BC's Carbon Tax Shift Is Working Well after Four Years (Attention Ottawa)
Bibliographic record
Abstract
La creation par la Colombie-Britannique en 2008 d’une taxe sur le carbone sans incidence sur les recettes a ete controversee. Dans cet article, nous comparons les changements qui sont survenus depuis dans la province en matiere de consommation de carburant, d’emissions de gaz a effet de serre et de produit interieur brut, et nous faisons la comparaison avec le reste du Canada. Nos resultats montrent que, depuis quatre ans que la taxe existe, la consommation de carburants a baisse de 19 % de plus que dans le reste du Canada. Ces resultats correspondent a ceux qu’ont obtenus les pays europeenes en creant eux aussi des taxes sur le carbone, et devraient donc enrichir le debat sur les politiques canadiennes en matiere de changements climatiques. Abstract: British Columbia’s introduction in 2008 of a revenue-neutral carbon tax shift was controversial. This analysis compares changes in fuel consumption, greenhouse gas emissions, and gross domestic product (GDP) between British Columbia and the rest of Canada. It finds that in the four years since the tax was introduced, British Columbia’s per capita consumption of fuels subject to the tax has declined by 19 percent compared to the rest of Canada. At the same time, its economy has kept pace with the rest of Canada. British Columbia’s experience mirrors the European experience with carbon tax shifting and should inform the federal debate on climate change policy.
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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 | 0.003 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".