Regionalised Life Cycle Assessment of Bio-Based Materials in Construction; the Case of Hemp Shiv Treated with Sol-Gel Coatings
Bibliographic record
Abstract
Interest in intrinsically low-energy construction materials is becoming mainstream, and bio-based materials form a key part of that group of materials. The goal of this study was to analyse the environmental impact of applying a sol-gel coating on hemp shiv, in order to improve the durability of this innovative bio-based material, using a regionalised LCA model, taking into account regional specific peculiarities. This study analysed the environmental performance of using bio-based materials in the building envelope compared with traditional synthetic construction materials, and compared the impact of a regionalised approach with a global approach. The carbon footprint of treated hemp shiv in a wall with a U-value of 0.15 W/m2.K was compared to untreated hempcrete and a reference cavity wall with the same U-value. Considering the environmental damage caused by the production of hemp shiv, nitrogen fertiliser was the hotspot. The LCA results showed that, using innovative bio-based materials in construction, treated hemp shiv with sol-gel can decrease the carbon footprint of a building envelope through carbon sequestration. Using the more accurate site-specific information in life cycle inventory and impact assessment methods will result in more consistent and site-appropriate environmental results for decision-making.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".