Canadian Electricity Markets during\nthe COVID-19 Pandemic: An Initial\nAssessment
Bibliographic record
Abstract
Les auteurs examinent l'effet de la pandemie de COVID-19 sur les marches de l'electricite dans certaines provinces canadiennes en s'appuyant sur les donnees disponibles. Leur analyse des donnees relatives a l'electricite a haute frequence revele que la demande d'electricite a diminue d'environ 10 % en Ontario, moins dans les autres provinces etudiees. Du cote de l'offre, en Alberta, ils observent que la production de certaines centrales au gaz naturel a diminue, mais que la production nette a partir des installations des sables bitumineux a augmente, tandis que l'Ontario a enregistre une augmentation des exportations nettes. Les repercussions de ces constats sur les politiques sont notamment leur incidence potentielle sur les tarifs en raison de frais fixes repartis sur une base tarifaire plus restreinte, l'utilisation potentielle des donnees sur l'electricite comme indicateur economique en temps reel pendant la pandemie et un cri du cceur pour que soit facilite l'acces aux donnees sur l'electricite dans toutes les provinces canadiennes. Abstract: This article examines the effect of the coronavirus disease 2019 (COVID-19) pandemic on electricity markets across select Canadian provinces, using available data. Using high-frequency electricity data, we find electricity demand declined by roughly 10 percent in Ontario and by about 5 percent in Alberta, British Columbia, and New Brunswick. On the supply side, in Alberta we find reductions from some natural gas plants and an increase in net generation from the oil sands region, whereas Ontario sees an increase in net electricity exports. Policy implications include potential rate impacts as a result of fixed charges spread over a smaller rate base, the potential use of electricity data as a real-time economic indicator during the pandemic, and a call to arms to make electricity data across all Canadian provinces more readily available.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| 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".