A National and Regional Greenhouse Gas Breakeven Assessment of EVs across North America
Bibliographic record
Abstract
Electrification is considered key to decarbonizing the transport sector. While electric vehicles (EVs) lack tailpipe emissions, battery and electricity production can lead to significant emissions. This study analysed whether EVs can effectively mitigate GHG emissions in North America, by calculating two GHG breakeven indicators for EVs and comparing them to internal combustion engine vehicles (ICEVs). EV life cycle emissions were compared to those of ICEVs in Canada, Mexico, and the USA. In addition, this study considered potential national electricity grids evolutions and improvements in battery production and vehicle efficiency. The study estimated that EVs in Canada, the USA, and Mexico would see environmental benefits after 18.0, 25.1, and 25.6 thousand driven kilometres, respectively, as compared to petrol vehicles. Regionally, Québec had the lowest emissions (12.9 tCO2eq) for EVs while Iowa (62.0) had the highest. In several states, EVs did not outperform ICEVs. Emissions from EVs are expected to decrease in coming years as the carbon intensity of electrical grids decreases. Policies should consider prioritising grid decarbonization over EV uptake where regional grid GHG intensity is high. This work provides one of the first regional and international case studies determining the environmental breakeven points of EVs when considering trade.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".