Material flow analysis (MFA) in general, is a term used to summarize a wide range of approaches to describe material stocks and flows in systems defined in space and time.
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.
In order to develop strategies as well as to define and reach goals concerning raw materials management, maps are needed to help navigate existing knowledge and data.
The purpose of this workshop was to further develop the roadmap for monitoring the physical economy. During the spring of 2018, the MinFuture project has held several commodity specific workshops to test the developed framework and to identify commodity specific trends, opportunities and challenges that can inform the MinFuture roadmap. Workshops has been held on aluminium, cobalt, neodymium, platinum, phosphorus and construction aggregates, and stakeholders from different parts of the supply chain has contributed to it.
This workshop is part of an EU Horizon 2020 project MinFuture (Global material flows and demand-supply forecasting for mineral strategies...
This workshop is part of an EU Horizon 2020 project MinFuture (Global material flows and demand-supply forecasting for mineral strategies; see details in the flyer attached or the project website: http://minfuture.eu/).
The MinFuture workshop synthesis brief describes the main insights from discussions on:
- How can we add more relevance and credibility to data published on raw materials? What context is missing that might enhance their status? How could we present data using a systemic MFA? perspective
- How do raw material data reporting schemes (information flows) currently operate at national, regional and global level?
- What raw material indicators are often used to identify issues with raw material supply/ demand? What are their strengths and weaknesses and how do they relate to material flow analysis?
Aimed at developing a common framework to analyse global mineral raw material flows, which can be agreed and used at international level, the MinFuture project intends to support data collectors, providers and users. Improving knowledge and quality of data on material cycles was found to be essential, but is faced with challenges such as interrupted information flows or lacking data availability. The first MinFuture Workshop (‘Methodology workshop’) served to discuss how MinFuture could support key data providers and users.
The purpose of this workshop was to initiate a dialogue with key stakeholders that report raw materials data, use data to develop MFA models, or use MFA models to inform decision making. The knowledge and needs of data providers, users and decision actors are different, but in order for a ‘common approach’ to be developed their input is required.
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.