Systematic Evaluation of Uncertainty in Material Flow Analysis

Bibliographic Data

ID19469596
AuthorsDavid Laner (0000-0003-1351-4468, TU Wien, corresponding author), Helmut Rechberger (0000-0002-7723-0635, TU Wien), T F Astrup (0000-0002-2853-1001), Thomas Astrup
Year2014
Volume18
Issue6
Pages859-870
Publication date2014-12-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueJournal of Industrial Ecology (JOURNAL)
Journal identifiersISSN: 1088-1980 • E-ISSN: 1530-9290
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1111/jiec.12143
OpenAlexW2158977061
LanguageEN
Citations received47
References cited44

Material flow analysis (MFA) is a tool to investigate material flows and stocks in defined systems as a basis for resource management or environmental pollution control. Because of the diverse nature of sources and the varying quality and availability of data, MFA results are inherently uncertain. Uncertainty analyses have received increasing attention in recent MFA studies, but systematic approaches for selection of appropriate uncertainty tools are missing. This article reviews existing literature related to handling of uncertainty in MFA studies and evaluates current practice of uncertainty analysis in MFA. Based on this, recommendations for consideration of uncertainty in MFA are provided. A five‐step framework for uncertainty handling is outlined, reflecting aspects such as data quality and goal/scope of the MFA. We distinguish between descriptive (quantification of material turnover in a region) and exploratory MFA (identification of critical parameters and system behavior). Whereas mathematically simpler concepts focusing on data uncertainty characterization are appropriate for descriptive MFAs, statistical approaches enabling more‐rigorous evaluation of uncertainty and model sensitivity are needed for exploratory MFAs. Irrespective of the level of sophistication, lack of information about MFA data poses a major challenge for meaningful uncertainty analysis. The step‐wise framework suggested here provides a systematic way to consider available information and produce results as precise as the data warrant

Business · Data quality · Industrial ecology · Management science · Material flow analysis · Operations research · Sophistication · Sustainability · Uncertainty analysis · Computer Science · Engineering · Environmental Impact and Sustainability · Extraction and Separation Processes · Recycling and Waste Management Techniques

Tools

Open DOISci-Hub