Willingness to pay for recreational fisheries in Europe
Bibliographic record
Abstract
Abstract Few studies have acknowledged and quantified the economic contribution in expenditures of recreational fisheries. Additional economic value attributed to fishers' willingness to pay (WTP) for recreational fisheries in excess of expenses was estimated for 33 countries in Europe. Benefit transfer was used in a meta‐regression analysis of 184 studies and 1001 observations of WTP per day for recreational fisheries. Most studies of fishing were in the USA, but also in Europe, Australia, New Zealand, South America and Canada. Mixed‐effects regression models were estimated with income, climate variables, population density and study characteristics as explanatory variables. Income and temperature positively affected WTP per day. Benefit transfers with these variables and different transfer methods among European countries showed that the estimated total WTP could amount to 11.4 billion USD (purchasing power parity corrected to 2020 prices). Variation in WTP per day was large, and ranged 9–62 USD among countries and transfer methods. For several countries, WTP for recreational fisheries exceeded 0.1% of gross domestic product.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".