Integrating E-Procurement and Green Procurement: Designing a Digital Framework for Sustainable Supplier Selection, Environmental Compliance, and Lifecycle Performance Monitoring
Bibliographic record
Abstract
The paper reviews how e-procurement merges with green procurement to develop a procurement that seeks to advance supplier identification, environmental standards, and life cycle assessment. E-procurement entails purchase activities that are planned, executed, and controlled using web-based tools; the system has emerged to be vital in improving the performance of the procurement function. Several systematically implemented approaches of green procurement aimed at buying environmentally friendly products and services include suppliers with an eco-labeling system. The study also reveals that these two systems need to be linked to achieve global sustainability goals since the existing applications do not address compliance and sustainability measures in real-time. Therefore, this study seeks to close this gap by proposing and validating a digital model that combines the above elements to monitor suppliers’ environmental performance continually and ensure legal compliance with their lifetime. Thus, the study helps to advance the research in sustainable digital procurement transformation and imparts knowledge of applying modern technologies like blockchain, big data, and AI to manage supply chains more efficiently.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".