Estimating Direct and Indirect Rebound Effects for UK Households

Dr Mona Chitnis, University of Surrey

Policymakers expect improved energy efficiency to play a key role in reducing GHG emissions. However, the energy and emissions savings from such improvements may be less than simple calculations suggest, owing to a variety of mechanisms that go under the heading of rebound effects.

Direct rebound effects result from increased demand for cheaper energy services: for example, insulation lowers heating costs and encourages households to heat their homes for longer and/or to higher temperatures. Indirect rebound effects result from re-spending the resulting cost savings on other goods and services: for example clothes that are manufactured in China and shipped to the UK. Energy efficiency improvements, such as cavity wall insulation, lead to both types of effect, while conservation measures such as lowering the thermostat lead only to indirect effects. In combination, they can be significant.

In this paper, we simulate a number of energy efficiency improvements and conservation measures by UK households and estimate the resulting direct and indirect rebound effects. The measures considered include insulation improvements, energy efficient lighting, fuel-efficient cars and walking/cycling instead of driving. We explore how rebound effects may vary between different income groups, and investigate how allowing for the capital cost and ‘embodied GHGs’ of the relevant measure can affect the results obtained.

We first estimate the GHG and cost savings from the different measures assuming that the demand for energy services remains unchanged. We then simulate the re-spending of these cost savings on different goods and services, calculate the implications for emissions and use these to estimate the rebound effects. Our calculations combine estimates of the GHG intensity and expenditure elasticity of different categories of household goods and services. The former are derived from an environmentally extended input output model and the latter from Engel curves estimated from cross-sectional data on UK household expenditure.

Our results indicate that:

•           if the capital cost and embodied GHGs of the relevant measures are ignored, the total rebound effect varies from 14% to 74%;

•           allowing for embodied GHGs significantly increases the estimated rebound effects, while allowing for capital costs significantly reduces them;

•           rebound effects are significantly higher for lower income groups.

The results demonstrate the importance of accounting for rebound effects when estimating the potential GHG savings from this type of measure.

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