A Pareto investigation on critical barriers in green supply chain management
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
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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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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