Back to top

Data

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.

Read More

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. 
This figure shows a sankey diagram of global steel flows from steelmaking to end-use goods. The width of the different arrows represent quantities flowing from one life cycle stage to the next; the thicker the arrow, the larger the quantities.
Result
MinFuture deliverable D3.2

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.

Event

This workshop is part of an EU Horizon 2020 project MinFuture (Global material flows and demand-supply forecasting for mineral strategies...

Event

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/).

Result

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?
Event

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.

Group photo of the participants of the 2nd MinFuture workshop
Event

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.

Result
MinFuture Deliverable D3.1

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.

Pages

Subscribe to Data