Circular economy (CE) offers a pathway to achieve long-term sustainability through material reuse, recycling, and reduction (3R). This work develops a dynamic optimization framework to identify optimal time-dependent strategies for implementing these three levers of CE to achieve global sustainability. An integrated planetary model is refined to capture the 3Rs. The model is a network of interconnected compartments representing agricultural producers, the industrial sector, human consumers, and natural resource pools resembling a complex food web. A Fisher Information (FI) based optimization framework is employed with minimization of the FI variance as the objective function to ensure system stability. The circulation fraction ( and the fraction of the population reusing goods are the dynamic decision variables. Simulations are conducted over a 200-year horizon with a one-week timestep. Results show that the business-as-usual scenario leads to system collapse at 111 years, driven by agricultural and industrial resources exhaustion. Static optimization (time-independent policy) achieves full-horizon sustainability with optimal values of = 0.3–0.45 and = 0.15–0.25, depending on reuse duration. Dynamic optimization (time-varying policy) maintains sustainability with 20–40% lower average intervention levels compared to static strategies. The findings also reveal an inverse relationship between the reuse time and circulation fraction, as well as the fraction of the population reusing. The most stable outcomes occur at a reuse time of five time-steps, where dynamic control yields minimal variability and sustained ecological equilibrium. These findings provide quantitative targets for achieving SDG 12.
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