A Supply Chain Inventory Model for Deteriorating Products with Carbon Emission-Dependent Demand, Advanced Payment, Carbon Tax and Cap Policy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Emissions are a major contributor to climate change. Some nations are now concentrating their efforts on lowering carbon emissions. In many nations, carbon taxes and caps are the main tools that are used to attain this goal. The majority of the inventory retailer-supplier model assumed that the retailer’s order cost should be paid to the supplier at that time when he gets their order. Few suppliers can expect to receive the entire or a portion of the total cost in advance from retailers in this real-life situation, and others will offer prepayment in numerous equal installments. The advance payment offers the customer the lowest price for the order, but it has the largest carbon footprint. The advance payment has a great impact on carbon emissions and production. Therefore, this study looked at a carbon tax and cap supply chain inventory model for deterioration with carbon emission-dependent demand, and Three payment options: Preliminary, cash, and post-payment have been considered. The model was constructed by first assessing the overall cost of supply chain participants with carbon tax regulation. Finally, we illustrate numerical examples of the proposed approach and its outcomes. The implications of adjusting the various parameters on the optimal total cost are also graphically and tabularly discussed in depth. With the help of Mathematica version-12, a sensitivity analysis was also performed. Several management takeaways are also emphasized. These findings are incredibly managerial and enlightening for enterprises seeking profitability while still fulfilling their environmental duties, and this study is extremely useful for any country’s government policy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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