Sustainability: Multi-Disciplinary Perspectives

Indexed in: Scopus, EBSCO.

The concept of sustainability is inherently multi-disciplinary because it concerns a complex system having economic, technological, ecological, political, and other perspectives. Consequently, any ...
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Industrial Ecology and Sustainable Development: Dynamics, Future Uncertainty and Distributed Decision Making

Pp. 243-272 (30)

Jim Petrie, Ruud Kempener and Jessica Beck


This chapter describes ways to enhance the operational potential of Industrial Ecology. Attention is paid to specific challenges for sustainable development beyond the broad aims of achieving economic competitiveness, ensuring environmental stewardship, and promoting both intra- and inter-generational equity. The focus is on the distributed decision making practices of individual agents within networks of industry, business and government, which, in various combinations, provide the underlying structure of any industrial ecology. Here, we consider the need for design and analysis tools to engage with the dynamics and uncertainty which characterize complex hierarchical socio-technical systems, including the ability to observe and interrogate system behaviour over multiple spatial and temporal scales, and to embrace the vitality which comes from human judgment in decision making within these systems. This capability should support the transition of such networks to ones which are both resilient and adaptive whilst pursuing.sustainability goals. We explore the role of both simulation and optimization toolkits in this regard, and conclude that there is value in a dual approach. Agent-based models of industrial ecosystems can be coupled with scenario analysis techniques to engage with future uncertainties. The way in which individual agents internalize such “world view” scenarios in their own distributed decision making practices, is highlighted. The effectiveness of agent interventions to support successful transitions to more sustainable practices can be measured against goal programming objectives, which in turn are defined by exploring the dynamic multiple objective optimisation decision space.


Industrial Ecology, Sustainable Development, Decision Making, Simulation, Agent-based modelling, Uncertainty industrial eco-systems, industrial networks, value chains, resource efficiency, robustness, resilience, adaptiveness, sustainability assessment, decision support frameworks, objectives hierarchies, decision trees, network structure, network characteristics, network performance, embeddedness, norms, routines, scenario analysis, cognition, mental models, bounded rationality, strategic choice.


School of Chemical and Bio-molecular Engineering, University of Sydney, NSW 2006, Australia