Consideration of Cost and Environmental Emissions of Solid Waste Management under Conditions of Uncertainty

Abstract

Among the many models and tools available for solid waste management (SWM), the integrated SWM decision support tool (ISWM DST) developed at North Carolina State University provides a comprehensive and integrated approach that considers cost and environmental factors associated with a large set of waste processing options. ISWM DST is designed to generate alternative SWM strategies that meet user-defined cost and environmental objectives. In addition to an array of site-specific inputs, this tool includes a large number of model parameters, which are currently treated deterministically with point estimates for inputs. A high degree of variability and uncertainty is known to exist in these input parameters, affecting the uncertainty in the model outputs. The absence of a systematic procedure to consider uncertainty in ISWM DST is a major drawback. The goal of this study is to develop and incorporate an uncertainty analysis component into ISWM DST. A Latin Hypercube Sampling (LHS) procedure is coupled with a simulation approach to enable uncertainty propagation. The capabilities of this new component are demonstrated using a realistic case study in which a series of scenarios are examined assuming uncertainty in a subset of the input parameters. For each scenario, the alternative strategy development capabilities of ISWM DST is first applied, the then each SWM strategy is evaluated under conditions of uncertainty. Performance of alternative strategies is compared, and more reliable or robust strategies are identified. New and useful insights that were not apparent under deterministic conditions were gained, contributing more information to assist in SWM decision making. Further, correlation analysis was conducted to identify the uncertain input parameters that contribute mostly to the output uncertainty. This information is also expected to be valuable in making more informed decisions. In summary, this research contributes by significantly enhancing via the uncertainty analysis component the broad array of powerful capabilities of ISWM DST, making this tool more applicable in SWM planning and design practice. <