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Impact of artificial intelligence on renewable energy supply chain vulnerability: Evidence from 61 countries

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  • Song, Yuegang
  • Wang, Ziqi
  • Song, Changqing
  • Wang, Jianhua
  • Liu, Rong

Abstract

By fully leveraging the mitigating effect of artificial intelligence (AI) on renewable energy, the supply chain vulnerability is referred to as the key to realizing the supply chain's safety, stability, reliability, and the continuous development of global environmental governance. Several databases have been selected here for the assessment: the UN Comtrade database, the global industrial robotics database, and the World Bank database covering the period of 2000 to 2019. This particular study examines the effect and mechanisms of AI on renewable energy supply chain vulnerability and explores the spatial spillover effects of AI in neighboring countries. The relevant findings are threefold. Firstly, the analysis of the mechanism of action demonstrates that AI can alleviate renewable energy supply chain vulnerability through technological innovation, governance system optimization, and trade network status promotion effects. Secondly, the heterogeneity analysis reveals that AI significantly alleviates renewable energy supply chain vulnerability in middle-income countries, high-vulnerability products, countries with high popularity of digital infrastructure, and countries in the initial stage of renewable energy industrial development. Thirdly, the spatial econometric results show that AI can directly alleviate domestic renewable energy supply chain vulnerability and indirectly alleviate vulnerability in neighboring countries through spatial spillover effects. This study expands the methods for identifying the impact of external environmental changes on renewable energy supply chain vulnerability. It provides an empirical reference for policymakers and professionals to maintain the security, stability, and reliability of renewable energy supply chains in the AI era.

Suggested Citation

  • Song, Yuegang & Wang, Ziqi & Song, Changqing & Wang, Jianhua & Liu, Rong, 2024. "Impact of artificial intelligence on renewable energy supply chain vulnerability: Evidence from 61 countries," Energy Economics, Elsevier, vol. 131(C).
    • Handle: RePEc:eee:eneeco:v:131:y:2024:i:c:s0140988324000653
    DOI: 10.1016/j.eneco.2024.107357
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    Cited by:

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    3. Ahmed Alyahya & Gomaa Agag, 2025. "Improving Corporate Environmental Performance Through Big Data Analytics Implementation: The Role of Industry Environment," Sustainability, MDPI, vol. 17(7), pages 1-22, March.
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    More about this item

    Keywords

    Renewable energy; Supply chain vulnerability; Artificial intelligence; Spatial spillover effect;
    All these keywords.

    JEL classification:

    • F18 - International Economics - - Trade - - - Trade and Environment
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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