Impact of Powertrain Type on Potential Life Cycle Greenhouse Gas Emission Reductions from a Real World Lightweight Glider
Bibliographic record
Abstract
This study investigates the life cycle greenhouse gas (GHG) emissions of a set of vehicles using two real-world gliders (vehicles without powertrains or batteries); a steel-intensive 2013 Ford Fusion glider and a multi material lightweight vehicle (MMLV) glider that utilizes significantly more aluminum and carbon fiber. These gliders are used to develop lightweight and conventional models of internal combustion engine vehicles (ICV), hybrid electric vehicles (HEV), and battery electric vehicles (BEV). Our results show that the MMLV glider can reduce life cycle GHG emissions despite its use of lightweight materials, which can be carbon intensive to produce, because the glider enables a decrease in fuel (production and use) cycle emissions. However, the fuel savings, and thus life cycle GHG emission reductions, differ substantially depending on powertrain type. Compared to ICVs, the high efficiency of HEVs decreases the potential fuel savings. BEVs are more efficient than HEVs but require heavy batteries to provide an acceptable driving range. A lightweight glider can allow a smaller battery to be used without sacrificing driving range. Battery downsizing is a secondary source of mass reduction that further decreases fuel use. A comparison of our results with those of other studies reveals inconsistencies and lack of powertrain-specific assumptions in the literature, which can mischaracterize the GHG emissions associated with producing lightweight vehicles, and those from potential fuel savings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".