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Controlling Ozone and Fine Particulates: Cost Benefit Analysis with Meteorological Variability

Author

Listed:
  • Shih, Jhih-Shyang
  • Bergin, Michelle S.
  • Krupnick, Alan J.
  • Russell, Armistead G.

Abstract

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.

Suggested Citation

  • Shih, Jhih-Shyang & Bergin, Michelle S. & Krupnick, Alan J. & Russell, Armistead G., 2003. "Controlling Ozone and Fine Particulates: Cost Benefit Analysis with Meteorological Variability," Discussion Papers 10735, Resources for the Future.
    • Handle: RePEc:ags:rffdps:10735
    DOI: 10.22004/ag.econ.10735
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    References listed on IDEAS

    as
    1. Krupnick, Alan J. & McConnell, Virginia D. & Cannon, Matthew & Stoessell, Terrell & Batz, Michael B., 2000. "Cost-Effective NOx Control in the Eastern United States," Discussion Papers 10483, Resources for the Future.
    2. Harvey J. Greenberg, 1995. "Mathematical Programming Models for Environmental Quality Control," Operations Research, INFORMS, vol. 43(4), pages 578-622, August.
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    Keywords

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    JEL classification:

    • C6 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling
    • Q2 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation
    • Q25 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Water
    • Q28 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Renewable Resources and Conservation - - - Government Policy

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