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A Transformer-Based Machine Learning Approach for Sustainable E-Waste Management: A Comparative Policy Analysis between the Swiss and Canadian Systems

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  • Saidia Ali

    (Ted Rogers School of Information Technology Management, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada)

  • Farid Shirazi

    (Ted Rogers School of Information Technology Management, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada)

Abstract

Efficient e-waste management is crucial to successfully achieve sustainable urban growth universally. The upsurge in e-waste has resulted in countries, including Canada, adopting a wide array of policies associated with sustainable management. In this study, we conducted a mixed-method analysis of Canadian e-waste management policies to showcase the opportunities and limitations of the current system. We examine and compare the effectiveness of electronic waste management strategies in Canada and Switzerland using a comparative policy evaluation and by quantitatively measuring their efficiencies through two efficiency methods, namely a transformer-based, bidirectional, unsupervised machine learning model for natural language processing (NLP) and data envelopment analysis (DEA). Switzerland is utilized as a comparison case due to its robust legal framework that has been in place for proper management e-waste in order to enhance Canada’s electronic waste management system. The policy considerations presented in this study are directed toward urban planners, policy makers, and corporate strategists. These involve a mix of political, economic, social, and environmental planning tools concerning how to communicate and foster competent e-waste management in these countries. This is the first study to incorporate DEA and NLP-based BERT analysis to identify the most efficient policy deployment concerning e-waste management.

Suggested Citation

  • Saidia Ali & Farid Shirazi, 2022. "A Transformer-Based Machine Learning Approach for Sustainable E-Waste Management: A Comparative Policy Analysis between the Swiss and Canadian Systems," Sustainability, MDPI, vol. 14(20), pages 1-22, October.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:13220-:d:942517
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    Cited by:

    1. Despotovic, Miroslav & Glatschke, Matthias, 2024. "Challenges and Opportunities of Artificial Intelligence and Machine Learning in Circular Economy," SocArXiv 6qmhf, Center for Open Science.
    2. Hilal Shams & Altaf Hossain Molla & Mohd Nizam Ab Rahman & Hawa Hishamuddin & Zambri Harun & Nallapaneni Manoj Kumar, 2023. "Exploring Industry-Specific Research Themes on E-Waste: A Literature Review," Sustainability, MDPI, vol. 15(16), pages 1-22, August.

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