10C-i 

APPENDIX 10C. REBOUND RATE

TABLE OF CONTENTS

10C.1 BACKGROUND ...................................................................................................10C-1

10C.2 OPERATING HOURS OF EFFICIENT AND STANDARD RESIDENTIAL 

LIGHTING IN CALIFORNIA ..............................................................................10C-3

10C.3 NATIONAL LIGHTING DENSITY AS THE DETERMINANT OF 

LIGHTING REBOUND ........................................................................................10C-3

10C.4 CONCLUSIONS....................................................................................................10C-5

LIST OF TABLES

Table 10C.2.1 Sample Disposition of Residential Lighting Studies.................................10C-3

Table 10C.3.1 U.S. Residential and Commercial Lighting in 2001 and 2010 ..................10C-4

Table 10C.3.2 Changes in Lamp Operating Hours Compared to Changes in Lighting 

Density, U.S. Residential and Commercial Sectors...................................10C-5

Table 10C.3.3 Estimate rebound rates based on LMC Data ..............................................10C-5
10C-1 

10C.1 BACKGROUND

 The U.S. Department of Energy (DOE) accounts for the direct rebound effect in its 

estimates of national energy savings.a Direct rebound is the concept that an increase in the 

energy efficiency of equipment may induce indirectly greater usage by the end-user, thereby 

undercutting expected energy savings. Economic theory suggests that if efficiency improvements 

decrease the cost of obtaining a desired service—lighting, in this case—then demand for that 

service will increase, assuming demand is not saturated. 

In the case of lighting, the increased usage should be normalized to floor area. Therefore, 

if a rebound is occurring there must be a change in lighting density (lumens per square foot). 

Any combination of the following potential consumer responses to increased lighting efficiency

could increase lighting density and, therefore, indicate a positive rebound effect: 

• an increase in the average operating hours of lamps, 

• a tendency to replace less efficient lamps with more lumenous efficient lamps, and 

• an increase in the number of fixtures or sockets per unit floor area (ft2

). 

While it is the change in service demand (lighting density in lm/ft2

), concomitant with an 

improvement in lighting efficiency, that is the final determinant of rebound, lacking such data, 

rebound researchers have relied on the individual factors listed previously as proxies for lighting 

rebound. (See, for example, the papers by Nadel (1993)1 and Greening et al. (2000),2 which were 

the origin of the rebound rates proposed by DOE in the framework document for this 

rulemaking.) In keeping with the literature on rebound, causation is not addressed in this 

analysis.

Lighting rebound can be quantified by the following formula:

Observed Savings = Expected Savings × (1 − rr)

Eq. 10C.1 

Where: 

Observed Savings = observed reduction in power consumption per square foot (W/ft2

) 

during a period, 

Expected Savings = expected reduction in power consumption per square foot (W/ft2

), 

given the efficiency improvements that occurred during that period, if lighting 

density (lm/ft2

) had remained constant, and 

rr = rebound rate. 

 

a DOE does not include rebound effects in its NPV calculation, however, for the following reasons: The take-back in 

energy consumption associated with the rebound effect provides consumers with increased value (e.g., increased 

lighting services). DOE believes that, if it were able to monetize the increased value to consumers of the rebound

effect, this value would be similar in value to the foregone energy savings. Therefore, the economic impacts on 

consumers with or without the rebound effect are the same, so DOE does not adjust operating cost savings in the 

NIA. 
10C-2 

 

Therefore, a direct rebound rate of 10 percent means that 10 percent of the energy savings 

expected to materialize from efficiency improvements alone would not materialize because of 

increased lighting density. 

During the framework public meeting, the Edison Energy Institute (EEI) and the 

Northwest Energy Efficiency Alliance (NEEA) both commented that the rebound rates proposed 

by DOE for residential and commercial lighting appear too high (EEI, Public Meeting 

Transcript, No. 10 at p. 241; NEEA, Public Meeting Transcript, No. 10 at p. 240). In the 

framework document, DOE proposed an 8.5 percent rebound rate for residential lighting, based 

on the average of two hours-of-use studies cited by Greening et al. (2000) and a one percent 

rebound rate for commercial and industrial lighting, also derived from Greening et al. (2000). 

