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Energy Policy
journal homepage: www.elsevier.com/locate/enpol
Competition and norms: A self-defeating combination? ^crossMa*
Genevieve Alberts a, Zeynep Gurguc b'*, Pantelis Koutroumpis b, Ralf Martin b, Mirabelle Muüls b,c, Tamaryn Napp c
a Energy Futures Lab, Imperial College London, South Kensington Campus, London SW7 2AAZ, United Kingdom b Imperial College Business School, Imperial College London, South Kensington Campus, London SW7 2AZ, United Kingdom
c Grantham Institute - Climate Change and the Environment, Imperial College London, South Kensington Campus, London SW7 2AAZ, United Kingdom
HIGHLIGHTS
• We investigate the effect of information feedback on residential energy consumption.
• A RCT tests whether norms affect the decisions of price-indifferent participants.
• Feedback mechanisms and norms reduce energy consumption by 22% on average.
• Introducing prize competition dissipates the impact of information feedback and norms.
ARTICLE INFO ABSTRACT
This paper investigates the effects of information feedback mechanisms on electricity and heating usage at a student hall of residence in London. In a randomised control trial, we formulate different treatments such as feedback information and norms, as well as prize competition among subjects. We show that information and norms lead to a sharp - more than 20% - reduction in overall energy consumption. Because participants do not pay for their energy consumption this response cannot be driven by cost saving incentives. Interestingly, when combining feedback and norms with a prize competition for achieving low energy consumption, the reduction effect - while present initially - disappears in the long run. This could suggest that external rewards reduce and even destroy intrinsic motivation to change behaviour.
© 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license
(http://creativecommons.org/Iicenses/by/4.0/).
Article history: Received 1 August 2015 Received in revised form 28 April 2016 Accepted 1 June 2016 Available online 25 June 2016
Keywords:
Behavioural intervention
Household energy demand
Randomised controlled trial
Information
Competition
1. Introduction
Reducing overall energy consumption, as well as managing energy market volatility and demand peaks are increasingly important issues with the growing focus on decreasing greenhouse gas (GHG) emissions and controlling climate change. Internet connectivity and electronic innovations now allow energy providers to develop demand side management systems instead of only concentrating on supply side management. Using combinations of information feedback loops and grid management techniques, operators have the potential to improve the management of
* Corresponding author. E-mail addresses: z.gurguc@imperial.ac.uk (Z. Gurguc), p.koutroumpis@imperial.ac.uk (P. Koutroumpis),
r.martin@imperial.ac.uk (R. Martin), m.muuls@imperial.ac.uk (M. Muûls), tamaryn.napp@imperial.ac.uk (T. Napp).
energy market volatility and demand peaks. This would lead to lower energy production costs and reduced emissions. As one third of aII greenhouse gas emissions come from residentiaI energy consumption (EPA, 2015), understanding how social dynamics can impact househoId energy demand is an important step in this direction.
In this paper, we investigate the effects feedback information and norms, as weII as prize competition, on energy consumption. We conduct a randomised controI triaI for a cohort of price-indifferent individuals at a student hall of residence in London. Our systematic literature review indicates that we are the first to test such a combination in this particuIar setting. We provide our subjects with individuaI as weII as group/comparative feedback. A cruciaI factor of our design is that, because participants do not pay for their energy consumption, the information effect is not confounded by any cost saving incentives. This aIIows us to soIeIy focus on the effects of behaviouraI interventions and norms as
http://dx.doi.org/10.1016/j.enpoI.2016.06.001
0301-4215/© 2016 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/Iicenses/by/4.0/).
opposed to price effects, which is present in other studies. We find that information mechanisms are strong: providing individuals with weekly feedback about their own consumption and their consumption relative to others leads to a 22% reduction in energy consumption on average. For a subset of our trial participants we combine the information treatment with a prize treatment; this group was promised a prize for the participant with the lowest energy consumption. This reveals an intriguing perverse effect. Whereas, for individuals with pure information treatment, the consumption effect is sustained throughout the trial, for the prize treated group the effect wears off completely after two weeks of treatment. We provide some evidence suggesting that this dropping off is caused by a reversal of efforts by individuals who realise that the prize is out of their reach as a consequence of the information treatment. This hints at a fundamentally different response mechanism when providing a prize: by strengthening external financial incentives, internal incentives such as the desire to reduce consumption because of detrimental social effects - e.g. through pollution - are weakened.
The focus of this paper is on household energy consumption, a key sector when considering energy efficiency and GHG emissions reductions. For example, in the UK, the domestic sector accounted for 27% of overall energy consumption in 20141 (DECC, 2015a) and 14% of total UK carbon emissions in 2013 (DECC, 2015b). These figures are expected to increase even further due to population growth and highlight the sector's growing importance. Statistics from the US and Western Europe reveal similar trends (Abrahamse et al., 2005; Gardner and Stern, 2002). In this context, as McMi-chael and Shipworth (2013) state: "various institutions are trying to encourage the adoption of behavioural energy-efficiency innovations through policy, building regulations and other measures such as direct engagement with communities and constituents".2
Our research relates to a vast literature analysing the factors that affect energy use in the residential domain. As described in Costa and Kahn (2013), household electricity consumption depends on individual choices and house characteristics, appliances and the intensity of their utilisation which are linked to the local climate, prices as well as the consumer's personal attributes and behaviour. At the macro level, factors including technological developments, demographic factors, household income and economic growth are also likely to affect consumption outcomes (Abrahamse et al., 2005). Additionally, Hori et al. (2013) show that regulation aimed at reducing energy use is much more effective in the industrial than in the residential sector. These results indicate the need to discover mechanisms that effectively induce lower energy consumption at the household level.
This paper also relates in particular to the literature on the behavioural dimension of energy use. As reviewed by Lopes et al. (2012), this subject pertains not only to economics but also to psychology (Biel and Thogersen, 2007; Frederiks et al., 2015). From an economics point of view, the base assumption will be that individuals are taking rational decisions when deciding on their energy use (Breukers et al., 2011; Wilson and Dowlatabadi, 2007). However, energy and electricity are not typical consumer products. These are rather an abstract, invisible, intangible and indirect by-product of other economic choices (Fischer, 2008). For example, Kempton and Layne (1994) compared energy consumption to shopping without price tags given that the customer only gets a quarterly bill.
Given these aforementioned characteristics, previous studies have examined dynamic pricing of electricity and shown that it
1 Energy consumption by the domestic sector was 38,162 thousand tonnes of oil equivalent (DEC, 2015a).
2 p.1 McMichael and Shipworth (2013)
effectively switches consumption from peak hours to non-peak hours during which it's priced lower (for a comprehensive review see Faruqui and Sergici (2014)). Despite these results, it has been demonstrated that decreasing overall energy consumption only through dynamic pricing is challenging (Faruqui and George, 2005; Faruqui et al., 2010). While some studies show a 'short-term effect of financial rewards' (Abrahamse et al., 2005), others conclude that effects of financial rewards for energy conservation are large and persistent over time (Dolan and Metcalfe, 2015). Additionally, policy makers are often wary of using imperfectly designed financial incentives that can distort behaviour and lead to undesired consequences for financially constrained portions of the population or the elderly, for example, by increasing health risks associated with reduced heat consumption (Barnicoat and Danson, 2015). As Buchanan et al. (2015) state, households already suffering from fuel poverty have little capacity to further reduce their energy consumption. Finally, the cost dimension of the energy savings also relates to rebound effect (Khazzoom, 1980; Saunders, 1992), i.e. households saving money from consuming less energy may spend their additional income on activities that can generate more emissions.
