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Unintended outcomes of electricity smart-metering: trading-off consumption and investment behaviour

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Abstract

Smart-metering allows electricity utilities to provide consumers with better information on their energy usage and to apply time-of-use pricing. These measures have been shown to reduce electricity consumption and induce time-shifting of demand. Less is known about how they affect residential energy efficiency investment behaviour. We use data from a randomised-controlled trial on a sample of almost 2500 Irish consumers, conducted over a 12-month period to investigate the effect of smart-metering and residential feedback on household investment behaviour. The results show that exposure to time-of-use pricing and information stimuli, while reducing overall and peak usage, can also have the unintended effect of reducing investment in energy efficiency measures within the home. Our findings indicate that households exposed to treatment were less likely to adopt any energy saving measure (23–28 % on average), and those households adopted less energy saving features than those in the control group (15–21 % on average). This result highlights the potential for behavioural interventions to have unintended consequences on behaviours other than those specifically targeted. Furthermore, it underlines the importance of examining a wider range of outcomes and allowing longer time-scales when evaluating this type of experiment.

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Notes

  1. 1 EU Energy Efficiency Directive 2012/27/EU (European Commission 2012)

  2. 2 EU members are required to proceed with roll-out, covering 80 % of consumers in their territory by 2020 (European Commission 2009).

  3. 3 This was through the use of IHDs, other information stimuli and time of use tariffs.

  4. 4 Similar results were found in Gans et al. (2013)

  5. 5 Similar findings have also recently been demonstrated in an Irish context for gas demand (Diffney et al. 2013). Other research on the particular dataset that we have used in this analysis has shown that feedback can significantly increase knowledge, but that this is not correlated with demand reductions (Carroll et al. 2014). This indicates that feedback may act more as a reminder and motivational tool, rather than providing educational benefits to the consumer.

  6. 6 CER Electricity Smart Metering Customer Behavioural Trial data.

  7. 7 See (CER 2011; Di Cosmo et al. 2014; Carroll et al. 2014). for further information on this trial and related research.

  8. 8 See pg 168 of http://www.cer.ie/docs/000340/cer11080(a)(ii).pdf for further information

  9. 9 We reject H 0 at a 1 % level of significance for treatment 2, and at a 5 % level for treatments 1 and 3

  10. 10 We reject H 0 at a 1 % level of significance for treatments 2 and 3, and at a 5 % level for treatment 1

  11. 11 These include socioeconomic characteristics, current stock of appliances, current stock of energy efficient measures, total energy usage for 6 month benchmark period, heating type, house characteristics and use of internet

  12. 12 See Table 10 in the Appendix.

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Acknowledgments

This material is based upon works supported by the Science Foundation Ireland under grant No.09/SRC/E1780. The funding from the ESRI Energy Policy Research Centre is also gratefully acknowledged. We are grateful to the Irish Social Science Data Archive for providing data. We would also like to thank Laura Malaguzzi Valeri and Geertje Schuitema for helpful advice and participants at the TCD MWG and ESRI/UCC Energy Modelling Workshop for comments. A special thanks is due to James Carroll for generously providing data-processing code.

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Authors and Affiliations

  1. The Economic and Social Research Institute, Economic Analysis, Whitaker Square, Sir John Rogerson’s Quay, Dublin 2, Ireland

    Daire McCoy & Sean Lyons

  2. Department of Economics, Trinity College Dublin, College Green, Dublin 2, Ireland

    Daire McCoy & Sean Lyons

Authors
  1. Daire McCoy
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  2. Sean Lyons
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Correspondence to Daire McCoy.

Appendix:

Appendix:

A characteristics of participants and dwellings

Table 8 Pre-trial distribution of socioeconomic characteristics
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Table 9 Pre-trial distribution of house characteristics
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Table 10 Pre-trial distribution of appliance stock and heating type
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Table 11 Regression results for all investments including household control variables
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Table 12 Regression results for all investments including household control variables continued
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McCoy, D., Lyons, S. Unintended outcomes of electricity smart-metering: trading-off consumption and investment behaviour. Energy Efficiency 10, 299–318 (2017). https://doi.org/10.1007/s12053-016-9452-9

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