In response to public comments, DOE conducted further research on rebound rates, 

starting with a review of the source materials quoted by Greening et al. (2000). The lighting 

rebound estimates quoted in the Greening paper were taken from Nadel (1993). Nadel, in turn, 

cited earlier surveys of lamp operating hours conducted by Pacific Gas & Electric Company 

(PG&E), Boston Edison (BE), and New England Electric System (NEES). Greening et al. used 

the results of quoted PG&E and BE studies only as the basis of their estimated 5 – 12 percent 

rebound for residential lighting. The results of the two NEES studies were not used, apparently 

because they had conflicting results. This may have been the genesis of the conclusion by

Greening et al. that the data available on lighting rebound were ‘inconclusive’.b

 

Given that the source data for the proposed rebound rates are now more than two decades 

old and were based on a single indicator of rebound (hours of use), which can be offset by other 

factors, DOE sought more recent evidence. Many authors have noted that appliance saturation 

effects can reduce rebound.

3,4 ,5,6,7

 Therefore, rebound rates can change significantly with time.

4

While Greening et al. (2000), and to a lesser extent Nadel (1993), has been repeatedly cited as a 

source for lighting rebound rates,5,

6,8,9 DOE found no studies that evaluated rebound rates based 

on more recent data. The National Energy Modeling System (NEMS) also cites Greening et al.2

as the source of its rebound rates, but NEMS uses a combined average rebound rate of 15 percent 

for all appliances and applies that to lighting, as well as to other technologies. The NEMS 

assumptions for commercial rebound are described in the Energy Information Administration’s 

Commercial Sector Demand Module documentation for NEMS10, the rebound rate used in the 

residential demand module was inferred. 

Lacking more recent journal publications on U.S. lighting rebound, DOE sought lighting

data from which rebound rates could be estimated. DOE identified two large studies of lamp 

operating hours in California that are directly comparable to the original studies cited by Nadel 

(1993) and Greening et al. (2000). DOE also used lighting data from its 2001 and 2010 U.S. 

Lighting Market Characterization (LMC) studies, which are a better source for national rebound 

rates, given their scope. 11,12 The results are described in sections 10C.2 and 10C.3. 

 

b See Table 3 footnote in Greening et al. (2000).
10C-3 

10C.2 OPERATING HOURS OF EFFICIENT AND STANDARD RESIDENTIAL

LIGHTING IN CALIFORNIA 

 A 2005 study by KEMA conducted for three California utilities (PG&E, Southern 

California Edison Company, and San Diego Gas and Electric Company) compared hours of use 

of compact fluorescent lamps (CFL)13 to hours of use of all residential lamp types that were 

reported in a 1999 California Energy Commission study for the same service territory.14 These 

studies were based on large and more recent data sets for service areas that overlapped those 

reported in Nadel (1993) and Greening et al. and used the same metric. The sample sizes used in 

the two studies and the reported hours of lamp use are reported in Table 10C.2.1. The studies 

found no discernible difference in lamps hours of use. Given that changes in lamp operating 

hours are only one possible cause of rebound, and the data are only for California, DOE sought 

national data to check the result. These are reported in the following section. 

Table 10C.2.1 Sample Disposition of Residential Lighting Studies 

KEMA 200513 CEC 199914

Residences 369 683

Fixtures 752 16,275

Lamps 1,514 26,203*

CFLs only All lamps

Reported Hours of Use of 

Lamps, Average Daily 2.3 hrs/day 2.34 hrs/day

* Total number of lamps calculated from reported average lamp per fixture values.

