Climate change mitigation potential of local use of harvest residues for bioenergy in Canada
Bibliographic record
Abstract
Abstract We estimate the mitigation potential of local use of bioenergy from harvest residues for the 2.3 × 10 6 km 2 (232 Mha) of Canada's managed forests from 2017 to 2050 using three models: Carbon Budget Model of the Canadian Forest Sector ( CBM ‐ CFS 3), a harvested wood products ( HWP ) model that estimates bioenergy emissions, and a model of emission substitution benefits from the use of bioenergy. We compare the use of harvest residues for local heat and electricity production relative to a base case scenario and estimate the climate change mitigation potential at the forest management unit level. Results demonstrate large differences between and within provinces and territories across Canada. We identify regions with increasing benefits to the atmosphere for many decades into the future and regions where no net benefit would occur over the 33‐year study horizon. The cumulative mitigation potential for regions with positive mitigation was predicted to be 429 Tg CO 2 e in 2050, with 7.1 TgC yr −1 of harvest residues producing bioenergy that met 3.1% of the heat demand and 2.9% of the electricity demand for 32.1 million people living within these regions. Our results show that regions with positive mitigation produced bioenergy, mainly from combined heat and power facilities, with emissions intensities that ranged from roughly 90 to 500 kg CO 2 e MWh −1 . Roughly 40% of the total captured harvest residue was associated with regions that were predicted to have a negative cumulative mitigation potential in 2050 of −152 Tg CO 2 e. We conclude that the capture of harvest residues to produce local bioenergy can reduce GHG emissions in populated regions where bioenergy, mainly from combined heat and power facilities, offsets fossil fuel sources (fuel oil, coal and petcoke, and natural gas).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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 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".