Towards a circular economy: lifecycle analysis of banana waste valorisation in thermal insulation, bioethanol production, and packaging material applications
Bibliographic record
Abstract
In three different applications – packaging, insulation, and bioethanol production – banana waste usage is thoroughly compared in this article. Banana leaves, rachis, and pseudo-stem wastes were used as raw materials for thermal insulation, bioethanol, and packaging material production. The goal of the study is to determine the environmental impact of using banana waste through conducting a comparative life cycle assessment (LCA) for three fruitful applications. A cradle-to-gate LCA using openLCA software, the ecoinvent database was used as a reference database. The findings show that the manufacturing of thermal insulation materials has far less influence on climate change (0.00057 points) than the manufacture of packaging materials (0.18467 points) and ethanol from banana rachis waste (1.84282 points). When compared with other materials, the thermal insulating board made from agricultural waste residue performs better environmentally. The research shows how much polystyrene was used in the thermal insulating board and how much of a contribution it made. The study also examines the amount of electricity used and the possibility of climate change in the manufacture of bioethanol, focusing on emissions from degradation and fossil fuel sources. The impact of banana waste on climate change is largely determined by the amount of trash used in the processes; higher emissions are noted when more waste is used.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".