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Artificial Intelligence and Green Collaborative Innovation: An Empirical Investigation Based on a High-Dimensional Fixed Effects Model

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  • Guanyan Lu

    (School of Economics and Management, Xi’an University of Technology, Xi’an 710054, China)

  • Bingxiang Li

    (School of Economics and Management, Xi’an University of Technology, Xi’an 710054, China)

Abstract

This study focuses on the intrinsic mechanisms and sustainable value of artificial intelligence (AI)-driven green collaborative innovation in enterprises amid the global green low-carbon transition, revealing new pathways for digital technology-enabled green development. Based on the data of China’s A-share listed companies jointly applying for green patents with other entities from 2010 to 2023, this study used a high-dimensional fixed effect model to empirically find that artificial intelligence significantly promotes green collaborative innovation. This promoting effect proved more pronounced in the case of high macroeconomic uncertainty, large enterprises and SOEs. A mechanism test revealed that artificial intelligence drives green collaborative innovation primarily by reducing transaction costs and optimizing the labor structure. A moderating effect analysis showed that green investor entry and CEO openness can strengthen the facilitating effect of artificial intelligence on green collaborative innovation. In addition, the facilitating effect of artificial intelligence on green collaborative innovation helps companies reduce carbon emissions and improve ESG performance, driving the transformation of business ecosystems toward environmental sustainability. From a technology–organization–environment co-evolution perspective, this research clarifies the micro-level operational chain of AI-enabled green innovation, providing theoretical support for developing countries to achieve leapfrog low-carbon transitions through digital technologies. Practically, it offers actionable insights for advancing AI-enabled green industries, constructing collaborative green innovation ecosystems, and supporting the realization of the United Nations Sustainable Development Goals (SDGs).

Suggested Citation

  • Guanyan Lu & Bingxiang Li, 2025. "Artificial Intelligence and Green Collaborative Innovation: An Empirical Investigation Based on a High-Dimensional Fixed Effects Model," Sustainability, MDPI, vol. 17(9), pages 1-41, May.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:9:p:4141-:d:1648864
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    1. Li, Cuilan & Chen, Hailin & Xie, Weilin & Wang, Ping, 2024. "Tax incentives for human capital accumulation and enterprise innovation quality: Evidence of a quasi-natural experiment in China," Finance Research Letters, Elsevier, vol. 70(C).
    2. Joseph G. Altonji & Todd E. Elder & Christopher R. Taber, 2005. "Selection on Observed and Unobserved Variables: Assessing the Effectiveness of Catholic Schools," Journal of Political Economy, University of Chicago Press, vol. 113(1), pages 151-184, February.
    3. Li Dong & Xin Zhang & Jinlong Chen, 2024. "Does Investor Sentiment Drive Corporate Green Innovation: Evidence from China," Sustainability, MDPI, vol. 16(8), pages 1-20, April.
    4. Zhong, Kai & Song, Liangrong, 2025. "Artificial intelligence adoption and corporate green innovation capability," Finance Research Letters, Elsevier, vol. 72(C).
    5. Chowdhury, Soumyadeb & Ren, Shuang & Richey, Robert Glenn, 2025. "Leveraging artificial intelligence to facilitate green servitization: Resource orchestration and Re-institutionalization perspectives," International Journal of Production Economics, Elsevier, vol. 281(C).
    6. Liu, Maotao & Fang, Xubing, 2025. "How does green investor entry affect corporate carbon performance? Evidence from China," Renewable Energy, Elsevier, vol. 244(C).
    7. Scott R. Baker & Nicholas Bloom & Steven J. Davis, 2016. "Measuring Economic Policy Uncertainty," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 131(4), pages 1593-1636.
    8. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The Skill Content of Recent Technological Change: An Empirical Exploration," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 118(4), pages 1279-1333.
    9. Cao, Qingfeng & Chi, Chuenyu & Shan, Junhui, 2025. "Can artificial intelligence technology reduce carbon emissions? A global perspective," Energy Economics, Elsevier, vol. 143(C).
    10. Arthur Lewbel, 1997. "Constructing Instruments for Regressions with Measurement Error when no Additional Data are Available, with an Application to Patents and R&D," Econometrica, Econometric Society, vol. 65(5), pages 1201-1214, September.
    11. De Giovanni, Pietro, 2020. "Blockchain and smart contracts in supply chain management: A game theoretic model," International Journal of Production Economics, Elsevier, vol. 228(C).
    12. Hao Liu & Xue Tang & LiYuan Liu & Hui Lai, 2024. "Foreign Institutional Investors and Corporate Green Innovation: Evidence from an Emerging Economy," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 60(9), pages 2096-2109, July.
    13. Wang, Lei & Zhou, Yahong & Chiao, Benjamin, 2023. "Robots and firm innovation: Evidence from Chinese manufacturing," Journal of Business Research, Elsevier, vol. 162(C).
    14. Upadhayay, Neha B. & Rocchetta, Silvia & Gupta, Shivam & Kamble, Sachin & Stekelorum, Rebecca, 2024. "Blazing the trail: The role of digital and green servitization on technological innovation," Technovation, Elsevier, vol. 130(C).
    15. Qu, Guimin & Jing, Hao, 2025. "Is new technology always good? Artificial intelligence and corporate tax avoidance: Evidence from China," International Review of Economics & Finance, Elsevier, vol. 98(C).
    16. Xue, Jing & Li, Guo & Ivanov, Dmitry, 2025. "Digital transformation in the blockchain era: Balancing efficiency and resilience in operations management," International Journal of Production Economics, Elsevier, vol. 282(C).
    17. Kobarg, Sebastian & Stumpf-Wollersheim, Jutta & Welpe, Isabell M., 2019. "More is not always better: Effects of collaboration breadth and depth on radical and incremental innovation performance at the project level," Research Policy, Elsevier, vol. 48(1), pages 1-10.
    18. Albats, Ekaterina & Bogers, Marcel & Podmetina, Daria, 2020. "Companies’ human capital for university partnerships: A micro-foundational perspective," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    19. Luo, Zijun & Liu, Yue & Xu, Weidong, 2025. "Carbon emission trading scheme and green investor entry: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 91(C).
    20. Yang, Hefan & Liu, Xilu & Meng, Yao & Feng, Baoyi & Chen, Zhijun, 2024. "Digital transformation and the allocation of decision-making rights within business groups – Empirical evidence from China," Journal of Business Research, Elsevier, vol. 179(C).
    21. Abdul Wahab Amadu & Michael Danquah, 2019. "R&D, Human Capital and Export Behavior of Manufacturing and Service Firms in Ghana," Journal of African Business, Taylor & Francis Journals, vol. 20(3), pages 283-304, July.
    22. Gao, Yang & Liu, Siqiang & Yang, Lu, 2025. "Artificial intelligence and innovation capability: A dynamic capabilities perspective," International Review of Economics & Finance, Elsevier, vol. 98(C).
    23. David H. Autor & Frank Levy & Richard J. Murnane, 2003. "The skill content of recent technological change: an empirical exploration," Proceedings, Federal Reserve Bank of San Francisco, issue nov.
    24. Sun, Xiuli & Li, Haizheng & Ghosal, Vivek, 2020. "Firm-level human capital and innovation: Evidence from China," China Economic Review, Elsevier, vol. 59(C).
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