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Estimating the U.S. Economy-Wide Rebound Effect
Brinda A. Thomas, Carnegie Mellon University, 240-672-1419, [email protected]
Overview
Governments invest in energy efficiency as a cost-effective tool to lower greenhouse gas emssions that induce
climate change. However, an investment in energy efficiency may not yield expected energy savings due to the
rebound effect. Greening et al. (2000) and Sorrell (2007) classify the rebound effect in terms of direct, indirect, and
economy-wide effects. The direct rebound effect, which varies between 0-50% depending on end-use (Greening et
al., 2000), occurs when an energy efficiency investment decreases the operating cost of an energy service, leading to
more consumption, or when additional income from energy cost savings are respent on other goods which consume
energy. The indirect rebound effect, which varies by country between 10% to more than 100% (Sorrell, 2007),
occurs when the energy cost savings from an efficiency investment lead to a change in demand for other goods and
services, which also require energy to produce (known as embodied energy). The economy-wide rebound effect
includes direct and indirect effects and the long-term structural shift in the economy toward energy-intensive sectors
due to the change in the relative price of energy services. A simple definition of the rebound effect, equation 1,
compares potential savings (either in energy or emissions), often derived from engineering calculations, with actual
savings, either directly measured or simulated taking into account consumer behavior and market effects.
R = 1-AES/PES
(1)
This study estimates economy-wide rebound due to respending energy cost savings, using an economic input-output
lifecycle assessment (IO-LCA) model (Hendrickson et al., 2006; www.eiolca.net), for the 2002 U.S. economy.
Methods
The IO method uses data about industry transactions in the form of purchases of commodities by one industry from
other industries, and data about direct environmental emissions of industries to estimate the total emissions
throughout the supply chain. The equation defining the model is given as:
Z = E (I-A)-1*Y
(2)
Where, E = Impact matrix or the global warming potential (GWP) Matrix [tons CO2e/$]
Y= Final demand vector or GDP for household consumption, either with or without energy cost savings [$]
A = an investment by industry (I x I) input requirements matrix [unitless]
Z = embodied energy/GHG emissions due to household consumption [tons CO2e]
We use the 2002 producer price model, which consists of 428 industries producing and purchasing 428 commodities.
Final demand was modeled using the Consumer Expenditure Survey (CES) for different income groups in the U.S.
collected by the Bureau of Labor Statistics (BLS). The scenarios chosen to evaluate the indirect and income effects
were to reduce the energy and the gasoline usage separately by 5% from the base case and re-distribute the energy
cost savings to the remaining 426-427 sectors, so that the total expenditure remains the same. To simulate the direct
rebound, we compare the emissions from energy demand when energy cost savings are entirely saved (PESdirect) or
respent in other sectors (AESdirect). The indirect rebound was modelled similarly, except the comparisons are done on
the embodied emissions of final demand. Respending scenarios were defined using Global Trade Analysis Project
(GTAP) U.S. income elasticities (Hertel, 1997), or assuming all energy cost savings are respent in either the lowestor highest-GHG-intensive sector.
Results
The direct rebound effect due to the income elasticity of the demand for energy, using U.S. 2002 GDP gross ouput
as final demand, is on the order of 1%, as seen in Table 1, compared to economy-wide rebound effects between 120% for 5% electricity efficiency and between 2-76% for 5% gasoline efficiency, assuming energy service prices are
held constant by IO-model definitions. In contrast, previous direct rebound effect estimates of 0-50%, some of which
are based on the own-price elasticity of demand for energy, or substitution effect, often assumes income is held
constant. Thus, our direct rebound estimates may be in addition to the direct rebound estimated from the own-price
elasticity of energy, although a dynamic macro-economic model, such as a computational general equilibrium (CGE)
model, may better show how price effects interact with income effects.
Table 1: Direct versus economy-wide rebound for 5% electricity and gasoline efficiency improvements
Electricity Efficiency
Gasoline Efficiency
Direct
Economy-wide
Direct
Economy-wide
Lower Bound
1%
2%
Income Elasticity
1%
6%
1%
25%
Upper Bound
20%
76%
Respending patterns will vary by income group, as the CES data show.
