Socio-economic development drives solid waste management performance in cities: A global analysis using machine learning

Velis, CA orcid.org/0000-0002-1906-726X, Wilson, DC, Gavish, Y et al. (2 more authors) (2023) Socio-economic development drives solid waste management performance in cities: A global analysis using machine learning. Science of the Total Environment, 872. 161913. ISSN 0048-9697

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Item Type: Article
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© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

Keywords: Municipal solid waste; Development indices; Sustainable development goals; Circular economy; Random forest
Dates:
  • Published: 10 May 2023
  • Published (online): 11 February 2023
  • Accepted: 26 January 2023
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Civil Engineering (Leeds)
Funding Information:
Funder
Grant number
GIZ Deutsche Gesellschaft Internationale
Not Known
Depositing User: Symplectic Publications
Date Deposited: 16 Mar 2023 10:34
Last Modified: 16 Mar 2023 10:34
Status: Published
Publisher: Elsevier
Identification Number: 10.1016/j.scitotenv.2023.161913
Open Archives Initiative ID (OAI ID):

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