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A field experiment using rebates and machine learnings to promote energy-saving behavior.
Last registered on July 12, 2020

Pre-Trial

Trial Information
General Information
Title
A field experiment using rebates and machine learnings to promote energy-saving behavior.
RCT ID
AEARCTR-0006139
Initial registration date
July 10, 2020
Last updated
July 12, 2020 10:23 PM EDT
Location(s)

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Primary Investigator
Affiliation
Kyoto University
Other Primary Investigator(s)
PI Affiliation
Kyoto University
PI Affiliation
Tohoku Gakuin University
PI Affiliation
Kyoto University
Additional Trial Information
Status
On going
Start date
2020-07-10
End date
2022-03-31
Secondary IDs
Abstract
We study the impact of rebate treatments on electricity conservation behavior. Furthermore, by linking household demographic information to electricity data, and using machine learning algorithm we will identify which households should be treated by rebates.
External Link(s)
Registration Citation
Citation
Ida, Takanori et al. 2020. "A field experiment using rebates and machine learnings to promote energy-saving behavior.." AEA RCT Registry. July 12. https://doi.org/10.1257/rct.6139-1.2000000000000002.
Experimental Details
Interventions
Intervention(s)
Rebate: 100 yen per kWh for the amount of energy saving from the previous month's average energy consumption between 5pm and 9pm in the winter and 1pm and 5pm in the summer.
Intervention Start Date
2020-08-24
Intervention End Date
2022-03-31
Primary Outcomes
Primary Outcomes (end points)
Electricity consumption
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
[Survey 1]
In conjunction with the power company, we will conduct a survey of its customers to obtain household demographics such as gender, age, and income of household members, as well as their use of home appliances. The survey can be completed either online or by mail. At the beginning of the survey, we will ask them for their consent to participate in this research experiment. Only those who have given their consent to participate will be included in the following experiments. In addition, we will pay a reward to the participants of the experiment for their participation in this survey and the following experiments and surveys.

[Experiment]
We randomly divide the participants into three groups. One will be the control group and will not offer a rebate. The other two will be the intervention group. One will offer a compulsory rebate and the other will offer a rebate at the participant's self-selection. Participants have one week to qualify for the rebate. During that period, we give a rebate of 100 yen per kWh for the amount of energy saving from the previous month's average energy consumption between 5pm and 9pm in the winter and 1pm and 5pm in the summer.

[Survey 2]
For those who did not opt for the rebate in the treatment group and those assigned to the control group, we will conduct a survey asking them if they are home or not home on a particular day. We will pay 500 yen to the respondent as a reward for this survey. The purpose of this study is to construct a model for predicting home and absenteeism from electricity consumption using machine learning methods based on electricity consumption data and information on home and absenteeism.
We will conduct Experiment and Survey (2) once every six months. Each participant will participate in the experiment or survey 2 at least four times. We also plan to regularly review the groupings and randomly group them again.
Experimental Design Details
Not available
Randomization Method
randomization by a computer.
Randomization Unit
households
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
N/A
Sample size: planned number of observations
3,800 households expectedly in the 2020 summer and the more in the following periods
Sample size (or number of clusters) by treatment arms
1520 households control, 1520 households treatment 1(compulsory), 760 households treatment 2(opt-in)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Committee on Experimental Ethics, Inter-Graduate School Program for Sustainable Development and Survivable Societies, Kyoto University, Japan.
IRB Approval Date
2019-12-02
IRB Approval Number
N/A