Carbon Emissions Prediction for the Transportation Industry with Consideration of China’s Peaking Carbon Emissions

Yutang Liu (), Anqiang Huang () and Zhou Yao ()
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Yutang Liu: Beijing Jiaotong University
Anqiang Huang: Beijing Jiaotong University
Zhou Yao: Beijing Jiaotong University

A chapter in LISS 2023, 2024, pp 406-426 from Springer

Abstract: Abstract China’s energy structure is undergoing significant changes as a result of the quick development of new energy technologies. Building and enhancing the carbon emission policy system for China’s transportation industry requires scientific prediction of carbon emissions with the aim of carbon peaking. This paper adopts the LMDI (Logarithmic Mean Divisia Index) method to decompose the driving factors and the LEAP (Long-Range Energy Alternatives Planning) model to predict the trajectory of China’s transportation sector’s carbon emissions. The findings indicate that while an increase in GDP per capita will result in an increase in carbon emissions, an improvement in the energy structure and a decrease in the intensity of transportation will contribute to a reduction. Additionally, the boosting effect of GDP per capita on carbon emissions in 2019 has been able to counteract the inhibiting effects of energy structure and transportation intensity, where road freight, which is primarily powered by diesel, is the largest source of carbon emissions. According to the baseline scenario, the first carbon peak will occur in 2023 due to the liberalization of epidemic prevention and control and the decrease in the share of road freight, while the second peak will happen in 2032 due to the combined effects of economic growth and advancements in carbon reduction technology. As a result, carbon emissions will exhibit a fluctuating peak tendency, indicating that the industry should pay attention to these features and adopt scientific development strategies. Therefore, the primary routes for China’s transportation sector to reach a carbon peak in the future include optimizing energy structure, developing carbon-reducing technologies, lowering the proportion of road freight, encouraging multi-modal transport development, and optimizing transport structure.

Keywords: carbon emission forecasting; transportation; LEAP model; LMDI factor decomposition; scenario analysis (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-981-97-4045-1_32

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