Enhancing e-waste estimates - improving data quality by multivariate input–output analysis

Wang, Feng, Huisman, Jaco, Stevels, Ab and Balde, Cornelis P., (2013). Enhancing e-waste estimates - improving data quality by multivariate input–output analysis. Journal of Waste Management, 2397-2407

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  • Sub-type Journal article
    Author Wang, Feng
    Huisman, Jaco
    Stevels, Ab
    Balde, Cornelis P.
    Title Enhancing e-waste estimates - improving data quality by multivariate input–output analysis
    Appearing in Journal of Waste Management
    Publication Date 2013-06
    Place of Publication Online
    Publisher Elsevier Ltd.
    Start page 2397
    End page 2407
    Abstract Waste electrical and electronic equipment (or e-waste) is one of the fastest growing waste streams, which encompasses a wide and increasing spectrum of products. Accurate estimation of e-waste generation is difficult, mainly due to lack of high quality data referred to market and socio-economic dynamics. This paper addresses how to enhance e-waste estimates by providing techniques to increase data quality. An advanced, flexible and multivariate Input–Output Analysis (IOA) method is proposed. It links all three pillars in IOA (product sales, stock and lifespan profiles) to construct mathematical relationships between various data points. By applying this method, the data consolidation steps can generate more accurate time-series datasets from available data pool. This can consequently increase the reliability of e-waste estimates compared to the approach without data processing. A case study in the Netherlands is used to apply the advanced IOA model. As a result, for the first time ever, complete datasets of all three variables for estimating all types of e-waste have been obtained. The result of this study also demonstrates significant disparity between various estimation models, arising from the use of data under different conditions. It shows the importance of applying multivariate approach and multiple sources to improve data quality for modelling, specifically using appropriate time-varying lifespan parameters. Following the case study, a roadmap with a procedural guideline is provided to enhance e-waste estimation studies.
    Copyright Holder Elsevier Ltd.
    Copyright Year 2013
    Copyright type All rights reserved
    DOI 10.1016/j.wasman.2013.07.005
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    Created: Mon, 14 Apr 2014, 17:12:26 JST