A state-of-art review on green supply chain management practices
Bibliographic record
Abstract
There is an emergent need for corporates to incorporate environment friendly practices into supply chain management. Green Supply Chain Management (GSCM) practices are the processes, which reduce the environment hazards from the supply chain. These practices help industries provide the competitive advantage from their core competitors by reducing environmental hazards. The literature gives an idea about a number of evidences of green supply-chain management practices, which are not developed. The study discusses the rules and the regulations made by the environmental authorities to meet social and environmental concerns to help in both developments of economies as well as business units suffering from insufficient GSCM practices. This research helps academicians, practitioners and researchers in incorporating and understanding GSCM practices in a broad manner. The research on the GSCM practices is at a very nascent stage in Indian manufacturing environment despite the fact that sustainability is the foremost worry of Indian industries. Using the rich literature, an attempt is made to bring out the need for GSCM practices and environmental sustainability of organizations. Finally, the findings and interpretations are summarized, and the main research issues and opportunities are highlighted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.006 |
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; both teacher heads agree on what is shown here.
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".