Building climate resilience, social sustainability and equity in global fisheries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 CO 2 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 CO 2 e trading price for 2022 and the social cost of carbon dioxide (SC-CO 2 ), for years 2025, 2030 and 2050. The application of the MBS scheme will result in 0.122 Gt CO 2 e sequestered or US$66 billion of potential benefits per year when considering 2050 SC-CO 2 . The latter also implies that if CO 2 e 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it