As an alternative to financial incentives, existing research has also analysed the importance of information, or feedback mechanisms, and their impact on energy use. Feedback entails providing information to households about their energy consumption or savings, and is a strategy often employed by energy conservation initiatives. The effectiveness of different types, frequencies, duration of feedback on reducing energy use differs at the group, or even individual level (Abrahamse et al., 2005). One possible channel for a potential effect of feedback mechanisms could be that they "rematerialize" energy consumption (e.g. Buchanan et al., 2014).
Norms have been conjectured to be another meaningful channel through which information and feedback will impact energy consumption. They can be descriptive or injunctive; the former simply inform about others' performance while the latter directly suggest what should be done. Feedback that is augmented with a norm, i.e. direct comparison with 'average' or 'normal' behaviour may prove more powerful. Thaler and Sunstein (2008) consider that this feedback bypasses the consumer's decision-making process and acts as a heuristic shortcut or "nudge". Additionally, Fuster and Meier (2010) suggest that financial incentives could be effective if they manage to change the social norm. The literature on the effectiveness of norms is rather inconclusive. Fischer (2008) reviews studies from 1987 to 2006 and finds that norms may not be an important element of feedback, as they do not affect consumption. More recent studies show on the contrary that norms do have a measureable impact on household consumption but some also argue that they can cause a boomerang effect3 (see for example Ayres et al. (2013), Nolan et al. (2008) or Schulz et al. (2007)). However, Harries et al. (2013) recently address a limitation of these studies: one should differentiate between the impact of pure feedback and that of norms. They find that the effect of norms is not statistically significant. Allcott and Rogers (2014) report how a utility company in the USA called OPOWER mailed home energy use reports, including social comparisons, to a selection of its customers. They find that it leads to energy consumption reduction, but that the frequency of the reports affects the persistence of their effect. Others demonstrate that social interaction and norms play a role in inducing energy saving behaviours at a decreasing rate over time (Dolan and Metcalfe, 2015; Hori et al., 2013). Additionally, post-consumption
3 Informing low energy consumers about the group norm may inadvertently inspire them to increase their energy consumption.
feedback has been an effective measure when accompanied by financial incentives (Abrahamse et aI., 2005).
Given descriptive norms inform the consumer about how she is performing reIative to her pairs, its driving force may be competitive behaviour against peers. PsychoIogists have Iong debated the role of competition in human behaviour (Deci et aI., 1981; Deci and Ryan, 1985; Kohn, 1986). In economics, rational choice theory takes humans' competitive spirit for granted. In this paper, we distinguish competition from descriptive norms. WhiIst the Iit-erature cited above has in some cases used the word 'competition' instead of 'descriptive norms', we instead use the former to describe prize competition - the case of peopIe entering a competition to win a prize. The Iiterature on the effect of prize competition on energy saving behaviour in particular is rather limited. However, previous evidence in psychoIogy suggests that tangibIe extrinsic rewards as weII as competition can become demotivating as they undermine intrinsic motivation4 (Deci, 1971; Deci et aI., 1981; Deci et aI., 1999; Lepper et aI., 1973; Reeve and Deci, 1996; Ryan and Deci, 2000;).
Our research bridges the areas expIoring norms, competition over energy IeveIs, financiaI rewards and reward competition. In our experiment, we reIy particuIarIy on descriptive norms to nudge our participants but aIso use some injunctive norms. We are particuIarIy interested in the effect of norms when they are free from price effects, as weII as of competition for a prize. We hypothesise that norms are IikeIy to manifest themseIves as intrinsic motivation such as a wiIIingness to preserve the environment out of own enjoyment. Whereas a prize competition, i.e. a type of fi-nanciaI incentive, adds an extrinsic reward dimension to the energy saving decision, thereby diminishing the norms' effect.
The rest of this paper is structured as foIIows. The next section wiII give a brief introduction to the theoreticaI modeI and experimentaI hypothesis, whiIe Section 3 wiII present the metho-doIogy used in this study. Section 4 wiII eIaborate on resuIts as weII as provide a short discussion of resuIts, and finaIIy Section 5 wiII concIude and discuss further possibIe research.
2. Theoretical framework
In this section, we present the framework used in the design and anaIysis of the RCT when considering how individuaIs choose their energy consumption. WhiIst different methodoIogies are utiIised in the Iiterature (WiIson and DowIatabadi, 2007), we take the economics approach as a starting point assuming that individuaIs make rationaI choices to maximise their satisfaction, aIso caIIed 'utiIity', given their budget constraints.5 However, our modeI conjectures that energy consumption decisions are not pureIy a rationaI choice,6 and therefore incIude the effect of norms in this context. We buiId on existing Iiterature showing that individuaIs care about norms (see for exampIe AkerIof, 1982; Arrow et aI., 2004; BaII et aI., 2001; BauIt et aI., 2008; Benabou and TiroIe 2011; DoIan and MetcaIfe 2015; Jones 1984; Luttmer 2005; Okuno-Fujiwara and PostIewaite 1995) and assume that the individuaI's utiIity couId be decreased by deviating from the norm. Moreover, given the participants in our triaI do not pay for their energy consumption we diverge from the existing Iiterature by aIso in-cIuding in our modeI the possibiIity for a price-indifferent
4 Intrinsic motivation is defined as the doing of an activity for its inherent satisfactions rather than some separabIe consequence (Deci and Ryan 1985)
5 A utiIity framework in this context stems from rationaI choice theory and aIIows us to provide a measure of satisfaction and individuaI preferences over the consumption of a good (Read, 2004), in our context, energy.
6 Hence, individuaIs do not onIy consider seIfishIy driven consumption
decisions.
individuaI to prioritise norm deviation over price-reIated matters.
Our modeI therefore focuses on energy consumption decisions by representing a typicaI individuaI as consumer i who maximises in period tan objective function that Iooks as foIIows
u(Et rit) = b(Eit) - ruw - eitC - v x n(eit - e^
where her utiIity is a function u of two components, Eit and rit. Eit represents the energy services enjoyed by individuaI i at time t: rather than benefiting from the energy consumption itseIf, individuaIs wiII derive satisfaction from the services it provides such as Iight, heat or powering of appIiances. The second item, rit, represents research on energy saving options, through which the individuaI can Iearn how to improve her efficiency. A consumer can improve her efficiency by engaging in research rit on energy saving options, which Ieads to cost of ritw.
The function b( Eit) measures the benefit, or satisfaction, derived from energy services. We assume it is a concave function,7 so that it exhibits decreasing returns: for exampIe, the benefit derived from boiIing water for my first cup of coffee in the morning wiII be Iarger than for the third.
ActuaI energy used is denoted by eit . We assume that:
where ait measures how efficientIy a consumer is using energy. The more efficient a consumer is the Iess actuaI energy eit is required to achieve a given energy service IeveI Eit. In our exampIe, a more efficient kettIe wiII mean a Iarger ait and hence a smaIIer quantity of energy used to produce the same energy service that is a cup of boiIing water.
We denote by C a consumer's unit energy cost, so that totaI energy expenditure is eitC. We assume that the individuaI's utiIity is decreased by this expenditure.
