@article{fdi:010095812,
  title = {{A} machine learning-based evidence map of ocean-related options for climate change mitigation and adaptation},
  author = {{V}eytia, {D}. and {M}ariani, {G}ael and {B}arclay, {V}. {M}. and {A}iroldi, {L}. and {C}laudet, {J}. and {C}ooley, {S}. and {M}agnan, {A}. and {N}eill, {S}. and {S}umaila, {U}. {R}. and {T}h{\'e}baud, {O}. and {V}oolstra, {C}. {R}. and {W}illiamson, {P}. and {B}onnin, {M}arie and {L}angridge, {J}. and {C}omte, {A}drien and {V}iard, {F}. and {S}hin, {Y}unne-{J}ai and {B}opp, {L}. and {G}attuso, {J}. {P}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}he ocean has a vital role to play in addressing the global challenge of climate change, which requires both mitigation and adaptation actions. {T}he exponential increase in research relating to ocean-related options ({ORO}s) requires a rapid and reproducible method to assess the state of knowledge. {W}e 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. {R}esearch proves to be unevenly distributed, concentrating on {ORO}s with mitigation objectives (80%), while revealing research gaps including under-researched ecosystems and an observed paucity of studies simultaneously assessing different {ORO} types. {W}e also uncover social inequalities driven by mismatches between the global distribution of research effort, climate change responsibility, and risk. {T}hese findings are important to maximise the efficacy of {ORO}s, position them within broader climate action portfolios, and inform future research priorities.},
  keywords = {{MONDE}},
  booktitle = {},
  journal = {{NPJ} {O}cean {S}ustainability},
  volume = {4},
  numero = {1},
  pages = {60 [13 p.]},
  year = {2025},
  DOI = {10.1038/s44183-025-00159-w},
  URL = {https://www.documentation.ird.fr/hor/fdi:010095812},
}