Consistent RESIDENTIAL Efficiency Improvements ACROSS END-U - PowerPoint Presentation

Slide1

Consistent RESIDENTIAL Efficiency Improvements ACROSS END-USES: Theoretical and Empirical InsightsMike BlackhurstAssistant ProfessorThe University Of Texas At AustinCivil, Architectural, & Environmental Engineering

mike.blackhurst@austin.utexas.eduSlide2

Multiple Perspectives on Technical EfficiencyWhat 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?”Slide3

The “Rebound Effect”aka “Jevon’s paradox” or “the energy efficiency paradox”Efficiency decreases resources needed for serviceEfficiency also decreases the cost of service, which…

Induces income and substitution effects and…

Likely

other behavioral

responses and drivers Slide4

Rebound TerminologyCategory

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 2

nd

refrigerator

Economy-wide 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 growthSlide5

Magnitude of Rebound DebatedNet Energy Elasticity (% Change in Energy / % Change in Efficiency)

Technically feasible

energy savingsSlide6

Start with technical definition of efficiency:Direct rebound usually estimated as own-price elasticity of demandIndirect rebound (re-spending) is estimated by modeling by income and substitution effects in response to a discrete efficiency change

Single-

S

ervice

R

ebound

M

odelSlide7

Challenge to Single Service ModelModified from Blackhurst and

Ghosh

(under review)Slide8

Two Service Model

0

0

0

0Slide9

Two Service ModelSlide10

Two Service

M

odel: Re-Arranged

technical response (1

st

and 2

nd

order)

direct

rebound for C

(1

st

order)

indirect rebound

from C

to T

ind. of e correlation (1

st order)indirect rebound from j to

i from e correlation (2nd

order)

indirect rebound

from

i

to j from

e

correlation (2

nd

order)Slide11

Application of Two-Service ModelWould homeowners in more efficient homes drive more?Include electricity (C) and transportation (T) services

Used constant elasticity of substitution (CES) production function

Can provide draft manuscript for more

detailsSlide12

Empirical Assumptions

Parameter

Base Case

Min.

Max.

Ref

Income category ($1,000)

$25-30

$40-45

$70-$75

BLS 2011

Short-run elasticity of

sub.,

s

SR

0.15

0.1

0.2

BLS 2011, Dahl 1993,

Brons

2008,

Graham 2002

Long-run elasticity of

sub.,

s

LR

0.8

0.7

0.9

Electricity Nominal Shares,

a

C

1.3%

0.4%

2.2%

BLS 2011

Gasoline Nominal Shares,

a

T

2.9%

0.8%

5.1%

BLS 2011

Electricity Real Shares

27%

26%

31%

BLS 2011

Gasoline Real

Shares

73%

74%

69%

BLS 2011

Efficiency correlation,

h

e

T

(

e

C

)

2.1

0.5

6

Replacements assuming different

code- and above-code performance

h

e

C

(

e

T

)

0.48

2.00

0.17Slide13

Energy

Elasticity,

(-1)

Direct

Rebound

[

h

e

i

(

E

i

)+1]

E

i

/E

Technically

feasible

elasticity

-1(

E

i

/E +

h

e

i

(

e

j

)

E

j

/E)

Cross-sector,

From trans

to

resid

with c.c.

h

e

i

(

e

j

)

h

e

j

(

E

i

)

E

i

/E

Cross-

sector (indirect),

independent

of

c.c

.

h

e

i

(

E

j

)

E

j

/E

Cross-sector,

From

resid

to

trans

with c.c

.

h

e

i

(

e

j

) [

h

e

j(Ej)+1] Ej/E

Short-run response

Long-run response

Rebound Across

Resid

and Trans Sectors:

Driven by Changes in Electricity Efficiency

Results shown for median income range ($40-$45k)Slide14

Energy Elasticity,

Direct

Rebound

[

h

e

i

(

E

i

)+1]

E

i

/E

Technically

feasible

elasticity

-1(

E

i

/E +

h

e

i

(

e

j

)

E

j

/E)

Cross-sector,

From trans

to

resid

with c.c.

h

e

i

(

e

j

)

h

e

j

(

E

i

)

E

i

/E

Cross-

sector (indirect),

independent

of

c.c

.

h

e

i

(

E

j

)

E

j

/E

Cross-sector,

From

resid

to

trans

with c.c

.

h

e

i

(

e

j

) [

h

e

j

(

Ej)+1] Ej/E

Short-run response

Long-run response

Rebound Across

Resid

and Trans Sectors:

Driven by Changes in Electricity Efficiency

Results shown for median income range ($40-$45k)Slide15

Rebound Across Resid and Trans Sectors: Driven by Changes in Vehicle Efficiency

Direct

Rebound,

h

e

i

(

E

i

)

Technically

feasible

elasticity

Cross-sector,

From

resid

to

trans

with c.c.

