Sustainable transportation system for electronic waste recycling from a social perspective

Abstract

E‐waste is currently identified as the world's fastest‐growing waste stream. The key driver behind this trend is the rapid technological advancement and socio‐economic development. When hazardous chemicals are not properly handled, e‐waste may harm ecosystems and human well‐being. Direct effects include releases of acids, heavy metal toxicity, carcinogens and indirect effects such as heavy metal bio‐enlargement, acids enlargement and toxicity problems. In this study, a specific type of system is utilised to collect and transport the different types of hazardous e‐waste using sustainable transportation with the decision‐making trial and evaluation laboratory (DEMATEL). The proposed method of DEMATEL is utilised to identify the relationship and the degree of impact among determined e‐waste recycling barriers. The experimental results suggest input material, lack of funds and most affecting factors that are required to be addressed for the improvement of e‐waste recycling infrastructure.

Análise da produção científica sobre a relação entre logística reversa e resíduos eletroeletrônicos

“…DRd -Desenvolvimento Regional em debate (ISSNe 2237-9029) v. 13, p. 589-607, 2023. O quarto agrupamento (Cluster 4) designado pela cor amarela é retratado como "Barreiras", que são as principais dificuldades enfrentadas à coleta seletiva desse tido de lixo tal como fora evidenciado nos estudos de Santos e Ogunseitan (2022) e Liang et al, (2020). DRd -Desenvolvimento Regional em debate (ISSNe 2237-9029) v. 13, p. 589-607, 2023.…”

Evaluation of practical edge computing CNN‐based solutions for intelligent recycling bins

“…The work by Li et al [2] describes a system that employs a convolutional neural network (CNN)-based detection and classification model to control the robot arm for sorting the waste at the recycling centre. Approaches for a sustainable transportation system for waste recycling [3] as well as managing medical waste in a post-pandemic era and in the context of a circular economy (CE) [4] have also been published recently by other researchers in the field.…”

“…Our dataset, however, has the benefit of introducing two additional classes being the 'hand' class, for identifying waste items where a hand is present, and an 'empty' class, corresponding to the scenario where no waste item has been presented. (3) Taking into account the limited computational resources available at the edge, this study proposed two practical, real-time, cost-efficient and energy-efficient systems for the purpose of realising an intelligent recycling bin. Specifically: � We fine-tuned the accuracy of MobileNet-based CNN models and targeted them for implementation on two edge platforms, namely a Jetson Nano and a K210 development platforms.…”

A systematic review on sustainable E-waste management: challenges, circular economy practices, and a conceptual framework

“…Additionally, the transboundary movement of E-waste, from developed to developing economies, complicates EWM adoption (Bhuyan et al ., 2022; Milovantseva and Fitzpatrick, 2015; Osibanjo and Nnorom, 2007). Insufficient technologies for E-waste treatment and disposal, storage and transportation hinder the adoption of EWM (Liang et al ., 2020). The adoption of EWM is also hampered by the enormous initial capital investment required by formal recycling plants (Kumar et al ., 2022a, b).…”