IDEAS home Printed from https://ideas.repec.org/a/eee/eneeco/v154y2026ics0140988325009508.html

Research on the impact of ultra-high voltage transmission on urban carbon neutral technology innovation: An empirical test based on double machine learning method

Author

Listed:
  • Han, Dongri
  • Wang, Ruiqi
  • Yuan, Yijia
  • Xiao, Deheng

Abstract

Energy infrastructure is a pivotal driver in reshaping the development trajectory of low-carbon technology as global carbon neutrality and the significant alteration of energy systems. Its innovation-driven efficacy has not been thoroughly investigated. This paper examines the “ultra-high voltage (UHV) transmission project,” which encompasses 270 Chinese cities at the prefecture level and above, as a quasi-natural experiment from 2006 to 2023. The difference-in-differences model and double machine learning are integrated to provide a causal inference framework that systematically reveals the multifaceted mechanism of energy infrastructure's impact on carbon neutral technology innovation. The findings indicate that UHV transmission project significantly increased carbon neutral technology innovation in pilot cities, enabling the optimal allocation of energy across regions. This supports the hypothesis of a network effect-innovation response mechanism driven by the dynamic adaptation of energy infrastructure. Further mechanism tests identify three transmission paths: government green development attention, informal environmental regulation, and energy consumption structure. Heterogeneity analysis reveals that these effects vary by region: energy-rich areas utilize UHV networks to break the resource curse; old industrial bases utilize it for green transitions; and small and medium-sized cities benefit from collaborative innovation. UHV transmission project reduces regional development gaps and weakens conventional geographic advantages. The paper provides precise policy targets for the energy revolution and regional coordination to support carbon neutrality, while providing practical guidance for infrastructure investment decisions.

Suggested Citation

  • Han, Dongri & Wang, Ruiqi & Yuan, Yijia & Xiao, Deheng, 2026. "Research on the impact of ultra-high voltage transmission on urban carbon neutral technology innovation: An empirical test based on double machine learning method," Energy Economics, Elsevier, vol. 154(C).
  • Handle: RePEc:eee:eneeco:v:154:y:2026:i:c:s0140988325009508
    DOI: 10.1016/j.eneco.2025.109120
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0140988325009508
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.eneco.2025.109120?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

    for a different version of it.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:eee:eneeco:v:154:y:2026:i:c:s0140988325009508. 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/locate/eneco .

    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.