La tarification du carbone et l’utilisation de ses revenus au Québec et au Canada
Bibliographic record
Abstract
Cet article répond à la question suivante : comment devrait-on distribuer les revenus générés par la tarification du carbone au Québec et au Canada ? Par exemple, devrait-on les distribuer directement aux citoyens ou les utiliser pour faciliter la transition énergétique ? Quels principes devraient orienter l’utilisation des fonds générés par la tarification du carbone ? Pour y répondre, cet article ciblera les principales mesures qui garantiraient une utilisation optimale des revenus au Québec et au Canada, en tenant compte des spécificités régionales. Pour ce faire, il met de l’avant une grille d’analyse basée sur trois variables : l’équité économique, l’acceptabilité sociale et la réduction des GES. L’article vise à souligner l’importance de ces trois variables dans la conception des politiques de tarification du carbone, permettant ainsi de montrer comment modéliser la tarification du carbone de manière juste, tout en assurant le soutien politique pour cette mesure dans les différents contextes politiques.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".