If a consumer cares about how she performs reIative to others -i.e. she is susceptibIe to norms - then v > 0 and the convex function n( eit - et-1) enters her utiIity function. We have et-1 denoting the energy consumption of the most efficient peer-group consumers in the previous period. If the individuaI i is Iagging behind, i.e. she is consuming more energy than her most efficient peers, then (eit - et-1) wiII be positive. Her utiIity wiII be decreased.
This Iag reIative to the efficient consumers is aIso IikeIy to affect the productivity of the individuaI's energy consumption. For sim-pIicity we assume that
% = °BAUieXP(rit[eit-1 - et-l])
which suggests that a consumer can improve her efficiency ait reIativeIy to a Business-as-usuaI (BAU) IeveI aBAUi. If she is Iagging behind, such that (eit - et-1) is positive, it means that there exists ways for her to reduce this Iag and consume a quantity of energy that is cIoser to her most efficient peers. If she is aware that she is Iagging behind, her search rit for energy efficiency is more effective if this consumer has a Iarger amount of catching up to do. If the consumer does no research, or if she is aIready among the most efficient, the expression in parenthesis in Eq. (3) wiII be equaI to zero, and her efficiency ait wiII remain at the IeveI aBAUi with no improvement.
The key parameter of interest for the purposes of this paper is v. Consider the hypothesis that v = 0; i.e. consumers are indifferent to descriptive norms. In that case, the Iast term of Eq. (1) is dropped. The IeveI of energy service demanded by the consumer wouId then be determined by maximising utiIity both with respect to energy services, obtaining the foIIowing first order condition
' E.g. consider b(Eit)=B xEit - e2
db(Eit dEit
as well as with respect to research effort, for which the first order condition is
c^[ eu-i ■
Combining Eqs. (2) and (5) we find that
c[ eit-i - et-i]
This equation indicates that the less costly it is to search for energy efficiency improving opportunities (the smaller w), the higher the energy cost c, and the larger the gap to the most efficient consumer, the lower energy consumption eit will be today. The important takeaway from this is that even if the consumer does not care about norms, her consumption will still be influenced by the consumption level of her peers: a large gap between her and her efficient peers means there are more learning opportunities to improve.
Note that if the marginal cost of energy is zero - c = 0 - then the first order condition for energy services derived in Eq. (4) becomes
db(Eit
As c = 0, improving her energy efficiency will not yield any reduction in cost: the marginal benefit from research is always smaller than its marginal cost. As a consequence, consumers would not undertake any research on energy saving options and consequently ait = aBAUi would be unaffected by the performance of the most efficient consumers, et-1. This means that if c = 0 and we nevertheless observe that consumers are responsive to information about peer energy consumption, it must be the case that V > 0.
In the RCT conducted in this paper, the trial group is provided with information about et, the control group is not, and marginal energy costs are zero for all. The regressions we report below can then be interpreted as a test of the hypothesis v = 0. If the hypothesis is true, we would not expect to find any effect of the treatment.
3. Methods
This study was implemented in a student residence in West London (UK) in the summer of 2013. At the time, 466 postgraduate students8 lived there, and had moved into their studio flats in October 2012. Each studio in this residence is occupied by one to two residents and is approximately the same size (varying between 18 m2 and 22 m2). Electricity and hot water for heating consumption are collected for each studio by Schneider Electric meters whose data-readings are stored on a server. This data was collated in weekly increments by Powerlogic ION Enterprise software that produces csv files with this information.
Prior to the field experiment, a survey to collect demographic and socio-economic characteristics of participants (see Appendix A) was distributed to all 466 residents. The main areas of the survey include: demographic information, experience with past energy usage and perceptions regarding energy use and the importance of conserving energy. A total of 89 respondents completed the questionnaire at least in part, implying a response rate
8 This was the whole population of the halls of residence to whom the survey was sent
of 19.3%.
The survey methodology consisted of sending out (i) an introductory letter, (ii) the questionnaire followed by a (iii) thank you/reminder. Moreover, an additional questionnaire was sent to non-respondents in order to collect more responses. At the beginning of the survey (see Appendix A), each participant was provided with information including the nature and purpose of our research, their role in the study, and a guarantee of confidentiality. The participants were then requested to acknowledge understanding and consent to the use of the subsequent data in order to continue with the survey. This permission was then further used to include consenting participants in the experimental RCT study. Each participant was provided the option to receive further information about the study or to withdraw from the study at any time, without penalty or loss. The online survey was designed and distributed electronically using Qualtrics software. A reminder was also sent before the survey closed. To incentivise completion of the survey, we promised and then offered a £50 Amazon voucher to a randomly selected participant.
In an attempt to monitor social dynamics within groups, respondents were asked to report the number of residents they were familiar with or interacted with on a regular basis. Approximately 50% of the sample reported regular interactions with only five individuals or less within the student residence; therefore, we do not suspect a strong communication bias among participants. The 89 residents that responded to the survey constitute the sample population for our randomised control trial. This was because the survey included a question about consent to using their energy consumption data for research. For these students, we inspected energy consumption data from the beginning of June (Monday the 3rd of June 2013) for 10 weeks. A first intervention report was distributed to participants at the beginning of week 5 (on Monday the 1st of July), and was sent out every Monday for a further 6 weeks. The energy use data collected for each week was used to inform the subjects about their own as well as relative energy consumption via their weekly energy reports.
This weekly energy report, emailed to participants, was developed based on a number of well known pre-established ones such as the OPOWER report (Allcott, 2011) and contains two main sections, namely a social comparison module and an action steps module (see Fig. 1).
The social (peer-group) comparison module consists of descriptive and injunctive norms. Descriptive norms, the first element in the social comparison module, are represented as a comparison of residents' individual energy use for a week to both the overall mean use denoted by 'all neighbours' and the mean of the 'efficient neighbours", the 20% of participating students using the least energy. In addition, this element also reports a weekly rank of the resident in comparison to the 89 participants. Furthermore, the energy use is further broken down into 'electricity' and 'heating' elements, which are then tracked on a weekly graph.
The second component of the social comparison module is a series of injunctive norms. The overall weekly comparison is rated as 'great', 'good', or 'more than average' coupled with appropriate smiley faces. Within the energy progress, a dashboard indicator moves between red, orange, light green and dark green status and a corresponding figure regarding how much energy has been used in comparison to the group.
In the second module, the action steps include a series of energy conservation tips. These tips, differing weekly, are selected based on relative simplicity and ease of implementation, and do not require any additional expenditure from the user. This part of the report is crucial as it helps us define how inputs (i.e. behaviours such as temperature of heating in the home, cooking food, etc.) translate into output (total resource use in kWh): it allows the participants to link the information about the norm behaviour in
Weekly Home Energy Use Report
Wood Lane Studios
Unit A114
Week: 15-21 July 2013
Fig. 1. Example of the study's weekly home energy report.
Notes: the weekIy energy report emaiIed to participants incIudes sociaI comparison and action steps moduIes. The sociaI comparison moduIe consists of (i) a comparison of residents' individuaI energy use for a week to both the overaII mean use denoted by 'aII neighbours' and the mean of the 'efficient neighbours", the 20% of participating students using the Ieast energy, (ii) a weekIy rank of the resident, (iii) eIectricity as weII as heating consumption, (iv) the overaII weekIy comparison rated as 'great', 'good', or 'more than average' coupIed with appropriate smiIey faces, and (v) a dashboard indicator. The action steps moduIe incIudes a series of energy conservation tips, differing weekIy.
their residential community to actual behavioural change.9
The randomised control trial was based on three experimental groups, namely the control group (1) and two treatment groups (2 and 3):
• Group 1 - Control: Received a single e-mail at the start of the experiment that contained a series of simple energy saving tips.
