Robust strategies for sustainable resource management depend on a solid understanding of the physical economy – the anthropogenic stocks and flows of matter and energy.
Data form the foundation of MFAs. Data represent observations of either stocks (at a given point in time) or flows (over a given time period). A system should be able to reflect reference points of measurement where data are collected. However, often enough, data collection on the physical dimensions of materials is not taking place having the system context in mind. The implication of this is ambiguous datasets that cannot represent the real system well and cannot be used to their full potential in MFA without introducing assumptions on their definition. This often has as a result the wrong interpretation of data. Ideally, data collection should be based on system understanding to reflect the real situation. This in turn will lead to high quality and robust data that can be used to monitor material systems. Data used in MFA are collected from a variety of sources, including national statistical offices, international trade statistics databases, data from geological surveys, trade associations and industry. Assumptions are often needed to fill in gaps in data. In addition, data from different sources are not usually harmonised, for example production and trade nomenclatures may use different definitions, which is also problematic during the MFA compilation process.
The five data challenges:
1. Uncertainties associated with data are not explicit.
2. Data of different data providers are often not harmonized and interoperable.
3. Metadata information to provide sufficient insight of the meaning of data and their boundaries is lacking.
4. Missing data, or limited data availability due to confidentiality or other legal constraints.
5. Data gaps are not explicitly presented.
The MinFuture hypotheses:
1. Statistical data on stocks and flows of goods or materials can always be placed within a system that accurately defines the reference point of where the measurement was taken.
2. Providing the system context (the “coordinates”) adds value to the data and allows for addressing of all of the above mentioned challenges.
Please note: The following text is taken from the existing MinFuture report "A systems approach for the monitoring of the physical economy". Please click on the afore-mentioned link to access and read the full publication.
Testing of the outlined hypotheses on selected companies, national authorities and at the European and global level. This is achieved through close collaboration between MFA specialists and representatives of the above mentioned institutions. Validation will illustrate how the five data challenges can be addressed effectively using the MinFuture hypotheses.
Data represent observations of either stocks (at a given point in time) or flows (over a given time period). Enabling an efficient monitoring of the physical economy is a data intensive task and requires data along the entire supply chain and across regions and nations. However, currently the data needed for compiling material cycles are often fragmented, inconsistent and not harmonized.
The current data collection is done by several governmental agencies for a variety of purposes. This often leads to different reference points for the measurement and further leads to difficulties in compiling data from several nations and institutions. Data used in
MFA are collected from a variety of sources, including national statistical offices, international trade statistics databases, data from geological surveys, trade associations and industry. The data is also not reported within a system context and for this reason individual MFA practitioners needs to gather data from a variety of sources, interpret the data and place them to the best of their abilities within a system. The implication of this is ambiguous datasets that cannot represent the real system well and cannot be used to their full potential in MFA without introducing assumptions on their definition. This often has as a result the wrong interpretation of data. An example of challenge is illustrated in Figure 7 below, in which two systems are shown one aggregated and one refined.
Figure 7: Issues with data use in crude systems vs the benefits of a refined system.
Due to the first system being on such an aggregated level it is not clear which measurement done by the USGS that should be placed on the flow between production and manufacturing, production or apparent consumption. However, in the refined system, serval of the reported measurements can be placed at the same time highlighting the gaps. To be able to monitor the physical economy in a consistent matter data needs to be collected and provided with a system context in mind. Reporting data within a systems context adds information and increases the robustness as it provides coordinates to the measurement.
In the figures shown in the menu Systems - Approaches, the reference points are mapped within a system which reduces the potential for miscommunication. It further allows for an increased level of transparency, by mapping reference points within a system, what we know and our knowledge gaps are made explicit.
- Data are measurements representing our coordinates.
- To be able to currently correctly place our data in the coordinate system, data needs to be reported with an explicit system context.
- Reporting data with a system context adds information and increases the robustness as it provides the coordinates to our measurements.
The intention of this conference "Monitoring the physical economy requires putting raw material data in a system context" was to present, reflect on and refine key recommendations from the MinFuture project on a monitoring system for the physical economy.
The presentations held and recommendations made will soon be available via this website.
This report aims to address existing challenges by developing a methodological framework for the monitoring of the physical economy that facilitates the users in reflecting more systematically about the problems mentioned above and in developing more effective strategies for addressing them. The framework proposed is based on Material Flow Analysis (MFA), a tool widely used for tracking materials and energy in the economy.
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.
On 11-12 December 2018, the MIN-GUIDE project team invites you to reflect upon the mining sector’s role for a circular economy at its 3rd Annual Conference. The conference will host about 200 participants from all over Europe and will provide an excellent opportunity to learn, explore, exchange, and network.
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.
MinFuture is a collaborative project funded by the Horizon 2020 framework, aiming to identify, integrate, and develop expertise for global material flow analysis and scenario modelling.
Vast quantities of scarce metals are being lost each year from Europe's urban mine of vehicles, batteries, mobile phones and electronic gadgets. To address this problem, the European research project ProSUM has compiled a new database that charts the metals in order to facilitate recycling.