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Sustainable Urban Logistics Distribution Network Planning with Carbon Tax

Author

Listed:
  • Jidong Guo

    (School of Emergency Management, Institute of Disaster Prevention, Beijing 101601, China)

  • Qiuhong Zhao

    (School of Economics and Management, Beihang University, Beijing 100191, China)

  • Menghao Xi

    (School of Emergency Management, Institute of Disaster Prevention, Beijing 101601, China)

Abstract

Global warming caused by excessive carbon dioxide emissions is threatening the sustainable development of human society. Considering the upcoming carbon tax policy in China, this paper studies the planning of dual-level urban logistics distribution network in the context of carbon emissions. Based on reasonable assumptions, a bi-objective mixed-integer programming model considering logistics operating costs and carbon emission costs is constructed. Given the problem size, an improved genetic algorithm is designed. Based on a numerical example, the optimization results of the improved genetic algorithm and the GLPK optimization suite are compared to verify the effectiveness of the proposed model and algorithm. Under different carbon tax rates, by adjusting the logistics distribution network along, the logistics operators can achieve a maximum cost savings of 19.4%, while carbon emissions can be reduced by up to 47.8%. The major conclusions include: carbon tax will bring about huge cost burdens for urban logistics operators; the reconfiguration of urban logistics network is a powerful measure to reduce carbon emission with the least extra costs; with problem size rises quickly, the intelligent algorithm as proposed in this article can always find near optimal solutions with acceptable time costs.

Suggested Citation

  • Jidong Guo & Qiuhong Zhao & Menghao Xi, 2022. "Sustainable Urban Logistics Distribution Network Planning with Carbon Tax," Sustainability, MDPI, vol. 14(20), pages 1-15, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13184-:d:942100
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    References listed on IDEAS

    as
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