Assessing the greenhouse gas effects of harvested wood products manufactured from managed forests in Canada
Bibliographic record
Abstract
We developed a life-cycle analysis (LCA) system to quantify the carbon dynamics for Canadian-made harvested wood products (HWP). We considered the carbon stocks of HWP in use and in landfills/dumps, emissions reduced by substituting HWP for non-wood construction materials, HWP production emissions and methane emissions from decomposing wood disposed of in landfills. Carbon dynamics analyses were conducted for five HWP production scenarios. Results indicate structural panels have the highest potential in mitigating greenhouse gases (GHG) emissions, followed by lumber and non-structural panels. Net GHG effects of Canadian-made HWP were evaluated by integrating HWP carbon dynamics with forest carbon analysis using four forest management units (a total of 2.21 million ha of forests managed for timber production) from Ontario, Canada, as a case study. If HWP substitution benefits were estimated using the average displacement factor, and the wood obtained by increasing harvesting (relative to the baseline harvest scenario) in these four management units is used for structural panel, lumber, non-structural panel and business as usual HWP production, 0, 21, 39 and 84 years are needed to achieve net emission reductions, respectively; net emission reductions were, respectively, estimated to be 112, 93, 66 and 21 Mt CO2-equivalent in 100 years. Our results suggest harvesting sustainably managed forests in Canada to produce long-lived solid HWP can significantly contribute to GHG mitigation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".