Implications of Successes and Failures of BEV-Focused Incentive Support for PEVs in the U.S., Canada and Europe
Bibliographic record
Abstract
An international comparative analysis of causes of variation of PEV sales rates per capita in selected U.S. states, Canadian provinces, and European Nations is conducted. 2014-15 light-duty PEV registrations/sales by make and model are examined, drawing heavily on 2014 data for aggregate comparisons. States, provinces, and nations with PEV success, but with widely varying PEV purchase incentives and charging infrastructure installations are examined. The paper focuses particularly presence or absence of long daily distance charging options for PEVs. Four questions are addressed. (1) European evidence is that PHEV powertrains are a very marketable option for large family vehicles. For small BEVs BMW i3 sales patterns indicate that range extension beyond 120 km but less than 240 km via gasoline significantly increases market share. BEV inter-city functionality provided by aggressive installation of DC fast charging was critical to overall PEV success in Norway. Norway, like Northern Europe and Canada has a utility network that is winter peaking, which allows provision of peak summer BEV long-distance charging needs without difficulty. This is not the case for the U.S., which is summer peaking. The reviewed states, provinces, and nations vary considerably in seasonal peak temperature extremes. These climate differences have a significant bearing on the local viability of PHEVs vs. BEVs. The long distance DC fast charging infrastructure investments needed to support BEV market success are not as large when PHEVs are preferred by consumers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".