Sustainable petroleum supply chains created during economic crisis in response to US Government policies
Bibliographic record
Abstract
Coronavirus disease (COVID-19) and the Saudi Arabia-Russia Oil Price War have created economic catastrophe. This crippled the US sustainable petroleum supply chain (SPSC), which is created in response to government policies, as a solution to global warming and achieving energy independency. Government and investors are striving to rescue the SPSC from bankruptcy. This motivated us to investigate creating a robust SPSC. Thus we extended the risk neutral study performed by Ghahremanlou and Kubiak (2020a) for regular economic conditions. To that end, we propose a risk averse approach by applying conditional value-at-risk (CVaR) and developing a two-stage stochastic programming model. We conduct a case study in Nebraska and provide investment decisions that can withstand economic crises. Our results show that for the survival of the SPSC, government must at least consider 2.151 $/gal tax credit for US cellulosic bioethanol blended with gasoline, and push the blend wall to at least 15%.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".