Figure 1 and Figure 2 show the GHG rebound from a 5% improvement in electricity and gasoline efficiency,
respectively. Lower income groups have a slightly higher rebound effect than higher income groups because a
smaller portion of their income is saved. The financial sectors are among the least GHG emissions-intensive sectors
in the U.S. economy, on a per dollar basis. However, the respending scenarios are highly sensitive to the GTAP
income elasticities, some of which are the result of a CGE model tuning, rather than empirical data, so further
investigation on income elasticities is necessary.
-300
6%
4%
Embodied GHG Reduc ons from 5%
Electricity Efficiency
-400
-500
-600
2%
-700
-800
$5 < $5
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0
0
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0,
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9
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1
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9,
>$ 999
15
0,
00
0
0%
Embodied GHG Rebound
Average U.S. Household Carbon Footprint = 15,750 kg CO2e/yr
Annual Income
Figure 1: GHG rebound for 5% electricity efficiency
varies by income group. Source/Notes: CES 2003,
EIOLCA 2002 model, GTAP income elasticities;
weighted average income elasticity of 1.07.
200
100
10%
8%
-100
Embodied GHG Reduc ons from
5% Gasoline Efficiency
6%
-200
-300
-400
-500
4%
-600
2%
Average U.S. Household Carbon Footprint = 15,750 kg CO2e/yr
0%
-700
-800
$5
$5
,0
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0
0
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0,
00 9,99
$1 0-$1 9
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00 4,99
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00 9,99
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00 9,99
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15 9
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-200
8%
Embodied GHG Rebound
from 5% Gasoline Efficiency
0
GHG Rebound (%)
GHG Rebound (%)
-100
Embodied GHG Rebound
12%
GHG Emissions (kg CO2e)
100
0
10%
14%
200
<
Embodied GHG Rebound from 5%
Electricity Efficiency
12%
GHG Emissions (kg CO2e)
14%
Annual Income
Figure 2: GHG rebound for 5% gasoline efficiency
varies by income group. Source/Notes: CES 2003,
EIOLCA 2002 model, GTAP income elasticities;
weighted average income elasticity of 1.07.
Conclusions
The results from this study show that GHG rebound effects vary by efficiency type, with 2-3% rebound effects
possible with electricity efficiency and 7-13% rebound effects possible with gasoline efficiency. GHG rebound
effects are higher for efficiency investments in lower GHG-intensity fuels, such as gasoline, than in high GHGintensity fuels such as electricity. Rebound effects are 5-6% lower for gasoline efficiency and 1% lower for
electricity efficiency for higher income groups, because of their higher saving rates. These results also show that
even in the upper bound case, with all respending in the greatest GHG-intensive sector, there is a high rebound of
76%, which is less than backfire (or rebound > 100%). The upper bound scenario of respending all energy cost
savings in the highest GHG-intensive sector is unlikely for households, so a realistic upper bound is probably much
less. The possibility of backfire seems small given the respending mechanism of the rebound effect.
References
Bureau of Energy Administration. (2002). Input-Output Tables.
Bureau of Labor Statistics. (2003). Consumer Expenditure Survey.
Greening, Lorna A., Greene, David L., and Difiglio, Carmen. (2000). "Energy efficiency and consumption -- the
rebound effect -- a survey." Energy Policy. Volume 28. 389-401.
Hendrickson, Chris T., Lave, Lester B., Matthews, H. Scott. (2006). Environmental Life Cycle Assessment of Goods
and Services: An Input-Output Approach. RFF Press; Washington, DC.
Hertel, Thomas W. (1997). Global Trade Analysis: Modeling and applications. Cambridge University Press; NY.
Sorrell, Steve. (2007). “The Rebound Effect: an assessment of the evidence for economy-wide energy savings from
improved energy efficiency.” UK Energy Research Centre Technical Report.