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Record W6951057495 · doi:10.5558/tfc2018-040

Ontario’s managed forests and harvested wood products contribute to greenhouse gas mitigation from 2020 to 2100

2018· article· en· W6951057495 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.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2018
Typearticle
Languageen
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsnot available
Fundersnot available
KeywordsGreenhouse gasBaseline (sea)Carbon stockTonneForest managementWood productionWood processingWood industryMethane emissions

Abstract

fetched live from OpenAlex

We used an integrated approach to estimate the greenhouse gas (GHG) mitigation potential of Ontario’s forestry sector, defined as the managed forests and the harvested wood products (HWP) originating from these forests. The 44.7 million ha of managed forests in this study included Crown forests designated as 41 forest management units (FMUs) for timber harvesting, productive forests north of the area of undertaking, large parks, and private forest land. Forests and HWP were simulated from approximately 2010 to 2100, with carbon (C) stocks and emissions reported for the period 2020 to 2100. A baseline scenario was defined to represent business as usual forestry operations in Ontario, in which the 41 FMUs and the private forests were harvested at historical (1990–2009) rates, and HWP production and end uses were assumed to follow Ontario’s historical values (1991–2010). In the baseline scenario, the forest C stocks were projected to increase from 7229.7 million tonnes (Mt C) in 2020 to 7424 Mt C in 2100. The C stocks of HWP originating from the 41 FMUs and the private forests were estimated to increase from 171.0 Mt C, the initial HWP C stocks in 2020 from previous harvesting, to 334.7 Mt C in 2100, in which the C stocks of HWP in use and in landfills, HWP production emissions, landfill methane emissions from decomposing mill residue and waste HWP discarded, and the reduced emissions from substituting HWP for non-wood materials in construction were all considered. On average, Ontario’s forestry sector was estimated to increase C stocks by 44.8 Mt per decade over the 80-year period. Six alternative scenarios were defined based on increased harvesting in the 41 FMUs and varied use of the increased harvested wood in HWP production and differing HWP end uses. Depending on how the increased harvested wood is used, increased forest harvesting (relative to historical rates) may increase or reduce the mitigation potential of Ontario’s forestry sector. Based on historical use statistics, the life-cycle analysis HWP C stocks/emissions plus HWP substitution benefits were insufficient to compensate for forest C decreases from increased harvesting. However, if the increased harvested wood was used to produce solid HWP and these products were used in construction, increasing forest harvesting to 95% of the maximum allowable harvest level would require 20.0 years to achieve a positive net mitigation contribution. Factoring out the decrease in forest C due to increased harvesting, this amounts to 187.9 Mt C of additional mitigation contribution by 2100. We conclude that Ontario’s forestry sector has the potential to contribute significantly to medium- and long-term GHG mitigation. Our results indicate that harvesting sustainably managed forests to produce solid HWP and using these HWP in long-lived end uses such as construction is a better mitigation option than protecting forest from harvesting.

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 categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.446
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.086
GPT teacher head0.420
Teacher spread0.334 · 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