Choose Wisely: Options and Trade-offs in Recycling Carbon Pricing Revenues
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Provinces can customize revenue recycling to achieve their own distinct prioritiesThis report further explores the provincial differences we first considered in The Way Forward.These differences-in economic structure, energy mixes, and policy context-provide provinces with a strong justification for designing and implementing their own carbon pricing policies.Revenue recycling is an opportunity to tailor carbon pricing policy to a province's unique circumstances.Some provinces are more exposed to competitiveness pressures created by carbon pricing (e.g., Alberta and Saskatchewan).Fairness concerns are heightened in provinces with carbon-intensive electricity systems (e.g., Alberta and Nova Scotia).Some provinces have much higher provincial debt (e.g., Quebec and Ontario), while others face more immediate fiscal challenges (e.g., Alberta).Still others have economic challenges associated with high income-tax rates (e.g., Quebec and Nova Scotia).Additional investments in emissionsreducing technology can make it possible to achieve ambitious targets (e.g., British Columbia and Ontario); technology investments could also be justified to improve the long-term performance of emissions-intensive sectors (e.g., Alberta and B.C.).How should provinces manage these trade-offs?In this report, we do not provide detailed, prescriptive recommendations to provinces: each one is best situated to make its own choices about revenue recycling.Instead, we provide broader guidance on the factors that policymakers should examine when considering trade-offs and making revenue-recycling choices.Our recommendations are as follows:
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it