Impact of non-petroleum vehicle fuel economy on GHG mitigation potential
Bibliographic record
Abstract
The fuel economy of gasoline vehicles will increase to meet 2025 corporate average fuel economy standards (CAFE). However, dedicated compressed natural gas (CNG) and battery electric vehicles (BEV) already exceed future CAFE fuel economy targets because only 15% of non-petroleum energy use is accounted for when determining compliance. This study aims to inform stakeholders about the potential impact of CAFE on life cycle greenhouse gas (GHG) emissions, should non-petroleum fuel vehicles displace increasingly fuel efficient petroleum vehicles. The well-to-wheel GHG emissions of a set of hypothetical model year 2025 light-duty vehicles are estimated. A reference gasoline vehicle is designed to meet the 2025 fuel economy target within CAFE, and is compared to a set of dedicated CNG vehicles and BEVs with different fuel economy ratings, but all vehicles meet or exceed the fuel economy target due to the policy's dedicated non-petroleum fuel vehicle incentives. Ownership costs and BEV driving ranges are estimated to provide context, as these can influence automaker and consumer decisions. The results show that CNG vehicles that have lower ownership costs than gasoline vehicles and BEVs with long distance driving ranges can exceed the 2025 CAFE fuel economy target. However, this could lead to lower efficiency CNG vehicles and heavier BEVs that have higher well-to-wheel GHG emissions than gasoline vehicles on a per km basis, even if the non-petroleum energy source is less carbon intensive on an energy equivalent basis. These changes could influence the effectiveness of low carbon fuel standards and are not precluded by the light-duty vehicle GHG emissions standards, which regulate tailpipe but not fuel production emissions.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".