The effect of U.S. electricity prices on the purchase of energy- efficient appliances
and implications for the effects of carbon pricing
Peter Schwarz, University of North Carolina Charlotte, 704.687.7614,
pschwarz@uncc.edu
Craig Depken, University of North Carolina Charlotte, cdepken@uncc.edu
Michael Herron, Premier Healthcare Alliance, mherron@uncc.edu
Benjamin Correll, Pricewaterhouse Coopers, benjcorrell@gmail.com
Overview
There is considerable disagreement, both theoretical and empirical, and consumer willingness to pay for
energy efficiency. We are among the few studies to use data, rather than surveys, to estimate the effect of
electricity prices on energy-efficient purchases. Most analyses rely upon survey data, which has wellknown biases of which the most fundamental is that consumer response is based upon stated rather than
revealed preferences. Consumer responses to surveys need not coincide with actual purchasing decisions.
We use U.S. Energy Information Administration Data on purchases of energy-efficient appliances –
appliances that qualify for the U.S. Environmental Protection Agency designation as ENERGY STAR
appliances—that save 15-20% of the electricity used by standard appliances. Our key independent
variable is average electricity price for each of the fifty U.S. states for the years 2000-2009, which is
provided by the U.S. Department of Energy. In addition to contributing to the research on the effect of
electricity prices on the purchase of energy-efficient appliances, our second focus is on the consequent
effect on carbon emissions with and without a price on carbon. Many studies estimate the potential
savings from avoiding costs associated with greenhouse gas emissions. But relatively few look at the
reduction in emissions from using energy-efficient appliances, with the majority of studies based on
building efficiency and automobile efficiency.
Methods
The first step is to estimate the effect of variation in state electricity prices on variation in
state market share of energy efficient appliances. Then we predict the change in the market share of
energy-efficient appliances after incorporating a carbon price into the price of electricity,
In order to estimate the first step:
Percent ENERGY STARi = β0 + β1 Electricity Pricei + β2 Per Capita Incomei + β3 Percent Owner
Occupiedi + β4 Percent Bachelorsi + εi
where Percent ENERGY STARi is the mean of the percent of all refrigerators sold that were ENERGY
STAR certified in state i, Electricity Pricei is the mean residential price of electricity (in
cents) in state i, Per Capita Incomei is the mean personal income in thousands of dollars
per capita in state i, Percent Owner Occupiedi is the mean of the percent of households that
are owner occupied in state i, and Percent Bachelorsi is the mean of the percent of the
population over age 25 with a bachelor’s degree or higher in state i. We also consider variables that
reflect a state’s attitude towards energy efficiency, alternatives and renewables. States that have a more
favorable view towards these attributes may have constituents who are more likely to purchase energyefficient appliances. Two approaches are used to account for this variation between states: in one
variation of the model, regional dummy variables are added; and in another variation, the American
Council for an Energy-Efficient Economy (ACEEE) score representing a state government’s commitment
to policies that support energy conservation is added. Regional dummy variables identify states by region
as defined by the U.S. Census Bureau. The U.S. EIA cautions against pooling the data for 2004-2009, as
the sample of retailers reporting the sale of energy-efficient appliances changes from year to year. Our
initial results are based upon averages over the five years. Ordinary least squares appears to provide a
better fit of the data than log-linear estimation. Also, the error terms appear to fit classical OLS
assumptions, as we did not see evidence of heteroskedasticity or spatial relationships in the residuals.
We also use the data in panel form, which gives give 500 rather than 50 observations, but question panel
data results where there intrastate variability within the ten years is much smaller than interstate
variability as in using state averages for the OLS estimates. We estimate consequent reductions in carbon
by relating energy use to carbon emissions, and the added effect of a carbon price on the electricity sector.
Results
We find average price elasticities of demand for room air conditioners, refrigerators, clothes washers, and
dishwashers of 0.29, 0.22, 0.04, and 0.01 respectively. Resources for the Future (2010) estimates that a carbon price
would increase the average electricity price by at most $0.04/kWh, which would increase the market share of
ENERGY STAR room air conditioners by 5%, 3% for refrigerators, and less than 1% for clothes washers and
dishwashers. In turn, a carbon price on the order of $0.04 per kilowatt hour would only increase market share by at
most 5% for room air conditioners and 3% for refrigerators and in turn, reduce carbon emissions by 0.2%.
Conclusions
The market share of ENERGY STAR appliances show an inelastic response to electricity prices, while also
responding to the share of adults with a bachelor’s degree, the percent of homes that are owner occupied, and
cultural attitudes. One factor that may dilute the effect of electricity prices is the availability of rebates on ENERGY
STAR appliances. Consumers showed a significant response to rebates, and an upfront incentive may be a more
powerful stimulus than electricity price, which factors in over the lifetime of the appliance. To the extent that these
results can be extrapolated to other types of ENERGY STAR appliances, or to other investments in energy
efficiency in general, reliance on a Pigovian tax or other mechanism to internalize the social costs of pollution from
using energy may not have as much impact as some might hope. If other market failures exist that prevent the
efficient level of investment in energy efficiency, such as asymmetric information, credit market inefficiencies,
bounded rationality, or the landlord-tenant problem, then several targeted policy approaches may be needed.