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Artificial intelligence in sustainable development research

Author

Listed:
  • C. Gohr

    (Leuphana University Lüneburg
    Leuphana University Lüneburg)

  • G. Rodríguez

    (Leuphana University Lüneburg
    Leuphana University Lüneburg)

  • S. Belomestnykh

    (Leuphana University Lüneburg)

  • D. Berg-Moelleken

    (Leuphana University Lüneburg
    Leuphana University Lüneburg)

  • N. Chauhan

    (Leuphana University Lüneburg
    Leuphana University Lüneburg)

  • J.-O. Engler

    (Leuphana University Lüneburg
    University of Vechta
    Vechta Institute of Sustainability Transformation in Rural Areas)

  • L. V. Heydebreck

    (Leuphana University Lüneburg
    Leuphana University Lüneburg)

  • M. J. Hintz

    (Potsdam Institute for Climate Impact Research
    Technical University Berlin
    Hertie School of Governance)

  • M. Kretschmer

    (Leuphana University Lüneburg)

  • C. Krügermeier

    (Leuphana University Lüneburg)

  • J. Meinberg

    (Leuphana University Lüneburg)

  • A.-L. Rau

    (Leuphana University Lüneburg
    Sustainability Education and Transdisciplinary Research Institute)

  • C. Schwenck

    (Leuphana University Lüneburg)

  • I. Aoulkadi

    (Leuphana University Lüneburg)

  • S. Poll

    (Leuphana University Lüneburg)

  • E. Frank

    (Leuphana University Lüneburg)

  • F. Creutzig

    (Potsdam Institute for Climate Impact Research
    Sussex Business School)

  • O. Lemke

    (Leuphana University Lüneburg)

  • M. Maushart

    (Hamburg University)

  • J. Pfendtner-Heise

    (Leuphana University Lüneburg
    Sustainability Education and Transdisciplinary Research Institute)

  • J. Rathgens

    (Helmholtz Centre Potsdam)

  • H. Wehrden

    (Leuphana University Lüneburg
    Leuphana University Lüneburg)

Abstract

Artificial intelligence (AI) holds significant potential to advance Sustainable Development Goals by enabling data-driven insights and optimizations. In this analysis, we review 792 articles that explore AI applications in Sustainable Development Goal-related research. The literature is organized along two dimensions: (1) the disciplinary spectrum, from natural sciences to the humanities, and (2) the focus, distinguishing economic from socioecological content. Deep learning and supervised machine learning were the most prominently applied algorithms for forecasting and system optimization. However, we identify a critical gap: only a few studies combine advanced AI applications with deep sustainability expertise. Sustainability needs to strike a balance between contextualization and generalizability to provide tangible knowledge that will lead to responsible change. AI must play a central role in this process. While expectations for AI’s transformative role in sustainable development are high, its full potential remains to be realized.

Suggested Citation

  • C. Gohr & G. Rodríguez & S. Belomestnykh & D. Berg-Moelleken & N. Chauhan & J.-O. Engler & L. V. Heydebreck & M. J. Hintz & M. Kretschmer & C. Krügermeier & J. Meinberg & A.-L. Rau & C. Schwenck & I. , 2025. "Artificial intelligence in sustainable development research," Nature Sustainability, Nature, vol. 8(8), pages 970-978, August.
    • Handle: RePEc:nat:natsus:v:8:y:2025:i:8:d:10.1038_s41893-025-01598-6
    DOI: 10.1038/s41893-025-01598-6
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    References listed on IDEAS

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