Research on Service Design of Garbage Classification Driven by Artificial Intelligence

Jingsong Zhang, Hai Yang () and Xinguo Xu
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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

Sustainability, 2023, vol. 15, issue 23, 1-16

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.

Keywords: AI; municipal solid waste classification; garbage classification; service design; intelligent recognition; management strategy (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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