Innovation in the Valuation of Ecosystems A Forest ApplicationEPA Grant Number: R824699
Title: Innovation in the Valuation of Ecosystems A Forest Application
Investigators: Russell, Clifford , Dale, Virginia
Institution: Vanderbilt University , Oak Ridge National Laboratory
EPA Project Officer: Lee, Sonja
Project Period: October 1, 1995 through September 1, 1997
Project Amount: $139,327
RFA: Valuation and Environmental Policy (1995) RFA Text | Recipients Lists
Research Category: Economics and Decision Sciences
Description:This project experiments with the use of Multi-attribute Utility (MAU) methods as a basis for structuring direct surveys of willingness to pay to maintain ecosystems in particular conditions. These methods are consistent with the multi-dimensional character of ecosystems, and may offer a way to simplify the cognitive task presented to lay people by the complex and unfamiliar situations for which benefit estimates are now being sought.
The heart of the project is the construction of the multiple dimensions (attributes, characteristics) that are used to describe alternative forest states. (The case-study forests are those of the southern Appalachians.) These are intended to be: 1) ecologically meaningful and based in principle, on measurements that could be made in real forests, and 2) related to respondents "values" for forests (the reasons people value forests; the "functions" forests perform).
The contrast to be kept in mind is between telling respondents that a forest is "good for" hiking or camping or wildlife viewing and describing a forest in such a way that each respondent can decide those matters for her/himself.
The MAU method involves describing the characteristics, eliciting importance ranks and weights and then introducing money as another characteristic of the situation. Respondents are asked to trade changes in characteristics off against each other, including tradeoffs involving the money characteristic.
The design of the survey instrument and supporting materials involves the extensive use of focus groups and one-on-one, "think aloud" interviews. It is anticipated that in order to maximize learning about the method and its problems, data will be gathered from two or three "deliberative polling" exercises in which 50 to 100 people will be collectively walked through the survey with extensive opportunity to ask questions and provide feedback. There will also be a comparison to the results of more traditionally structured contingent valuation questions involving narrative descriptions of alternative forest conditions that "smear" together the attributes from the MAU exercise.
The results of this project should give at least a preliminary indication whether or not MAU techniques really will help make direct benefit estimation methods more robust in the face of complex, multi-dimensional environmental problem settings.