Modeling Plastic Circularity under Material Degradation Constraints: An Agent-Based Study of Delaware’s Recycling Supply Chain

Efforts to advance circular plastic economies often prioritize technological solutions while underestimating material degradation and actors’ behavioral complexity. Yet such simplifications risk overstating long-term gains in recycled material uptake and emissions reduction. This study addresses that gap by employing an agent-based model to simulate the recycled plastic supply chain in the US State of Delaware, incorporating behavioral decision rules, policy influence, and a cap on mechanical recycling cycles. The model analyzes three scenarios: deterministic, yield-stochastic, and yield-and-demand-stochastic, each tested with and without degradation of plastic waste in recycling. Results show that while degradation constraints limit ultimate system performance, they also stabilize emissions and uptake, reducing the volatility of economic and emission metrics by around 10%. These findings underscore the importance of embedding material feedback and behavioral adaptation into circular economy models. The evaluation under different levels of uncertainty highlights that disruptions that affect the yields of the recycling plants can be absorbed by the consumers, which adapt their interactions. Such uncertainty in the yield even reduces the uncertainty of the economic and environmental indicators by nearly 80%. However, the uncertainty related to the consumers slightly increases the uncertainty over the deterministic case, remarking its critical influence on the overall supply chain performance.