Socio-economic correlation analysis and hybrid artificial neural network model development for provincial waste electrical and electronic equipment generation forecasting in China

Ability to forecast the waste electrical and electronic equipment (WEEE) generation can help formulate a robust future WEEE management system. Previous studies applied forecasting models, such as the grey model and artificial neural networks, to predict WEEE generation from a country perspective, leading to less accurate forecasts due to huge socio-economic differences in rural and urban areas. Additionally, there has been the incompatibility of a single forecasting model for all WEEE types, and this remained a research gap. Taking advantage of respective forecasting models, this study presents a hybrid model, Grey Artificial Neural Network, to forecast the WEEE generation of 31 province-level regions in China while evaluating the socio-economic analysis of seven WEEE types via Pearson correlation analysis. More than 70% of WEEE from province-level regions strongly correlates (R > ±0.8) with the gross domestic product and the population, whereas some top WEEE-generating province-level regions (i.e., Tianjin and Shanghai) correlate weakly to moderately. The root mean square error and mean absolute error of the developed Grey Artificial Neural Network hybrid model are the lowest at 8.29 and 6.48, compared to the grey model (13.53 and 11.13) and back-propagation neural network (9.21 and 7.22). Though the Grey Artificial Neural Network hybrid model has the lowest error, posterior mean square deviation ratio analysis indicates that this hybrid model is only suitable for washing machines, refrigerators, color televisions, and personal computers (urban area), while the back-propagation neural network is suitable for monochrome televisions, air conditioners, and personal computers (rural area). Compared to 2019, it is projected that an additional 32.92 M units of WEEE will be generated by 2025, suggesting that China should build at least 15 extra recycling centers (14% more based on 2016) to handle the increased WEEE generation. This study provides policy implications for effective WEEE monitoring and collection systems to build resilient WEEE management.