Peter Schwarz
Professor of Economics, Belk College of Business and
Associate, Energy Production and Infrastructure Center (EPIC)
UNC Charlotte, Charlotte NC 28223-00001, USA and
Visiting Professorship,
China University of Mining and Technology, Xuzhou, China
Craig A. Depken, II
Professor of Economics, Belk College of Business
UNC Charlotte, Charlotte, NC 28223-0001, USA
Michael Herron
Data Scientist
Premier Healthcare Alliance
13034 Ballantyne Corporate Place, Charlotte, NC 28277
Ben Correll
Analyst
PricewaterhouseCoopers Advisory Services LLC
1333 Main Street #30 Columbia, SC 29201
For presentation at the International Association of Energy
Economics North American Meeting, Pittsburgh PA,
October 25-28, 2015
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Introduction
Literature
Data & Empirical Approach
Results
Policy Implications
Conclusions
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•
ENERGY STAR
• Introduced by USEPA in 1992, USDOE joined in 1996
• voluntary labeling program intended to encourage
purchase of energy-efficient products
• Provides information on energy savings for four appliances
• refrigerators, room air conditioners, clothes washers,
dishwashers
•
Stated Justification:
• Reduce carbon emissions
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•
Energy efficiency gap: consumers apply too high a discount rate.
• Hausman (1979), Dubin and McFadden (1984), most recently Parry,
Evans, and Oates (2014).
• Some studies dispute a gap.
•
Allcott and Greenstone (2012), Francois Cohen, Matthieu Glachant
and Magnus Soderberg (2014), Lance Davis, et al. (2014), Fowlie, et al.
(NBER 2015)
•
•
Renters less energy-efficient than owners.
• Schwarz (1991), Davis (2008).
Behavioral explanations:
• Defective telescopic faculty, misperceptions, temptation and selfcontrol.
• Pigou (1932), Parry, Evans and Oates (2014),
Tsvetanov and Segerson (2014)
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Other variables that affect market share of ES
appliances
Attitude towards energy efficiency
ACEEE index (Murray and Mills 2011)
Rebates
Used same ES data set (Datta and Gulati 2014)
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• U.S. Energy Information Administration, a division of USDOE
• Appliance sales data from national retail chains, representing 70% of
market
• Residential electricity prices
• Rebates
• U.S. Census Bureau
• Percent of housing units that are owner occupied
• Percent of adults over age 25 with at least a bachelor’s degree
• U.S. Bureau of Economic Analysis
• Per capita income
• American Council for an Energy-Efficient Economy (ACEEE)
• State energy-efficiency score
All variables are at the state level for the years 2000-2009
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All States 2000-2009 (500 obs.)
Variable
ENERGY STAR market share
State Means (50 obs.)
Mean
Std Dev
Min.
Max.
Std Dev
Min.
Max.
Refrigerator
Dishwasher
Clothes Washer
Air Conditioner
29.02
59.39
28.88
34.54
8.21
27.01
13.84
14.84
10.54
3.90
3.26
4.09
57.21
99.00
60.04
69.81
3.03
2.76
6.01
6.31
42.28
53.17
18.18
21.11
36.50
65.03
42.21
49.99
Refrigerator
Dishwasher
3.75
2.33
12.67
9.11
0
0
85.18
53.21
9.85
5.95
0
0
47.47
26.33
Clothes Washer
3.86
14.68
0
113.57
9.83
0
54.06
Air Conditioner
--
--
--
--
--
--
--
Residential electricity price
(cents/kWh, 2009 dollars)
10.71
3.29
6.39
32.38
3.16
7.09
22.80
Per capita income (2009 dollars)
37,672
5,496
26,866
57,787
5,330
29,919
53,656
Percent of households
owner-occupied
70.22
4.88
53.40
81.30
4.73
54.81
78.17
Percent of population with
bachelor’s degree or higher
26.25
4.63
15.30
38.20
4.53
16.37
36.26
ACEEE Scores
14.85
10.33
0
50
10.01
0.67
41.17
Incentives (2009 Dollars)
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(Percent ENERGY STAR)jit = β0 + β1 (Electricity Price)it +
β2 (Per Capita Income)it + β3 (Percent Owner Occupied)it +
β4 (Percent Bachelors)it + γXit + εit
• Results using state means from 2000 to 2009, j = appliance, i = state, t = year
• Xit is a matrix of additional control variables
• Regional dummies, incentives, and ACEEE score were added in alternative variants
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• Attempted to take advantage of panel aspect of
data
• Fixed and random effects models performed
poorly
• Primarily because of very low
volatility in price of electricity
within states.
• Because of the poor performance of the fixed and
random effects models, we use
• Between estimator, which uses sample
means for each state.
• Regression diagnostics reveal no problems with
non-normal, heteroscedastic, or spatially
autocorrelated errors.
