"AI large language models" by Wes Cockx & Google DeepMind via betterimagesofai.org. (CC-BY 4.0).

Our living planet and the climate system are changing at an unprecedented pace. The years 2023 and 2024 were characterized by unprecedented warming across the globe.1 Ocean heat reached its highest observed levels in 2024,2 causing severe ecological pressure on marine life and putting communities who depend on the ocean for their food security, way of life, and livelihoods at increased risk.3 Children born this decade are expected to experience an unprecedented number of extreme weather events during their lifetime.4 The continued loss of ecosystems and biodiversity,5 and the transgression of several planetary boundaries—the estimated safe operating space for Earth System stability and human prosperity—pose severe risks to society.6 The time for forceful and evidence-based action that secures a sustainable future for all is now.

These profound changes on the planet are unfolding at the same time as another important global shift: a period of rapid technological change and disruption. Sensors, autonomous systems, artificial intelligence (AI), and other technological advances are on the verge of disrupting multiple aspects of societies and economies. Interestingly, advances in AI are also contributing to novel scientific breakthroughs.7 Examples include mathematics and computer sciences,8 the prediction of protein structures,9 and the life sciences,10 to mention only a few.

Could uses of AI in science also help us better understand and tackle the complex social, economic, and ecological repercussions of a changing planet? This report focuses on this particular question with a focus on issues related to the sustainability sciences. This field is inherently inter- and transdisciplinary; integrates the analysis of humans, climate, and nature; and strives to inform people’s and societies’ ambitions to secure a sustainable future for all.11,12 As we discuss here, AI offers untapped and under-explored potential to accelerate this type of research.

Our ambition

This report explores the potential of AI as a set of research methods in a number of issue areas associated with the sustainability sciences. At the core of the sciences is the understanding that the biosphere, with resilient ecosystems and the services they provide, is vital for human health and prosperity, and that human stewardship is key to supporting ecosystem resilience.12Technology, traditionally encompassing built and engineered environments, but now ever more digital technologies, is an integral part of these social-ecological-technological systems,13 with far-reaching impact. As we discuss in this report, AI is at breakneck speed becoming increasingly engrained in shaping how we understand and interact with the world around us and, indeed, our living planet.

Our work complements previous attempts to explore uses of AI for climate change and related issues (e.g., Earth System forecasting14 and biodiversity monitoring15). Our approach is deliberately broader than that of other synthesis reports. We do not focus on direct applications of AI or prescribe specific implementation strategies for the public or private sectors.16,17 Instead, we emphasize the systemic, cross-cutting implications of AI for sustainability research. We choose areas where science and real-world action meet, in an attempt to lay a foundation for an “AI for Sustainability Science” agenda.

Our broad systems perspective is certainly more demanding. However, we argue that it’s urgently needed to build the capacity required to tackle the system dynamics of interconnected sustainability challenges, especially in light of the accelerating pace of planetary change.

Our approach

This report is the result of a collaboration that started with a two-day workshop hosted in Stockholm in mid-January 2025, with >20 scientists from various scientific backgrounds, including ecology, sustainability, social, Earth System, and computer science. Many have experience from work in NGOs, private companies, and public universities of applying and developing AI methods in their research. Our work has been structured in the following way:

  1. Editorial team started with the identification of issue areas (November and December 2024)
  2. Systematic collection and AI-augmented analysis of relevant literature for each issue area (November 2024 to June 2025)
  3. Domain expert analysis and redrafting of issue areas, partly informed by the identified literature analysis (January 2025 to August 2025)
  4. Iterated discussions with chapter author teams about content of identified issue areas (February 2025 to June 2025)
  5. Collective grading, and analysis of potential and gaps (July 2025 to August 2025)

Domain expert discussions and writing have been structured around three main subareas, summarized in Fig. 1: AI for data collection and generation; AI-assisted predictive modeling; and AI-assisted decision-making.

