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

Solid waste management (SWM) is an integral component of civil infrastructure and the broader U.S. economy and policy makers have taken an increasing interest in reducing environmental impacts associated with SWM. 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. SWOLF uses a mixed integer linear programming model to determine optimal SWM strategies while considering the interdependencies among processes in the SWM system. SWOLF is generalizable to include numerous SWM treatment facilities and collection options, and solves in less than two hours using readily available hardware and software.;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 results indicated that GHG emissions can increase with increased diversion, which suggests that diversion targets and material disposal bans may be counterproductive towards reducing GHG emissions in some instances. Relatedly, the model found that SWM strategies designed to reduce GHG emissions were more cost effective at reducing GHG emissions than SWM strategies designed to increase diversion, which indicates that SWM decision makers should focus on the environmental impacts they wish to reduce, instead of using potentially problematic proxies such as landfill diversion. The case study provided numerous insights that were only possible through the use of a stage-wise life-cycle optimization framework. For example, both the diversion maximizing and GHG minimizing scenarios showed stage-wise switching of anaerobic digestion (AD) and composting throughputs based on changes to waste composition and generation.;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. Minimum cost SWM strategies with GHG emission and diversion targets were affected by a carbon policy. Specifically, the model found that relative GHG benefits of WTE (and other electricity generating technologies) were dependent on waste composition (e.g., percent of paper and plastic) and electricity GHG intensity (e.g., relative contribution of coal, natural gas and renewables). These dynamic interdependencies can only be analyzed through the use of a multi-stage optimization framework. The analyses showed that it is critical for SWM decision makers to systematically consider changes to waste composition and generation, SWM policy, the U.S. energy system, and potential future GHG mitigation policies when develop.