The Cradle-to-Gate Life Cycle Assessment on Hardwood Lumber Production in New Brunswick, Canada
Bibliographic record
Abstract
This case study conducted a cradle-to-gate life cycle assessment (LCA) on the production of hardwood lumber in New Brunswick, Canada, evaluating the environmental impacts from raw material extraction to the point where lumber exited the mill as rough green lumber, the primary input for manufacturing pallets and railway ties. Data on annual production, material flow, and energy use for harvesting and sawmilling were gathered through survey questionnaires and on-site visits. The life cycle inventory (LCI) was developed in SimaPro software, the life cycle impact assessment (LCIA) was conducted using the North American TRACI method to quantify impact categories, and the cumulative energy demand (CED) method was employed to analyze the total energy consumption. It was found that rough green hardwood lumber production emitted approximately 41 kg CO2−eq/m3 (excluding biogenic carbon storage), with manufacturing accounting for 42% of total emissions and consuming 736 MJ/m3 of total energy, 98% of which came from non-renewable sources. The carbon stored in lumber was 975 CO2 eq/m3, 24 times greater than its cradle-to-gate emissions, highlighting its significant environmental benefit. Future research will aim to conduct a cradle-to-grave LCA on hardwood production by including drying and planing and increase the number of sawmill samples.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".