IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-981-16-9264-2_11.html
   My bibliography  Save this book chapter

Identify the Worldwide Industrial Transfer Pattern

In: Complex Network-Based Global Value Chain Accounting System

Author

Listed:
  • Lizhi Xing

    (Beijing University of Technology)

Abstract

Industrial Transfer is an inevitable trend in the process of vertical specialization. The traditional industrial transfer theory tends to adopt partial data and methodologies from reductionism, and thus can not tackle with the highly non-linear systematic problems, such as the evolutionary mechanism and path of global economic system. With the properties of structural complexity, dynamic evolution and multiple linkages, complex networks can better reflect the interdependent and mutually restricted relations between different levels and components of the industrial structure, pinpoint the key to optimization and control. Currently, there are only a few available studies on such weighted, directed, and dense networks, which reflect the topological complexity of GVC with the results being unsystematic and impractical. This chapter utilizes the binary GISRN model to describe the trajectory of the most crucial value stream on the GVC, making it possible to find the transfer paths between economies. Also, methods of defining and measuring the networks’ redundancies are devised to figure out the fundamental laws of worldwide industrial transfer pattern based on Link Prediction, thus blazing a new trial for the evolutionary economics.

Suggested Citation

  • Lizhi Xing, 2022. "Identify the Worldwide Industrial Transfer Pattern," Springer Books, in: Complex Network-Based Global Value Chain Accounting System, chapter 0, pages 261-285, Springer.
    • Handle: RePEc:spr:sprchp:978-981-16-9264-2_11
    DOI: 10.1007/978-981-16-9264-2_11
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    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:spr:sprchp:978-981-16-9264-2_11. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.