Climate change substitution factors for Canadian forest-based products and bioenergy
Bibliographic record
Abstract
Evaluating the climate change mitigation potential of the forest sector requires a holistic approach based on forest carbon (C) sequestration, C storage in harvested wood products (HWP) and substitution on markets. High uncertainty is associated with substitution factors, that express avoided fossil greenhouse gas (GHG) emissions from the use of forest-based products in replacement of GHG-intensive materials and fossil fuels. Few studies have focused on the development of substitution factors in Canada, resulting in the use of unrepresentative generic data. Here, we provide a framework to reduce uncertainties related to substitution factors for primary wood products in a Canadian context. A life cycle assessment framework is used to quantify fossil GHG emissions for a baseline and a wood-intensive scenario. For solid product substitution, we focused on the construction sector and analyzed a range of innovative wood buildings with steel and reinforced concrete as alternative materials. We found non-weighted averages of 0.80 tC/tC for sawnwood and 0.81 tC/tC for panels. For energy substitution, we analyzed cases with different specifications on biomass product, facility type and alternative fossil fuel source in non-residential heat production and biofuel transportation sectors. We found a non-weighted average of 0.80 tC/tC for non-residential heat production and 0.51 tC/tC for biofuel transportation, that can be interpreted as 0.91 tC/tC for heavy fuel oil, 0.69 tC/tC for light fuel oil and 0.68 tC/tC for natural gas substitution. These results provide a benchmark for substitution factors in Canada, to help guide forest management strategies for climate change 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".