AN ANALYSIS OF THE AN ANALYSIS OF THE RESIDENTIAL PREFERENCES RESIDENTIAL PREFERENCES

FOR FOR GREEN POWER-THE ROLE OF GREEN POWER-THE ROLE OF

BIOENERGYBIOENERGYKim Jensen, Jamey Menard, Kim Jensen, Jamey Menard, Burt English, and Paul JakusBurt English, and Paul Jakus

Professor, Research Associate, and Professor, Research Associate, and Professor, Agricultural Economics, Professor, Agricultural Economics, University of Tennessee, Associate University of Tennessee, Associate Professor, Economics, Utah State Professor, Economics, Utah State

UniversityUniversityStudy funded in part by a grant from the USDA National Research Initiative Program.

BioenergyBioenergy• Potential to expand industrial consumption Potential to expand industrial consumption

of agricultural commodities, adding rural of agricultural commodities, adding rural jobs and increasing economic activity in jobs and increasing economic activity in rural regionsrural regions

• Uses renewable resources such as fast Uses renewable resources such as fast growing agricultural crops and trees or growing agricultural crops and trees or forest products wastes to produce forest products wastes to produce electricityelectricity

• Not emission free, but compared with coal, Not emission free, but compared with coal, significantly lower sulfur emissionssignificantly lower sulfur emissions

• Considered carbon neutral Considered carbon neutral

Introduction

ConsiderationsConsiderations

• Hydroelectric, wind, and photovoltaic Hydroelectric, wind, and photovoltaic do not produce CO2 or SO2 emissionsdo not produce CO2 or SO2 emissions

• Hydroelectric power faces Hydroelectric power faces environmental barriers related to environmental barriers related to construction of damsconstruction of dams

• Wind machines can be noisy and have Wind machines can be noisy and have significant impacts on the landscapesignificant impacts on the landscape

• Photovoltaic costs are relatively highPhotovoltaic costs are relatively high

Introduction

Costs of RenewablesCosts of Renewables• Guey-Lee renewable sources, utilities to non-utilities, Guey-Lee renewable sources, utilities to non-utilities,

cents/kWhcents/kWh– 6.86 conventional hydro6.86 conventional hydro– 11.77 landfill gas11.77 landfill gas– 11.64 wind11.64 wind– 15.80 solar15.80 solar– 9.67 wood/wood waste9.67 wood/wood waste– 6.27 municipal solid waste and landfills6.27 municipal solid waste and landfills– 12.31 other biomass 12.31 other biomass

• Other estimates from biomass-fired plantsOther estimates from biomass-fired plants– 9 cents per kWh (Department of Energy, Energy Efficiency 9 cents per kWh (Department of Energy, Energy Efficiency

and Renewable Energy Network) and Renewable Energy Network) – 6.4 to 11.3 cents per kWh (Oak Ridge National Laboratory)6.4 to 11.3 cents per kWh (Oak Ridge National Laboratory)

Introduction

Study PurposeStudy Purpose• Residential electricity consumers’ willingness Residential electricity consumers’ willingness

to pay (WTP) for electricity from bioenergy to pay (WTP) for electricity from bioenergy and other renewable sourcesand other renewable sources

• Expands on prior research by dividing Expands on prior research by dividing bioenergy into two sources: bioenergy from bioenergy into two sources: bioenergy from agricultural crops and bioenergy from forest agricultural crops and bioenergy from forest products wastes. Other sources examined products wastes. Other sources examined include solar, wind, and landfill wastesinclude solar, wind, and landfill wastes

• WTP is compared across sourcesWTP is compared across sources• The effects of demographics, such as income The effects of demographics, such as income

and education, on willingness to pay are also and education, on willingness to pay are also examinedexamined

Introduction

Prior StudiesPrior Studies• % WTP more for electricity from % WTP more for electricity from

renewable sources ranges from 30 to renewable sources ranges from 30 to 93 (Farhar; Farhar and Coburn; 93 (Farhar; Farhar and Coburn; Farhar and Houston; Rowlands et al.; Farhar and Houston; Rowlands et al.; Tarnai and Moore; Zarnikau)Tarnai and Moore; Zarnikau)

