Cradle-to-gate life cycle assessment of hemp utilization for biocomposite pellet production: A case study with data quality assurance process
Bibliographic record
Abstract
Natural fiber biomass pre-processing practices, including collection and particle size reduction, are crucial for sustainable manufacturing. This industrial case study evaluates the environmental impact of producing a fully hemp-derived biopolymer/lignin biocomposite pellet using different biomass collection and pre-processing equipment configurations, in order to identify the most efficient and eco-friendly operational option. The system boundary follows cradle-to-gate approach, covering upstream activities such as cultivation, harvesting, size reduction, transportation, and pellet manufacturing. To this end, an attributional Life Cycle Assessment (LCA) is performed using a functional unit of 1 tonne of biocomposite, comparing four supply chain (SC) design alternatives involving different baler (round/square) and size reduction equipment (full/half screen hammer mill) options. We specifically delve into the relative difference of the full-screen hammer mill and square baler (as a best-case/reference) with the half-screen hammer mill and round baler (as a worst-case). Results indicated that the half-round alternative exhibited 30-44% higher environmental impacts due to 30% higher harvested biomass and 9% higher diesel usage per tonne of produced biocomposite, but resulted in higher product quality compared to the full-square alternative. The harvesting stage, linked to the use of biomass, fertilizers, and diesel fuels, is identified as a critical contributor to the environmental impact in all the important impact categories. Sensitivity analysis revealed that a 10-30% increase in biomass yield could reduce impacts across all categories by approximately 7-20%. A scenario-based improvement model integrating substitution of nitrogen fertilizer with compost, diesel-to-natural gas switching, ethanol recycling, and increased hemp yield demonstrated up to 85% GWP reduction compared to the baseline. The improved biocomposite scenario achieved 57% lower GWP and 43% lower smog formation than virgin PET, also outperforming it in fossil fuel depletion. These findings support the viability of hemp-based biocomposites under improved conditions and emphasize the importance of operational SC decisions for sustainable material 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".