The effect of plug-in hybrid electric vehicle charging on fuel consumption and tail-pipe emissions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 gCO 2 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 gCO 2 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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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it