Solid waste management through the application of AI and ICT: a systematic literature review
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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