The current "take-make-use-dispose" paradigm of an extractive linear economy has led to unnecessary waste, pollution, and extraction of natural resources. The circular economy (CE) or “circularity” is a framework that aims to eliminate the extraction of materials from, and disposal/emission of waste/pollution to, the environment, by keeping products in use as long as possible and, rather than disposing them as waste at their end-of-life (EoL), restoring or regenerating them to their original form through biological decomposition or recirculation into the value chain through recycle, refurbishment, or repair.1 However, CE remains poorly defined, and although various metrics and indicators have been proposed to quantify circularity for an actor in a supply chain network,2,3 there is a lack of understanding of how these improvements in these indicators affect other actors in the CE network.
In this work we develop a generic framework for dynamic modeling of CE initiatives that can be used to understand how circular initiatives at the individual level affect other actors’ circular initiatives and the overall circularity of the network. We assume different actors in a generic supply chain include manufacturers, consumers, material recovery facilities (MRFs), and recycling facilities, which can be combined to generate synthetic CE networks for different case studies. The effects of different circularity initiatives on downstream actors are simulated by introducing step changes to the networks and comparing the resulting circular steady states using indicators proposed in the literature, such as those from MICRON and Circulytics.2,3 Sensitivity analyses are performed to analyze the effect of market parameters and consumer behavior on the circular steady states.
We use this methodology to model the supply chain for plastic packaging, a significant contributor to pollution in landfills and waterways.4,5 Our preliminary results indicate that circular initiatives at the actor level benefit some actors at the expense of others and do not always result in improved circularity of the overall network. For example, the consumer maximizes their circularity by using the product as long as possible, but this lowers the amount of recycled material recirculated into the value chain, increasing the cost of recycled material due to unfavorable economies of scale and thus limiting the amount of product sourced from recycled material.
(1) MacArthur, E. Towards the Circular Economy. J. Ind. Ecol. 2013, 2 (1), 23–44.
(2) Baratsas, S. G.; Pistikopoulos, E. N.; Avraamidou, S. A Quantitative and Holistic Circular Economy Assessment Framework at the Micro Level. Comput. Chem. Eng. 2022, 160, 107697. https://doi.org/10.1016/j.compchemeng.2022.107697.
(3) The EllenMacArthur Foundation. Circulytics- Method Introduction, Indicators, Definitions, Indistry Classification. The EllenMacArthur Foundation. https://www.ellenmacarthurfoundation.org/resources/circulytics/resources (accessed 2024-03-11).
(4) Tsakona, M.; Baker, E.; Rucevska, I.; Maes, T.; Rosendahl Appelquist, L.; Macmillan-Lawler, M.; Harris, P.; Raubenheimer, K.; Langeard, R.; Savelli-Soderberg, H.; Woodall, K.; Dittkrist, J.; Zwimpfer, T.; Aidis, R.; Mafuta, C.; Schoolmeester, T. Drowning in Plastics: Marine Litter and Plastic Waste Vital Graphics; 2021. https://doi.org/10.13140/RG.2.2.13444.65928.
(5) Smith, R. L.; Takkellapati, S.; Riegerix, R. C. Recycling of Plastics in the United States: Plastic Material Flows and Polyethylene Terephthalate (PET) Recycling Processes. ACS Sustain. Chem. Eng. 2022, 10 (6), 2084–2096. https://doi.org/10.1021/acssuschemeng.1c06845.