•
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Base Model with ACEEE & Regional Dummy
DW
CW
RF
AC
Electricity Price
0.282*
0.103
0.706***
0.296
Per Capita Income
Percent Owner
Occupied
Percent with
Bachelors
South
0.008
0.060
-0.042
-0.107
0.142*
0.333**
0.288***
0.370**
0.270***
0.314
0.172*
0.264
0.384
-7.777***
-0.605
-5.521*
West
2.923**
0.759
2.864**
-7.300**
Midwest
2.781**
-2.259
1.352
-2.054
ACEEE Scores
0.088**
0.159**
0.081**
0.289***
Incentives
--
--
--
--
Constant
Adjusted R2
37.553
-5.641
-4.304
2.081
0.649
0.679
0.733
0.538
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Base Model with Incentives & ACEEE
DW
CW
RF
AC
0.004
0.596*
0.599***
--
-0.003
0.107
-0.020
--
0.068
0.365**
0.250***
--
0.301**
0.383
0.153
--
--
--
--
--
West
--
--
--
--
Midwest
--
--
--
--
ACEEE Scores
0.101**
0.155**
0.091**
--
Incentives
Constant
Adjusted R2
0.038
0.212
0.079**
--
45.184
-20.332
0.143
--
0.500
0.490
0.626
--
Electricity Price
Per Capita Income
Percent Owner
Occupied
Percent with
Bachelors
South
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Price coefficient is positive in 24/25
specifications/models
Statistically significant at 5% level in 11/25 variants.
Refrigerators
Always positive and significant at 1% level.
Air conditioners: Positive and significant for two
of four specifications
Base model and specification with only ACEEE score
included.
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Dishwashers
At best only weakly statistically related
Statistically significant at 5% level only for the base model
with regional dummies.
At the 10% level in only two other specifications.
Clothes Washers
Only significant in base model with incentives
included
Insignificant at 5% level in all other models.
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Results agree with intuition
Dishwashers:
Electricity savings not large enough to justify premium
for more efficient appliance
Air conditioners:
Quickest payback period and largest elasticity
Though still in inelastic range.
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Percent owner-occupied housing positive and
significant at 10% or better in 16/25 models
Significant at 5% in 9/12 models for RF and AC
Generally insignificant for DW
ACEEE scores positive, statistically significant at
5% or better for all appliances across the three
models where it is included.
Incentives positive and significant at 5% level for
RF and CW when added to the base model
Positive but less significant when added to models
including the ACEEE score or regional dummy
variables.
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Percentage of population in state with at least a
Bachelor’s degree generally positively related to
ES market share.
Significant at 5% or better in 11/25 models across
the four appliances.
Significant at 5% in 9/12 models for RF and AC
Generally insignificant for DW
Regional dummies added to base model
South has lowest share of ES appliances relative to
Northeast; West has greater market shares (except
for room AC).
Coefficient on income is insignificant in all
specifications
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Results are marginal changes in market shares
over the ten year period 2000-2009.
Primary reason for using state sample means is that
electricity prices change very slowly within states
Fixed and random effects estimators inappropriate and
not well behaved.
How stable are parameter estimates across
distribution of market shares?
More sensitive to changes in electricity price at
lower or upper end of price distribution?
Quartile regression applied to all the specifications shows
all parameter estimates are very stable.
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Clustered standard errors by region of country
Very little change in significance.
Clustered standard errors based on ACEEE score
regardless of where state was located within the
country
Again, very little change in parameter estimates.
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• Elasticities for base model are 0.34 for AC, 0.21 for RF, 0.05 for
CW, and 0.04 for DW.
• Given these relatively inelastic responses, even a large
increase in electricity prices might not increase market
shares by very much.
• Resources for the Future estimates that a carbon price
would increase electricity price by at most 4 cents/kWh.
• Based on 2009 data, market share for ES room air
conditioners would increase from 41.4% to 46.2%, for
ES refrigerators from 33.4% to 36.0%, for ES clothes
washers from 37.0% to 37.6%, and for ES dishwashers
from 79.3% to 79.7%.
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• Based on 2009 data, the decrease in energy use from the four ES
appliances including the 4 cents/kWh carbon price is almost
2,000,000 MWh/year.
• Just over 100,000 MWh of the reduction is due to the
4 cent/kWh carbon price.
• Using the approximation that each MWh of electricity
generated emits 0.5 metric tons of carbon, ES appliances
reduce C emissions by just under 1,000,000 metric tons
• Close to 50,000 MWh due to the 4 cent/kWh C tax.
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According to the EIA (2012), the U.S. emitted 5.290 billion metric
tons in 2012.
• The total percentage reduction is approximately 0.02% per
year, or 0.2% over ten years due to the carbon price,
assuming average appliance life.
• The annual reduction in carbon emissions is the
equivalent of taking just over 200,000 cars off the road,
based on the U.S EPA estimate that the average
automobile emits 4.7 metric tons of carbon per year.
• Of this total, about 50,000 metric tons are due to the
carbon tax
• Equivalent of a little over 10,000 cars per year
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• In all, the ES program has a modest effect on
energy use
• And a more modest effect on carbon
reductions
• A carbon tax would have an even smaller
marginal indirect contribution through the ES
program.
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Thank you
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