Circular economy in automotive disassembly
Bibliographic record
Abstract
The transition towards a circular economy is a pressing priority for addressing the environmental and economic challenges posed by end-of-life vehicles (ELVs). This study critically analyzes global disassembly practices and their alignment with circular economy objectives, focusing on the reuse, recycling, and remanufacturing of high-value components such as lithium-ion batteries and catalytic converters. Using the PRISMA framework and a thematic analytical synthesis, a systematic literature review examines practices across Europe, Asia, Africa, North America, South America, and Oceania. Key findings highlight significant regional disparities: Europe excels with advanced infrastructures and robust regulations; Asia demonstrates innovation with Japan’s 95% vehicle reuse rate and China’s leadership in lithium-ion battery recycling; North America achieves high recycling rates for steel and aluminum but faces challenges with EV battery recycling. Africa and South America struggle with limited infrastructure but show progress in initiatives like EV battery repurposing in South Africa and structured recycling markets in Brazil. Oceania benefits from government-supported recycling but lacks scaling capacity. Emerging technologies such as robotics and artificial intelligence offer transformative solutions. The study proposes actionable pathways to enhance material recovery, integrate reverse logistics, and advance Industry 4.0/5.0 technologies, supporting sustainable development goals in the automotive sector.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".