CONSISTENT RESIDENTIAL EFFICIENCY
IMPROVEMENTS ACROSS END-USES:
THEORETICAL AND EMPIRICAL INSIGHTS
Mike Blackhurst
Assistant Professor
The University Of Texas At Austin
Civil, Architectural, & Environmental Engineering
mike.blackhurst@austin.utexas.edu
Multiple Perspectives on Technical Efficiency
What happens if you double the efficiency of your air conditioner?
The technologist says, “You use half the energy.”
The economist says, “You turn down the thermostat.”
The social scientist says, “Who made the decision?”
The “Rebound Effect”
o aka “Jevon’s paradox” or “the energy efficiency paradox”
o Efficiency decreases resources needed for service
o Efficiency also decreases the cost of service, which…
o Induces income and substitution effects and…
o Likely other behavioral responses and drivers
Rebound Terminology
Category
Description
Example
Direct
rebound
Homeowners use more of the
more efficient service
Consumer drives more with a more fuel
efficient car
Indirect
rebound
Homeowners re-spending on
other goods and services
Savings from efficient lighting spend on 2nd
refrigerator
Economywide
rebound
More efficient production and
shifts in demand alter economic
structure and growth
A more efficient steam engine increases
production changes structural relationships
and leads to economic growth
Magnitude of Rebound Debated
Technically Feasible
After Direct Rebound
After direct + indirect rebound
Net Energy Elasticity
(% Change in Energy / % Change in Efficiency)
0%
-20%
-40%
-60%
-80%
-100%
Technically feasible
energy savings
Single-Service Rebound Model
o Start with technical definition of efficiency: E = S / e
¶E e
¶e E
= he ( E) = he (S )-1
he (E) = -1 when he (S ) = 0
o Direct rebound usually estimated as own-price elasticity of demand
he (E) ~ -h p( E)-1
o Indirect rebound (re-spending) is estimated by modeling by income
and substitution effects in response to a discrete efficiency change
Avg. Technology Choices per Household (STech/SHHs)
Challenge to Single Service Model
4
OTHERS
HEAT PUMP AC
WEATHERIZED
3
CFLs
WELL
INSULATED
2
PROGRAM.
THERMOSTAT
ES CLOTHES
WASHER
1
ES DISHWASHER
ES REFRIGERATOR
0
<
5k
$1
5k
$1
.9
19
$
-
k
$
k
20
29
-$
k
.9
0k
$3
-
9k
9.
3
$
0k
$4
-
9k
4.
5
$
5k
$5
-
9k
9.
7
$
0k
$8
-
9.
$9
9k
2009 Annual Household Before-Tax Income
0
$1
0k
-
Modified from Blackhurst and Ghosh (under review)
99
$1
k
.9
>
k
20
1
$
Two Service Model
E = Ei + E j = f ( pi , p j ,ei ,e j , M ,s )
0
0
0
0
¶E ¶f ¶f ¶pi ¶f ¶p j ¶f ¶e j ¶f ¶M ¶f ¶s
+
+
+
+
+
=
¶ei ¶ei ¶pi ¶ei ¶p j ¶ei ¶e j ¶ei ¶M ¶ei ¶s ¶ei
Two Service Model
¶E ¶f
¶f ¶e j
=
+
¶ei ¶ei ¶e j ¶ei
he ,cc(Ei ) = he ( Ei )+ he (E j )he (ei )
i
i
i
j
he ,cc(E j ) = he (E j )+ he ( E j )he (e j )
i
i
j
i
Ej
Ei
he ,cc ( Ei + E j ) = he ,cc( Ei ) + he ,cc( E j )
i
i
i
E
E
Two Service Model: Re-Arranged
he
C
=
,cc (EC +ET )
æE
ET ö
C
- ç
+ he (eT )
÷
C
E ø
è E
E
+ [he ( EC )+1] C
C
E
+ he ( ET )
C
+
C
direct rebound for C (1st order)
ET
E
he (eT )he ( EC )
C
technical response (1st and 2nd order)
indirect rebound from C to T ind. of e correlation (1st order)
EC
E
E
+ he (eT )éëhe ( ET )+1ùû T
C
T
E
indirect rebound from j to i from e correlation (2nd order)
indirect rebound from i to j from e correlation (2nd order)
Application of Two-Service Model
Would homeowners in more efficient homes drive more?
