A Comprehensive Literature Review of Green Supply Chain Management
Bibliographic record
Abstract
In a competitive market, organizations expand their supply chain on a global scale.Pressure from customers, stakeholders, legislation and environmental organizations have pushed companies to be more considerate of the environmental impacts of their supply chain.This development has put focus on sustainability within supply chains, leading to the rise of Green Supply Chain Management (GSCM).The purpose of this research is to create a conceptual model to present the vastly varied literature within the area of GSCM in a structured way, in order to promote environmental and in turn, overall supply chain performance.The research methodology includes a literature review using 125 peer-reviewed journal articles from 2013 to 2016 published in 19 journals.Out of the 125, 10 journal articles were selected based on their focus in regard to the subject.The articles were chosen to attain a vantage point in view of critical factors within environmentally sustainable supply chains with a focus on optimizing performance.This paper contributes to theory by presenting a conceptual model for optimizing performance in green supply chains.Drivers, which promote Green Supply Chain (GSC) are classified as re-active and pro-active, and the main methods used for optimizing performance are concluded to be collaboration, metrics to monitor performance and practices such as green purchasing, ecodesign, reverse logistics and legislation.The review may be of use to both academics and companies as it outlines proven ways to implement green supply chain with high performance.
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 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".