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Enhancing Recycling of Construction Materials: An Agent Based Model with Empirically Based Decision Parameters

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Abstract

Recycling of construction material is a valuable option for minimizing construction & demolition waste streams to landfills and mitigating primary mineral resource depletion. Material flows in the construction sector are governed by a complex socio-technical system in which awarding authorities decide in interaction with other actors on the use of construction materials. Currently, construction & demolition waste is still mainly deposited in landfills, as construction actors lack the necessary information and training regarding the use of recycled materials, and as a result have low levels of acceptance for them. This paper presents an agent-based model of the Swiss recycled construction material market based on empirical data derived from the agent operationalization approach. It elaborates on how recycling of construction materials can be enhanced by analysing key factors affecting the demand for recycled construction materials and developing scenarios towards a sustainable construction waste management. Doing so it demonstrates how detailed empirical agent decision data were incrementally included in the ABM model. Raising construction actors’ awareness of recycled materials as a decision option, in combination with small price incentives was most effective for enhancing the use of recycled materials. This could lead to a 50% reduction of construction & demolition waste streams to landfills, and significantly reduce the environmental impacts related to concrete applications. From a methodological perspective, although the agent operationalization approach provides a large empirical foundation, incremental model development turned out to be particularly important for the traceability of results and a realistic system representation.

Suggested Citation

  • Christof Knoeri & Igor Nikolic & Hans-Joerg Althaus & Claudia R. Binder, 2014. "Enhancing Recycling of Construction Materials: An Agent Based Model with Empirically Based Decision Parameters," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 17(3), pages 1-10.
    • Handle: RePEc:jas:jasssj:2012-124-3
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    Cited by:

    1. Oluwole Abayomi Soyinka & Mesthrige Jayantha Wadu & Udara Willhelm Abeydera Lebunu Hewage & Timo Olugbenga Oladinrin, 2023. "Scientometric review of construction demolition waste management: a global sustainability perspective," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(10), pages 10533-10565, October.
    2. Claudius Gräbner & Catherine S. E. Bale & Bernardo Alves Furtado & Brais Alvarez-Pereira & James E. Gentile & Heath Henderson & Francesca Lipari, 2019. "Getting the Best of Both Worlds? Developing Complementary Equation-Based and Agent-Based Models," Computational Economics, Springer;Society for Computational Economics, vol. 53(2), pages 763-782, February.
    3. Valeria Superti & Cynthia Houmani & Ralph Hansmann & Ivo Baur & Claudia R. Binder, 2021. "Strategies for a Circular Economy in the Construction and Demolition Sector: Identifying the Factors Affecting the Recommendation of Recycled Concrete," Sustainability, MDPI, vol. 13(8), pages 1-32, April.
    4. Rizzati, Massimiliano & Landoni, Matteo, 2024. "A systematic review of agent-based modelling in the circular economy: Insights towards a general model," Structural Change and Economic Dynamics, Elsevier, vol. 69(C), pages 617-631.
    5. Busch, Jonathan & Roelich, Katy & Bale, Catherine S.E. & Knoeri, Christof, 2017. "Scaling up local energy infrastructure; An agent-based model of the emergence of district heating networks," Energy Policy, Elsevier, vol. 100(C), pages 170-180.
    6. Zhikun Ding & Wenyan Gong & Shenghan Li & Zezhou Wu, 2018. "System Dynamics versus Agent-Based Modeling: A Review of Complexity Simulation in Construction Waste Management," Sustainability, MDPI, vol. 10(7), pages 1-13, July.

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