The Activity-based Spatial MFA (AS-MFA) method was developed by Geldermans et al. (2017) in the European Horizon 2020 REPAiR project. As the name suggests, it is an MFA that emphasises the importance of opening up the black box by analysing the (economic) activities that take place in a city, their relationships and respective actors (companies) linked to those activities. The actors and their connections provide the “spatial” aspect of this method, since the actors can be georeferenced and their interrelations are expressed by the material flows between them.

The AS-MFA method has already been applied in the case studies of Naples and Amsterdam (Geldermans et al. 2019), Łódź (Czapiewski et al. 2018), Pécs (Varjú et al. 2018) and Hamburg (Arlati et al. 2018). The material scopes for these case studies were wastes along the supply chain, therefore taking into account upstream and downstream processes with the goal to create circular streams and close loops. Therefore, the supply chain perspective was taken starting at the point where the material scope under study became waste for the first time, which in the case of food waste was already at the farmer, for example.

The strengths of this method lie in the refined network approach that highlights the necessity of systems thinking and demonstrates how very many things are interconnected and dependent on each other. The multi-scale consideration aids in this as well, where in principle data can be aggregated from the actor to neighbourhood to district to city to regional levels, illustrating where exactly hotspots occur. By having the flows and stocks spatially mapped (spatial Sankey diagrams) hotspots are revealed, which can inform decision makers.

However, the decision makers won’t find a connection to costs or other economic data, as the AS-MFA doesn’t account for it, at least in the way that the method is defined so far. Another weakness is that energy flows are not integrated as of yet. Finally, it requires a good understanding of supply chains and flow networks and a significant amount of data on e.g. actor locations, relationships of economic activities, various material amounts and destinations, some of which isn’t readily available or can be costly to obtain.