o Include electricity (C) and transportation (T) services
o Used constant elasticity of substitution (CES) production function
V(M , pC , pT ,eC ,eT ) = max {U(Y )}
M ³ X + pC EC + pT ET
EC = C / eC ; ET = T / eT
s
( s -1) s
( s -1) s
( s -1) s ù
é
Y = ë(1- aC - aT )X
+ aC C
+ aT T
û
o Can provide draft manuscript for more details
( s -1)
Empirical Assumptions
Parameter
Base
Case
Income category ($1,000) $25-30
Min.
$40-45
Max.
$70$75
Ref
BLS 2011
Short-run elasticity of sub., sSR
0.15
0.1
0.2
Long-run elasticity of sub., sLR
0.8
0.7
0.9
BLS 2011, Dahl 1993,
Brons 2008,
Graham 2002
Electricity Nominal Shares, aC
1.3%
0.4%
2.2%
BLS 2011
Gasoline Nominal Shares, aT
2.9%
0.8%
5.1%
BLS 2011
Electricity Real Shares
27%
26%
31%
BLS 2011
Gasoline Real Shares
73%
74%
69%
Efficiency correlation, heT(eC)
2.1
0.5
6
heC(eT)
0.48
2.00
0.17
BLS 2011
Replacements assuming different
code- and above-code
performance
Rebound Across Resid and Trans Sectors:
Driven by Changes in Electricity Efficiency
Technically
Technically
feasible elasticity
Direct
Direct
rebound
Rebound
feasible elasticity
i/E + hei(ej)Ej/E)
-1(E-1(E
i/E + hei(ej)Ej/E)
Cross-sector
(indirect),
Cross-sector,
independent
from C to T
of c.c. of c.c.
independent
+
e (Ei)+1]EE/E
/E
[he[h
i(Eii)+1]
ii
+
ei(Ej) Ej/E
hhei(E
j) Ej/E
Cross-sector,
Cross-sector,
Fromfrom
trans
toCresid
T to
with
with c.c.
c.c.
Cross-sector,
Cross-sector,
From resid
to trans
from C to T
withwith
c.c.c.c.
ei(ej) hej (Ei )Ei/E
+heh(e
+ hei(ej) [hej(Ej)+1] Ej/E =
i j) hej (Ei )Ei/E hei(ej) [hej(Ej)+1] Ej/E
-0.5
(-1)
Energy
Elasticity,
DE E
De e
-1.5
-2.5
-3.5
Short-run response
Long-run response
-4.5
Results shown for median income range ($40-$45k)
Net
Energ
Elastici
Rebound Across Resid and Trans Sectors:
Driven by Changes in Electricity Efficiency
Technically
Technically
feasible elasticity
Direct
Direct
rebound
Rebound
feasible elasticity
i/E + hei(ej)Ej/E)
-1(E-1(E
i/E + hei(ej)Ej/E)
Cross-sector
(indirect),
Cross-sector,
independent
from C to T
of c.c. of c.c.
independent
+
e (Ei)+1]EE/E
/E
[he[h
i(Eii)+1]
ii
+
ei(Ej) Ej/E
hhei(E
j) Ej/E
Cross-sector,
Cross-sector,
Fromfrom
trans
toCresid
T to
with
with c.c.
c.c.
