A conditional value-at-risk approach to studying the sustainable crude oil supply chains evolved due to change in government policies
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it