IDEAS home Printed from https://ideas.repec.org/a/taf/ecsysr/v37y2025i1p76-94.html
   My bibliography  Save this article

An electricity big data application to reveal the chronological linkages between industries

Author

Listed:
  • Kehan He
  • D’Maris Coffman
  • Xingzhe Hou
  • Jinkai Li
  • Zhifu Mi

Abstract

Effective integration and compromise between theories and empirical data are essential for an operational economic model. However, existing economic models often neglect the intricate fluctuations and transitions that occur in weeks and days. This research proposes an Input–Output-based algorithm to introduce the time domain into economic modelling. Using daily electricity consumption big data in Chongqing as a proxy for economic activities, we quantitatively analyse the chronological interactions among industrial sectors and reveal that a longer duration is required by the heavy industry sector to signal an intermediate production in the service sector than any other sectors in this municipality. With the proposed model, we forecast the economic impact induced by demand changes for consumer goods under three growth scenarios. The model not only serves as a methodological bridge between theoretical and data-driven approaches but also offers new insights into the dynamic interplay of sectoral activities over time.

Suggested Citation

  • Kehan He & D’Maris Coffman & Xingzhe Hou & Jinkai Li & Zhifu Mi, 2025. "An electricity big data application to reveal the chronological linkages between industries," Economic Systems Research, Taylor & Francis Journals, vol. 37(1), pages 76-94, January.
  • Handle: RePEc:taf:ecsysr:v:37:y:2025:i:1:p:76-94
    DOI: 10.1080/09535314.2024.2357167
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/09535314.2024.2357167
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/09535314.2024.2357167?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:ecsysr:v:37:y:2025:i:1:p:76-94. 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/CESR20 .

    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.