Cross-sector,
Cross-sector,
From resid
to trans
from C to T
withwith
c.c.c.c.
ei(ej) hej (Ei )Ei/E
+heh(e
+ hei(ej) [hej(Ej)+1] Ej/E =
i j) hej (Ei )Ei/E hei(ej) [hej(Ej)+1] Ej/E
-0.5
Energy
Elasticity,
DE E
De e
-1.5
-2.5
-3.5
Short-run response
Long-run response
-4.5
Results shown for median income range ($40-$45k)
Net
Energ
Elastici
Rebound Across Resid and Trans Sectors:
Driven by Changes in Vehicle Efficiency
Technically
feasible elasticity
-1(Ei/E + hei(ej)Ej/E)
1.5
0.5
Energy
Elasticity,
DE E
De e
Technically
feasible elasticity
Direct
rebound
+
[hei(Ei)+1] Ei/E
Direct
Rebound,
hei(Ei)
+
Cross-sector,
from resid. to trans.
independent of c.c.
hei(Ej) Ej/E
Cross-sector
(indirect),
independent
of c.c.
+
Cross-sector,
from trans. to resid.
with c.c.
hei(ej) hej (Ei )Ei/E
+
Cross-sector,
From resid to trans
with c.c.
Cross-sector,
from resid. to trans.
with c.c.
hei(ej) [hej(Ej)+1] Ej/E =
Cross-sector,
From trans to resid
with c.c.
Technical
response
-0.5
-1.5
Short-run response
Long-run response
-2.5
Results shown for median income range ($40-$45k)
E
Other Behavioral Drivers
Behavior or Driver
Effect on technology…
Choice
Cost minimization,
income constraint
Reference(s)
Use
High implicit discount rate observed
Hausman 1979;
Sanstad et al. 1995
Demographic
Education levels, ownership, & tenure
increased technology adoption
?
Hartman 1998;
Michelson & Madner
2011
Physical household
characteristics
Increased home age and size promote
technology adoption
?
Michelson and Madner
2011
Environmental
awareness and
valuation
Increased awareness & valuation
increased adoption
?
Cummings and Taylor
1999; Hanley et al.
1990; Bateman et al.
2011
Technological
awareness
Homeowners misperceive technology
performance at extremes;
Self-reported awareness increased
adoption
?
Attari 2010;
Nair et al 2010
Other Behavioral Drivers
Do homeowners correlate or compensate drivers of
energy technology choice and use?
o Limited qualitative insights
• Correlation and compensation observed across a
variety of “green” behaviors [Thøgersen & Ölander 2003]
• Self-reported behavior changes with PV adoption
[Keirstead 2007; McAndrews; Schweizer-Reis et al. 2000 ]
o Implications for rebound?
Empirical Research
o Estimate the impact of marginal technical change
within and across end uses on electricity use and
rebound
• If choose technology A versus
• If choose both technology A and technology B
Pecan Street Research Institute
Static data
High resolution consumption data
Representative Sample Data
Sample includes one year of monthly electricity consumption for 79 homes
Variable
Range
Climate
Monthly CDD
Mean= 292, SD= 257
Structural
Floorspace (square feet)
Windows area (square feet)
Age of the house
Occupancy
Tenure
HH income
Thermostat set point – summer
TV hours per month
Dishwasher loads per month
Clothes washer loads per month
Education (interval)
Attic insulation R-value
Air conditioning Energy Efficiency Ratio (EER)
No. of devices
Dummy variables, Programmable thermostat, Double pane windows, Energy
star appliances, Solar PV (count = 37), EV (count = 14), Electric heater
Electricity consumption (KWh/month)
Mean= 2,019, SD= 719
Mean= 245, SD= 106
Mean= 21.4, SD= 23.6
