Macroeconomic impact of an international fishery regulation on a small island country
Bibliographic record
Abstract
Abstract This paper examines the macroeconomic impact of regulating tuna fishing on a small island economy that relies heavily on tourism and fishing for its foreign exchange earnings. While there is scientific consensus to limit the use of drifting fish aggregating devices (dFADs) worldwide, there is no agreement on their optimal number at sea. Resolution 23/02, adopted by the Indian Ocean Tuna Commission (IOTC) in February 2023, proposed a 72-day moratorium on dFADs, but this resolution has met with resistance from many contracting parties, including developing countries. To understand the reasons for this resistance, a recursive, multi-sectoral dynamic general equilibrium model is developed for the Republic of Seychelles, a small tuna-dependent country. The model assesses the short- and medium-term macroeconomic impacts of a seasonal dFAD closure for the Indian Ocean tuna fishery. The analysis suggests that a 12% decline in canned tuna exports would result in a −8.8% deviation from the real gross domestic product trend after seven years. Such an impact would have far-reaching effects on the domestic economy, affecting all components of aggregate demand. Consequently, the economy would become more dependent on tourism, which has shown its vulnerability during the recent pandemic crisis. The study highlights the importance of considering social and economic aspects in sustainable fisheries management and provides insights into the potential consequences of dFAD regulations for small island economies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".