Bibliographic record
Abstract
To explore the potential energy impacts of widespread PHEV use, an innovative, three-part survey instrument collected data from 877 new vehicle buyers in California. This analysis combines all the available information from each respondent—driving, recharge potential, and PHEV design priorities—to estimate the energy impacts of the respondents’ existing travel and understandings of PHEVs under a variety of recharging scenarios. Results suggest that the use of PHEV vehicles could halve gasoline use relative to conventional vehicles—the majority of this reduction being due to increases in charge sustaining (CS) fuel economy. Using three scenarios to represent potential boundary conditions on PHEV driver recharge patterns (unconstrained, universal workplace recharging, and off-peak only charging), we estimate tradeoffs between the magnitude and timing of PHEV electricity use. In the unconstrained “Plug and Play†recharge scenario, recharging peaks at 6:15 p.m., following a far more dispersed pattern throughout the earlier part of the day than anticipated by previous research. PHEV electricity use could be increased through policies increasing non-home recharge opportunities (e.g., the “Enhanced Workplace Access†scenario), but most of this increase occurs during daytime hours and could contribute to peak electricity demand (depending on a given region’s definition of “peakâ€). We also demonstrate how deferring all recharging to off-peak hours (8:00 p.m. to 6:00 a.m.) could eliminate all additions to daytime electricity demand from PHEVs. However, in such a scenario less electricity is used due to the elimination of daytime recharge opportunities and less gasoline is displaced. Overall, policy, technology, and energy providers may use this information to understand whether their plans, designs, and goals align with these present empirically informed understandings.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".