Bioenergy from Mountain Pine Beetle Timber and Forest Residuals: A Cost Analysis
Bibliographic record
Abstract
In light of the large volumes of pine killed in the interior forests of British Columbia (BC) by the mountain pine beetle, many forest sector participants are keen to employ forest biomass as an energy source. To assess the feasibility of a wood biomass‐fired power plant in the BC interior, it is necessary to know both how much physical biomass might be available over the life of a plant and its location as transportation cost is likely to be a major operating cost for any facility. To address these issues, we construct a mathematical programming model of fiber flows in the Quesnel Timber Supply Area of BC over a 25‐year time horizon. The focus of the model is on minimizing the cost of supplying feedstock through space and time. Results indicate that over the life of the project, feedstock costs will more than double, increasing from $54.60/bone‐dry tonnes (BDt) ($0.039/kWh) to $116.14/BDt ($0.083/kWh). En raison de l’important volume de pins dévastés par le dendroctone du pin ponderosa dans les forêts de l’intérieur de la Colombie‐Britannique (BC), de nombreux participants du secteur forestier sont désireux d’utiliser la biomasse forestière comme source d’énergie. Pour évaluer la faisabilité d’une centrale alimentée à la biomasse ligneuse à l’intérieur de la Colombie‐Britannique, il faut connaître la quantité de biomasse disponible pendant la durée de vie de la centrale ainsi que l’emplacement de cette biomasse étant donné que les coûts de transport risquent de représenter des coûts d’exploitation importants pour n’importe quelle installation. Afin d’examiner ces aspects, nous avons élaboré un modèle de programmation mathématique de l’approvisionnement en matière ligneuse dans la zone d’approvisionnement forestier de Quesnel en Colombie‐Britannique, sur un horizon de 25 ans. Le modèle visait principalement à réduire le coût liéà l’approvisionnement en matière première à travers l’espace et le temps. D’après nos résultats, les coûts de la matière première feront plus que doubler au cours de la durée du projet, passant de 54,60 $/tonne anhydre (t.a.) (0,039 $/kWh) à 116,14 $/t.a. (0,083 $/kWh).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".