IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i6p2652-d1614157.html
   My bibliography  Save this article

How Does Digital Innovation Empower the Development of New Quality Productive Forces? An Empirical Study Based on Double Machine Learning

Author

Listed:
  • Jingwen Zhang

    (Business School, Jiangxi Normal University, Nanchang 330022, China)

  • Yi Liu

    (Research Center of Management Science and Engineering, Jiangxi Normal University, Nanchang 330022, China)

Abstract

New quality productive forces (NQPFs) are a key driver for sustainable and high-quality development, where digital innovation (DI) plays a crucial role in promoting the evolution of NQPFs. Based on this, this paper takes 2740 A-share listed companies from 2011 to 2022 as research samples and utilizes double machine learning to explore the impact and transmission mechanisms of DI on NQPFs. The study finds that DI significantly empowers the development of NQPF; mechanism-wise, DI achieves this through industry–university–research cooperation (IURC), increasing market concentration (MC) and enhancing government innovation subsidies (GISs); heterogeneity analysis reveals that the empowering effect of DI on NQPFs is stronger in large cities, small cities, the region northwest of the Hu Line, and the old industrial bases, whereas in megacity behemoths, megacities, regions along the Hu Line and the southeast region, and non-old industrial base enterprises, the effects are relatively smaller. This study provides both theoretical and empirical insights into how DI drives the development of NQPFs and supports sustainable economic growth, offering valuable guidance for future development strategies.

Suggested Citation

  • Jingwen Zhang & Yi Liu, 2025. "How Does Digital Innovation Empower the Development of New Quality Productive Forces? An Empirical Study Based on Double Machine Learning," Sustainability, MDPI, vol. 17(6), pages 1-29, March.
  • Handle: RePEc:gam:jsusta:v:17:y:2025:i:6:p:2652-:d:1614157
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/6/2652/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2071-1050/17/6/2652/
    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. Aghion, Philippe & Howitt, Peter, 1992. "A Model of Growth through Creative Destruction," Econometrica, Econometric Society, vol. 60(2), pages 323-351, March.
    3. Helmut Farbmacher & Martin Huber & Lukáš Lafférs & Henrika Langen & Martin Spindler, 2022. "Causal mediation analysis with double machine learning [Mediation analysis via potential outcomes models]," The Econometrics Journal, Royal Economic Society, vol. 25(2), pages 277-300.
    4. Tabares, Sabrina & Parida, Vinit & Chirumalla, Koteshwar, 2025. "Twin transition in industrial organizations: Conceptualization, implementation framework, and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 213(C).
    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. Gupta, Ranjit & Mejia, Cristian & Kajikawa, Yuya, 2019. "Business, innovation and digital ecosystems landscape survey and knowledge cross sharing," Technological Forecasting and Social Change, Elsevier, vol. 147(C), pages 100-109.
    7. Liu, Yang & Dong, Jiuyu & Ying, Ying & Jiao, Hao, 2021. "Status and digital innovation: A middle-status conformity perspective," Technological Forecasting and Social Change, Elsevier, vol. 168(C).
    8. Wen Jun & Muhammad Hamid Nasir & Zahid Yousaf & Amira Khattak & Muhammad Yasir & Asad Javed & Syed Hamad Shirazi, 2021. "Innovation performance in digital economy: does digital platform capability, improvisation capability and organizational readiness really matter?," European Journal of Innovation Management, Emerald Group Publishing Limited, vol. 25(5), pages 1309-1327, May.
    9. Liu, Yang & Dong, Jiuyu & Mei, Liang & Shen, Rui, 2023. "Digital innovation and performance of manufacturing firms: An affordance perspective," Technovation, Elsevier, vol. 119(C).
    Full references (including those not matched with items on IDEAS)

