Sustainable supply chain initiatives in reducing greenhouse gas emission within the road freight industry
Bibliographic record
Abstract
Background: Global supply chains have evolved from a traditional simple supply chain to one that is filled with complexities and uncertainties. The increase in road freight transportation has resulted in an escalation in the emission of greenhouse gases (GHG) into the atmosphere, impacting climate change.Objectives: The purpose of this article was to investigate the implementation of sustainable supply chain initiatives in reducing GHG emissions within the South African road freight transport industry.Method: This research utilised a case study approach and used primary data, obtained through self-administered questionnaires, to explore the adopting sustainable transport management practices.Results: The main drivers for implementing sustainable initiatives are pressure from consumer and brand protection, pressure from top management, and cost saving and revenue. Eco-driving, eco-routing and increasing vehicle carrying capacity are the most adopted sustainable supply chain initiatives implemented. The key benefits resulting from implementing sustainable initiatives were operational cost savings, improved competitive advantage and enhanced supplier relationships. Also, the lack of government support, lack of understanding of the cost and insufficient manpower were identified as the foremost challenges associated with the implementation of these sustainable initiatives.Conclusion: The results reveal that organisations are placed under enormous pressure to implement sustainable practices. This study identifies various sustainable initiatives to reduce GHG emissions and addresses the associated benefits and challenges when implementing these initiatives.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".