IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i6p2652-d1614157.html

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. 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.
    2. 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.
    3. Aghion, Philippe & Howitt, Peter, 1992. "A Model of Growth through Creative Destruction," Econometrica, Econometric Society, vol. 60(2), pages 323-351, March.
    4. 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.
    5. Liu, Yang & Dong, Jiuyu & Mei, Liang & Shen, Rui, 2023. "Digital innovation and performance of manufacturing firms: An affordance perspective," Technovation, Elsevier, vol. 119(C).
    6. 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).
    7. 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.
    8. 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.
    9. 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).
    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. 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.
    3. 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.
    4. 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.
    5. 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).
    6. 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.
    7. Aleksei Opacic, 2025. "Monotonic Path-Specific Effects: Application to Estimating Educational Returns," Papers 2508.13366, arXiv.org.
    8. 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.
    9. 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.
    10. 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.
    11. Rahul Singh & Liyuan Xu & Arthur Gretton, 2021. "Sequential Kernel Embedding for Mediated and Time-Varying Dose Response Curves," Papers 2111.03950, arXiv.org, revised Mar 2025.
    12. Wang, Lulu & Lyu, Jie & Wang, Shanshan & Zhang, Junyan, 2025. "Unveiling the influence of agricultural mechanization on greenhouse gas emission intensity: Insights from China using causal machine learning model," Agricultural Systems, Elsevier, vol. 226(C).
    13. AmirEmad Ghassami & Andrew Ying & Ilya Shpitser & Eric Tchetgen Tchetgen, 2021. "Minimax Kernel Machine Learning for a Class of Doubly Robust Functionals with Application to Proximal Causal Inference," Papers 2104.02929, arXiv.org, revised Mar 2022.
    14. Lulu Wang & Jie Lyu & Junyan Zhang, 2024. "Explicating the Role of Agricultural Socialized Services on Chemical Fertilizer Use Reduction: Evidence from China Using a Double Machine Learning Model," Agriculture, MDPI, vol. 14(12), pages 1-16, November.
    15. Li, Zhen & Chai, Fan & Han, Jinrong, 2025. "Digital product trade liberalization and enterprises’ digital innovation in China——Evidence from the ITA expansion," Finance Research Letters, Elsevier, vol. 80(C).
    16. Song, Hongti & Chen, Wei, 2025. "A digital blueprint for sustainability: Can digital infrastructure policies promote renewable energy innovation?," Renewable Energy, Elsevier, vol. 244(C).
    17. Ruonan Cai & Wencan Tian & Rundong Luo & Zhichao Fang & Zhigang Hu, 2025. "Do articles with multiple corresponding authorships have a citation advantage? A double machine learning analysis approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(5), pages 2523-2550, May.
    18. Jonathan Fuhr & Philipp Berens & Dominik Papies, 2024. "Estimating Causal Effects with Double Machine Learning -- A Method Evaluation," Papers 2403.14385, arXiv.org, revised Apr 2024.
    19. Nora Bearth & Michael Lechner, 2024. "Causal Machine Learning for Moderation Effects," Papers 2401.08290, arXiv.org, revised Jan 2025.
    20. Dongli Chen & Qianxuan Huang, 2024. "The New Policy for Innovative Transformation in Regional Industrial Chains, the Conversion of New and Old Kinetic Energy, and Energy Poverty Alleviation," Energies, MDPI, vol. 17(11), pages 1-37, May.

    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.