Hidden costs and propped-up profits: unraveling the economics of Europe’s purse-seine tuna fishing industry
Bibliographic record
Abstract
Despite tuna fisheries’ global economic significance and contributions to food security and trade, we find that without substantial subsidies, notably fuel tax exemptions and fishing access agreement fees supported by public funds from the European Union, the European purse seine tuna industry would be highly unprofitable. Between 2010 and 2023, EUR 2.1 B in subsidies were funnelled into the sector (EUR 179.4 M from fishing access agreements, and EUR 1.9 B from fuel tax concessions), masking underlying financial issues and commercial non-viability. French companies reported continuous losses in recent years, even with these subsidies. Spanish firms performed better, likely due to complex ownership structures that facilitate tax optimization strategies, lower operating costs, and increased access to fishing grounds and seafood markets. Additionally, the sector’s economic survival appears to come at the expense of working conditions and pay, environmental sustainability, and legal compliance, including wage suppression and fishing violations. This research highlights the urgent need for policy reform in the European tuna fishing industry, addressing the reliance on harmful subsidies and the intensification of labor exploitation as a means to avoid economic unviability. The abolishment of harmful subsidies, sustainable fishing practices, and fairer employment conditions must be integrated to prevent a looming crisis that threatens ecological systems, economic stability and livelihoods.
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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 | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| 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".