Variability in costs of electrifying passenger cars in Canada
Bibliographic record
Abstract
Abstract The high cost of purchasing electric vehicles (EVs) compared to internal combustion engine vehicles (ICEVs) is a major barrier to their widespread adoption. Additionally, the price disparity is not the same for all households. We conducted a total cost of ownership (TCO) analysis to compare the net present value of EV versus ICEV ownership for various household categories across Canada. We observed spatial and behavioral factors, including variations in costs of electricity, temperature, household archetypes and their purchase decisions, and access to charging infrastructure. We found that EVs are more cost-effective than ICEVs for certain daily driving distances, but typical households in Canada generally do not drive enough for lifecycle costs of EVs to be less than ICEVs. The province of Quebec has the most favorable conditions for EV ownership due to high purchase subsidies and low electricity prices. Variability in costs across other provinces and territories is mainly due to differences in rebates, electricity and gasoline prices, and tax rates. Our findings have implications for policymakers and consumers. For consumers comparing ICEVs to EVs based on a fixed budget, which may be consistent with how many households frame their purchase decision, willingness to accept smaller, non-luxury EVs can result in large cost savings. We also find that although temperature variation has a minimal effect on TCO, it does impact the ‘number of charge-ups’—a metric that we introduce to compare how many charging cycles a user may expect over the lifetime of a vehicle. The policy implication of this would be a need to consider regional differences in cold weather patterns when planning charging infrastructure deployment and the extent to which households in shared dwellings may face additional costs. Lastly, our findings strengthen the argument that equitably decarbonizing transportation will also require investment in strategies other than electrifying personal vehicles.
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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 | 0.001 | 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 | 0.001 |
| 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".