Maximizing Automotive Parts Reuse, Remanufacturing, and Recycling Through Effective End-of-Life Vehicle Management: A Different Perspective on What Needs to be Done
Bibliographic record
Abstract
Life-cycle assessments (LCAs) conducted, to date, of the end-of-life phase of vehicles rely significantly on assumed values and extrapolations within models. The end phase of vehicles, however, has become all the more important as a consequence of increasing regulatory requirements on materials recovery, tightening disposal restrictions, and the rapid introduction of new materials and electronics, all potentially impacting a vehicle's efficacy for achieving greater levels of sustainability. This article presents and discusses selected research results of a comprehensive gate-to-gate life-cycle-inventory (LCI) of end-of-life vehicle (ELV) dismantling and shredding processes, constructed through a comprehensive and detailed case study, and argues that managing and implementing creative dismantling practices can improve significantly the recovery of both reusable and recyclable materials from end-of-life vehicles. Although the amount of parts and materials recovered and directed for reuse, remanufacturing or recycling may be as much as 11.6% by weight of the ELVs entering a dismantling process [1], greater rates of reuse and/or recycling may be achieved by the strategic management of the ELVs entering the dismantling process according to age. Late model, high-salvage ELVs (HSELVS) of an optimum age range (e.g., 5-9 years) could be targeted for maximum recovery of parts for reuse and remanufacture. Older low-salvage ELVs (LSELVs) would be targeted principally for materials recovery and recycling. This paper discusses the challenges anticipated with the development of an ELV management system promoting maximum parts reuse/remanufacturing and materials recycling.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".