Methodology for environmental impact and performance assessment of derivative electronic products

Abstract

This study therefore uses a back-propagation neural network (BPNN) to establish a model for analysis of hazardous chemicals and energy consumption of the product life cycle of derivative electronics, helping enterprises, at the product design stage, estimate hazards of derivative electronic products on the environment across major phases of the product life cycle. In addition, to clarify the performance of a newly developed product's ecological design, this paper applies TOPSIS to develop a performance assessment model for product design for environment (DfE). With the analysis provided above, we may help enterprises better understand DfE performance of its new product as well as similar products of competitors as a reference for modification of product design. © 2010 Springer-Verlag London Limited.