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experiment in Japan
Kazuma Murakami 1 *, Ikuho Kochi 2
1

The University of Shiga Prefecture, 2 Kanazawa University
April 2024

*Corresponding author. e-mail: murakami.k@ses.usp.ac.jp
Abstract
In Japan, households are required to save more electricity in response to electricity supply shortages
after the Great East Japan Earthquake and meet the 66% greenhouse gas reduction target in theresidential sector under the Paris Agreement. This study examines the effects of providing
information to promote the implementation of green curtains, a summer electricity-saving behavior
at home, using a randomized controlled trial for residents in Japan. Green curtains are made by
growing annual vines, like curtains on a net stretched across a window or a wall, and have the effectof reducing room temperature by providing shade. Based on their characteristics, being visible toothers and a low implementation rate, we examine the effects of the perceptions and cognitions of
the implementation of green curtains in the city (subjective descriptive norm) and the information
provided on the trends in green curtain implementation in neighboring districts (objective
descriptive norm) on the respondents' willingness to implement. The results show that subjective
descriptive norms influence the willingness to implement. Further, the information aboutneighboring districts with slightly higher green curtain implementation rates is more effective thaninformation about neighboring districts with significantly higher rates. This examination of thecomprehensive influence of descriptive norms and the effect of the dynamic and relatively
comparative forms of providing descriptive norm information have implications for studies in otherfields on promoting prosocial behaviors visible to others and with low implementation rates.
Keywords: social comparison, descriptive norm, green curtains, information provision, energysaving behavio u r, randomized controlled trial

11. Introduction
In Japan, growing green curtains is a way to keep cool at home. Green curtains are made of annual
vines, such as morning glory, loofah, and bitter gourd, which are grown like curtains on a net
stretched across a window or wall (Fig. 1). The shading effect lowers the room temperature andreduces the cooling demand, thereby saving electricity. According to a review by Abe et al. (2020),
the indoor temperature reduction effect of green curtains ranges from 0.7–4.1°C. In response to thepower shortage caused by the accident at the Fukushima Daiichi Nuclear Power Plant following theGreat East Japan Earthquake in 2011, the Ministry of the Environment and the Ministry of Land,
Infrastructure, Transport, and Tourism promoted green curtains, and many local governments
implemented promotional activities. However, even in Fukuchiyama City in Kyoto Prefecture,
which had promoted green curtains before the earthquake and aimed to be the No.1 city in green
curtain implementation rate, the implementation rate of green curtains remains 11.7%. 1< Fig. 1>
Most studies on green curtains have focused on examining their effectiveness in lowering room
temperature; no studies exist on information provision measures that promote their implementationthat would be beneficial for local governments. Many studies have used the Home Energy Report(HER) as an information provision measure to promote energy-saving behaviors (Allcott, 2011;
Allcott and Rogers, 2014; Brandon et al., 2019; Andor et al., 2020). These studies used randomized
controlled trials (RCTs) to examine the effect of providing information about the difference inelectricity consumption between one's own household and neighboring households to promoteenergy conservation. However, most of them have examined the effect of reducing electricity
consumption (kWh) by presenting the electricity consumption of neighboring households as an
objective descriptive norm (Cialdini et al., 1991). Nor have many studies examined the effect on
specific electricity conservation behaviors (McAndrew et al., 2021). Therefore, to propose
information provision measures promoting the adoption of green curtains, it is necessary to examine
the effect of objective descriptive norm information about green curtains (e.g., X% of households
in district Z implement green curtains) on their implementation by using RCTs.
Unlike indoor energy-saving behaviors such as limiting the use of air conditioners and lighting,green curtains are outdoor energy-saving behaviors that are visible to o th er s outside the family.
Therefore, social learning based on the perceptions and cognitions of green curtains implemented
by others in the city may influence the decision to implement green curtains. The incentives forbehaviors aimed at acquiring signal values, such as reputation and social status, are expected to behigher than those for indoor energy-saving behaviors. This suggests that not only the objective
descriptive norm information provided in the RCT but also the subjective descriptive norm of each
person's perceptions and cognitions of others' green curtain implementation may influence its
overall implementation. Examining the effects of subjective descriptive norms of green curtains

1

Calculated as number of households implementing green curtains/total households. These figures are forthe summer of 2017, when this study was conducted and based on a visual survey conducted by the staff ofthe Fukuchiyama City Environmental Council.

2

visible to others may reveal drivers that are different from those that promote indoor energy-saving
behavior.
This study aims to examine the provision of information to promote the implementation of green
curtains using an RCT. Specifically, it examines the effects of perceptions and cognitions about theimplementation of green curtains in the city (subjective descriptive norms of green curtains) andthe information provided in the RCT about the implementation rate in neighboring districts(objective descriptive norms of green curtains), and the money saved by implementing green
curtains, on the willingness to implement. In Japan, the shortage of electricity supply since theGreat East Japan Earthquake in 2011 has led to the need to reduce maximum electricity consumption(kW) in the summer. In addition, based on the Paris Agreement, the target for the household sector
is a 66% reduction in greenhouse gas emissions by 2030 over 2013 , 2 which requires a further
reduction in household electricity consumption (kWh). To this end, we believe that building specificpower-saving behaviors is an effective measure and focus on promoting the implementation of green
curtains in this study. In addition to promoting power-saving behaviors visible to others, which are
unique to each country and region, the results of this study will also have implications for researchon promoting environmentally conscious behaviors visible to others, such as buying
environmentally labeled products and organic vegetables in stores, using "my bag" and "my mug,"and participating in environmental organizations and environmental projects.
This study comprised two experiments. Although the implementation of green curtains results inelectricity savings, it is time-consuming to grow plants outdoors. It is also unclear whether people
perceive it as an effective electricity-saving behavior. Therefore, it is necessary to examine thegeneral drivers of energy-saving behavior, namely, economic incentives and descriptive norms. In
Experiment 1, we test whether information about the amount of money saved as an economic
incentive to implement green curtains increased the willingness to implement among residents of
two municipalities with different levels of green curtain promotion measures. In addition, we
identify the factors that promote green curtain adoption, including the influence of perceptions andcognitions regarding such implementation in the city (subjective descriptive norms). In Experiment2, we test the robustness of the green curtain promotion factors presented in Experiment 1, andwhether information about green curtain implementation rates in neighboring districts (objective
descriptive norms) increases the willingness to implement.
By conducting an analysis based on the two characteristics of green curtains, visibility to others
and low implementation rate, the study contributes to two research areas. First, as a study on energy
conservation promotion using social comparison (Festinger, 1954) and descriptive norms (Cialdini
et al., 1991), it reveals the effects of both subjective and objective descriptive norms. Comparedwith studies based on objective descriptive norms using HER (Allcott, 2011; Allcott and Rogers,
2014; Brandon et al., 2019; Andor et al., 2020), this study’s novelty lies in its exploration of thecomprehensive influence of descriptive norms. Second, it reveals the effect of the dynamic andrelative comparative form of providing descriptive norm information that can promote behaviors in

2

Headquarters for Promotion of Global Warming Countermeasures in Japan (2021).

3

case of low implementation rates. The results of this analysis provide a possible solution to the
problem in which the provision of low-descriptive norm-level information generates negative
effects (Frey and Meier, 2004; Cialdini et al., 2006).
The paper is structured as follows. Section 2 presents the focus and uniqueness of the study basedon a review of the literature, Section 3 presents the design of the two experiments and theiranalytical models, Section 4 outlines and analyses the results, and Section 5 concludes with thepolicy implications.
2. Literature review
2.1 Green curtains as an indirect green façade
Green curtains are implemented in public facilities and educational institutions, such as
kindergartens, primary and secondary schools, shops, offices, and homes. They improve the indoor
thermal environment and save electricity by shielding the heat energy of solar radiation andsuppressing radiant heat through the transpiration of leaves. The relationship between the functionsof green curtains and electricity savings is shown in Fig. A1. This information was presented torespondents in our questionnaire survey. Climatically, the seeding season is from late April to lateMay, and the green curtains reduce the room temperature from July to September during the growing
season and are removed at the end of September when the summer heat wanes.
Green curtains are classified as indirect green façades in the vertical greenery systems (VGS)(Safikhani et al., 2014; Manso and Castro-Gomes, 2015). Most studies on VGS and indirect green
façades have focused on large-scale systems in which entire building walls are covered withperennial grasses. The effectiveness of improving the thermal environment of city blocks andbuilding units as a heat island countermeasure has been examined and cultivation techniques andmethods have been studied (Perez et al., 2014; Ascione et al., 2020; Oquendo-Di Cocola et al.,2022; Susca et al., 2022). Studies have also been conducted on the effects of green curtains withannual plants in reducing indoor temperatures in detached dwellings, mainly in Japan and China(Hoyano, 1988; Koyama et al., 2013; Kato et al., 2013a; Abe et al., 2020; Zheng et al., 2020; Zhang
et al., 2022). Although there are differences in the scale of indirect green facades, most studies haveexamined the effectiveness of improving the thermal environment, and few have identified thefactors that promote the implementation of such facades. Because green curtains use annual plants,
implementation decisions are made once a year during the early spring planting season. Comparedwith perennial plants, opportunities for decision-making are more; therefore, it is important toclarify the provision of information to motivate implementation.
2.2 Influence of subjective descriptive norms on PV interest and adoption
To test the effects of social comparison (Festinger, 1954) and descriptive norms (Cialdini et al.,1991) on household CO 2 reduction, studies on promotion of the adoption of solar PVs, a renewableenergy device, have examined whether perceptions and cognitions of PV systems owned byneighboring households, as the subjective descriptive norms, influence the decision to adopt PV

