Exploring the Relationship between Supply Chain Management Practices and Environmental Performance in the Freight Forwarding Industry: A Case Study in Malaysia
Bibliographic record
Abstract
Within the bustling freight forwarding industry of Malaysia, the effects of supply chain management on environmental performance stand largely uncharted. This article delves deep into the intricate relationships between logistic practices, reversed logistics, fleet management, and their cumulative environmental ramifications. Spurred by the surge in demand for freight forwarding and its consequential environmental footprint, the research underscores the transformative potential of green supply chain adoption. Not only as a beacon of environmental stewardship but as a formidable competitive edge. As the freight forwarding realm grapples with ambiguity over the true environmental weight of their supply chain operations, our findings unveil pivotal insights. By bridging this knowledge chasm, the research paves the way for fostering sustainable practices that harmoniously marry operational prowess with ecological prudence. Beyond its immediate audience, this article beckons industries far and wide, championing the pivotal role of environmental conscientiousness in modern supply chain dynamics.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".