A Review of Cradle-to-Gate Greenhouse Gas Emission Factors for Canada’s Harvested Wood Products
Why this work is in the frame
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Bibliographic record
Abstract
Abstract With the previous decade’s (2010 through 2019) greenhouse gas emissions remaining the highest on record, focus on emissions mitigation efforts is paramount. Harvested wood products (HWPs) can store carbon for various timespans depending on the product and its end uses. Life cycle inventories (LCIs) are the base for life cycle analyses (LCAs), as they represent a comprehensive catalogue of the raw data essential to complete an LCA. However, most LCI documentation is in the form of case studies of different types of HWPs, with varying LCI results that reflect varied system boundaries, case-specific conditions, and assumptions. Our goal was to conduct a systematic literature review to evaluate, analyze, and synthesize previously reported Canadian HWP data and to initiate a Canadian database based on reported cradle-to-gate HWP emission factors. HWPs were categorized as lumber, traditional structural panels, mass timber, nonstructural panels, and wood pellets. Based on our analysis, we found that softwood lumber produced the lowest cradle-to-gate emission factor (61.99 kg of CO 2 equivalent [CO 2 eq] per m 3 HWP) while I-joists produced the highest (218.55 kg of CO 2 eq per m 3 HWP). Resource extraction emissions accounted for most of the overall emissions for softwood lumber, oriented strand board, cross-laminated timber, and glue-laminated timber. Meanwhile, manufacturing accounted for most of the emissions for plywood, I-joists, cellulosic fiberboard, particleboard, and wood pellets. Substantial gaps exist in published LCI data and, when possible, publishing detailed LCI data is encouraged to support additional HWP life cycle analyses.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it