Cost–Benefit Analysis and System-Level Evaluation of Recycling Metallized Biaxially Oriented Polypropylene Film as a Filler in Polyurethane Foam
Bibliographic record
Abstract
This study presents a comprehensive evaluation of the economic viability, environmental impact, and social value of valorizing metallized biaxially oriented polypropylene (BOP) film waste as a nonreactive filler in flexible polyurethane (PU) foam synthesis. Given the challenges of recycling complex multilayer films and the high carbon footprint of virgin-polyol-based PU production, this approach offers a circular solution to reduce petrochemical dependency and landfill-bound waste. Through a multiscalar cost–benefit analysis (CBA) and social impact assessment (SIA), the integration of up to 15 wt % BOP filler is shown to deliver material cost savings of approximately $300 per metric ton while reducing lifecycle greenhouse gas emissions by over 90% compared to incineration. Additionally, decentralized waste valorization supports the creation of up to 210 direct and indirect jobs per 10,000 tons of BOP waste processed annually, enhancing inclusive labor markets in developing regions. Despite technical and regulatory limitations such as filler dispersion, aesthetic constraints, and a lack of harmonized standards, the strategy aligns with key Sustainable Development Goals and presents scalable pathways for industrial decarbonization and Environmental, Social, and Governance (ESG) performance enhancement. The findings position BOP-recycled PU foam as a viable, low-carbon innovation in polymer circularity and sustainable materials management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".