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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".