Imperial College London
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Professor Sue Grimes

Faculty of Engineering, Department of Civil and Environmental Engineering

RAEng Chair in Waste & Resource Management
 
 
 
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Contact

 

+44 (0)20 7594 5966s.grimes

 
 
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Location

 

233Skempton BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Velis:2023:10.1016/j.scitotenv.2023.161913,
author = {Velis, CA and Wilson, DC and Gavish, Y and Grimes, SM and Whiteman, A},
doi = {10.1016/j.scitotenv.2023.161913},
journal = {Science of the Total Environment},
title = {Socio-economic development drives solid waste management performance in cities: a global analysis using machine learning},
url = {http://dx.doi.org/10.1016/j.scitotenv.2023.161913},
volume = {872},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Mismanaged municipal solid waste (MSW), the major source of plastics pollution and a key contributor to climate forcing, in Global South cities poses public health and environmental problems. This study analyses the first consistent and quality assured dataset available for cities distributed worldwide, featuring a comprehensive set of solid waste management performance indicators (Wasteaware Cities Benchmark Indicators – WABI). Machine learning (multivariate random forest) and univariate non-linear regression are applied, identifying best-fit converging models for a broad range of explanatory socioeconomic variables. These proxies describe in a variety of ways generic levels of progress, such as Gross Domestic Product – Purchasing Power per capita, Social Progress Index (SPI) and Corruption Perceptions Index. Specifically, the research tests and quantitatively confirms a long-standing, yet unverified, hypothesis: that variability in cities' performance on MSW can be accounted for by socioeconomic development indices. The results provide a baseline for measuring progress as cities report MSW performance for the sustainable development goal SDG11.6.1 indicator: median rates of controlled recovery and disposal are approximately at 45 % for cities in low-income countries, 75 % in lower-middle, and 100 % for both upper-middle and high-income. Casting light on aspects beyond the SDG metric, on the quality of MSW-related services, show that improvements in service quality often lag improvements in service coverage. Overall, the findings suggest that progress in collection coverage, and controlled recovery and disposal has already taken place in low- and middle-income cities. However, if cities aspire to perform better on MSW management than would have been anticipated by the average socioeconomic development in their country, they should identify ways to overcome systemic underlying failures associated with that socioeconomic level. Most alarmingly, ‘bus
AU  - Velis,CA
AU  - Wilson,DC
AU  - Gavish,Y
AU  - Grimes,SM
AU  - Whiteman,A
DO  - 10.1016/j.scitotenv.2023.161913
PY  - 2023///
SN  - 0048-9697
TI  - Socio-economic development drives solid waste management performance in cities: a global analysis using machine learning
T2  - Science of the Total Environment
UR  - http://dx.doi.org/10.1016/j.scitotenv.2023.161913
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000948124700001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR  - https://www.sciencedirect.com/science/article/pii/S0048969723005284
UR  - http://hdl.handle.net/10044/1/107866
VL  - 872
ER  -
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