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Does artificial intelligence promote green innovation? An assessment based on direct, indirect, spillover, and heterogeneity effects

Author

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  • Qiang Wang
  • Tingting Sun
  • Rongrong Li

Abstract

This paper investigates the intricate relationship between artificial intelligence (AI) and green innovation within the context of sustainable development goals. As societies strive to achieve sustainability, understanding the dynamics between technological advancements and environmental progress becomes paramount. Drawing from panel data encompassing 51 countries between 2000 and 2019, this study employs fixed-effects models, mediated effects models, and spatial Durbin models to meticulously examine the influence of AI on green innovation. The empirical findings reveal a robust and significantly positive correlation between AI and green innovation, highlighting the critical role of AI in fostering environmental innovation. Heterogeneity analysis across developed and developing economies delineates variations in the impact of AI on green innovation, shedding light on the influence of economic development levels and financial structures. Developed nations showcase a more pronounced AI-green innovation relationship compared to their developing counterparts, highlighting the complexities of technology adoption within distinct economic landscapes. Moreover, this study delves into the transmission mechanisms underlying the AI-green innovation nexus, revealing the mediating roles of industrial structure and human capital. Industrial upgrading and the enhancement of human capital emerge as crucial pathways through which AI indirectly stimulates green innovation. Spatial analyses reveals the spatial relevance of green innovation globally, emphasizing AI's substantial impact not only within domestic spheres but also across neighboring regions. There are significant direct, indirect, and total effects of AI on green innovation, highlighting its spillover characteristics and the catalytic role it plays in driving collaborative AI development on a global scale. This research contributes nuanced insights into the interplay between AI and green innovation, providing a foundation for policymakers, businesses, and researchers to comprehend the multifaceted dimensions of technological interventions in fostering sustainable innovation. The findings emphasize the imperative of collaborative efforts in utilizing AI's potential to propel green innovation, thereby advancing global sustainability agendas.

Suggested Citation

  • Qiang Wang & Tingting Sun & Rongrong Li, 2025. "Does artificial intelligence promote green innovation? An assessment based on direct, indirect, spillover, and heterogeneity effects," Energy & Environment, , vol. 36(2), pages 1005-1037, March.
    • Handle: RePEc:sae:engenv:v:36:y:2025:i:2:p:1005-1037
    DOI: 10.1177/0958305X231220520
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    Cited by:

    1. Ulug, Mehmet & Caglar, Abdullah Emre & Avci, Mehmet Alpertunga & Avci, Salih Bortecine, 2025. "Germany's sustainable future: How artificial intelligence and energy innovation shape the carbon neutrality roadmap?," Energy, Elsevier, vol. 335(C).
    2. Sun, Yemeng & Zhang, Xiaoxia & Dong, Zhuoqi, 2025. "The impact of top management team heterogeneity and financial misallocation on corporate resource allocation efficiency," International Review of Economics & Finance, Elsevier, vol. 103(C).
    3. Lu Wang & Ziying Zhao & Xiaojun Xu & Xiaoli Wang & Yuting Wang, 2025. "How Does the Construction of New Generation of National AI Innovative Development Pilot Zones Affect Carbon Emissions Intensity? Empirical Evidence from China," Sustainability, MDPI, vol. 17(15), pages 1-26, July.
    4. Li, Rongrong & Chen, Qian & Wang, Qiang, 2025. "Emerging topic identification in carbon capture utilization and storage (CCUS) for advancing climate action (SDG 13)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 222(C).

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