A conceptual framework for siting biorefineries in the Canadian Prairies
Bibliographic record
Abstract
Abstract Ethanol is increasingly used as a means to reduce gasoline consumption. As a result, it has also attracted analysis of its economic, social, and environmental merit. In order for the ethanol production industry to continue to expand, these issues must be confronted in future development. Although technological development is often relied upon, carefully considered ethanol refinery siting also mitigates some of these concerns. Five alternative siting locations were selected in the western Canadian Prairies. These were evaluated using 12 criteria which represent regional resources, economic conditions, government support, or social indicators. The criteria were weighted to represent the perspectives of two stakeholders. The Preference Ranking Organization Method for Enrichment and Evaluations (PROMETHEE) method was applied to this data, ranking the alternative sites. Several future scenarios were created to analyze the sensitivity of the results to both statistical data and subjective inputs. The rankings proved to be robust, and varied little in the different scenarios. Southern Alberta had an advantage with a high ethanol byproduct demand, education level, and ethanol demand. Southern Manitoba benefitted from the lowest labor and miscellaneous costs, due to higher unemployment. Saskatchewan suffers from low byproduct demand and a decrease in water availability while having a heated economy which increases costs. In addition, Saskatchewan as a whole is currently the leader in ethanol production, resulting in less net demand, reduced access to government incentives, and more local competition. Southern Alberta and Southern Manitoba are the optimal regions for future ethanol biorefinery, where as the Saskatchewan locations are the least attractive. © 2010 Society of Chemical Industry and John Wiley & Sons, Ltd
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.010 | 0.013 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".