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Artificial Intelligence (AI) Adoption and Green Investment: Driving Corporate Environmental Responsibility

Author

Listed:
  • Rabia Akram

    (Guilin University of Electronic Technology)

  • Xiaojing Shi

    (Zhengzhou University)

  • Fahad Khalid

    (Guilin University of Electronic Technology)

  • Mohit Srivastava

    (Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School)

Abstract

Extant research has provided initial evidence on the application of artificial intelligence (AI) for corporate sustainable development. However, a research gap remains in understanding how AI specifically drives corporate green development, particularly in the realm of green investments. This research investigates the impact of AI adoption on corporate green investment (CGI) based on the sample of China's A‐share listed companies (2008–2022). By employing econometric techniques to examine panel data, an instrument variable with a two‐stage least squares approach, the Heckman selection model, and a series of robustness tests, this research finds that AI adoption positively correlates with CGI. The mechanism analysis reveals that AI adoption curbs managerial myopia and enhances technological resources to drive green investment levels. Moreover, the impact of AI adoption on CGI is pronounced when companies are headed by younger CEOs, those with higher education qualifications, and those with global exposure. This research enhances our overall comprehension of how digital innovation may be strategically utilized to promote sustainable development. It also provides significant insights for managers and policymakers who seek to facilitate environmentally friendly economic growth.

Suggested Citation

  • Rabia Akram & Xiaojing Shi & Fahad Khalid & Mohit Srivastava, 2025. "Artificial Intelligence (AI) Adoption and Green Investment: Driving Corporate Environmental Responsibility," Post-Print hal-05576277, HAL.
    • Handle: RePEc:hal:journl:hal-05576277
    DOI: 10.1002/bse.70069
    as

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