Trade-off model for carbon market sensitive green supply chain network design
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
Regulatory frameworks to reduce greenhouse gases (GHGs) emissions are currently being developed in many countries around the globe. As a consequence, companies need to consider the different available options and mechanisms to meet their legal obligation. This paper introduces a mathematical model and a solution methodology for the ‘carbon market sensitive green supply chain network design’ (CMS/GSCND) problem. Specifically, carbon trading are integrated within the supply chain (SC) network design phase and the problem formulated as multiobjective mixed integer linear optimisation programme to decide on the SC configuration. The solution methodology allows the economic evaluation of different strategic decisions such as supplier and subcontractor selection, product allocation, capacity utilisation, transportation configuration and their impact in term of carbon footprint. It also provides decision makers with the ability to understand the trade-offs between total logistics costs and GHGs reduction. Model validation and extended analysis are demonstrated via a numerical study.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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