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

Introduction

Prior StudiesPrior Studies• Farhar-69 percent placed “Wind” in their top three Farhar-69 percent placed “Wind” in their top three

choices, only 26 percent placed “Biomass” in their choices, only 26 percent placed “Biomass” in their top three choicestop three choices

• 93 percent somewhat or strongly favored solar 93 percent somewhat or strongly favored solar power, while 64 percent and 59 percent somewhat power, while 64 percent and 59 percent somewhat or strongly favored landfill gas and forest waste, or strongly favored landfill gas and forest waste, respectivelyrespectively

• 53 percent would be willing to pay at least $4 a 53 percent would be willing to pay at least $4 a month more for electricity generated from biomass. month more for electricity generated from biomass. In contrast, 65 percent said they would be willing to In contrast, 65 percent said they would be willing to pay $6 per month more for wind powerpay $6 per month more for wind power

• Farhar and Coburn-Colorado homeowners’ Farhar and Coburn-Colorado homeowners’ preferences-1.5 percent listed biomass as their top preferences-1.5 percent listed biomass as their top choice, while 33 percent listed solar cells as their top choice, while 33 percent listed solar cells as their top choicechoice

Introduction

SurveySurvey

• A survey was conducted by mail in A survey was conducted by mail in Spring/Summer of 2003. Prior to the Spring/Summer of 2003. Prior to the field survey, a pretest survey of 50 field survey, a pretest survey of 50 randomly selected residents was randomly selected residents was conductedconducted

• A sample of 3,000 Tennessee residents A sample of 3,000 Tennessee residents was randomly drawn. A survey, cover was randomly drawn. A survey, cover letter, and information sheet about the letter, and information sheet about the renewable energy sources under study renewable energy sources under study were mailed to individuals in the samplewere mailed to individuals in the sample

Study Methods

SurveySurvey

SectionsSections• Support for and willingness to pay some Support for and willingness to pay some

positive amount for energy from renewable positive amount for energy from renewable sourcessources

• Consumers’ willingness to pay for renewable Consumers’ willingness to pay for renewable energy from several sources, including energy from several sources, including solar, wind, landfill wastes, bioenergy from solar, wind, landfill wastes, bioenergy from fast growing crops, and bioenergy from fast growing crops, and bioenergy from forest products wastesforest products wastes

• Socioeconomics and demographics, such as Socioeconomics and demographics, such as age, education, incomeage, education, income

Study Methods

SurveySurvey• Participants are asked to treat the hypothetical Participants are asked to treat the hypothetical

scenario as realistically as possible and they scenario as realistically as possible and they are reminded of their budget constraint are reminded of their budget constraint (Kotchen and Reiling; Cummings and Taylor)(Kotchen and Reiling; Cummings and Taylor)

• By allowing respondents to express support for By allowing respondents to express support for renewable energy without requiring a price renewable energy without requiring a price premium, bias associated with ‘yea saying’, premium, bias associated with ‘yea saying’, perceived pressure to provide a “socially perceived pressure to provide a “socially responsible” answer, may be minimized responsible” answer, may be minimized (Blamey et al.)(Blamey et al.)

Study Methods

SurveySurvey• Information sheet comparing land use, emissions, Information sheet comparing land use, emissions,

and other environmental impacts across specified and other environmental impacts across specified renewable energy sources and coalrenewable energy sources and coal

• Sample evenly divided among five premium Sample evenly divided among five premium levels for a 150kWh block of green power to be levels for a 150kWh block of green power to be purchased on the respondents’ monthly electric purchased on the respondents’ monthly electric bill ($1.65, $3.75, $4.50, $6.00, and $13.00)bill ($1.65, $3.75, $4.50, $6.00, and $13.00)

• Premium levels, block of electricity sold Premium levels, block of electricity sold hypothetically are based on data from existing hypothetically are based on data from existing green power programsgreen power programs

• Referendum format-respondents asked to Referendum format-respondents asked to indicate whether they would be willing to indicate whether they would be willing to purchase the block of power at the specified purchase the block of power at the specified premium levelpremium level

