A Pareto investigation on critical barriers in green supply chain management
Bibliographic record
Abstract
More and more organizations are involved in green supply chain practices to sustain business market competition, achieve customer loyalty, improve brand image, and minimize negative environmental impacts. Examples of these practices are green design, green purchasing, green manufacturing, green packaging, green logistics, and green marketing. In this paper, we investigate barriers in green supply chain management and identify the ‘critical’ or ‘vital’ using Pareto analysis. The data for green supply chain barriers is extracted using literature review and expert surveys. Pareto analysis is conducted on the two data sources to identify the priority barriers and the common barriers are determined as ‘vital few’. The results of our study yield ‘difficulty in transforming positive environmental attitudes into action’ and ‘lack of awareness about reverse logistics adoption’ as the top priority barriers followed by ‘high cost of hazardous waste disposal’, ‘perception of “out of responsibility” zone’, ‘lack of R&D capability on ESER (Environmental and Sustainability Education Research)’, and ‘lack of corporate social responsibility’. These barriers are related to awareness, cost, commitment and resources. Interested organizations should therefore put focus on these barriers to make green supply chain practices successful.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".