Controlling Ozone and Fine Particulates: Cost Benefit Analysis with Meteorological Variability
Alan Krupnick (),
Jhih-Shyang Shih (),
S. Bergin and
Discussion Papers from Resources For the Future
In this paper, we develop an integrated cost-benefit analysis framework for ozone and fine particulate control, accounting for variability and uncertainty. The framework includes air quality simulation, sensitivity analysis, stochastic multi-objective air quality management, and stochastic cost-benefit analysis. This paper has two major contributions. The first is the development of stochastic source-receptor (S-R) coefficient matrices for ozone and fine particulate matter using an advanced air quality simulation model (URM-1ATM) and an efficient sensitivity algorithm (DDM-3D). The second is a demonstration of this framework for alternative ozone and PM2.5 reduction policies. Alternative objectives of the stochastic air quality management model include optimization of the net social benefits and maximization of the reliability of satisfying certain air quality goals. We also examine the effect of accounting for distributional concerns.
Keywords: ambient air; ozone; particulate matter; risk management; public policy; cost-benefit analysis; variability and uncertainty; stochastic simulation; stochastic multi-objective programming; decisionmaking; National Ambient Air Quality Standards (search for similar items in EconPapers)
JEL-codes: C6 Q2 Q25 Q28 (search for similar items in EconPapers)
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Working Paper: Controlling Ozone and Fine Particulates: Cost Benefit Analysis with Meteorological Variability (2003)
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Persistent link: https://EconPapers.repec.org/RePEc:rff:dpaper:dp-03-55
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