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On the Complexity of Life Cycle Inventory Networks: Role of Life Cycle Processes with Network Analysis
Author(s) -
NavarreteGutiérrez Tomás,
Rugani Benedetto,
Pigné Yoann,
Marvuglia Antonino,
Benetto Enrico
Publication year - 2016
Publication title -
journal of industrial ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.377
H-Index - 102
eISSN - 1530-9290
pISSN - 1088-1980
DOI - 10.1111/jiec.12338
Subject(s) - social connectedness , computer science , network analysis , dynamic network analysis , node (physics) , centrality , numéraire , process (computing) , emergy , complex network , relevance (law) , sustainability , mathematics , engineering , computer network , econometrics , psychology , ecology , structural engineering , combinatorics , world wide web , law , political science , electrical engineering , psychotherapist , biology , operating system
Summary Determining the relevance and importance of a technosphere process or a cluster of processes in relation to the rest of the industrial network can provide insights into the sustainability of supply chains: those that need to be optimized or controlled/safeguarded. Network analysis (NA) can offer a broad framework of indicators to tackle this problem. In this article, we present a detailed analysis of a life cycle inventory (LCI) model from an NA perspective. Specifically, the network is represented as a directed graph and the “emergy” numeraire is used as the weight associated with the arcs of the network. The case study of a technological system for drinking water production is presented. We investigate the topological and structural characteristics of the network representation of this system and compare properties of its weighted and unweighted network, as well as the importance of nodes (i.e., life cycle unit processes). By identifying a number of advantages and limitations linked to the modeling complexity of such emergy‐LCI networks, we classify the LCI technosphere network of our case study as a complex network belonging to the scale‐free network family. The salient feature of this network family is represented by the presence of “hubs”: nodes that connect with many other nodes. Hub failures may imply relevant changes, decreases, or even breaks in the connectedness with other smaller hubs and nodes of the network. Hence, by identifying node centralities, we can rank and interpret the relevance of each node for its special role in the life cycle network.