Sharing the Burden for Climate Change Mitigation in the Canadian Federation
Bibliographic record
Abstract
Dividing the burden for greenhouse gas abatement amongst the provinces has proven challenging in Canada, and is a major factor contributing to Canada's poor historic performance on greenhouse gas abatement. As the country aims to achieve substantial cuts to emissions over the next decade and by mid-century, such burden sharing considerations are likely to be elevated in importance. This paper uses a calibrated multi-region multi-sector computable general equilibrium model to compare a number of archetypal rules for sharing the burden of a joint commitment amongst members for the case of greenhouse gas reductions in Canada. Because of the substantial heterogeneity amongst Canadian provinces, these different burden sharing rules imply signifcantly different relative abatement effort amongst provinces, and also signifcantly different welfare implications. When emission permits are allocated on an equal per capita basis, welfare is increased in Ontario, British Columbia, Quebec, and Manitoba, and signifcantly reduced in Alberta and Saskatchewan. In contrast, when emission permits are allocated based on historic emissions, Alberta and Saskatchewan are made better off, and Ontario, British Columbia, Quebec, and Manitoba are made worse off. We compare these archetypal burden sharing rules to existing provincial emission reduction commitments, and find that none of the standard burden sharing rules comes close to existing commitments. We argue that the debate on burden sharing of greenhouse gas abatement in Canada could be objectified if informed by coherent quantitative analysis such as the one presented here.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".