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.

Under the carbon peaking and carbon neutrality goals, the new energy industry has emerged as a new engine for China’s economic development. Establishing a collaborative innovation system serves as a critical pathway to accelerate the growth of strategic emerging industries such as new energy. Critical minerals represented by nickel ore play an essential role as raw materials in advancing the new energy sector. This study constructs a tripartite evolutionary game model involving the government, nickel mining enterprises, and new energy enterprises from a systems perspective. It analyzes behavioral choices and equilibrium states during collaborative innovation processes and simulates the evolution of interactions among these stakeholders. The research reveals two evolutionary equilibrium points in this collaborative innovation system. The initial willingness of participating entities significantly influences the system’s convergence speed toward stability—higher initial willingness accelerates this process. Furthermore, reducing government supervision costs, reasonably increasing financial support, enhancing enterprises’ excess returns, and appropriately lowering information platform construction costs collectively foster the development of this collaborative innovation system.