Economic experiments used for the evaluation of

Economic experiments used for the evaluation of building users’ energy-saving behavior Book or Report Section Accepted Version

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Martín, N. G., Sabater-Grande, G., García-Gallego, A., Georgantzis, N., Barreda-Tarrazona, I. and Belenguer, E. (2015) Economic experiments used for the evaluation of building users’ energy-saving behavior. In: Energy Performance of Buildings. Springer, pp. 107-121. ISBN 9783319208305 doi: 10.1007/978-3-319-20831-2_7 Available at http://centaur.reading.ac.uk/48489/

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ACCEPTED VERSION Economic experiments used for the evaluation of building users’ energy saving behavior Nieves García Martín1 Gerardo Sabater-Grande2 Aurora García-Gallego3 Nikolaos Georgantzis4 Iván Barreda-Tarrazona5 Enrique Belenguer Balaguer6 Abstract Different treatments that could be implemented in the home environment are evaluated with the objective of reaching a more rational and efficient use of energy. We consider that a detailed knowledge of energy-consuming behaviour is paramount for the development and implementation of new technologies, services and even policies that could result in more rational energy use. The proposed evaluation methodology is based on the development of economic experiments implemented in an experimental economics laboratory, where the behaviour of individuals when making decisions related to energy use in the domestic environment can be tested.

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N. García () Department of Industrial Systems Engineering, Universitat Jaume I, Castellón, Spain e-mail: [email protected] 2 G. Sabater-Grande () LEE and Economics Department, Universitat Jaume I, Castellón, Spain e-mail: [email protected] 3

A. García-Gallego () LEE and Economics Department, Universitat Jaume I, Castellón, Spain e-mail: [email protected] 4

N. Georgantzis () School of Agriculture Policy and Development, University of Reading, UK e-mail: [email protected] 5

I. Barreda-Tarrazona () LEE and Economics Department, Universitat Jaume I, Castellón, Spain e-mail: [email protected] 6 E. Belenguer () Department of Industrial Systems Engineering, Universitat Jaume I, Castellón, Spain e-mail: [email protected]

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Keywords: Experimental economy, energy efficiency, residential sector, energy savings, energy-consuming behaviour, feedback technologies.

1.1 Object The demand for energy in the residential sector in developed countries has been rising continuously over the last 30 years, although this tendency to increase has slowed down significantly since 2008 due to the economic crisis. In Spain, in the year 2011, the energy consumed in the residential sector accounted for 25% of the country’s total electricity consumption, as well as 17% of the total energy consumption. Given the importance of the residential sector in the consumption of energy in a country, researchers have proposed different methods to reduce household consumption. However, test results are not conclusive. On the one hand, informative campaigns have yielded poor results or they have not been time-persistent. On the other, the results of more sophisticated methods, such as feedback information and control technologies, are difficult to compare and validate due to the use of different sample sizes and their implementation in countries with very different climates and consumption habits. In this paper, we present the development of an economic experiment that was designed and performed in a laboratory with two main objectives. The first was to analyse different systems or treatments for improving energy efficiency at home in a controlled environment, regardless of geographic and climatic variations, with a similar number of samples under equal conditions. This provides us with data from different treatments that can be compared properly, thereby simplifying the data acquisition process and reducing the cost of the test development. The second objective is related to the importance of human behaviour in the success of each treatment. It is well known that human behaviour consists of a combination of rational and irrational (emotional) decisions. A detailed analysis of this behaviour is critical, in our opinion, to determine the scope and effects of each method. We cannot reasonably expect a realistic representation of a household to be obtained just by using a computer simulation, and therefore the results of the experiment about energy savings, as absolute values, will not be exact. Nevertheless, the experiment is useful as a means to analyse and quantify the differences between the methods, so the proportional variations between them will be established, that is to say, the experiment provides a scale.

