Blue Economy in the face of Climate Change: Insights from resource availability projections
Bibliographic record
Abstract
The Blue Economy aims to foster socially equitable, environmentally sustainable, and economically viable ocean resources, ensuring that conservation efforts harmonize with long-term economic growth. However, climate change significantly impacts the availability and management of these resources by altering ecosystems and habitats, affecting ocean water quality, biodiversity, and key factors such as temperature, sea level rise, and changes in wind and current patterns. In this study, we integrate state-of-the-art global datasets to project the future distribution of resources required for fisheries, aquaculture, blue carbon, bioprospecting, ecotourism, and offshore wind energy sectors. Our results show a projected negative balance within the tropics for most of these sectors. Notably, significant declines are expected closer to coastal areas, where marginalized coastal communities—particularly in Small Island Developing States (SIDS) and Least Developed Countries (LDCs)—are most vulnerable, exacerbating the ongoing challenges these states are already facing. We stress that considering the impacts of climate change on these sectors is crucial for developing realistic scenarios and informing effective policy responses. Climate adaptation and resource management strategies must prioritize the needs of these coastal communities, promote their active participation and leadership, and ensure they benefit from innovations in renewable energy, aquaculture, and other ocean-based resources. Likewise, technological advancements, international cooperation, and community engagement are essential for building adaptive capacities and fostering inclusive economic opportunities for marginalized groups. Finally, we highlight that adopting these measures is critical for fostering an equity-focused approach to resilience in ocean-dependent economies and sustaining marine resources amid changing climatic conditions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".