• Group 2 - 'Information, feedback and norms' treatment: Received the energy weekly report described above.
• Group 3 - 'Information, feedback, norms and prize competition' treatment: Received the same energy weekly report as G2 participants. They were additionally informed that a competition was running to find the resident with the lowest energy consumption over the course of the project, although, no specific prize was communicated.
Finally, in order to gather first-hand information regarding participants' thoughts, feelings and reactions to the study, we conducted a
9 AIso noted by DoIan and MetcaIfe (2015) about various simiIar studies, "what is missing here, is the abiIity of the person to understand how to transform that norm into observabIe behaviour change. Therefore the norm has to be accompanied by the information to actuaIIy change behaviour".
small-scale feedback review from participants. Its main goal was to collect information on what participants thought worked well or was irrelevant, whether they were able to understand the weekly report and their preferred method or frequency of communication. The questions were posed as a combination of structured and open-ended form, in order to encourage honest and detailed feedback and were distributed via a bulk email a week after our experiment concluded. The benefits of this setting for our RCT are emphasised given the existing literature on energy consumption decisions. These decisions are complex for households (Lutzenhiser, 1992) and isolating factors in a field experiment can be difficult. However, we are in a relatively easier position to isolate particular aspects of decision-making: participating students have identical rooms, use identical appliances (fridge, cooker, heating) and similar income status. Of course, the generalisability of results from experiments with student subjects has long been debated; for example, Druckman and Kam (2011) show that in fact student samples are comparable to general population. In our study, their student status makes them easily comparable and allows us to control aspects that we could not have done with a wider population.
To measure and establish the impact of our treatments from the experimental part of the study we implement a simple difference in difference (DID) specification; i.e. we run regressions of the following generic form:
AeWi = ßlTreatedi x PostTreatW + ß2Treatedi + ß3PostTreatW
+ ßxXWt + eWi
Table 1
Survey results for demographic variables.
where Aewi is the percentage change in weekly energy consumption for student i relative to a pre-intervention base week, which is week 4 in our results below. We compute percentage changes as
Mv = ■
0. 5x(ew + e4)
i.e. the change in consumption relative to the average consumption in week w and week 4. This allows us to compute AeW even if consumption is 0 in either week w or week 4, which is relevant for heat consumption, where most students had weeks without using heating.
Treatedj is an indicator variable that is equal to 1 for students that received one of our interventions. If p2, the parameter associated with this indicator is not significantly different from zero, then we can conclude that the treatment and control group are comparable before treatment. PostTreatW indicates weeks during which the treatment is active. The main parameter of interest is pv which given the random assignment of treatment gives us an estimate of the average causal impact of the treatment. Because we use percentage changes as the dependent variable the parameter estimate for ¡¡v can be interpreted as the percentage reduction energy consumption due to the treatment. XWt is a vector of additional control variables such as nationality or gender of the student. However, note that given the random assignment of treatment it is not necessary to include additional control variables to get unbiased estimates of ftvw
4. Results and discussion
4.1. Survey results
Our main goal in conducting this survey in the first stage of our study was to gain further information into the characteristics of the residents taking part in the study, in particular in relation to their energy use. The descriptive statistics for the results of our questionnaire are shown below. A summary and the summary of descriptive statistics of all the relevant variables can be found in Appendix C.
4.1.1. Demographic variables
The demographic variables include gender, nationality, income/ budget, parental education level and previous residential status. Appendix C shows that the three treatment groups are comparable in terms of those observables as could be expected from the random treatment of the students.
Table 1 shows an almost equal split between male and female and the budget level of the respondents is predominantly between £10,000 and £19,000 per annum (41% of respondents). We also observe a very high level of parental education amongst respondents, with 96% having attained some college or higher high school and a further 55% having a graduate degree. This is well above the UK national average - which approximates 79% of adults having completed an upper secondary education (Eurostat, 2013).
4.1.2. Historical energy use
We find that for a large proportion of our student sample, the energy bill at their previous place of residence was either paid for by their parents (38%) or by someone else (2%). Others had
Gender Male Female
45% 55%
Nationality UK Rest of Europe Asia Other
19% 31% 33% 17%
Previous UK Rest of Europe Asia Other
residence 44% 22% 16% 19%
Previous With With others (non- Alone
living family family)
48% 33% 19%
Annual Bud- Above 30,000-39,999 20,000- 10,000-
get (£) 40,000 29,000 19,000
7% 2% 17% 41%
Funding Self- Scholarship Students Other
source funded loans
48% 21% 7% 24%
Parental Doctoral Masters College /some High
education degree degree college school or
24% 31% 41% 4%
Below 10,000
10 Our main results below do not include further controls but we examined the
robustness of our results to including such controls and can make them available on request.
Notes: 89 participants completed the questionnaire at least in part and hence was included in our RCT. The above results are based on the answers of 42 participants whom have fully completed the survey.
experienced a flat rate (26%) or the payment was included in the rent (22%). Hence, we conclude that a maximum of 12% of participants have experience of paying for their energy bills and have some awareness about the cost of energy.
4.1.3. Energy related intent and perceptions
In the residence, flats are supplied with basic appliances, namely, a microwave oven, refrigerator and thermostat. To fully understand the students' means to reduce energy consumption, we collected information on appliances used in the flats, such as laptops.11 We also asked our subjects whether they felt if they knew how to save energy. A large majority (90%) thought they were aware of necessary measures. We nevertheless included saving tips in the feedback report of our study such as to ensure that 100% of participants understood how to link the change in their behaviour regarding their energy consumption into energy saving output. Literature shows that technology or intervention acceptance increases with perceived usefulness (Davis et al., 1989; Szajna, 1996). The students were also well aware of their daily energy use pattern, with 83% suggesting they used the most energy in the evening.
In order to gauge the perception of our subjects about their own behaviour, we posed some questions about their energy saving habits. We find that lifestyle and comfort take precedence over energy saving with respect to laptop use, as 26% of respondents will always leave it on and 33% only 'when working on something important'. However, we still observe a large positive response (95% of participants) towards the importance of energy saving, with some 83% of participants declaring they always switch the lights off when leaving a room.
Despite not paying for their energy bills, respondents were asked to rank the reasons for which they would consider energy saving as important. The resulting order was (1) saving money -50% (2) energy security - 43% (3) climate change - 36%. This result is interesting given that the RCT's setting means monetary
Typical energy usage for these appliances is given in Table C2, Appendix C.
—•— Control —•— Treated
Fig. 2. The effect of information treatment on changes in energy consumption. Notes: the figure shows the average change (separateIy for aII anaIysed weeks) reIative to the Iast pre-treatment week (week 4) in energy consumption for both the treatment group (i.e. those students that received energy consumption feedback) and the controI group that received no feedback for every anaIysed week.
incentives are not part of the main intervention. It aIso shows that the popuIation in our survey is not primariIy environmentaIIy driven, as couId have been the case in a student haII.
4.2. Randomised control trial
This section reports the RCT resuIts using a difference-in-dif-ference approach as described in Eq. (8) of Section 3.
