关键词: 交通工程； 碳排放； 机器学习； 情景分析； 灰狼优化算法(GWO)

Abstract: The upper reaches of the Yangtze River serve as a critical area for implementing Chinas “dual carbon” strategy and constructing the ecological barrier of the Yangtze River Economic Belt, whose carbon emission governance in the transportation sector plays a significant role in achieving the objective of “synergistic enhancement of pollution reduction and carbon reduction.” Based on data from Chongqing, Guizhou, Sichuan, and Yunnan provinces/municipalities from 2000 to 2021, Lasso regression was employed to address multicollinearity of the STIRPAT model, a carbon emission prediction model integrating the grey wolf optimizer algorithm with random forest (GWO-RF) was constructed, and multi-scenario analysis was carried out. The research results indicate that under the baseline and low-carbon scenarios, carbon emissions from the transportation sector in this region are projected to peak in 2032, with peaks of 109.19 Mt CO2 and 104.08Mt CO2, respectively. Under the high-carbon scenario, the peak is delayed to 2034, with the peak value rising to 117.51 Mt CO2. From 2022 to 2040, carbon emissions from transportation sector in this region generally follow an evolutionary path of “initial growth followed by peaking, then gradual stabilization and decline”. This achievement can provide methodological references and decision-making basis for accurate prediction of carbon emissions and design of peak-reaching pathways in cross-regional transportation sectors.

Key words: traffic engineering; carbon emissions; machine learning; scenario analysis; grey wolf optimizer algorithm (GWO)