The conceptual framework of MinFuture is structured as a pyramid with seven components related to Material Flow Analysis (i.e., systems, data, uncertainty, models, indicators, visualisation, and strategy & decision support). The hierarchical structure of components implies that the robustness of upper components largely relies on a good understanding of the lower components. Six material-specific workshop were organised under the umbrella of MinFuture project, aiming to identify gaps that hamper a robust mapping of raw material cycles. In each material-specific workshop, stakeholders from academia, industry, and government were brought together to discuss and comment on the current status of each material cycle. Challenges or issues of each material cycle were identified through interactive sessions. Drawn upon discussions and comments from the six material-specific workshops, we found several common gaps in Material Flow Analysis:
- The current system definition does not correspond to challenges;
- Data gaps need to be bridged via data harmonization and improving data reporting;
- End-of-life management challenges are not always well conceived because some of them are yet to come;
- Better demand-supply forecasting approaches and methods are needed;
- Quantitative understanding of drivers and uncertainties are needed to forecast the future demands.
Possible solutions or directions that might fill the gaps were pointed out in the workshops:
- A high resolution of life cycle stages, especially intermediate processes, is necessary in Material Flow Analysis, in order to reflect the complexity of routes and linkages in material cycles. A good dialogue with industry is of great importance when developing a system.
- Data gaps are commonly found in output/shipment, market share, and material content at product level. The foremost task is to improve current data reporting system by incorporating material-related information. Even if data are available in some cases, inconsistent commodity code systems for shipment and trade data are still one of the major challenges. Therefore, data should be reported with metadata, which could help map data in a system.
- To understand the dynamics and feedbacks within a material cycle or across material cycles, stocks (including reserves and societal stocks) should be put emphasis on in demand and supply forecasting models. Quantitative forecasting models and scenarios should refine their resolution.