Using trading ratios to correct for systematic selection in performance-based water quality trading programs
Date
2015
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
University of Delaware
Abstract
Scientific-based water quality models are used in water quality trading
programs to assess nutrient loadings. Maryland Nutrient Trading Tool (MDNTT)
calculates loading reductions on a land use, land cover map of all fields in the
Maryland and Delaware portion of the Chesapeake Bay Watershed for a host of
potential best management practices (BMPs). However, uncertainty remains. This
work hypothesizes that the BMP projects with largest overestimated abatement error
would be supplied first into trading program, which is called as a “first-worst”
selection.
An algorithm is developed to perform the simulation of abatement error and
four alternative systematic selection mechanisms: “first-worst” selection, “first-best”,
acreage targeting and benefits targeting. There are 100 treatments consisting of 10
possible levels of abatement heterogeneity and 10 possible levels of budget. The rule
of prioritization of the four mechanisms is different. The outputs of the simulation
experiment are the sum of error, the proportion of error of selected projects, and the
predicted values of the percentage of error from nonlinear model estimation.
As for “first-worst” selection, the results show that there is a convex
relationship between error and the budget ratio. Holding heterogeneity constant and in
each level of heterogeneity, total error of selected projects will increase as the budget
ratio increase from 10% to 50%, reaching the peak value, and then it will decrease as
budget ratio continues to rise. Holding the budget constant, the error will experience
an unstable growth as heterogeneity increases. The results offer an empirical estimate
of how best to vary trading ratios. When it comes to “first-best” selection, the results
show that under the largest level of abatement heterogeneity and in a thin market,
there is 50% more abatement than the estimates. This indicates that if policymakers
can target those underestimated projects, they can expect more abatement than
estimation with just a small amount of budget. However, trading cannot achieve the
“first-best” solution due to the information asymmetry. The results of two targeting
strategies show that benefit targeting can be regarded as a “second-best” solution.