The effect of plug-in hybrid electric vehicle charging on fuel consumption and tail-pipe emissions
Bibliographic record
Abstract
Abstract Plug-in hybrid electric vehicles (PHEV) have an electric motor and an internal combustion engine and can reduce greenhouse gas emissions (GHG) from transport. However, their environmental benefit strongly depends on the charging behaviour. Several studies have analysed the GHG emissions from upstream electricity production, yet the impact of individual charging behaviour on PHEV tail-pipe carbon emissions has not been quantified from empirical data so far. Here, we use daily driving data from 7,491 Chevrolet Volt PHEV with a total 3.4 million driving days in the US and Canada to fill this gap. We quantify the effect of daily charging on the electric driving share and the individual fuel consumption. We find that even a minor deviation from charging every driving day significantly increases fuel consumption and thus tail-pipe emissions. Our results show that reducing charging from every day to 9 out of 10 days, increases fuel consumption on average by 1.85 ± 0.03 l/100 km or 42.7 ± 0.8 gCO2 km−1 tail-pipe emissions (± on standard error). Charging more than once per driving day has less impact in our sample, this must occur during at least 20% of driving days to have a noteworthy effect. Even then, a 10% increase in frequency only has moderate effect of decreasing fuel consumption on average by 0.08 ± 0.02 l/100 km or 1.86 ± 0.46 gCO2 km−1 tail-pipe emissions. Our results illustrate the importance of providing adequate charging infrastructure and incentives for PHEV users to charge their vehicles on a regular basis in order to ensure that their environmental impact is small as even long-range PHEVs can have a noteworthy share of conventional fuel use when not regularly charged.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".