An Association Rule Mining approach to explore the dynamics in plastic recycling business

Cleaner Waste Systems Pub Date : 2024-12-01 DOI:10.1016/j.clwas.2024.100186

Abdullah Al Hasan , Quazi Hamidul Bari , Philipp Lorber , Islam M. Rafizul , Jobaer Ahmed Saju , Eckhard Kraft

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引用次数: 0

Abstract

Understanding plastic recycling practices is vital for policy intervention. Association Rule Mining (ARM) is a powerful tool for extracting insights from complex data, though it hasn't been used for plastic recycling analysis before. This study aimed to apply ARM to data from the questionnaire survey of Recycling Shop (RS) owners in Khulna City to identify patterns of recycling practices and recommend suitable policies. Key findings revealed that RS owners rely on local and external sources for plastic waste (Rules with support 0.061–0.242, confidence 0.667–0.8, lift > 1), recommending stronger recycling supply chain policies. Significant links between sourcing and impurities (Rules with support 0.061–0.091, confidence 0.5–0.667, lift > 1.2) suggest better quality control. Disposal methods of non-recyclables like burning (Rules with support 0.03–0.212, confidence 0.714–1, lift > 1) suggest policies for non-recyclables. Using ARM in this study offers a novel approach to developing efficient, sustainable waste management strategies in Khulna City.

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来源期刊

Cleaner Waste Systems

CiteScore

2.60

自引率

0.00%

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