Optimal harvesting and taxation when accounting for the marine environmental quality of the fishery
Bibliographic record
Abstract
Abstract We consider a fishery described by two state variables, namely, the stock of fish and its marine environmental quality, operationalized as an index of habitat extent and quality, which influences the growth rate and the carrying capacity (MEQ). Assuming that myopic fishing agents exploit the fishery, we characterize and contrast the steady‐state values in two scenarios: (a) a scenario where the agents (correctly) perceive that the MEQ is nonconstant and (b) a scenario where they behave as if the MEQ is a given constant. Not unexpectedly, the harvest rates differ across the two scenarios and consequently lead to different steady states. Interestingly, for some parameter values, we obtain that assuming a constant MEQ has a conservation flavor, that is, it results in a larger stock of fish and higher MEQ in the steady‐state. We show that there exists a steady‐state solution to the planner's problem and that it can be supported by a large number of appropriately designed tax schemes, while the approach path to the steady‐state depends on the implemented tax scheme. We also discuss the implications for optimal regulation under open access when habitat matters Recommendations for Resource Managers We take into account the marine environmental quality (MEQ) in a fishery model and assess how it affects harvesting behavior and the fish stock. For some parameter values, we obtain that assuming a constant MEQ has a conservation flavor, that is, it results in a larger stock of fish and higher MEQ in the steady‐state. We show that there exists a steady‐state solution to the planner's problem that can be supported by different tax schemes.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".