Optimization of carbon peaking achieving paths in China's transportation sector under digital feature clustering

Abstract

Digital development disparities, technological diversity, and regional imbalances have led to a shortage of tailored carbon reduction strategies for the transportation sector. Understanding the mechanisms and future trajectories of transportation carbon emission (TCE) drivers across varying digitization levels is crucial. To address this issue, we propose a strategy that classifies Chinese provinces into four distinct digitization regions based on their digital development characteristics. First, we extend the STIRPAT-PLS framework to analyze the historical impacts of external governance and internal development factors on TCE. Second, we use scenario settings and Monte Carlo simulation techniques to simulate future trends of TCE. Third, we conduct a dynamic sensitivity analysis to assess the contributions of factors, aiming to optimize the TCE peaking path. Our results indicate that the digitization context cannot fully explain the differences in TCE; even in regions with similar levels of digitization, the contributions of influencing factors vary significantly. Under the BAU scenario, existing policies and technologies may be insufficient to achieve TCE peaking before 2030. This work guides achieving carbon peaking in China's transportation sector. It emphasizes that we should build upon the digital development context and harness the positive effects of different influences to promote context-specific policy implementation.