State Water Pollution Control Policy Insights from a Reduced-Form Model
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Schultz, Martin T. et al.; State Water Pollution Control Policy Insights from a Reduced-Form Model; Journal of Water Resources Planning and Management, Volume 130, Issue 2, 19 February, 2004; https://doi.org/10.1061/(ASCE)0733-9496(2004)130:2(150)
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This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
Public Domain Mark 1.0
This work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
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Abstract
Regulatory policy analyses are often based on the results of computer-intensive models that have limitations resulting from their complexity, size, and run-time requirements. This paper describes and applies a reduced-form model (RFM) of a large-scale water quality model developed for regulatory decision support. The RFM addresses the needs of decision makers who are interested in assessing uncertainty in model estimates and developing prescriptive applications of the model. This paper describes the RFM and demonstrates its applicability to four U.S. states that vary in environmental and socioeconomic characteristics. An application to combined sewer overflow (CSO) policy is developed to illustrate how the RFM can improve decision support. Economic benefits of CSO controls are simulated using the RFM and compared with the U.S. Environmental Protection Agency’s control cost estimates. The sensitivity of these benefits to assumptions of the benefit-cost analysis is tested. In terms of environmental decision making, the RFM reveals that it is more important to resolve what loading rates are most appropriate for benefit-cost analysis than it is to precisely model wet-weather hydrology.