Life Cycle Emissions of Electric Vehicles in North America: Temporal Dynamics and Policy Assessment
Bibliographic record
Abstract
Electrification of transport is often considered one of the key steps along the path towards a climate-friendly future. Consequently, many countries are implementing policies encouraging this transition and investing in infrastructure such as charging stations. While electric vehicles (EVs) are considered environmentally friendly because they have no tailpipe emissions, battery production produces significant greenhouse gas emissions and the well-to-wheel emissions vary depending on the composition of the electricity grid. This study analysed whether promoting EVs is a valid policy for mitigating GHG emissions in North America, by calculating three environmental performance indicators for EVs. This study applies calculations from the literature to calculate EV life cycle emissions in Canada, Mexico and the USA, as well as for the 50 states, D.C. and Québec. Additionally, this study looks at temporal aspects of how national electricity grids may change depending on current policies as well as the evolution of battery production and vehicle efficiency. The study found that EVs in Canada would have lower life-cycle emissions than in the other nations, but that overall, EVs had lower life-cycle emissions than petrol and diesel vehicles in all three nations. At the regional level, Québec had the lowest emissions for an EV while Iowa and Alaska had the highest. Emissions from EVs are expected to decrease across nations and states over the next few years. Policies should reflect the regional variation by prioritising grid decarbonisation over EV uptake in certain areas.
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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 | 0.001 |
| 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) | 0.000 | 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".