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An investigation into modelling approaches for industrial symbiosis: a literature review

Author

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  • Demartini, Melissa
  • Bertani, Filippo
  • Tonelli, Flavio
  • Raberto, Marco
  • Cincotti, Silvano

Abstract

The aim of this paper is to understand how to model industrial symbiosis networks in order to favour its implementation and provide a framework to guide companies and policy makers towards it. Industrial symbiosis is a clear example of complex adaptive systems and traditional approaches (i.e., Input/Output analysis, Material flow analysis) are not capable to capture these dynamics behaviours. Therefore, the aim of this literature review is to investigate: i) the most used modelling and simulation approaches to analyse industrial symbiosis and ii) their characteristics in terms of simulation methods, interaction mechanisms and simulations software. Findings from our research suggest that a hybrid modelling and simulation approach, based on agent-based and system dynamics, could be an appropriate method for industrial symbiosis analysis and design.

Suggested Citation

  • Demartini, Melissa & Bertani, Filippo & Tonelli, Flavio & Raberto, Marco & Cincotti, Silvano, 2021. "An investigation into modelling approaches for industrial symbiosis: a literature review," MPRA Paper 107448, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:107448
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    References listed on IDEAS

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    1. Fraccascia, Luca & Yazan, Devrim Murat & Albino, Vito & Zijm, Henk, 2020. "The role of redundancy in industrial symbiotic business development: A theoretical framework explored by agent-based simulation," International Journal of Production Economics, Elsevier, vol. 221(C).
    2. Fraccascia, Luca & Albino, Vito & Garavelli, Claudio A., 2017. "Technical efficiency measures of industrial symbiosis networks using enterprise input-output analysis," International Journal of Production Economics, Elsevier, vol. 183(PA), pages 273-286.
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    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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