10C.3 NATIONAL LIGHTING DENSITY AS THE DETERMINANT OF 

LIGHTING REBOUND

DOE sought evidence of U.S. lighting rebound based on national lighting data reported in 

DOE’s 2001 and 2010 U.S. LMC studies. 11,12As indicated in section 10C.1, by definition a 

positive lighting rebound occurs if there is an increase in lighting density (lm/ft2

) concomitant 

with an increase in lighting efficiency. A decrease in lighting density yields a negative rebound 

rate, indicating a greater energy savings than expected from efficiency improvements alone. 

 DOE used data from the two LMC reports to estimate lighting density in 2001 and 2010 

using the following equation: 

Lighting Density (lm/ft2) = 

Nlamps ∗ Wlamp ∗ εlamp

Floorspace

Eq. 10C.2 

Where:

 Nlamps = the number of lamps in the category under consideration in the United States,

 Wlamp = the average wattage (W) of the lamp category, 
10C-4 

 εlamp = the average efficacy (lm/W) of the lamp category, and

 Floorspace = the total floor space in the sector (ft2). 

As shown in Table 10C.3.1, while reported lighting efficiency increased from 2001 to 

2010, lighting density decreased, implying a negative rebound rate. The table reports the 

associated percentage decreases in power consumption per lumen: 5 percent in the residential 

sector and 21 percent in the commercial sector. In the absence of rebound, lighting power density 

(W/ft2) would be expected to decrease by the same amounts. Instead, it drops by considerably 

larger percentages: 29 and 31 percent in the residential and commercial sectors, respectively. 

This implies a large reduction in lighting demand: from 30 to 23 lumens per square foot in the 

residential sector, and from 90 to 75 lumens per square foot in the commercial sector. 

Table 10C.3.1 U.S. Residential and Commercial Lighting in 2001 and 2010 Average Efficacy1

Elamp (lm/W)

Change in Watts per Lumen

Lamps1 (Millions)

Average Lamp 

Power1 (W) 

Floor Space1

(Billion ft2) 

Average Lighting 

Density (lm/ft2) 

Lighting Power 

Density (W/ft2) 

Change in Lighting Power 

Density (W/ft2) 

2001201020012010200120102001201020012010200120102010-EXP2 

Residential 18 19 -5% 4611 5812 63 46 174 223 30 23 1.7 1.2 1.6 -29%

Commercial 55 70 -21% 1966 2069 56 42 67 81 90 75 1.6 1.1 1.3 -31% 1 Data taken from LMC reports. All other values calculated. 2 The expected power density in 2010 in the absence of rebound.

According to the LMC data, only in the residential sector are the reductions in lighting 

density explained (at least in part) by reductions in operating hours. As shown in Table 10C.3.2, 

in the residential sector lamp operating hours were estimated to have decreased by 10 percent, 

explaining only part of the estimated 23 percent reduction in lighting density. In the commercial 

sector the reported lamp operating hours increased. However, LMC 2010 indicates that the large 

change in estimated operating hours could be an artifact of changes in the characterization of 

commercial versus industrial lighting. Therefore, the results should not be considered conclusive. 
10C-5 

Table 10C.3.2 Changes in Lamp Operating Hours Compared to Changes in Lighting 

Density, U.S. Residential and Commercial Sectors 

Sector and Lamp Type

Average Daily

Operating Hours1 

Percent 

Change in 

Operating 

Hours 

 

Average Lighting 

Density (lm/ft2

) 

Percent 

Change in

Average 

Lighting 

Density

2001 2010 2001 2010

Residential (Total) 2 1.8 -10% 30 23 -23%

Commercial (Total) 9.9 11.2 13% 90 75 -17% 1 Data taken from LMC reports. All other values calculated.

 Using the result from Table 10C.3.2, DOE estimated the rebound rates implied by the 

LMC data as follows: 

rr = 1 −

observed power savings

expected power savings

Eq. 10C.3 

Where: 

observed power savings = the difference between the 2001 and 2010 lighting power 

density, and 

expected power savings = percentage change in watts per lumen multiplied by the 2001 

lighting power density. 

