Mitigation Potential of Ecosystem-Based Forest Management under Climate Change: A Case Study in the Boreal-Temperate Forest Ecotone
Bibliographic record
Abstract
The forest sector can help reduce atmospheric CO2 through carbon (C) sequestration and storage and wood substitution of more polluting materials. However, climate change can have an impact on the C fluxes we are trying to leverage through forestry. We calculated the difference in CO2 eq. fluxes between ecosystem-based forest management and total forest conservation in the context of the temperate-boreal forest ecotone of Quebec (Canada), taking into account fluxes from forest ecosystems, wood product life cycle, and the substitution effect of wood products on markets. Over the 2020–2120 period, in the absence of climate change, ecosystem-based forest management and wood production caused average net annual emissions of 66.9 kilotonnes (kt) of CO2 eq. year−1 (relative to forest conservation), and 15.4 kt of CO2 eq. year−1 when assuming a 100% substitution effect of wood products. While management increased the ecosystem C sink, emissions from degradation of largely short-lived wood products caused the system to be a net source. Moreover, climate warming would decrease the capacity of ecosystems to sequester C and cause a shift towards more hardwood species. Our study highlights the need to adapt the industrial network towards an increased capacity of processing hardwoods into long-lived products and/or products with high substitution potential.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".