Research Grants/Fellowships/SBIR

The Role of Information and Learning in Nonpoint Source Pollution Control

EPA Grant Number: U915184
Title: The Role of Information and Learning in Nonpoint Source Pollution Control
Investigators: Kaplan, Jonathan D.
Institution: University of California - Davis
EPA Project Officer: Jones, Brandon
Project Period: January 1, 1997 through January 1, 2000
Project Amount: $102,000
RFA: STAR Graduate Fellowships (1997) RFA Text |  Recipients Lists
Research Category: Academic Fellowships , Economics and Decision Sciences , Fellowship - Economics



The objectives of this research is three fold. (1) construct a theoretical nonpoint source (NPS) pollution control model to better understand the optimal budget tradeoff between direct treatment of sources versusand data collection, and to acquire information and learn the relative pollution loading among the sources; Second, (2) develop a maximum entropy specification of the Kalman filter in order to statistically update the estimated NPS pollution loading parameters as new data becomes available; (3) apply the model and estimation approach with data collected from the management of sediment control in Redwood National Park in Orick, California.


The theoretical NPS pollution control model depicts the behavior of a budget-constrained manager, who minimizes the pollution-related damage by choosing the levels of data collection to acquire information on pollution loading and direct treatment to control the pollution-loading sources. This model examines the tradeoff between data collection and treatment. A numerical example is used to illustrate the role of information and learning in reducing NPS pollution uncertainty, and thus minimizing pollution-related damages. To properly estimate the NPS pollution loading from among the various sources, a sequential entropy-specified Kalman filter, which can accommodate ill-posed NPS pollution data, is developed and compared with traditional approaches to statistical Bayesian updating. This statistical updating or learning method then is then applied to data provided by Redwood National Park. Finally, the theoretical and methodological results are incorporated into a sediment control model for the Redwood National Ppark. To provide empirical policy analysis to the sediment control problem in Redwood National Park, sensitivity analysis is conducted with respect to the level and type of data collection to determine changes in the optimal treatment and data collection strategies.

Supplemental Keywords:

fellowship, nonpoint source pollution control, NPS, Redwood National Park, California, CA., RFA, Economic, Social, & Behavioral Science Research Program, Scientific Discipline, Economics and Business, decision-making, Ecology and Ecosystems, Economics & Decision Making, nonpoint source pollution, maximum entropy specification, Bayesian approach, economic benefits, decision making, pollution control model, behavior change