1. Results Conditional Logit and Mixed Logit Regression Results: Willingness to Pay Results: Wind Energy in Your Community: Choice Experiment Will Scott & Buddy Reed - EC476 Seminar: The Economics of Ecosystems and Biodiversity Literature Review “A Spatial Hedonic Analysis of the Effects of Wind Energy Facilities on Surrounding Property Values in the United States” - Ben Hoen et. all Researchers collected data on 50,000 home sales among 27 counties in 9 states. The homes were within 10 miles of 67 different wind facilities Data spans period before announcement to after construction Used OLS and hedonic models to estimate the home-value impacts of the wind facilities Researchers found no statistical evidence that home values near turbines were affected “Wind Energy Facilities and Residential Properties: The Effect of Proximity and View on Sales Prices” Ben Hoen et. all Researchers investigated 7,500 sales of single-family homes surrounding 24 existing wind facilities in the United States Used four different hedonic models and various tests of robustness to determine impact of wind facilities on housing prices Results were consistent between models: neither the view of the wind facility nor the distance of the home to those facilities is found to have a statistically significant effect on sales prices Introduction Conclusions Different people have different preferences, and our research highlights some of these differences. The biggest differences in willingness to pay for wind installations can be found in the different income levels Major differences in the importance of community engagement as well as major differences in the importance of environmental precautions exist Other significant differences can be found between different age groups as well as genders Though many of our results are significant, our sample size is relatively small and generalizations of the U.S population cannot be made. Our results supplement precious theories that people are, in general, less willing to accept big, intrusive, and disruptive wind installments, however the extent of such objections differs among certain demographic groups. This research can be used to help assess which communities would be most willing to accept the construction of a new wind farm, thus creating an easier and less expensive permitting process for renewable energy companies. Methods In order to test our research questions, we created a choice experiment. A choice experiment works by asking survey respondents to choose between choices A, B, or C, each of which have different levels of different attributes. By having each respondent answer multiple questions and having a large number of respondents, we are able to use their data to determine consumer preferences. For our choice experiment, we created 3 different survey versions with 7 questions per survey. In each survey, there are six individual questions with the seventh question identical to the first. This was done because it sometimes takes respondents one question to understand the process. To find the appropriate attributes to include in our survey, we talked to industry executives who had previous experience working with communities in Maine. The attributes we used and their respectively levels are listed below: In addition to the questions about attributes, we added demographic questions to each survey to determine if there were any recurring themes between subgroups. Below is an example of a choice question for one of the surveys: Why do we care? Wind energy only accounts for 4% of energy used in the power sector Local communities show resistance against wind energy for a variety of reasons Wind energy produces clean, renewable energy for communities What can we do? Understand reasons for resistance against wind energy Incentivize citizens and communities to adopt wind energy Create choice experiment to survey citizens How can we do it? Use Conditional and Mixed Logit regression analysis to understand consumer preferences References Hoen, B., R. Wiser, P. Cappers, M. Thayer, and G. Sethi. The Impact of Wind Power Projects on Residential Property Values in the United States: A Multi-Site Hedonic Analysis. Lawrence Berkeley National Laboratory, Berkeley, CA, 2009. Hoen, B., Wiser, R., Cappers, P., Thayer, M. and Sethi, G. (2011)Wind Energy Facilities and Residential Properties: The Effect of Proximity and View on Sales Prices. Journal of Real Estate Research. 33(3): 279-316. Acknowledgements We would like to acknowledge the help we received from Professor Sahan T. M. Dissanayake in creating this project. In addition, we would like to thank Matt Kearns, VP of Business Development at First Wind, for his help. Research Questions Why do people oppose wind facilities in their local communities? What incentives can best be used to motivate local communities and residents to adopt wind energy? Which demographic groups are more or less likely to oppose wind energy? Respondent Demographics Respondent Education Distribution Discussion Out of the 199 respondents, we can assume that only 102 of said respondents were well-informed, having correctly answered a question hidden in the description of the survey. Looking at the results above, there are many important findings: In the original clogit and mixlogit regressions, all of the attributes were statistically significant except for benefit distribution. Out of the five attributes, environmental protection had the largest coefficient, consistent with our subsequent regressions. For the mixed and conditional logit willingness to pay, the results between the two are fairly consistent. Noticeably, the distance attribute is only statistically significant in the conditional logit willingness to pay test. In terms of differences between genders, males are found to be willing to pay for increased engagement and distance at a statistically significant level, while both groups are willing to pay for increased environmental precautions and project size. When looking at the difference between respondents over and under 45 years old, it is interesting to note the different attributes that are statistically significant. Younger people are willing to pay for larger projects, while older people are willing to pay to projects farther away.