The results are reported in Table 10C.3.3. 

Table 10C.3.3 Estimate rebound rates based on LMC Data

Observed Power Savings Expected Power Savings Estimated Rebound Rate

Residential 0.5 0.09 -4.9 (-490%)

Commercial 0.5 0.34 -0.5 (-50%)

10C.4 CONCLUSIONS

DOE concludes that the most recent available data do not support a lighting rebound 

effect for either the residential or commercial sector. The data may indicate a systematic trend in 

residential sector rebound, moving from a modest positive rebound in the early 1990s, to zero 

rebound in 2005, to a very large negative rebound in 2012. An initial reduction in rebound rates 

from positive to zero is what is expected if demand for a service saturates. GoldsteinError! Bookmark 

not defined. explains that negative rebounds can occur if consumers use efficiency cost savings to 

invest in more energy efficiency rather than in more energy consumption. The same may be true 

of commercial lighting, though there are fewer data to support such a conclusion. However, 
10C-6 

given the uncertainties inherent in comparing different data sources for different times, DOE 

took a conservative approach in estimating energy savings and assumed in this preliminary 

analysis no rebound effect for its reference scenario for both sectors. DOE also conducted a 

sensitivity analysis to determine the impact of the change in the assumed rebound rates compared 

to what had been proposed in the framework document and what has been used in NEMS. The 

results are described in chapter 10.

REFERENCES

1 Nadel, S.M. The Takeback Effect: Fact or Fiction? 1993. ACEEE Publication, American 

Council for an Energy-Efficient Economy. Washington, D.C. Available online at 

<http://www.aceee.org/research-report/u933>.

2 Greening, L.A., D.L. Greene, and C. Difiglio. Energy Efficiency and Consumption—the 

rebound effect—a survey. Energy Policy. 2000. 28: pp. 389–401.

3

Gottron, F. Energy Efficiency and the Rebound Effect. 2001. Congressional Research Service. 4

 Goldstein, D.B, Martinez, S., and Roy, R. 2011 Are There Rebound Effects from Energy 

Efficiency?- An Analysis of Empirical Data, Internal Consistency and Solutions.

<Electricitypolicy.com> (Last accessed December 11. 2012). 

5 Sorrell, S. Jevons’ Parodox revisited: The Evidence for backfire from improved energy 

efficiency. Energy Policy. 2009. 37: pp. 1456-1469. 

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<aceee.org/files/pdf/white-paper/rebound-large-and-small.pdf>

8 Herring, H. and Roy, R. Technological innovation, energy efficient design and the rebound 

effect. Technovation. 2007. 27: pp. 194-203.

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and research needs. Energy. 2009. 34: pp. 370-376. 

10 U.S. Department of Energy—Energy Information Administration. Office of Integrated 

Analysis and Forecasting, Commercial Sector Demand Module of the National Energy Modeling 

System: Model Documentation 2010, p. 57. May 2010. Washington, D.C. p. 57. 

11 Navigant Consulting Inc. 2001 U.S. Lighting Market Characterization. Department of Energy, 

Solid State Lighting Program. September, 2002. 

< http://apps1.eere.energy.gov/buildings/publications/pdfs/ssl/lmc_vol1_final.pdf> 
10C-7 

12 Navigant Consulting Inc. 2010 U.S. Lighting Market Characterization. Department of Energy, 

Solid State Lighting Program. January, 2012. Available online at: 

<http://www1.eere.energy.gov/buildings/ssl/tech_reports.html >

13 KEMA Inc., CFL Metering Study: Final Report, Prepared for Pacific Gas & Electric 

Company, San Francisco, California, San Diego Gas & Electric Company, San Diego, 

California, and Southern California Edison Company, Rosemead, California. Prepared by 

KEMA Inc., Oakland, California, February 25, 2005, page 4-1, 4-2.

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