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The Nexus of Artificial Intelligence and Green Innovation: a Cross-Density Analysis at the Country Level

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  • Youngsam Chun

    (Seoul National University
    Seoul National University
    Korea Telecom (KT))

  • Junseok Hwang

    (Seoul National University
    Seoul National University
    Seoul National University)

Abstract

This study explored the synergistic effects of artificial intelligence (AI) on green innovation at the national level by identifying three core activities in green innovation: entry, exit, and sustained specialization in green technologies. Employing a comprehensive three-way fixed-effects panel model, we analyzed green innovation trends in 139 countries over a 30-year period. Our findings emphasize that technological capability, especially in green and AI technologies, is more pivotal than economic wealth in securing comparative advantages in green technologies. One notable observation is the path-dependent development of green technology, which suggests a propensity for nations to build new technologies based on existing strengths. A key contribution of this study is the development of the “AI-Green Cross-Density” model. This innovative method quantifies the interactions between green and AI technologies and highlights the role of AI in promoting green innovation. The influence of AI on the introduction of new green technologies has become more pronounced in recent years, particularly in economically less-developed countries. Our findings indicate that the integration of modern AI can significantly enhance green technology sectors in mid-to-low-income countries, providing vital insights for policymakers striving to foster a sustainable and technologically advanced future.

Suggested Citation

  • Youngsam Chun & Junseok Hwang, 2025. "The Nexus of Artificial Intelligence and Green Innovation: a Cross-Density Analysis at the Country Level," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 1688-1716, March.
    • Handle: RePEc:spr:jknowl:v:16:y:2025:i:1:d:10.1007_s13132-024-02076-8
    DOI: 10.1007/s13132-024-02076-8
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