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
<div class="section abstract"><div class="htmlview paragraph">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.</div><div class="htmlview paragraph">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 [<span class="xref">1</span>], 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.</div></div>
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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 | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".