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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
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    References listed on IDEAS

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    1. Li, Fengyun & Zhang, Junxia & Li, Xingmei, 2023. "Energy security dilemma and energy transition policy in the context of climate change: A perspective from China," Energy Policy, Elsevier, vol. 181(C).
    2. Zhengliang Zhang & Junfei Teng, 2023. "Role of Government in the Construction of Zero-Waste Cities: A Case Study of China’s Pearl River Delta City Cluster," Sustainability, MDPI, vol. 15(2), pages 1-19, January.
    3. Wenjie Liu & Yuqing Chen & Peng Zhu & Jinjie Tong, 2024. "Can carbon reduction policies promote sustainable construction development? Evidence from China’s green building market," PLOS ONE, Public Library of Science, vol. 19(5), pages 1-24, May.
    4. Rongrong Li & Zhuang Yang & Qiang Wang, 2025. "Does renewable energy reduce energy intensity? A matter of income inequality," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-22, December.
    5. Victor Chernozhukov & Denis Chetverikov & Mert Demirer & Esther Duflo & Christian Hansen & Whitney Newey & James Robins, 2018. "Double/debiased machine learning for treatment and structural parameters," Econometrics Journal, Royal Economic Society, vol. 21(1), pages 1-68, February.
    6. Khosravi, Fatemeh & Lowes, Richard & Ugalde-Loo, Carlos E., 2023. "Cooling is hotting up in the UK," Energy Policy, Elsevier, vol. 174(C).
    7. Zeng, Shihong & Gu, Yuxiao & Su, Bin & Li, Tengfei, 2025. "Energy consumption transition and green total factor productivity in Chinese prefecture-level cities," Energy Economics, Elsevier, vol. 142(C).
    8. Shu, Haicheng & Wang, Yu & Umar, Muhammad & Zhong, Yifan, 2023. "Dynamics of renewable energy research, investment in EnvoTech and environmental quality in the context of G7 countries," Energy Economics, Elsevier, vol. 120(C).
    9. Philipp Bach & Victor Chernozhukov & Malte S. Kurz & Martin Spindler & Sven Klaassen, 2021. "DoubleML -- An Object-Oriented Implementation of Double Machine Learning in R," Papers 2103.09603, arXiv.org, revised Jun 2024.
    10. Wang, Shuang & Tian, Guixian, 2025. "Does renewable energy consumption reduce the energy security risk?," Energy, Elsevier, vol. 320(C).
    11. Guo, Bingnan & Feng, Weizhe & Lin, Ji, 2024. "The impact of green finance on labor income share: Evidence from green finance reform and innovation pilot zone," Economic Analysis and Policy, Elsevier, vol. 84(C), pages 1347-1358.
    12. Tianyu Qin & Lingling She & Zhaolong Wang & Luosong Chen & Wanyi Xu & Gaoming Jiang & Zhe Zhang, 2022. "The Practical Experience of “Zero Waste City” Construction in Foshan City Condenses the Chinese Solution to the Sustainable Development Goals," Sustainability, MDPI, vol. 14(19), pages 1-16, September.
    13. Stefan Wager & Susan Athey, 2018. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1228-1242, July.
    14. Wang, Tianyang & Umar, Muhammad & Li, Menggang & Shan, Shan, 2023. "Green finance and clean taxes are the ways to curb carbon emissions: An OECD experience," Energy Economics, Elsevier, vol. 124(C).
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    Cited by:

    1. Zhang, Ge & Liu, Songxian & Guo, Bingnan, 2025. "The impact of administrative monopoly regulation on industrial enterprises' green transformation: Evidence from China's Fair Competition Review System," International Review of Financial Analysis, Elsevier, vol. 106(C).
    2. Lu Lv & Bingnan Guo, 2025. "Do Pilot Zones for Green Finance Reform and Innovation Policy Enhance China’s Energy Resilience?," Sustainability, MDPI, vol. 17(13), pages 1-22, June.

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