Hemp fiber and its bio-composites: a short review part II—applications and life cycle assessment
Bibliographic record
Abstract
The urgent need to address global sustainability challenges, including climate change and resource depletion, has sparked renewed interest in renewable materials and carbon-negative solutions. This review examines Cannabis sativa L. (hemp) as a versatile and sustainable crop that aligns with the United Nations Sustainable Development Goals. Hemp’s remarkable attributes, including its rapid growth cycle, low cultivation requirements, and carbon sequestration capabilities, position it as a promising alternative to conventional materials. The review systematically analyzes hemp’s diverse applications across multiple sectors, including textiles, construction, automotive, paper production, and biofuel generation. Of particular interest are hemp fiber’s mechanical properties, which rival synthetic counterparts, making them ideal for eco-friendly composites and structural applications. The plant’s versatility extends beyond industrial uses to encompass food security, pharmaceutical applications, and environmental remediation. With over 30 countries currently cultivating hemp, led by China and followed by significant production in Europe and Canada, the crop is experiencing a global renaissance. This comprehensive analysis also explores emerging innovative applications in medical therapeutics, cosmeceuticals, phytoremediation, and wastewater treatment while evaluating life cycle assessments to demonstrate hemp’s potential in addressing contemporary environmental and health challenges. As society grapples with mounting resource demands and environmental concerns, this review underscores hemp’s role in transitioning toward a more sustainable and regenerative future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".