Bibliographic record
Abstract
Various second‐best policy mixes of investment measures and environmental taxes for a polluted, small open economy with foreign capital and immovable trade restrictions are examined. The optimal policy mix depends on the types of trade restrictions. When tariffs are in place, strict policies of pollution taxes and export requirements are optimal for alleviating tariff‐induced consumption and production distortions. When involuntary quotas are used in lieu of tariffs, however, the optimal policy mix is a zero export requirement and Pigouvian taxes on pollution. For the case of VERs, however, the optimal policy demands export requirements and a less stringent pollution tax. TRIMs, impôts environnementaux et investissement étranger. Ce mémoire examine certains ensembles de politiques de second‐ordre portant sur l'investissement et les impôts environnementaux dans une petite économie ouverte polluée où il y a capital étranger et restrictions au commerce international. Quand on a des droits de douane en place, des politiques de fiscalité environnementale et d'exportations requises sont optimales pour corriger les distorsions aux patterns de production et de consommation engendrées par les droits de douane. Cependant quand des contingentements involontaires sont en place plutôt que des droits de douane, le mélange optimal de politiques est de ne pas requérir d'exportation mais d'imposer des taxes à la Pigou sur la pollution. Pour ce qui est des restrictions volontaires à l'exportation, le mélange optimal de politiques réclame une taxe moins forte sur la pollution accompagnée par un certain niveau d'exportation requis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 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".