A machine-learning-based evidence map of ocean-related options for climate change mitigation and adaptation

Veytia, Devi, Mariani, Gaël, Barclay, Vicki Marti, Airoldi, Laura, Claudet, Joachim, Cooley, Sarah, Magnan, Alexandre, Neill, Simon, Sumaila, Rashid, Thébaud, Olivier, Voolstra, Christian R., Williamson, Phillip, Bonnin, Marie, Langridge, Joseph, Comte, Adrien, Viard, Frédérique, Shin, Yunne-Jai, Bopp, Laurent and Gattuso, Jean-Pierre (2025) A machine-learning-based evidence map of ocean-related options for climate change mitigation and adaptation. NPJ Ocean Sustainability, 4. ISSN 2731-426X

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Abstract

The ocean has a vital role to play in addressing the global challenge of climate change, which requires both mitigation and adaptation actions. The exponential increase in research relating to ocean-related options (OROs) requires a rapid and reproducible method to assess the state of knowledge. We train a state-of-the-art large language model to characterise the landscape of ORO research by classifying 44,193 (±11,615) articles across various descriptors. Research proves to be unevenly distributed, concentrating on OROs with mitigation objectives (80%), while revealing research gaps including under-researched ecosystems and an observed paucity of studies simultaneously assessing different ORO types. We also uncover social inequalities driven by mismatches between the global distribution of research effort, climate change responsibility, and risk. These findings are important to maximise the efficacy of OROs, position them within broader climate action portfolios, and inform future research priorities.

Item Type:	Article
Additional Information:	Data availability: The datasets generated and/or analysed during the current study are available in a public repository (Veytia, D., Mariani, G. & Marti, V. Ocean-related options for climate change mitigation and adaptation: Data https://zenodo.org/records/13349908 (2024)). The protocol for the systematic map can be found on Protocol exchange (Veytia, D. et al. Ocean-related options for climate change mitigation and adaptation: a machine learning-based evidence map protocol. PROTOCOL (version 1). Protocol Exchange (2024). Code availability: All code used to produce these results are available in online repositories (Veytia, D. & Marti, V. ORO map relevance https://github.com/dveytia/ORO-map-relevance (2024); Veytia, D. & Mariani, G. ORO map figures https://github.com/dveytia/ORO-map-figures (2024)). Funding information: The postdoctoral project of D.V. and the expert panel meetings were funded by the French Priority Research Programme (PPR) on Ocean & Climate. We acknowledge the support of Supercomputing Wales, a project partly funded by the European Regional Development Fund (ERDF) under grant reference 80898.
Uncontrolled Keywords:	sdg 14 - life below water ,/dk/atira/pure/sustainabledevelopmentgoals/life_below_water
Faculty \ School:	Faculty of Science > School of Environmental Sciences University of East Anglia Research Groups/Centres > Theme - ClimateUEA
Depositing User:	LivePure Connector
Date Deposited:	24 Nov 2025 14:30
Last Modified:	26 Nov 2025 17:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/101093
DOI:	10.1038/s44183-025-00159-w

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