Material flow accounting (MFA) (Fischer-Kowalski et al. 2011) is a method to systematically compile data on the total material use (input) and discards (output) of society/economy. Its system boundaries are consistent with the System of National Accounts and it is an established accounting methodology with a comprehensive database (Resource Panel IRP). As an accounting scheme, MFA provides sector and country level data for direct flows, i.e., the material flows that directly flow into the economies and sectors for which they are reported. In supply chain assessment, direct flows are labelled scope 1 indicators. MFA data are used to build environmental extension tables and calculate stressor matrices for input-output tables (Miller and Blair 2009). These derived data, in turn, are used for calculating the material footprint, a supply chain or scope 3 indicator with multi-regional input-output models (MRIO) (Wiedmann et al. 2015; Miller and Blair 2009).
The system definition of MFA (Fig. 1) shows the main flows that are accounted for. A crucial step in MFA is to not only quantify industrial material commodities themselves but the total raw materials extracted and processed, as these flows can be much larger than the flows of useful materials and are major drivers of environmental impacts such as land use, GHG emissions, or toxicity impacts.
As explained by Eurostat (Eurostat 2022):
“The simple weight of traded goods provides an incomplete picture as it does not take into account the raw materials originally necessary to produce these traded goods. A more comprehensive picture on the ‘material footprints’ can be obtained by converting the traded goods into their raw material equivalents (RME), i.e. the amounts of raw materials required to provide the respective traded goods. Especially for finished and semi-finished products, imports and exports in RME are much higher than their corresponding physical weight.
Imports in RME are the amount of raw material required to produce the goods imported into the economy.
Exports in RME are the amount of raw material required to produce the goods exported from the economy.
Raw material input (RMI) is the amount of raw materials required to produce the goods, which are available for use in production and consumption activities of the economy.
Raw material consumption (RMC), measures the total amount of raw materials required to produce the goods used by the economy (also called ‘material footprint’).
Raw material input = Domestic extraction + Imports in RME
Raw material consumption = Domestic extraction + Imports in RME – Exports in RME = Raw material input – Exports in RME”
One can thus speak of the raw material equivalent (RME) as a function (the material footprint indicator calculation) that converts a given commodity flow into its raw material equivalent, i.e., all material that had to be processed to produce the material or commodity at hand.
Fig. 1: System definition of economy-wide material flow accounting. Source: Screenshot of Figure 1 in Fischer-Kowalski et al. (2011). Note that for the correct calculation of TMR and TMC, the trade flows need to be converted to their raw material equivalents (RME).
To show the different flows and their meaning more explicitly, the following system definition and indicator list is provided (Fig. 2).
Fig. 2: Material flow accounting flows and indicators in a material flow analysis-type system definition. Five different layers are shown. They are needed to denote the different flow measures and indicators: total commodity mass (1), dry commodity mass (2), waste/emissions (3), raw material equivalent (RME) of dry commodity mass (4), and total material requirements (TMR) of dry commodity mass (5). The physical trade balance is defined in Schandl et al. (2017). Source: Pauliuk (2022)
In the figure above, five layers, at which material flows can be quantified, are introduced and listed for each flow or indicator: total commodity mass (1), dry commodity mass (2), waste/emissions (3), raw material equivalent (RME) of dry commodity mass (4), and total material requirements (TMR) of dry commodity mass (5).
For LCA- and MRIO-type calculations, the two indicators RMI and RMC are the same and are commonly labelled as material footprint of a product or service. This is because a product system does not contain any exports of other products and commodities, all export flows are zero and therefore, the RMI indicator equals the material footprint.
Next to the RME, there is also the total material requirement of a commodity:
“Total Material Requirement is a compound indicator reflecting all of the physical materials that are mobilized each year to support an economy, including “hidden”, non-economic materials such as mineral overburden, processing waste and soil erosion. The TMR includes an aggregate indicator and disaggregated sub-indicators by resource sector.” (UN ESA 2022)
It also needs to be noted that DMI and DMC are not consistent across layers:
“As part of the material flow accounts, Eurostat produces indicators among which is the indicator domestic material consumption (DMC). Those accounts, however, do not provide an entirely consistent picture of global material footprints because they record imports and exports in the actual weight of the traded goods when they cross country borders instead of the weight of materials extracted to produce them. As the former are lower than the latter, economy-wide material flow accounts (EW-MFA) and the derived DMC underestimate the material footprint. To adjust for this, the weight of processed goods traded internationally is converted into the corresponding raw material extractions they induce.” (Eurostat MFA 2022)
This inconsistency across layers led to the definition of the raw material equivalent (RME) or material footprint. With RME as a function, we can write:
DE = RME(DM) (1)
FE = RME(sum(Ix)) (2)
With the help of MFA indicators, we can also establish the economy-wide material flow balance for a national economy:
DE + SUM(I_x) = SUM(E_x) + SUM(B_x) + ΔS (3)
Here, ΔS denotes the stock change or net stock accumulation of goods and materials in the use phase (change of in-use stocks, not shown in Fig. 2)
Data sources for material flow accounting
The most comprehensive database for scope 1 material flow accounts is hosted by the International Resource Panel of the United Nations Environmental Program:
Eurostat also hosts a material flow accounts and resource productivity database:
Material footprint calculation requires supply chain modelling. Here, first, the contributions of the different process steps are calculated with a Leontief input-output (IO) model (see IEooc_Methods4_Lecture1 for details). In a second step, environmental stressor multipliers are used to calculate the material input flows into the supply chain. In the third and final step, the different material input flows are converted to raw material equivalents and added together to form the aggregated material footprint.
