Regional sensitivity analysis to assess critical parameters in circular economy interventions: An application to the dynamic MFA model

In evaluating a circular economy (CE), one needs to address the complexity arising from indicators with multiple objectives, multiple means of implementation with combinations of CE strategies, and the uncertainty inherent in resource cycle systems. Regional sensitivity analysis (RSA) is a version of global sensitivity analysis that can be used to determine whether the output variables of a mathematical model lie within a certain range. Although RSA has found application in a wide range of fields, no prior studies sought to apply the method to industrial ecology. In this study, RSA is applied to a dynamic material flow analysis (MFA) model to identify the essential factors and analyze the conditions under which two indicators, greenhouse gas emissions and total material requirement, are influenced, in the case studies for digital cameras and smartphones. To this end, RSA with 10,000 Monte Carlo simulations were performed. Two factors were found to be especially important: (1) controlling the collection channels of end-of-life products, and (2) encouraging consumers to use products over a longer period. It was also suggested that, for ambitious reductions in environmental impacts, the achievement of targets should be given priority over the speed of implementation of strategies. To avoid catastrophic environmental impacts, the first step should be to ensure higher recycling rates using well-developed collection routes. This study represents a major step forward from simply forecasting future cycles with the dynamic MFA model to the application of RSA to systematically consider parameter uncertainties with various possibilities of combined circular economy strategies.