in energy conservation and environmental protection and implementing vehicle regulation.

The reduction of CO₂ emission has become one of the significant tasks to control climate change in China. This study employs Exploratory Spatial Data Analysis (ESDA) to identify the provinces in China with different types of spatiotemporal transition, and applies the Logarithmic Mean Divisia Index (LMDI) to analyze the influencing factors of industrial CO₂ emissions. Spatial autocorrelation of provincial industrial CO₂ emissions from 2003 to 2017 has been demonstrated. The results are as follows: (1) 30 provinces in China are categorized into 8 types of spatiotemporal transition, among which 24 provinces are characterized by stable spatial structure and 6 provinces show significant spatiotemporal transition; (2) For all types of spatiotemporal transition, economic scale effect is mostly contributed to industrial CO₂ emission, while energy intensity effect is the most crucial driving force to reduce industrial carbon dioxide emission; (3) provinces of type HH-HH, HL-HL and HL-HH are most vital for CO₂ emission reduction, while the potential CO₂ emission increase of developing provinces in LL-LL, LH-LH and LL-LH should also be taken into account. Specific measures for CO₂ emission reduction are suggested accordingly.