Inventory Models Under Carbon Tax and Cap-and-Trade Policies: A Comparative Analysis of Decentralized and Centralized Approaches
Bibliographic record
Abstract
Due to tougher carbon restrictions and regulations, businesses have been researching approaches to decrease the amount of carbon emissions throughout the inventory supply process and achieve sustainable development.The two most common approaches are (i) decentralized, which involves implementing a carbon tax or cost for emitting carbon, and (ii) centralized, which includes introducing an emissions trading (cap-and-trade) mechanism.Within this research, we optimize a two-stage supply management system under FPH(finite planning horizon) while taking into consideration these two policies.Using a linear time and inventory-dependent demand model, we investigated various techniques within a specific time frame.We created and solved two distinct MINLP (Mixed Integer Non-Linear Programming) approaches for each carbon strategy.These models can assist businesses/firms in determining the minimum overall cost, optimal order quantity, optimal replenishment time, and replenishment cycles.Using mathematical tools, our sensitivity evaluations indicate that organizations can reduce overall projected emissions and costs by making parameter variations under both carbon regimes.We additionally showed that while both approaches optimize the overall supply chain cost, the order quantity and total emissions remain constant.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".