Marginal\nGreenhouse Gas Emissions of Ontario’s\nElectricity System and the Implications of Electric Vehicle Charging
Bibliographic record
Abstract
To\nestimate greenhouse gas (GHG) emission reductions of electric\nvehicles (EVs) deployment, it is important to account for emissions\nfrom electricity generation. Since such emissions change according\nto temporal patterns of electricity generation and EV charging, this\nstudy operationalizes the concept of marginal emission factors (MEFs)\nand uses person-level travel activity data to simulate charging scenarios.\nOur study is set in the Greater Toronto and Hamilton Area in Ontario,\nCanada. After generating hourly MEFs using a multiple linear regression\nmodel, we estimated GHG emissions for EV charging at two EV penetration\nrates, 5% and 30%, and five charging scenarios: home, work and shopping,\nnight, downtown vs suburb, and an optimal low emission charging scenario,\nmatching charging time with the lowest available MEF. We observed\nthat vehicle electrification substantially reduces GHG emissions,\neven when using MEFs that are up to seven times higher than average\nelectricity emission factors. With Ontario’s 2017 electricity\ngeneration mix, EVs achieve over 80% lower fuel cycle emissions compared\nwith equivalent sets of gasoline vehicles. At 5% penetration, night\ncharging nearly matches low emission charging, but night charging\nemissions increase with 30% EV penetration, suggesting a need for\npolicy that can smooth out charging demand after midnight.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".