Population growth and the acceleration of urbanization have led to a sharp increase in municipal solid waste production, and researchers have sought to use advanced technology to solve this problem. Machine learning (ML) algorithms are good at modeling complex nonlinear processes and have been gradually adopted to promote municipal solid waste management (MSWM) and help the sustainable development of the environment in the past few years. In this study, more than 200 publications published over the last two decades (2000–2020) were reviewed and analyzed. This paper summarizes the application of ML algorithms in the whole process of MSWM, from waste generation to collection and transportation, to final disposal. Through this comprehensive review, the gaps and future directions of ML application in MSWM are discussed, providing theoretical and practical guidance for follow-up related research.

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This study uses the statistical and meta-analysis methods to comprehensively review 324 LCZ papers during 2012-2020, 202 of which are categorized as LCZ mapping papers. We present a bibliometric analysis of LCZ mapping papers from literature statistics, research topics, city distribution, institutions and cooperation, and research projects.

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