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Practical AI Cases for Solving ESG Challenges

Author

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  • Evgeny Burnaev

    (Applied AI center, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
    Autonomous Non-Profit Organization Artificial Intelligence Research Institute (AIRI), 105064 Moscow, Russia
    These authors contributed equally to this work.)

  • Evgeny Mironov

    (LLC “Gazpromneft-Digital Solutions”, 196084 Saint Petersburg, Russia
    These authors contributed equally to this work.)

  • Aleksei Shpilman

    (“Gazprom Neft”, 190121 Saint Petersburg, Russia
    Department of Informatics, HSE Campus in St. Petersburg, HSE University, 190121 Saint Petersburg, Russia)

  • Maxim Mironenko

    (Applied AI center, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia)

  • Dmitry Katalevsky

    (Applied AI center, Skolkovo Institute of Science and Technology, 121205 Moscow, Russia
    Graduate School of Business, Lomonosov Moscow State University, 119234 Moscow, Russia)

Abstract

Artificial intelligence (AI) is a rapidly advancing area of research that encompasses numerical methods to solve various prediction, optimization, and classification/clustering problems. Recently, AI tools were proposed to address the environmental, social, and governance (ESG) challenges associated with sustainable business development. While many publications discuss the potential of AI, few focus on practical cases in the three ESG domains altogether, and even fewer highlight the challenges that AI may pose in terms of ESG. The current paper fills this gap by reviewing practical AI applications with a main focus on IT and engineering implementations. The considered cases are based on almost one hundred publicly available research manuscripts and reports obtained via online search engines. This review involves the study of typical business and production problems associated with each ESG domain, gives background details on several selected cases (such as carbon neutrality, land management, and ESG scoring), and lists challenges that the smart algorithms can pose (such as fake news generation and increased electricity consumption). Overall, it is concluded that, while many practical cases already exist, AI in ESG is still very far away from reaching its full potential; however, one should always remember that AI itself can lead to some ESG risks.

Suggested Citation

  • Evgeny Burnaev & Evgeny Mironov & Aleksei Shpilman & Maxim Mironenko & Dmitry Katalevsky, 2023. "Practical AI Cases for Solving ESG Challenges," Sustainability, MDPI, vol. 15(17), pages 1-15, August.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:17:p:12731-:d:1222887
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    References listed on IDEAS

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    Cited by:

    1. Da HUO & Tianying SUN & Wenjia GU & Aidi TANG, 2025. "AI and ESG - New Quality Productivity in Digital Economy," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 5-23, July.
    2. Jun Xu, 2024. "AI in ESG for Financial Institutions: An Industrial Survey," Papers 2403.05541, arXiv.org.
    3. Zizhe Du & Chao Chen, 2025. "AI vs. ESG? Uncovering a Bidirectional Struggle in China’s Sustainable Finance," Sustainability, MDPI, vol. 17(9), pages 1-23, May.
    4. Svetozar D. Jankovic & Dejan M. Curovic, 2023. "Strategic Integration of Artificial Intelligence for Sustainable Businesses: Implications for Data Management and Human User Engagement in the Digital Era," Sustainability, MDPI, vol. 15(21), pages 1-19, October.

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