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  Artificial intelligence in sustainable development research

Gohr, C., Rodríguez, G., Belomestnykh, S., Berg-Moelleken, D., Chauhan, N., Engler, J.-O., Heydebreck, L. V., Hintz, M. J., Kretschmer, M., Krügermeier, C., Meinberg, J., Rau, A.-L., Schwenck, C., Aoulkadi, I., Poll, S., Frank, E., Creutzig, F., Lemke, O., Maushart, M., Pfendtner-Heise, J., Rathgens, J., von Wehrden, H. (2025): Artificial intelligence in sustainable development research. - Nature Sustainability, 8, 970-978.
https://doi.org/10.1038/s41893-025-01598-6

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 Creators:
Gohr, C.1, Author
Rodríguez, G.1, Author
Belomestnykh, S.1, Author
Berg-Moelleken, D.1, Author
Chauhan, N.1, Author
Engler, J.-O.1, Author
Heydebreck, L. V.1, Author
Hintz, Marie Josefine2, Author           
Kretschmer, M.1, Author
Krügermeier, C.1, Author
Meinberg, J.1, Author
Rau, A.-L.1, Author
Schwenck, C.1, Author
Aoulkadi, I.1, Author
Poll, S.1, Author
Frank, E.1, Author
Creutzig, Felix2, Author                 
Lemke, O.1, Author
Maushart, M.1, Author
Pfendtner-Heise, J.1, Author
Rathgens, J.1, Authorvon Wehrden, H.1, Author more..
Affiliations:
1External Organizations, ou_persistent22              
2Potsdam Institute for Climate Impact Research, Potsdam, ou_persistent13              

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 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.

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Language(s): eng - English
 Dates: 2025-01-062025-06-112025-07-212025-08-01
 Publication Status: Finally published
 Pages: 12
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1038/s41893-025-01598-6
PIKDOMAIN: RD5 - Climate Economics and Policy - MCC Berlin
Organisational keyword: RD5 - Climate Economics and Policy - MCC Berlin
Working Group: Cities: Data Science and Sustainable Planning
Research topic keyword: Sustainable Development
Model / method: Machine Learning
MDB-ID: No MDB - stored outside PIK (see locators/paper)
OATYPE: Hybrid Open Access
 Degree: -

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Title: Nature Sustainability
Source Genre: Journal, SCI, Scopus
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Pages: - Volume / Issue: 8 Sequence Number: - Start / End Page: 970 - 978 Identifier: CoNE: https://publications.pik-potsdam.de/cone/journals/resource/nature-sustainability
Publisher: Nature