Bibliographic record
Abstract
Abstract I examine the environmental and economic effects of pricing greenhouse gas emissions from livestock in Canada. Using a partial equilibrium model, I consider three different pricing policies: a consumer level tax, a producer level tax, and a producer subsidy. All policies price emissions at $50 per tonne of carbon dioxide equivalent. The producer level tax generates the greatest reduction in emissions at the lowest social cost per unit of emissions abatement. The producer subsidy results in a smaller reduction in emissions at a higher social cost, relative to the producer tax. However, the subsidy provides a substantial increase in producer surplus, which may make it a politically feasible second‐best policy. The consumer level tax results in a trivial reduction in emissions, at a social cost that is greater than the price placed on emissions. J'examine les effets environnementaux et économiques de l'imposition d'un prix sur les gaz à effets de serre provenant des animaux d'élevage canadiens. Au moyen d'un modèle d'équilibre partiel, je tiens compte de trois différentes politiques d'imposition de prix : taxer le consommateur, taxer le producteur, et subventionner le producteur. Toutes les politiques imposent les émissions au prix de 50$ par tonne en équivalent de dioxyde de carbone. Taxer l'éleveur génère la plus grande réduction des émissions au plus bas coût social par unité de réduction des émissions. Subventionner l'éleveur entraéne une plus petite réduction des émissions à coût social plus élevé, en comparaison û la taxe imposée à l'éleveur. Par contre, le choix de subventionner fournit une augmentation substantielle de surplus pour l'éleveur, faisant de cette option un bon deuxième choix envisageable au niveau politique. Taxer le consommateur ne produit qu'une réduction négligeable des émissions, à coût social plus élevé que le taux imposé sur les émissions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".