An AHP-ELECTRE framework to evaluate barriers to green supply chain management in the leather industry
Bibliographic record
Abstract
The leather-processing industry (LPI) is constantly polluting the environment in Bangladesh. As a result, stakeholders are continuously pressurizing managers working in LPI to embrace green leather-processing activities. Thus, the green concept is attracting significant attention from managers in the Bangladeshi LPI. However, the industry is struggling with many barriers to implementing green supply chain management (GSCM). There are many studies regarding barriers to GSCM. However, those studies failed to show the possible pathways to implement GSCM. This study addresses the gap by evaluating barriers to GSCM considering effective pathways to GSCM. In this study, the Analytical Hierarchy Process (AHP) is integrated with Elimination Et Choix Traduisant La Realite (ELECTRE-I) method to identify and prioritize the barriers and to rank the possible pathways to implementing GSCM in the leather industry. To accredit the proposed framework, it is implemented on a leather-processing factory in Bangladesh. A sensitivity analysis is performed to inspect the strength of the outcome of this method. The outcome of this study indicates that the high cost of advanced technology is the most important barrier to implement GSCM while green technology and techniques are the most effective pathways to GSCM. The findings of this research will support researchers and practitioners by giving insights on barriers and possible pathways to implementing GSCM.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".