Renewable Energy from Forest Residues—How Greenhouse Gas Emission Offsets Can Make Fossil Fuel Substitution More Attractive
Bibliographic record
Abstract
Burning forest biomass from renewable sources has been suggested as a viable strategy to help offset greenhouse gas (GHG) emissions in the energy generation sector. Energy facilities can, in principle, be retrofitted to produce a portion of their energy from biomass. However, supply uncertainties affect costs, and are an important impediment to widespread and sustained adoption of this strategy. In this paper, we describe a general approach to assess the cost of offsetting GHG emissions at co-generation facilities by replacing two common fossil fuels, coal and natural gas, with forest harvest residue biomass for heat and electricity production. We apply the approach to a Canadian case study that identifies the price of GHG offsets that could make the use of forest residue biomass feedstock attractive. Biomass supply costs were based on a geographical assessment of industrial harvest operations in Canadian forests, biomass extraction and transportation costs, and included representation of basic ecological sustainability and technical accessibility constraints. Sensitivity analyses suggest that biomass extraction costs have the largest impact on the costs of GHG emission offsets, followed by fossil fuel prices. In the context of other evaluations of mitigation strategies in the energy generation sector, such as afforestation or industrial carbon capture, this analysis suggests that the substitution of fossil fuels by forest residue biomass could be a viable and reasonably substantive short-term alternative under appropriate GHG emission pricing schemes.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".