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

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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Acknowledgements

We are grateful to the editors and anonymous reviewers for their constructive comments on this study. This work was supported by the Doctoral Research Fund of Nantong University (Grant No. 2018-33), the graduate research innovation project of Jiangsu Province (Grant No. KYCX20_2823).

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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by RG, HML and HHS. The first draft of the manuscript was written by RG and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Conceptualization: HML; Methodology: RG, DW; Formal analysis and investigation: HHS, DW, DDRA; Writing—original draft preparation: RG; Writing—review and editing: HML, LY; Funding acquisition: HML, HY; Resources: HHS; Supervision: LY.

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Correspondence to Lu Yao.

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Guo, R., Liu, HM., Sun, HH. et al. Forecasting of municipal solid waste generation in China based on an optimized grey multiple regression model. J Mater Cycles Waste Manag (2022). https://doi.org/10.1007/s10163-022-01479-6

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Keywords

  • Municipal solid waste
  • Grey multiple non-linear regression model
  • Forecasting
  • Grey relational analysis