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INTEGRATED UNCERTAINTY ANALYSIS TO SUPPORT EFFECTIVE ENVIRONMENTAL DECISION-MAKING
Impact/Purpose:
Every environmental management decision, whether evaluating a site-specific remedial action or developing national policy alternatives, relies on one or more predictive models. Each of these models generates outputs with associated uncertainties that are used as inputs for subsequent models across a set of linked modules. There are numerical and analytical methods available for propagating uncertainty across modules for quantifiable sources of uncertainties. For uncertainties that are difficult to quantify, there are scenario-based methods that can be used to bound the results. However, propagating uncertainty across models and modules represents only one part of the puzzle: the results of these analyses require communication to decision-makers and other interested stakeholders. Uncertainty analyses, particularly multi-dimensional probabilistic analyses, can be difficult to communicate and to understand, hindering their effective use in decision-making. To address these issues, the objectives of this research are to 1) quantify uncertainty in several models used in many Regulatory Impact Analyses (RIAs) using exposure to mercury as a case study, 2) develop an integrated uncertainty analysis following the conceptual framework used in the mercury RIA (CAMR, EPA, 2005), and, 3) work with two regional stakeholder panels with the goal of identifying strategies that lead to greater understanding of uncertainty in integrated models used to support decision-making.
Description:
The expected results of this research are to 1) contribute to our understanding of dominant uncertainties in models typically used across many RIAs, 2) identify integrated uncertainty analysis strategies that the EPA and other regulatory agencies can use to evaluate overall uncertainty in RIAs, and, 3) identify effective communication and presentation strategies elicited through a stakeholder panel consisting of regional decision-makers.