Building climate resilience, social sustainability and equity in global fisheries
Bibliographic record
Abstract
Abstract Although the Paris Agreement establishes targets to limit global warming—including carbon market mechanisms—little research has been done on developing operational tools to achieve them. To cover this gap, we use CO2 permit markets towards a market-based solutions (MBS) scheme to implement blue carbon climate targets for global fisheries. The scheme creates a scarcity value for the right to not sequester blue carbon, generating an asset of carbon sequestration allowances based on historical landings, which are considered initial allowances. We use the scheme to identify fishing activities that could be reduced because they are biologically negative, economically inefficient, and socially unequitable. We compute the annual willingness to sequester carbon considering the CO2e trading price for 2022 and the social cost of carbon dioxide (SC-CO2), for years 2025, 2030 and 2050. The application of the MBS scheme will result in 0.122 Gt CO2e sequestered or US$66 billion of potential benefits per year when considering 2050 SC-CO2. The latter also implies that if CO2e trading prices reach the 2050 social cost of carbon, around 75% of the landings worldwide would be more valuable as carbon than as foodstuff in the market. Our findings provide the global economy and policymakers with an alternative for the fisheries sector, which grapples with the complexity to find alternatives to reallocate invested capital. They also provide a potential solution to make climate resilience, social sustainability and equity of global fisheries real, scientific and practical for a wide range of social-ecological and political contexts.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".