Smart Meter Devices and The Effect of Feedback on Residential Electricity Consumption: Evidence from a Natural Experiment in Northern Ireland

By Will Gans, Anna Alberini, A. Longo. 2013

286 citations recorded by the source corpus.

Using a unique set of data and exploiting a large-scale natural experiment, we estimate the effect of real-time usage information on residential electricity consumption in Northern Ireland. Starting in April 2002, the utility replaced prepayment meters with “smart” meters that allow the consumer to track usage in real-time. We rely on this event, account for the endogeneity of price and plan with consumption through a plan selection correction term, and find that the provision of information is associated with a decline in electricity consumption of up to 20%. We find that the reduction is robust to different specifications, selection-bias correction methods and subsamples of the original data. At £15-17 per tonne of CO2e (2009£), the smart meter program delivers cost-effective reductions in carbon dioxide emissions.

Subfields: Economics and Econometrics, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/0166cf51191f808c81e7270f2c5e9664dba928f3

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In Economics and Econometrics

1. Arnold Picot
2. Xavier Gassmann
3. Thomas Meissner
4. Corinne Faure
5. J. Schleich
6. David Popp

In Renewable Energy, Sustainability and the Environment

1. Xavier Gassmann
2. Thomas Meissner
3. Corinne Faure
4. J. Schleich
5. Patrik Thollander
6. T. Fleiter

Citations in the corpus

• The role of information for energy efficiency in the residential sector (2015, unknown): “Similarly, Gans et al. (2013) found that energy use in Ireland dropped between 11% and 17% when smart meters replaced old meters in a natural experiment.”
• The role of smart metering and decentralized electricity storage for smart grids: The importance of positive externalities (2012, background): “Other research found that replacing standard meters with smart meters leads to an electricity consumption decrease of 3.7% (Schleich et al., 2011) to 15–20% (Gans et al., 2011) or found that smart meters help combat electricity thefts (Depuru et al., 2011).”
• Making the implicit explicit: A look inside the implicit discount rate (2016, background): “Providing households with information on their electricity consumption has generally been found to be effective (e.g. Wilhite and Ling, 1995; Ehrhardt-Martinez et al., 2010; Gleerup et al., 2010; Gans et al., 2012; Schleich et al., 2013).”
• Sensitivity of Price Elasticity of Demand to Aggregation, Unobserved Heterogeneity, Price Trends, and Price Endogeneity: Evidence from U.S. Data (2016, unknown): “, 2011), cross-sections (Nesbakken, 1999; Krishnamurthy and Kriström, 2013; Quigley and Rubinfeld, 1989; Boogen et al., 2014; Reiss and White, 2005; Gans et al., 2013), or time series (Kamerschen and Porter, 2004, or Dergiades and Tsoulfidis, 2008)—geography, and level of aggregation of the data.”