Investigating the effect of carbon tax and carbon quota policy to achieve low carbon logistics operations

Yan Li, Ming Lim, Jiayao Hu, Ming-Lang Tseng

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20 Citations (Scopus)
51 Downloads (Pure)


Developing a low-carbon economy and reducing carbon dioxide emission have become a consensus for both academics and practitioners. However, the existing literature did not pay enough attention in interrogating the impacts of Carbon Tax (CT) and Carbon Quota (CQ) policy on distribution costs and carbon dioxide emission in the field of vehicle routing problem. Moreover, the investigated subsidies factor is also incomplete. This research stands on the position of the company to study the impact of CT and CQ policy on aforementioned two aspects. A mathematical model is developed to achieve the best low carbon vehicle routing under the optimal policy. The optimization goal of this research is to minimize the total cost that includes vehicle-using, transportation, CT, CQ, and raw material subsidy costs. An improved optimization algorithm, namely Genetic Algorithm-Tabu Search (GA-TS), is proposed to solve a given business case. In the simulation experiments, GA-TS and a traditional GA are compared and the results show the advantage of GA-TS on reducing the total cost and carbon dioxide emission. Furthermore, the experiments also explore the total cost and carbon dioxide emission under three scenarios (Benchmark, CT and CQ), incorporating four policies: CT, Carbon Tax Subsidy (CTS), CQ, and Carbon Quota Subsidy (CQS). It is concluded that CQS is the ideal policy to minimize distribution cost and carbon dioxide emission. In addition, the impact of vehicles’ capacities on the total cost and carbon dioxide emission is also analyzed in this research. This research also aimed at assisting practitioners in better formulating delivery routes, as well as policy makers in developing carbon policies. Finally, the limitations and the future research directions of this research are also discussed.
Original languageEnglish
Article number104535
JournalResources, Conservation and Recycling
Early online date26 Nov 2019
Publication statusPublished - Mar 2020

Bibliographical note

NOTICE: this is the author’s version of a work that was accepted for publication in Resources, Conservation and Recycling. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Resources, Conservation and Recycling, 154 (2020)
DOI: 10.1016/j.resconrec.2019.104535

© 2020, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International


Chongqing Science and Technology Commission (Project no. cstc2019jscx-msxmX0189)


  • Low carbon logistics
  • Carbon tax
  • Vehicle routing
  • Genetic algorithm


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