Better Together? The Implications of Linking Canada-US Greenhouse Gas Policies
Bibliographic record
Abstract
The Canadian and American economies are inextricably intertwined through trade. As the two countries debate plans to curb greenhouse gas (GHG) emissions, policymakers in both countries must consider how emissions policies, such as an emissions trading system that sets economy-wide limits on GHG emissions and allows firms to trade GHG emissions permits for the right to pollute, might coexist. This paper analyzes the implications of linking elements of potential Canadian and American GHG emissions trading systems, including the scope of emissions covered by the systems, national emissions-reduction targets, emissions permit prices, and cross-border trade of emissions permits. This assessment indicates that linked allowance trade with the US would not necessarily be the best policy for Canada to pursue, as the US develops its own system. Instead, Canada should forge ahead with its own system, while minimizing the risk of getting too far out of step with the US on relative carbon prices. A policy of “go-it-alone” with similar carbon price expectations, and a targeted innovation agenda, seems to be a low-risk strategy for Canada as it develops its emissions policies.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.027 | 0.021 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.033 | 0.026 |
| Insufficient payload (model declined to judge) | 0.008 | 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".