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How Does Critical Peak Pricing Boost Urban Green Total Factor Energy Efficiency? Evidence from a Double Machine Learning Model

Author

Listed:
  • Da Gao

    (School of Law and Business, Wuhan Institute of Technology, Wuhan 430205, China)

  • Qingshuo Wang

    (School of Law and Business, Wuhan Institute of Technology, Wuhan 430205, China)

  • Qingjiang Han

    (School of Law and Business, Wuhan Institute of Technology, Wuhan 430205, China)

Abstract

Green and low-carbon development constitutes an essential pathway toward high-quality socioeconomic transformation, with improving urban green total factor energy efficiency (GTFEE) critical to achieving this objective. Based on the sample data of Chinese cities from 2013 to 2022, this study systematically investigated the impact and mechanism of critical peak pricing on urban GTFEE by using the double machine learning method, effectively supplementing the existing literature. This study finds that this policy significantly enhances urban GTFEE. Mechanism analysis indicates that critical peak pricing generates a dual effect by increasing the price difference between peak and off-peak hours and enhancing energy efficiency through two important channels: market expansion and technology-driven innovation. Heterogeneity analysis indicates that the critical peak pricing policy has a more significant promotion effect on non-resource-based, strong government administrative power, as well as central and eastern regions. These findings advance the power marketization reform framework and provide new theoretical support for promoting low-carbon energy transformation.

Suggested Citation

  • Da Gao & Qingshuo Wang & Qingjiang Han, 2025. "How Does Critical Peak Pricing Boost Urban Green Total Factor Energy Efficiency? Evidence from a Double Machine Learning Model," Energies, MDPI, vol. 18(18), pages 1-21, September.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:18:p:4970-:d:1752827
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