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IMA Journal of Management Mathematics Advance Access originally published online on March 14, 2007
IMA Journal of Management Mathematics 2008 19(4):417-435; doi:10.1093/imaman/dpm005
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© The authors 2007. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.

This article appears in the following IMA Journal of Management Mathematics issue: Special Issue Stochastic Programming [View the issue table of contents]

Modelling health-based restrictions on pesticides in groundwater under uncertainty: a policy application

Keith Willett{dagger}

Department of Economics and Legal Studies in Business, College of Business Administration, Oklahoma State University, Stillwater, OK 74078, USA

Dolores Willett

Center for Innovation and Economic Development, Oklahoma State University, Stillwater, OK 74078, USA

{dagger} Email: keith.willett{at}okstate.edu

Accepted on 31 January 2007.

This paper has two objectives. First, the opportunity cost of health-based restrictions on pesticides in groundwater under uncertainty is examined under varying conditions. The determination of opportunity cost is based on a protocol which combines a stochastic simulation model with mathematical programming techniques that incorporate health-based risk considerations as well as elements of uncertainty. The model structure used is a safety-rule framework and begins with the establishment of a target for human health-related exposure to pesticides in groundwater. The changes in key parameters that impact the opportunity cost for a specific health-based level of pesticides in groundwater are identified and discussed. An empirical implementation of the model along with an analysis of the results is presented. The second objective of this paper is to develop an optimal tax based on the standards and charges approach. The numerical results from the empirical model are then used to calculate the various tax rates.

Keywords: pesticide contamination of groundwater; environmental risk indices; environmental taxation; nonpoint source pollution; stochastic programming; uncertainty


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