IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v14y2022i11p6789-d829926.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/14/11/6789/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/14/11/6789/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Zhao, Huiru & Guo, Sen, 2016. "An optimized grey model for annual power load forecasting," Energy, Elsevier, vol. 107(C), pages 272-286.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Alexandra L’Heureux & Katarina Grolinger & Miriam A. M. Capretz, 2022. "Transformer-Based Model for Electrical Load Forecasting," Energies, MDPI, vol. 15(14), pages 1-23, July.
    3. Kong, Xiangyu & Li, Chuang & Wang, Chengshan & Zhang, Yusen & Zhang, Jian, 2020. "Short-term electrical load forecasting based on error correction using dynamic mode decomposition," Applied Energy, Elsevier, vol. 261(C).
    4. Sen Guo & Haoran Zhao & Huiru Zhao, 2017. "A New Hybrid Wind Power Forecaster Using the Beveridge-Nelson Decomposition Method and a Relevance Vector Machine Optimized by the Ant Lion Optimizer," Energies, MDPI, vol. 10(7), pages 1-20, July.
    5. Wang, Qiang & Li, Shuyu & Li, Rongrong & Ma, Minglu, 2018. "Forecasting U.S. shale gas monthly production using a hybrid ARIMA and metabolic nonlinear grey model," Energy, Elsevier, vol. 160(C), pages 378-387.
    6. Horațiu Vermeșan & Ancuța-Elena Tiuc & Marius Purcar, 2019. "Advanced Recovery Techniques for Waste Materials from IT and Telecommunication Equipment Printed Circuit Boards," Sustainability, MDPI, vol. 12(1), pages 1-23, December.
    7. Song, Wanqing & Cattani, Carlo & Chi, Chi-Hung, 2020. "Multifractional Brownian motion and quantum-behaved particle swarm optimization for short term power load forecasting: An integrated approach," Energy, Elsevier, vol. 194(C).
    8. Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.
    9. Ding, Song, 2018. "A novel self-adapting intelligent grey model for forecasting China's natural-gas demand," Energy, Elsevier, vol. 162(C), pages 393-407.
    10. Wang, Qiang & Li, Shuyu & Li, Rongrong, 2018. "China's dependency on foreign oil will exceed 80% by 2030: Developing a novel NMGM-ARIMA to forecast China's foreign oil dependence from two dimensions," Energy, Elsevier, vol. 163(C), pages 151-167.
    11. Yi-Chung Hu, 2017. "Nonadditive Grey Prediction Using Functional-Link Net for Energy Demand Forecasting," Sustainability, MDPI, vol. 9(7), pages 1-14, July.
    12. Moral-Carcedo, Julián & Pérez-García, Julián, 2017. "Integrating long-term economic scenarios into peak load forecasting: An application to Spain," Energy, Elsevier, vol. 140(P1), pages 682-695.
    13. Wang, Lin & Hu, Huanling & Ai, Xue-Yi & Liu, Hua, 2018. "Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm," Energy, Elsevier, vol. 153(C), pages 801-815.
    14. Wang, Zheng-Xin & Wang, Zhi-Wei & Li, Qin, 2020. "Forecasting the industrial solar energy consumption using a novel seasonal GM(1,1) model with dynamic seasonal adjustment factors," Energy, Elsevier, vol. 200(C).
    15. Ksenija Denčić-Mihajlov & Mladen Krstić & Dejan Spasić, 2020. "Sensitivity Analysis as a Tool in Environmental Policy for Sustainability: The Case of Waste Recycling Projects in the Republic of Serbia," Sustainability, MDPI, vol. 12(19), pages 1-19, September.
    16. Xiong, Xin & Hu, Xi & Guo, Huan, 2021. "A hybrid optimized grey seasonal variation index model improved by whale optimization algorithm for forecasting the residential electricity consumption," Energy, Elsevier, vol. 234(C).
    17. Tingting Liu & Qian Zhang & Zichen Zheng & Shangyun Wu & Zhixiong Weng, 2022. "Stakeholder Analysis of the Waste Electrical and Electronic Equipment Internet Recycling Industry," IJERPH, MDPI, vol. 19(16), pages 1-13, August.
    18. Wang, Qiang & Song, Xiaoxin, 2019. "Forecasting China's oil consumption: A comparison of novel nonlinear-dynamic grey model (GM), linear GM, nonlinear GM and metabolism GM," Energy, Elsevier, vol. 183(C), pages 160-171.
    19. de Oliveira Ventura, Lucas & Melo, Joel D. & Padilha-Feltrin, Antonio & Fernández-Gutiérrez, Juan Pablo & Sánchez Zuleta, Carmen C. & Piedrahita Escobar, Carlos César, 2020. "A new way for comparing solutions to non-technical electricity losses in South America," Utilities Policy, Elsevier, vol. 67(C).
    20. Ang Li & Bo Li & Xia Liu & Ying Zhang & Haiyan Zhang & Xuyang Lei & Suxia Hou & Bin Lu, 2022. "Characteristics and Dynamics of University Students’ Awareness of Retired Mobile Phones in China," Sustainability, MDPI, vol. 14(17), pages 1-14, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:14:y:2022:i:11:p:6789-:d:829926. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.