4

systems. These studies can be broadly divided into two categories: those that use objective data on
descriptive norms, and those that use residents' awareness of descriptive norms.
Studies using objective data identify the location and timing of household PV installations basedon information from GIS, zip codes, and subsidy programs and examine the distance and range (0km, regional/district level, zip code level, street level, etc.) at which peer effects occur (Bollinger
and Gillingham, 2012; Müller and Rode, 2013; Graziano and Gillingham, 2015; Rode and Weber,
2016; Kosugi et al., 2019; Barton-Henry et al., 2021; Irwin, 2021). Studies using residents'
awareness captured through questionnaires have confirmed the neighborhood and peer effects ofperceptions and cognitions of PVs owned by neighboring households on interest in and adoption ofPVs (Woersdorfer and Kaus, 2011; Rai and Robinson, 2013; Palm, 2017; Wolskea et al., 2017;
Curtius et al., 2018; Mundaca and Samahita, 2020; Hansen et al., 2022). As green curtains use
annual plants, there are no objective data on the location and timing of implementation, such as for
PV. Therefore, this study measures the perceptions and cognitions of green curtains as subjective
descriptive norms using a questionnaire. As green curtain implementation is cheaper and easier thanPV implementation, it may be influenced more by subjective descriptive norms; however, this has
not yet been tested.
Subjective descriptive norms are not always perfect information; these are based on thesubjectivity of each individual, which leads to heterogeneity (Allcot, 2011). Nyborg et al. (2006)
find, as did Tversky and Kahneman (1973), that the purchase of green consumer goods is influenced
by the perceived purchase status of others as an availability heuristic. Further, some people may
have incentives to acquire signals such as reputation and social status. In addition to the objective
descriptive norm information presented as an RCT, we examine the effect of evaluations of others'
implementation status as subjective descriptive norms on willingness to implement green curtains.
2.3 Reference group setting and form of presenting descriptive norms
In the case of a majority behavior, providing information about the situation is likely to encourage
it. Therefore, providing information with a high level of descriptive norms is significant (Cialdini
et al., 1990, 1991; Cialdini, 2003; Schultz et al., 2007). Green curtains are minor behaviors withlow descriptive norms. Therefore, it is vital to devise a reference group setting and a form forpresenting descriptive norms based on previous studies.
First, regarding the reference group setting, some studies have examined the effectiveness of theproximity of the reference group. Hallsworth et al. (2017) find that in situations where tax payment
rates as descriptive norm levels are equal at the country and local area levels, presenting tax payment
rate information at the local area level increases tax payment rates more than at the national level.
Goldstein et al. (2008) reveal that presenting the information that the towel reuse rate of hotel guests
in the room they used was 75%, increased towel reuse more than presenting the information that theoverall towel reuse rate of guests was 75%. However, Czajkowskia et al. (2019) find that providing

country-level information is more effective in increasing the willingness to recycle at home thancity-level information, at the same level of the descriptive norm presented.

5

In the field of electricity conservation, Brülisauer et al. (2020) use RCT to examine the differencein the impact of electricity consumption feedback of residents in the same apartment as the subject
and that of residents in a different apartment on the subject's electricity conservation behavior but
do not find a statistically significant difference. Similarly, Shen et al. (2016) examine thedifferences in the impact of electricity consumption feedback between neighborhoods, streets, andnext-door neighbors on a subject's electricity-saving behavior, yet find that the most significant
effect was on street-level feedback and not next-door neighbor-level feedback. In addition,
descriptive norm levels differ across intervention groups in these studies, and the effects ofdescriptive norm levels and proximity were not tested separately. By contrast, Hallsworth et al.
(2017) and Goldstein et al. (2008) set the descriptive norm level equally across intervention groups
but targeted major behaviors with high implementation rates. In other words, the effects ofproximity on behaviors with low descriptive norm levels in the field of power saving have not been
fully examined. Additionally, the effect of proximity on behaviors easily visible to others has notbeen tested. Therefore, this study examines the effect of reference group proximity on power-saving
behavior that is highly visible to others and has a low implementation rate.
Next, we present the descriptive norms. For behaviors with low levels of descriptive norms,
providing information at that level can backfire (Frey and Meier, 2004; Cialdini et al., 2006; deGroot and Schuitema, 2012; Kormos et al., 2014; Kalch et al., 2021). Therefore, recent studies haveexamined whether presenting dynamic descriptive norms as levels that change over time, rather thanas static levels, can promote prosocial behavior. For example, water conservation behavior during
tooth brushing in Mortensen et al. (2017), reduced meat consumption in Sparkman and Walton(2017) and de Groot et al. (2021), and reduced sugary drink consumption in Sparkman and Walton(2019) and Loschelder et al. (2019) aimed to promote the use of reusable mugs.
However, most studies differ from static descriptive norms by adding messages such as "~ hasincreased in recent years" or "Recent surveys show that ~" or "In the last three years ~"; few show
specific level changes. Mortensen et al. (2017) examine the effect of the presentation form
"increased from 37% to 48% in two years" as trending minority norms. However, this study only
provides information on changes in descriptive norms and does not include a social comparison
perspective. As studies using HER have shown, the effect of showing the relative difference between
own behavior and others’ is likely to be significant. Czajkowskia et al. (2019) present recycling
behavior rates as a descriptive norm for one's own neighborhood and other neighborhoods together
as relative versus absolute norms and examine the behavior change effect; however, their form ofpresentation is static. Against these, this study examines the effect of dynamic and relative
comparative forms by presenting information on changes in green curtain implementation rates in
the respondent's district and its neighboring districts, making respondents aware of intragroupsolidarity and intergroup competition.

63. Methodology
3.1 Research design
The price-based information intervention was conducted in RCT Experiment 1 in September 2017
and the descriptive norm information intervention was conducted in RCT Experiment 2 inSeptember 2018. Each experiment was conducted using a questionnaire to examine the effects ofdifferent information provisions. Their subjects were different, too. Using screening questions, thestudy subjects were those who had not implemented green curtains in the summer of the current
year, and who were subscribers to Kansai Electric Power Co., and residents of detached houses. 3
These were based on the experimental details described in the following subsections. In bothexperiments, information was randomly provided as an intervention after obtaining the respondents'
demographics and other information. Willingness to implement green curtains the following
summer was measured.
3.1.1 Experiment 1
We examine whether providing information about the amount of money saved by implementing
green curtains increased willingness to implement green curtains. To this end, we established twointervention groups, Own and Others, with different reference points (Kahneman and Tversky,
1979; Tversky and Kahneman, 1991) to examine whether the same amount of savings but different
framing made a difference in the willingness to implement.
The specific information provided to the three groups (own, others, and control) is presented inFig. 2. The power-saving effects of implementing green curtains are presented in the text, anddifferent graphics are inserted for each group. The amount of money saved by the Own and Others
groups was approximately 300 yen/month (approximately $2.7/month). 4 If the green curtain works
for approximately three months (from July to September), it will save about 900 yen.
< Fig. 2 >࣭ Information provided to own group: A household with standard electricity consumption(260 kWh/month) 5 subscribing to Kansai Electric Power Co. 6 will save approximately
300 yen/month by implementing green curtains (top of Fig. 2).
࣭

Information provided to the others group: A household with standard electricity
consumption subscribing to Kansai Electric Power Co. will save about 300 yen/month by

3

Implementing green curtains in apartments is relatively difficult, and the relative lack of interest amongresidents of apartments could have led to bias in the responses. Therefore, we limited our sample to residents
of detached houses.
4
Calculated using the September 2017 exchange rate of $1=110.71 yen.
5
Japanese households can confirm their electricity consumption and electricity charges from their monthly
meter readings. Each major electricity power company publishes the monthly electricity charge for ahousehold with standard electricity consumption (260 kWh/month) on its website. To examine theinformation provision by local governments, we present household electricity charges with standard
consumption as information easily collected by local governments and understood by consumers.
6
Japan's electricity retail market was fully liberalized in April 2016. The survey subjects, residents ofFukuchiyama City and Kameoka City in Kyoto Prefecture, received electricity from Kansai Electric PowerCo. prior to April 2016. Therefore, the survey subjects were subscribers of Kansai Electric Power Co., andhad not changed power companies as of September 2017, the time of the survey.

7

implementing green curtains, equal to the difference in electricity charges for a householdwith standard electricity consumption contracted by Chubu Electric Power Co., 7 which
has a lower electricity unit price (middle part of Fig. 2).
࣭

Information provided to the control group: The three-month trend in electricity charges
paid by a household with standard electricity consumption that subscribed to Kansai
Electric Power Co. (bottom of Fig. 2).

The purpose of establishing the Others group is as follows. Other information indicates adifference in the amount of electricity charges paid by others who contract with Chubu ElectricPower Co., which has a lower price per unit, even though their electricity consumption is the same.
We hypothesize that behavioral change is more likely to occur due to inequity aversion (Fehr andSchmidt, 1999) related to social comparison. We test whether Others information as the reference
point, rather than the simple amount saved in Own information, further increases the willingness to
implement green curtains. 8
3.1.2 Experiment 2
We examine which forms of objective descriptive norm information about the implementation ofgreen curtains can motivate people to implement green curtains, and therefore, the effects ofproximity and different levels of descriptive norms.
The specific information provided to the three groups (modest, high, and control) is presented inFig. 3. The study subjects were residents of Sasabe district in Fukuchiyama City (Fig. A2, A3). We
show the trend of the implementation rate of green curtains in the Sasabe district, and the trend in
the implementation rate in the reference districts, which differs from group to group. The Sasabe,
Seijin, and Senkyo Districts are adjacent to each other 9 (Fig. A3).
< Fig. 3 >࣭ Information provided to the modest group: Trends in the implementation rate of green
curtains in Sasabe and adjacent Seijin districts over the past three years (top of Fig. 3).
࣭ Information provided to the high Group: Trends in the rate of implementation of green
curtains in Sasabe and adjacent Senkyo districts over the past three years (middle part of
Fig. 3).
࣭ Information provided to the control group: Trends in the implementation rate of green
curtains in Sasabe district and Fukuchiyama City over the past three years (Figure 3,
bottom panel).

7

We use the data for Chubu Electric Power Co., Japan’s third largest power company after Tokyo ElectricPower Company and Kansai Electric Power Co. It was a major power company before the full liberalizationof the electricity retail market in April 2016, and thus highly recognized by consumers. Before thederegulation, this company's electricity service area was adjacent to that of Kansai Electric Power Co.
8
We also analyze the willingness to change electricity companies as an objective variable (see, Appendix
C).
9
As of the 2015 census, the number of ordinary households living in residences was 1,408 in Seijin and
1,859 in Senkyo district. Sasabe district has 3,385 households and Fukuchiyama City has 31,115 households.

