Nestedness of eco‐industrial networks: Exploring linkage distribution to promote sustainable industrial growth. (28th August 2020)

• Record Type: Journal Article
• Authors: Brehm, Colton; Layton, Astrid
• Is Part Of: Journal of industrial ecology. Volume 25:Number 1(2021)
• Page Start: 205
• Page End: 218
• Publication Date: 2020-08-28
• Subjects: bio‐inspired -- eco‐industrial networks -- industrial symbiosis -- industrial ecology -- nestedness -- sustainability; Industrial ecology -- Periodicals; Product life cycle -- Environmental aspects -- Periodicals; Industrial management -- Environmental aspects -- Periodicals; Écologie industrielle -- Périodiques; 658.56
• DOI: 10.1111/jiec.13057
• Languages: English
• ISSNs: 1088-1980

Abstract

Abstract: Eco‐industrial networks (EINs, of which eco‐industrial parks are a subset) have gained support as a solution that simultaneously reduces environmental burdens and promotes economic interests. EINs operate under a mutualistic framework, where waste materials and energy are exchanged between industries to their mutual benefit, creating a diverse web of flows. Recent studies have focused on analogies between food webs (FWs) and EINs, measuring a network's success at ecological imitation as representative of its sustainability. Studies have focused heavily on the number of links and nodes in a network, but have neglected the economic reality that each investment comes at the opportunity cost of all alternatives. This analysis focuses on the nestedness metric as used by ecologists to address this pivotal facet to the FW‐EIN analogy. Nestedness describes an ecological strategy for the position of links between nodes in a network in a way that maximizes network cycling for a given number of connections. This metric presents many advantages for EIN design and analysis, including maturity independence, size normalization, and a strong statistical record in highly mutualistic ecological systems. Application of nestedness to EINs indicates a lower presence of nested structures and more randomness than what is typically seen in FWs. The industrial networks also display a correlation between high nestedness and internal cycles, suggesting that the reuse of materials and energy in EINs can be improved upon by increasing the nestedness of structures.