The brief presents key discussion items and main findings from the June 2017 Workshop in Vienna. In order to tackle challenges such as insufficient information flows or lacking data availability, a (more) systemic understanding of global mineral raw material flows is needed. Mapping the system context and making data/information gaps explicit will help identifying possible improvements.
A model can never perfectly represent a natural system and due to paucity of data and limited system understanding, MFA is naturally confronted with uncertainty. Data for material flow analysis originate from different sources and vary in terms of availability and quality, particularly if material stocks and flows of large-scale systems, such as regions or whole economies, are investigated. Previously, ignoring uncertainty in MFA has raised doubts about the reliability of MFA results.
Methods to deal with uncertainty in MFA range from qualitative discussions to sophisticated statistical approaches. Different methods will be evaluated. The majority of methods can be classified into four groups:
- Data classification methods
- Uncertainty analysis approaches
- Sensitivity analysis approaches
- Comparisons of model structures
A step-wise procedure (framework) how to deal with uncertainty in MFA will be established. The procedure should relate to MFA with a focus on understanding mechanisms relevant for material flows in the system context and producing results as precise as possible.