Mean= 2.7, SD= 1.2
Mean= 6.6, SD= 7.6
Mean= $128k SD= $62k
Mean= 76.9, SD= 2.2
Mean= 107, SD= 71.9
Mean= 14.3, SD= 8.1
Mean= 17.1, SD= 9.2
Demographic
Selfreported
behaviors
Technology
choices
Electricity
Mean= 28.6, SD= 8.4
Mean= 10.5, SD= 1.7
Mean= 3.34, SD= 1.8
Mean= 963, SD= 938
Model Specification
o Where
• Yitλ represents monthly electricity consumption
• βj are the predictor coefficient fixed effects
• βi are the coefficient estimates for random effects
• Sijλ represents a series of household structural factors
• Dijλ represents a series of household demographic factors
• Bijλ represents household behaviors and cognitive factors
• Xij interaction terms for different technology choice combinations
• Ri represents the household identification codes
Results with No Interaction Terms
Explanatory variable
ProgTherm
ES Refrig
1/sqrt(Sq Ft)
Devices
CWloads
Home R value
Cooling Degree Days
EV
ES DW
2-P window
ES Clothes washer
PV
AC EER
1/occupancy
Dishwasher loads
1/Window Sq Ft
Income
Constant (b0)
Coefficient
-0.236
-0.164
-75.7
0.067
-1.958
-0.009
0.001
0.087
-0.085
-0.081
-0.069
0.054
0.006
0.141
0.001
-2.086
2.00E-07
8.348
p-value
0.026
0.025
< .0001
0.027
0.053
0.087
< .0001
0.339
0.325
0.346
0.378
0.487
0.738
0.465
0.879
0.924
0.78
< .0001
% change in Y for: 1 unit (or *10%) increase in X
-21.0%
-15.1%
8.35%*
7.00%
1.20%*
-0.91%
0.14%
9.14%
-8.14%
-7.78%
-6.68%
5.61%
0.65%
-0.21%*
0.09%
0.09%*
0.00%
-
Rebound from Marginal Efficiency Gains:
Demonstrative Empirical Results
Single Pane Window
Multi-pane window
7.1
Fitted Value (kWh)
7
6.9
6.8
6.7
6.6
6.5
6.4
6.3
6
7
8
9
10
11
12
Air Conditioning Efficiency (EER)
13
14
Rebound with Marginal Efficiency Gains
Individual Coeff.--> -0.24 *
Prog.
Therm.
Prog. Therm.
Multi-Pane Windows
<19
Home
19-29
Insulation
>30
AC
7
Energy
11
Efficiency
14
ES Refrigerator
ES Dishwasher
*
ES Clothes Washer
*
1
Devices
4
(count)
9
-0.081
Multi
Pane
-0.009 *
Home R value
<19 19-29 >30
0.006
AC EER
7
11
14
*
*
*
*
*
*
-0.16 * -0.085 -0.069
0.068 *
ES
ES
Devices (count)
ES CW
Refrig. Dishw.
1
4
9
*
*
*
*
*
*
*
Multi-pane
windows* installed,
*
*
increased
* AC efficiency
*
*
*
*
*
*
*
*
*
*
Multi-pane
windows installed
*
at indicated AC efficiency
*
*
*
*
*
*
Legend
Increase in consumption (rebound)
No effect
Net energy savings
AC = Air conditioning; EER = energy efficient ratio; ES = Energy Start; CW = Clothes washer
* Significant to 10% level
Rebound with Marginal Efficiency Gains
Individual Coeff.--> -0.24 *
Prog.
Therm.
Prog. Therm.
Multi-Pane Windows
<19
Home
19-29
Insulation
>30
AC
7
Energy
11
Efficiency
14
ES Refrigerator
ES Dishwasher
*
ES Clothes Washer
*
1
Devices
4
(count)
9
-0.081
Multi
Pane
-0.009 *
Home R value
<19 19-29 >30
0.006
AC EER
7
11
14
*
*
*
*
*
*
-0.16 * -0.085 -0.069
ES
ES
ES CW
Refrig. Dishw.
*
*
*
*
*
*
*
*
*
*
*
*
*
0.068 *
Devices (count)
1
4
9
*
*
*
*
*
*
*
*
*
*
*
*
*
*
*
Legend
Increase in consumption (rebound)
No effect
Net energy savings
AC = Air conditioning; EER = energy efficient ratio; ES = Energy Start; CW = Clothes washer
* Significant to 10% level
Preliminary PV Results
o Order of technical change matters
Order of technical
change
Increase AC
Efficiency
Increase Insul.
