Leveraging Biomass Procurement to Mitigate Carbon Emissions at the Stand Level: A Case Study in Eastern Canadian Forests
Bibliographic record
Abstract
ABSTRACT Many jurisdictions within the boreal and temperate biomes have adopted targets to increase the contribution of forest bioenergy for climate change mitigation. Using residual forest biomass as feedstock is considered, but the carbon emission reductions associated with this practice remain controversial. Our study evaluated how intensifying wood procurement for bioenergy production, alongside supplying fiber for conventional wood industries, can support low‐carbon forest management. We used six sites established in eastern Canada as a case study. We compared the carbon balance of four harvesting scenarios with increasing wood procurement intensity (from procuring sawtimber only to procuring sawtimber, pulpwood and biomass) to three scenarios of unharvested forests, two of which experienced natural disturbances. We modeled carbon fluxes over a 100‐year simulation period, considering biogenic and fossil emissions from aboveground forest ecosystems, harvested wood products, and wood supply and manufacturing. We assessed the mitigation potential of procuring biomass to produce bioenergy in the form of stemwood, treetops (including branches) or pulpwood. We found that forest harvesting, regardless of the wood procurement intensity, offered limited carbon benefits compared to the referenced undisturbed mature stands in most cases. However, increasing wood procurement can reduce the carbon footprint of wood supply chains, with pulpwood identified as a key feedstock. Compared with harvesting roundwood for conventional industries only, procuring biomass for bioenergy is likely to increase carbon emissions unless it substitutes high‐emission energy sources on markets or enhances the next‐rotation stand yield, which seems achievable in the context we studied. Bioenergy displacement factors should range from 0.072 to 0.701 tonne of carbon emission reduction per tonne of carbon in the bioenergy product, depending on stand characteristics, biomass feedstock, and cutting cycle length. Our findings provide a foundation for assessing the GHG reduction potential of harvesting activities at a broader scale, considering varying feedstock recovery intensities.
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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 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".