Wissenschaftler im Kompetenzbereich „Umwelt und Ressourcen“, RWI – Leibniz-Institut für Wirtschaftsforschung
Professor, Institute for Applied Microeconomics, Universität Bonn
Professor, University of Alabama; Wissenschaftler, RWI–Leibniz-Institut für Wirtschaftsforschung
Präsident, RWI – Leibniz-Institut für Wirtschaftsforschung
Wissenschaftler im Kompetenzbereich „Umwelt und Ressourcen“, RWI – Leibniz-Institut für Wirtschaftsforschung
Doktorandin, Bonn Graduate School of Economics
The project investigates the potential for resource conservation that results from using smart meters to provide detailed data on energy and water consumption during showering. Interventions are used to test how smart meter data can be employed to motivate changes in behavior. A welfare analysis also takes into account for the first time the psychological cost-and-benefit factors of smart meter applications.
The growing demand for energy and water is causing global environmental problems and social tensions and calls for effective policies to conserve resources. This project investigates the potential for resource conservation offered by digitally measuring household consumption using so-called smart meters (intelligent electricity meters). By testing various behavioral interventions based on smart meter applications for the shower, we can test how smart meter data can be used to motivate behavioral changes. We also carry out a welfare analysis to determine the value that users attach to these interventions.
The European Union (EU) has set itself the ambitious goal of introducing smart meters in 80% of households and expects to install 200 million smart meters for electricity and 45 million for gas by 2020, with an estimated total cost of around 45 billion euros. Given the high investment costs, there are considerable differences of opinion in the individual countries as to whether these costs are really outweighed by the benefits of these smart meter technologies.
Studies on smart meters in the electricity sector have found the effects of smart meter applications on electricity consumption to be rather small (a 3 to 5% reduction in consumption) (McKerracher and Torriti 2013). However, initial studies on water consumption have shown average effects of over 20% from smart meter applications in showers in Switzerland and Singapore (Tiefenbeck et al. 2018, 2019).
As showering is an activity with very high energy and water consumption, a successful use of smart meters could save substantial resources. In a typical household, a shower of less than 5 minutes consumes about 45 liters of hot water and thus about as much energy as two refrigerators and the entire lighting of an average household per day (Tiefenbeck et al. 2018).
We will conduct a field study in which 600 households will receive smart meters for the shower. A randomized, controlled field experiment will evaluate the impact of different ways of providing consumers with smart meter information on resource consumption. We will also conduct a welfare analysis to determine the value that consumers attach to this information.
Besides deepening knowledge of the savings that can be achieved with smart meters, the welfare analysis also provides information on how consumers subjectively assess the provision of information. Are they happy to learn more about their consumption behavior, or does this spoil the pleasure of showering? This question is of considerable relevance for assessing the acceptance and potential of the widespread use of smart meters and similar technologies.
McKerracher, C., & Torriti, J. (2013). Energy consumption feedback in perspective: Integrating Australian data to meta-analyses on in-home displays. Energy Efficiency, 6(2), 387-405.
Tiefenbeck, V., Götte, L., Degen, K., Tasic, V., Fleisch, E., Lalive, R., & Staake, T. (2018). Overcoming salience bias: How real-time feedback fosters resource conservation. Management Science, 64(3), 1458-1476.
Tiefenbeck, V., Wörner, A., Schöb, S., Fleisch, E., & Staake, T. (2019). Real-time feedback promotes energy conservation in the absence of volunteer selection bias and monetary incentives. Nature Energy, 4(1), 35.
