Assessment of risk propagation in an e-waste collection system using Bayesian networks

Abstract

The widespread use of electrical and electronic devices has become integral to modern life, transforming communication and day-to-day work; however, this has led to a significant challenge in effectively managing the growing volume of electronic waste (e-waste). Effective e-waste management faces a substantial challenge as the collection rates remain low, primarily due to inadequate collection systems and socioeconomic disparities. The present study investigates the assessment of various prominent risks affecting the e-waste collection system. It aims to examine the e-waste collection risk propagation categorized into social, environmental, economic, technical, and policy aspects. The Bayesian network approach is utilized to address a range of potential risks. The key findings indicate inconsistencies in the data collected on e-waste, including information such as collection date and time, location, and technical details. These inconsistencies are observed both between users or customers and e-waste collection agencies, as well as among the country's administration officials. In improving the e-waste collection system, the pivotal factors contributing to improvement were found to be technical and social risks. The insights of this study provide valuable information for policymakers to make informed decisions about promoting sustainable e-waste management practices.