8

The effect of proximity was clarified by comparing the control and modest groups. The level ofand trend in the implementation rate of green curtains in Fukuchiyama City in the control group andin Seijin District in the modest group were almost the same. Similar to Hallsworth et al. (2017) andGoldstein et al. (2008), we test whether the effect of information in the Seijin district is larger.
To examine the effect of different levels of descriptive norms, we select Senkyo District, which
has a relatively high rate of green curtain implementation, as the high group. This is because higher
descriptive norm levels are more likely to promote behavioral change, as suggested by Cialdini etal. (1990, 1991), Cialdini (2003), and Schultz et al. (2007).
However, according to Festinger (1957), an object too far removed from one's own situation is
not subject to social comparison. Ashraf and Bandiera (2018) review the difference between
horizontal (peers at the same level) and vertical (individuals at different levels) peer effects in their
discussion of social incentives. In a study on goal setting in electricity conservation, Lazaric andToumi (2022) used an RCT to examine the difference in the impact of ambitious and modest goals
on electricity consumption reduction. Harding and Hsiaw (2014) show that setting realistic
electricity conservation goals reduced consumption more than when unrealistically high goals wereset. These studies suggest that if the goal level is too high, people may become demotivated and notchange their behavior. This study aligns the proximity of two districts and examine whether a highor affordable level of information is more effective as a target for social comparison.
3.2 Setting up the survey subjects
The target area should be an area where green curtains are implemented to the extent that people
can see them, even if they are not strongly aware of them. Therefore, we selected areas based on
the level of activity of local governments’ green curtain promotion policies. We assume that if apromotion policy is active, there will be many green curtains in the city, and selected Fukuchiyama
City in the Kyoto Prefecture as it has been promoting green curtains since 2007 with the goal of
becoming Japan's No.1 city for green curtains. The city has created a character named "Goya Sensei"(Bitter Melon Teacher) and conducted various activities, such as holding green curtain seminars,
distributing seedlings, and advertising on how to grow green curtains in public relations magazinesand newspapers. In addition to public facilities and schools, many green curtains were installed inbusinesses and stores and the rate of implementation in each district was measured. The following
describes the selection method of the survey subjects for Experiments 1 and 2.
3.2.1 Experiment 1
Another municipality has a level of green curtain implementation different from that of
Fukuchiyama City. Kameoka City (Fig. A2), also in Kyoto Prefecture, was selected as amunicipality that implemented green curtain promotion policies to some extent, though less than in
Fukuchiyama City. The selection was based on a survey of the literature and websites ofmunicipalities near Fukuchiyama City that have similar climate and other factors. This was done toverify the robustness of the perceptions and cognitions regarding the implementation in the city to
influence the willingness to implement green curtains.

9Next, we selected the areas in each municipality where the questionnaire was to be distributed.
In Fukuchiyama City, we selected several areas known to have a large number of green curtains,based on the results of a survey on the implementation rate of green curtains for the past three years
provided by Fukuchiyama City and documents from the executive committee of the Fukuchiyama
Environmental Conference that conducts the survey. Similarly, in Kameoka City, based on the
results of the green curtain Model Project 10 for the past nine years provided by the City, we selectedareas where the facilities that had conducted the project for the past five consecutive years werelocated. The target areas in both cities were those where green curtains had been visible for several
years without a strong awareness. Questionnaires were randomly distributed to the residents of theseareas designated mail 11 and collected by mail.
3.2.2 Experiment 2
In Experiment 2, we examine the differences in the effect of the objective descriptive norm
information on willingness to implement green curtains. The participants were residents of Sasabe
district in Fukuchiyama City. Questionnaires were randomly distributed to and collected from theresidents by the designated delivery area by mail. 12 In Kameoka City, no surveys were conductedon the green curtain implementation rate.
3.3 Analytical frames and models
The analytical model for Experiment 1 is validated using ordinary least squares (OLS):
Y2018 i =Į+ȕ1 Own i +ȕ2 Others i +ȕ3 X i +႒ i (1)Y2018 i is respondent i’s willingness to implement green curtains next summer (2018) measured
using a 6-point Likert scale. The questionnaire, including other variables, is presented in Table A1.
Own i is a dummy variable that takes the value of 1 if respondent i is in the own group and Others i
is a dummy variable that takes the value of 1 if respondent i is in the Others group. ȕ1 and ȕ2
measure the average treatment effect (ATE) for each information provision on outcome Y2018. X iis the vector of control variables for the socioeconomic characteristics of respondent i and otherinformation on respondent i from the questionnaire survey. ႒ i is an individual-specific error term.
X includes respondent characteristics such as age, sex, children under 18 living at home, solar
power at home, and gardening practices at home (growing flowers and vegetables).
We also set subjective descriptive norms for green curtains (two) and awareness of electricity
conservation (three). The subjective descriptive norms for green curtains were: perceptions andcognitive status of green curtains in public facilities and schools in the city, and perceptions andcognitive status of green curtains in residences in the city. We distinguished and examined the
behaviors that were affected. Awareness of electricity conservation was divided into: money saving
10

A model project to distribute seedlings to nursery schools, kindergartens, elementary and junior highschools, and nursing homes willing to implement and grow green curtains.
11
A service provided by Japan Post that delivers to all households in a specified zip code. Envelopes
containing one of three types of information provided by RCT were mixed and brought to the Fukuchiyama
and Kameoka post offices for delivery; 1,623 letters were distributed in Fukuchiyama City and 1,781 in
Kameoka City.
12
After randomly selecting zip codes within the Sasabe district, 2,099 letters were sent.

10

as an economic incentive, contribution to global warming prevention as environmental awareness,
and awareness of the electricity-saving status of others around them as a social norm.
The following analytical model was chosen for Experiment 2 and validated using OLS.
Y2019 i 㸻Į㸩ȕ1 Modest i 㸩ȕ2 High i 㸩ȕ3 X i 㸩႒ i (2)Y2019 i is respondent i’s willingness to implement green curtains next to summer (2019). Modest i
is a dummy variable that takes the value of 1 if respondent i is in the Modest group; High i is adummy variable that takes the value of one if respondent i is in the High group; X i is the vector ofcontrol variables for the socioeconomic characteristics of respondent i and other information on
respondent i from the questionnaire survey; ႒ i is an individual-specific error term; and X contains
the same indicators as in Models (1).
4. Results and discussion
4.1 Experiment 1
Descriptive statistics are presented in Table B1 for Fukuchiyama City and Table B2 for Kameoka
City, respectively. The sample sizes are 237 and 333 in Fukuchiyama and Kameoka, respectively,
the latter being larger because of the large number of residents who did not implement green curtains
that year. The mean and standard deviation for Y2018 in Fukuchiyama City were 3.034 and 1.200,
respectively, while those in Kameoka City were 3.021 and 1.226, respectively. The results of thebalance test by the difference of means test showed significant differences at the 5% level in Moneysaving awareness in Fukuchiyama City, PV ownership, and Environmental awareness in Kameoka
City. These variables are included in the model as control variables.
The results of the analysis of model (1) with Y2018 as the objective variable are presented inTable 1 for Fukuchiyama. The average treatment effect is shown in Columns (1) and (2), and noneof the coefficients of O wn and O ther s are significant. Cognition of public facilities is significant inColumn (2), and the adjusted R2 is improved from that in Column (1). These results confirm theinfluence of perceptions and cognitions regarding the implementation of green curtains in public
facilities and schools in the city on the willingness to implement green curtains. In column (2), thecoefficients of Children, Gardening, and Awareness of their surroundings are positive andsignificant, whereas PV ownership is negative and significant.
< Table 1 >Next, we examine the heterogeneity. We examine the interaction between subjective descriptive
norms and information provision in Columns (3) and (4). The results show that all four coefficientsare negative and significant. This implies that those who perceive and recognize green curtains inpublic facilities, schools, or residences in the city are less willing to implement green curtains when
receiving information about the amount of money saved by green curtains.
Table 2 presents the results for Kameoka. The average treatment effect is shown in Columns (1)and (2), and the coefficients of Own and Others are insignificant. Cognition in residences issignificant in Column (2), and the adjusted R2 improved from that in Column (1). These results
confirm the influence of perceptions and cognition s of the implementation of green curtains in11residences in the city on the willingness to implement green curtains. In column (2), G ardening is
positive and significant.
< Table 2 >Regarding heterogeneity, we examine the interaction between awareness of electricity
conservation and information provision in Columns (5)–(7), and Own × Awareness of surroundings
is negative and significant. This implies that those who are aware of the power-saving status of theirsurroundings are less willing to implement green curtains when they receive Own information.
Based on the above analysis of both cities, two points are clear. The first is the influence of
subjective descriptive norms and their differences in both cities. Fukuchiyama was more active than
Kameoka in administrative measures for promoting green curtains. As shown in Tables B1 and B2,
the levels of cognition in the public facilities in Fukuchiyama City are higher than those in Kameoka
City. Therefore, the green curtains implemented by the government have a stronger influence on thewillingness to implement in Fukuchiyama City.
The second concerns the lack of an effect and backfire in the savings information of green curtain
implementation. The effect of Own and Others information on the willingness to implement green
curtains does not seem to exist. Savings compared to the cost of growing plants outdoors cannot
influence the willingness to implement green curtains, even with a different framing. Nor ismonetary saving awareness effective. Especially in Kameoka City, the other energy conservation
awareness variables, environmental awareness, and awareness of surroundings were not significant.
Furthermore, one’s Own × Awareness of surroundings has a negative effect. In Kameoka City, green
curtains are not considered an energy-saving behavior but an extension of gardening. This may be
due to the low government promotions in Kameoka City.
In Fukuchiyama City, many green curtains have been implemented in schools because of thepromotion policy, and families with children may be highly interested in implementing green
curtains at home as part of their environmental learning. The negative and significant PV ownershipin Fukuchiyama City needs to be verified, but this may be due to moral licensing (Miller and Effron,2010; Tiefenbeck et al., 2013), in which people do not take other environmental measures because
they have already adopted PV adoption. However, Children and PV ownership were not significant
in Experiment 2, as discussed below. Therefore, the results are not necessarily robust.
In Fukuchiyama City, negative effects were observed at the intersection of subjective descriptive
norms and information provision. Fukuchiyama City has an active promotion policy, but it focuseson providing information on how to grow green curtains, functional aspects related to lowering
room temperature, and environmental aspects related to preventing global warming, but not on
economic aspects, such as the amount of money saved on electricity. Therefore, those more awareof green curtains because of their perception and cognition of green curtains were the first torecognize the small amount of money saved, leading to a decrease in their willingness to implement.
4.2 Experiment 2
The descriptive statistics are presented in Table B3. The mean and standard deviation for Y2019
are 3.000 and 1.361, respectively. The results of the balance test using the difference of means test
12

