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Enhancing e-waste estimates: improving data quality by multivariate Input-Output Analysis.
Feng Wang1, Jaco Huisman, Ab Stevels
1Institute for Sustainability and Peace, United Nations University, Hermann-Ehler-Str. 10, 53113 Bonn, Germany; Design for Sustainability Lab, Faculty of Industrial Design Engineering, Delft University of Technology, Landbergstraat 15, 2628CE Delft, The Netherlands.
Waste Management (New York, N.Y.)
|August 1, 2013
Summary
Accurate waste electrical and electronic equipment (e-waste) estimation is challenging due to data limitations. This study introduces an advanced Input-Output Analysis (IOA) method to improve e-waste data quality and reliability.
Area of Science:
- Environmental Science
- Waste Management
- Data Science
Background:
- Waste electrical and electronic equipment (e-waste) is a rapidly growing global waste stream.
- Accurate e-waste generation estimation is hindered by insufficient market and socio-economic data.
- Existing estimation methods often lack comprehensive data integration.
Purpose of the Study:
- To enhance the accuracy and reliability of e-waste generation estimates.
- To introduce an advanced, flexible, and multivariate Input-Output Analysis (IOA) method.
- To provide a procedural guideline for improving e-waste estimation studies.
Main Methods:
- Development of a multivariate Input-Output Analysis (IOA) model linking product sales, stock, and lifespan profiles.
- Application of data consolidation techniques to generate accurate time-series datasets.
- Case study in the Netherlands to validate the advanced IOA model.
Main Results:
- Obtained complete datasets for all variables required for e-waste estimation.
- Demonstrated significant disparities between estimation models due to data variations.
- Highlighted the importance of multivariate approaches and multiple data sources for improved modeling.
Conclusions:
- The advanced IOA method significantly enhances e-waste estimation reliability.
- Using time-varying lifespan parameters and multiple data sources is crucial for accurate modeling.
- A roadmap is provided to guide future e-waste estimation research and practice.
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