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Quantity Prediction of Construction and Demolition Waste Using Weighted Combined Grey Theory and Autoregressive Integrated Moving Average Model

Author

Listed:
  • Yuan Fang

    (School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China)

  • Xinyi Shi

    (School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China)

  • Yuan Chen

    (School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China)

  • Jialiang He

    (School of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, China)

Abstract

With rapid urban development, the “waste-free city” concept has emerged. Therefore, the accurate prediction of the amount of C&D waste is of great importance. However, many countries and regions, including China, have not yet established C&D waste databases and standard prediction methods. This study proposed a method using a weighted combination of the grey theory model (GM) and the autoregressive integrated moving average (ARIMA) model to predict the quantity of urban C&D waste in the future. Based on a case study in Guangzhou, this study compared the prediction results of three prediction models, namely the GM, the ARIMA, and the proposed weighted combined model of the GM and the ARIMA (GM-ARIMA). The results of this study proved that the proposed combined GM-ARIMA model had a better predictive performance than both the separated models. The mean absolute percentage errors (MAPE) of the GM and ARIMA models were 12.11% and 14.26%, respectively, whereas the proposed GM-ARIMA model had a lower MAPE (8.5%). This study found that the generation of C&D waste in Guangzhou will continue to grow steadily. From 2024 to 2035, the quantity of C&D waste is expected to reach 850 million tons cumulatively, with an annual growth rate of 7.1%.

Suggested Citation

  • Yuan Fang & Xinyi Shi & Yuan Chen & Jialiang He, 2024. "Quantity Prediction of Construction and Demolition Waste Using Weighted Combined Grey Theory and Autoregressive Integrated Moving Average Model," Sustainability, MDPI, vol. 16(12), pages 1-20, June.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:12:p:5255-:d:1418805
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    References listed on IDEAS

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    1. Yuan, Chaoqing & Liu, Sifeng & Fang, Zhigeng, 2016. "Comparison of China's primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM(1,1) model," Energy, Elsevier, vol. 100(C), pages 384-390.
    2. Ting Wang & Kaiyi Li & Defu Liu & Yang Yang & Dong Wu, 2022. "Estimating the Carbon Emission of Construction Waste Recycling Using Grey Model and Life Cycle Assessment: A Case Study of Shanghai," IJERPH, MDPI, vol. 19(14), pages 1-16, July.
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    Cited by:

    1. Mona Salah & Emad Elbeltagi & Meshal Almoshaogeh & Fawaz Alharbi & Mohamed T. Elnabwy, 2025. "Identifying Root Causes and Sustainable Solutions for Reducing Construction Waste Using Social Network Analysis," Sustainability, MDPI, vol. 17(17), pages 1-27, August.

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