Anticipating plug-in hybrid vehicle energy impacts in California: Constructing consumer-informed recharge profiles
Bibliographic record
Abstract
Plug-in hybrid electric vehicles (PHEVs) can be powered by gasoline, grid electricity, or both. To explore potential PHEV energy impacts, a three-part survey instrument collected data from new vehicle buyers in California. We combine the available information to estimate the electricity and gasoline use under three recharging scenarios. Results suggest that the use of PHEV vehicles could halve gasoline use relative to conventional vehicles. Using three scenarios to represent plausible conditions on PHEV drivers’ recharge patterns (immediate and unconstrained, universal workplace access, and off-peak only), tradeoffs are described between the magnitude and timing of PHEV electricity use. PHEV electricity use could be increased through policies supporting non-home recharge opportunities, but this increase occurs during daytime hours and could contribute to peak electricity demand. Deferring all recharging to off-peak hours could eliminate all additions to daytime electricity demand from PHEVs, although less electricity is used and less gasoline displaced.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".