show significant differences at the 5% level for money-saving awareness, cognition in public
facilities, and cognition in residences. These variables were included in the model and analyzed ascontrol variables.
Table 3 presents the results of the analysis of Model (2) with Y2019 as the objective variable.
Similar to the results for Fukuchiyama City in Experiment 1 (Table 1), Cognition in public facilities
is significant in Column (2), and the adjusted R2 improved compared to that in Column (1). As forthe average treatment effect, the modest coefficient is significant in Column (2). As in Experiment
1, the coefficient of the awareness of surroundings is positive and significant.
< Table 3 >Next, the heterogeneity results are presented. Columns (3) and (4) show the interaction between
objective and subjective descriptive norms. The test was conducted to determine whether positive
evaluations of subjective descriptive norms could compensate for the influence of lower levels ofobjective descriptive norms. None of the results are statistically significant. Columns (5)–(7) show
the interaction between awareness of electricity conservation and objective descriptive norms.
Modest × M oney-saving awareness is negative and significant in Column (5), and High × Awarenessof surroundings is positive and significant in Column (7). This implies that those who are aware of
the money saved are less willing to implement green curtains when they receive modest information,and those who are aware of their surroundings' energy-saving status are more willing to implement
green curtains when they receive high information.
Based on the results of the analysis, three points are derived. The first is the robustness of theresults of cognition in public facilities and awareness of their surroundings, whose results are
similar to those of Experiment 1 in Fukuchiyama City. The results for cognition in public facilities
suggest that the effect of the implementation of green curtains by the government spreads beyond
areas with many green curtains, such as in Experiment 1. The results on awareness of surroundings
suggest that respondents aware of the power-saving status of their surroundings understand that
others who are implementing green curtains are doing so as a power-saving behavior and that
becoming aware of their implementation increases respondents' motivation to implement green
curtains.
The second factor is the effect of proximity. Information about Seijin District helped increase thewillingness to implement green curtains more than the information about Fukuchiyama City, withalmost the same level and trend. The results of this proximity effect are similar to those reported byHallsworth et al. (2017) and Goldstein et al. (2008). While Czajkowskia et al. (2019) found noproximity effect at low descriptive norm-level behaviors, this study confirms the proximity effectby providing dynamic and relative comparative information.
The third inference is the effects of modest information and the presence of heterogeneity. An
average treatment effect is observed for modest information, indicating that it increased thewillingness to implement green curtains. In the context of Festinger's (1957) discussion of social
comparison, modest information is more appropriate as a reference. However, it has a negative
effect on residents conscious of saving electricity for money. Given the results of the savings factors
in Experiment 1, one possible interpretation of the results is that respondents with a conservation
13

mindset for money-saving purposes thought that the reason for the implementation level in Seijin
district not being high was that green curtains could not lead to large savings. However, a high levelof information has a positive effect on respondents aware of the energy-saving status of others. This
could be because of the benchmarking of others with higher implementation levels. The finding thathigher descriptive norm levels are more likely to promote behavioral change (Cialdini et al., 1990,
1991; Cialdini, 2003; Schultz et al. 2007) is confirmed only for those who were aware of the powersaving status of others around them.
5. Conclusions and policy implications
To examine the information provision for promoting the implementation of green curtains, thisstudy examines the effects, on the willingness to implement, of perceptions and cognitions of green
curtain implementation in a city (subjective descriptive norm), of information provided on the trends
in the implementation rate of green curtains in neighboring districts (objective descriptive norm),and of information provided on the amount of money saved. The results of the analysis, based on
the two characteristics of green curtains – visibility to others and low implementation rate – areexpected to contribute to the literature on the comprehensive influence of descriptive norms and theeffect of a dynamic and relative comparative form of providing descriptive norm information. These
results have implications for studies on the promotion of prosocial behaviors that are visible toothers and have low implementation rates.
The policy implications are presented in terms of subjective and objective descriptive norms.
First, regarding subjective descriptive norms, it is desirable for local governments to promote green
curtain policies and their own green curtain implementation evidence easily visible to residents:
green curtain seminars, presentation of growing methods in public information magazines,
distribution of seeds and seedlings to places where many people gather (commercial, entertainment,
cultural, sports facilities, etc.), apart from in individual households, public facilities, and schools.
Such efforts would raise the level of perception and cognition of green curtains among residents.
The adoption of green curtains may then spread in a cascade, influenced by subjective descriptive
norms and by residents aware of their neighbors’ energy-saving status.
Next, regarding objective descriptive norms, it is preferable to provide information on theimplementation rate of green curtains in neighboring districts rather than any other municipality
average. Furthermore, it is desirable to provide information on districts with implementation rates
not much higher than those of residential districts.
However, this objective descriptive norm information requires the following attention. It isnecessary to verify the extent of the proximity effect. Experiment 2 shows that information on
districts adjacent to the respondents' residential districts is more effective than information on
Fukuchiyama City. If the spatial size of Fukuchiyama City impairs social proximity as a referencegroup, it is possible that the same effect could be obtained not only from information on adjacentdistricts but also from information on non-adjacent neighboring districts, slightly distant districts,
and distant districts. This verification, depending on the results, could lead to less time and effort
to prepare information provision.
14

Acknowledgements

This work was supported by JSPS KAKENHI [Grant Numbers JP17K04318, JP20K03296].
References
Abe, H., Rijal, H.B., Hiroki, R., Iijima, K., Ohta, A., 2020. Thermal mitigation of the indoor and outdoor
climatebygreen

curtains

inJapanese

condominiums.

Climate.

8(1),

8.

https://doi.org/10.3390/cli8010008
Allcott, H., 2011. Rethinking real-time electricity pricing. Resource and Energy Economics. 33(4), 820842. https://doi.org/10.1016/j.reseneeco.2011.06.003
Allcott, H., Rogers, T., 2014. The short-run and long-run effects of behavioral interventions:
Experimental evidence from energy conservation. American Economic Review. 104(10), 3003-3037.
https://doi.org/10.1257/aer.104.10.3003
Andor, M.A., Gerster, A., Peters, J., Schmidt, C.M., 2020. Social norms and energy conservation beyond
the

US,

JournalofEnvironmental

Economics

andManagement.

103,102351.

https://doi.org/10.1016/j.jeem.2020.102351
Ascione, F., De Masi, R.F., Mastellone, M., Ruggiero, S., Vanoli, G.P., 2020. A critical review:
Knowledge gaps, design parameters, thermal performances and multi-criteria design approaches.
Energies. 13(9), 2296. https://doi.org/10.3390/en13092296
Ashraf, N., Bandiera, O., 2018. Social incentives in organizations. Annual Review of Economics. 10(1),439-463. https://doi.org/10.1007/978-3-319-24612-3_1834
Barton-Henry, K., Wenz, L., Levermann, A., 2021. Decay radius of climate decision for solar panels in
the city of Fresno, USA. Scientific Reports. 11(1), 8571. https://doi.org/10.1038/s41598-021-87714w
Bollinger, B., Gillingham, K., 2012. Peer effects in the diffusion of solar photovoltaic panels. Marketing
Science. 31(6), 900–912. https://doi.org/10.1287/mksc.1120.0727
Brandon, A., List, J.A., Metcalfe, R.D., Price, M.K., Rundhammer, F., 2019. Testing for crowd out insocial nudges: Evidence from a natural field experiment in the market for electricity. PNAS. 116(12),5293-5298. https://doi.org/10.1073/pnas.1802874115
Brülisauer, M., Goette, L., Jiang, Z., Schmitz, J., Schubert, R., 2020. Appliance-specific feedback andsocial comparisons: Evidence from a field experiment on energy conservation. Energy Policy. 145,
111742. https://doi.org/10.1016/j.enpol.2020.111742
Cialdini, R., Reno, R.R., Kallgren, C.A., 1990. A focus theory of normative conduct: Recycling theconcept of norms to reduce littering in public places. Journal of Personality and Social Psychology.
58(6), 1015–1026. https://doi.org/10.1037/0022-3514.58.6.1015
Cialdini, R.B., Kallgren, C.A., Reno, R.R., 1991. A focus theory of normative conduct: A theoreticalrefinement and reevaluation of the role of norms in human-behavior. Advances in Experimental
Social Psychology. 24, 201–234. https://doi.org/10.1016/S0065-2601(08)60330-5
Cialdini, R.B., 2003. Crafting normative messages to protect the environment. Current Directions in
Psychological Science. 12(4), 105–109. https://doi.org/10.1111/1467- 8721.01242
15

Cialdini, R.B., Demaine, L.J., Sagarin, B.J., Barrett, D.W., Rhoads, K., Winter, P.L., 2006. Managing
socialnorms

forpersuasive

impact.

SocialInfluence.

1(1),

3–15.

https://doi.org/10.1080/15534510500181459
Curtius, H.C., Hille, S.L., Berger, C., Hahnel, U.J.J., Wüstenhagen, R., 2018. Shotgun or snowball
approach? Accelerating the diffusion of rooftop solar photovoltaics through peer effects and social
norms. Energy Policy. 118, 596–602. https://doi.org/10.1016/j.enpol.2018.04.005
Czajkowski, M., Zagórska, K., Hanley, N., 2019. Social norm nudging and preferences for household
recycling.

Resource

andEnergyEconomics.

58,

101110.

https://doi.org/10.1016/j.reseneeco.2019.07.004
de Groot, J.I.M., Schuitema, G., 2012. How to make the unpopular popular? Policy characteristics,
social norms and the acceptability of environmental policies. Environmental Science and Policy. 1920, 100–107. https://doi.org/10.1016/j.envsci.2012.03.004
de Groot, J.I.M., Bondy, K., Schuitema, G., 2021. Listen to others or yourself? The role of personalnorms on the effectiveness of social norm interventions to change pro-environmental behavior.
Journal of Environmental Psychology. 78, 101688. https://doi.org/10.1016/j.jenvp.2021.101688
Fehr, E., Schmidt, K.M., 1999. A theory of fairness, competition, and cooperation. Quarterly Journal of
Economics. 114(3), 817-868. https://doi.org/10.1162/003355399556151
Festinger, L., 1954. A theory of social comparison processes. Human Relations. 7(2), 117-140.
https://doi.org/10.1177/001872675400700202
Frey, B.S., Meier, S., 2004. Social comparisons and pro-social behavior: Testing “conditional
cooperation”

in

a

field

experiment.

AmericanEconomic

Review.

94(5),

1717–1722.

https://doi.org/10.1257/0002828043052187
Goldstein, N.J., Cialdini, R.B., Griskevicius, V., 2008. A room with a viewpoint: Using social norms tomotivate environmental conservation in hotels. Journal of Consumer Research. 35(3), 472–482.
https://doi.org/10.1086/586910
Graziano, M., Gillingham, K., 2015. Spatial patterns of solar photovoltaic system adoption. Journal of
Economic Geography. 15(4), 815–839. https://doi.org/10.1093/jeg/lbu036
Hallsworth, M., List, J.A., Metcalfe, R.D., Vlaev, I., 2017. The behavioralist as tax collector: Using
natural field experiments to enhance tax compliance. Journal of Public Economics. 148, 14–31.
https://doi.org/10.1016/j.jpubeco.2017.02.003
Hansen, A.R., Jacobsen, M.H., Gram-Hanssen, K., 2022. Characterizing the Danish energy prosumer:
Who buys solar PV systems and why do they buy them?. Ecological Economics. 193, 107333.
https://doi.org/10.1016/j.ecolecon.2021.107333
Harding, M., Hsiaw, A., 2014. Goal setting and energy conservation. Journal of Economic Behavior andOrganization. 107(Pt A), 209–227. https://doi.org/10.1016/j.jebo.2014.04.012
Headquarters for Promotion of Global Warming Countermeasures in Japan, 2021. Global warming
countermeasuresplan(Cabinetdecision

https://www.kantei.go.jp/jp/singi/ondanka/index.html

16on

October

22.

