Digital Technologies in E-Waste Management: A Systematic Literature Review
Bibliographic record
Abstract
The fast growth of electronic waste or e-waste is considered one of the serious economic and environmental concerns in the world. Traditional e-waste management (EMW) is unable to deal with the increased volume and complexity of this waste. This issue makes digitalization to be considered as a new solution. Digital technologies such as Artificial Intelligence (AI), Internet of Things (IoT), Blockchain, Big Data, Cloud Computing, Cyber-Physical Systems (CPS), Edge Computing, Robotics, and Digital Twins have great potential to improve the efficiency, traceability, and sustainability of EWM. This study presents a systematic literature review (SLR) to examine how these technologies are adapted in different e-waste processes and what challenges there are in their implementation. The aim of this review is to present a structured classification of digital technologies based on their function in e-waste processes. By taking a comprehensive approach, this study helps clarify how digital tools are used in e-waste management and suggests directions for future research and policymaking.
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.024 | 0.024 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".