Fig. 1. Flowchart illustrating AI-assisted research processes. Adapted from Camps-Valls et al., 2025, p. 2.18

Limitations

A large part of this report centers around eight broad issue areas. The areas were preselected by the editorial team and discussed, and reformulated after discussions with contributing participants of the workshop and co-authors. The issue areas have been chosen because of their inter- and transdisciplinary features, as well as their importance to people in a time of rapid change for our living planet and climate system. It is important to acknowledge that the issue areas and questions could have been framed differently, and additional ones could have been included. Such changes might have influenced the outcomes and insights presented. The analysis also relies heavily on the composition of invited co-authors and their expertise. We hope, however, that the approach chosen here can inspire further analyses by others.

Potential of work

Why is this report needed? We believe that while AI has much to offer to the sustainability sciences, it has so far been unclear how the potential could be fulfilled. A number of reports have explored the potential of AI for science in general,43–45 but few have addressed issues related to people, climate, nature, and their interactions. This is surprising considering the importance of a stable climate system and a resilient living planet for human development and well-being, economic prosperity, and innovation.

The work presented here should be of interest to:

  • Sustainability researchers, by offering an overview of uses of AI for sustainability research in various areas
  • AI developers in the private sector looking to contribute to research and development in areas of urgent importance to society
  • Public agencies interested in assessing how AI may be used in their work to understand sustainability challenges
  • Research agencies and philanthropies looking to assess emerging areas where novel applications of AI methods could help drive scientific breakthroughs

Bibliography

  1. Schaeffer, R. et al. Ten New Insights in Climate Science 2024. One Earth 101285 (2025) doi:10.1016/j. oneear.2025.101285.
  2. Pan, Y. et al. Ocean heat content in 2024. Nat Rev Earth Environ 6, 249–251 (2025).
  3. Smith, K. E. et al. Biological Impacts of Marine Heatwaves. Rev. Mar. Sci. 15, 119–145 (2023).
  4. Grant, L. et al. Global emergence of unprecedented lifetime exposure to climate extremes. Nature 641, 374–379 (2025).
  5. Dasgupta, P. The Economics of Biodiversity: The Dasgupta Review. (Cambridge University Press, 2024). doi:10.1017/9781009494359.
  6. Richardson, K. et al. Earth beyond six of nine planetary boundaries. Adv. 9, eadh2458 (2023).
  7. Science in the age of AI | Royal Society. https://royalsociety.org/news-resources/projects/science-in-the-age-of-ai/.
  8. Gibney, E. DeepMind unveils ‘spectacular’ general-purpose science AI. Nature 641, 827–828 (2025).
  9. Callaway, E. Chemistry Nobel goes to developers of AlphaFold AI that predicts protein structures. Nature 634, 525–526 (2024).
  10. Committee on Assessing and Navigating Biosecurity Concerns and Benefits of Artificial Intelligence Use in the Life Sciences et al. The Age of AI in the Life Sciences: Benefits and Biosecurity Considerations. 28868 (National Academies Press, Washington, D.C., 2025). doi:10.17226/28868.
  11. Clark, W. C. & Dickson, N. M. Sustainability science: The emerging research program. Natl. Acad. Sci. U.S.A. 100, 8059–8061 (2003).
  12. Folke, C., Biggs, R., Norström, A. V., Reyers, B. & Rockström, J. Social-ecological resilience and biosphere-based sustainability science. E&S 21, art41 (2016).
  13. McPhearson, T. et al. A social-ecological-technological systems framework for urban ecosystem services. One Earth 5, 505–518 (2022).
  14. Bodnar, C. et al. A foundation model for the Earth system. Nature 641, 1180–1187 (2025).
  15. Reynolds, S. A. et al. The potential for AI to revolutionize conservation: a horizon scan. Trends in Ecology & Evolution 40, 191–207 (2025).
  16. Yaakoubi, Y. Grand Challenge Initiatives in AI for Climate & Nature: Landscape Assessment and Recommendations. https://ccai-reports.s3.us-east-2.amazonaws.com/ grand-challenge-initiatives-ai-climate-nature.pdf (2024).
  17. Accelerating Sustainability with AI: Innovations for a Better Future. (2025).
  18. Camps-Valls, G. et al. Artificial intelligence for modeling and understanding extreme weather and climate events. Nat Commun 16, 1919 (2025).
Topics: Leveraging technologies
Published: 2025-11-05

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