| Title |
Prediction of WEEE Recycling in China Based on an Improved Grey Prediction Model |
| ID_Doc |
9477 |
| Authors |
Xiao, Q; Wang, HS |
| Title |
Prediction of WEEE Recycling in China Based on an Improved Grey Prediction Model |
| Year |
2022 |
| Published |
Sustainability, 14.0, 11 |
| DOI |
10.3390/su14116789 |
| 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. |
| Author Keywords |
waste electrical and electronic equipment (WEEE); recycling; grey prediction; NGBM (1; 1) model; PSO algorithm |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED); Social Science Citation Index (SSCI) |
| EID |
WOS:000809864500001 |
| WoS Category |
Green & Sustainable Science & Technology; Environmental Sciences; Environmental Studies |
| Research Area |
Science & Technology - Other Topics; Environmental Sciences & Ecology |
| PDF |
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