Install multipane
Windows
Purchase
EnergyStar
Appliances
Have PV before
efficiency
-
-
+
-
Install PV after
efficiency change
+ low EER
- high EER
+ low R-values
- high R-values
-
-
+ Statistically significant increase in electricity consumption
-
Statistically significant decrease in electricity consumption
Implications
o Literature is mixed as to whether consumers correlate or
compensate valuations across energy technology choice/use
o Empirical work suggests consumers MAY leverage efficiency
gains for services ACROSS end uses; our results are also mixed
o Rebound is relative to the current efficient technical state of
the home and order of technical change
o These findings suggest the dominant singleservice rebound paradigm is misleading
Implications
o Consistent efficiency change across end uses can mitigate
consumer responses; however…
o Consumers can and do expend energy services; thus…
o Models of rebound need to recognize service expansion
Implications
o The literature assumes PV exclusively replaces conventional
grid energy sources; however…
o Behavioral implications of PV are entirely unclear
o Consumers will treat long-run operating cost of PV as zero
o Results are mixed with respect to consumers responses to
both efficiency change and installation of PV
Related Ongoing/Future Work
o Rebound across resources (water/electricity/natural
gas/gasoline)
o Comparing Empirical and Estimated Energy Consumption
(RECS/BeOpt)
o Does Weather Influence the Use of PV for Discretionary
Electricity End Uses?
o Estimating Total and End-Use Residential Water (Energy)
Demands Using Energy (Water) Demands
o Comparing the Observed and Estimated Performance of
Residential Water Efficient Fixtures and Appliances
Acknowledgements
o This work was funded by
• The University of Texas at Austin
• Bill and Melinda Gates Foundation Fellowship
o PhD students
• Nour El-Imane Bouhou
• Pamela Torres
• Alison Wood
o MS Students
• Bruk Berhanu
• Neftali Torres
o Post doc
• Sarah Taylor-Lange
CONSISTENT RESIDENTIAL EFFICIENCY
IMPROVEMENTS ACROSS END-USES:
THEORETICAL AND EMPIRICAL INSIGHTS
Mike Blackhurst
Assistant Professor
The University Of Texas At Austin
Civil, Architectural, & Environmental Engineering
mike.blackhurst@austin.utexas.edu
References
o
Blackhurst, MF, and NK Ghosh. “The Rebound Effect with Consistent Efficiency Improvements and Implications for Cross-Sector Rebound.”
Ecological Economics (submitted for review).
o
Attari, S. Z., M. L. DeKay, C. I. Davidson, and W. B. de Bruin. 2010. “Public Perceptions of Energy Consumption and Savings.” Proceedings
of the National Academy of Sciences 107 (37): 16054–16059.
o
Thøgersen, J., and F. Ölander. 2003. “Spillover of Environment-Friendly Consumer Behaviour.” Journal of Environmental Psychology 23 (3):
225–236.
o
Keirstead, J. 2007. “Behavioural Responses to Photovoltaic Systems in the UK Domestic Sector.” Energy Policy 35 (8): 4128–4141.
o
McAndrews, K. “To Conserve or Consume: Behavior Change in Residential Solar PV Owners.” The University of Texas at Austin, 2012.
o
Hausman, Jerry A. “Individual Discount Rates and the Purchase and Utilization of Energy-Using Durables.” The Bell Journal of Economics
10, no. 1 (April 1, 1979): 33–54. doi:10.2307/3003318.
o
Sanstad, Alan H., Carl Blumstein, and Steven E. Stoft. “How High Are Option Values in Energy-Efficiency Investments?” Energy Policy 23,
no. 9 (1995): 739–743.
o
Hartman, R. S. “Self-Selection Bias in the Evolution of Voluntary Energy Conservation Programs.” The Review of Economics and Statistics
(1988): 448–458.
o
Michelsen, C., and R. Madlener. “Homeowners’ Preferences for Adopting Residential Heating Systems: A Discrete Choice Analysis for
Germany.” FCN Working Papers (2011).
o
Cummings, Ronald G., and Laura O. Taylor. “Unbiased Value Estimates for Environmental Goods: A Cheap Talk Design for the Contingent
Valuation Method.” The American Economic Review 89, no. 3 (June 1, 1999): 649–665.
o
Nair, Gireesh, Leif Gustavsson, and Krushna Mahapatra. “Factors Influencing Energy Efficiency Investments in Existing Swedish Residential
Buildings.” Energy Policy 38, no. 6 (June 2010): 2956–2963. doi:10.1016/j.enpol.2010.01.033.
o
Bateman, Ian J., Georgina M. Mace, Carlo Fezzi, Giles Atkinson, and Kerry Turner. “Economic Analysis for Ecosystem Service Assessments.”
