A systematic literature review on 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
Green supply chain management has emerged as a trending topic of discussion for organisations thriving for enhanced competitive advantages, increased customer satisfaction, improved brand image, and of course minimum adverse impacts on the environment. The primary objective of this research is to perform literature analysis on the green supply chain barriers and propose a classification framework to prioritise the most impactful ones. Six different categories of classification are proposed to analyse the barriers namely multiple Ms (eight Ms), supply chain processes (design, purchasing, production, testing and inspection, packaging, transportation, warehousing, after sales service, and recycling), stakeholders (employees, customers, suppliers, government/regulatory, and non-government organisations), sustainability areas (societal, economic, environmental, technical), organisational hierarchy (top management/executive level, middle management/departmental level, worker/supervisory level) and others (psychological, technological, knowledge, and strategical). Classification of barriers using the proposed categories will assist decision makers in prioritising actions and channelling resources in the right direction for achieving sustainability objectives for green supply chain management.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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