Ontario’s managed forests and harvested wood products contribute to greenhouse gas mitigation from 2020 to 2100
Why this work is in the frame
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Bibliographic record
Abstract
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.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it