Systematic Exploration of Efficient Strategies to Manage Solid Waste in U.S. Municipalities: Perspectives from the Solid Waste Optimization Life-Cycle Framework (SWOLF)

Abstract

Solid waste management (SWM) systems must proactively adapt to changing policy requirements, waste composition, and an evolving energy system to sustainably manage future solid waste. This study represents the first application of an optimizable dynamic life-cycle assessment framework capable of considering these future changes. The framework was used to draw insights by analyzing the SWM system of a hypothetical suburban U.S. city of 100 000 people over 30 years while considering changes to population, waste generation, and energy mix and costs. The SWM system included 3 waste generation sectors, 30 types of waste materials, and 9 processes for waste separation, treatment, and disposal. A business-as-usual scenario (BAU) was compared to three optimization scenarios that (1) minimized cost (Min Cost), (2) maximized diversion (Max Diversion), and (3) minimized greenhouse gas (GHG) emissions (Min GHG) from the system. The Min Cost scenario saved $7.2 million (12%) and reduced GHG emissions (3%) relative to the BAU scenario. Compared to the Max Diversion scenario, the Min GHG scenario cost approximately 27% less and more than doubled the net reduction in GHG emissions. The results illustrate how the timed-deployment of technologies in response to changes in waste composition and the energy system results in more efficient SWM system performance compared to what is possible from static analyses.