Assessing the Effects of Different Harvesting Practices on the Forestry Sector’s Climate Benefits Potential: A Stand Level Theoretical Study in an Eastern Canadian Boreal Forest
Bibliographic record
Abstract
The contribution of the forest sector to climate change mitigation needs to rely on optimal strategies that include forest management, wood supply, wood product disposal, and replacement of GHG-intensive materials and energy sources. Our study aimed to assess the impact of alternative forest management practices applied at the stand scale on the capacity of the forest sector to reduce its radiative forcing, using the boreal forests of eastern Canada as a case study. We simulated management of a balsam fir–white birch stand over a sixty-year period and determined the sectorial carbon and radiative forcing budget for a reference scenario (no harvest) and for nineteen clearcut and partial cut alternatives. The results suggest that logging may not significantly reduce carbon emissions compared to a preservation practice and does not yield any climate benefits in terms of radiative forcing. In a context for which the substitution effect of wood products on markets is expected to be limited, the mitigation potential of a scenario is mostly driven by the capacity of the forest ecosystem carbon sink to compensate for the substantial CO2 and CH4 emissions from wood product decay in landfills. The improved assessment of carbon emission temporality, incorporation of ecosystem carbon dynamics, and improved consideration of substitution and the decay of wood products are essential in the development of any forest management strategy. Neglecting these elements can lead to misconceptions and prevent informed mitigation decisions.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".