IDEAS home Printed from https://ideas.repec.org/a/eee/transa/v184y2024ics0965856424001149.html
   My bibliography  Save this article

A low-carbon transportation network: Collaborative effects of a rail freight subsidy and carbon trading mechanism

Author

Listed:
  • Yin, Chuanzhong
  • Zhang, Zi-Ang
  • Fu, Xiaowen
  • Ge, Ying-En

Abstract

Optimizing a transportation network is an effective way to reduce carbon emissions. To examine the collaborative effects of a rail freight subsidy and carbon trading mechanism in a low-carbon transportation network, a multiobjective 0–1 mathematical model that considers transportation cost, carbon trading cost and transportation time is established in this paper, and the NSGA-II algorithm is used to solve it. The Pareto optimal frontier solution set is found for the model, and the optimal solution is determined using the evaluation function of the ideal point method. The performance and effectiveness of the NSGA-II algorithm is analyzed by means of a sample example. A case study of the Yangtze River Economic Belt region in China is conducted to demonstrate the application and practicality of the model. Sensitivity analysis is carried out on rail freight subsidy, carbon quota and carbon trading price. The numerical results highlight that the rail freight subsidy significantly contributes to the design of the low-carbon transportation network, while the low carbon trading price shows a limited effect, which also leads to a weak effect of carbon quota on low-carbon transportation network design. These findings provide decision-making support for optimizing the low-carbon transportation network design and improving the carbon trading mechanism.

Suggested Citation

  • Yin, Chuanzhong & Zhang, Zi-Ang & Fu, Xiaowen & Ge, Ying-En, 2024. "A low-carbon transportation network: Collaborative effects of a rail freight subsidy and carbon trading mechanism," Transportation Research Part A: Policy and Practice, Elsevier, vol. 184(C).
  • Handle: RePEc:eee:transa:v:184:y:2024:i:c:s0965856424001149
    DOI: 10.1016/j.tra.2024.104066
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0965856424001149
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.tra.2024.104066?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:transa:v:184:y:2024:i:c:s0965856424001149. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/547/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.