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AN ANALYSIS OF THE RESIDENTIAL PREFERENCES FOR GREEN POWER-THE ROLE OF BIOENERGY Kim Jensen, Jamey Menard, Burt English, and Paul Jakus Professor, Research.

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Presentation on theme: "AN ANALYSIS OF THE RESIDENTIAL PREFERENCES FOR GREEN POWER-THE ROLE OF BIOENERGY Kim Jensen, Jamey Menard, Burt English, and Paul Jakus Professor, Research."— Presentation transcript:

1 AN ANALYSIS OF THE RESIDENTIAL PREFERENCES FOR GREEN POWER-THE ROLE OF BIOENERGY Kim Jensen, Jamey Menard, Burt English, and Paul Jakus Professor, 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 Bioenergy Potential to expand industrial consumption of agricultural commodities, adding rural jobs and increasing economic activity in rural regionsPotential 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 electricityUses 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 emissionsNot emission free, but compared with coal, significantly lower sulfur emissions Considered carbon neutralConsidered carbon neutral Introduction

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

4 Costs of Renewables Guey-Lee renewable sources, utilities to non-utilities, cents/kWhGuey-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 plantsOther 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) Introduction

5 Study Purpose Residential electricity consumers’ willingness to pay (WTP) for electricity from bioenergy and other renewable sourcesResidential 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 wastesExpands 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 sourcesWTP is compared across sources The effects of demographics, such as income and education, on willingness to pay are also examinedThe effects of demographics, such as income and education, on willingness to pay are also examined Introduction

6 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)% 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)Actual customer participation 4% or less (Swezey and Bird) Introduction

7 Prior Studies Farhar-69 percent placed “Wind” in their top three choices, only 26 percent placed “Biomass” in their top three choicesFarhar-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, respectively93 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 power53 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 choiceFarhar and Coburn-Colorado homeowners’ preferences-1.5 percent listed biomass as their top choice, while 33 percent listed solar cells as their top choice Introduction

8 Survey A survey was conducted by mail in Spring/Summer of Prior to the field survey, a pretest survey of 50 randomly selected residents was conductedA survey was conducted by mail in Spring/Summer of 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 sampleA 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 Study Methods

9 Survey Sections Support for and willingness to pay some positive amount for energy from renewable sourcesSupport 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 wastesConsumers’ 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, incomeSocioeconomics and demographics, such as age, education, income Study Methods

10 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)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.)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.) Study Methods

11 Survey Information sheet comparing land use, emissions, and other environmental impacts across specified renewable energy sources and coalInformation 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)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 programsPremium 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 levelReferendum format-respondents asked to indicate whether they would be willing to purchase the block of power at the specified premium level Study Methods

12 Economic Model Possible outcomesPossible 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 Study Methods

13 Economic model Probability will pay the premium 1-(1/[1+ exp( α + δ X– β Prem)]) Probability will pay something, but less than the premium (1/[1+ exp( α + δ X – β Prem)])-(1/(1+exp[ α + δ X])) (1/[1+ exp( α + δ X – β Prem)])-(1/(1+exp[ α + δ X])) Probability that will not pay any 1/[1+exp( α + δ X)] Prem=premium, X=demographics, etc., α, δ, β are parameters to be estimated WTP=ln[1+exp( α + δ X)]/ β 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) Study Methods

14 A total of 421 responded to the surveyA total of 421 responded to the survey 38.05% percent were willing to pay something more for renewable energy38.05% percent were willing to pay something more for renewable energy Results

15 Estimated Models Results

16 Premium was significant in both modelsPremium was significant in both models Income $25,000 or less was significant and negative in the modelsIncome $25,000 or less was significant and negative in the models Income from $60,001 to $75,000 was significant and positive in both modelsIncome 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 payCollege 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 significantThe 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 omittedOther variables, such as age, gender, recycling, and having had a home energy audit, were not significant in any of the models and were omitted Results Estimated Models

17 WTP Estimates Across Profiles WTP estimates calculated at sample means and for two profilesWTP 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. Results

18 WTP Estimates Across Profiles Results

19 $5$7$9$11$13$15$17$19 Crops $5.77 $ 7.19 $ 8.62 CL Mean CU Forest $5.47 $ 6.87 $ 8.28 CL Mean CU Landfill Wastes $7.35 $ 9.75 $12.14 CL Mean CU Solar $9.39 $12.71 $16.03 CL Mean CU Wind $11.03 $15.48 $19.94 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 Estimated WTP and 95% Confidence Intervals Across Energy Sources Results

20 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 energyMost 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 sourcesReasons 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 Results

21 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 percentPercentage 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 sourcesSomewhat 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 wastesHowever, no statistical difference between WTP for bioenergy and energy from landfill wastes Conclusions

22 About a $5-6 per month gap in WTP between solar and bioenergy sources (about $ per kWh)About a $5-6 per month gap in WTP between solar and bioenergy sources (about $ per kWh) About an $8-9 per month difference in WTP for wind compared with bioenergy sources (about $ per kWh)About an $8-9 per month difference in WTP for wind compared with bioenergy sources (about $ 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 sourcesWTP 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 Conclusions

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


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