Industrial system and policy making lie at the core of the transition towards a sustainable economy. The interaction between industry, resources and policy is complex, requiring the maximization of synergies from the bottom-up (industrial) and top-down (policy) perspectives within the economy. In this paper, a hybrid Agent Based-System Dynamics modelling is employed to capture the complexity of such a system and focus efforts to model industrial symbiosis (IS) processes (i.e. the ability of one industry to imitate the other) and effects on the transition towards a zero-waste economy. In IS two or more companies cooperate by making sustainable business decisions to utilize common resources, so that the waste of one company becomes the raw material of the other (Chertow 2000). All kinds of resources, be it energy, labour, knowledge, logistics and expertise are efficiently shared. The fundamental precondition for IS is cooperation among the companies, hence the need to investigate and understand these cooperation mechanisms. The interaction of IS between industry, resources and policy is complex, requiring the maximization of synergies from the bottom-up (industrial) and top-down (policy) perspectives within the economy. IS exhibits both detailed and dynamic complexity due to the degree of circularity and uncertainty on forward-reverse logistics modelling and optimization levels, in fact it represents a form of the Complex Adaptive System (CAS) involving multiple sectors and agents and displaying non-linear and non-rational interacting behaviours. Traditional approaches such as General Equilibrium Model (GEM), Input/Output analysis or Discrete Event Simulation (DES) are not capable of understanding these dynamic behaviours. Therefore, a new paradigm, which is able to capture the complexity of such a system and focus its efforts towards modelling the industrial symbiosis processes, is necessary.

Industrial Innovation Analysis and Evaluation Towards Industrial Symbiosis application in the Circular Economy Transformation

Demartini M.;Bertani F.;Barberis F.;Raberto M.;Cincotti S.;Tonelli F.
2019-01-01

Abstract

Industrial system and policy making lie at the core of the transition towards a sustainable economy. The interaction between industry, resources and policy is complex, requiring the maximization of synergies from the bottom-up (industrial) and top-down (policy) perspectives within the economy. In this paper, a hybrid Agent Based-System Dynamics modelling is employed to capture the complexity of such a system and focus efforts to model industrial symbiosis (IS) processes (i.e. the ability of one industry to imitate the other) and effects on the transition towards a zero-waste economy. In IS two or more companies cooperate by making sustainable business decisions to utilize common resources, so that the waste of one company becomes the raw material of the other (Chertow 2000). All kinds of resources, be it energy, labour, knowledge, logistics and expertise are efficiently shared. The fundamental precondition for IS is cooperation among the companies, hence the need to investigate and understand these cooperation mechanisms. The interaction of IS between industry, resources and policy is complex, requiring the maximization of synergies from the bottom-up (industrial) and top-down (policy) perspectives within the economy. IS exhibits both detailed and dynamic complexity due to the degree of circularity and uncertainty on forward-reverse logistics modelling and optimization levels, in fact it represents a form of the Complex Adaptive System (CAS) involving multiple sectors and agents and displaying non-linear and non-rational interacting behaviours. Traditional approaches such as General Equilibrium Model (GEM), Input/Output analysis or Discrete Event Simulation (DES) are not capable of understanding these dynamic behaviours. Therefore, a new paradigm, which is able to capture the complexity of such a system and focus its efforts towards modelling the industrial symbiosis processes, is necessary.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/984225
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