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Effects of feedback on residential electricity demand: Findings from a field trial in Austria

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  • Schleich, Joachim
  • Klobasa, Marian
  • Götz, Sebastian
  • Brunner, Marc

Abstract

This paper analyzes the effects of providing feedback on electricity consumption in a field trial involving more than 1,500 households in Linz, Austria. About half of these households received feedback together with information about electricity-saving measures (pilot group), while the remaining households served as a control group. Participation in the pilot group was random, but households were able to choose between two types of feedback: access to a web portal or written feedback by post. Results from cross section OLS regression suggest that feedback provided to the pilot group corresponds with electricity savings of around 4.5 % for the average household. Our results from quantile regressions imply that for house-holds in the 30th to the 70th percentile, feedback on electricity consumption is statistically significant and effects are highest in absolute terms and as a share of electricity consumption. For percentiles below or above this range, feedback ap-pears to have no effect. Finally, controlling for a potential endogeneity bias induced by non random participation in the feedback type groups, we find no difference in the effects of feedback provided via the web portal and by post. --

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Bibliographic Info

Paper provided by Fraunhofer Institute for Systems and Innovation Research (ISI) in its series Working Papers "Sustainability and Innovation" with number S8/2012.

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Date of creation: 2012
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Handle: RePEc:zbw:fisisi:s82012

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Keywords: smart metering; feedback; household electricity consumption;

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  1. Salies, Evens, 2013. "Real-time pricing when some consumers resist in saving electricity," Energy Policy, Elsevier, vol. 59(C), pages 843-849.
  2. Paul J. Ferraro & Juan Jose Miranda & Michael K. Price, 2011. "The Persistence of Treatment Effects with Norm-Based Policy Instruments: Evidence from a Randomized Environmental Policy Experiment," American Economic Review, American Economic Association, vol. 101(3), pages 318-22, May.
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  8. Bradford Mills & Joachim Schleich, 2012. "Residential Energy-Efficient Technology Adoption, Energy Conservation, Knowledge, and Attitudes: An Analysis of European Countries," Grenoble Ecole de Management (Post-Print) hal-00805711, HAL.
  9. Isamu Matsukawa, 2004. "The Effects of Information on Residential Demand for Electricity," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 1-18.
  10. Su, Qing, 2012. "A quantile regression analysis of the rebound effect: Evidence from the 2009 National Household Transportation Survey in the United States," Energy Policy, Elsevier, vol. 45(C), pages 368-377.
  11. Ian Ayres & Sophie Raseman & Alice Shih, 2009. "Evidence from Two Large Field Experiments that Peer Comparison Feedback Can Reduce Residential Energy Usage," NBER Working Papers 15386, National Bureau of Economic Research, Inc.
  12. John R. Boyce, 2013. "Prediction and Inference in the Hubbert-Deffeyes Peak Oil Model," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
  13. Allcott, Hunt, 2011. "Social norms and energy conservation," Journal of Public Economics, Elsevier, vol. 95(9-10), pages 1082-1095, October.
  14. Kempton, Willett & Layne, Linda L., 1994. "The consumer's energy analysis environment," Energy Policy, Elsevier, vol. 22(10), pages 857-866, October.
  15. Torriti, Jacopo, 2012. "Demand Side Management for the European Supergrid: Occupancy variances of European single-person households," Energy Policy, Elsevier, vol. 44(C), pages 199-206.
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