The concept of “industrial” or “societal” metabolism is a simple model of the interrelation between the economy and the environment, in which the economy is an embedded subsystem of the environment. Similar to living beings, this subsystem is dependent on a constant throughput of materials and energy. Raw materials, water and air are extracted from the natural system as inputs, transformed into products and finally re-transferred to the natural system as outputs (waste and emissions).
Total inputs from natural systems into the economy must by definition equal total outputs plus net accumulation of materials in the socio-economic system. Hence, gathering information on human’s material consumption is important, as many of today’s most pressing environmental problems such as climate change, biodiversity loss or water scarcity are directly linked to the scale of our material throughput.
The internationally standardised method for the measurement and analysis of raw material use on the national level is called Economy-Wide Material Flow Accounting and Analysis (EW-MFA).
Indicators derived from the EW-MFA accounts inform about the overall physical size of an economy and can be set in relation to economic indicators, allowing assessments of material productivity and the decoupling performance of countries. They are particularly useful for monitoring overarching policy goals and targets, such as those defined in the context of the Sustainable Development Goals (SDGs).
The interactive figure below illustrates the different EW-MFA indicators and their interrelationships in the context of the material balance on the national level. All illustrated indicators refer to materials that are economically used, i.e. become a physical part of a production output.
Other EW-MFA indicators also include materials that are extracted or moved in extraction processes but do not become part of a product. Indicators including this ‘unused material extraction’ are not included in the figure.
Move the mouse over a specific indicator to learn more about the questions each indicator addresses, its underlying data sources and the methods to calculate the indicator.