Bibliographic record
Abstract
The automotive industry is beginning a paradigm shift from a linear "take-make-dispose" model to a circular economy (CE) framework. This transition is driven by emerging environmental regulations, resource scarcity, and the urgent need to mitigate the carbon footprint of the mobility sector following the recent adoption of the Sustainable Development Goals (SDGs) and the lead-up to the COP21 Paris climate negotiations. This research article explores the current state-of-the-art in End-of-Life Vehicle (ELV) recycling and the sustainable reuse of automotive materials. We analyze technical advancements in the recovery of ferrous and non-ferrous metals, the challenges of polymer and composite recycling, and the nascent field of electric vehicle (EV) battery second-life applications. The study further examines "Design for Disassembly" (DfD) strategies and the potential role of early Industry 4.0 technologies, such as automated sorting and digital material tracking, in enhancing recovery rates. By synthesizing data from industrial implementations and academic studies (2008–2015), this paper provides a comprehensive roadmap for achieving a sustainable automotive ecosystem, accounting for technological, economic, and geopolitical variables.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".