Cross-

sector (indirect),

independent

of

c.c

.

Cross-sector,

From

trans

to

resid

with c.c

.

Energy

Elasticity,

Short-run response

Long-run response

Results shown for median income range ($40-$45k)Slide16

Other Behavioral DriversBehavior or Driver

Effect on technology…

Reference(s)

Choice

Use

Cost minimization, income constraint

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 Slide17

Other Behavioral DriversDo homeowners correlate or compensate drivers of energy technology choice and use?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 ]

Implications for rebound?Slide18

Empirical ResearchEstimate the impact of marginal technical change within and across end uses on electricity use and reboundIf choose technology A versus If choose both technology A and technology BSlide19

Pecan Street Research InstituteStatic data

High resolution consumption dataSlide20

Representative Sample Data

Variable

Range

Climate

Monthly CDD

Mean= 292,

SD= 257

Structural

Floorspace

(

square feet)

Windows area (square feet)

Age of the house

Mean= 2,019,

SD=

719

Mean= 245,

SD=

106

Mean= 21.4,

SD=

23.6

Demog-raphic

Occupancy

Tenure

HH income

Mean=

2.7, SD=

1.2

Mean= 6.6,

SD=

7.6

Mean=

$

128k SD

=

$

62k

Self-reported behaviors

Thermostat set point – summer

TV hours

per month

Dishwasher

loads per month

Clothes

washer loads per

month

Education (interval)

Mean= 76.9,

SD=

2.2

Mean= 107,

SD=

71.9

Mean= 14.3,

SD=

8.1

Mean= 17.1,

SD=

9.2

Technology choices

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),

E

lectric

heater

Mean= 28.6,

SD= 8.4Mean= 10.5, SD= 1.7Mean= 3.34, SD= 1.8 ElectricityElectricity consumption (KWh/month)Mean= 963, SD= 938Sample includes one year of monthly electricity consumption for 79 homesSlide21

Model SpecificationWhereYitλ represents monthly electricity

consumption

β

j

are the predictor

coefficient fixed effects

β

i

are the coefficient estimates for random

effects

S

ijλ

represents a series of household structural factorsDijλ

represents a series of household demographic factorsBijλ

represents household behaviors and cognitive factors X

ij interaction terms for different technology choice combinations

Ri represents the household identification codesSlide22

Results with No Interaction TermsExplanatory variable

Coefficient

p-value

% change in

Y for:

1 unit (or *10%

)

increase in X

ProgTherm

-0.236

0.026

-21.0%

ES

Refrig

-0.164

0.025

-15.1%

1

/

sqrt

(

Sq

Ft

)

-75.7

< .0001

8.35

%*

Devices

0.067

0.027

7.00%

CWloads

-1.958

0.053

1.20

%*

Home

R value

-0.009

0.087

-0.91%

Cooling

Degree Days

0.001

< .0001

0.14%

EV

0.087

0.339

9.14%

ES

DW

-0.085

0.325

-8.14%

2

-P window

-0.081

0.346

-7.78%

ES Clothes

washer

-0.069

0.378

-6.68%

PV

0.054

0.487

5.61%

AC

EER

0.006

0.738

0.65%

1/occupancy

0.141

0.465

-0.21

%*

Dishwasher

loads

0.001

0.879

0.09%

1/Window

Sq

Ft

-2.086

0.924

0.09%*Income2.00E-070.780.00%Constant (b0)8.348

< .0001-Slide23

Rebound from Marginal Efficiency Gains: Demonstrative Empirical ResultsSlide24

Rebound with Marginal Efficiency Gains

Multi-pane windows installed,

AC efficiency increased

Multi-pane windows installed at indicated AC efficiencySlide25

Rebound with Marginal Efficiency GainsSlide26

Preliminary PV ResultsOrder of technical change matters

Order of technical

change

Increase AC Efficiency

Increase

Insul

.