Economic experiments used for the evaluation of building users’ energy saving behavior

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1.2 Literature Review Feedback information varies from advices on energy usage or comparison with peers to more complex feedback technologies providing real-time consumption information through internet-enabled devices. Estimates of the energy savings from feedback technologies vary widely, from none to as much as 20% (Faruqui et al. 2010; Ehrhardt-Martinez et al. 2010). Delmas et al. (2013) offers the most comprehensive meta-analysis of information-based energy conservation experiments conducted to date. The study shows that strategies providing individualized audits and consulting are comparatively more effective for conservation behaviour than strategies that provide historical, peer-comparison energy feedback. Overall, this meta-analysis suggests that information strategies do induce energy conservation, but it is less clear which strategies work best, in part because many experiments simultaneously use more than one strategy, thus leading to confounding issues. Delmas et al. (2013) claimed that additional experiments are needed to better identify the winning strategies. Studies that have analysed the impact of peer comparisons on conservation have yielded mixed results. For example, Goldstein et al. (2008) found that social norms can increase towel reuse by hotel guests. Yet, in a literature review of the effect of feedback on home energy consumption, Fischer (2008) noted that of the dozen studies testing the impact of comparisons with others that she reviewed, none showed an effect. She attributed the failure to the boomerang problem, where informing individuals of typical peer behaviour inadvertently inspires those who have been underestimating the prevalence of an activity to increase the unwanted behaviour. Cialdini et al. (1991) argued that combining injunctive norms (norms that express social values rather than actual behaviour) with descriptive norms can neutralize the boomerang effect. Schultz et al. (2007) conducted a randomized field study in San Marcos, California, about the effectiveness of social norms messaging (alongside energy-saving tips) to reduce home energy consumption. They found that combining descriptive and injunctive messages lowered energy consumption and reduced the undesirable boomerang effect. Ayres et al. (2009) found that by providing customers with feedback on home electricity and natural gas usage with a focus on peer comparisons, utilities can reduce energy consumption at a low cost. Nowadays, it is possible to think of sophisticated demand response systems, where smart meters could allow consumers to adjust consumption in response to price signals that vary in time, leading to a more efficient electric system. 7 Brophy et al. (2009) investigated the international experience in liberalized electricity markets and highlighted smart metering as a necessary tool to be able to overcome barriers like information asymmetry.

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Conchado and Linares (2010) is an interesting survey of the state of the art in the quantification of demand response programme benefits.

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Fisher (2008) offered a theoretical perspective on the matter and introduced a psychological model to explain why feedback works. She holds that successful feedback has to capture consumers’ attention, link specific actions to their effects and activate various motives. One unanimous finding is that feedback is more effective when it is more detailed and more closely linked to consumption actions. Several8 field studies analyse feedback-induced energy savings using different in-home monitors, including aggregate-level and appliance-specific real-time feedback. Other studies are focused on web-based feedback technologies. In this line, Abrahamse et al. (2007) found that European households were capable of generating average savings of 5.1% using an internet-based tool. Benders et al. (2006) concluded that a web-based application produced average household savings of 8.5% in the Netherlands. Additionally, Ueno et al. (2006a and 2006b) found average savings of 18% in Japan. Generally speaking, the most directly related experimental literature on electricity markets can be classified in two main categories: field experiments and lab experiments. In the first category, Battalio et al. (1979) ran an interesting field experiment designed to determine the effects of various price and non-price policies on electricity consumption. Five different price policies were tested. The results show that price policies work much better than informational policies. Faruqui and Sergich (2009) reviewed the most recent experimental evidence on the effectiveness of residential dynamic pricing programmes. Their review of 15 different field experiments on pricing reveals that the demand response impacts from different pilot programmes vary from modest to substantial, largely depending on the data used in the experiments and the availability of enabling technologies. In the category of lab experiments, Adilov et al. (2004) and Adilov et al. (2005) conducted experiments on demand-side and full two-sided electricity markets, respectively. The aim of both papers was to test the efficiency of two alternative forms of active demand-side participation. They evaluated different experimental price structures that represent end-use consumers who can substitute part of their usage between day and night: fixed price, a demand-response programme with fixed price and credit for reduced purchase, and a real-time pricing system where prices are forecast for the coming day/night. Their main conclusion is that the realtime programme results in the greatest market efficiency. Barreda et al. (2012) studied consumers’ behaviour in experimental electricity markets, finding that a dynamic system for prices is more efficient than a fixed one. They showed that a dynamic scheme with sanctions, although less preferred by consumers, is more effective than the one with bonuses in order to reduce peak consumption.

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See Carroll (2009) and Carroll et al. (2009), for example.


