Canadian Subnational Climate Change Policy
Bibliographic record
Abstract
Using a framework, this paper evaluates British Columbia’s and Alberta’s carbon tax and Ontario’s and Quebec’s cap and trade system, to determine how effective these policies will be at reducing GHG emissions cumulatively. The framework has been primarily shaped via a literature review. The framework consists of the following evaluative criteria: A) policy effectiveness, B) allocation of public resources and C) policy design. Each criterion consists of multiple questions and sub-questions which are used to determine the effectiveness of the policy. The criterions take into account things such as the carbon scope, price of carbon, the extent of emission reductions, actual and anticipated reductions, allocation of generated revenues, political acceptability, gaming prevention, policy rigorousness, evaluation, and transparency. Since all policies besides BC’s are in their infancy, to satisfy the criteria, this paper primarily utilizes government documents, working paper, and commentaries. Recommendations and findings are summarized in the appendix. \n \nCurrent modeling and data suggest that all four policies will not result in enough emission reductions to allow the respective provinces to achieve their emissions reduction goals. Although, some are further off the mark than others. However, it is blatantly clear that the recommendations that are required with the timeframe allotted is steep to say the least. Ultimately, each policy can benefit from a price on carbon that is significantly greater than $30/tCO2e and a much leaner scope. Particularly, Alberta and Ontario damage their scope substantially to preserve their large emitters. Blanketed exemptions seem to be a popular theme between these two provinces. Better redistribution of revenues to achieve further reductions can also be had, particularly from British Columbia. Notably, Quebec sets the pace for good transparency and something that the other three policies should aspire too. All provinces can also improve their reporting and evaluation processes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 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".