Decision Making and Modeling Approaches in Circular Economy

Dear Colleagues,

Accelerating the transition towards a Circular Economy at scale requires evaluation of the complex trade-offs within the target state as well within the transition from a linear to a more circular configuration of value chains [1,2,3]. Consequently, the implementation of Circular Economy Initiatives requires the following:

• Collaboration across different actors along the value chain of materials, components, and products;
• The evaluation of reverse options currently not yet developed at scale;
• The modelling of complex interactions between actors exposed to shifting collaboration and competition opportunities.

As actors evaluate options across different key performance indicators with varying weights, the process of decision making to accelerate the transition towards more circular configurations at scale becomes increasingly complex and frequently requires modelling approaches to identify, initiate, and implement Circular Economy Initiatives [4,5].

As a result, practitioners in the private and public sector, as well as academics, need to construct, instantiate, and solve decision support models at different levels of aggregation, ranging from micro-, meso- and macro-economic models covering individual business cases for inter-company decision making to sector-spanning econometric modelling to inform policy initiatives at the intra-governmental scale [6].

Both original research and review works are welcome for submission. Research topics of interest include, but are not limited to, the following:

• Multi-criteria decision making for implementing Circular Economy value chains and systems [7];
• Modelling and decision support approaches, methods, and tools for an accelerated transition towards a Circular Economy at scale [8,9];
• Whole systems and sub-system modelling for Circular Economy [13,14];
• Agent-based modelling and other advanced methods for modelling, evaluating, and optimising alternative Circular Economy value chain configurations [17,18];
• Definition and prioritisation of key performance indicators, objective functions, and constraints to optimise decision making and modelling for a Circular Economy [10];
• Decision support systems for Circular Economy business model evaluation;
• Data collection and modelling for comparative modelling of alternative Circular Economy system configurations [15,16];
• Modelling and decision making under uncertainty in the Circular Economy [11];
• Case examples and best practices in decision making and modelling for a Circular Economy [12];
• Modelling of value creation and value capture approaches for a Circular Economy [19,20].

The implementation of the Circular Economy requires the reconfiguration of value chains from a linear to a more circular system configuration. Consequently, this topic fits the scope of Systems.

[1] Corona, B., Shen, L., Reike, D., Rosales Carreón, J., & Worrell, E. (2019).6 Towards sustainable development through the circular economy—A review and critical assessment on current circularity metrics. Resources, Conservation and Recycling, 151, 104498.

[2] Ahmed, A. A., Nazzal, M. A., Darras, B. M., & Deiab, I. M. (2022).9 A comprehensive multi-level circular economy assessment framework. Sustainable Production and Consumption, 32, 700-717.

[3] Balasbaneh, A. T., & Yeoh, C. C. (2025). A Systematic Review of Implementing Multi-Criteria Decision-Making (MCDM) Approaches for the Circular Economy and Cost Assessment.Sustainability, 17(11), 5007.1

[4] Hopkinson, P. De Angelis, R and Marcus Zils, (2020) Systemic building blocks for creating and capturing value from circular economy, Resources, Conservation and Recycling, Volume 155, 2020, 104672, ISSN 0921-3449,

[5] Potting, J., Hekkert, M., Worrel, E. & Hanemaaijer, A. (2017): Circular Economy: Measuring innovation in the product chain, Policy Report. https://www.pbl.nl/sites/default/files/downloads/pbl-2016-circular-economy-measuring-innovation-in-product-chains-2544.pdf

[6] Walzberg Julien , Lonca Geoffrey , Hanes Rebecca J. , Eberle Annika L. , Carpenter Alberta , Heath Garvin A. (2021) Do We Need a New Sustainability Assessment Method for the Circular Economy? A Critical Literature Review, Frontiers in Sustainability
Volume 1 – 2020
URL=https://www.frontiersin.org/journals/sustainability/articles/10.3389/frsus.2020.620047 DOI=10.3389/frsus.2020.620047 ISSN=2673-4524

[7] Meng, Y., Pigosso, D. C., & McAloone, T. C. (2025). Probabilistic Multi-Criteria Decision-Making for Circularity Performance of Modern Methods of Construction Products. ResearchGate.

[8] Zocco, F., & Malvezzi, M. (2024).4 Circular Economy Design through System Dynamics Modeling.Systems, 12(7), 223.

[9] Lahcen, B., Eyckmans, J., Rousseau, S., Dams, Y., & Brusselaers, J. (2022).5 Modelling the circular economy: Introducing a supply chain equilibrium approach. Ecological Economics, 197, 107451.

[10] Kerdlap, P., & Chavalparit, O. (2024). Towards Circular Building Key Performance Indicators. In Circular Economy and Sustainability (pp. 415-430). Springer, Cham.

[11] Stingl, V., & Geissdoerfer, M. (2021). Uncertainty in circular business model innovation: A systematic review. Journal of Cleaner Production, 311, 127670.

[12] Zils, M., Howard, M., & Hopkinson, P. (2023). Circular economy implementation in operations & supply chain management: Building a pathway to business transformation. Production Planning & Control, 36(4), 501–520. https://doi.org/10.1080/09537287.2023.2280907

[13] Bibas, R., Chateau, J., and Lanzi, E. (2021). Policy scenarios for a transition to a more resource efficient and circular economy. OECD Environment Working Papers, (169).

[14] Donati, F., Aguilar-Hernandez, G. A., Sigüenza-Sánchez, C. P., de Koning, A., Rodrigues, J. F., and Tukker, A. (2020). Modeling the circular economy in environmentally extended input- output tables: Methods, software and case study. Resources, Conservation and Recycling, 152:104508.

[15] Pronos & DevStat (2023a): Eurostat funded project. Lot 6: Economic aspects of the circular economy. Task 2 – Data compilation and dissemination. Data Note: Methods to compile data. June 2023. https://circabc.europa.eu/ui/group/b01d2930-990e-44fb-9121-a9a6b00a1283/library/5d95ee90-ff2b-447f-9c55-1d8469922273

[16] CES (Conference of European Statisticians) (2021): In-depth Review of Measuring Circular Economy, https:// unece.org/sites/default/files/2021-05/08_Measuring_Circular_Economy_full_version_ENG.pdf

[17] Tolk, Andreas & Richkus, Jennifer & Shaikh, Yahya. (2024). Towards Hybrid Modelling and Simulation Concepts for Complex Socio-technical Systems. 10.1007/978-3-031-59999-6_4.

[18] Walzberg, J., Lathuilliere, M. J., & Isenhour, C. (2023). Agent-Based Modeling and Simulation for the Circular Economy: Lessons Learned and Path Forward. Resources, Conservation and Recycling, 188, 106675.

[19] D. Guzzo, D.C.A. Pigosso, N. Videira, J. Mascarenhas,
A system dynamics-based framework for examining Circular Economy transitions,
Journal of Cleaner Production,
Volume 333, 2022, 129933, ISSN 0959-6526, https://doi.org/10.1016/j.jclepro.2021.129933.

[20] McCarthy, A., R. Dellink and R. Bibas (2018): The Macroeconomics of the Circular Economy Transition: A Critical Review of Modelling Approaches, OECD Environment Working Papers, No. 130, OECD Publishing. https://doi.org/10.1787/af983f9a-en

Prof. Dr. Markus Zils
Prof. Dr. Peter Hopkinson
Guest Editors

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• circular economy
• systems modelling
• circular economy value creation and capture
• circular economy key performance indicators
• multi-criteria decision making under uncertainty
• decision support systems and models
• circular economy business cases
• case examples and best practice
• advanced methods models for decision making