Towards Carbon Free Energy Systems in Canada: Case Studies in the Ontario Transportation and Canadian Energy Export Sectors
Bibliographic record
Abstract
With climate change being arguably the most important problem of this century, solutions must be researched, developed and implemented across the globe. Canada has shown progress over time in the electricity sector to implement renewable energy sources and limit the impact of climate change, however there is still substantial work to be done in both the transportation and energy export sectors. Research into each sector is conducted as two separate studies; one comparing the impact of battery electric vehicles and hydrogen fuel cell vehicles on the Ontario energy generation system, and the other showing the required renewable energy systems necessary to transition the Trans Mountain pipeline expansion to using hydrogen fuel instead of diluted bitumen. In the case of the first study, the electrical demand pro le in the province in any given season is able to be level when fuel cell vehicles and hydrogen generation are introduced. Conservatively only 3,000 MW of clean, base-load generation would have to be added in order to entirely eliminate the need for oil and gas in the province, and this required capacity becomes less as the e ciency of electrolysis improves over time. In the case of the second study, it is shown that with the large scale development of renewable energy systems (mostly wind generation), the need for oil and gas in the energy export sector can be transitioned to a cleaner alternative in hydrogen, whether it be the present-day exports to the United States of America or the future exports to the Asia-Pacific region by means of the new Trans Mountain pipeline expansion. In each case, overwhelmingly positive environmental and economic impacts are evident for specific provinces, and Canada as a whole.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".