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
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. 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).
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Khosravi, Fatemeh & Lowes, Richard & Ugalde-Loo, Carlos E., 2023. "Cooling is hotting up in the UK," Energy Policy, Elsevier, vol. 174(C).
    8. 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).
    9. 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).
    10. 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.
    11. Wang, Shuang & Tian, Guixian, 2025. "Does renewable energy consumption reduce the energy security risk?," Energy, Elsevier, vol. 320(C).
    12. 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.
    13. 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.
    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).
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Michael Lechner & Jana Mareckova, 2024. "Comprehensive Causal Machine Learning," Papers 2405.10198, arXiv.org, revised Feb 2025.
    2. Justin Whitehouse & Morgane Austern & Vasilis Syrgkanis, 2025. "Inference on Optimal Policy Values and Other Irregular Functionals via Smoothing," Papers 2507.11780, arXiv.org.
    3. Davide Viviano & Jelena Bradic, 2019. "Synthetic learner: model-free inference on treatments over time," Papers 1904.01490, arXiv.org, revised Aug 2022.
    4. Elek, Péter & Bíró, Anikó, 2021. "Regional differences in diabetes across Europe – regression and causal forest analyses," Economics & Human Biology, Elsevier, vol. 40(C).
    5. Michael C. Knaus, 2021. "A double machine learning approach to estimate the effects of musical practice on student’s skills," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(1), pages 282-300, January.
    6. Kyle Colangelo & Ying-Ying Lee, 2019. "Double debiased machine learning nonparametric inference with continuous treatments," CeMMAP working papers CWP54/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
    7. Bokelmann, Björn & Lessmann, Stefan, 2024. "Improving uplift model evaluation on randomized controlled trial data," European Journal of Operational Research, Elsevier, vol. 313(2), pages 691-707.
    8. Chunrong Ai & Oliver Linton & Kaiji Motegi & Zheng Zhang, 2021. "A unified framework for efficient estimation of general treatment models," Quantitative Economics, Econometric Society, vol. 12(3), pages 779-816, July.
    9. Daniel Goller, 2023. "Analysing a built-in advantage in asymmetric darts contests using causal machine learning," Annals of Operations Research, Springer, vol. 325(1), pages 649-679, June.
    10. Wu, Guojun & Song, Ge & Lv, Xiaoxiang & Luo, Shikai & Shi, Chengchun & Zhu, Hongtu, 2023. "DNet: distributional network for distributional individualized treatment effects," LSE Research Online Documents on Economics 122895, London School of Economics and Political Science, LSE Library.
    11. Black, Dan A. & Grogger, Jeffrey & Kirchmaier, Tom & Sanders, Koen, 2023. "Criminal charges, risk assessment and violent recidivism in cases of domestic abuse," LSE Research Online Documents on Economics 121374, London School of Economics and Political Science, LSE Library.
    12. Augusto Cerqua & Marco Letta & Gabriele Pinto, 2024. "On the (Mis)Use of Machine Learning with Panel Data," Papers 2411.09218, arXiv.org, revised May 2025.
    13. Krikamol Muandet & Wittawat Jitkrittum & Jonas Kubler, 2020. "Kernel Conditional Moment Test via Maximum Moment Restriction," Papers 2002.09225, arXiv.org, revised Jun 2020.
    14. Krekel, Christian & Srisuma, Sorawoot, 2024. "Talking Therapy: Impacts of a Nationwide Mental Health Service in England," IZA Discussion Papers 16839, Institute of Labor Economics (IZA).
    15. Mark Kattenberg & Bas Scheer & Jurre Thiel, 2023. "Causal forests with fixed effects for treatment effect heterogeneity in difference-in-differences," CPB Discussion Paper 452, CPB Netherlands Bureau for Economic Policy Analysis.
    16. Michael C Knaus, 2022. "Double machine learning-based programme evaluation under unconfoundedness [Econometric methods for program evaluation]," The Econometrics Journal, Royal Economic Society, vol. 25(3), pages 602-627.
    17. Yuya Sasaki & Takuya Ura & Yichong Zhang, 2022. "Unconditional quantile regression with high‐dimensional data," Quantitative Economics, Econometric Society, vol. 13(3), pages 955-978, July.
    18. Agboola, Oluwagbenga David & Yu, Han, 2023. "Neighborhood-based cross fitting approach to treatment effects with high-dimensional data," Computational Statistics & Data Analysis, Elsevier, vol. 186(C).
    19. Patrick Rehill & Nicholas Biddle, 2024. "Heterogeneous treatment effect estimation with high-dimensional data in public policy evaluation -- an application to the conditioning of cash transfers in Morocco using causal machine learning," Papers 2401.07075, arXiv.org, revised Mar 2024.
    20. Aditya Ghosh & Dominik Rothenhausler, 2025. "Assumption-robust Causal Inference," Papers 2505.08729, arXiv.org, revised Jun 2025.

    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: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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.