[Regional Transport Carbon Emission Forecasting and Peak Carbon Pathway Planning in China].

Abstract

Reduction in regional traffic emission is crucial for achieving overall emission reduction and low-carbon development in the transportation sector. To assist China's transportation sector in early realizing of its carbon peak and carbon neutrality goals, panel data from 30 provinces （municipalities, autonomous regions） of China from 2000 to 2021 were utilized. Various machine learning regression algorithms were employed to construct a predictive model for regional transportation carbon emissions, among which the model combining the Lasso regression and support vector machine algorithms performed the best. Taking the transportation sector in Guangdong, Shanghai, Shandong, and Sichuan as examples, three future development scenarios-baseline, energy-saving emission reduction, and technology-driven emission reduction-were set. The predictive model （Lasso_SVM） was used to forecast the carbon emissions from transport sector in these provinces from 2022 to 2035. The results indicated that under the baseline, energy-saving emission reduction, and technology-driven emission reduction scenarios, the earliest peak times for carbon emissions from transport sector in Guangdong, Shanghai, Shandong, and Sichuan were the years 2029, 2028, 2030, and 2029, respectively, with peak values of 73.59, 52.16, 55.08, and 33.46 Mt, respectively. Finally, based on the carbon emission forecasts under different scenarios for the four provinces, scientifically feasible emission reduction pathways were formulated to provide technical support for advancing the carbon peak achievement in China's transport sector.