Quantifying the biophysical climate change mitigation potential of Canada's forest sector
Bibliographic record
Abstract
Abstract. The potential of forests and the forest sector to mitigate greenhouse gas (GHG) emissions is widely recognized, but challenging to quantify at a national scale. Forests and their carbon (C) sequestration potential are affected by management practices, where wood harvesting transfers C out of the forest into products, and subsequent regrowth allows further C sequestration. Here we determine the mitigation potential of the 2.3 × 106 km2 of Canada's managed forests from 2015 to 2050 using the Carbon Budget Model of the Canadian Forest Sector (CBM-CFS3), a harvested wood products model that estimates emissions based on product half-life decay times, and an account of emission substitution benefits from the use of wood products and bioenergy. We examine several mitigation scenarios with different assumptions about forest management activity levels relative to a base-case scenario, including improved growth from silvicultural activities, increased harvest and residue management for bioenergy, and reduced harvest for conservation. We combine forest management options with two mitigation scenarios for harvested wood product use involving an increase in either long-lived products or bioenergy uses. Results demonstrate large differences among alternative scenarios, and we identify potential mitigation scenarios with increasing benefits to the atmosphere for many decades into the future, as well as scenarios with no net benefit over many decades. The greatest mitigation impact was achieved through a mix of strategies that varied across the country and had cumulative mitigation of 254 Tg CO2e in 2030, and 1180 Tg CO2e in 2050. We conclude that (i) national-scale forest sector mitigation options need to be assessed rigorously from a systems perspective to avoid the development of policies that deliver no net benefits to the atmosphere, (ii) a mix of strategies implemented across the country achieves the greatest mitigation impact, and (iii) because of the time delays in achieving carbon benefits for many forest-based mitigation activities, future contributions of the forest sector to climate mitigation can be maximized if implemented soon.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 teacher head, 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".