Study Methods

Economic ModelEconomic Model

• Possible outcomesPossible outcomes– not willing to pay any premium not willing to pay any premium – would pay some nonzero premium less would pay some nonzero premium less

than the suggested premium than the suggested premium – would be willing to pay at least the would be willing to pay at least the

suggested premiumsuggested premium

Study Methods

Economic modelEconomic model

Probability will pay the premiumProbability will pay the premium1-(1/[1+ exp(α+1-(1/[1+ exp(α+δδXX– βPrem)])– βPrem)])

Probability will pay something, but less than Probability will pay something, but less than the premiumthe premium

(1/[1+ exp(α+(1/[1+ exp(α+δδXX – βPrem)])-(1/(1+exp[α+ – βPrem)])-(1/(1+exp[α+δδX]X]))))

Probability that will not pay anyProbability that will not pay any1/[1+exp(α+1/[1+exp(α+δδXX)])]Prem=premium, X=demographics, etc., α,Prem=premium, X=demographics, etc., α,δδ,, β are β are

parameters to be estimatedparameters to be estimated

WTP=ln[1+exp(α+WTP=ln[1+exp(α+δδXX)]/β)]/β

Spike model helps account for large spike or Spike model helps account for large spike or responses at 0 (not willing to pay anything or responses at 0 (not willing to pay anything or willing to pay some amount less than the willing to pay some amount less than the premium provided) (Kriström)premium provided) (Kriström)

Study Methods

• A total of 421 responded to the survey A total of 421 responded to the survey • 38.05% percent were willing to pay 38.05% percent were willing to pay

something more for renewable energysomething more for renewable energy

30.38 30.68

32.45

33.9234.51

28

30

32

34

36

% Who

Would Pay

Energy Source

Forest Products Wastes Crops

Landfill Wastes Solar

Wind

Results

Table 1. Estimated Spike Models of WTP for Bioenergy from Crops and from Forest Products Wastes.a

Fast Growing

Crops Forest Products

Wastes Intercept -1.5608 *** -1.5844 *** (.3523) (.3616) Premium -.0617 *** -.0649) *** (.0121) (.0126) Income $25,000 or less -.6711 * -.7500 *** (.3900) (.4051) Income $60,001 to $75,000 .5903 * .6166 * (.3345) (.3320) At Least Some College Education .7968 ** .8376 ** (.3455) (.3577) Contribution of Time or Money to Environmental Organization .8225 *** .9483 *** (.2748) (.2695) County Population (10,000) .0007 * .0006 * (.0004) (.0004) LLF 267.8647 268.3115 N 335 335 % Correctly Classified

a ***=significant at α=.01, **=significant at α=.05, *=significant at α=.10.

Estimated ModelsEstimated ModelsResults

• PremiumPremium was significant in both models was significant in both models• Income $25,000 or less was significant and Income $25,000 or less was significant and

negative in the modelsnegative in the models• Income from $60,001 to $75,000 was significant Income from $60,001 to $75,000 was significant

and positive in both models and positive in both models • College education and contribution of time or College education and contribution of time or

money to an environmental organization had money to an environmental organization had positive influences willingness to pay positive influences willingness to pay

• The coefficient on county population was positive The coefficient on county population was positive and significantand significant

• Other variables, such as age, gender, recycling, Other variables, such as age, gender, recycling, and having had a home energy audit, were not and having had a home energy audit, were not significant in any of the models and were omittedsignificant in any of the models and were omitted

Results

Estimated ModelsEstimated Models

WTP Estimates Across ProfilesWTP Estimates Across Profiles

• WTP estimates calculated at sample WTP estimates calculated at sample means and for two profilesmeans and for two profiles– The first profile is income $25,000 or less, The first profile is income $25,000 or less,

not college educated, not a contributor to not college educated, not a contributor to an environmental organization, and living an environmental organization, and living in a county with 100,000 population. in a county with 100,000 population.

– The second profile is income $60,001 to The second profile is income $60,001 to 75,000, college educated, contributor to 75,000, college educated, contributor to an environmental organization, and living an environmental organization, and living in a county with a population of 600,000. in a county with a population of 600,000.

