The Extent to Which Hemp Insulation Materials Can Be Used in Canadian Residential Buildings
Bibliographic record
Abstract
The embodied carbon of building materials is a significant contributor to greenhouse gas (GHG) emissions. Hemp is widely acknowledged as the most used vegetal insulation in building and construction due to its comparable thermal properties and better environmental performance than that of mainstream insulation materials (MIMs). However, the application of hemp insulation materials (HIMs) in Canada is still in its infancy. Canada is currently the largest hemp oil and seed producer in the world. Most recent research on hemp in Canada has focused on the impact of legalising marijuana and the popularisation of hemp health products and cannabidiol (CBD). There is a lack of studies addressing the holistic impact of hemp in reducing emissions in Canadian residential buildings. This paper exams the feasibility of large-scale hemp cultivation in Canada and the suitability of HIMs for Canadian private dwellings. Material flow analysis (MFA) and life cycle assessment (LCA) were applied to evaluate different levels of carbon mitigation over time produced by HIM substitution. The results show that Canada has sufficient farmland and perfect geographic location and weather to implement large-scale hemp cultivation. HIM substitution can be accomplished for 81% of Canadian residential buildings. Full HIM substitution fulfilled through 5% hemp fibre insulation (HF) and 95% hempcrete (HC) will mitigate 101% of the GHG emissions caused by existing MIMs and contribute up to a 7.38% reduction in emissions to achieve the net zero emissions target by 2050.
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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 | 0.002 | 0.002 |
| 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".