2021).

URL.

Hoyano, A., 1988. Climatological uses of plants for solar control and the effects on the thermal
environment of a building. Energy and Buildings. 11, 181-199. https://doi.org/10.1016/03787788(88)90035-7
Irwin, N.B., 2021. Sunny days: Spatial spillovers in photovoltaic system adoptions. Energy Policy. 151,
112192. https://doi.org/10.1016/j.enpol.2021.112192
Kahneman, D., Tversky, A., 1979. Prospect theory: An analysis of decisions under risk. Econometrica.
47(2), 263-291. https://doi.org/10.2307/1914185
Kalch, A., Bilandzic, H., Sappler, A., Stellinger, S., 2021. Am I responsible? The joint effect ofindividual responsibility attributions and descriptive normative climate messages on climate
mitigation

intentions.

JournalofEnvironmental

Psychology.

78,

101711.

https://doi.org/10.1016/j.jenvp.2021.101711
Kato, M., Iwata, T., Ishii, N., Hino, K., Tsutsumi, J., Nakamatsu, R., Nishime, Y., Miyagi, K., Suzuki,M., 2013a. Effects of Green Curtains to Improve the Living Environment, Kawakami, M., Shen, Z.,
Pai, J.T., GAO, X.S., Zhang, M. (ed). Spatial Planning and Sustainable Development: Approaches
for Achieving Sustainable Urban Form in Asian Cities, Springer book, Chapter 15, 1-286.
Kato, M., Ishii, N., Hino, K., Suzuki, H., 2013b. Research on the electricity-saving effects of green
curtains: Questionnaire survey in Hamamatsu City. Journal of the Japanese Society of Revegetation
Technology. 39(1), 133-136. https://doi.org/10.7211/jjsrt.39.133 (Japanese)
Kormos, C., Gifford, R., Brown, E., 2014. The influence of descriptive social norm information onsustainable transportation behavior: A field experiment. Environment and Behavior. 47(5), 479–501.
https://doi.org/10.1177/0013916513520416
Kosugi, T., Shimoda, Y., Tashiro, T., 2019. Neighborhood influences on the diffusion of residential
photovoltaic systems in Kyoto City, Japan. Environmental Economics and Policy Studies. 21(4),
477–505. https://doi.org/10.1007/s10018-019-00239-5
Koyama, T., Yoshinaga, M., Hayashi, H., Maeda, K., Yamauchi, A., 2013. Identification of key plant
traits contributing to the cooling effects of green façades using freestanding walls. Building andEnvironment. 66, 96-103. https://doi.org/10.1016/j.buildenv.2013.04.020
Lazaric, N., Toumi, M., 2022. Reducing consumption of electricity: A field experiment in Monaco withboostsandgoal

setting.

Ecological

Economics.

191,

107231.

https://doi.org/10.1016/j.ecolecon.2021.107231
Loschelder, D.D., Siepelmeyer, H., Fischer, D., Rubel, J.A., 2019. Dynamic norms drive sustainableconsumption: Norm-based nudging helps café customers to avoid disposable to-go-cups. Journal of
Economic Psychology. 75, 102146. https://doi.org/10.1016/j.joep.2019.02.002
Manso, M., Castro-Gomes, J., 2015. Green wall systems: A review of their characteristics. Renewable
and Sustainable Energy Reviews. 41, 863-871. https://doi.org/10.1016/j.rser.2014.07.203
McAndrew, R., Mulcahy, R., Gordon, R., Russell-Bennett, R., 2021. Household energy efficiency
interventions:A

systematic

literature

review.

https://doi.org/10.1016/j.enpol.2021.112136

17

EnergyPolicy.

150,

112136.

Miller, D.T., Effron, D.A., 2010. Psychological license: When it is needed and how it functions.
Advances in Experimental Social Psychology. 43, 115-155. https://doi.org/10.1016/S00652601(10)43003-8
Mortensen, C.R., Neel, R., Cialdini, R.B., Jaeger, C.M., Jacobson, R.P., Ringel, M.M., 2017. Trending
norms: A lever for encouraging behaviors performed by the minority. Social Psychology andPersonality Science. 10(2), 201–210. https://doi.org/ 10.1177/1948550617734615
Müller, S., Rode, J., 2013. The adoption of photovoltaic systems in Wiesbaden, Germany. Economics
of

Innovation

and

New

Technology.

22(5),

519–535.

http://dx.doi.org/10.1080/10438599.2013.804333
Mundaca, L., Samahita, M., 2020. What drives home solar PV uptake? Subsidies, peer effects andvisibility

in

Sweden.

EnergyResearchandSocialScience.

60,

101319.

https://doi.org/10.1016/j.erss.2019.101319
Murakami, K., Ida T., 2019. Deregulation and status quo bias: Evidence from stated and revealed
switching behaviors in the electricity market in Japan. Discussion papers e-19-001, Graduate Schoolof Economics, Kyoto University.
Nyborg, K., Howarth, R.B., Brekke, K.A., 2006. Green consumers and public policy: On sociallycontingent

moral

motivation.

Resource

andEnergyEconomics.

28(4),

351–366.

https://doi.org/10.1016/j.reseneeco.2006.03.001
Oquendo-Di Cosola, V., Olivieri, F., Ruiz-García, L., 2022. A systematic review of the impact of green
walls on urban comfort: Temperature reduction and noise attenuation. Renewable and Sustainable
Energy Reviews. 162, 112463. https://doi.org/10.1016/j.rser.2022.112463
Palm, A., 2017. Peer effects in residential solar photovoltaics adoption: A mixed methods study ofSwedishusers.

EnergyResearchandSocialScience.

26,

1–10.

https://doi.org/10.1016/j.erss.2017.01.008
Pérez, G., Coma, J., Martorell, I., Cabeza, L.F., 2014. Vertical Greenery Systems (VGS) for energy
saving in buildings: A review. Renewable and Sustainable Energy Reviews. 39, 139-165.
https://doi.org/10.1016/j.rser.2014.07.055
Rai, V., Robinson, S.A., 2013. Effective information channels for reducing costs of environmentallyfriendly technologies: Evidence from residential PV markets. Environmental Research Letters. 8(1),014044. https://doi.org/10.1088/1748-9326/8/1/014044
Rode, J., Weber, A., 2016. Does localized imitation drive technology adoption? A case study on rooftop
photovoltaic systems in Germany. Journal of Environmental Economics and Management. 78, 38–48.
https://doi.org/10.1016/j.jeem.2016.02.001
Safikhani, T., Abdullah, A.M., Ossen, D.R., Baharvand, M., 2014. A review of energy characteristic ofverticalgreenerysystems.

Renewable

andSustainable

EnergyReviews.

40,

450–462.

https://doi.org/10.1016/j.rser.2014.07.166
Schultz, P.W., Nolan, J.M., Cialdini, R.B., Goldstein, N.J., Griskevicius, V., 2007. The constructive,
destructive, and reconstructive power of social norms. Psychological Science. 18(5), 429-434.
https://doi.org/10.1111/j.1467-9280.2007.01917.x

18

Shen, M., Young, R., Cui, Q., 2016. The normative feedback approach for energy conservation behavior
in the military community. Energy Policy. 98, 19–32. https://doi.org/10.1016/j.enpol.2016.08.014
Sparkman, G., Walton, G.M., 2017. Dynamic norms promote sustainable behavior, even if it iscounternormative.

Psychological

Science.

28(11),

1663–1674.

https://doi.org/10.1177/0956797617719950
Sparkman, G., Walton, G.M., 2019. Witnessing change: Dynamic norms help resolve diverse barriers topersonalchange.

Journalof

Experimental

Social

Psychology.

82,

238–252.

https://doi.org/10.1016/j.jesp.2019.01.007
Susca, T., Zanghirella, F., Colasuonno, L., Del Fatto, V., 2022. Effect of green wall installation on urban
heat island and building energy use: A climate-informed systematic literature review. Renewable andSustainable Energy Reviews. 159, 112100. https://doi.org/10.1016/j.rser.2022.112100
Tiefenbeck, V., Staake, T., Roth, K., Sachs, O., 2013. For better or for worse? Empirical evidence ofmoral licensing in a behavioral energy conservation campaign. Energy Policy. 57, 160–171.
https://doi.org/10.1016/j.enpol.2013.01.021.
Tversky, A., Kahneman, D., 1973. Availability: A heuristic for judging frequency and probability.
Cognitive Psychology. 5, 207-232. https://doi.org/10.1016/0010-0285(73)90033-9
Tversky, A., Kahneman, D., 1991. Loss aversion in riskless choice: A reference-dependent model.
Quarterly Journal of Economics. 106(4), 1039 -1061. https://doi.org/10.2307/2937956
Woersdorfer, J.S., Kaus, W., 2011. Will nonowners follow pioneer consumers in the adoption of solar
thermal systems? Empirical evidence for northwestern Germany. Ecological Economics. 70(12),2282-2291. https://doi.org/10.1016/j.ecolecon.2011.04.005
Wolske, K.S., Stern, P.C., Dietz, T., 2017. Explaining interest in adopting residential solar photovoltaic
systems in the United States: Toward an integration of behavioral theories. Energy Research andSocial Science. 25, 134–151. https://doi.org/10.1016/j.erss.2016.12.023
Zhang, Y., Yang, Y., Zhang, L., Zhao, C., Yan, J., Liu, M., Zhao, L., 2022. Seasonal variation in leaf
area index and its impact on the shading effects of vertical green facades in subtropical areas.
Building and Environment. 225, 109629. https://doi.org/10.1016/j.buildenv.2022.109629
Zheng, X., Dai, T., Tang, M., 2020. An experimental study of vertical greenery systems for window
shadingforenergysavinginsummer.

Journalhttps://doi.org/10.1016/j.jclepro.2020.120708

19of

Cleaner

Production.

259,

120708.