Environmental and Resource Economics 48, no. 2 (2011): 177–218.
o
Dahl, C. A. “A Survey of Energy Demand Elasticities in Support of the Development of the NEMS” (1993). http://mpra.ub.unimuenchen.de/13962/.
o
Brons, Martijn, Peter Nijkamp, Eric Pels, and Piet Rietveld. “A Meta-Analysis of the Price Elasticity of Gasoline Demand. A SUR Approach.” Energy
Economics 30, no. 5 (September 2008): 2105–2122. doi:10.1016/j.eneco.2007.08.004.
o
Graham, Daniel J., and Stephen Glaister. “The Demand for Automobile Fuel: A Survey of Elasticities.” Journal of Transport Economics and Policy (2002):
1–25.
o
BLS (U.S. Bureau of Labor Statistics). “Consumer Expenditure Survey,” 2011. http://www.bls.gov/cex/.
Single service rebound model
o Using technical definition of efficiency: E = S / e
¶E e
= he ( E) = he (S )-1
¶e E
he (E) ~ -h p( E)-1
o Using CES production function
V(M , p,e ) = max {U(Y )}
M ³ X + pE
E = S /e
s
( s -1) s
( s -1) s ù
é
Y = ë(1- a S )X
+ aS S
û
he (E) ~ -h p( E)-1
( s -1)
Rebound with Marginal Efficiency Gains
Individual Coeff.--> -0.24 *
Prog.
Therm.
Prog. Therm.
Multi-Pane Windows
<19
Home
19-29
Insulation
>30
AC
7
Energy
11
Efficiency
14
ES Refrigerator
ES Dishwasher
*
ES Clothes Washer
*
1
Devices
4
(count)
9
-0.081
Multi
Pane
-0.009 *
Home R value
<19 19-29 >30
0.006
AC EER
7
11
14
*
*
*
*
*
*
-0.16 * -0.085 -0.069
0.068 *
ES
ES
Devices (count)
ES CW
Refrig. Dishw.
1
4
9
*
*
*
*
*
*
*
Multi-pane
windows* installed,
*
*
increased
* AC efficiency
*
*
*
*
*
*
*
*
*
*
Multi-pane
windows installed
*
at indicated AC efficiency
*
*
*
*
*
*
Legend
Increase in consumption (rebound)
No effect
Net energy savings
AC = Air conditioning; EER = energy efficient ratio; ES = Energy Start; CW = Clothes washer
* Significant to 10% level
Rebound Across Resid and Trans Sectors:
Driven by Changes in Electricity Efficiency
Technically
Technically
feasible elasticity
Direct
Direct
rebound
Rebound
feasible elasticity
i/E + hei(ej)Ej/E)
-1(E-1(E
i/E + hei(ej)Ej/E)
Cross-sector
(indirect),
Cross-sector,
independent
from C to T
of c.c. of c.c.
independent
+
e (Ei)+1]EE/E
/E
[he[h
i(Eii)+1]
ii
+
ei(Ej) Ej/E
hhei(E
j) Ej/E
Cross-sector,
Cross-sector,
Fromfrom
trans
toCresid
T to
with
with c.c.
c.c.
Cross-sector,
Cross-sector,
From resid
to trans
from C to T
withwith
c.c.c.c.
ei(ej) hej (Ei )Ei/E
+heh(e
+ hei(ej) [hej(Ej)+1] Ej/E =
i j) hej (Ei )Ei/E hei(ej) [hej(Ej)+1] Ej/E
-0.5
(-1)
Energy
Elasticity,
DE E
De e
-1.5
-2.5
-3.5
Short-run response
Long-run response
-4.5
Results shown for median income range ($40-$45k)
Net
Energ
Elastici