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1.3 Experimental Design We conducted an experiment consisting in simulating consumers’ energy behaviour in a virtual home according to the information they were provided with. Participants were selected among students who volunteered of Universitat Jaume I, Castellón, Spain. We assumed that the residence was occupied by three members (father, mother and son) and consisted of a living room, kitchen, two bedrooms and a bathroom. Heating and hot water ran on gas whereas lighting and all other appliances worked on electric energy. In this framework, energy consumption had to be chosen by subjects for each period representing a typical winter or summer day 9. The 18period sequence implemented included 3 winter days, 3 summer days, 3 winter days and so on. In order to simulate this environment we designed a Programming Screen (PS), see Fig. 1.1, on which subjects decided what appliances to switch on or off in order to maximize their net gains, calculated as the difference between their valuation of the energy consumed and the money spent to buy this energy.

Fig. 1.1 Programming screen

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Differences between winter and summer days are focused on the possibility of using a heating system that consumes gas energy.

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As is common in laboratory experiments, the valuation of purchases was preassigned to buyers through an Induced Utility Function10 (IUF), which determined the maximum price the consumer was willing to pay for each unit of energy. On the PS, subjects decided on energy consumption for seven different groups of items. 1. Lighting. We assumed there were four lighting selection11 points: kitchen, living room and the two bedrooms. 2. Oven, microwave, toaster, iron and heater. For this group of appliances subjects had to choose whether or not to use each of them, the number of days per week they were to be used and the number of hours per day. 3. Washing machine and dishwasher. In addition to the features considered in the previous group, subjects had to select the type of wash programme and, when not in use, whether to leave the appliances in standby mode or switch them off. 4. Refrigerator and router. Neither of them allowed the subjects any kind of choice, as they were necessarily running 24 hours a day. However, they represented an energy consumption shown on the PS. 5. TV, stereo, desktop computer and laptop. In this group participants had to select the number of hours they wanted to use them and, in the case of no use, choose between the stand-by or off modes. 6. Personal Hygiene. In this case the subject had to choose the type (fast shower, slow shower and bath) and the number of daily washes (none, one or two for all types) for each family member. 7. Heating. Subjects could choose use and temperature for 4 time slots (7h-12h, 13h-18h, 19h-24h and 1h-6h), but only for winter days. On summer days this item was removed from the PS. Participants were not given any time restriction when it came to making their choices. However, there was a timer on their computer screen counting down the seconds from 300 to 0. Once the first five minutes were over, the counter turned red, indicating that a reasonable amount of time had already passed. Once decisions had been made for these seven groups of items, subjects received information about their gas and electricity expenditure along with utility and net gains achieved in ExCUs (Experimental Currency Units) on the Invoice screen (see Fig. 1.2). In order to study the effect of information on energy consumption, five different treatments were considered. In the baseline treatment (T0), subjects were provided with a gas and electricity bill for each period after their decisions had been made. In treatment 1 (T1) before energy consumption was chosen, participants 10

The utility provided by the use of electricity depends on the amount consumed according to a concave function. Parameterizations of these functions for each appliance are available upon request. 11 Lighting is selected 24 hours per period by default and subjects must turn off the light at undesired hours by clicking on the square to the right of the corresponding hour.


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were additionally provided with advice 12 about energy savings, which were repeated in every period. In treatment 2 (T2) along with the aforementioned bill, individuals also received information about instantaneous gas and electricity consumption in kWh and their corresponding cost in ExCUs for the entire household through a smart meter display. In treatment 3 (T3) subjects disposed of a set of five smart meters, showing the instantaneous consumption of each separate group13 of appliances in kWh or ExCUS. In treatment 4 (T4) after consumption decisions had been made for each period, subjects were provided with comparative information about average and minimum energy consumptions in their market, along with the standard gas and electricity bill. T4 was repeated in two different sessions in order to increase the number of independent observations.

Fig. 1.2 Invoice screen

The differences introduced on the PS were minimal, consisting in the type of information provided depending on the treatment that was implemented. In Table 1.1 we present a summary of the experimental sessions carried out. A total of 173 subjects (30 per session with the exception of T2, where there were 23) were recruited from among undergraduate students from different economics- or business-related courses, using standard recruitment procedures with Advice that was provided included sentences such as: (a) Turn off equipment when it’s not in use, (b) The microwave uses less energy than the oven and it can cook the same foods, (c) Remember to turn off lights when you leave the room, (d) A bath is more expensive than a shower, etc. 13 Independent smart meters are offered for lighting, kitchen, gas consumption, leisure (TV, stereo and computers) and others (washer, dishwasher, iron and bathroom heater). 12

