Life cycle analysis for green composites: A review of literature including considerations for local and global agricultural use
Bibliographic record
Abstract
Increasing concerns regarding human-driven effects on the biosphere have led to the development and adoption of environmentally friendly “green” composites. Unlike conventional synthetic composites, green composites are made of natural materials in either the matrix or the fiber reinforcement (or both). They are claimed to have lower negative environmental effects due to their sustainability and easier recyclability. To assess the environmental impacts associated with any product, a life cycle assessment (LCA) is needed. This literature review summarizes the individual steps undertaken in an LCA study and discusses their relevance within the field of green composites. Similarly, an outline of life cycle costing (LCC), a type of study which determines the economic implications of a product, is incorporated. Since some phases of a product’s life cycle can have significant environmental effects, parameters affecting the time-dependant degradation of green composites and their significance in LCA studies were also explored. Finally, criteria for choosing natural fibers and biopolymers for green composites in engineering applications were considered, and case studies of hemp and flax as candidates for fiber cultivation in Alberta, Canada are provided throughout.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".