Results

Crops

5.77

0.89

16.39

7.19

1.78

22.31

8.62

2.65

28.24

0 10 20 30

Sample

Profile 1

Profile 2

CL Means CU

Forest Products Wastes

1.04

0.7

16.83

6.87

1.52

22.7

12.71

2.33

28.5

0 10 20 30

Sample

Profile 1

Profile 2

WTP WTP Estimates Estimates Across ProfilesAcross Profiles

Results

$5$5 $7$7 $9$9 $11$11 $13$13 $15$15 $17$17 $19$19

Crops

$5.77 $ 7.19 $ 8.62CL 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

Reasons for Not Paying MoreReasons for Not Paying More• Most who would not pay more, did support Most who would not pay more, did support

electricity from renewable sources, but they electricity from renewable sources, but they were not willing to pay any more. Only about were not willing to pay any more. Only about 7 percent of the respondents did not support 7 percent of the respondents did not support the concept of electricity from renewable the concept of electricity from renewable energyenergy

• Reasons why not willing to pay more for Reasons why not willing to pay more for energy from specified sources energy from specified sources – Wind-visual appearance of the windmills and Wind-visual appearance of the windmills and

concerns about bird migration/deaths concerns about bird migration/deaths – Solar-disposal of the solar cellsSolar-disposal of the solar cells– Landfill wastes-air emissions from burning Landfill wastes-air emissions from burning – Bioenergy from crops-environmental impacts of Bioenergy from crops-environmental impacts of

agriculture and displacement of acreage for foodagriculture and displacement of acreage for food– Bioenergy from forest products wastes-Bioenergy from forest products wastes-

deforestation and concerns air emissions from deforestation and concerns air emissions from burningburning

Results

• Percentage of residential electricity consumers Percentage of residential electricity consumers who are willing to pay premiums for electricity who are willing to pay premiums for electricity is much lower than found in prior studies, at 38 is much lower than found in prior studies, at 38 percent compared with estimates as high as 90 percent compared with estimates as high as 90 percentpercent

• Somewhat lower preference for electricity from Somewhat lower preference for electricity from crops or forest wastes than for electricity from crops or forest wastes than for electricity from solar or wind sourcessolar or wind sources

• However, no statistical difference between WTP However, no statistical difference between WTP for bioenergy and energy from landfill wastesfor bioenergy and energy from landfill wastes

Conclusions

• About a $5-6 per month gap in WTP between About a $5-6 per month gap in WTP between solar and bioenergy sources (about $.03-.04 solar and bioenergy sources (about $.03-.04 per kWh)per kWh)

• About an $8-9 per month difference in WTP for About an $8-9 per month difference in WTP for wind compared with bioenergy sources (about wind compared with bioenergy sources (about $.05-.06 per kWh)$.05-.06 per kWh)

• WTP estimates are compared with estimated WTP estimates are compared with estimated costs of generation from prior research (Guey-costs of generation from prior research (Guey-Lee; Department of Energy, Energy Efficiency Lee; Department of Energy, Energy Efficiency and Renewable Energy Network; Oak Ridge and Renewable Energy Network; Oak Ridge National Laboratory), gaps between WTP and National Laboratory), gaps between WTP and costs appear to be greatest for solar and costs appear to be greatest for solar and bioenergy sourcesbioenergy sources

Conclusions

• Income and education levels, contribution to Income and education levels, contribution to environmental organizations, and urbanization environmental organizations, and urbanization influence willingness to pay- suggest potential for influence willingness to pay- suggest potential for target marketing of electricity from renewable target marketing of electricity from renewable sourcessources

• Study confined geographically to one state, Study confined geographically to one state, capabilities to examine effects of geographic capabilities to examine effects of geographic location were limited location were limited

• Future research should examine WTP across regions Future research should examine WTP across regions of the United Statesof the United States

• Future research might also examine how investment Future research might also examine how investment in local green power projects versus purchases off in local green power projects versus purchases off green power markets affect willingness to pay for green power markets affect willingness to pay for bioenergybioenergy

Conclusions