Fig. 1 Green curtains

20

Own groupŶEffects of green curtains on electricity saving
࣭Green curtains reduce summer electricity consumption by 4.5% on average.
(Results of a questionnaire survey of 187 green curtains implementers in Hamamatsu City, Shizuoka Prefecture *)࠙ReferenceࠚElectricity charges
The electricity charge for a standard household (260Kwh) contracting with Kansai Electric Power Co. is estimated to be
6,721 yen this August (6,890 yen in July and 6,747 yen in September). About 4.5% of 6,721 yen (i.e., about the same as the㻣㻘㻜㻜㻜㼥㼑㼚 power-saving effect of the Green Curtain) is about 300 yen.
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Graphics in the Others group
The electricity charge for a standard household (260Kwh) contracting with Kansai Electric Power Co. is estimated to be
6,721 yen this August (6,890 yen in July and 6,747 yen in September). The difference in electricity chage this August for a㻣㻘㻜㻜㻜㼥㼑㼚 standard household (260Kwh) contracting with Chubu Electric Power Co. will be about 300 yen. This 300 yen is equivalent
to about 4.5% of 6,721 yen (i.e., about the same as the energy-saving effect of the Green Curtain).
㻢㻘㻤㻥㻜㼥㼑㼚

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Graphics in the Control group
The electricity charge for a standard household (260Kwh) contracting with Kansai Electric Power Co. is estimated to be
6,721 yen this August (6,890 yen in July and 6,747 yen in September).
㻣㻘㻜㻜㻜㼥㼑㼚

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㻢㻘㻣㻞㻝㼥㼑㼚

㻢㻘㻣㻠㻣㼥㼑㼚

㻭㼡㼓㻚

㻿㼑㼜㻚

㻢㻘㻡㻜㻜㼥㼑㼚

㻢㻘㻜㻜㻜㼥㼑㼚

㻞㻜㻝㻣 㻶㼡㼘㻚

Fig. 2The specific information provided for the three groups in Experiment 1

21

In the information provided to the Others group and Control group, the Own group graphic at the top is replacedby the respective graphic. The results of these questionnaires are shown outside of these frames to be
based on Kato et al. (2013b).

22

Modest group㼀㼞㼑㼚㼐㼟㻌㼕㼚㻌㻳㼞㼑㼑㼚㻌㻯㼡㼞㼠㼍㼕㼚㻌㻵㼙㼜㼘㼑㼙㼑㼚㼠㼍㼠㼕㼛㼚㻌㻾㼍㼠㼑㼟㻌㼕㼚㻌㻿㼍㼟㼍㼎㼑㻌㼍㼚㼐㻌㻿㼑㼕㼖㼕㼚㻌㼐㼕㼟㼠㼞㼕㼏㼠㼟㻌㻔㻞㻜㻝㻡㻙㻞㻜㻝㻣㻕
㻝㻡㻚㻜㻑
㻝㻟㻚㻜㻑

Seijindistrict

㻝㻝㻚㻜㻑

㻝㻜㻚㻢㻑
㻥㻚㻜㻑
㻣㻚㻜㻑

Sasabedistrict

Sasabedistrict

㻢㻚㻥㻑

㻣㻚㻝㻑

Seijindistrict

Sasabedistrict

㻝㻜㻚㻜㻑

㻥㻚㻤㻑

Seijindistrict

㻝㻝㻚㻝㻑

㻡㻚㻜㻑

㻞㻜㻝㻡

㻞㻜㻝㻢

㻞㻜㻝㻣

High group㼀㼞㼑㼚㼐㼟㻌㼕㼚㻌㻳㼞㼑㼑㼚㻌㻯㼡㼞㼠㼍㼕㼚㻌㻵㼙㼜㼘㼑㼙㼑㼚㼠㼍㼠㼕㼛㼚㻌㻾㼍㼠㼑㼟㻌㼕㼚㻌㻿㼍㼟㼍㼎㼑㻌㼍㼚㼐㻌㻿㼑㼚㼗㼥㼛㻌㼐㼕㼟㼠㼞㼕㼏㼠㼟㻌㻔㻞㻜㻝㻡㻙㻞㻜㻝㻣㻕Senkyodistrict

㻝㻡㻚㻜㻑

㻝㻡㻚㻝㻑

Senkyodistrict

㻝㻟㻚㻜㻑

㻝㻞㻚㻢㻑

Senkyodistrict

㻝㻝㻚㻜㻑

㻝㻜㻚㻞㻑

㻥㻚㻜㻑
㻣㻚㻜㻑

Sasabedistrict

Sasabedistrict

㻢㻚㻥㻑

㻣㻚㻝㻑

Sasabedistrict

㻥㻚㻤㻑

㻡㻚㻜㻑

㻞㻜㻝㻡

㻞㻜㻝㻢

㻞㻜㻝㻣

Control group㼀㼞㼑㼚㼐㼟㻌㼕㼚㻌㻳㼞㼑㼑㼚㻌㻯㼡㼞㼠㼍㼕㼚㻌㻵㼙㼜㼘㼑㼙㼑㼚㼠㼍㼠㼕㼛㼚㻌㻾㼍㼠㼑㼟㻌㼕㼚㻌㻿㼍㼟㼍㼎㼑㻌㼐㼕㼟㼠㼞㼕㼏㼠㻌㼍㼚㼐㻌㻲㼡㼗㼡㼏㼔㼕㼥㼍㼙㼍㻌㻯㼕㼠㼥㻌㻔㻞㻜㻝㻡㻙㻞㻜㻝㻣㻕
㻝㻡㻚㻜㻑
㻝㻟㻚㻜㻑
㻝㻝㻚㻜㻑

Fukuchiyama
City

㻥㻚㻜㻑

㻝㻜㻚㻜㻑

㻣㻚㻜㻑

Fukuchiyama
City

㻝㻜㻚㻝㻑

Sasabedistrict

Sasabedistrict

㻢㻚㻥㻑

㻣㻚㻝㻑

Fukuchiyama
City
Sasabedistrict

㻝㻝㻚㻣㻑

㻥㻚㻤㻑

㻡㻚㻜㻑

㻞㻜㻝㻡

㻞㻜㻝㻢

㻞㻜㻝㻣

Fig. 3The specific information provided for the three groups in Experiment 2
The source of the data is indicated outside these frames as "Source: Fukuchiyama City Environmental Policy Office".

23

Table 1 Analysis results in Experiment 1 (Fukuchiyama City).
(1)
(2)
(3)
(4)
(5)
(6)
(7)
0.092
0.072
1.081 㻖1.421 㻖㻖 -0.066
0.090
0.095
(0.190)(0.179)(0.550)(0.556)(1.198)(0.802)(0.552)Others0.012-0.053
1.176 㻖1.158 㻖-0.716-1.1120.066(0.178)(0.176)(0.623)(0.613)(1.129)(0.769)(0.511)
Age
0.089
0.071
0.057
0.071
0.069
0.067
0.071
(0.062)(0.058)(0.059)(0.057)(0.059)(0.058)(0.058)
Female-0.031-0.117-0.163-0.104-0.118-0.102-0.117(0.150)(0.145)(0.147)(0.144)(0.144)(0.143)(0.145)
Children
0.399 㻖㻖0.402 㻖㻖㻖 0.394 㻖㻖
0.444 㻖㻖㻖 0.399 㻖㻖
0.396 㻖㻖0.401 㻖㻖㻖(0.155)(0.153)(0.153)(0.154)(0.154)(0.153)(0.154)
PV ownership-0.412-0.447 㻖-0.417 㻖-0.444 㻖-0.448 㻖㻖 -0.427 㻖-0.455 㻖㻖(0.257)(0.228)(0.227)(0.233)(0.227)(0.231)(0.229)Gardening
0.448 㻖㻖0.400 㻖㻖0.372 㻖㻖0.398 㻖㻖0.407 㻖㻖0.412 㻖㻖0.401 㻖㻖(0.191)(0.183)(0.178)(0.176)(0.183)(0.181)(0.182)
Money-saving awareness0.006-0.0030.000
0.010-0.054-0.001-0.002(0.112)(0.106)(0.103)(0.103)(0.158)(0.106)(0.106)
Environmental awareness0.143 㻖
0.078
0.084
0.082
0.075
-0.0110.080(0.079)(0.081)(0.080)(0.077)(0.082)(0.127)(0.082)
Awareness of surroundings
0.261 㻖㻖㻖 0.202 㻖㻖㻖 0.203 㻖㻖㻖 0.192 㻖㻖㻖 0.201 㻖㻖㻖 0.195 㻖㻖㻖 0.215 㻖㻖(0.069)(0.070)(0.069)(0.068)(0.070)(0.070)(0.107)
Cognition in public facilities
0.172 㻖㻖0.333 㻖㻖㻖 0.161 㻖㻖
0.176 㻖㻖0.172 㻖㻖0.173 㻖㻖(0.072)(0.083)(0.074)(0.073)(0.072)(0.073)
Cognition in residences
0.121
0.138
0.377 㻖㻖㻖 0.119
0.131
0.120
(0.086)(0.084)(0.121)(0.087)(0.086)(0.087)
Own×Cognition in public facilities-0.244 㻖(0.127)
Others×Cognition in public facilities-0.293 㻖㻖(0.139)
Own×Cognition in residences-0.379 㻖㻖(0.152)Others×Cognition in residences-0.339 㻖㻖(0.159)
Own×Money-saving awareness0.025(0.235)
Others×Money-saving awareness
0.131(0.222)
Own×Environmental awareness-0.005(0.167)
Others×Environmental awareness
0.222(0.157)
Own×Awareness of surroundings-0.006(0.157)
Others×Awareness of surroundings-0.034(0.143)
Constant
0.413
0.019
-0.635-0.8960.310
0.443-0.036(0.657)(0.615)(0.630)(0.670)(0.917)(0.762)(0.684)
2
0.147
0.200
0.212
0.217
0.194
0.201
0.193
Adj.R
N
237
237
237
237
237
237
237
Own

Robust standard errors in parentheses. ***, ** and * indicate statistical significance at the 1%, 5%, 10% level,
respectively.

