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Decomposition Analysis of Energy-Related CO 2 Emissions and Decoupling Status in China’s Logistics Industry

Author

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  • Shiqing Zhang

    (School of Economics and Management, Chang’an University, Middle Section of South Second Ring Road, Xi’an 710064, China)

  • Jianwei Wang

    (Research Center for Low-carbon Transportation, School of Economics and Management, Chang’an University, Middle Section of South Second Ring Road, Xi’an 710064, China)

  • Wenlong Zheng

    (School of Economics and Management, Chang’an University, Middle Section of South Second Ring Road, Xi’an 710064, China)

Abstract

The logistics industry is one of the major fossil energy consumers and CO 2 emitters in China, which plays an important role in achieving sustainable development as well as China’s emission reduction targets. To identify the key influencing factors regarding the logistics of CO 2 reductions and ensure that the development of China’s logistics industry becomes less dependent on CO 2 emissions, this paper built an extended log-mean Divisia index model (LMDI) to decompose the logistics of CO 2 changes between 1985 and 2015. Then, we introduced a decoupling model that combined the decomposition results to analyze the decoupling state and identify the main factors that influenced the decoupling relationship. The results show the following. (1) The urbanization effect was the decisive factor in CO 2 emissions increases, followed by structural adjustment effects, while technological progress effects played a major role in inhibiting CO 2 emissions. Particularly, the energy structure showed great potential for CO 2 emissions reduction in China. (2) Highways appeared to have dominant promoting roles in increasing CO 2 emissions regarding transportation structure effects; highways and aviation proved to have the largest impact on CO 2 emission reduction. (3) There has been an increase in the number of expansive negative decoupling states between 2005 and 2015, which implies that the development of the logistics industry has become more dependent on CO 2 emissions. Finally, this paper puts forward some policy implications for CO 2 emission reductions in China’s logistics industry.

Suggested Citation

  • Shiqing Zhang & Jianwei Wang & Wenlong Zheng, 2018. "Decomposition Analysis of Energy-Related CO 2 Emissions and Decoupling Status in China’s Logistics Industry," Sustainability, MDPI, vol. 10(5), pages 1-21, April.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:5:p:1340-:d:143264
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    5. Darío Serrano-Puente, 2021. "Are we moving towards an energy-efficient low-carbon economy? An input-output LMDI decomposition of CO2 emissions for Spain and the EU28," Working Papers 2104, Banco de España.
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    8. Shining Zhang & Fang Yang & Changyi Liu & Xing Chen & Xin Tan & Yuanbing Zhou & Fei Guo & Weiyi Jiang, 2020. "Study on Global Industrialization and Industry Emission to Achieve the 2 °C Goal Based on MESSAGE Model and LMDI Approach," Energies, MDPI, vol. 13(4), pages 1-21, February.
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    14. Darío Serrano-Puente, 2021. "Are we moving toward an energy-efficient low-carbon economy? An input–output LMDI decomposition of CO $$_{2}$$ 2 emissions for Spain and the EU28," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 12(2), pages 151-229, June.
    15. Oliver I. Inah & Fidelis I. Abam & Bethrand N. Nwankwojike, 2022. "Exploring the CO2 emissions drivers in the Nigerian manufacturing sector through decomposition analysis and the potential of carbon tax (CAT) policy on CO2 mitigation," Future Business Journal, Springer, vol. 8(1), pages 1-22, December.
    16. Menglu Li & Wei Wang & Gejirifu De & Xionghua Ji & Zhongfu Tan, 2018. "Forecasting Carbon Emissions Related to Energy Consumption in Beijing-Tianjin-Hebei Region Based on Grey Prediction Theory and Extreme Learning Machine Optimized by Support Vector Machine Algorithm," Energies, MDPI, vol. 11(9), pages 1-15, September.
    17. Lele Xin & Junsong Jia & Wenhui Hu & Huiqing Zeng & Chundi Chen & Bo Wu, 2021. "Decomposition and Decoupling Analysis of CO 2 Emissions Based on LMDI and Two-Dimensional Decoupling Model in Gansu Province, China," IJERPH, MDPI, vol. 18(11), pages 1-20, June.
    18. Wang, Juan & Li, Ziming & Wu, Tong & Wu, Siyu & Yin, Tingwei, 2022. "The decoupling analysis of CO2 emissions from power generation in Chinese provincial power sector," Energy, Elsevier, vol. 255(C).

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