Simulation of urban transport carbon dioxide emission reduction environment economic policy in China: An integrated approach using agent-based modelling and system dynamics

Reducing greenhouse gas emissions has become a hot topic in the 21st century. As the main source of urban carbon dioxide (CO2) emissions, reducing transportation CO2 emission has also attracted great attention worldwide. To reduce traffic CO2 emissions, we propose a complex model that combines multiagent-based models and system dynamics models. From the perspective of the job-housing balance, we explore the inter-action among urban socioeconomic development, low-carbon transportation, environmental economic policies (prices, charging policies, etc.), and other factors, simulate the urban traffic CO2 emissions system and policies, investigate the impact of different strategies on traffic CO2 emissions reduction and the economic advantages to the environment, and then select an optimal policy scheme. The analysis results indicate that if we want to achieve the goals of Beijing's future development planning through comprehensive industrial structure adjust-ment and policy adjustment, then Beijing should accelerate the implementation of the policy of collecting traffic congestion charges. The pricing of traffic congestion charges should be 40 CNY, the parking fees should be increased to 13 CNY, the starting fares of buses and subways should be increased to 2.6 CNY and 4 CNY, respectively, and taxi starting prices should be raised to 17 CNY. At the same time, through the simulation results of the coupling model, the CO2 emissions reduction effect of each scenario in 2050 is better than that of business as usual scenario (BAU). Compared with BAU, C8 scheme has the largest emission reduction, with emission reduction of 19.5975 million tons in 2050, and CO2 emissions reduction intensity of more than 50%. As a result, given a comprehensive scenario, C8 is the optimal long-term development route. Our findings serve as a foun-dation for key Beijing administrative departments to enhance the jobs-housing link, as well as a guide for the development and execution of environmental and economic policies to reduce traffic CO2 emissions.

Automated summary

To reduce traffic CO2 emissions, we propose a complex model combining multiagent-based and system dynamics models. By exploring the interaction among urban socioeconomic development, low-carbon transportation, environmental economic policies, and other factors, we simulate the urban traffic CO2 emissions system and policies, investigate the impact of different strategies and select an optimal policy scheme.