Solid waste management through the application of AI and ICT: a systematic literature review
Bibliographic record
Abstract
Solid waste management (SWM) poses a major global challenge with significant environmental implications. The integration of artificial intelligence (AI) and information and communication technology (ICT) has emerged as a promising solution to revolutionise waste management practices. This systematic literature review, which examines the application of AI and ICT in SWM over the past 5 years (2018–2023) and analyses 152 research papers, explores their integration at various stages. In the production phase, AI-driven predictive models have outperformed traditional methods, improving waste forecasting accuracy and facilitating recycling initiatives. In waste collection, AI and ICT enable real-time route optimisation, dynamic scheduling, and sensor-based monitoring, enhancing service delivery while reducing operational costs. Furthermore, AI-powered technologies have revolutionised waste sorting, precisely identifying and segregating recyclables from mixed waste streams, thereby increasing recycling rates and alleviating the burden on landfills. The article also identifies the constraints and challenges associated with these technologies and discusses potential strategies to address them. The main objective of this review is to provide guidance to SWM researchers interested in utilising these technologies within their field. In addition, it aims to enrich the ongoing conversation about sustainable waste management by offering insights into current practices and future trends.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.016 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".