Abstract
Switching to more energy-efficient appliances may lead to higher energy demand. This phenomenon is known as the rebound effect, which may lead to less power saving than expected prior to the switch. Using a combination of propensity score matching with the difference-in-differences method, we examine the change in household electricity consumption that may be caused by replacing air conditioners with more energy-efficient ones. Based on the results of our estimations, we calculate the magnitude of the rebound effect for summer and winter. We find that the rebound effect is positive in summer and winter, and the magnitude is higher in winter (7.87% versus almost 100%, respectively). The estimated rebound effect is small in summer, implying that the power-saving effect due to switching to energy-efficient air conditioners is sizable. On the other hand, no power-saving effect due to the switch was found in winter.
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Notes
Almost every air conditioner that has been sold on the Japanese market for the past couple of decades has both cooling and heating functions. Therefore, we consider that the air conditioners in this paper have both functions.
APF (Annual Performance Factor) means the cooling and heating capacity per 1 kWh of an air conditioner that is used under fixed conditions through the year.
That is, although the assignment of treatment might be dependent on the observable covariates, if we control for these covariates, we can think that the treatment was assigned almost randomly.
Kansai area is comprised of Osaka, Kyoto, Hyogo, Nara, Shiga, and Wakayama prefectures.
The data collection was conducted by an online survey company. This company has their own registered households and asked those who are living in Kansai area to participate to the survey. The participating households are selected by first come first served basis, until the number reaches 800.
These data are provided in Microsoft Excel file format and include not only the monthly electricity consumption, but also the date of meter reading, number of days of utilization, and the monthly electricity bill.
We assume that the size of the air conditioners before and after the replacement is the same.
This program was created to promote the manufacture of energy-efficient electronic appliances and automotive vehicles. This program set energy-efficient targets based on the value of the most energy-efficient products in the market, which all machinery and equipment covered by the program should exceed. Companies that continue to manufacture and sell products that fail to meet the targets are publicized and penalized.
Since electricity consumption data are available only for 2 years and our survey has been implemented for 2 months; data for February and March are available only for limited samples. Thus, we omitted these 2 months.
We assume that these characteristics do not change during the study period.
The daily outdoor temperature data of many location of Japan is available from the Japan Meteorological Agency (http://www.jma.go.jp/jma/indexe.html). Average temperature of nearest observatory is calculated based on information of participants’ address and their days of electricity consumption for each season.
We conducted matching for each season from spring to winter. After matching, there are no statistical differences in the independent variables between the treatment and the control groups in other seasons. Full estimation results for each season are available upon request.
The mean bias is commonly used for evaluating the effects of shrinking the differences of covariates (e.g., age, income, temperature, etc., in our study) between treatment group and control group before or after conducting PS matching. Generally, if the value of the mean bias after matching becomes below 5.0 (or the bias reduction rate, which is the ratio of the mean bias after matching to the mean bias before matching, becomes over 0.7), the accuracy of PS matching is considered sufficient. Here, the values of mean bias (bias reduction rate) of each season after matching are below 5.0 (over 0.7).
The technological saving rate is calculated by a vintage approach (Rapson, 2014). It is estimated by comparing the average power consumption of the year in which the previous air conditioner was bought with the year in which the new air conditioner was bought. The calculation is made for each season.
This comparison is only a guide. The average rebound effect in our study is calculated over 5 months (July, August, September, December, and January), which means that the rebound effect of the remaining 7 months (April, May, June, October, November, February, and March) is assumed to be zero. Therefore, it is difficult to directly compare our estimation of average rebound effect with the estimated annual rebound effect in previous studies.
The share of all electric homes in Japan was 10.2% in 2012 (Fuji Keizai Marketing Research & Consulting Group: https://www.fuji-keizai.co.jp/profile/profile/en_index.html).
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Acknowledgements
This research is supported by the Japan Society for the Promotion of Science [Grant-in-Aid for Young Scientists (B) #26780164; Grant-in-Aid for Scientific Research (B) #16H03006].
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Mizobuchi, K., Takeuchi, K. Rebound effect across seasons: evidence from the replacement of air conditioners in Japan. Environ Econ Policy Stud 21, 123–140 (2019). https://doi.org/10.1007/s10018-018-0224-y
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DOI: https://doi.org/10.1007/s10018-018-0224-y