31st USAEE/IAEE North American Conference Online Proceedings (Poster)

i. Title of Poster:

Direct and Indirect Rebound Effects for U.S. Households with Input-Output Analysis

ii. Authors and their organizational affiliation:

Brinda A. Thomas Carnegie Mellon University

iii. Complete contact details for the lead author/student

Brinda A. Thomas Postdoctoral Fellow Department of Engineering and Public Policy Carnegie Mellon University 5000 Forbes Ave., 129 Baker Hall Pittsburgh, PA, 15213 Email address: brindat@cmu.edu

Rebound Model = Direct (Own-Price Elasticity, ) + Indirect (Cross-Price Elasticity )

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Direct and Indirect rebound effects in primary energy for residential efficiency investments are modest (15-25%). Rebound effects in energy and CO2e emissions are important to assess the net economic and environmental impacts of energy efficiency policy goals. The higher the direct rebound effect, the lower the indirect rebound and vice versa. As the household re-spends energy-cost savings on greater utilization of the efficient appliance, there is less money available for re-spending on other goods and services, assuming households spend within their budgets. The Indirect rebound for electricity service efficiency depends on grid emissions factor, gasoline budget share and income elasticity, and electricity and gasoline prices. This implies that carbon prices will not eliminate indirect rebound effect in percent, but might lower the emissions consequences of the indirect rebound effect. It also implies considerable regional and individual variation in indirect rebound effects, with highest indirect rebound effects in states with expensive, low-carbon electricity, and for households with highly income-elastic or large budget shares for gasoline-based driving. Rebound analysis might be used to target efficiency investments by those fuels and end-use with lower rebound effects. In addition, the consideration of rebound effects could improve assessments of the cost-effectiveness of energy efficiency investments, and the development of energy forecasting models.

Where: •  !S,Ps is the uncompensated own-price elasticity for electricity

services such as air conditioning (Dubin et al., 1986), natural gas services such as space heating (Greening et al., 2000), and gasoline services such as driving (Greene, 2012),

•  !S,I, is the income elasticity for energy service s, and •  !O,I, the income elasticity for good o, (Taylor and Houthakker, 2010), •  wo and ws, are the household budget shares for good o and good s,

respectively,(2004 Consumer Expenditure Survey in 2002$) •  Eo-avg =SEowo/wo, , is the primary energy or emissions intensity per

average dollar of expenditure on other goods, •  "(EowoI)/"I=SEowo!O,I , is the primary energy or emissions intensity

per marginal dollar of expenditure on other goods, and •  Es, is the average energy or emissions intensity per dollar of

expenditure on an energy service, (2002 EIO-LCA model; Hendrickson et al., 2006; www.eiolca.net)

Research Goal: How large are direct and indirect rebound effects in the residential sector? This work simulates direct and indirect rebound effects for the average U.S. household using direct rebound effects from literature to construct cross-price elasticities to estimate the indirect rebound effect. Indirect rebound effects also include the embodied energy of respending energy-cost savings from efficiency on other goods and services which I estimate using an economic input-output lifecycle assessment (EIO-LCA) model (www.eiolca.net) for the 2002 U.S. economy. Rebound = 1 – [Actual Savings]/[Potential Savings] Direct rebound = greater energy consumption through greater efficient appliance utilization Indirect rebound = energy requirements from re-spending energy-cost savings and embodied energy Macroeconomic rebound = greater energy consumption from lower market price of energy, economic structure, etc.

Direct and Indirect Rebound Effects for U.S. Households with Input-Output Analysis Brinda A. Thomas (brindat@cmu.edu) Department of Engineering & Public Policy, Carnegie Mellon University, Pittsburgh, PA

1. Objective and Definitions

2. Methods and Data

4. Discussion & Policy Implications

Acknowledgements: This research was made possible through support from the Climate and Energy Decision Making (CEDM) and the Climate Decision Marking Center (CDMC) of the Department of Engineering and Public Policy. This Center has been created through a cooperative agreement between the National Science Foundation (SES-034798) and Carnegie Mellon University. The views state are those of the authors and not of

the NSF. Support has also been provided by the Betty and Gordon Moore Foundation.

3. Results

*Eo

Es

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Expected Efficiency Savings = x% reduction in annual household energy expenditures = EswsIx!

Assumptions 1. Each fuel provides a single energy service, s 2. Basic elasticity properties hold (Engel Agg., Cournot

Agg. & Slutsky Decomp.) 3. Constant-utility (compensated) cross-price elasticities

for all non-energy services are constant 4. Assume zero incremental capital costs for efficiency

(overestimates rebound: Henly et al., 1987)

Indirect rebound effects depend on average and marginal household spending patterns, budget share, and the emissions intensity of expenditures.

Rebound effects in emissions depend on policy goal. Rebound in energy and emissions is just as important as percent rebound.

Indirect rebound effect for electricity service efficiency is sensitive to grid emissions factors, energy prices and gasoline demand.

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