AN ANALYSIS OF THE RESIDENTIAL PREFERENCES FOR GREEN POWER-THE ROLE OF BIOENERGY

1. AN ANALYSIS OF THE RESIDENTIAL PREFERENCES FOR GREEN POWER-THE ROLE OF BIOENERGY Kim Jensen, Jamey Menard, Burt English, and Paul JakusProfessor, Research Associate, and Professor, Agricultural Economics, University of Tennessee, Associate Professor, Economics, Utah State University Study funded in part by a grant from the USDA National Research Initiative Program.

2. Introduction Bioenergy • Potential to expand industrial consumption of agricultural commodities, adding rural jobs and increasing economic activity in rural regions • Uses renewable resources such as fast growing agricultural crops and trees or forest products wastes to produce electricity • Not emission free, but compared with coal, significantly lower sulfur emissions • Considered carbon neutral

3. Introduction Considerations • Hydroelectric, wind, and photovoltaic do not produce CO2 or SO2 emissions • Hydroelectric power faces environmental barriers related to construction of dams • Wind machines can be noisy and have significant impacts on the landscape • Photovoltaic costs are relatively high

4. Introduction Costs of Renewables • Guey-Lee renewable sources, utilities to non-utilities, cents/kWh • 6.86 conventional hydro • 11.77 landfill gas • 11.64 wind • 15.80 solar • 9.67 wood/wood waste • 6.27 municipal solid waste and landfills • 12.31 other biomass • Other estimates from biomass-fired plants • 9 cents per kWh (Department of Energy, Energy Efficiency and Renewable Energy Network) • 6.4 to 11.3 cents per kWh (Oak Ridge National Laboratory)

5. Introduction Study Purpose • Residential electricity consumers’ willingness to pay (WTP) for electricity from bioenergy and other renewable sources • Expands on prior research by dividing bioenergy into two sources: bioenergy from agricultural crops and bioenergy from forest products wastes. Other sources examined include solar, wind, and landfill wastes • WTP is compared across sources • The effects of demographics, such as income and education, on willingness to pay are also examined

6. Introduction Prior Studies • % WTP more for electricity from renewable sources ranges from 30 to 93 (Farhar; Farhar and Coburn; Farhar and Houston; Rowlands et al.; Tarnai and Moore; Zarnikau) • Actual customer participation 4% or less (Swezey and Bird)

7. Introduction Prior Studies • Farhar-69 percent placed “Wind” in their top three choices, only 26 percent placed “Biomass” in their top three choices • 93 percent somewhat or strongly favored solar power, while 64 percent and 59 percent somewhat or strongly favored landfill gas and forest waste, respectively • 53 percent would be willing to pay at least $4 a month more for electricity generated from biomass. In contrast, 65 percent said they would be willing to pay $6 per month more for wind power • Farhar and Coburn-Colorado homeowners’ preferences-1.5 percent listed biomass as their top choice, while 33 percent listed solar cells as their top choice

8. Study Methods Survey • A survey was conducted by mail in Spring/Summer of 2003. Prior to the field survey, a pretest survey of 50 randomly selected residents was conducted • A sample of 3,000 Tennessee residents was randomly drawn. A survey, cover letter, and information sheet about the renewable energy sources under study were mailed to individuals in the sample

9. Study Methods Survey Sections • Support for and willingness to pay some positive amount for energy from renewable sources • Consumers’ willingness to pay for renewable energy from several sources, including solar, wind, landfill wastes, bioenergy from fast growing crops, and bioenergy from forest products wastes • Socioeconomics and demographics, such as age, education, income

10. Study Methods Survey • Participants are asked to treat the hypothetical scenario as realistically as possible and they are reminded of their budget constraint (Kotchen and Reiling; Cummings and Taylor) • By allowing respondents to express support for renewable energy without requiring a price premium, bias associated with ‘yea saying’, perceived pressure to provide a “socially responsible” answer, may be minimized (Blamey et al.)