4.2.1. Basic Results
A first anaIysis of the RCT resuIts shows in Fig. 2 that there is an aggregate effect of the intervention on energy consumption, with groups 2 and 3 combined as the "treated". We find that as a resuIt of the intervention, the treated groups showed a drop in consumption after treatment begins in week 5, whereas before the treatment in weeks 1 -4 they foIIow simiIar trends on average. In TabIe 2, we confirm that before the treatment both average energy consumption and the average growth in energy consumption are statisticaIIy indistinguishabIe before treatment begins between treatment and controI groups (rows 1 and 2 of TabIe 2), which is to
Table 2
Descriptive statistics.
Total energy consumption pre treatment in kW h
Average Average growth
Treatment groups Control 24.509 -0.046
All Treated 27.679 - 0.053
Information 29.367 -0.031
Competition 25.992 -0.076
Position relative to Marginal 40.281 -0.038
Non Marginal 21.612*** -0.056
Notes: The tabIe reports descriptive statistics on energy consumption for different subsampIes participating in our RCT. Stars indicate if means are significantIy different between different groups. *** = significant at 1%. In PaneI 1 difference test are reIative to controI group. In paneI 2 (comparing position reIative to peers) difference test is between the marginaI and non-marginaI group. A marginaI group is defined as a student who is not in the bottom 20 in week 5 and neither in the top 5 in week 6. This definition is further expIained in Section 4.2.3 Incentives and Learning.
Table 3
Basic DD estimation.
Percentage change in
Dependent variabIes (1) Total energy (2) Electricity (3) Heat
Treated 0.009 - 0.067 0.178
(0.081) (0.079) (0.114)
Treatment Period - 0.198** - 0.062 - 0.488***
(0.091) (0.073) (0.131)
Treated X Treatment -0.215** -0.077 - 0.469***
Period
(0.106) (0.084) (0.173)
Observations 890 890 890
Number of rooms 89 89 89
R-squared 0.127 0.034 0.192
Notes: Dependent variabIes are computed as percentage changes reIative to the Iast
pre treatment week; i.e ew - e4— where ew denotes energy consumption in
0.5 x (ew + e4)
week w. Robust standard errors. CIusters at the IeveI of individuaI rooms. ** = significant at 5%, *** = significant at 1%
be expected given the random assignment of treatment. Average energy consumption is 25-28 kW h per week for both treated and controI groups. Energy consumption decIines on average per week by 5%. In TabIe 3, we confirm the impact of treatment by fitting the regression suggested in Eq. (8). CoIumn 1 reports resuIts for totaI energy, coIumn 2 for eIectricity and coIumn 3 for heat energy consumption. Across aII specifications the treatment and controI groups foIIow the same trends, which is reflected by the "Treated" dummy not being statisticaIIy significant. The coefficients of the treatment period dummy interacted with the treated group indicator - "Treated X Treatment Period" - we can interpret as the percentage causaI impact of treatment; i.e. the treatment reduces the (totaI) energy consumption of the treated by approximateIy 22% on average. The treatment effect seems to arise primariIy from reductions in heating energy consumption. This might be due to the fact that the RCT being conducted in summer means that heating is Iess of a necessity and its demand is more eIastic. Note that the majority of students participating in the triaI (68%) have been using their heating during the triaI period.
4.2.2. Information versus Competition
Within our treatment group of 60 residents we administered two different kinds of treatment: pure norm onIy, which we refer to as 'information' in the tabIes and figures, or the same norm but combined with prize competition, which we refer to as 'competition'. Fig. 3 Iooks at these two groups separateIy. This reveaIs an interesting pattern. InitiaIIy, in weeks 5 and 6, both treatment groups behave very simiIarIy, by reducing their energy consumption once exposed to treatment. However, starting in week 7, the competition group starts to faII back so that the average growth in energy consumption resembIes more that of the controI group. Indeed, the regression resuIts in the first coIumn of TabIe 4 confirm that in weeks 5 and 6 (Period 1) the energy consumption for both treatment groups reduces significantIy by around 20% re-Iative to the controI group. However, in treatment period 2 (weeks 7-10), the gap between the competition and the controI group is no Ionger significant and onIy around 9%. For the information (norm onIy) group on the other hand, the reduction gap deepens to around 30%. Rows 3 and 4 of TabIe 2 confirm that neither in terms of IeveIs of energy consumption nor in terms of growth rates are there significant differences between the two different treatment groups and the controI group. Non-significant differences in pre-treatment trends are equaIIy confirmed by the non-significant
—•— Control —•— Information
—•— Competition
Fig. 3. The effect of information treatment on changes in energy consumption for different treatment groups.
Notes: the figure shows the average change (separately for all analysed weeks) relative to the last pre-treatment week (week 4) in energy consumption separately for the two treatment groups (i.e. pure information feedback and feedback in combination with a competition to reduce consumption) and the control group that received no feedback for every analysed week.
Table 4
DD of the intervention for treatment and control groups - distinguishing between different treatments.
Dependent Variable Percentage change in Total energy consumption
Information - 0.019
(0.086)
Competition 0.038
(0.091)
Treatment period 1 - 0.128 - 0.135
(0.087) (0.083)
Treatment period 2 - 0.234** - 0.240**
(0.102) (0.093)
Period 1 X Info - 0.190 - 0.209**
(0.115) (0.102)
Period 1 X Comp - 0.250** -0.212*
(0.108) (0.107)
Period 2 X Info - 0.289** - 0.308**
(0.137) (0.126)
Period 2 X Comp - 0.137 -0.099
(0.131) (0.133)
Observations 890 890
Rooms 89 89
R-squared 0.144 0.143
Notes: Dependent variables are computed as percentage changes relative to the last pre treatment week; i.e. where ew denotes energy consumption in
week w. Robust standard errors. Clusters at the level of individual rooms. * = significant at 10%, ** = significant at 5%.
dummies for "information" and "competition" groups in column 1 (row 1 and 2) of Table 4. Given this absence of significant pre-trend differences between the two treatment and the control groups, we report in column 2 of Table 4 the same regression as in column 1, but restricting the two treatment group dummies to zero. This improves the efficiency of the regression, and confirms statistically the differential evolutions of the information and competition groups in periods 1 and 2 of the treatment weeks.
4.2.3. Incentives and learning
The results above raise the question of why the two treatment groups behave differently after week 6. We explore an explanation that combines two elements. Firstly, we suggest that treatment period 1 serves as a learning period. Individuals discover how they perform relative to others. Secondly, we propose that the behavioural drivers are fundamentally different between the two treatment groups. The information group is driven by the individuals' desire to reduce energy consumption in order to achieve societal objectives (i.e. preventing emissions and climate change). However, given the possibility of a financial gain is introduced for the competition group, their behaviour is driven by the individuals' efforts to maximise this gain.
Only the student with the lowest energy consumption would win a prize. Hence, if this becomes the main behavioural driver, there is little incentive to change behaviour if it seems completely out of reach to be the best student at reducing energy consumption and winning the prize. From the information and norms provided during the trial, students were able to assess their chances of coming first. Hence, we might be able to explain the pattern seen in Fig. 3 by some students initially trying to win the final prize but after week 6 realising that it is out of reach to them, thereby abandoning their previous efforts. We may call such students the marginal group.
To explore this potential explanation, we define this marginal group as follows: a student who is not in the bottom 20 in week 5 and neither in the top 5 in week 6. This will therefore include students who, in the light of week 5 comparison, would have a chance to win the prize competition - they are not those with the highest energy consumption, ranked the worst - but who, by week
6, realise they are unlikely to be the winner of the prize -they are not in the top 5 consuming the least energy.
