Policy Forum: Carbon Taxes and Fiscal Federalism in Canada—A New Wrinkle to an Old Problem
Bibliographic record
Abstract
The federal government intends to increase its minimum carbon tax from $40 per tonne of carbon emissions to $170 per tonne by 2030. The carbon tax increase will have uneven and potentially large impacts on provincial emissions and carbon tax revenue, but little is known about how decisions to recycle these revenues will affect equalization payments to provinces. This article compares baseline equalization payments with simulated payments under various revenue-recycling scenarios given a $170 minimum carbon tax. The simulations demonstrate that recycling carbon tax revenues with offsetting reductions in provincial personal or business income taxes, for example, lowers overall disparities in provincial governments' revenue-raising abilities and reduces the size of the equalization program needed to address these disparities. The article draws attention to an already controversial design feature of the program, the fixed-growth rule. The simulations show that the fixed-growth rule limits the impact of higher carbon tax revenues on equalization, by adjusting payments to ensure that the overall size of the program grows roughly in line with the economy. As a result, any potential savings in aggregate equalization payments from revenue recycling are not realized. The distribution of payments is also affected by the fixed-growth rule. Overequalization often results, with Quebec and sometimes Ontario as the main beneficiaries.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".