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an open call for subjects through our lab’s website. Before the beginning of each session, subjects were given written instructions, which were also read aloud by the organizers, and any remaining issues were resolved on an individual basis. Table 1.1 Summary of experimental sessions Treatment

Information

Sessions

Subjects

T0

No

1

30

T1

Advice

1

23

T2

Aggregated Consumption

1

30

T3

Disaggregated Consumption

1

30

T4

Consumption Comparison

2

60

At the end of each session, which typically lasted 90–120 minutes (depending on the experimental treatment), subjects responded to an environmental awareness questionnaire. After that, they were paid privately in cash, the average earnings being about 20 €. All sessions were computerized and carried out in a specialized computer lab, using original software programmed in JAVA.

1.4 Results

In this section we analyse electricity and gas consumption of participants and corresponding consumer surplus obtained in the five implemented treatments. Table 1.2 (In Table 1.2 we observe that, with the exception of treatment 3, all treatments showed a decrease in electricity consumption relative to the baseline treatment. However this reduction is not associated with an increase in surplus. On the contrary, all treatments exhibit higher surplus than the baseline treatment. Regarding gas consumption, with the exception of the advice treatment, we observe in Error! Not a valid bookmark self-reference. that all treatments present higher consumption and lower surplus than the baseline treatment. Table 1.3) present averages and standard deviations of electricity (gas) consumption and corresponding surplus for all treatments. Table 1.2 Summary of statistical descriptions. Electricity. Treatment

Electricity Consumption (c€)

Electricity Surplus (c€)

Average

Standard deviation

Average

Standard deviation

T0

3363,75

1059,77

501,78

67,03

T1

3154,37

1464,79

507,31

85,10

T2

3179,01

1180,91

539,40

92,12

T3

3856,77

1420,80

567,18

89,56


T4

3091,17

1185,52

508,13

9

94,20

In Table 1.2 we observe that, with the exception of treatment 3, all treatments showed a decrease in electricity consumption relative to the baseline treatment. However this reduction is not associated with an increase in surplus. On the contrary, all treatments exhibit higher surplus than the baseline treatment. Regarding gas consumption, with the exception of the advice treatment, we observe in Error! Not a valid bookmark self-reference. that all treatments present higher consumption and lower surplus than the baseline treatment. Table 1.3 Summary of statistical descriptions. Gas. Treatment

Gas surplus (Winter) (c€)

Gas Surplus (Summer) (c€)

Average

Standard deviation

Average

Standard deviation

T0

1738,77

754,1

91,70

30,29

T1

1646,11

701,27

94,00

33,91

T2

2371,54

1378,61

85,72

36,16

T3

1986,00

1065,06

86,08

27,97

T4

1874.96

711,47

82,27

29,90

Fig. 1.3 shows the average values of electricity consumption for each treatment as a percentage with respect to the optimum. The optimum point is defined as the use of each piece of equipment at its point of maximum benefit, and it was designed as the most energy-efficient behaviour without loss of comfort. The values at the optimum point are shown in Table 1.4. Table 1.4 Electricity and gas. Optimum values Variable Electricity Consumption

Electricity Surplus

Gas Consumption

Gas Surplus

Season

Optimum value (c€)

Winter

4150,1

Summer

3586,4

Average

3868,3

Winter

941,3

Summer

816,4

Average

878,9

Winter

3306,7

Summer

567,0

Average

Not appropriate

Winter

207,5

Summer

75

Average

Not appropriate

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As the difference between summer and winter in electricity is minor, the average of the whole year is shown. Statistical significance of all the differences has been tested 14 with MannWhitney tests: results can be checked in Table 1.6. Table 1.5 Shapiro-Wilk tests Electricity Consumption p-value

Electriciy Surplus p-value

Treatment

Winter

Summer

All

Winter

Summer

All

T0

0,001

0,001

0,001

0,545

0,781

0,773

T1

0,001

0,005

0,006

0,379

0,827

0,815

T2

0,059

0.238

0,093

0,905

0,662

0,878

T3

0,019

0,032

0,016

0,030

0,632

0,548

T4

0,039

0,002

0,015

0,692

0,024

0,232

Table 1.6 Mann-Whitnney of the variable Electrical Consumption Item1 Treatment

Optimum

T0

T1

T2

T0