The material footprint therefore is a good example for a supply chain indicator that is built on the extension account to an IO model. It is calculated as the product of all process outputs in product’s supply chain with the material input intensity of each process, then multiplied with each material’s raw material equivalent, and then summed up to the four broad categories: biomass, fossil fuels, metal ores, non-metallic minerals, and the total.
Traditionally, the material footprint is calculated for countries and regions, using multiregional IO models that contain national sector-specific material flow accounts at scale to construct the material intensity multipliers (Wiedmann et al. 2015). EXIOBASE is a global multi-regional IO database that contains the material flow accounts described above:
In process-based life cycle assessment (LCA), commonly used for product-level supply chain studies, a corresponding impact assessment method to determine the material footprint of product systems was lacking for some time. Thanks to the work by Mostert and Bringezu (2019), a list of 286 characterization factors for the material footprint of products and services, expressed as raw material equivalents (RME/RMI) and total material requirement (TMR) of individual natural resources, is now available for ecoinvent 3.1. In LCA databases and software, the second step of the footprint calculation, the conversion of process output to material input, is done by adding the material input to the process inventories in the database. The third step, the raw material equivalent conversion, is done by multiplying the matrix of characterization factors for raw material equivalent to the vector of material inputs into the product system.
In a recent report (Pauliuk 2022), an attempt to transfer and extend the material-specific MFA characterization factors (RMI and TMR) compiled by Mostert and Bringezu (2019) to process-based LCA in form of the databases ecoinvent 3.7.1 and 3.8 (Wernet et al. 2016) is described. This report is available for download here:
The dataset with the characterization factors for openLCA is available on Zenodo. For using it, an ecoinvent-openLCA license for the versions 3.7.1 or 3.8 is required:
Compared to scarcity-weighted mineral resource scarcity or fossil resource scarcity indicators, the MFA-based material footprint answers a different question and is much simpler, as it simply converts and adds up all reported resource inputs into processed raw materials, using RME conversion factors, containing current average/typical ore grades. Its main advantage over price and scarcity-weighted material depletion indicators is that the total mass input to production is very easy to interpret. Next to using the aggregated indicator, LCA software allows for breaking down total impacts into individual materials, process contributions, and resource flows, which means that material footprint LCA indicators can be used to quickly identify the most relevant materials in a product’s material footprint and where they occur. An in-depth impact assessment of the product system’s most relevant material flows regarding land transformation, eutrophication, or toxicity can then follow.
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Li, L., J. Wang, L. Zhang, R. Deng, S. Zhou, and G. Wang. 2021. Strength and Durability Properties of Antimony Tailing Coarse Aggregate (ATCA) Concrete. Materials .
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Miller, R.E. and P.D. Blair. 2009. Input-Output Analysis Foundations and Extensions. New York: Cambridge University Press.
Mostert, C. and S. Bringezu. 2019. Measuring Product Material Footprint as New Life Cycle Impact Assessment Method : Indicators and Abiotic Characterization Factors. Resources 8: 61.
Pauliuk 2022: Characterization factors for material flow accounting (material footprint) for process-based LCA – Documentation for ecoinvent 3.7.1 and 3.8 in openLCA. Industrial Ecology Freiburg (IEF) Working Paper 3(2022), University of Freiburg, Germany, DOI 10.6094/UNIFR/226265
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Schandl, H., M. Fischer-Kowalski, J. West, S. Giljum, M. Dittrich, N. Eisenmenger, A. Geschke, et al. 2017. Global Material Flows and Resource Productivity: Forty Years of Evidence. Journal of Industrial Ecology 22(4): 827–838. https://doi.org/10.1111/jiec.12626.
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Wang, J., L. Li, L. Zhang, B. Li, R. Deng, and D. Shi. 2021. Sustainable Applications for Utilizing Antimony Tailing Coarse Aggregate (ATCA) in Concrete: Characteristic of ATCA and Toxicity Risks of Concrete. Materials .
Wernet, G., C. Bauer, B. Steubing, J. Reinhard, E. Moreno-Ruiz, and B. Weidema. 2016. The ecoinvent database version 3 (part I): overview and methodology. The International Journal of Life Cycle Assessment 21(9): 1218–1230. https://doi.org/10.1007/s11367-016-1087-8.
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Web Links, access in January 2022:
Resource Panel IRP: https://www.resourcepanel.org/global-material-flows-database
- New Guidelines for Data Modeling and Data Integration for Material Flow Analysis
- Young researcher seminar on Material Flow Analysis and sustainable material cycles
- Growth of in-use stocks: Central obstacle to closing material cycles
- Short lifetime of metals in the anthroposphere illustrates the challenge of closing material cycles
- Introducing ODYM-RECC – A community model for circular economy and material efficiency assessments