Understanding Emission Trends, Regional Distribution Differences, and Synergistic Emission Effects in the Transportation Sector in Terms of Social Factors and Energy Consumption

Yu Zhao and Prasanna Divigalpitiya ()
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Yu Zhao: Graduate School of Human-Environment Studies, Kyushu University, Fukuoka 819-0395, Japan
Prasanna Divigalpitiya: Faculty of Human-Environment Studies, Kyushu University, Fukuoka 819-0395, Japan

Sustainability, 2024, vol. 16, issue 24, 1-18

Abstract: China’s transportation sector plays a significant role in reducing carbon dioxide (CO 2 ) and air pollution. Previous studies have predominantly utilized scenario analysis to forecast emissions for the next 30 to 50 years based on coefficients from a base year. To elucidate the current state of gas emissions in the transportation sector, this study employed panel data for 10 types of gas emissions from 2001 to 2020, analyzing their emission characteristics, tendencies, and synergistic effects. Utilizing the Kaya equation and the logarithmic mean division index (LMDI) decomposition method, we developed a model of pollutant emissions that considers the synergistic effects, pollution emission intensity, energy mix, energy consumption intensity, and population. The results show that all pollutants in the transportation sector decreased except for NH 3 and CO 2 . There was a synergistic effect between air pollutants and CO 2 emissions, but the reduction was not significant. From 2013 to 2020, the transportation sector shifted from a high emission intensity with low synergy to a low emission intensity with high synergy. The results indicate that off-road mobile vehicles, on-road diesel vehicles, and motorcycles became the main source of emissions from transportation in certain provinces, and a key area requiring attention in policy development. Gasoline consumption was identified as the primary contributor to the significant increase in synergistic emission variability in the transportation sector. These results provide policymakers with practical ways to optimize emission reduction pathways.

Keywords: transportation sector; synergistic effect; synergistic emission variability; emission trends; pollution emissions intensity; sustainable transport (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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