IDEAS home Printed from https://ideas.repec.org/a/taf/transp/v46y2023i5p573-598.html
   My bibliography  Save this article

Unveiling drivers of sustainability in Chinese transport: an approach based on principal component analysis and neural networks

Author

Listed:
  • Peter Fernandes Wanke
  • Amir Karbassi Yazdi
  • Thomas Hanne
  • Yong Tan

Abstract

The paper analyzes the sustainability of the Chinese transportation sector by examining the relationship between energy consumption (and CO2 emissions), transportation modes, and macroeconomic variables. Principal Component Analysis (PCA) and Neural Networks (NN) are combined using monthly data from January 1999 to December 2017. Our goal is to propose a model that links China's transportation footprint to major macroeconomic factors while simultaneously controlling each mode of transportation. Inflation and credit policies exert relatively weak effects on the explained variable. In contrast, trade and fixed asset investments, as well as monetary and fiscal policies, show a positive and significant impact. The use of waterways and airways plays an imperative role in sustainable development compared to the use of roads.

Suggested Citation

  • Peter Fernandes Wanke & Amir Karbassi Yazdi & Thomas Hanne & Yong Tan, 2023. "Unveiling drivers of sustainability in Chinese transport: an approach based on principal component analysis and neural networks," Transportation Planning and Technology, Taylor & Francis Journals, vol. 46(5), pages 573-598, July.
    • Handle: RePEc:taf:transp:v:46:y:2023:i:5:p:573-598
    DOI: 10.1080/03081060.2023.2198517
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/03081060.2023.2198517
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/03081060.2023.2198517?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.

    More about this item

    Statistics

    Access and download statistics

    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:taf:transp:v:46:y:2023:i:5:p:573-598. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/GTPT20 .

    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.