Weather rebate contracts for different risk attitudes of supply chain members
Bibliographic record
Abstract
Firms that deal with weather-sensitive products are often exposed to weather risk. How a weather rebate contract can be implemented to improve the performance of a supplier-retailer supply chain is investigated. The supplier offers a weather rebate to the retailer to compensate for the loss of sales due to weather risk. Depending on certain weather conditions, the retailer receives the rebate if it orders beyond a predefined ordering quantity. Depending on its risk attitude, the supplier uses weather derivatives to minimize the downside risk. The performance of weather rebate contracts is analyzed for three cases: risk-neutral supplier and risk-averse retailer, risk-averse supplier and risk-neutral retailer, and risk-averse supplier and risk-averse retailer. Conditional Value at Risk (CVaR) is used as the risk measure. The supply chain coordination under weather risk is investigated, and the specific conditions of a weather rebate contract leading to a Pareto-improving solution for both parties are obtained. To the best of the authors’ knowledge, this is the first study that investigates the weather rebate contract incorporating the risk attitude of the firms using a cooling degree days (CDD)/heating degree days (HDD)-based rebate structure. A comparative analysis between the weather rebate and wholesale price contracts is carried out based on the actual temperature and demand data. The results show that the weather rebate contract outperforms the regular wholesale price contract in all three cases. The study also demonstrates how to use weather derivatives to improve the performance of a supply chain dealing with weather-sensitive products.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".