Mapping the evidence of "ocean-based solutions" to address climate challenges

Ocean-based "solutions" are receiving increasing attention for their potential to mitigate greenhouse gas emissions and to help in climate change adaptation, with high expectations for their success. Whether this optimism is justified based on scientific evidence remains elusive; the large volume of literature makes it difficult to synthesize the state of knowledge in terms of implementation and effectiveness to contribute to the solution space and support decision making. With recent advancements in large language machine learning models, it is now possible to address this need. Here we present a machine learning-based map of the scientific evidence surrounding these options. This map informs a suite of evidence syntheses that contribute finer-scale nuance to crucial research questions.  

Our evidence map charts the knowledge clusters and gaps characterizing the landscape of this burgeoning field of almost 45,000 scientific articles. We show that research is siloed across the different options, implying a lack of evidence on the synergies and trade-offs between multiple options. We also find that research is constrained to specific ecosystems and climatic-impact drivers. The geographical distribution of scientific literature reveals spatial mis-matches between research effort and needs, where developing coastal countries at high-risk from coastal hazards receive less empirical research. 

From this uneven research landscape, we identify crucial evidence synthesis needs across four key research themes: disentangling links between the research, policy and deployment of mitigation options; biodiversity impacts of ocean-related options; the effectiveness of restoration options for climate action; and options for adaptive fisheries in a changing ocean. The addition of these cutting-edge findings adds sharper contours to our understanding of the effectiveness, equitability, safety and influences of these options.