24

Table 2 Analysis results in Experiment 1 (Kameoka City).
(1)
(2)
(3)
(4)
(5)
(6)
(7)
0.087
0.136
0.020
-0.3420.758
0.080
0.950 㻖(0.162)(0.158)(0.511)(0.509)(0.989)(0.832)(0.487)
Others0.044
0.035-0.127-0.017
0.359
0.670
0.388
(0.157)(0.155)(0.481)(0.554)(1.132)(0.791)(0.434)
Age
0.063
0.036
0.036
0.034
0.039
0.036
0.043
(0.056)(0.056)(0.056)(0.057)(0.056)(0.056)(0.056)
Female0.043-0.075-0.070-0.077-0.069-0.073-0.062(0.142)(0.146)(0.146)(0.145)(0.146)(0.145)(0.145)
Children
0.215
0.234
0.237
0.235
0.240
0.226
0.224
(0.156)(0.155)(0.154)(0.155)(0.153)(0.154)(0.154)
PV ownership-0.234-0.325-0.329-0.361-0.332-0.324-0.334(0.237)(0.239)(0.239)(0.241)(0.240)(0.236)(0.241)
Gardening
0.511 㻖㻖㻖 0.449 㻖㻖㻖 0.447 㻖㻖㻖 0.454 㻖㻖㻖 0.445 㻖㻖㻖 0.468 㻖㻖㻖 0.446 㻖㻖㻖(0.154)(0.152)(0.153)(0.152)(0.151)(0.152)(0.152)
Money-saving awareness-0.033-0.042-0.041-0.0460.024-0.041-0.037(0.103)(0.103)(0.103)(0.104)(0.146)(0.102)(0.103)
Environmental awareness
0.083
0.071
0.071
0.069
0.069
0.116
0.067
(0.082)(0.087)(0.088)(0.087)(0.086)(0.138)(0.088)
Awareness of surroundings
0.052
0.040
0.039
0.037
0.039
0.044
0.141
(0.059)(0.058)(0.058)(0.058)(0.058)(0.059)(0.090)Cognition in public facilities
0.056
0.031
0.055
0.055
0.054
0.059
(0.075)(0.098)(0.076)(0.075)(0.074)(0.075)
Cognition in residences
0.218 㻖㻖0.215 㻖㻖0.160
0.222 㻖㻖0.213 㻖㻖0.222 㻖㻖(0.092)(0.094)(0.134)(0.092)(0.092)(0.092)
Own×Cognition in public facilities
0.033(0.141)Others×Cognition in public facilities
0.046(0.131)
Own×Cognition in residences
0.154(0.159)Others×Cognition in residences
0.016(0.166)
Own×Money-saving awareness-0.123(0.197)
Others×Money-saving awareness-0.063(0.219)Own×Environmental awareness
0.014(0.172)Others×Environmental awareness-0.135(0.162)
Own×Awareness of surroundings-0.244 㻖(0.134)
Others×Awareness of surroundings-0.103(0.124)
Constant
1.719 㻖㻖㻖 1.220 㻖㻖
1.318 㻖1.460 㻖㻖
0.868
0.994
0.805
(0.625)(0.616)(0.699)(0.721)(0.847)(0.796)(0.659)
2
0.026
0.064
0.059
0.062
0.060
0.062
0.069
Adj.R
N
333
333
333
333
333
333
333
Own

Robust standard errors in parentheses. ***, ** and * indicate statistical significance at the 1%, 5%, 10% level,
respectively.

25

Table 3 Analysis results in Experiment 2
(1)
(2)
(3)
(4)
(5)
(6)
(7)
0.250
0.341 㻖0.376
-0.0102.242 㻖㻖
0.730
0.273
(0.202)(0.200)(0.624)(0.687)(1.126)(0.907)(0.644)
High
0.213
0.236
0.254
-0.2380.492-0.120-0.746(0.199)(0.196)(0.778)(0.792)(0.960)(0.802)(0.621)
Age
0.060
0.060
0.060
0.060
0.059
0.055
0.039
(0.068)(0.068)(0.069)(0.067)(0.067)(0.069)(0.068)
Female
0.059
0.032
0.032
0.038
0.045
0.026
0.006
(0.165)(0.166)(0.168)(0.165)(0.166)(0.167)(0.165)
Children-0.008-0.087-0.087-0.094-0.086-0.108-0.142(0.181)(0.181)(0.181)(0.181)(0.180)(0.185)(0.182)
PV ownership-0.093-0.093-0.092-0.103-0.085-0.109-0.112(0.239)(0.238)(0.241)(0.240)(0.236)(0.241)(0.233)
Gardening
0.329 㻖
0.260
0.260
0.256
0.317 㻖0.300
0.227
(0.172)(0.175)(0.176)(0.174)(0.181)(0.184)(0.173)
Money-saving awareness
0.064
0.050
0.050
0.055
0.220
0.060
0.052
(0.105)(0.103)(0.104)(0.104)(0.158)(0.101)(0.098)Environmental awareness0.248 㻖㻖㻖 0.216 㻖㻖
0.216 㻖㻖0.217 㻖㻖0.212 㻖㻖0.212
0.206 㻖㻖(0.085)(0.085)(0.085)(0.086)(0.085)(0.158)(0.083)
Awareness of surroundings
0.260 㻖㻖㻖 0.231 㻖㻖㻖 0.231 㻖㻖㻖 0.232 㻖㻖㻖 0.236 㻖㻖㻖 0.229 㻖㻖㻖 0.139(0.080)(0.080)(0.080)(0.079)(0.080)(0.081)(0.133)
Cognition in public facilities
0.185 㻖㻖0.191
0.189 㻖㻖0.197 㻖㻖0.189 㻖㻖0.197 㻖㻖(0.091)(0.150)(0.092)(0.093)(0.091)(0.091)
Cognition in residences
0.037
0.036
-0.044
0.017
0.030
0.043
(0.110)(0.111)(0.176)(0.110)(0.109)(0.107)
Modest×Cognition in public facilities-0.009(0.154)
High×Cognition in public facilities-0.004(0.191)
Modest×Cognition in residences
0.095(0.190)
High×Cognition in residences
0.128(0.213)Modest×Money-saving awareness-0.384 㻖(0.227)
High×Money-saving awareness-0.057(0.198)Modest×Environmental awareness-0.083(0.196)
High×Environmental awareness
0.076(0.176)
Modest×Awareness of surroundings
0.020(0.173)
High×Awareness of surroundings
0.290 㻖(0.173)
Constant-0.136-0.615-0.635-0.349-1.446 㻖-0.617-0.152(0.629)(0.623)(0.753)(0.755)(0.854)(0.813)(0.742)
2
0.148
0.172
0.165
0.166
0.178
0.167
0.178
Adj.R
N
227
227
227
227
227
227
227
ModestRobust standard errors in parentheses. ***, ** and * indicate statistical significance at the 1%, 5%, 10% level,
respectively.

26

Appendix A.
ᴾ
Table A1 Model variables.
Variable names
Dependent variable
Y2018
Change Company
Y2019
Independent variable
Age
FemaleChildrenPV ownership
Gardening
Money-saving awareness
Environmental awareness
Awareness of surroundings
Cognition in public facilities
Cognition in residences

Definition
I would like to implement green curtains in summer 2018(6 = strongly agree to 1 = not at all agree)
I would consider changing my power supplier (6 = strongly agree to 1 = not at all agree)
I would like to implement green curtains in summer 2019 (6 = strongly agree to 1 = not at all agree)
Age in years
Male = 0, Female = 1
Children under 18 years old living with me (No = 0 , Yes = 1 )Solar power generation at home (No = 0 , Yes = 1 )Gardening at home (growing flowers or vegetables in my yard) (No = 0 , Yes = 1 )Saving electricity saves money for my household (6 = strongly agree to 1 = not at all agree)
Saving electricity contributes to reducing global warming (6 = strongly agree to 1 = not at all agree)
I save electricity because (I think) people around me save electricity (6 = strongly agree to 1 = not at all agree)
I often see green curtains in public facilities and schools in the city (6 = strongly agree to 1 = not at all agree)
I often see green curtains at residences in the city (6 = strongly agree to 1 = not at all agree)

ᴾ
ᴾ
ᴾ
ᴾ
ᴾ
ŶWhat are green curtains?
࣭Green curtains are curtain-like covers made of vines, such as bitter gourd, morning glory, andloofah, by crawling them under a net.
࣭Grow seedlings or seeds in a planter near a window or wall. (For bitter melon, sow seeds fromlate April to late May)ŶFunctions of green curtains(1) Prevents sunlight from shining through the window (shading effect).
࣭The large number of leaves blocks direct sunlight from entering throughthe window, thus suppressing the rise in indoor temperature.
࣭The curtains cut about 80% of the heat energy of solar radiation (50 to
60% for bamboo screen, 55% for high performance shading glass).
(2) It suppresses the temperature around the house (cooling effect bytranspiration).
࣭Plants absorb water from their roots and evaporate it from their leaves,which removes radiant heat from the ground and walls near the window.
࣭This leads to a decrease in the body temperature, which makes you feelcooler.
Fig. A1. Green curtains Information in the Questionnaireᴾ27

Fig. A2. Location of Fukuchiyama City and Kameoka City in Kyoto Prefecture, Japan

Fig. A3. Location of Sasabe, Seijin and Senkyo districts in Fukuchiyama City
Source: Fukuchiyama City website

28

Appendix B.
Table B1 Descriptive statistics and balance tests on independent variables in Experiment 1(Fukuchiyama City).

Total
Age
FemaleChildrenPV ownership
Gardening
Money-saving awareness
Environmental awareness
Awareness of surroundings
Cognition in public facilities
Cognition in residencesN

5.848(1.303)0.426(0.496)0.392(0.489)0.101(0.302)0.751(0.433)5.051(0.752)4.768(0.983)3.489(1.152)4.203(1.249)3.646(1.105)237

Controlgroup
6.041(1.287)0.486(0.503)0.351(0.481)
0.095(0.295)0.770(0.424)5.216(0.763)4.824(0.970)3.473(1.219)4.203(1.385)3.514(1.113)74

Standard deviations in parentheses.

29

Own
group
5.750
(1.355)0.388(0.490)0.338(0.476)0.113(0.318)0.700(0.461)4.988(0.738)4.750(0.921)
3.563(1.123)
4.138(1.166)3.625(1.107)80

OthersP-valueP-valuegroup(C-Own) (C-Others)
5.771
0.174
0.188
(1.262)
0.410
0.219
0.337
(0.495)
0.482
0.858
0.098
(0.503)
0.096
0.717
0.970
(0.297)
0.783
0.326
0.848
(0.415)
4.964
0.061
0.038
(0.740)
4.735
0.627
0.582
(1.060)
3.434
0.637
0.835
(1.128)
4.265
0.753
0.766
(1.211)
3.783
0.535
0.129
(1.094)83

Table B2 Descriptive statistics and balance tests on independent variables in Experiment 1 (Kameoka
City).

Total
Age
FemaleChildrenPV ownership
Gardening
Money-saving awareness
Environmental awareness
Awareness of surroundings
Cognition in public facilities
Cognition in residences
N

5.517(1.405)0.580(0.494)0.511(0.501)
0.093(0.291)0.790(0.408)5.054(0.866)4.721(1.088)3.330(1.263)3.514(1.253)3.177(1.004)333

Controlgroup
5.449(1.369)0.602(0.492)0.576(0.496)0.051(0.221)0.788(0.410)5.127(0.790)4.890(0.923)3.449(1.318)3.534(1.238)3.229(0.947)
118

Standard deviations in parentheses.

