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Modeling Metal Flow Systems

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  • L. Andrew Bollinger
  • Chris Davis
  • Igor Nikolić
  • Gerard P.J. Dijkema

Abstract

Substance flow analysis (SFA) is a frequently used industrial ecology technique for studying societal metal flows, but it is limited in its ability to inform us about future developments in metal flow patterns and how we can affect them. Equation‐based simulation modeling techniques, such as dynamic SFA and system dynamics, can usefully complement static SFA studies in this respect, but they are also restricted in several ways. The objective of this article is to demonstrate the ability of agent‐based modeling to overcome these limitations and its usefulness as a tool for studying societal metal flow systems. The body of the article summarizes the parallel implementation of two models—an agent‐based model and a system dynamics model—both addressing the following research question: What conditions foster the development of a closed‐loop flow network for metals in mobile phones? The results from in silico experimentation with these models highlight three important differences between agent‐based modeling (ABM) and equation‐based modeling (EBM) techniques. An analysis of how these differences affected the insights that could be extracted from the constructed models points to several key advantages of ABM in the study of metal flow systems. In particular, this analysis suggests that a key advantage of the ABM technique is its flexibility to enable the representation of societal metal flow systems in a more native manner. This added flexibility endows modelers with enhanced leverage to identify options for steering metal flows and opens new opportunities for using the metaphor of an ecosystem to understand metal flow systems more fully.

Suggested Citation

  • L. Andrew Bollinger & Chris Davis & Igor Nikolić & Gerard P.J. Dijkema, 2012. "Modeling Metal Flow Systems," Journal of Industrial Ecology, Yale University, vol. 16(2), pages 176-190, April.
  • Handle: RePEc:bla:inecol:v:16:y:2012:i:2:p:176-190
    DOI: 10.1111/j.1530-9290.2011.00413.x
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    1. Daniel G. Brown & Rick Riolo & Derek T. Robinson & Michael North & William Rand, 2005. "Spatial process and data models: Toward integration of agent-based models and GIS," Journal of Geographical Systems, Springer, vol. 7(1), pages 25-47, October.
    2. van Vuuren, D. P. & Strengers, B. J. & De Vries, H. J. M., 1999. "Long-term perspectives on world metal use--a system-dynamics model," Resources Policy, Elsevier, vol. 25(4), pages 239-255, December.
    3. Steve Bankes, 1993. "Exploratory Modeling for Policy Analysis," Operations Research, INFORMS, vol. 41(3), pages 435-449, June.
    4. Leigh Tesfatsion & Kenneth L. Judd (ed.), 2006. "Handbook of Computational Economics," Handbook of Computational Economics, Elsevier, edition 1, volume 2, number 2.
    5. Tesfatsion, Leigh & Judd, Kenneth L., 2006. "Handbook of Computational Economics, Vol. 2: Agent-Based Computational Economics," Staff General Research Papers Archive 10368, Iowa State University, Department of Economics.
    6. Cao, Kai & Feng, Xiao & Wan, Hui, 2009. "Applying agent-based modeling to the evolution of eco-industrial systems," Ecological Economics, Elsevier, vol. 68(11), pages 2868-2876, September.
    7. David F. Batten, 2009. "Fostering Industrial Symbiosis With Agent‐Based Simulation and Participatory Modeling," Journal of Industrial Ecology, Yale University, vol. 13(2), pages 197-213, April.
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    Cited by:

    1. Michael Saidani & Alissa Kendall & Bernard Yannou & Yann Leroy & François Cluzel, 2019. "Closing the loop on platinum from catalytic converters: Contributions from material flow analysis and circularity indicators," Post-Print hal-02094798, HAL.
    2. Sinha, Rajib & Laurenti, Rafael & Singh, Jagdeep & Malmström, Maria E. & Frostell, Björn, 2016. "Identifying ways of closing the metal flow loop in the global mobile phone product system: A system dynamics modeling approach," Resources, Conservation & Recycling, Elsevier, vol. 113(C), pages 65-76.
    3. Joris Baars & Mohammad Ali Rajaeifar & Oliver Heidrich, 2022. "Quo vadis MFA? Integrated material flow analysis to support material efficiency," Journal of Industrial Ecology, Yale University, vol. 26(4), pages 1487-1503, August.
    4. Sauvageau, Gabriel & Frayret, Jean-Marc, 2015. "Waste paper procurement optimization: An agent-based simulation approach," European Journal of Operational Research, Elsevier, vol. 242(3), pages 987-998.
    5. Karan Bhuwalka & Randolph E. Kirchain & Elsa A. Olivetti & Richard Roth, 2023. "Quantifying the drivers of long‐term prices in materials supply chains," Journal of Industrial Ecology, Yale University, vol. 27(1), pages 141-154, February.

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