Life Cycle Assessment Sheds New Insights Toward Sustainable Management of Biodegradable Resin Blends Used in Packaging: A Case Study on PBAT
Bibliographic record
Abstract
Bioplastics are gaining attention as eco-friendly alternatives to conventional plastics, with Polybutylene Adipate Terephthalate (PBAT) emerging as a promising biodegradable substitute for polyethylene (PE) in food packaging. Commercial PBAT is often blended with other plastics or bio-based fillers to improve mechanical properties and reduce costs, though these additives can influence its environmental footprint. Therefore, this study quantifies the environmental impacts of producing PBAT resin blends reinforced with common inorganic fillers and compares end-of-life (EoL) performance against PE. While prior studies have largely assessed virgin PBAT or PBAT/Polylactic Acid (PLA) systems, systematic LCA of commercial-style PBAT blends with inorganic fillers and screening LCA level for comparisons of composting vs. landfill remain limited. The contributions of this study are to: (i) map gate-to-gate environmental hotspots for PBAT-blend conversion, (ii) provide a screening gate-to-grave comparison of PBAT composting vs. PE landfill using ReCiPe 2016 and IPCC GWP100 methods, and (iii) discuss theoretical implications for material substitution in the context of EoL strategies. The results indicated that producing 1 kg of PBAT blend generated a single score impact of 921 mPt with Human Health and Resource categories contributing similarly, and a GWP of 8.64 kg CO2-eq, dominated by mixing and drying processes. EoL screening showed PBAT composting offered clear advantages over landfilling PE, yielding −53.9 mPt and 11.35 kg CO2-eq savings, effectively offsetting production emissions. In contrast, landfilling PE resulted in 288.8 mPt and 2.2 kg CO2-eq emissions. Sensitivity analysis further demonstrated that a 30% reduction in electricity use could decrease impacts by up to 10%, underscoring the importance of energy efficiency improvements and renewable energy adoption for sustainable PBAT development.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".