Prediction of electric vehicles CO2 emission trajectory and peak time series in China

Abstract

Transportation has emerged as the second largest source of global CO₂ emissions. As an applied practice of decarbonization and electrification, electric vehicles (EVs) present significant potential for reducing CO₂ emissions during the use phase. However, the coal-dominated energy mix in China introduces variability in the effectiveness of CO₂ emission reductions associated with EVs, both temporally and spatially. Therefore, the designation of targeted policy research on road transportation and transportation industries in different regions is an urgent problem to be solved at present. In this study, electric vehicle ownership data from 31 provinces across China is used as an intermediate variable. Carbon emissions are predicted based on the three distinct provincial cluster development scenarios. The results show that a combined Support Vector Regression (SVR) and Wavelet Decomposition (WD) model demonstrates robust predictive performance with a mean absolute percentage error (MAPE) of 8.02 %. With the road transportation CO₂ emission inventory, the CO₂ emission intensity and the time series of emissions peaks across 31 provinces were obtained. Due to the rapid proliferation of electric vehicles, the peak of CO₂ emissions from road transportation sector in China is projected to occur in 2028. Finally, the 31 provinces in China are categorized into four groups based on the predicted CO₂ emission intensity and electric vehicle market penetration in different provinces. Corresponding policy recommendations are proposed for the four categories of cities, including accelerating the construction of charging infrastructure, increasing the EV licensing policies, adjusting license plate quota regulations, and delaying the decline of EV subsidies.