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Does Artificial Intelligence Promote Firms’ Green Technological Innovation?

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  • Hanna Li

    (School of Economics and Management, Zhengzhou University of Light Industry, Zhengzhou 450001, China)

  • Yu Chen

    (School of Economics and Management, Zhengzhou University of Light Industry, Zhengzhou 450001, China)

Abstract

Green technological innovation represents one of the critical driving forces for addressing environmental issues and advancing the sustainable development process. As a key driver of the new round of technological transformation, artificial intelligence is bound to exert significant impacts on firms’ green technological innovation. In this study, green technology innovation is divided into clean production and pollution control technology innovation according to the production link. A double fixed-effects model was used to test the impact of AI using data from Chinese listed companies from 2006 to 2020. The research findings are as follows: First, artificial intelligence has a significant contribution to green technology innovation in different segments. Second, mechanism analysis reveals that artificial intelligence enhances green technological innovation by improving human capital caliber and firm efficiency. Third, heterogeneity analysis shows that the greater the intensity of environmental regulation a firm faces, the greater the incentive for the firm to use AI for green technology innovation; its effect on pollution control technological innovation is more significant for firms in high-pollution industries; and its effect on clean production technological innovation is more prominent for enterprises in low-pollution industries.

Suggested Citation

  • Hanna Li & Yu Chen, 2025. "Does Artificial Intelligence Promote Firms’ Green Technological Innovation?," Sustainability, MDPI, vol. 17(11), pages 1-24, May.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:11:p:4900-:d:1665145
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    References listed on IDEAS

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    1. Fan, Haichao & Hu, Yichuan & Tang, Lixin, 2021. "Labor costs and the adoption of robots in China," Journal of Economic Behavior & Organization, Elsevier, vol. 186(C), pages 608-631.
    2. Paul Lanoie & Michel Patry & Richard Lajeunesse, 2008. "Environmental regulation and productivity: testing the porter hypothesis," Journal of Productivity Analysis, Springer, vol. 30(2), pages 121-128, October.
    3. Lv, Chengchao & Shao, Changhua & Lee, Chien-Chiang, 2021. "Green technology innovation and financial development: Do environmental regulation and innovation output matter?," Energy Economics, Elsevier, vol. 98(C).
    4. Zhang, Hongsheng & Chen, Ziyi & Wei, Yueling, 2025. "Robot adoption and export sophistication: Firm-level evidence from China," Journal of Asian Economics, Elsevier, vol. 98(C).
    5. Magazzino, Cosimo & Mele, Marco & Morelli, Giovanna & Schneider, Nicolas, 2021. "The nexus between information technology and environmental pollution: Application of a new machine learning algorithm to OECD countries," Utilities Policy, Elsevier, vol. 72(C).
    6. He, Xiaogang & Teng, Ruifeng & Feng, Dawei & Gai, Jiahui, 2024. "Industrial robots and pollution: Evidence from Chinese enterprises," Economic Analysis and Policy, Elsevier, vol. 82(C), pages 629-650.
    7. Wayne B. Gray, 1997. "Manufacturing Plant Location: Does State Pollution Regulation Matter?," NBER Working Papers 5880, National Bureau of Economic Research, Inc.
    8. Daron Acemoglu & Pascual Restrepo, 2019. "Automation and New Tasks: How Technology Displaces and Reinstates Labor," Journal of Economic Perspectives, American Economic Association, vol. 33(2), pages 3-30, Spring.
    9. Morgenstern, Richard D. & Pizer, William A. & Shih, Jhih-Shyang, 2002. "Jobs Versus the Environment: An Industry-Level Perspective," Journal of Environmental Economics and Management, Elsevier, vol. 43(3), pages 412-436, May.
    10. Daron Acemoglu & Pascual Restrepo, 2020. "Robots and Jobs: Evidence from US Labor Markets," Journal of Political Economy, University of Chicago Press, vol. 128(6), pages 2188-2244.
    11. Huang, Geng & He, Ling-Yun & Lin, Xi, 2022. "Robot adoption and energy performance: Evidence from Chinese industrial firms," Energy Economics, Elsevier, vol. 107(C).
    12. Liu, Jun & Chang, Huihong & Forrest, Jeffrey Yi-Lin & Yang, Baohua, 2020. "Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    13. Wang, En-Ze & Lee, Chien-Chiang & Li, Yaya, 2022. "Assessing the impact of industrial robots on manufacturing energy intensity in 38 countries," Energy Economics, Elsevier, vol. 105(C).
    14. Rennings, Klaus, 2000. "Redefining innovation -- eco-innovation research and the contribution from ecological economics," Ecological Economics, Elsevier, vol. 32(2), pages 319-332, February.
    15. Lee, Chien-Chiang & Qin, Shuai & Li, Yaya, 2022. "Does industrial robot application promote green technology innovation in the manufacturing industry?," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
    16. Antonioli, Davide & Marzucchi, Alberto & Rentocchini, Francesco & Vannuccini, Simone, 2024. "Robot adoption and product innovation," Research Policy, Elsevier, vol. 53(6).
    17. El-Kassar, Abdul-Nasser & Singh, Sanjay Kumar, 2019. "Green innovation and organizational performance: The influence of big data and the moderating role of management commitment and HR practices," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 483-498.
    18. Sharfaei, Shahab & Bittner, Jan, 2024. "Technological employment: Evidence from worldwide robot adoption," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
    19. Cao, Yuanyuan & Chen, Shaojian & Tang, Heyan, 2025. "Robot adoption and firm export: Evidence from China," Technological Forecasting and Social Change, Elsevier, vol. 210(C).
    Full references (including those not matched with items on IDEAS)

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