Electricity Market Deregulation and CO2 Emissions Reduction: Dancing at Different Tune across Canada and U.S. Border
Bibliographic record
Abstract
Canada has ratified the Kyoto Protocol while the United States, its main trading partner, have not. A major concern of Canadian industrial producers is the negative impact on competitiveness of progorams designed to reduce greenhouse gas emissions (GHG). To alleviate this concern, the Government of Canada is proposing an approach that puts a ceiling on the price of emission permits paid by industrial users and that allocate emission permits on the base of output. We analyze how such a scheme would affect electricity production and trade among three Canadian provinces (Ontario, Québec and New Brunswick) and two U.S. regions (New England and New York), which are linked by large interconnections and which exchange electricity on other wholesale markets. We find that the Canadian government approach has almost no effect on electricity production and trade flows; so it is very effective at protecting the competitive position of electricity producers. However it does little to reduce GHG emissions. If we were to unbundle emission permits allocation and production an let the Canadian electricity producers face the permit price ceiling of $15 per tonne of CO2 equivalent, then the effect on electricity production in Canada depends on whether the U.S. regions persue an aggressive or lenient policy with respect to GHG emissions. The overall impact on GHG emissions in the five regions is rather small.
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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 | 0.003 |
| 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".