Linear Pigovian taxes and the optimal size of a polluting industry
Bibliographic record
Abstract
Confusion surrounding the appropriateness of long‐run considerations in effluent regulation has arisen in the literature and recently carried over into textbooks. We use a factor input model under oligopsony to show that, when firms can influence the level of marginal damages, a linear pollution tax does not satisfy the long‐run entry‐exit condition. Previous results to the contrary are shown to depend on restrictive assumptions. Efficient policy design requires a lump‐sum refund or any one of various non‐linear pricing schemes. JEL Classification: Q2, L1 Il y a beaucoup de confusion dans les débats qui entourent la réglementation des effluents, et son caractère plus ou moins appropriéà long terme, tant dans la littérature spécialisée que dans les manuels. Les auteurs tentent d'éliminer cette confusion en analysant les émissions à l'aide d'un modèle standard de demande d'intrant. Quand les entreprises peuvent influencer la valeur présente des dommages marginaux, un impôt linéaire sur la pollution n'entraîne pas nécessairement les décisions appropriées d'entrée et de sortie. On peut corriger le problème à l'aide d'un remboursement forfaitaire ou de l'une ou l'autre des formes de tarification non‐linéaire.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".