The precise thresholds are of course arbitrary and we have explored various alternatives - some reported in the Appendix D -which show that our main results are robust to variety of different thresholds. Table 2 (in rows 5 and 6) reports average energy consumption and growth trends pre treatment for the two groups. In terms of consumption levels, non-marginals have on average significantly lower energy consumption: 21kWh as opposed to 40. However, in terms of growth trends - which is what we rely on for identification of effects - they are statistically not distinguishable. In Fig. 4, we report average trends over our 10-week sample for these two groups. Consider first the top row of the diagrams. They compare the two treatment groups; i.e. information treatment only vs. information and competition/prize treatment. We see that in weeks 5 and 6 the two groups trend similarly. However, in week
7, we observe the opening of a gap for the marginal group: energy consumption for the information treated students continues to decline whereas the competition treated students increase their energy consumption on average. In the non-marginal group no such gap opens up.
In the figures in the second row, we compare the competition treated students with the control group that received no treatment at all. For the marginal group we see that a gap opens up in weeks 5 and 6 but that the gap becomes smaller after week 6. For the non-marginal group we do not find any such pattern.
In Table 5, we report regressions corresponding to the four diagrams of Fig. 4. We regress Aew on a treatment period 2 dummy and on interactions between the period 2 dummy and each group. In column 1, we only include in the sample the marginal group and excluding control group students. Hence, the coefficients on the interaction dummies, 'Competition X Period' correspond to the average difference in energy growth between the two treatment groups in each period. As seen visually in Fig. 4, the average gap is small and indeed negative in period 1 but not significant: the average reduction for competition treated students
Marginal Group
Non Marginal Group
Control
Competition
Fig. 4. Distinguishing between different treatments for the MarginaI and Non MarginaI group of treated students.
Notes: these figures report average trends for change in energy consumption over our 10-week sampIe for marginaI and non-marginaI groups. MarginaI group is defined as students who are not in the bottom 20 in week 5 and neither in the top 5 in week 6. In the top row, two treatment groups are compared (Information treatment onIy vs. information and competition/prize treatment). In second row, the competition treated students are compared to the controI group that received no treatment at aII.
Table 5
Marginal vs non marginal consumers.
(1) (2) (3) (4)
Competition vs Info Competition vs ControI
Dependent VariabIes Marginal Non Marginal Non marginal
marginal
Treatment Period 2 - 0.202* -0.218 -0.075 - 0.163
(0.110) (0.261) (0.141) (0.166)
Competition X Period 1 -0.004 0.048 - 0.360*** 0.062
(Mean difference in (0.098) (0.149) (0.121) (0.180)
Period 1)
Competition X Period 2 0.265** 0.134 -0.218 0.094
(Mean difference in (0.125) (0.346) (0.137) (0.261)
Period 2)
Observations 276 84 240 114
Rooms 46 14 40 19
Rooms in ControI Group 0 0 19 10
Rooms in Info Group 25 5 0 0
Rooms in Competition 21 9 21 9
R-squared 0.062 0.022 0.081 0.021
Notes: Dependent variabIes are computed as percentage changes reIative to the Iast pre treatment week; i.e. ^e4e ) where ew denotes energy consumption in
week w. Robust standard errors. CIusters at the IeveI of individuaI rooms. * = significant at 10%, ** = significant at 5%, *** = significant at 1%.
was 0.4% points higher than for information treated students. However, in treatment period 2 the average gap becomes a positive and significant 26% points.
In coIumn 2, we restrict the sampIe to non-marginaI students. In neither period is there a significant gap. In coIumn 3, we incIude marginaI students from treatment group 3 and marginaI controI group students. We see that, on average, competition treated students reduce their energy consumption by a significant 36% points more than students in the controI group. However, this gap reduces to non-significant 21.8% points in period 2. Again, we cannot detect a significant gap in the non-marginaI group (coIumn 4).
Hence, both the graphicaI anaIysis and the regression anaIysis suggest that the dropping-off effect identified in Fig. 2 for the competition treated group is driven by students in the marginaI group.
5. Conclusions and policy implications
Our societies are facing important chaIIenges, such as cIimate change, that require a new Iow carbon energy system. DeIivering such a soIution, whiIst addressing fueI poverty and ensuring energy security, raises many issues on different fronts. A smart grid, integrating network and demand controI technoIogies, wiII need
to ensure the balancing of supply and demand. Another key element will be the improvement of energy efficiency. Policies such as the rollout of smart meters rely on the hypothesis that considerable efficiency improvements can be achieved through providing consumers with more information about both their own energy consumption as well as that of others. In this paper, we explore these questions in the unique setting of a student hall with a large number of identical and smart metered studio flats.
As we described above, various previous studies have established that feedback provision can have sustained impact on energy consumption. The main contribution of this paper is that we can examine the effects of feedback in a setting where the treated consumers do not have to pay for the energy they consume. We find a strong and significant impact of providing residents with feedback on both their own consumption and how it stands relative to other consumers: on average total energy consumption reduces by more than 20% relative to a control group that receives no information. This suggests that such effects are - at least in part - driven by intrinsic motivations - e.g. the willingness to reduce pollution - and the desire to comply with norms rather than by external rewards from reduced costs. Awareness seems to be key for energy management even with no specific price motives, corroborating similar findings in the literature regarding the use of social norms.
For a subset of our sample population we combine the feedback treatment with a prize for the resident with the lowest energy consumption. Hence, we re-introduce an external reward. Interestingly, for this group we find that the treatment effect dissipates after two weeks. This could imply that external incentives, far from re-enforcing intrinsic motivation could cancel it out and be less effective overall. This shows how important the design of such interventions can be. These results can be useful to energy market operators who need to carefully plan their approach towards energy demand management through various instruments such as the inclusion of norms with prize competition. By understanding individual characteristics and behavioural responses, they will be in a better position to estimate the energy elasticity of demand. This means they can meet their environmental objectives and target customers in a more efficient way.
The results of this paper are also an interesting contribution to the existing evidence. Even though the opportunity to track their energy consumption proved to be valuable to our participants in reducing their consumption and hence improving energy efficiency, we highlight the delicate relationship between the use of norms and behavioural change in energy demand. Other studies have similarly observed that once households realise their energy saving potential they might become frustrated and demotivated (Hargreaves et al., 2013). Besides, our RCT design benefited from and its results reinforce those of existing studies, including highlights of the importance of feedback (Allcott and Rogers, 2014) or the persistence of energy consumption reductions (Dolan and Metcalfe, 2015).
Our study also has a number of limitations. While the context of a student residence provides for highly comparable treatment and control units and a well-developed metering infrastructure, it raises questions regarding external validity. Our prior would be that 'real' households, with a higher and more diverse usage of energy, also provide more opportunities for saving energy. Indeed, students in our study are equipped with basic appliances and a self-adjustable thermostat, such that the variation in their energy consumption is much more limited than a typical household. We could expect to find stronger effects in a 'real' household context. On the other hand, students provide a sample that is more highly educated and driven by intrinsic incentives than the rest of the population, which might imply that effects in such a context would be more muted. Ultimately, the temporal dimension of the experiment is ten weeks thus limiting the identification of potential seasonality in energy demand elasticity. We are currently working with several energy retailers and service companies to conduct similar experiments in other settings, which should provide clarity on these questions in the near future.
Finally, the finding of a weaker effect when providing what appears to be more high-powered incentives, i.e. prize competition, is a surprising and unexpected result that deserves further attention. Can this effect be replicated in other settings? Is it contingent on providing external rewards via a competition where the winner takes it all, or would it also occur with more balanced rewards? These lines of inquiry are left for further research.
