Salvage harvesting for bioenergy in Canada: From sustainable and integrated supply chain to climate change mitigation
Bibliographic record
Abstract
Driven by the policy imperatives of mitigating greenhouse gas (GHG) emissions and improving energy security, an increasing proportion of global energy demand is being met by nonfossil energy sources. The socioeconomic and environmental benefits of replacing fossil fuels with bioenergy are complex; however, debate continues about issues such as best practices for biomass removal, stable supply chains, and GHG mitigation. With the greatest biomass per capita in the world, Canada could play an increasing role in the future of global bioenergy and the emerging bioeconomy. This paper reviews the utilization of feedstock salvaged after natural disturbances (fire and insect outbreaks) to supply wood‐based bioenergy, by addressing the following multidisciplinary questions: (1) How much salvaged feedstock is available, and what are the uncertainties around these estimates? (2) How can sustainable practices to support increased removal of biomass be implemented? (3) What are the constraints on development of an integrated supply chain and cost‐effective mobilization of the biomass? (4) Is the quality of biomass from salvaged trees suitable for conversion to bioenergy? (5) What is the potential for climate change mitigation? In average, salvaged feedstock from fire and insects could theoretically provide about 100 × 10 6 oven Dry ton (ODT) biomass per year, with high variability over time and space. Existing policies and guidelines for harvesting of woody biomass in Canadian jurisdictions could support sustainable biomass removal. However, uncertainties remain as to the development of competitive and profitable supply chains, because of the large distances between the locations of this feedstock and available processing sites. Another uncertainty lies in the time needed for a benefit in climate change mitigation to occur. A flexible supply chain, integrated with other sources of biomass residues, is needed to develop a cost‐efficient bioenergy sector. This article is categorized under: Bioenergy > Climate and Environment
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".