  Industrial symbiosis involves creating integrated cycles of by-products and
waste between networks of industrial actors in order to maximize economic
value, while at the same time minimizing environmental strain. In such a
network, the global environmental strain is no longer equal to the sum of the
environmental strain of the individual actors, but it is dependent on how well
the network performs as a whole. The development of methods to understand,
manage or optimize such networks remains an open issue. In this paper we put
forward a simulation model of by-product flow between industrial actors. The
goal is to introduce a method for modelling symbiotic exchanges from a macro
perspective. The model takes into account the effect of two main mechanisms on
a multi-objective optimization of symbiotic processes. First it allows us to
study the effect of geographical properties of the economic system, said
differently, where actors are divided in space. Second, it allows us to study
the effect of clustering complementary actors together as a function of
distance, by means of a spatial correlation between the actors' by-products.
Our simulations unveil patterns that are relevant for macro-level policy.
First, our results show that the geographical properties are an important
factor for the macro performance of symbiotic processes. Second, spatial
correlations, which can be interpreted as planned clusters such as
Eco-industrial parks, can lead to a very effective macro performance, but only
if these are strictly implemented. Finally, we provide a proof of concept by
comparing the model to real world data from the European Pollutant Release and
Transfer Register database using georeferencing of the companies in the
dataset. This work opens up research opportunities in interactive data-driven
models and platforms to support real-world implementation of industrial
symbiosis.
