Enhancing efficiency in supply chain management: A synergistic approach to production, logistics, and green investments under different carbon emission policies
Bibliographic record
Abstract
The study examines the influence of different carbon policies and the incorporation of green technologies in a two-echelon supply chain, with a focus on carbon emissions generated during transportation, production, and storage phases. The study evaluates three strategies for controlling carbon emissions: setting a maximum limit on total emissions, implementing carbon-taxation, and adopting a cap-and-trade framework. The proposed model assists businesses determine the optimal production and delivery volumes, as well as calculate the most effective investment in green technologies to reduce costs in the context of different carbon emission regulations. Furthermore, this study offers practical guidance for policymakers, highlighting the importance of balancing environmental sustainability with economic growth. Results indicate that companies are more inclined to pursue advanced green technology solutions under a carbon tax policy. The analysis highlights that carbon emissions per unit of production and transportation distance significantly impact overall emissions. The imposed emission cap has a stronger influence than the emission reduction potential of green technologies. The study recommends that governments establish realistic emission limits in cap-and-trade schemes to prevent excessive trading of emission allowances by suppliers.
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.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".