Plug-in Hybrid Vehicle GHG Impacts in California: Integrating Consumer-Informed Recharge Profiles with an Electricity-Dispatch Model
Bibliographic record
Abstract
Estimating greenhouse gas (GHG) emissions of plug-in hybrid vehicles (PHEVs) is challenging because PHEVs are powered by gasoline and grid electricity—in a variety of proportions across individual consumers. Previous GHG estimates emissions postulate consumer behavior and simplify interactions with the electricity grid. We construct PHEV emissions scenarios to address inherent relationships between vehicle design, driving and recharging behaviors, seasonal and time-of-day variation in GHG-intensity of electricity, and total GHG emissions. From a survey of 877 California new vehicle buyers we elicit driving patterns, time of day recharge access, and PHEV design interests. The elicited data differ substantially from those used in previous analyses—including substantial interest in PHEVs with no true all-electric driving. We construct electricity demand profiles scaled to one million PHEVs and input them into an hourly California electricity supply model to simulate GHG emissions scenarios. Compared to conventional vehicles, consumerdesigned PHEVs cut marginal (incremental) GHG emissions by more than one third in current California energy scenarios and by a quarter in future energy scenarios— -2- reductions similar to those simulated for all-electric PHEV designs. Across the emissions scenarios realization of long-term GHG reductions depends on reducing the carbon intensity of the grid.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".