Integrating E-Procurement and Green Procurement: Designing a Digital Framework for Sustainable Supplier Selection, Environmental Compliance, and Lifecycle Performance Monitoring
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
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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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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