Artificial intelligence in sustainable development research
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In: Nature Sustainability, 2025.
Research output: Journal contributions › Journal articles › Research › peer-review
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TY - JOUR
T1 - Artificial intelligence in sustainable development research
AU - Gohr, Charlotte
AU - Rodríguez Aboytes, Jorge Gustavo
AU - Belomestnykh, S.
AU - Berg-Moelleken, Dagmar
AU - Chauhan, Neha
AU - Engler, John-Oliver
AU - von Heydebreck, Linda
AU - Hintz, Marie J.
AU - Kretschmer, Max-Friedemann
AU - Krügermeier, Carlo
AU - Meinberg, Jule
AU - Rau, Anna-Lena
AU - Schwenck, Christoph
AU - Aoulkadi, I.
AU - Poll, S.
AU - Frank, Elisabeth
AU - Creutzig, Felix
AU - Lemke, O.
AU - Maushart, M.
AU - Pfendtner-Heise, Jannis
AU - Rathgens, Julius
AU - von Wehrden, Henrik
N1 - Publisher Copyright: © The Author(s) 2025.
PY - 2025
Y1 - 2025
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=105011342860&partnerID=8YFLogxK
U2 - 10.1038/s41893-025-01598-6
DO - 10.1038/s41893-025-01598-6
M3 - Journal articles
AN - SCOPUS:105011342860
JO - Nature Sustainability
JF - Nature Sustainability
SN - 2398-9629
ER -