Reducing Carbon Emissions from Transportation: Pathway Setting Strategy and Prediction Uncertainty Analysis

Abstract

Carbon emissions from transportation contribute over 25% of global carbon emissions, with China’s accounting for approximately 10%. This is expected to increase as China’s economy grows, conflicting with carbon peak targets. However, various factors make this prediction uncertain, for example, there is a lack of research into the implementation of emission reduction measures. To fill this gap, this study proposes an uncertainty assessment method based on pathway selection. Specifically, six emission reduction paths (focusing on the reduction intensity change pre-2030 and post-2030) were designed for four reduction measures; the path combination with the lowest 2030 peak carbon emissions was chosen for uncertainty analysis. A refined parameter uncertainty assessment method was then introduced, considering both influencing factor initial uncertainty and reduction measure implementation uncertainty; the Monte Carlo method was then used to generate carbon emission uncertainty. The results indicate that, regardless of the challenges in early and late implementation of emission reduction measures, the post-2030 intensity is crucial, while greater pre-2030 intensity favors carbon peak target achievement. The pathway setting in this study suggests that the transportation sector can meet its target for a peak in carbon emissions of 1.3 billion tons around 2030. Prediction uncertainty suggests that, compared with 2023, the 95% confidence interval for carbon emissions will increase by 2.05, 3.60, 4.75, and 5.16 times in 2025, 2030, 2035, and 2040, respectively. This study contributes to the carbon reduction strategy setting and uncertainty management; it can also be applied to various emission reduction predictions supported by scenario settings.