A Primer on Nonmarket Valuation

• Author(s): Patricia A. Champ, Kevin J. Boyle, Thomas C. Brown,
• Publisher: Springer
• Pages: 576
• ISBN_10: 1402014457
  ISBN_13: 9781402014451
  
• Language: en
• Categories: Business & Economics / Economic History , Business & Economics / Public Finance , Business & Economics / Economics / General , Business & Economics / Economics / Theory , Business & Economics / Industries / Agribusiness , Business & Economics / Environmental Economics , Technology & Engineering / Agriculture / General ,

Description:... A Primer on Nonmarket Valuation is unique in its clear descriptions of the most commonly used nonmarket valuation techniques and their implementation. Individuals working for government agencies, attorneys involved with natural resource damage assessments, graduate students, and others will appreciate the non-technical and practical tone of this book.
The first section of the book provides the context and theoretical foundation of nonmarket valuation, along with practical data issues.
The middle two sections of the Primer describe the major stated and revealed nonmarket valuation techniques. For each technique, the steps involved in implementation are laid out and described. Both practitioners of nonmarket valuation and those who are new to the field will come away from these methods chapters with a thorough understanding of how to design, implement, and analyze a nonmarket valuation study.
The concluding section takes stock of the usefulness of nonmarket valuation, highlighting chapters on benefit transfer, the role of nonmarket valuation in real decisions about natural resources, and where nonmarket valuation is headed in the future.
As a companion to A Primer on Nonmarket Valuation, a website has been developed, http://www.fs.fed.us/nonmarketprimerdata/. This website includes downloadable datasets for each of the techniques described in the Primer, as well as links to published journal articles and reports based on the data. The website also provides an opportunity for students to estimate models using the data.