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A spatial agent based model for simulating and optimizing networked eco-industrial systems

Author

Listed:
  • J. Raimbault
  • J. Broere
  • M. Somveille
  • J. M. Serna
  • E. Strombom
  • C. Moore
  • B. Zhu
  • L. Sugar

Abstract

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.

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  • J. Raimbault & J. Broere & M. Somveille & J. M. Serna & E. Strombom & C. Moore & B. Zhu & L. Sugar, 2020. "A spatial agent based model for simulating and optimizing networked eco-industrial systems," Papers 2003.14133, arXiv.org.
    • Handle: RePEc:arx:papers:2003.14133
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    References listed on IDEAS

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    1. Rizzati, Massimiliano & Landoni, Matteo, 2024. "A systematic review of agent-based modelling in the circular economy: Insights towards a general model," Structural Change and Economic Dynamics, Elsevier, vol. 69(C), pages 617-631.
    2. Sergio Barile & Clara Bassano & Raffaele D’Amore & Paolo Piciocchi & Marialuisa Saviano & Pietro Vito, 2021. "Insights of Digital Transformation Processes in Industrial Symbiosis from the Viable Systems Approach ( vSa )," Sustainability, MDPI, vol. 13(17), pages 1-14, August.
    3. Benjamin Patrick Evans & Kirill Glavatskiy & Michael S. Harré & Mikhail Prokopenko, 2023. "The impact of social influence in Australian real estate: market forecasting with a spatial agent-based model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 18(1), pages 5-57, January.
    4. Ali Utku Akar & Sukran Yalpir & Suleyman Sisman & Gamze Goktepeli & Esra Yel, 2023. "A deterministic approach in waste management: delineation of potential territories in Turkey for industrial symbiosis of olive pomace, marble wastes and plastics by integrating Fuzzy AHP to GIS," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(6), pages 5635-5662, June.
    5. Benjamin Patrick Evans & Kirill Glavatskiy & Michael S. Harr'e & Mikhail Prokopenko, 2020. "The impact of social influence in Australian real-estate: market forecasting with a spatial agent-based model," Papers 2009.06914, arXiv.org, revised Feb 2021.

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