Investigating barriers in green supply chain management using the FANP method - case study: Iranian industrial companies
Bibliographic record
Abstract
Environmental management has become a standard component among societies with an emphasis on environmental protection, leading to the creation of a new concept of an environment-friendly business called 'green supply chain management - GSCM'. This study aims to identify the significant barriers to establishing green supply chains in developing countries such as Iran using the multi-criteria decision-making (MCDM) method. Investigating such industries as petrochemicals, detergents, automotive, etc., the researchers will identify the critical components of GSC barriers, using phase network analysis, and then they will be ranked in the order of importance. The results of this study yield that the main categories of barriers are knowledge-related, product and design-related, modern technology and encouragement-related. This study is the first study conducted in various industrial sectors in Iran and stands out because the barriers are ranked, a strong point that helps policy planners, managers, and decision-makers make GSCM practices a success.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 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) | 0.002 | 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".