    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. Kangqi Jiang & Xiaofeng Chen & Jiayun Li & Mengling Zhou, 2025. "Technology adoption and extreme stock risk: Evidence from digital tax reform in China," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-20, December.
    2. Zhang, Yingheng & Li, Haojie & Ren, Gang, 2025. "Analysing the role of traffic volume as mediator in transport policy evaluation with causal mediation analysis and targeted learning," Transportation Research Part A: Policy and Practice, Elsevier, vol. 192(C).
    3. Aleksei Opacic, 2025. "Monotonic Path-Specific Effects: Application to Estimating Educational Returns," Papers 2508.13366, arXiv.org.
    4. Rahul Singh & Liyuan Xu & Arthur Gretton, 2020. "Kernel Methods for Causal Functions: Dose, Heterogeneous, and Incremental Response Curves," Papers 2010.04855, arXiv.org, revised Oct 2022.
    5. Azoulay, Pierre & Greenblatt, Wesley H. & Heggeness, Misty L., 2021. "Long-term effects from early exposure to research: Evidence from the NIH “Yellow Berets”," Research Policy, Elsevier, vol. 50(9).
    6. Su, Xiaoying & Zha, Donglan & Zhang, Chaoqun & Zhao, Kuokuo, 2025. "Does household lifestyle impact the decarbonization effect of carbon generalized system of preferences? Evidence from household survey in China," Journal of Asian Economics, Elsevier, vol. 98(C).
    7. 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.
    8. Yangyang Zhong & Yilin Zhong & Longpeng Zhang & Zhiwei Tang, 2024. "The Path to Urban Sustainability: Urban Intelligent Transformation and Green Development—Evidence from 286 Cities in China," Sustainability, MDPI, vol. 16(23), pages 1-32, November.
    9. Xinyu Wei & Mingwang Cheng & Kaifeng Duan & Xiangxing Kong, 2024. "Effects of Big Data on PM 2.5 : A Study Based on Double Machine Learning," Land, MDPI, vol. 13(3), pages 1-21, March.
    10. Yunpeng Fu & Zixuan Wang & Wenjia Zhao, 2025. "The Impact of Information Consumption Pilot Policy on Urban Land Green Use Efficiency: An Empirical Study from China," Land, MDPI, vol. 14(5), pages 1-31, April.
    11. Linlin Wu & Zhen Qin & Peng Jing & Ying Xue & Danning Shao & Pan Luo, 2025. "The Causal Effect of Active School Travel on Children’s Subjective Well-Being: Evidence from the China Family Panel Survey," Journal of Happiness Studies, Springer, vol. 26(6), pages 1-28, August.
    12. Li, Pengshi & Lin, Yan & Yu, Xing & Liu, Guifang, 2025. "Does bid-ask spread explains the smile? On DVF and DML," Pacific-Basin Finance Journal, Elsevier, vol. 90(C).
    13. Lu Kang & Jie Lv & Haoyang Zhang, 2024. "Can the Water Resource Fee-to-Tax Reform Promote the “Three-Wheel Drive” of Corporate Green Energy-Saving Innovations? Quasi-Natural Experimental Evidence from China," Energies, MDPI, vol. 17(12), pages 1-38, June.
    14. Evan D. Peet & Dana Schultz & Susan Lovejoy & Fuchiang (Rich) Tsui, 2024. "The infant health effects of doulas: Leveraging big data and machine learning to inform cost‐effective targeting," Health Economics, John Wiley & Sons, Ltd., vol. 33(6), pages 1387-1411, June.
    15. Chenglei Xu & Shuxin Zhu & Boru Yang & Bin Miao & Yi Duan, 2023. "A Review of Policy Framework Research on Promoting Sustainable Transformation of Digital Innovation," Sustainability, MDPI, vol. 15(9), pages 1-26, April.
    16. Linsen Zhu & Yan Li & Lei Suo & Haiying Feng, 2025. "The Impact of High-Quality Development of Foreign Trade on Marine Economic Quality: Empirical Evidence from Coastal Provinces and Cities in China," Sustainability, MDPI, vol. 17(17), pages 1-29, August.
    17. Michael Lechner, 2023. "Causal Machine Learning and its use for public policy," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-15, December.
    18. Ruiyu Hu & Zemenghong Bao & Zhisen Lin & Kun Lv, 2024. "The Innovative Construction of Provinces, Regional Artificial Intelligence Development, and the Resilience of Regional Innovation Ecosystems: Quasi-Natural Experiments Based on Spatial Difference-in-D," Sustainability, MDPI, vol. 16(18), pages 1-37, September.
    19. Jiahui Li & Yu Yang & Yuqi Ye, 2025. "Rural Tourism, Economic Growth, and Environmental Sustainability: Empirical Evidence Based on County-Level Data in China," Sustainability, MDPI, vol. 17(20), pages 1-32, October.
    20. Yuchen Lu & Jiakun Zhuang & Jun Chen & Chenlu Yang & Mei Kong, 2025. "The Impact of Farmland Transfer on Urban–Rural Integration: Causal Inference Based on Double Machine Learning," Land, MDPI, vol. 14(1), pages 1-30, January.

    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:6:p:2652-:d:1614157. 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.