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The Impact of AI on Corporate Green Transformation: Empirical Evidence from China

Author

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  • Zhen-Er Jiang

    (School of Business, Nanjing University, Nanjing 210093, China)

  • Fu Huang

    (Department of Economics and Management, Jiangsu College of Administration, Nanjing 210009, China)

  • Qiang Wu

    (Yangtze River Delta Economic and Social Development Research Center, Nanjing University, Nanjing 210093, China)

Abstract

With the rapid advancement of artificial intelligence (AI), its deep integration into corporate operations has become the key driver for firms to reconfigure factor resources, boost green total factor productivity, and achieve green transformation. This analysis empirically investigates the influence of AI on corporate green transformation using panel data of China’s listed companies from 2015 to 2022. This research employs a multidimensional fixed effects linear model to analyze the relationship, finding that AI significantly enhances corporate green transformation. Mechanism analysis reveals that AI promotes green transformation by enhancing firm research and development (R&D) and firm green innovation capabilities. Heterogeneity analysis shows that the positive impact of AI on corporate green transformation is more significant in the eastern region, post-COVID−19, and in low-pollution industries. The impact is also significantly and positively moderated by the development of the non-state-owned economy and the development degree of product markets. These findings suggest that AI is a critical tool for promoting sustainable economic growth and green transformation in businesses.

Suggested Citation

  • Zhen-Er Jiang & Fu Huang & Qiang Wu, 2025. "The Impact of AI on Corporate Green Transformation: Empirical Evidence from China," Sustainability, MDPI, vol. 17(17), pages 1-20, August.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:17:p:7782-:d:1737396
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    References listed on IDEAS

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    1. Daron Acemoglu, 2025. "The simple macroeconomics of AI," Economic Policy, CEPR, CESifo, Sciences Po;CES;MSH, vol. 40(121), pages 13-58.
    2. Du, Kerui & Cheng, Yuanyuan & Yao, Xin, 2021. "Environmental regulation, green technology innovation, and industrial structure upgrading: The road to the green transformation of Chinese cities," Energy Economics, Elsevier, vol. 98(C).
    3. 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).
    4. Pandey, Dharen Kumar & Hunjra, Ahmed Imran & Bhaskar, Ratikant & Al-Faryan, Mamdouh Abdulaziz Saleh, 2023. "Artificial intelligence, machine learning and big data in natural resources management: A comprehensive bibliometric review of literature spanning 1975–2022," Resources Policy, Elsevier, vol. 86(PA).
    5. Mariani, Marcello M. & Machado, Isa & Magrelli, Vittoria & Dwivedi, Yogesh K., 2023. "Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions," Technovation, Elsevier, vol. 122(C).
    6. Rammer, Christian & Fernández, Gastón P. & Czarnitzki, Dirk, 2022. "Artificial intelligence and industrial innovation: Evidence from German firm-level data," Research Policy, Elsevier, vol. 51(7).
    7. Bahoo, Salman & Cucculelli, Marco & Qamar, Dawood, 2023. "Artificial intelligence and corporate innovation: A review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    8. Zhang, Bingbing & Yu, Lan & Sun, Chuanwang, 2022. "How does urban environmental legislation guide the green transition of enterprises? Based on the perspective of enterprises' green total factor productivity," Energy Economics, Elsevier, vol. 110(C).
    9. James Heckman, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    10. Wang, Zongrun & Zhang, Taiyu & Ren, Xiaohang & Shi, Yukun, 2024. "AI adoption rate and corporate green innovation efficiency: Evidence from Chinese energy companies," Energy Economics, Elsevier, vol. 132(C).
    11. Yongjie Wu & Jingwen Wang & Mengxuan Jia, 2025. "Measurement, Regional Differences and Convergence Characteristics of Comprehensive Green Transformation of China’s Economy and Society," Sustainability, MDPI, vol. 17(9), pages 1-23, April.
    12. Birger Wernerfelt, 1984. "A resource‐based view of the firm," Strategic Management Journal, Wiley Blackwell, vol. 5(2), pages 171-180, April.
    13. Song, Malin & Fisher, Ron & Kwoh, Yusen, 2019. "Technological challenges of green innovation and sustainable resource management with large scale data," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 361-368.
    14. Liu, Liang & Yang, Kun & Fujii, Hidemichi & Liu, Jun, 2021. "Artificial intelligence and energy intensity in China’s industrial sector: Effect and transmission channel," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 276-293.
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    Cited by:

    1. He, Wensheng & Ding, Qiaoying & Zhou, Tao, 2025. "How fiscal policy drives corporate digital transformation: an analysis of the synergistic effects of tax incentives and special subsidies," International Review of Economics & Finance, Elsevier, vol. 103(C).

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