Master's Defense: Patricia Hickson
The public is invited to attend Patricia Hickson's Master's defense
Tuesday, June 12, 10:00am
Room 100 Hendricks Hall
Explaining Willingness to Pay for Watershed Protection.
Sense of Place Matters
Master of Community and Regional Planning, June 2012
Payment for watershed service (PWS) programs are increasing in popularity as a watershed conservation strategy. Already there are at least 216 active PWS programs in 24 different countries, including at least 32 programs in the U.S. As a market-based conservation mechanism, PWS programs rely on those who benefit from a healthy watershed to fund some portion of actions planned to restore or protect watershed health. That is, the success of PWS programs hinges on beneficiaries being willing to pay (WTP) for watershed protection. But what predicts a beneficiary’s willingness-to pay?
Sense of place has not been well studied as a characteristic for predicting WTP for environmental goods even though many conservation efforts assume developing a relationship with a landscape is important for encouraging positive environmental attitudes and behaviors. This study uses the response data generated by a survey of Eugene Water and Electric Board residential water rate payers to examine the effect of “sense of place” on rate payers’ WTP for protection of their local watershed. Using a regression analysis I compared the effect of sense of place on WTP to two other respondent characteristics already known to be strongly correlated with WTP for environmental goods: intensity of use and political ideology. I found that sense of place is an important predictor of WTP. It explains WTP better than user intensity and at least as well as political ideology when controlling for the socio-demographic characteristics of income, age, gender and education. This result suggests that policy makers considering a PWS program should evaluate the strength of the relationship PWS program payers have with the watershed in question at least as much as other demographic characteristics when trying to predict program support.