Striving for Equivalency Across the British Columbia, Alberta, Québec and Ontario Carbon Pricing Systems: The Pan-Canadian Carbon Pricing Benchmark
Bibliographic record
Abstract
The Pan-Canadian Framework on Clean Growth and Climate Change is designed to put Canada on track to meet its Paris commitments. A key pillar of the plan is the introduction of a pan-Canadian carbon price by 2018. However, four Canadian provinces, nearly 85% of the Canadian economy and population, have already implemented carbon pricing systems. British Columbia (BC) has a carbon tax. Alberta has transitioned from an output-based allocation system for industrial emitters to a hybrid system combining a carbon levy and refined output-based system. Quebec and Ontario have implemented cap-and-trade systems, linked to California. Recognizing these existing systems, rather than impose a single carbon pricing mechanism, the Pan-Canadian Approach to Carbon Pricing gives provinces and territories the flexibility to adopt a carbon tax, a hybrid system, or a cap-and-trade system. To address concerns relating to ‘fairness’ and equivalency of carbon price, a federal carbon pricing benchmark establishes criteria relating to minimum ‘common scope’ and ‘increases in stringency’ that provincial and territorial carbon pricing systems must meet. This paper explores the design features of the existing Alberta, BC, Ontario and Quebec carbon pricing systems, and considers how the benchmark affects stringency and addresses equivalency of carbon price across these different systems. *Tables excluded.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".