Cost of climate change mitigation in Canada’s forest sector
Bibliographic record
Abstract
Managing forests and forest products has substantial potential to help mitigate climate change but the cost has not been extensively examined in Canada. We estimated the cost of seven forest-related mitigation strategies in Canada’s 230 million hectares of managed forest, divided into 32 spatial units. For each strategy and spatial unit, we determined forest sector mitigation cost per tonne (t) using estimated impacts on forest sector greenhouse gas emissions and removals and net revenue. National cost curves showed that mitigation averaged 11.0 Mt CO2e·year–1 in 2015–2050 at costs below $50·t CO2e–1 for a strategy of increased recovery of harvested biomass, increased salvage, extraction of harvest residues for bioenergy, and increased production of longer lived products. We also examined national portfolios in which the strategy selected for each spatial unit (from among the seven examined) was chosen to maximize mitigation or minimize costs. At low levels of mitigation, portfolios chosen to minimize costs were much cheaper than those that maximized mitigation, but overall, they yielded less than half the total mitigation of the latter portfolios. Choosing strategies to maximize mitigation in 2015–2050 yielded an average of 16.5 Mt·year–1 at costs below $50·t CO2e–1. Our analysis suggests that forest-related strategies may be cost-effective choices to help achieve long-term emission reductions in Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".