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Big Data Innovative Development Experiments, Sci-Technology Finance Ecology, and the Chinese Path to Sustainable Modernization—A Quasi-Natural Experiment Based on SDID and DML

Author

Listed:
  • Qi Liu

    (Business School, Ningbo University, Ningbo 315211, China)

  • Tianning Guan

    (Graduate Institute for Taiwan Studies, Xiamen University, Xiamen 361005, China)

  • Siyu Liu

    (Faculty of Architecture and Art, Ningbo Polytechnic University, Ningbo 315800, China)

  • Juncheng Jia

    (Business School, Ningbo University, Ningbo 315211, China)

  • Chenxuan Yu

    (Business School, Ningbo University, Ningbo 315211, China)

  • Kun Lv

    (Business School, Ningbo University, Ningbo 315211, China
    Merchants’ Guild Economics and Cultural Intelligent Computing Laboratory, Ningbo University, Ningbo 315211, China)

Abstract

Modernization in developing countries such as China has long been unsustainable. As a result, China has set the goal of achieving sustainable modernization characterized by harmony between humanity and nature. Against this backdrop, in this study, we apply spatial difference-in-differences (SDID) and double machine learning (DML) models using panel data from 30 provincial-level regions in China from 2009 to 2021. We examine the impacts of the National Big Data Comprehensive Pilot Zone policy and sci-technology financial ecology on the Chinese Path to Sustainable Modernization. The results show that big data pilot zones significantly enhance modernization and generate positive spatial spillover effects through demonstration and diffusion. Sci-technology financial ecology improves sustainable modernization and amplifies the role played by pilot zones. Heterogeneity tests reveal stronger effects in eastern provinces and in areas implementing urban–rural integration or green finance reforms. The results of the mechanism analysis show that big data innovation promotes modernization by strengthening sci-technology financial ecology, raising government attention, fostering inclusive intelligence development, enhancing green innovation efficiency, and upgrading industrial structures.

Suggested Citation

  • Qi Liu & Tianning Guan & Siyu Liu & Juncheng Jia & Chenxuan Yu & Kun Lv, 2025. "Big Data Innovative Development Experiments, Sci-Technology Finance Ecology, and the Chinese Path to Sustainable Modernization—A Quasi-Natural Experiment Based on SDID and DML," Sustainability, MDPI, vol. 17(18), pages 1-23, September.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:18:p:8227-:d:1748452
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    References listed on IDEAS

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    1. Chagas, André L.S. & Azzoni, Carlos R. & Almeida, Alexandre N., 2016. "A spatial difference-in-differences analysis of the impact of sugarcane production on respiratory diseases," Regional Science and Urban Economics, Elsevier, vol. 59(C), pages 24-36.
    2. Xing Gao & Keyu Zhai, 2021. "Spatial Mechanisms of Regional Innovation Mobility in China," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 156(1), pages 247-270, July.
    3. Xuejun Jin & Xiao Pan, 2023. "Government Attention, Market Competition and Firm Digital Transformation," Sustainability, MDPI, vol. 15(11), pages 1-27, June.
    4. Yudong Qi & Xi Chu, 2022. "Development of the digital economy, transformation of the economic structure and leaping of the middle-income trap," China Political Economy, Emerald Group Publishing Limited, vol. 5(1), pages 14-39, November.
    5. Shiguang Li & Yixiang Tian, 2023. "How Does Digital Transformation Affect Total Factor Productivity: Firm-Level Evidence from China," Sustainability, MDPI, vol. 15(12), pages 1-17, June.
    6. Bei Liu & Yukun Li & Xiaoya Tian & Lipeng Sun & Pishi Xiu, 2023. "Can Digital Economy Development Contribute to the Low-Carbon Transition? Evidence from the City Level in China," IJERPH, MDPI, vol. 20(3), pages 1-19, February.
    7. 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.
    8. Cortes, Gustavo S. & Gao, George P. & Silva, Felipe B.G. & Song, Zhaogang, 2022. "Unconventional monetary policy and disaster risk: Evidence from the subprime and COVID–19 crises," Journal of International Money and Finance, Elsevier, vol. 122(C).
    9. Yihao Cao & Ehsan Elahi & Zainab Khalid & Ping Li & Pengsheng Sun, 2023. "How Do Intellectual Property Rights Affect Green Technological Innovation? Empirical Evidence from China," Sustainability, MDPI, vol. 15(10), pages 1-17, May.
    10. Joseph E Stiglitz, 2018. "Where modern macroeconomics went wrong," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, vol. 34(1-2), pages 70-106.
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