Deep decarbonization potential and implementation path under provincial differences in China's fleet electrification.

ABSTRACT

Fleet electrification is considered to be an important measure for reducing carbon emissions in the road transport industry. Considering the heterogeneity of the NEV market penetration and the vehicle types in different provinces, how to design targeted and time-sequenced road transport decarbonisation reduction strategies has become a key issue that needs to be discussed urgently. In this study, the NEVs ownership in China's 31 provinces is used as an intermediate variable. Considering the process of energy transition and changes in vehicle structure, a two-layer scenario framework that combines Shared Socioeconomic Pathways scenarios and model structure was developed to predict carbon emissions. This study firstly analyzes the electrification process and carbon emission reduction potential of provincial road transport industry by region, vehicle type and stage. The potential for reducing carbon emissions was determined under benchmark, transition, and electrification scenarios. The results indicate that the Pearson Correlation Coefficient-Discrete Wavelet Transform-Bidirectional Long Short-term Memory prediction model has an mean absolute percentage error of 8.583 and an R-squared of 0.975. China's road transportation industry total carbon emissions will reach its peak as early as 2027, due to the rapid implementation of renewable energy and fleet electrification. Shanghai, Jiangsu, Shandong, Henan, and Guangdong have set carbon peak targets that can be achieved faster with the transition plan for new energy vehicles to replace fossil fuel vehicles. This paper proposes a timing-responsive deep decarbonization path and policy recommendations for China's road transport industry in sub provincial and time-series settings.