Evidence map›Paper›PMID 37362015›Full record

ReviewEnvironmental chemistry letters2023

Artificial intelligence for waste management in smart cities: a review.

Bingbing Fang, Jiacheng Yu, Zhonghao Chen, Ahmed I Osman, Mohamed Farghali, Ikko Ihara, Essam H Hamza, David W Rooney, Pow-Seng Yap

Abstract readReview
In one paragraph

Review in Environmental chemistry letters, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

24 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
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  10. Effective waste management in service industry: Fuzzy-based modelling approach for strategic decision-making.Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA · 2025
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Bingbing Fang *Department of Civil Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, 215123 China.
Jiacheng Yu *Department of Civil Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, 215123 China.
Zhonghao ChenDepartment of Civil Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, 215123 China.
Ahmed I OsmanSchool of Chemistry and Chemical Engineering, Queen's University Belfast, David Keir Building, Stranmillis Road, Belfast, BT9 5AG Northern Ireland UK.ORCID 0000-0003-2788-7839
Mohamed Farghali *Department of Agricultural Engineering and Socio-Economics, Kobe University, Kobe, 657-8501 Japan.
Ikko IharaDepartment of Agricultural Engineering and Socio-Economics, Kobe University, Kobe, 657-8501 Japan.
Essam H HamzaElectric and Computer Engineering Department, Aircraft Armament (A/CA), Military Technical College, Cairo, Egypt.
David W RooneySchool of Chemistry and Chemical Engineering, Queen's University Belfast, David Keir Building, Stranmillis Road, Belfast, BT9 5AG Northern Ireland UK.
Pow-Seng YapDepartment of Civil Engineering, Xi'an Jiaotong-Liverpool University, Suzhou, 215123 China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The rising amount of waste generated worldwide is inducing issues of pollution, waste management, and recycling, calling for new strategies to improve the waste ecosystem, such as the use of artificial intelligence. Here, we review the application of artificial intelligence in waste-to-energy, smart bins, waste-sorting robots, waste generation models, waste monitoring and tracking, plastic pyrolysis, distinguishing fossil and modern materials, logistics, disposal, illegal dumping, resource recovery, smart cities, process efficiency, cost savings, and improving public health. Using artificial intelligence in waste logistics can reduce transportation distance by up to 36.8%, cost savings by up to 13.35%, and time savings by up to 28.22%. Artificial intelligence allows for identifying and sorting waste with an accuracy ranging from 72.8 to 99.95%. Artificial intelligence combined with chemical analysis improves waste pyrolysis, carbon emission estimation, and energy conversion. We also explain how efficiency can be increased and costs can be reduced by artificial intelligence in waste management systems for smart cities.

Indexed as

Artificial intelligenceChemical analysisCost efficiencyOptimizationWaste management

Identifiers

PMID37362015
PMCPMC10169138

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.