A Mathematical Programming Life-Cycle Assessment Model for Solid Waste Management Decision Making

In the future, greenhouse gas (GHG) mitigation policies that affect the U.S. energy mix as well as the cost of energy and emissions could significantly impact the strategic direction of SWM. As such, SWM systems must proactively adapt to changing waste composition, policy requirements, and an evolving energy system to cost-effectively and sustainably manage future solid waste. SWM life-cycle assessment (LCA) models integrated into an optimization framework can simultaneously consider all possible waste collection and treatment alternatives to find the combination of technologies that optimizes environmental and economic objectives. Such a framework must be able to represent multi-stage decisions to consider the changes to the SWM system over time. The objectives of this research are: to develop the Solid Waste Optimization Life-cycle Framework (SWOLF); to illustrate the use of the framework to analyze the economic and environmental impacts and trade-offs associated with SWM systems based on future changes to waste generation, waste composition, and energy projections; and to analyze the illustrative results to understand how variations in the energy system, GHG policy, and SWM policy affect optimal SWM decisions. Two case studies were developed that represent the first applications of an optimizable dynamic life-cycle assessment framework for SWM. The applicability of SWOLF to provide insights into a realistic SWM system was shown through a case study of a hypothetical suburban city over the next 30 years. The model was then used to investigate the effects of energy, GHG, and SWM policies on optimal SWM strategies. This case study required integrating SWOLF with energy system modeling results to investigate how changes in GHG policy and the energy system affect SWM system performance