Importantly, our findings inform policy through the insights they bring on energy management and efficiency. The smart meter rollout that is being required by EU and UK policy will be used in the future to give all consumers a real-time feedback on their energy consumption. Understanding the optimal amount, content and frequency of information feedback, as well as its combination with other financial incentives and prizes, is a necessary input in the design of smart meter interfaces that are a key element of the smart grid needed to face society's challenges. The results of this paper points towards the fact that incentives, norms and feedback, if used in a well-thought combination, reduce overall consumption. It is clear that in the area of residential energy use, implementing system monitoring and intelligent control require a strong knowledge of consumer behaviour.
Acknowledgements
We thank British Academy Grant no SG113013 and The Digital City Exchange Project funded by RCUK Grant no EP/I038837/1.
Appendix A. Survey communications
Al: Survey questionnaire
Energy Consumption and Perceptions
Thinking about your energy use, please answer the following questions by checking the box that corresponds to your opinion.
Questionnaire Number
SECTION 1: DEMOGRAPHIC INFORMATION
1 Institution: .........................................................................................................
2 Room no: .........................................................................................................
3 Name: .........................................................................................................
4 Gender: Male | | Female | |
5 Nationality: ......................................................................................................
6 What is your annual budget:
1 Below £10,000
2 £10,000 - £19,999
3 £20,000 - £29,999
4 £30,000 - £39,999
5 Above £40,000
What is your main source of funding:
1 Self-funded (e.g. savings or part-time job)
2 Student loans
3 Scholarship
4 Other
8 In which city and country were you living before moving to Wood Lane Studios:
9 For how long did you live in your previous address (before moving to Wood Lane Studios):
1 1 year or less
2 More than 1 year and less than 5 years
3 More than 5 years
10 In your previous address (before moving to Wood Lane Studios) did you live:
1 By yourself
2 With non-family members
3 With parents or other family members
11 What is the highest education level of your parents (if you have parents with differing education levels please state the highest among them):
1 Less than high school
2 High school/GED
3 Some college
4 College degree
5 Masters degree
6 Doctoral degree
SECTION 2: HISTORICAL ENERGY USE
12 In your previous address (before moving to Wood Lane Studios) how did you pay for electricity:
1 Utility bills included in the rent
2 Individual meter with flat electricity tariff
3 Individual meter with top-up
4 Other payment schemes
5 1 did not pay for electricity
6 My parents paid for electricity
Would you say the cost of electricity at your previous address (before moving to Wood Lane Studios)
1 Cheap
2 Fair
3 Expensive
4 1 don't know
14 How much did you pay on average per month for electricity at your previous address:
1 £0 - £20
2 £20 - £50
3 £50 - £100
4 £100 or more
SECTION 3: ENERGY BEHAVIOUR AND PERCEPTIONS
Do you feel you know how you can save energy:
16 Do you switch off the light every time you leave a room
17 Which of the following appliances do you have in your studio at Wood Lane [select all that apply]:
1 Electric leater
2 TV or computer monitor
3 Laptop
4 Desktop computer
5 Sound system
6 Hair dryer
7 Iron
8 Kettle
9 Toaster
10 None of the above
11 Other (please specify)
Do you have a habit of turning off appliances when you do not use them [rate on a scale of 1 (never) to 5 (always)]:
Do you often leave your computer / laptop on standby while not using
1 Always
2 When I'm working on something important
3 Every now and then
4 Never
20 Do you think it is important to save energy:
3 1 don't know
21 If yes, why: [Rank the following reasons from 1 (most important) to 4 (least important)]:
1 Saving money
2 Energy security
3 Climate change
4 Other (please specify)
When do you use the most
energy:
1 Morning
2 Afternoon
3 Evening
4 Night
5 1 don't know
SECTION 4: EXPERIMENT CONTAMINATION CHECK
23 How many of the other residents at Wood Lane Studios do you know:
1 None
2 Less than 5
3 Between 5 and 10
4 Between 11 and 20
5 More than 20
24 How many of the other residents at Wood Lane Studios do you interact with on a regular basis:
1 None
2 Less than 5
3 Between 5 and 10
4 Between 11 and 20
5 More than 20
A2: Survey launch letter
Dear Student,
We wouId greatIy appreciate your input in an academic research study being conducted at the Grantham Institute for CIi-mate Change and Business SchooI at ImperiaI CoIIege. The main aim of the study is to understand peopIe's awareness of energy consumption in our community. It's a short questionnaire and for useful results your response is very important.
By fiIIing in the survey you wiII be entered in a draw to win a £50 Amazon voucher*! The deadIine for entering the draw has been extended to the 9th of June at midnight.
Follow this link to the Survey:
${I://SurveyLink? d=Take the Survey}.
Or copy and paste the URL beIow into your Internet browser: ${I://SurveyURL}.
We very much appreciate your heIp.
Best regards,
Dr. MirabeIIe MuuIs.
*Note: The draw for the £50 Amazon voucher wiII take pIace on the 3rd of June and the winner wiII be notified by emaiI. The voucher wiII be vaIid for 11 months and redeemabIe on www. amazon.co.uk.
FoIIow the Iink to opt out of future emaiIs:
${I://OptOutLink? d = CIick here to unsubscribe}.
Appendix B. Energy consumption communications
B1: Singular energy saving report (control)
Standby to shutdown!
When you're away or asleep, switch your laptop or computer off It's better for the device and your energy bill too
Unplug appliances when they're not in use. Even chargers continue to use electricity when they aren't charging
Switch off and save!
The lights in your home contribute 20% to your electricity use. If you're out or in another room - kill the lights
Remember to turn off plugs when they're not in use!
Shrink your bill not your clothes! Washing your clothes at a lower temperature will make them both clean and more energy friendly
Go easy on the heating!
Now that summer is here you can turn down the heating! Just one degree uses much less electricity and you won't notice the difference And make sure to turn down the thermostat when you open the windows!
Turning off the heating at night and when you're away can save £££...
Every drop counts!
Showering at a lower temperature during summertime will save energy and leave you feeling as cool as a cucumber.
Report water leaks or dripping taps Leaving them could pour 5,500 litres of water down the drain a year
Turning off the tap while you're shaving or brushing your teeth saves more than six litres of water a minute!
Don't forget the kitchen!
Kettles use a lot of electricity! Fill up and boil only the water you need, saving time and electricity.
Using the right size pot or pan for the heating plate is a good energy idea
Put a lid on it! You'll be surpnsed how much quicker your pan reaches boiling point.
B2: Weekly energy report (experiment)
Weekly Home Energy Use Report
Wood Lane Studios
Unit A114
Week: 15-21 July 2013
B3: Qualitative feedback communication
Dear Resident,
As you are no doubt aware, you have been receiving a perso-naIised Home Energy Reports weekIy for the past few weeks. This piIot project has now come to an end and we wouId Iike to thank you for your participation.
We wouId aIso Iike to ask you a few short questions about the study. RepIying shouId onIy take a few minutes of your time, and wiII be extremeIy vaIuabIe in improving the study going forward:)
1) Did you understand the report? PIease expIain any aspects that were confusing or converseIy that you feIt were effective.
2) Did you find the feedback report vaIuabIe?
3) WouId you Iike to receive a report Iike this in future? If so, how often?
4) Were you motivated to decrease your energy consumption when receiving the report? Why?
