Veytia D., Mariani Gael, Barclay V. M., Airoldi L., Claudet J., Cooley S., Magnan A., Neill S., Sumaila U. R., Thébaud O., Voolstra C. R., Williamson P., Bonnin Marie, Langridge J., Comte Adrien, Viard F., Shin Yunne-Jai, Bopp L., Gattuso J. P. (2025). A machine learning-based evidence map of ocean-related options for climate change mitigation and adaptation. NPJ Ocean Sustainability, 4 (1), p. 60 [13 p.].
Titre du document
A machine learning-based evidence map of ocean-related options for climate change mitigation and adaptation
Année de publication
2025
Auteurs
Veytia D., Mariani Gael, Barclay V. M., Airoldi L., Claudet J., Cooley S., Magnan A., Neill S., Sumaila U. R., Thébaud O., Voolstra C. R., Williamson P., Bonnin Marie, Langridge J., Comte Adrien, Viard F., Shin Yunne-Jai, Bopp L., Gattuso J. P.
Source
NPJ Ocean Sustainability, 2025,
4 (1), p. 60 [13 p.]
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.
Plan de classement
Sciences fondamentales / Techniques d'analyse et de recherche [020]
;
Sciences du milieu [021]
;
Limnologie physique / Océanographie physique [032]
;
Documentation [124]
Description Géographique
MONDE
Localisation
Fonds IRD [F B010095812]
Identifiant IRD
fdi:010095812
