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Solid waste generation prediction model framework using socioeconomic and demographic factors with real-time MSW collection data

Laurie Fontaine, Robert Legros and Jean‐Marc Frayret

Article (2024)

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Abstract

This article proposes a framework for developing predictive models of end-of-life product flows, highlighting the importance of conducting thorough analyses before developing waste management and end-of-life product flow strategies. The framework emphasizes the importance of recognizing the nature and quality of the available data and finding a balance between model development time and detail requirements. It is designed to adapt to source material heterogeneity and address varying data availability scenarios, such as the presence or absence of radio frequency identification chips. A case study for the city of Gatineau is presented, showcasing the framework’s application through agent-based simulation models in a geographic information systems environment. The study focuses on creating models of municipal solid waste generation based on socioeconomic and demographic factors and collection data to accurately predict the quantity and quality of waste streams, enabling municipalities to assess the environmental impact of their waste management strategies.

Uncontrolled Keywords

Municipal solid waste; agent-based simulation models; waste prediction; GIS environment; household behaviours; end-of-life product flows; socioeconomic and demographic factors

Department: Department of Chemical Engineering
Department of Mathematics and Industrial Engineering
PolyPublie URL: https://publications.polymtl.ca/57589/
Journal Title: Waste Management & Research
Publisher: SAGE Publishing
DOI: 10.1177/0734242x241231414
Official URL: https://doi.org/10.1177/0734242x241231414
Date Deposited: 25 Mar 2024 15:25
Last Modified: 02 May 2024 00:12
Cite in APA 7: Fontaine, L., Legros, R., & Frayret, J.‐M. (2024). Solid waste generation prediction model framework using socioeconomic and demographic factors with real-time MSW collection data. Waste Management & Research, 15 pages. https://doi.org/10.1177/0734242x241231414

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