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. 2017 Mar 21;114(12):3091-3096.
doi: 10.1073/pnas.1615290114. Epub 2017 Mar 6.

Consequences of a price incentive on free riding and electric energy consumption

Mikael Elinder  1   2   3 , Sebastian Escobar  4   2 , Ingel Petré  4
Affiliations

Consequences of a price incentive on free riding and electric energy consumption

Mikael Elinder et al. Proc Natl Acad Sci U S A. .

Abstract

This article shows that a simple monetary incentive can dramatically reduce electric energy consumption (EEC) in the residential sector and simultaneously achieve a more desirable allocation of EEC costs. The analyses are based on data from a policy experiment conducted in 2011 and 2012 by a private housing company in about 1,800 apartments. Roughly 800 of the tenants (treatment group) were subject to a change from having unlimited EEC included in their rent to having to pay the market price for their own EEC. This change was achieved by installing EEC meters in each apartment. Tenants in the other 1,000 apartments (control group) experienced no policy change and were subject to apartment-level billing and metering during the entire study period. Using a quasiexperimental research design and daily data on EEC from 2007 to 2015, we estimate that apartment-level billing and metering permanently reduce EEC by about 25%. Moreover, we show that households reduce EEC immediately after being informed that they will be billed for EEC, the reduction is larger when the production cost is higher, and the reduction in EEC comes almost exclusively from households with very high EEC before the policy change. Finally, we show that apartment-level billing and metering are cost-effective, with a cost per reduced kilowatt hour of US$0.01, and for each invested dollar, the social value of reductions in air pollution, including CO2 emissions, is $2.

Keywords: energy conservation; environment; quasiexperiment; smart meters; sub metering.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1.
Fig. 1.
Daily EEC in the treatment and control groups (kilowatt hours per square meter). A data point for a specific day shows the annual consumption if consumption is constant at the level of that day for an entire year. The vertical lines denote the times when the tenants were informed of the policy change, the meters were installed, and the billing started. The figure is based on 72,065 data points from 22 bulk meters and 3,286 d.
Fig. 2.
Fig. 2.
The effect of the policy change is estimated to be a reduction in EEC by 6.2% during the announcement period, 18.1% during the period after the meters had been installed but before billing started (December 1, 2011 to February 29, 2012), and 24.4% during the period after billing was introduced (March 1, 2012 to December 31, 2015). All effects are relative to the mean EEC in the treatment group before June 1, 2011 (92.99 kWh/m2). All estimates are statistically significant at the 1% level. The figure is based on 72,065 data points from 22 bulk meters and 3,286 d. More details on the estimated model are provided in SI Appendix, section C.2, and detailed regression results are in SI Appendix, Table S2.
Fig. 3.
Fig. 3.
The effect of the policy change is estimated to be a reduction in EEC of 21.8% during the remainder of 2012 (March 1 to December 31), 24.1% during 2013, 24.7% during 2014, and 26.4% during 2015. All effects are relative to the mean EEC in the treatment group before June 1, 2011 (92.99 kWh/m2). All estimates are statistically significant at the 1% level. The figure is based on 72,065 data points from 22 bulk meters and 3,286 d. More details on the estimated model are provided in SI Appendix, section C.4, and detailed regression results are in SI Appendix, Table S3.
Fig. 4.
Fig. 4.
The reduction in EEC caused by the policy change is estimated to vary between 14.6% in July and 30.8% in December. All effects are relative to the mean EEC in the treatment group for the corresponding month in the pretreatment period (before June 1, 2011). All estimates are statistically significant at the 1% level. The estimates are based on 66,264 data points from 22 bulk meters and 3,012 d. More details on the estimated model are provided in SI Appendix, section C.5, and detailed regression results are in SI Appendix, Table S4.
Fig. 5.
Fig. 5.
The reduction in EEC caused by the policy change varies with prebilling EEC. For the six deciles with lowest EEC in January of 2012, we see small or statistically insignificant reductions in EEC between January and December of 2012. For deciles 7 and 8, the reduction is modest (around 15%), and for the deciles 9 and 10, we see very large reductions (26.3 and 36.1%, respectively). All effects are relative to the mean EEC in the respective deciles in January of 2012. The figure is based on 106,256 data points from 61 d and 1,742 apartment meters. More details on the estimated model are provided in SI Appendix, section C.6, and detailed regression results are in SI Appendix, Table S5.
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