Life Cycle Air Emissions Impacts and Ownership Costs of Light-Duty Vehicles Using Natural Gas As a Primary Energy Source
Bibliographic record
Abstract
This paper aims to comprehensively distinguish among the merits of different vehicles using a common primary energy source. In this study, we consider compressed natural gas (CNG) use directly in conventional vehicles (CV) and hybrid electric vehicles (HEV), and natural gas-derived electricity (NG-e) use in plug-in battery electric vehicles (BEV). This study evaluates the incremental life cycle air emissions (climate change and human health) impacts and life cycle ownership costs of non-plug-in (CV and HEV) and plug-in light-duty vehicles. Replacing a gasoline CV with a CNG CV, or a CNG CV with a CNG HEV, can provide life cycle air emissions impact benefits without increasing ownership costs; however, the NG-e BEV will likely increase costs (90% confidence interval: $1000 to $31 000 incremental cost per vehicle lifetime). Furthermore, eliminating HEV tailpipe emissions via plug-in vehicles has an insignificant incremental benefit, due to high uncertainties, with emissions cost benefits between -$1000 and $2000. Vehicle criteria air contaminants are a relatively minor contributor to life cycle air emissions impacts because of strict vehicle emissions standards. Therefore, policies should focus on adoption of plug-in vehicles in nonattainment regions, because CNG vehicles are likely more cost-effective at providing overall life cycle air emissions impact benefits.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".