Volume 24 Issue 4
Aug.  2024
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CHEN Tao, LI Xiao-yang, CHEN Bin. Decoupling effect and peak prediction of carbon emission in transportation industry under dual-carbon target[J]. Journal of Traffic and Transportation Engineering, 2024, 24(4): 104-116. doi: 10.19818/j.cnki.1671-1637.2024.04.008
Citation: CHEN Tao, LI Xiao-yang, CHEN Bin. Decoupling effect and peak prediction of carbon emission in transportation industry under dual-carbon target[J]. Journal of Traffic and Transportation Engineering, 2024, 24(4): 104-116. doi: 10.19818/j.cnki.1671-1637.2024.04.008
shu

Decoupling effect and peak prediction of carbon emission in transportation industry under dual-carbon target

doi: 10.19818/j.cnki.1671-1637.2024.04.008
  • 1.

    School of Automobile, Chang'an University, Xi'an 710064, Shaanxi, China

  • 2.

    Institute of Transportation Development Strategy and Planning of Sichuan Province, Chengdu 610001, Sichuan, China

Funds:

National Natural Science Foundation of China 51978075

Transportation Science and Technology Project of Sichuan Province 2021-ZL-02

More Information
  • Author Bio:

    CHEN Tao(1974-), male, professor, PhD, chentao@chd.edu.cn

  • Received Date: 2024-02-20
    Available Online: 2024-09-26
  • Publish Date: 2024-08-28
    Abstract
  • To help the transportation industry achieve the strategic development goals of carbon peaking and carbon neutrality, the change trend and influencing factors of carbon emission in China's transportation industry were analyzed from two perspectives of historical verification and future prediction. The logarithmic mean Divisia index (LMDI) model was used to decompose the influencing factors of CO2 emission change in China's transportation industry from 2000 to 2020. The decoupling state of carbon emission and economic development in the industry and the driving factors of decoupling were analyzed by combining the Tapio decoupling model. The decomposition results of influencing factors were used as the basis for the selection of the indicators in the scenario analysis method, and the variations of prediction indicators under different scenarios were set. A prediction model of stochastic impacts by regression on population, affluence, and technology (STIRPAT) was constructed by using ridge regression. Analysis results show that the total CO2 emission exhibits an increasing trend year by year during the study period, with a cumulative increase of 694 million tons from 2000 to 2020. The decrease in transportation intensity is the main inhibiting factor for the increase in carbon emission, with a cumulative effect of -626 million tons. The growth of per capita GDP is the most important factor promoting the increase in carbon emission, and the cumulative effect is 1 294 million tons. The energy consumption is still dominated by fossil fuels, and the energy structure is not significantly optimized. The decoupling index of industrial carbon emission is in a stable decline stage, and the decoupling state improves, mainly manifesting in three states, such as the expansion negative decoupling, growth connection, and weak decoupling. The optimization of energy structure is the most potential factor to help the decoupling. In the future, the carbon emission in China's transportation industry will rapidly grow at first, slow down near the peak, reach a plateau for a short period after the peak, and finally decline. In the baseline, pessimistic, and optimistic scenarios, the peak CO2 emission in China's transportation industry will occur in 2040, 2045, and 2035, respectively, with peaks of about 1.210 billion, 1.263 billion, and 1.130 billion tons, respectively.

     

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