Researchers Lauren Knapp M.S. Yiting Li M.S. Yufeng Ma M.S., M.A.E. Matthew Rife M.S. Co-Authors Jarod Kelly, Ph.D. University of Michigan Craig

Transcription

1 Researchers Lauren Knapp M.S. Yiting Li M.S. Yufeng Ma M.S., M.A.E. Matthew Rife M.S. Co-Authors Jarod Kelly, Ph.D. University of Michigan Craig Landry, Ph.D. East Carolina University Great Lakes Wind Collaborative Webinar January 13, 2014

2 Introduction and Research Objectives Project Deliverables Survey Instrument: Methodology and Responses WindPro Simulations Data Analysis: Approaches Results and Analysis Discussion & Conclusions 2

3 Key research question What are the underlying factors indicative of support or opposition for offshore wind development in Lake Michigan? Study regions Mason and Oceana Counties, MI Evanston, Rogers Park and Wilmette, IL Objectives Estimate resident preferences for offshore wind farm scenarios 3, 6, and 10 miles (+) or (-) price impact on electricity bill Analyze information sources, demographic characteristics, and opinion variables that could explain local opposition/support Estimate the non-market value of the lake view impact 3

4 Ladenberg and Dubgaard (2007, 2009) found a + WTP when residents in Denmark were presented with an option to move the wind farm to 12, 18, or 50 km from the coast relative to a 8 km baseline, suggesting a visual disamenity at the closest siting location. Kreuger et al. (2011) using CE found an annual social cost per inland, Delaware residents household for an offshore wind farm of $18.86, $8.74, $0.74, and $0 at 0.9, 3.6, 6, and 9 miles from shore. Landry, et al. (2012) results from a travel cost CE suggest while residents are averse to placement of wind turbines 1 mile off the coast, this aversion is no longer present at 4 miles and some respondents consider wind farm development adds value to the viewshed. 4

5 Citizens Greener Evanston reports 2013 Poster presentations 4 th National Forum on Socioeconomic Research in Coastal Systems (New Orleans, LA) American Wind Energy Association (AWEA) WindPower Annual Conference (Chicago, IL) Working draft journal articles Local welfare effects of wind development on Lake Michigan, Resource and Energy Economics Energy Policy 5

6 Format: Stated preference, contingent valuation method (CVM) survey with 5 sections Sampling: Geographic, systematic sampling Contact protocol: Priming letter + follow-up call, postcards Response bias controls: Self-selection bias (mailing language) Hypothetical bias ( Cheap talk ) Primacy effect (simulation randomization) Geographic Sample Initial sample (N) # Responses (n) Response Rate Illinois 2, % Mason County, MI % Oceana County, MI % 6

7 7

8 8

9 9

10 10

11 Random effects binary probit model: outcome variable = yes for CVM scenario Repeated measurement (3 observations per respondent) Dummy variables (demographics, opinions, simulation ordering effect) Average willingness to pay (WTP) 11

12 12

13 Michigan (n=122) Oppose 39% I have not yet made up my mind Undecided 0% 26% Inclination Oppose 7% Need more info 7% I have not yet made up my mind Oppose 0% 15% Illinois (n=208) Undecided 53% Inclination Oppose 6% Support 35% Support 12% Support 32% Support 24% Need more info 23% 13

14 Michigan (n=122) Illinois (n=208) 100% 80% 60% 40% 20% 0% -20% -40% -60% -80% -100% Improve No Impact Worsen 100% 80% 60% 40% 20% 0% -20% -40% -60% -80% -100% 14

15 Results indicate statistically significant effects for both of the CV scenario attributes (p<0.05, <0.01), but they differ notably in magnitude: The coefficient on bid price is negative, indicating the electricity rate impact variable holds an inverse relationship with the probability of support. There are strong preferences for wind farms sited > 3 miles from shoreline. Significant predictors for a wind farm 6- and 10-miles from shoreline, relative to the 3-mile near-shore distance, exhibit positive effects on support. Other positive statistically significant predictors of support: continuous household income and whether a respondent felt local jobs would improve. Respondents that support coal generation: statistically significant lower probability of support for offshore wind scenarios. 15

16 Wind Farm Distance Mean Willingness to Pay (WTP) / Month 3 miles offshore -$ miles offshore -$ miles offshore $29.08 To achieve 50% supportive vote for the wind farm sited at 3 and 6 miles offshore, the average respondent would need to be compensated. Results suggest respondents would pay a premium to have a wind farm 10 miles offshore. 16

17 Much more solidified opinions in Michigan region Prior exposure to Scandia proposal Higher uncertainty in Illinois region Empirical findings suggest that pre-existing support for traditional (coal) electricity-generation outlook negatively influence preferences for offshore wind development Yet, people that think jobs/local economy would improve support of offshore wind farm scenarios Positive WTP findings indicate people would value building an offshore wind farm further than 6 miles = social benefit Informational guide for policymaking: CBA Education is critical 17

18