Economic viability-driven biorefinery site selection for cellulosic biofuel production in Western Canada
Bibliographic record
Abstract
There is a substantial body of current research on biofuel feedstock assessment and biorefinery site identification. Most of the literature in this field focuses on selecting suitable biorefineries by minimising costs, particularly transportation costs, rather than maximising economic profits. The latest studies on site location have started to introduce financial feasibility as a criterion for site selection. However, there remains a significant gap in the literature regarding the rapidly evolving sector of advanced biofuels like cellulosic biofuels. Addressing this gap, this study innovatively applies a Net Present Value (NPV) framework and a mathematical programming approach, incorporating economic viability, investment support, and carbon credits into the decision-making process for site selection in Western Western Canada. This approach offers insightful revelations regarding economically viable biomass supply and optimal site identification, highlighting the extent of governmental support essential to fostering the growth of the cellulosic biofuel industry. Key findings include: (1) An economic viability-based assessment indicated a substantially lower feedstock supply, about 20 % compared to evaluations based solely on feasible travelling distance; (2) Governmental intervention emerged as a pivotal element influencing the economic viability of cellulosic biofuel refineries; (3) Varied parameters, including production capacity, capital investment subsidies, and maximum transport distance, have significant impacts on economic feasibility and site selection outcomes. The results of this research add to the understanding of current cellulosic biofuel developments. They offer valuable insights into predicting feedstock supply, choosing the best locations for biofuel plants, and designing effective policies. • Novel financial feasibility approach for biorefinery site location identification. • Economic feasibility of biomass supply lower than distance-based estimates. • Government intervention key to biorefinery economic viability. • Varying impacts of production capacity, subsidies and maximum distance analyzed. • Insights offered for biofuel feedstock forecasting and policy design.
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".