Data form the foundation of MFAs. They represent observations of either stocks (at a given point in time) or flows (over a given time period).
Systems represent the totality of the stocks and flows within boundaries defined in space and time at a chosen level of (dis-)aggregation. They include observed and unobserved stocks and flows. Adding a system definition to observed data adds information: Systems define the context of observed flows and they allow for calculation of unobserved flows using mass balance
Models in this context are mathematical representations of material cycles. They reflect the system definition and the drivers of cycles such as population growth or technologies used. They are used to simulate MFA-based trends and developments. Scenarios here are assumptions of plausible future cycles that are consistent with the mass balance principals and the assumed drivers . They can be used to make forecasts or to evaluate the effectiveness of alternative strategies.
Uncertainty is inherent in all MFAs of historical or future cycles due to errors in system definitions and the data used. Approaches to uncertainty analysis aim at making uncertainties transparent and reducing them. They enable the modeller to make more robust assumptions and become aware of the model’s strengths and limitations.
Visualisations here are different maps of complex systems. They can inform decision making in industry and government, by visualising current status and historical trends, and potential future developments under different conditions. Visualisation tools are developed to support the recording (monitoring), exploration (analysis), and explanation (interpretation) of information.
Indicators stands for quantitative measures that aim to reflect the status of complex systems. They are used to analyse and compare performance of businesses, sectors or economies across countries and to determine policy priorities.
Decision support in the MinFuture context is looking at the uptake of the Common MFA methodology for:
(1) Supporting political decisions, or strategy development, or reporting against set goals and targets , for example those of the Strategic Implementation Plan (SIP) of the European Innovation Partnership on Raw Materials, the Circular Economy Action Plan or the SDG’s;
(2) Supporting the harmonisation and interoperability of data, models and tools;
(3) Promoting its use and benefits to offer to industries, governments or research disciplines.
In the MinFuture project we aim to integrate four core dimensions, (1) Stages (2) Trade (3) Linkages (4) Time.
The development of a glossary is a crucial part of the common MFA methodology being developed in MinFuture. Developing terminology that is agreed by the multidisciplinary stakeholders of MinFuture is the first step towards harmonisation and it is therefore seen as an important function of the common MFA methodology.
Case studies demonstrate the foundational roles of systems and data in MFA. They illustrate how gaps and barriers identified in the four dimensions of MFA (i.e., stages of material cycles, trade, linkages, and time) for specific materials can be addressed in a generally agreed common methodology.