IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i11p5039-d1668586.html
   My bibliography  Save this article

Impact of Zero-Waste City Pilot Policies on Urban Energy Consumption Intensity: Causal Inference Based on Double Machine Learning

Author

Listed:
  • Bingnan Guo

    (School of Humanities and Social Sciences, Jiangsu University of Science and Technology, Zhenjiang 212000, China)

  • Yuren Qian

    (School of Humanities and Social Sciences, Jiangsu University of Science and Technology, Zhenjiang 212000, China)

  • Xinyan Guo

    (School of Economics and Management, Gannan University of Science and Technology, Ganzhou 341000, China)

  • Hao Zhang

    (School of Humanities and Social Sciences, Jiangsu University of Science and Technology, Zhenjiang 212000, China)

Abstract

To scientifically assess the energy-saving effects of China’s zero-waste city pilot (ZWCP) policies and provide empirical evidence and policy insights for advancing pilot policies and accelerating energy conservation and emission reduction goals, this study selected 274 cities in China from 2010 to 2022 as the research sample, employing a double machine learning model to empirically analyze the impact of pilot policies on urban energy consumption intensity. The research results demonstrate that the ZWCP policies significantly reduced energy consumption intensity in pilot areas. Channel analysis reveals that this policy exerted a restraining effect on energy consumption intensity through industrial structure upgrading, green technology innovation, and enhanced environmental awareness. Heterogeneity analysis shows that policy effects were more pronounced in non-urban agglomeration regions, inland areas, and small-to-medium-sized cities. This study provides crucial decision-making references for the promotion and implementation of ZWCP policies during the “14th Five-Year Plan” period.

Suggested Citation

  • Bingnan Guo & Yuren Qian & Xinyan Guo & Hao Zhang, 2025. "Impact of Zero-Waste City Pilot Policies on Urban Energy Consumption Intensity: Causal Inference Based on Double Machine Learning," Sustainability, MDPI, vol. 17(11), pages 1-25, May.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:11:p:5039-:d:1668586
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/11/5039/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/11/5039/
    Download Restriction: no
    ---><---

    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:gam:jsusta:v:17:y:2025:i:11:p:5039-:d:1668586. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.