A decision-support system for recycling of residents' waste plastics in China based on material flow analysis and life cycle assessment

Abstract

Recycling waste plastics is one of the important ways to save petroleum resources and reduce carbon emissions. However, the current recycling rate of waste plastics is still low. Material flow analysis can help determine the flow of waste plastics, and life cycle assessment (LCA) can be used to quantify environmental impacts. The present study integrates these two methods into the model construction of the residents' waste plastics recycling decision-support system. This model construction is followed by sensitivity analysis of the relevant parameters affecting the performance of the waste plastics recycling system. Finally, the present study forecasts the recycling system's performance and environmental impacts by setting four optimization scenarios based on sensitivity analysis. The results show that in 2019, a total of 8.39 million tons of high-end applications were recovered, carbon emissions during the recycling process were 34.9 million tons, and dioxin emissions were 316.11 g TEQ, with a total emission reduction of 24.47 million tons of CO₂ compared to the original production. Sensitivity analysis shows that the selection rate of waste plastic recycling, the re-sorting rate of waste plastic recycling plant, and the classification recovery rate of mixed waste had relatively high effects on the recovery performance and environmental benefits of the recycling system. In the scenario of comprehensive improvement, in 2035, the recycling volume of high-end applications will rise to 33.96 million tons, the carbon emissions will rise to 64.73 million tons, the dioxin emissions will drop to 165.98 g TEQ, and the carbon emission reduction will rise to 99.06 million tons. This study has a certain guiding role for policy-makers to formulate industry norms and related policies for waste plastic recycling.

Keywords: Life cycle assessment; Material flow analysis; Residents waste plastics; Scenario prediction; Sensitivity analysis.