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.

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 However, existing studies suffer from outdated data, a small scope, and a lack of research into new influencing factors.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.