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Prediction of WEEE Recycling in China Based on an Improved Grey Prediction Model

Author

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  • Qiang Xiao

    (School of Economics and Management, Lanzhou Jiaotong University, Lanzhou 730070, China)

  • Hongshuang Wang

    (School of Economics and Management, Lanzhou Jiaotong University, Lanzhou 730070, China)

Abstract

Accurate waste electrical and electronic equipment (WEEE) recycling forecast is an essential reference for optimizing e-waste industry layout and division of labor policies, conducive to better guiding enterprises’ recycling activities and improving the efficiency of WEEE recycling in China. The nonlinear grey Bernoulli model (NGBM (1,1)) was constructed by analyzing the recycling data characteristics of WEEE from 2012 to 2020, and a particle swarm optimization (PSO) algorithm was introduced to solve the model parameters and optimize the background value coefficients. The prediction results were compared with other grey prediction models to verify the effectiveness of the improved NGBM (1,1) model for WEEE recycling prediction in China and the applicability of the PSO algorithm for improving the prediction accuracy of each grey model. Statistical data were used to forecast the WEEE recycling volume in China from 2021 to 2023, and the results show that the value of WEEE recycling will continue to grow at 9%. The value of recycling will reach 16 billion yuan by 2023, while the quantity of WEEE recycling will see a slight decline. Based on the calculation results, the WEEE recycling industry development trend is predicted to guide the promotion of the WEEE industry recycling program and the national circular economy program.

Suggested Citation

  • Qiang Xiao & Hongshuang Wang, 2022. "Prediction of WEEE Recycling in China Based on an Improved Grey Prediction Model," Sustainability, MDPI, vol. 14(11), pages 1-14, June.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6789-:d:829926
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    References listed on IDEAS

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    1. Zhao, Huiru & Guo, Sen, 2016. "An optimized grey model for annual power load forecasting," Energy, Elsevier, vol. 107(C), pages 272-286.
    2. Rong Wang & Yi Deng & Shuyuan Li & Keli Yu & Yi Liu & Min Shang & Jiqin Wang & Jiancheng Shu & Zhi Sun & Mengjun Chen & Qian Liang, 2021. "Waste Electrical and Electronic Equipment Reutilization in China," Sustainability, MDPI, vol. 13(20), pages 1-9, October.
    3. Huihui Liu & Xiaolin Wu & Desheng Dou & Xu Tang & G. Keong Leong, 2018. "Determining Recycling Fees and Subsidies in China’s WEEE Disposal Fund with Formal and Informal Sectors," Sustainability, MDPI, vol. 10(9), pages 1-14, August.
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    Cited by:

    1. Fangzhong Qi & Leilei Zhang & Kexiang Zhuo & Xiuyan Ma, 2022. "Early Warning for Manufacturing Supply Chain Resilience Based on Improved Grey Prediction Model," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
    2. Hilal Shams & Altaf Hossain Molla & Mohd Nizam Ab Rahman & Hawa Hishamuddin & Zambri Harun & Nallapaneni Manoj Kumar, 2023. "Exploring Industry-Specific Research Themes on E-Waste: A Literature Review," Sustainability, MDPI, vol. 15(16), pages 1-22, August.

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