TY - JOUR
T1 - A machine-learning-based evidence map of ocean-related options for climate change mitigation and adaptation
AU - Veytia, Devi
AU - Mariani, Gaël
AU - Barclay, Vicki Marti
AU - Airoldi, Laura
AU - Claudet, Joachim
AU - Cooley, Sarah
AU - Magnan, Alexandre
AU - Neill, Simon
AU - Sumaila, Rashid
AU - Thébaud, Olivier
AU - Voolstra, Christian R.
AU - Williamson, Phillip
AU - Bonnin, Marie
AU - Langridge, Joseph
AU - Comte, Adrien
AU - Viard, Frédérique
AU - Shin, Yunne-Jai
AU - Bopp, Laurent
AU - Gattuso, Jean-Pierre
N1 - 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.
PY - 2025/11/19
Y1 - 2025/11/19
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/105022421016
U2 - 10.1038/s44183-025-00159-w
DO - 10.1038/s44183-025-00159-w
M3 - Article
SN - 2731-426X
VL - 4
JO - NPJ Ocean Sustainability
JF - NPJ Ocean Sustainability
M1 - 60
ER -
SDG 13 Climate Action
SDG 14 Life Below Water