Artificial intelligence in sustainable development research

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. Aoulkadi, S. Poll, E. Frank, F. Creutzig, O. Lemke, M. Maushart, J. Pfendtner-Heise, J. Rathgens and H. Wehrden
Additional contact information
C. Gohr: Leuphana University Lüneburg
G. Rodríguez: Leuphana University Lüneburg
S. Belomestnykh: Leuphana University Lüneburg
D. Berg-Moelleken: Leuphana University Lüneburg
N. Chauhan: Leuphana University Lüneburg
J.-O. Engler: Leuphana University Lüneburg
L. V. Heydebreck: Leuphana University Lüneburg
M. J. Hintz: Potsdam Institute for Climate Impact Research
M. Kretschmer: Leuphana University Lüneburg
C. Krügermeier: Leuphana University Lüneburg
J. Meinberg: Leuphana University Lüneburg
A.-L. Rau: Leuphana University Lüneburg
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
O. Lemke: Leuphana University Lüneburg
M. Maushart: Hamburg University
J. Pfendtner-Heise: Leuphana University Lüneburg
J. Rathgens: Helmholtz Centre Potsdam
H. Wehrden: Leuphana University Lüneburg

Nature Sustainability, 2025, vol. 8, issue 8, 970-978

Abstract: 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.

Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41893-025-01598-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natsus:v:8:y:2025:i:8:d:10.1038_s41893-025-01598-6

Ordering information: This journal article can be ordered from
https://www.nature.com/natsustain/

DOI: 10.1038/s41893-025-01598-6

Access Statistics for this article

Nature Sustainability is currently edited by Monica Contestabile

More articles in Nature Sustainability from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().