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Journal of Material Cycles and Waste Management

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10-08-2022 | ORIGINAL ARTICLE

Forecasting of municipal solid waste generation in China based on an optimized grey multiple regression model

Authors: Rong Guo, Hong-Mei Liu, Hong-Hao Sun, Dong Wang, Hao Yu, Diana Do Rosario Alves, Lu Yao

Published in: Journal of Material Cycles and Waste Management | Issue 6/2022

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Abstract

The massive generation of municipal solid waste (MSW) has become an essential social problem that not only damages the ecological environment but also affects human health. To effectively manage MSW, it is necessary to forecast waste generation accurately. In this study, a grey multiple non-linear regression (GMNLR) model is developed to achieve the effective forecasting of MSW generation in China. Using grey relational analysis (GRA) to rank the influential factors of MSW generation, it is found that urban road area, residential consumption level, and total population are the main factors. Then, these factors are used as the input variables of the model to forecast MSW generation. Meanwhile, four performance indicators with adjusted \(R^{2}\) (\(R_{adj}^{2}\)), absolute percentage error (APE), mean absolute percentage error (MAPE), and root mean square error (RMSE) are used to evaluate the performance of these models. The results demonstrate that the GMNLR model has a highest prediction accuracy among the four models. According to the forecast results, China's MSW generation will reach 332.41 million tons in 2025, with an annual growth rate of 8.28%. The combined model proposed in this paper is helpful for the government in policies and regulations making for MSW management.

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Title: Forecasting of municipal solid waste generation in China based on an optimized grey multiple regression model
Authors: Rong Guo
Hong-Mei Liu
Hong-Hao Sun
Dong Wang
Hao Yu
Diana Do Rosario Alves
Lu Yao
Publication date: 10-08-2022
Publisher: Springer Japan
Published in: Journal of Material Cycles and Waste Management / Issue 6/2022
Print ISSN: 1438-4957
Electronic ISSN: 1611-8227
DOI: https://doi.org/10.1007/s10163-022-01479-6

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