Akademik Veri Yönetim Sistemi
Leveraging AI and Nanotechnology for Materials, Devices, and Manufacturing, Ashok Vaseashta,Ioan Stamatin, Editör, IGI Global, Pennsylvania, ss.267-304, 2025
Electronic waste (e-waste) is the fastest-growing waste stream worldwide, posing environmental, economic, and health challenges. Despite containing valuable precious and rare metals, less than 22% of e-waste is recycled, worsening resource loss and pollution. This study estimates and forecasts Germany's e-waste generation from 1960 to 2040. Using Robinson's (2008) method and complying with the EU WEEE Directive, total e-waste was estimated until 2025. After testing nonlinearity, a Nonlinear Autoregressive Neural Network (NARNET) forecasted trends through 2040, showing steady growth. Results highlight trade-offs: pre-ElektroG models recovered value but were costly, while post- ElektroG bulk processing cut costs but limited reuse. We propose a hybrid approach-value-focused collection for highyield items and bulk processing for low- value waste-to optimize economic and environmental outcomes. Policy recommendations stress collaboration, awareness, and adaptive frameworks to foster a circular economy. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Use of this publication to train generative artificial intelligence (AI) technologies is expressly prohibited. The publisher reserves all rights to license its use for generative AI training and machine learning model development.