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Analysis of uncertainty in material flow analysis

Authors :
Yi-Shin Wang
Hwong-Wen Ma
Source :
Journal of Cleaner Production. 170:1017-1028
Publication Year :
2018
Publisher :
Elsevier BV, 2018.

Abstract

Material flow analysis (MFA) is a method of depicting the flow of materials in the anthroposphere and the environment to support environmental management. However, MFA results can contain uncertainties that may be related to the investigation method, calculation process, data quality, data source, or study assumptions. Uncertainty is already an important consideration in tools for environmental management; for example, Life Cycle Analysis (LCA) has prompted research discussions related to the uncertainty in databases, periods and characteristic factors. However, few studies have addressed the uncertainty in MFA. The uncertainty issue in MFA is increasingly important to policy makers coping with resource management issues. The objectives of this research are to perform uncertainty analysis for data sets of different materials in MFA and to compare the results of two important uncertainty analysis methods, the Hedbrant and SÓ§rme (HS) method and Monte Carlo (MC) simulation. In addition, we provide suggestions for research regarding MFA uncertainty analyses in the future. The uncertainty range and likely values of flow data can vary considerably based on the HS method or MC simulation. After analyzing the relationship between the uncertainty range and flow data, likely values can be derived via comparisons of uncertainty method processes, and the uncertainty analysis results of the HS may deviate greatly from those of the MC method. In this scenario, MC simulation is needed to analyze MFA uncertainty, although it requires greater efforts. The results presented here provide new insights and recommendations for further MFA research.

Details

ISSN :
09596526
Volume :
170
Database :
OpenAIRE
Journal :
Journal of Cleaner Production
Accession number :
edsair.doi...........d4123af612eef727b8f96db3a49bc351
Full Text :
https://doi.org/10.1016/j.jclepro.2017.09.202