2019/27 | LEM Working Paper Series | ||||||||||||||||
Estimating the Economy-Wide Rebound Effect Using Empirically Identified Structural Vector Autoregressions |
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Stephan B. Bruns, Alessio Moneta and David I. Stern |
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Keywords | |||||||||||||||||
Energy efficiency; Rebound effect; Structural VAR; Impulse response functions; Independent component analysis.
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JEL Classifications | |||||||||||||||||
C32, Q43
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Abstract | |||||||||||||||||
The size of the economy-wide rebound effect is crucial for estimating the contribution that
energy efficiency improvements can make to reducing greenhouse gas emissions and for
understanding the drivers of energy use. Existing estimates, which vary widely, are based on
computable general equilibrium models or partial equilibrium econometric estimates. The
former depend on many a priori assumptions and the parameter values adopted, and the latter do
not include all mechanisms that might increase or reduce the rebound and mostly do not
credibly identify the rebound effect. Using a structural vector autoregressive (SVAR) model, we
identify the dynamic causal impact of structural shocks, including an energy efficiency shock,
applying identification methods developed in machine learning. In this manner, we are able to
estimate the rebound effect with a minimum of a priori assumptions. We apply the SVAR to
U.S. monthly and quarterly data, finding that after four years rebound is around 100%. This
implies that policies to encourage cost-reducing energy efficiency innovation are not likely to
significantly reduce energy use and greenhouse gas emissions in the long run.
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