A cost analysis of bioenergy-generated ash disposal options in Canada
Bibliographic record
Abstract
The burning of wood for bioenergy produces significant amounts of ash residue that requires disposal. We constructed a cost model to evaluate the unit costs of three ash disposal methods in the Canadian context: landfills owned and operated by the bioenergy facility, municipal landfills, and forest site application. The model accounts for costs related to the pretreatment, transportation, and disposal of ash at a landfill or forest site. Model parameter values were assigned appropriate distributions (based on published literature and industry surveys), and Monte Carlo simulations were employed to produce a range of model outputs for each disposal option. Results indicate that existing landfills (if available for ash disposal) are likely the most cost-effective option (median value of $77 per tonne), although applying ash to a forest site is only ∼15%–20% more costly (median value of $92 per tonne). Indeed, the unit cost estimates across disposal options have considerable overlap. This suggests that close examination of firm-specific circumstances is highly warranted when choosing a disposal approach, even in the absence of accounting for potential environmental benefits associated with forest site disposal of ash.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 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".