11. Study Methods Survey • Information sheet comparing land use, emissions, and other environmental impacts across specified renewable energy sources and coal • Sample evenly divided among five premium levels for a 150kWh block of green power to be purchased on the respondents’ monthly electric bill ($1.65, $3.75, $4.50, $6.00, and $13.00) • Premium levels, block of electricity sold hypothetically are based on data from existing green power programs • Referendum format-respondents asked to indicate whether they would be willing to purchase the block of power at the specified premium level

12. Study Methods Economic Model • Possible outcomes • not willing to pay any premium • would pay some nonzero premium less than the suggested premium • would be willing to pay at least the suggested premium

13. Study Methods Economic model • Spike model helps account for large spike or responses at 0 (not willing to pay anything or willing to pay some amount less than the premium provided) (Kriström) Probability will pay the premium 1-(1/[1+ exp(α+δX– βPrem)]) Probability will pay something, but less than the premium (1/[1+ exp(α+δX – βPrem)])-(1/(1+exp[α+δX])) Probability that will not pay any 1/[1+exp(α+δX)] Prem=premium, X=demographics, etc., α,δ, β are parameters to be estimated WTP=ln[1+exp(α+δX)]/β

14. Results • A total of 421 responded to the survey • 38.05% percent were willing to pay something more for renewable energy

15. Results Estimated Models

16. Results Estimated Models • Premium was significant in both models • Income $25,000 or less was significant and negative in the models • Income from $60,001 to $75,000 was significant and positive in both models • College education and contribution of time or money to an environmental organization had positive influences willingness to pay • The coefficient on county population was positive and significant • Other variables, such as age, gender, recycling, and having had a home energy audit, were not significant in any of the models and were omitted

17. Results WTP Estimates Across Profiles • WTP estimates calculated at sample means and for two profiles • The first profile is income $25,000 or less, not college educated, not a contributor to an environmental organization, and living in a county with 100,000 population. • The second profile is income $60,001 to 75,000, college educated, contributor to an environmental organization, and living in a county with a population of 600,000.

18. Results WTP Estimates Across Profiles

19. Results Wind $11.03 $15.48 $19.94 CL Mean CU Solar $9.39 $12.71 $16.03 CL Mean CU Landfill Wastes $7.35 $ 9.75 $12.14 CL Mean CU • No difference in WTP between bioenergy sources or between bioenergy and landfill wastes • WTP for energy from solar or wind> WTP for bioenergy Crops $5.77 $ 7.19 $ 8.62 CL Mean CU Forest $5.47 $ 6.87 $ 8.28 CL Mean CU Estimated WTP and 95% Confidence Intervals Across Energy Sources

20. Results Reasons for Not Paying More • Most who would not pay more, did support electricity from renewable sources, but they were not willing to pay any more. Only about 7 percent of the respondents did not support the concept of electricity from renewable energy • Reasons why not willing to pay more for energy from specified sources • Wind-visual appearance of the windmills and concerns about bird migration/deaths • Solar-disposal of the solar cells • Landfill wastes-air emissions from burning • Bioenergy from crops-environmental impacts of agriculture and displacement of acreage for food • Bioenergy from forest products wastes-deforestation and concerns air emissions from burning

21. Conclusions • Percentage of residential electricity consumers who are willing to pay premiums for electricity is much lower than found in prior studies, at 38 percent compared with estimates as high as 90 percent • Somewhat lower preference for electricity from crops or forest wastes than for electricity from solar or wind sources • However, no statistical difference between WTP for bioenergy and energy from landfill wastes

22. Conclusions • About a $5-6 per month gap in WTP between solar and bioenergy sources (about $.03-.04 per kWh) • About an $8-9 per month difference in WTP for wind compared with bioenergy sources (about $.05-.06 per kWh) • WTP estimates are compared with estimated costs of generation from prior research (Guey-Lee; Department of Energy, Energy Efficiency and Renewable Energy Network; Oak Ridge National Laboratory), gaps between WTP and costs appear to be greatest for solar and bioenergy sources

23. Conclusions • Income and education levels, contribution to environmental organizations, and urbanization influence willingness to pay- suggest potential for target marketing of electricity from renewable sources • Study confined geographically to one state, capabilities to examine effects of geographic location were limited • Future research should examine WTP across regions of the United States • Future research might also examine how investment in local green power projects versus purchases off green power markets affect willingness to pay for bioenergy