Transportation de-carbonization pathways and effect in China: A systematic analysis using STIRPAT-SD model

Transportation de-carbonization is a complex problem involving the economy, population, technology and environment. Implementing the pathway simulation based on systematic methods will help to optimize the transportation sustainability plan. This study's key motivation is that earlier research ignored the relationship between driving factors and the transmission process. To quantitatively identify the path and process of emission reduction, a hybrid system dynamics STIRPAT-SD model is proposed to explore the transportation optimization's de-carbonization ability. This study fully considers the composition of elements and subsystems based on the STIRPAT theoretical model and visually shows the system's feedback relationship. Transportation structural and technical optimization scenarios are set to identify the threshold reduction paths. It is found these optimization strategies have significant de-carbonization effects. And transportation structure policy has the highest de-carbonization efficiency, the emission intensity decreased by 9.1% under the TSS2 scenario (Transportation structure scenario). This study proposes a novelty model combining dynamic simulating processes with a significantly theoretical model to improve simulation and factor composition accuracy. And the joint scenario setting identifies the most effective de-carbonization pathway and clarifies the threshold of all possible pathways. Research findings can effectively track, test, predict the achievement of policy goals, and provide policy optimization references for the sustainable development related to the transportation system in practice.