As one of the important sources of carbon emissions, the transportation industry should be given attention. This study investigates the relationship between vehicle ownership, economic growth, and environmental pressure on the Chongqing transportation industry (CQTI) based on CQTI data, then constructs a comprehensive regression model and couples the EKC curve and Tapio model for integrated analysis, and finally constructs a LEAP-Chongqing model to forecast CQTI from multiple perspectives. The innovations are that the multi-model examines the effects of different variables and has a better classification of transportation modes in scenario simulation. The results show that: (1) there is an inverse N-shaped relationship between car ownership, economic growth, and environmental pressure of CQTI; (2) every 1% of transportation output, urbanization rate, or car ownership will cause 0.769%, 0.111%, and 0.096% of carbon emission change, respectively; (3) gasoline, diesel and aviation kerosene consumption account for 80–90%, private cars cause 41–52% of carbon emissions, and the energy structure and transportation structure of CQTI are unreasonable; (4) the results of a multi-scenario simulation show that the energy saving and emission reduction effect of a single policy is not satisfactory, and the integration of energy saving and emission reduction measures should be strengthened.

The accelerated flow of resources and the population has resulted in the coexistence of the expansion and shrinkage of cities. The shrinking city is not a new thing, but it is a new problem that needs to be solved urgently in China. Urban resources reveal the direct cause of the shrinking city: attraction, which reflects the competition for external resources and the endowment of the intrinsic resources of cities. Therefore, this paper established the Urban Resource Degree (URD) model to measure the urban resource degree of the shrinking city. Factors were then selected to analyze the ways in which they influence the shrinking cities. Given the spillover effect and heterogeneity of the influencing factors, a Spatial Durbin Model (SDM) and a Spatiotemporal Geographically Weighted Regression Model (GTWR) were used to conduct a spatial spillover (SSA) and spatial heterogeneity (SHA) analysis of the URD of the shrinking cities in Northeast China. The results show that the ability gap to compete for and control resources between prefecture-level shrinking cities and county-level shrinking cities is narrowed. From the SSA and SHA perspectives, the influence factors can be categorized into three types: “High West–Low East and Negative–Positive Spillover”, “Low West and High East Positive–Negative Spillover”, “Low Northwest and High Southeast Negative Spillover”. Finally, policy implications are proposed to provide support for policy-making.

With increasing urbanisation, urban household electricity consumption (UHEC) has become the most dynamic aspect of China’s energy growth. However, existing studies suffer from outdated data, a small scope, and a lack of research into new influencing factors. There are significant challenges to the promotion of urban household energy-efficiency strategies, which may arise from the intervention of several new inter-provincial differences and other influencing factors. To better understand the variability, volatility characteristics, and influencing factors of change in provincial UHEC, this study analyses and assesses the influencing factors based on an extended STIRPAT model of Chinese provincial panel data from 2005 to 2020. The findings revealed rapid increases in provincial urban household electricity consumption and significant provincial differences in UHEC in China stemming from variation in economic level and energy use. Urbanisation, income, the size of the older population, and area per capita contributed to household electricity consumption. Conversely, household size, heating days (HDD), and air conditioning dampened household electricity consumption. However, television and cooling days (CDD) did not accurately explain the variation in household electricity use in this study. Finally, this study suggests targeted policy recommendations that could promote the implementation of energy-efficiency strategies in Chinese urban households.