30

Own
group
5.629
(1.558)0.581(0.496)0.457(0.501)0.152(0.361)0.800(0.402)4.962(0.887)4.657(1.159)3.267(1.195)3.476(1.225)3.057(1.073)
105

OthersP-valueP-valuegroup(C-Own) (C-Others)
5.482
0.365
0.853
(1.290)
0.555
0.755
0.474
(0.499)
0.491
0.076
0.198
(0.502)
0.082
0.014
0.352
(0.275)
0.782
0.828
0.908
(0.415)
5.064
0.146
0.579
(0.921)
4.600
0.102
0.040
(1.167)
3.264
0.279
0.280
(1.268)
3.527
0.727
0.969
(1.304)
3.236
0.209
0.953
(0.995)110

Table B3 Descriptive statistics and balance tests on independent variables in Experiment 2.

Total
Age
FemaleChildrenPV ownership
Gardening
Money-saving awareness
Environmental awareness
Awareness of surroundings
Cognition in public facilities
Cognition in residencesN

5.587(1.445)0.575(0.495)0.437(0.497)0.158(0.365)0.753(0.432)4.960(0.919)4.696(1.063)3.405(1.222)
3.935(1.225)
3.534(1.054)227

Controlgroup
5.685(1.332)0.630(0.486)0.425(0.498)0.123(0.331)
0.795(0.407)4.795(0.881)
4.699(0.953)3.397(1.175)
4.123(1.117)3.781(1.044)73

Standard deviations in parentheses.

31

Modestgroup
5.439(1.451)0.531(0.502)0.449(0.500)0.153(0.362)0.735(0.444)5.082(0.916)4.694(1.030)3.429(1.276)3.724(1.306)3.327(1.023)78

P-valueHigh(C-Modest)
group
5.684
0.252
(1.542)0.579
0.193(0.497)0.434
0.753(0.499)0.197
0.577(0.401)0.737
0.362(0.443)4.961
0.040(0.944)4.697
0.975(1.211)3.382
0.868(1.211)4.026
0.033(1.189)3.566
0.005(1.063)76

P-value(C-High)

0.998
0.526
0.907
0.220
0.409
0.269
0.994
0.936
0.609
0.215

Appendix C.

In September 2017, a little over a year after the deregulation, the share of electricity sold by
new entrants to the market was only 7.1%, indicating a low awareness of the liberalization of theretail electricity market and the prices of other companies. Therefore, we assume that theprovision of prices of other electricity companies could have an effect on the willingness tochange electricity companies. Model (3) was set up with the objective variable of willingness toswitch electricity suppliers and examined using OLS.
Company change i 㸻Į㸩ȕ1 Own i 㸩ȕ2 Others i 㸩ȕ3 X i +႒ i (3)Company change i represents respondent i’s willingness to change electricity companies. This
was also tested because the experiment provided information on the electricity prices of Chubu
Electric Power Co., lower than those of Kansai Electric Power Co. with which the respondent has
a contract. We examine whether there was a secondary effect on their willingness to switch
electricity companies.
Table C1 shows the results for Fukuchiyama City and Table C2 for Kameoka City, respectively.
The mean and standard deviation for company changes in Fukuchiyama City are 2.477 and 1.156,
respectively, and 2.694 and 1.233, respectively, in Kameoka City. No effect of information
provision was observed in either city and nor was any effect of intersection terms confirmed.
Murakami and Ida (2019) find that Japanese households did not change electricity providers
even if a 5% cost reduction was realized from it, which they attributed to a status quo bias. The
4.5% cost reduction achieved by switching to Chubu Electric Power Co. in this experiment was
found to have no effect on the willingness to switch electricity providers.

32Table C1 Analysis results in Experiment 1 (Dependent variable; Company change, Fukuchiyama
City).
Own
OthersAge
FemaleChildrenPV ownership
Gardening
Money-saving awareness
Environmental awareness
Awareness of surroundings
Cognition in public facilities
Cognition in residences
Own×Cognition in public facilities
Others×Cognition in public facilities

(1)
0.052
(0.177)0.269(0.191)-0.163 㻖㻖(0.066)-0.219(0.150)-0.064(0.177)-0.079(0.319)0.014(0.179)-0.151(0.106)0.212 㻖㻖(0.082)0.016(0.071)(2)
0.056(0.176)0.272(0.195)-0.167 㻖㻖(0.064)-0.239(0.148)-0.079(0.174)-0.087(0.317)0.002(0.180)-0.152(0.106)
0.189 㻖㻖(0.083)0.015(0.074)0.090(0.088)-0.042(0.097)(3)
0.236(0.573)0.742(0.700)-0.170 㻖㻖(0.066)-0.249 㻖(0.148)-0.084(0.174)-0.080(0.313)-0.014(0.180)-0.155(0.104)0.190 㻖㻖(0.083)0.018(0.074)0.138(0.127)-0.038(0.099)-0.044(0.139)-0.111(0.159)

Own×Cognition in residences

(4)
(5)
(6)
(7)
0.405
-0.088-0.2270.260(0.528)(1.135)(0.793)(0.564)0.845-0.381-0.8090.329(0.649)(1.315)(0.816)(0.578)-0.167 㻖㻖㻖 -0.169 㻖㻖㻖 -0.169 㻖㻖㻖 -0.167 㻖㻖㻖(0.064)(0.064)(0.063)(0.064)-0.229-0.239-0.226-0.240(0.151)(0.148)(0.148)(0.148)-0.072-0.082-0.088-0.074(0.175)(0.174)(0.175)(0.174)-0.089-0.089-0.070-0.087(0.311)(0.313)(0.310)(0.317)-0.0090.009
0.011-0.002(0.180)(0.181)(0.179)(0.182)-0.148-0.204-0.152-0.151(0.106)(0.174)(0.107)(0.106)0.187 㻖㻖0.186 㻖㻖0.080
0.191 㻖㻖(0.083)(0.082)(0.122)(0.084)
0.013
0.014
0.007
0.039
(0.074)(0.074)(0.074)(0.117)
0.087
0.093
0.091
0.087
(0.086)(0.089)(0.087)(0.088)0.049-0.043-0.034-0.040(0.140)(0.098)(0.095)(0.097)-0.099(0.149)-0.158(0.169)Others×Cognition in residences
Own×Money-saving awareness

0.027(0.218)0.129(0.260)

Others×Money-saving awarenessOwn×Environmental awareness

0.058(0.166)0.226(0.174)

Others×Environmental awareness
Own×Awareness of surroundings-0.058(0.154)
Others×Awareness of surroundings-0.016(0.157)
Constant
3.128 㻖㻖㻖 3.067 㻖㻖㻖 2.888 㻖㻖㻖 2.758 㻖㻖㻖 3.358 㻖㻖㻖 3.592 㻖㻖㻖 2.978 㻖㻖㻖(0.681)(0.659)(0.703)(0.749)(1.002)(0.756)(0.742)
2
0.020
0.017
0.011
0.012
0.010
0.016
0.009
Adj.R
N
237
237
237
237
237
237
237
Robust standard errors in parentheses. ***, ** and * indicate statistical significance at the 1%, 5%, 10% level,
respectively.

33

Table C2 Analysis results in Experiment 1 (Dependent variable; Company change, Kameoka City).
(1)
(2)
(3)
(4)
(5)
(6)
(7)
-0.074-0.060-0.287-0.184-0.442-0.324-0.223(0.159)(0.159)(0.474)(0.508)(0.966)(0.741)(0.431)Others
0.057
0.054
0.319
-0.424-0.7110.568
0.320(0.152)(0.152)(0.484)(0.570)(1.042)(0.771)(0.455)Age-0.031-0.040-0.040-0.039-0.038-0.041-0.045(0.054)(0.053)(0.053)(0.053)(0.054)(0.053)(0.054)
Female0.019-0.018-0.034-0.001-0.023-0.018-0.027(0.136)(0.138)(0.139)(0.139)(0.138)(0.138)(0.140)Children0.579 㻖㻖㻖 0.583 㻖㻖㻖 0.579 㻖㻖㻖 0.579 㻖㻖㻖 0.592 㻖㻖㻖 0.575 㻖㻖㻖 0.579 㻖㻖㻖(0.138)(0.139)(0.139)(0.138)(0.140)(0.139)(0.139)PV ownership-0.534 㻖㻖 -0.565 㻖㻖 -0.574 㻖㻖 -0.569 㻖㻖 -0.564 㻖㻖 -0.566 㻖㻖 -0.563 㻖㻖(0.228)(0.228)(0.223)(0.227)(0.228)(0.226)(0.229)Gardening
0.557 㻖㻖㻖 0.533 㻖㻖㻖 0.537 㻖㻖㻖 0.532 㻖㻖㻖 0.537 㻖㻖㻖 0.556 㻖㻖㻖 0.548 㻖㻖㻖(0.152)(0.154)(0.154)(0.153)(0.155)(0.155)(0.156)
Money-saving awareness-0.016-0.018-0.019-0.019-0.099-0.016-0.017(0.091)(0.092)(0.092)(0.091)(0.155)(0.091)(0.093)
Environmental awareness
0.094
0.090
0.085
0.090
0.091
0.108
0.096
(0.081)(0.082)(0.082)(0.081)(0.081)(0.132)(0.084)
Awareness of surroundings
0.108 㻖0.103 㻖0.105 㻖0.105 㻖0.102 㻖0.109 㻖0.115
(0.058)(0.057)(0.057)(0.058)(0.057)(0.059)(0.083)
Cognition in public facilities
0.040
0.050
0.035
0.041
0.037
0.043
(0.067)(0.092)(0.066)(0.067)(0.066)(0.067)
Cognition in residences
0.054
0.052
-0.005
0.053
0.049
0.047
(0.082)(0.083)(0.117)(0.083)(0.083)(0.083)
Own×Cognition in public facilities
0.065(0.135)
Others×Cognition in public facilities-0.076(0.132)
Own×Cognition in residences
0.037(0.150)
Others×Cognition in residences
0.148(0.164)
Own×Money-saving awareness
0.074(0.186)
Others×Money-saving awareness
0.150(0.200)Own×Environmental awareness
0.057(0.152)Others×Environmental awareness-0.110(0.157)
Own×Awareness of surroundings
0.051(0.122)Others×Awareness of surroundings-0.080(0.125)1.470 㻖㻖1.674 㻖1.168
1.230 㻖㻖Constant
1.453 㻖㻖㻖 1.270 㻖㻖
1.275 㻖㻖(0.537)(0.551)(0.596)(0.628)(0.870)(0.727)(0.611)
2
0.108
0.108
0.106
0.105
0.105
0.107
0.106
Adj.R
N
333
333
333
333
333
333
333
Own

Robust standard errors in parentheses. ***, ** and * indicate statistical significance at the 1%, 5%, 10% level,
respectively.

34