Regional-level analysis for the material flows and process energy demands of aluminum and steel in the American automotive industry
Bibliographic record
Abstract
Aluminum and steel are the two most prevalent metals in light duty vehicles (LDVs) today, yet the flows of these automotive metals have not been closely evaluated. This study develops and implements a method for regionalizing sector-specific material flows and and presents the results of such models for aluminum and steel entering the American automotive industry. These results were then used to identify regional process energy demands associated with each metal. Aluminum entering the American automotive industry, as sheet and extrusion mill product, is primarily sourced from the NPCC (23%), SERC (20%), MRO (18%), and RFC (13%) NERC regions and a spatially unresolved Local region within the USA and Canada (18%). Primary aluminum used for these mill products comes largely from the Canadian province of Quebec (69%). Further upstream, alumna and bauxite come primarily from international sources (91% for alumina and 100% for bauxite). These patterns are reflected in regional process energy demands. Further, the regional distribution of total embodied process energy is largely influenced by that of primary aluminum, highlighting the significant energy required for primary aluminum production. Finished steel entering the American automotive industry comes primarily from the RFC (63%) and SERC (20%) regions within the USA Crude steel for this finished steel is similarly dominated by the RFC (69%) and SERC (7%) regions. The majority of raw materials including coke, coking coal, iron ore, lime, and steel scrap are sourced from the USA with only direct reduced iron (DRI) and pig iron as exceptions. The regional distribution of total embodied process energy for this steel is again dominated by the RFC (54%) and SERC (10%) regions, but in slightly smaller shares due to international sourcing of energy intensive DRI and pig iron. The results from this study can help guide sustainability improvements in American automotive, aluminum, and steel industries and can be integrated into future life cycle assessment (LCA) models to provide more geographically specific energy demand data.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".