5) Did the study resuIt in a change in your energy consumption behaviour? In what way? PIease expIain any steps or actions you took that were different after receiving the weekIy emaiI.
6) PIease rank how usefuI you found the foIIowing parts of the report: (1 = completely useless; 2 = useless; 3 = I'm not sure; 4 =
useful; 5 = very useful)
i) EIectricity and heating consumption ii) Energy saving
graphs tips
iii) Neighbour's energy consumption iv) WeekIy rank
PIease rank your change in the foIIowing: (1 = significantIy worse; 2 = worse; 3 = no change; 4 = better; 5 = significantIy better).
i) KnowIedge about saving ii) Attitude towards saving
energy energy
iii) Intention to save energy iv) AbiIity to save energy
If there is anything eIse you wouId Iike to teII us about, or feedback on pIease feeI free to do so!. Thank you again. Kind regards, ICL Energy Team.
Appendix C. Summary of survey descriptive statistics
see: Tables C1 and C2.
A summary of the various descriptive elements of the survey, including the percentage where relevant, mean score, and standard deviation for the group in total as well as per experimental group are found below in Table 1. Despite some variation in de-
mographic variables (such as gender and nationality) between groups, the summary shows that an even spread of question response was achieved. We conclude that the randomisation of groups based on demographic and other independent variables have been effective since the results of each group are closely aligned with one another as well as the group as a whole.
Table C1
Summary of survey descriptive statistics by total and experimental group.
Variable
Coding
Group 1
Group 2
Group 3
Demographic information
V1: Institution V2: Gender
V3: Nationality
V4: Annual budget V7: Funding source V9: Residence period
V10: Living situation V11: Parental education level
V12: Energy payment method
Historical energy use
V13: Previous energy cost
V14: Avg. energy bill
Energy behaviour and perceptions
V15: Know how to save
V16: Switch off light V18: Turn off appliances V19: Leave PC on standby
1 Science, technology and medicine
2 Business, economics and finance
3 Other (humanities, art, law) 1 Male 2 Female
1 Europe 2 United Kingdom 3 North America 4 Caribbean
5 Asia 6 India 7 Middle East 8 Africa
1 Below £10k 2£10k-£19,999
3£20 k-£29,999 4£30 k-£39,999 5 Above £40k
1 Self-funded 2 Student loans 3 Scholarship 4 Other
1 1 year or less
2 More than 1 year and less than 5
3 More than 5 years
1 By yourself
2 With non-family members
3 With parents or family members
1 Less than high school
2 High school / GED
3 Some college
4 College degree
5 Master's degree
6 Doctoral degree
1 Utility bills included in the rent
2 Individual meter - flat tariff
3 Individual meter with top-up
4 Other payment schemes
5 I did not pay for electricity
6 My parents paid for electricity
1 Cheap 2Fair
3 Expensive 4 I do not know
1£0-£20 2£20-£50 3£50-£100 4£100 +
1 Yes 2 No 1 Yes 2 No 1 Always - 5 Never
1 Always
2 Working on something important
3 Every now and then
1: 81% 2: 14% 3: 5% 45% male 1: 31% 2: 19% 3:2% 4: 5%
5: 24% 6: 10% 7: 5% 8: 5%
2.10 (1.11)
2.21 (1.26)
2.02 (.86)
2.29 (.76)
4.55 (1.18)
3.57 (2.09)
2.48 (.98)
2.05 (.96)
1.10 (.30)
1.17 (.37)
3.98 (1.06)
2.31 (1.03)
1: 87% 2: 13%
67% male
1: 53% 2: 20%
5: 20% 8: 7%
2.12 (1.11)
2.22 (1.28)
2.00 (.86)
2.27 (.77)
4.56 (1.19)
3.51 (2.09)
2.05 (.97)
1.10 (.30)
1.15 (.35)
4.02 (1.02)
2.32 (1.05)
1: 71% 2: 14% 3: 14% 29% male
1: 14% 2: 14% 4: 7%
5: 36% 6: 7% 7: 14% 8: 7%
2.08 (1.13)
2.32 (1.28)
2.00 (.86)
2.32 (.76)
4.55 (1.23)
3.61 (2.13)
2.09 (1.00)
1.08 (.27)
1.16 (.36)
(1.04)
(1.05)
1: 85% 2: 15%
38% male
1: 23% 2: 23% 3: 8%
4: 8% 5: 15% 6: 23%
2.10 (1.14)
2.28 (1.26)
2.08 (.85)
2.30 (.78)
4.58 (1.18)
3.68 (2.09)
2.53 (.97)
2.08 (.97)
1.10 (.30)
1.18 (.38)
3.95 (1.07)
2.30 (1.05)
4 Never
Table C1 (continued )
Variable Coding All Group 1 Group 2 Group 3
Demographic information
V20: Important to save energy 1 Yes 2 No 1.10 1.10 1.11 1.10
(.43) (.43) (.45) (.44)
V22: Usage pattern 1 Morning 2 Afternoon 3.50 3.51 3.50 3.50
3 Evening 4 Night
5 I do not know (.82) (.83) (.82) (.84)
Experimental check
V23: Resident familiarity 1 None 2 Less than 5 3.60 3.56 3.53 3.60
3 5 - 10 4 11-20
5 20 + (1.16) (1.15) (1.19) (1.18)
V24: Resident interaction 1 None 2 Less than 5 2.57 2.56 2.61 2.60
3 5 - 10 4 11-20
5 20 + (1.00) (1.01) (1.04) (1.02)
Notes: % indicates the spIit between categories. Standard deviations appear in parentheses beIow means. SampIe size (N): AII = 42; Group 1 = 15; Group 2 = 14; Group 3 = 13
Table C2
TypicaI energy usage for househoId appIiances. (Source: Adapted from US DoE, 2012; Warwickshire Switch It Off Campaign, n.d.)
Appliance Maximum power (W) Typical standby (W)
Microwave oven 750-1100 2
Refrigerator 90-120 n/a
Laptop 50 -
KettIe 900-1200 -
Hair dryer / straightener 1200-1875 -
Toaster 800-1400 -
EIectric heater 750-1500 400
Monitor 150 30
Iron 1000-1800 n/a
Appendix D. Appendix D. Marginal vs non marginal groups
see: TabIe D1. see: Figs. D1 and D2.
Table D1
Electricity and heat energy consumption.
Electricity consumption pre treatment in kWh Heat energy consumption pre treatment in kWh
Average Average growth Average Average growth
Treatment groups Control 18.198 - 0.037 6.310 - 0.123
All Treated 17.329 0.009 10.350 -0.300
Information 17.167 0.022 12.200* -0.230
Competition 17.492 - 0.004 8.500** -0.387
Position relative to peers Marginal 23.958 0.009 16.323 - 0.139
Non Marginal 15.269*** - 0.011 6.342*** - 0.330
Notes: * = significant at 10%, ** = significant at 5%, *** = significant at 1%.
Marginal Group
Non Marginal Group
Fig. D1. Marginal vs Non Marginal group - Top 10 threshold.
Notes: As Fig. 4 but marginal group only includes students that are ranked 10 or lower in week 6 (rather than ranked 5 or lower).
Marginal Group
Non Marginal Group
5 6 week
Control
Competition
Fig. D2. Marginal vs Non Marginal group - Bottom 25 threshold.
Notes: As Fig. 4 but marginal group excludes bottom 25 (rather than bottom 20).
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