Socio-economic Correlation Analysis of E-waste and Prediction of E-waste Generation via Back-propagation Neural Network

Abstract

With the rapid electronics production in China, the lack of proper collection and disposal mechanisms of e-waste is a growing concern. Accurate predictions of e-waste can help improve the efficiency of e-waste collection and disposal. As China’s capital city with a high gross domestic product, studying the e-waste generation in Beijing could provide an example for other province-level administrations. Analysing the correlation between e-waste generation with their socio-economic factors (e.g., gross domestic product, unemployment rate, educational level) is vital in revealing their influential critical factors. This study analyses Beijing’s socio-economic correlation with e-waste using Pearson correlation analysis and develops a Back-propagation Neural Network to predict e-waste generation. The major socio-economic factors influencing e-waste generation are municipal solid waste generation, population, gross domestic product, and carbon dioxide emissions. The Back-propagation Neural Network prediction shows the air-conditioner type e-waste with the greatest growth rate, 40.24 % from 2019 to 2025, while the washing machine type achieves the least. Through this study, the identified correlated socio-economic factors and the predictive model could provide e-waste growth rate and socio-economic influencing factors of e-waste to Beijing’s government in planning a more sustainable e-waste management system.