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Research on Service Design of Garbage Classification Driven by Artificial Intelligence

Author

Listed:
  • Jingsong Zhang

    (School of Design and Architecture, Zhejiang University of Technology, Hangzhou 310014, China)

  • Hai Yang

    (Hangzhou Zhongwei Ganlian Information Technology Co., Ltd., Hangzhou 310023, China)

  • Xinguo Xu

    (School of Design and Architecture, Zhejiang University of Technology, Hangzhou 310014, China)

Abstract

This paper proposes a framework for AI-driven municipal solid waste classification service design and management, with an emphasis on advancing sustainable urban development. This study uses narrative research and case study methods to delve into the benefits of AI technology in waste classification systems. The framework includes intelligent recognition, management strategies, AI-based waste classification technologies, service reforms, and AI-powered customer involvement and education. Our research indicates that AI technology can improve accuracy, efficiency, and cost-effectiveness in waste classification, contributing to environmental sustainability and public health. However, the effectiveness of AI applications in diverse city contexts requires further verification. The framework holds theoretical and practical significance, offering insights for future service designs of waste management and promoting broader goals of sustainable urban development.

Suggested Citation

  • Jingsong Zhang & Hai Yang & Xinguo Xu, 2023. "Research on Service Design of Garbage Classification Driven by Artificial Intelligence," Sustainability, MDPI, vol. 15(23), pages 1-16, November.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:23:p:16454-:d:1291659
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    References listed on IDEAS

    as
    1. Ming-Hui Zhou & Shui-Long Shen & Ye-Shuang Xu & An-Nan Zhou, 2019. "New Policy and Implementation of Municipal Solid Waste Classification in Shanghai, China," IJERPH, MDPI, vol. 16(17), pages 1-10, August.
    2. Chetan A. Jhaveri & Jitendra M. Nenavani, 2020. "Evaluation of eTail Services Quality: AHP Approach," Vision, , vol. 24(3), pages 310-319, September.
    3. David H. Autor, 2015. "Why Are There Still So Many Jobs? The History and Future of Workplace Automation," Journal of Economic Perspectives, American Economic Association, vol. 29(3), pages 3-30, Summer.
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