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The Impact of Big Data Pilot Zones on Urban Ecological Resilience: Evidence from a Machine Learning Approach

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Listed:
  • Wei Wen

    (School of Economic and Management, Northeast Agricultural University, Harbin 150030, China)

  • Kangan Jiang

    (School of Economic and Management, Northeast Agricultural University, Harbin 150030, China)

  • Xiaojing Shao

    (School of Economic and Management, Northeast Agricultural University, Harbin 150030, China)

Abstract

Against the backdrop of the structural transition in China’s economic landscape, the implementation of digital economy policies—particularly through the Broadband China Demonstration Cities initiatives—has significantly enhanced urban ecological resilience. Based on panel data from 280 prefecture-level cities in China over the period 2013–2022, this study employs the national big data comprehensive pilot zone as a quasi-natural experiment and utilizes the dual machine learning method to examine how pilot zone construction influences urban ecological resilience. This analysis provides theoretical support for fostering green urban development. The results are summarized as follows. (1) The construction of national big data comprehensive pilot zones significantly enhances urban ecological resilience. The conclusion is robust to various tests, including the removal of outliers, changes in sample splitting ratios, and alterations in machine learning algorithms. (2) The construction of national big data comprehensive pilot zones indirectly improves urban ecological resilience through pathways of green innovation and energy efficiency. (3) This study assesses the heterogeneity of policy effects based on the generalized random forest (GRF) model to identify the sources of heterogeneity in policy effects, and conducts a comprehensive heterogeneity analysis from the three dimensions of resource endowments, geographical location characteristics, and the attributes of environmental protection zones. These findings enrich the analysis of the consequences of national big data comprehensive pilot zone policies and offer a theoretical basis and policy reference for how constructing big data pilot zones can better serve urban ecological development.

Suggested Citation

  • Wei Wen & Kangan Jiang & Xiaojing Shao, 2025. "The Impact of Big Data Pilot Zones on Urban Ecological Resilience: Evidence from a Machine Learning Approach," Sustainability, MDPI, vol. 17(7), pages 1-22, March.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:7:p:2846-:d:1618593
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    References listed on IDEAS

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    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. Zhang, Yiren & Ran, Congjing, 2023. "Effect of digital economy on air pollution in China? New evidence from the “National Big Data Comprehensive Pilot Area” policy," Economic Analysis and Policy, Elsevier, vol. 79(C), pages 986-1004.
    3. Yali Liu & Zhi Li & Haonan Chen & Xiaoning Cui, 2024. "Impact of Big Data on Carbon Emissions: Empirical Evidence from China’s National Big Data Comprehensive Pilot Zone," Sustainability, MDPI, vol. 16(19), pages 1-23, September.
    4. Tang, Jiaomei & Li, Wanting & Hu, Jiahan & Ren, Yayun, 2025. "Can government digital transformation improve corporate energy efficiency in resource-based cities?," Energy Economics, Elsevier, vol. 141(C).
    5. Youzhi Zhang & Jingyi Wang & Yinke Liu & Jing Zhao, 2024. "The Impact of the Digital Economy on Urban Ecological Resilience: Empirical Evidence from China’s Comprehensive Big Data Pilot Zone Policy," Sustainability, MDPI, vol. 16(9), pages 1-27, April.
    6. Dai, Jiapeng & Mehmood, Usman & Nassani, Abdelmohsen A., 2025. "Empowering sustainability through energy efficiency, green innovations, and the sharing economy: Insights from G7 economies," Energy, Elsevier, vol. 318(C).
    7. Jilin Wu & Manhong Yang & Jinyou Zuo & Ningling Yin & Yimin Yang & Wenhai Xie & Shuiliang Liu, 2024. "Spatio-Temporal Evolution of Ecological Resilience in Ecologically Fragile Areas and Its Influencing Factors: A Case Study of the Wuling Mountains Area, China," Sustainability, MDPI, vol. 16(9), pages 1-21, April.
    8. Du, Kerui & Li, Pengzhen & Yan, Zheming, 2019. "Do green technology innovations contribute to carbon dioxide emission reduction? Empirical evidence from patent data," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 297-303.
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