Exploring the Impacts of Carbon Pricing on Canada’s Electricity Sector
Bibliographic record
Abstract
Canadian provinces are required to regulate their power sectors using carbon pricing systems that meet national minimum stringency standards, which are set by the federal government. A diverse set of systems has emerged as a result. However, there has been limited assessment of how different pricing mechanisms impact the evolution of Canada’s electricity system. To address this gap, we use an electricity system planning model called COPPER and a scenario-based approach to assess if, and to what extent, different policy regimes impact power sector greenhouse gas emissions and costs. Our results show that carbon pricing systems currently in place lead to significant carbon reductions over the long term, provided that free emissions allocations are reduced. However, the cost-optimal pathway for the power sector differs across provinces depending on the carbon pricing mechanism. Some provinces achieve least-cost emissions reductions by switching from high-carbon technologies to renewables, while others are better served by replacing high-carbon technologies with low-carbon fossil fuel alternatives. Further, provinces that implement cap-and-trade systems may affect the transitions of interconnected jurisdictions. Power sector climate policy design should reflect the heterogeneity of available assets, resources, and neighbouring approaches.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".