Impacts of Geographic Variation on Aluminum Lightweighted Plug-In Hybrid Electric Vehicle Greenhouse Gas Emissions
Bibliographic record
Abstract
Increasing fuel prices, environmental concerns, and fuel efficiency regulations are precipitating the adoption of new vehicle construction and propulsion technologies that are sensitive to location of vehicle production and use. This sensitivity to location stands in contrast to the dominant vehicle technologies of the last 100 years. Plug-in hybrid electric powertrains and lightweight automotive aluminum are especially location sensitive as vehicle battery charging and aluminum production consume large amounts of electricity from a geographically variable electricity grid. This thesis focused on the impact of geographic variation on lifetime greenhouse gas emissions of aluminum lightweighted plug-in hybrid electric vehicles. We conducted a high resolution characterization of U.S. primary aluminum production, paying special attention to the methods used to allocate consumed electricity emissions, and performed a case study in which a plug-in hybrid vehicle’s conventional steel hood was lightweighted with aluminum. By understanding the impact of regional variations in material production and vehicle use, we wish to inform decision makers of potential hotspots within their vehicle design and material supply chain strategies. This information can help direct attention to the most impactful parts of the vehicle’s lifecycle and ensure that strategies designed to lower the lifetime greenhouse gas emissions of personal transport have the desired effect.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".