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Record W4385074785 · doi:10.1111/gcbb.13081

Under what circumstances can the forest sector contribute to 2050 climate change mitigation targets? A study from forest ecosystems to landfill methane emissions for the province of Quebec, Canada

2023· article· en· W4385074785 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 · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsCanadian Sport Centre PacificNatural Resources CanadaUniversité Laval
FundersNatural Resources Canada
KeywordsGreenhouse gasEnvironmental scienceBaseline (sea)Climate change mitigationClimate changeCarbon sequestrationSustainabilityForest managementBusiness as usualSustainable forest managementForest productEnvironmental protectionNatural resource economicsAgroforestryCarbon dioxideEcologyEconomics

Abstract

fetched live from OpenAlex

Abstract Meeting climate change mitigation targets by 2050, as outlined in international pledges, involves determining optimal strategies for forest management, wood supply, the substitution of greenhouse gas‐intensive materials and energy sources, and wood product disposal. Our study quantified the cumulative mitigation potential by 2050 of the forest sector in the province of Quebec, Canada, using several alternative strategies and assessed under what circumstances the sector could contribute to the targets. We used the Carbon Budget Model of the Canadian Forest Sector to project ecosystems emissions and sequestration of seven alternative and one baseline (business‐as‐usual [BaU]) forest management scenarios over the 2018–2050 period. Three baskets of wood products were used in a Harvested Wood Products model to predict wood product emissions. The mitigation potential was determined by comparing the cumulative CO 2 e budget of each alternative scenario to the BaU. The proportion of methane emissions from landfills (RCH 4 %) and the required displacement factor (RDF) to achieve mitigation benefits were assessed both independently and jointly. The fastest and most efficient way to improve mitigation outcomes of the forest sector of Quebec is to reduce end‐of‐life methane emissions from wood products. By reducing methane emissions, the RDF for achieving mitigation benefits through intensification strategies can be reduced from 1.2–2.3 to 0–0.9 tC/tC, thus reaching the current provincial mean DF threshold (0.9). Both a reduction and an increase in the harvested volume have the potential to provide mitigation benefits with adequate RCH 4 % and RDF. Increased carbon sequestration in ecosystems, innovations in long‐lived wood products, and optimal substitution in markets offer potential avenues for the forest sector to contribute to mitigation benefits but are subject to significant uncertainties. Methane emission reduction at the end of wood product service life is emerging as a valuable approach to enhance mitigation benefits of the forest sector.

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 score0.102
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.016
GPT teacher head0.244
Teacher spread0.228 · 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