An agent-based simulation of wheat based ethanol plant location decisions for Saskatchewan
Bibliographic record
Abstract
First generation ethanol production has experienced rapid expansion but is now at a crossroads facing impending industry transformation. While Saskatchewan’s ethanol industry has benefited from demand and policy instruments that have guided substantial growth in recent years, changing policy and market dynamics present new challenges which are compelling the industry to adjust. This thesis examines three factors that are suspected to influence ethanol plant locational decisions. The development of an agent-based simulation model in this thesis will ascertain how transportation networks, market synergies, and subsidization influence location stability for an ethanol plant. The long term interaction of these factors is unknown, therefore do tradeoffs exist between these factors or is it conditional for all to be present? Modeling factors that affect location stability through an agent-based approach creates a dynamic framework to understand how location attributes impact an ethanol agent’s longevity. It was found that location stability is affected by an ethanol agent’s distance to both primary transportation networks as well as product markets. Surprisingly, distance to DDGS (dried distillers grain with solubles) markets, a low value by-product of ethanol production, has a profound effect on location stability. Policy instruments and industry subsidization are considered key ethanol development drivers and the surge in ethanol industry growth brought hopes of rural revitalization. In Saskatchewan, policy was developed to support small ethanol plants, those 25 Mmly (million litres per year) or smaller, aimed at increasing farmer investment and alternative markets for wheat. Measuring the effect of subsidization on location stability was fundamental to understanding how a post subsidized ethanol industry may look. The research found that subsidization of Saskatchewan’s ethanol industry dramatically affected economies of scale and location decisions, which left ethanol agents unable to compete in an increasingly competitive ethanol industry.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".