With the increasing demand for electricity, the phasing out of what were once guaranteed sources of energy supply, and the switching of energy production to renewable sources, there is a need to have a more flexible demand for electricity. There are two principal approaches to lowering electricity demand: (1) more efficient technologies and buildings, (2) incentives for consumers to modify their electricity consuming behaviour. The present paper focuses on the latter.
In the experimental literature, households are incentivised to lower their consumption through financial (monetary incentives, monetary information), and non-financial (personalised advice, individual and real-time feedback, social and injunctive norms) incentives. The incentives are designed to either make it more costly for consumers to consume as they typically do, to provide them with greater information about their consumption so that they can target types of consumption which can be reduced, or to exploit behavioural biases to “nudge” consumers towards lowering their consumption of electricity.
The present paper employs a meta-analysis approach to analyse the results of recent field experiments and pilot studies which explore the effects of different methods of incentivising energy consumption reduction on residential consumers’ energy demand. Both data from peer-reviewed research and from grey literature; utility or government reports are used. Previous meta-analyses have reviewed studies from the 1970s and 1980s onwards and conclude that feedback which can be immediately related to the energy consuming activity, as well as tailored advice and energy conservation tips are most effective. However, such analyses have considered large timeframe and so their estimates of the effect of incentives on residential electricity consumption may be greater than the effects we can expect to see today (Darby, 2006; Ehrhardt-Martinez et al., 2010; Faruqui et al., 2010; Delmas et al. 2013; Faruqui and Sergici, 2013, McKerracher and Torriti, 2013). By focussing only on recent studies (2005 onwards), as well including results from the grey literature, the present paper provides an analysis of studies carried out when more advanced technology has been used and when there has been a greater understanding of the risks of climate change. The analysis includes 105 treatment observations from 39 papers.
After an analysis to determine that publication bias is not a significant issue in the sample of studies used for the present meta-analysis, results show that, on average, an experimental study of the effect of an incentive on residential electricity consumption can be expected to see a 3.37% reduction in energy consumption, or a 1.85% reduction when primary study sample size is accounted for. Real-time feedback and monetary information incentives have the greatest effect on energy consumption with an average reduction in consumption of almost 3% (weighted by sample size of study). Monetary incentives have the smallest effect on energy consumption with a weighted average reduction in consumption of 0.99%. This has important policy implications given that dynamic pricing is often not readily accepted by consumers (Alexander 2010.
A graphical analysis shows that in studies in which households are randomly assigned to a treatment there is a smaller variation in average effects. In addition, in studies in which households choose to participate, there is a greater dispersion of average effects. These households may have motivations to take part in energy consumption field experiments and pilot studies that are not necessarily accounted for in the experiment. These participants may be predisposed to make a greater effort than if the incentive were to be implemented at a national level. This provides support for the idea that a one-size-fits-all may not be the most effective. However, a tailored approach may not be feasible.
Keywords: conservation, consumption, electricity, feedback, incentives, nudges, residential.Buckley-Incentivising-households-to-reduce-electricity-consumption.pdf 1.32 MB