Impact of government policies on Sustainable Petroleum Supply Chain (SPSC): A case study – Part I (Models)
Bibliographic record
Abstract
Environmental concerns and energy security have led governments to establish legislation to convert a Conventional Petroleum Supply Chain (CPSC) to a Sustainable Petroleum Supply Chain (SPSC). The United States (US), one of the biggest oil consumers in the world, has created regulations to manage ethanol production and consumption over the last half century. Although these regulations have created new opportunities, they have also added new burdens to the obligated parties. It is thus key for the government, the obligated parties, and related businesses to study the impact of the policies on the SPSC. We develop a two-stage stochastic programming model, General Model (GM), which incorporates Renewable Fuel Standard 2 (RFS2), Tax Credits, Tariffs, and Blend Wall (BW) to study the policy impact on the SPSC using cellulosic ethanol. The model, as with any other general model available in the literature, makes it highly impractical to study the policy impact due to the model’s computational complexity. We use the GM to derive a Lean Model (LM) to study the impact by running computational experiments more efficiently and consequently by arriving much faster at robust managerial insights. We present a case study of the policy impact on the SPSC in the State of Nebraska using the LM in the accompanying part II (Ghahremanlou and Kubiak, 2020).
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".