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Can artificial intelligence technology improve companies' capacity for green innovation? Evidence from listed companies in China

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  • Liu, Yingji
  • Shen, Fangbing
  • Guo, Ju
  • Hu, Guoheng
  • Song, Yuegang

Abstract

Green innovation in the digital economy is characterized by complex adaptive systems. It is challenging to effectively improve corporate green innovation capacity (CGIC) by relying on traditional technological innovation. The integration of enterprises' green innovation and artificial intelligence technology (AIT) is becoming a significant driver for addressing global environmental challenges and promoting enterprises' energy efficiency and low-carbon development. Combining patent datasets for listed companies in China, this study explores the driving effect of AIT on CGIC from the perspective of “artificial intelligence +”. This study empirically examines how AIT affects CGIC, and further investigates AIT's impact on the duration of enterprises' green innovation. The findings reveal that AIT's intervention can effectively improve CGIC. Enterprises with different characteristics have heterogeneous effects on improving CGIC by applying AI, indicating that the application of AIT has a more prominent impact on heavily polluting, nontechnology-intensive, and highly competitive enterprises. Mechanism analysis demonstrates that AIT can improve CGIC by absorbing high-skilled labor and increasing investment in research and development. Further examination reveals that AIT application can significantly reduce the potential for enterprises interrupting green innovation activities and prolong the duration of green innovation. This study provides valuable insights concerning the effect of enterprises' AIT application on improving CGIC, empowering enterprises to improve energy efficiency and achieve low-carbon development.

Suggested Citation

  • Liu, Yingji & Shen, Fangbing & Guo, Ju & Hu, Guoheng & Song, Yuegang, 2025. "Can artificial intelligence technology improve companies' capacity for green innovation? Evidence from listed companies in China," Energy Economics, Elsevier, vol. 143(C).
    • Handle: RePEc:eee:eneeco:v:143:y:2025:i:c:s0140988325001033
    DOI: 10.1016/j.eneco.2025.108280
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    JEL classification:

    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • O32 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Management of Technological Innovation and R&D
    • Q55 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Environmental Economics: Technological Innovation

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