Cradle-to-Gate Life-Cycle Assessment of a Glued-Laminated Wood Product from Quebec's Boreal Forest
Bibliographic record
Abstract
Abstract The building sector is increasingly identified as being energy and carbon intensive. Although the majority of emissions are linked to energy usage during the operation part of a building's life cycle, choice of construction materials could play a significant role in reducing greenhouse gas emissions and other environmental end-point damages. Increasing the use of wood products in buildings may contribute to the solution, but their environmental impacts are difficult to assess and quantify because they depend on a variety of uncertain parameters. The present cradle-to-gate life-cycle analysis (LCA) focuses exclusively on a glued-laminated wood product (glulam) produced from North American boreal forests located in the province of Quebec, Canada. This study uses primary data to quantify the environmental impacts of all necessary stages of products' life cycle, from harvesting the primary resources, to manufacturing the transformed product into glulam. The functional unit is 1 m3 of glulam. This is the first study based on primary data pertaining to Quebec's boreal forest. Quebec's boreal glulam manufacturing was compared with two other LCAs on glulam in Europe and the United States. Our results show that Quebec's glulam has a significantly smaller environmental footprint than what is reported in the literature. From an LCA perspective, there is a significant advantage to producing glulam in Quebec, compared with the European and American contexts. The same holds true in regard to the four end-point damage categories.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".