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.
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
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
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)
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
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)
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
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
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
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.)
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
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
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
Results
• A total of 421 responded to the survey
• 38.05% percent were willing to pay something more
for renewable energy
36
33.92
34
% Who
Would Pay
32
32.45
30.38 30.68
30
28
Energy Source
Forest Products Wastes
Crops
Landfill Wastes
Solar
Wind
34.51
Results
Estimated Models
Table 1. Estimated Spike Models of WTP for Bioenergy from
Crops and from Forest Products Wastes.a
Fast Growing Forest Products
Crops
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.
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
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.
Results
Crops
Profile 2
WTP Estimates
Across Profiles
16.39
2.65
1.78
0.89
Profile 1
8.62
7.19
5.77
Sample
Forest Products Wastes
Profile 2
16.83
0
22.7
10
28.5
2.33
1.52
0.7
Profile 1
Sample
1.04
0
6.87
10
12.71
20
22.31
28.24
30
CL
20
Means
CU
30
Results
$11.03
CL
Solar
$9.39
$12.71
CL
Mean
Landfill Wastes
$7.35 $ 9.75 $12.14
•No
CL
Mean
CU
Wind
$15.48
Mean
$19.94
CU
$16.03
CU
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
$5
Mean
$7
CU
$9
$11
$13
$15
$17
Estimated WTP and 95% Confidence Intervals
Across Energy Sources
$19
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
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
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
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