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Record W4413357349 · doi:10.1111/gcbb.70067

Leveraging Biomass Procurement to Mitigate Carbon Emissions at the Stand Level: A Case Study in Eastern Canadian Forests

2025· article· en· W4413357349 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueGCB Bioenergy · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Management and Policy
Canadian institutionsUniversité LavalNatural Resources Canada
FundersFonds de recherche du Québec – Nature et technologiesNatural Resources CanadaCanadian Forest ServiceNatural Sciences and Engineering Research Council of Canada
KeywordsBiomass (ecology)Environmental scienceGreenhouse gasBioenergyCarbon fibersBiofuelAgroforestryProcurementCarbon sequestrationRenewable energyEnvironmental protectionForestryEcologyWaste managementCarbon dioxideBusinessGeographyEngineeringBiology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.381
Threshold uncertainty score0.475

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.275
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it