Cost-benefit and Uncertainty Analysis for Ambient Ozone Reductions: Development and Demonstration of an Integrated Model and FrameworkEPA Grant Number: R825821
Title: Cost-benefit and Uncertainty Analysis for Ambient Ozone Reductions: Development and Demonstration of an Integrated Model and Framework
Investigators: Krupnick, Alan J. , Russell, Armistead G. , Shih, Jhih-Shyang
Current Investigators: Krupnick, Alan J. , Bergin, Michelle S. , Russell, Armistead G. , Shih, Jhih-Shyang
Institution: Resources for the Future , Georgia Institute of Technology
EPA Project Officer: Lee, Sonja
Project Period: October 1, 1997 through September 30, 2000 (Extended to September 30, 2002)
Project Amount: $300,000
RFA: Decision-Making and Valuation for Environmental Policy (1997) RFA Text | Recipients Lists
Research Category: Economics and Decision Sciences
The process of developing strategies to meet national ambient air quality standards has been a subject of considerable debate among the states and between them and the USEPA. Among the issues are: (i) the way the standards and demonstrations of attainment are formulated fails to take meteorological and other uncertainties into account, resulting in an unrealistically tight de facto standard; (ii) the lack of emphasis on and incentives for finding cost-effective approaches to meeting standards and (iii) the lack of recognition that different control policies affect environmental endpoints differently.
The proposed research has two primary goals. The first is to model the ozone non-attainment problem by integrating a stochastic photochemical model of ozone formation into an economic framework for controlling emission of the precursors of ozone under uncertainty. A stochastic air quality simulation framework and a stochastic cost-benefit analysis framework to be developed will allow us to examine the effects of uncertain inputs within the air quality simulation, exposure-response, and health valuation models. These effects include: methods for computing the effect of changes in inputs on model estimations, i.e., sensitivity analysis; methods for calculating the uncertainty in the model outputs induced by the uncertainties in its inputs, i.e. uncertainty propagation, and methods for comparing the importance of the input uncertainties in terms of their relative contributions to uncertainty in the output, i.e., importance analysis.
The second objective is to demonstrate how to conduct analyses of alternative ozone reduction policies using this modeling approach. Stochastic multi-objective programming models will be developed for this purpose. Alternative objective functions will be examined, including optimization of the stochastic responses of social benefits, optimization of the benefit obtained at individual receptor regions and maximization of the reliability of satisfying certain benefit goals. These models will be able to consider ozone concentrations at multiple locations. These models will yield information on emission control strategies of primary precursors from individual sources, the approximated distribution of net social benefits, and the approximate ozone concentration distribution at various geographical locations. This illustrative analysis will be conducted for the northeastern U.S.
The modeling framework and illustration may serve to improve our understanding of how uncertainties in ozone formation and movement, in costs and effectiveness, and in benefits alter possible control strategies relative to those arising from studies that do not take such effects into account. If the strategies appear greatly affected, a possible result would be for changes in EPA guidance to the states in their modeling and demonstration of attainment as part of their State Implementation Plans (SIPs) under the Clean Air Act. This research might also affect EPA deliberations and rulemakings on implementing the new ozone standards recently proposed.
The model to be developed for this project will be used to search for efficient NOx pollution control programs, such as emissions or ambient trading, in the Eastern U.S. This work will differ from (and, in effect, extend) the Ozone Transport Assessment Group's (OTAG's) deterministic modeling by including an environmental benefit component and stochastic elements of the air quality, cost, and benefit components.