A conditional value-at-risk approach to studying the sustainable crude oil supply chains evolved due to change in government policies
Bibliographic record
Abstract
Recently US oil and bioethanol industries have faced drastic economic damage due to the 2020 Saudi Arabia-Russia oil price war and coronavirus disease (COVID-19), resulting in many bankruptcies. Government policies have brought these two main industries together to ensure sustainable crude oil supply chains, to combat global warming and energy insecurity. This motivated us to extend the study of Ghahremanlou and Kubiak (2021a) to protect the current and new SCOSCs against financial risks during economic crises by providing insights for the government and the investors, working to rescue the industries. We employ conditional value-at-risk, and develop a two-stage stochastic programming model. We perform a case study of the State of Nebraska by carrying out a computational experiment with 10,710 different policy scenarios. We recommend robust strategic investment decisions to businesses during policy changes within economic crises. We also identify resilient strategic investment decisions.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".