Install

multi-pane 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

-Slide27

ImplicationsLiterature is mixed as to whether consumers correlate or compensate valuations across energy technology choice/useEmpirical work suggests consumers MAY leverage efficiency gains for services ACROSS end uses; our results are also mixedRebound is relative to the current efficient technical state of the home and order of technical change

These findings suggest the dominant single

-service rebound paradigm is

misleadingSlide28

ImplicationsConsistent efficiency change across end uses can mitigate consumer responses; however…Consumers can and do expend energy services; thus…Models of rebound need to recognize service expansionSlide29

ImplicationsThe literature assumes PV exclusively replaces conventional grid energy sources; however…Behavioral implications of PV are entirely unclear Consumers will treat long-run operating cost of PV as zero

Results are mixed with respect to consumers responses to both efficiency change and installation of PVSlide30

Related Ongoing/Future WorkRebound across resources (water/electricity/natural gas/gasoline)Comparing Empirical and Estimated Energy Consumption (RECS/BeOpt)Does Weather Influence the Use of PV for Discretionary Electricity End Uses?

Estimating

Total

and E

nd-Use Residential Water (Energy

)

Demands Using Energy (Water

)

Demands

Comparing the Observed and Estimated Performance of Residential Water Efficient Fixtures and AppliancesSlide31

AcknowledgementsThis work was funded by The University of Texas at AustinBill and Melinda Gates Foundation FellowshipPhD studentsNour El-

Imane

Bouhou

Pamela Torres

Alison Wood

MS Students

Bruk

Berhanu

Neftali

Torres Post docSarah Taylor-LangeSlide32

Consistent RESIDENTIAL Efficiency Improvements ACROSS END-USES: Theoretical and Empirical InsightsMike BlackhurstAssistant ProfessorThe University Of Texas At AustinCivil, Architectural, & Environmental Engineering

mike.blackhurst@austin.utexas.eduSlide33

ReferencesBlackhurst, MF, and NK Ghosh. “The Rebound Effect with Consistent Efficiency Improvements and Implications for Cross-Sector Rebound.” Ecological Economics (submitted for review).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.

Thøgersen

, J., and F.

Ölander

. 2003. “Spillover of Environment-Friendly Consumer

Behaviour

.”

Journal of Environmental Psychology 23 (3): 225–236.Keirstead, J. 2007. “

Behavioural Responses to Photovoltaic Systems in the UK Domestic Sector.” Energy Policy 35 (8): 4128–4141.McAndrews, K. “To Conserve or Consume: Behavior Change in Residential Solar PV Owners.” The University of Texas at Austin, 2012

.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

.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.Hartman, R. S. “Self-Selection Bias in the Evolution of Voluntary Energy Conservation Programs.” The Review of Economics and Statistics (1988): 448–458.

Michelsen, C., and R. Madlener. “Homeowners’ Preferences for Adopting Residential Heating Systems: A Discrete Choice Analysis for Germany.” FCN Working Papers (2011

).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.

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

.

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

.

Dahl, C. A. “A Survey of Energy Demand

Elasticities

in Support of the Development of the NEMS” (1993).

http://mpra.ub.uni-muenchen.de/13962/

.

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

.

Graham, Daniel J., and Stephen

Glaister

. “The Demand for Automobile Fuel: A Survey of

Elasticities

.”

Journal of Transport Economics and Policy

(2002): 1–25

.

BLS (U.S. Bureau of Labor Statistics). “Consumer Expenditure Survey,” 2011. http://

www.bls.gov

/

cex

/

.Slide34

Single service rebound modelUsing technical definition of efficiency:Using CES production functionSlide35

Rebound with Marginal Efficiency Gains

Multi-pane windows installed,

AC efficiency increased

Multi-pane windows installed at indicated AC efficiencySlide36

Energy

Elasticity,

(-1)

Direct

Rebound

[

h

e

i

(

E

i

)+1]

E

i

/E

Technically

feasible

elasticity

-1(

E

i

/E +

h

e

i

(

e

j

)

E

j

/E)

Cross-sector,

From trans

to

resid

with c.c.

h

e

i

(

e

j

)

h

e

j

(

E

i

)

E

i

/E

Cross-

sector (indirect),

independent

of

c.c

.

h

e

i

(

E

j

)

E

j

/E

Cross-sector,

From

resid

to

trans

with c.c

.

h

e

i

(

e

j

) [

h

e

j(Ej)+1] Ej/E

Short-run response

Long-run response

Rebound Across

Resid

and Trans Sectors:

Driven by Changes in Electricity Efficiency

Results shown for median income range ($40-$45k)