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A system dynamics-based framework for examining Circular Economy transitions

  • D. Guzzo
  • , D.C.A. Pigosso*
  • , N. Videira
  • , J. Mascarenhas
  • *Corresponding author for this work
    • University of São Paulo
    • NOVA University Lisbon

    Research output: Contribution to journalJournal articleResearchpeer-review

    1108 Downloads (Orbit)

    Abstract

    Decision-makers in the public policy and business arenas need tools to deal with multiple sources of complexity in Circular Economy (CE) transitions. System Dynamics (SD) facilitates coping with increased complexity by enabling closed-loop thinking via identifying the causal structures underlying behaviour and permitting to proactively experiment with the system through simulation. This research aims to propose and test an SD-based framework for examining CE transitions to supporting decision-making at the micro-, meso-, and macro-levels. Two inductive model-based cases studies led to formalising the framework, finally tested in a third deductive model-based case study. The framework is built upon the well-known stages for building SD simulation models and complemented with domain-specific activities, guiding questions, and expected outcomes when examining CE transitions. The SD-based framework is the first modelling-oriented prescriptive approach to help researchers and practitioners examining CE transitions on their journeys to understand and facilitate changes through SD simulation models.
    Original languageEnglish
    Article number129933
    JournalJournal of cleaner production
    Volume333
    Number of pages20
    ISSN0959-6526
    DOIs
    Publication statusPublished - 2022

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 8 - Decent Work and Economic Growth
      SDG 8 Decent Work and Economic Growth
    2. SDG 12 - Responsible Consumption and Production
      SDG 12 Responsible Consumption and Production

    Keywords

    • Circular economy
    • System dynamics
    • Simulation models
    • Sustainability transitions
    • Policy experimentation

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