Dynamic life cycle assessment of Canadian glued-laminated (Glulam) timber: a pathway to sustainable structural systems in construction
Bibliographic record
Abstract
Abstract The environmental footprint of building materials has become a focal point in the global effort to decarbonize the construction sector, which contributes approximately 33% of global greenhouse gas (GHG) emissions. This study conducts a dynamic life cycle assessment (DLCA) of Glued-Laminated Timber (Glulam) manufactured in British Columbia (BC), Canada, to assess its cradle-to-gate environmental impacts under current and evolving scenarios. A hybrid methodology combining process-based LCA with a system dynamics (SD) model was implemented. Real-time production data from a Glulam facility in Castlegar, BC, were integrated with region-specific energy and forestry profiles and assessed using the ReCiPe 2016 Midpoint (E) method. Results indicate that the Global Warming Potential (GWP) of BC Glulam is significantly lower than comparable products in other regions, primarily due to hydroelectric-powered manufacturing and efficient clean wood waste recovery. Sensitivity analysis identified transportation distances, adhesive type and usage, timber yield, and energy mix as the most critical impact drivers. The SD model projects the evolution of emissions, energy use, and waste generation from 2020 to 2050 under different demand and efficiency trajectories. The findings underscore Glulam’s potential as a low-carbon structural alternative to steel and concrete, especially in regions with renewable energy infrastructure and sustainable forest practices. Policy insights are provided to support the broader adoption of engineered wood products in climate-aligned construction.
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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) | 0.001 | 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".