This study, using Chongqing City of China as an example, predicts the future motor vehicle population using the Gompertz Model and the motorcycle population using the piecewise regression model, and predicts and analyzes fuel consumption and greenhouse gas (GHG) emissions of motor vehicles from 2016 to 2035 based on the bottom-up method under different scenarios of improving the fuel economy of conventional vehicles, promoting alternative fuel vehicles, and the mixed policy of the above two policy options. The results indicate that the total population of motor vehicles in Chongqing will increase from 4.61 million in 2015 to 10.15 million in 2035. In the business-as-usual scenario, the road-transportation energy demand in Chongqing will keep increasing from 2015 and will peak in 2030, before it begins to decline by 2035. The trends for the tank to wheel (TTW) and well to wheel (WTW) GHG emissions are similar to that of energy demand. The WTW GHG emissions will increase from 24.9 Mt CO₂e in 2016 to 50.5 Mt CO₂e in 2030 and will then gradually decline to 48.9 Mt CO₂e in 2035. Under the policy scenarios of improving fuel economy of conventional fuel passenger cars, promoting alternative fuel vehicles, and their mixed policy, direct energy consumption and TTW and WTW GHG emissions from 2016 to 2035 will be reduced to different levels. It is also found that the two types of policies have a hedging effect on the direct energy-consumption saving, TTW, and WTW GHG emission reductions. Sensitivity analysis of key parameters and policy settings is conducted to investigate the impact of their changes on the vehicle population projection, direct energy demand, and WTW GHG emissions. Some policy implications are suggested to provide reference for the formulation and adjustment of Chongqing’s, or even China’s, low-carbon road transportation policies in the future based on the analysis results.

Megacities are the main sources of carbon emissions and are pillars of socioeconomic development due to economic prosperity, industrial development, and population agglomeration. Taking the megacity of Shenzhen, China, as an example, this research explored the advantages of low-carbon transformation in both climate change mitigation and socioeconomic progress. Soft linking of the Low Emissions Analysis Platform (LEAP) model with input–output analysis demonstrates that adopting feasible low-carbon transformation strategies has the potential to reverse the ongoing trend of carbon emission growth in Shenzhen, resulting in a peak before 2025. The peaks in carbon intensity and carbon emissions per capita occurred earlier than those in total carbon emissions. Furthermore, a total low-carbon investment of 462.04 billion CNY could yield approximately 799.49 billion CNY in output, 311.42 billion CNY in value-added, and 156.10 billion CNY in resident remuneration and create 1.79 million job opportunities during policy implementation. Taking into account both the reduction in carbon emissions and the socioeconomic benefits of low-carbon transformation, this research elucidates a potential pathway for Shenzhen to achieve synergy between mitigating climate change and promoting socioeconomic development, thus offering a valuable model for cities facing similar challenges.

Assessing transport CO₂ emissions is important in the development of low-carbon strategies, but studies based on mixed land use are rare. This study assessed CO₂ emissions from passenger transport in traffic analysis zones (TAZs) at the community level, based on a combination of the mixed-use development model and the vehicle emission calculation model. Based on mixed land use and transport accessibility, the mixed-use development model was adopted to estimate travel demand, including travel modes and distances. As a leading low-carbon city project of international cooperation in China, Shenzhen International Low-Carbon City Core Area was chosen as a case study. The results clearly illustrate travel demand and CO₂ emissions of different travel modes between communities and show that car trips account for the vast majority of emissions in all types of travel modes in each community. Spatial emission differences are prominently associated with inadequately mixed land use layouts and unbalanced transport accessibility. The findings demonstrate the significance of the mixed land use and associated job-housing balance in reducing passenger CO₂ emissions from passenger transport, especially in per capita emissions. Policy implications are given based on the results to facilitate sophisticated transport emission control at a finer spatial scale. This new framework can be used for assessing the impacts of urban planning on transport emissions to promote sustainable urbanization in developing countries.

Industrial relocation and ecological subsidy are viewed as effective ways to overcome transboundary industrial pollution. In this paper, we study the transboundary industrial pollution control problem in the Beijing-Tianjin-Hebei region in the context of industrial relocation. Firstly, we construct an economic model of pollution control with relevant variables such as environmental tax and environmental damage coefficient. Secondly, we solve the economic contributions by using the proportional split-off solution. Finally, we compare the optimal relocation quantity and welfare functions in both cooperative and non-cooperative cases. Our research finds that: 1) The optimal strategy is closely related to utility coefficient, environmental loss coefficient and incentive intensity coefficient. 2) The welfare function and the optimal relocation quantity in the cooperative case are significantly greater than those in the non-cooperative case. Based on the analysis, some suggestions are provided for transboundary industrial pollution management.