The IPCC report lists a number of mitigation technologies for the energy sector (IPCC 2015, p.569):
- energy efficiency improvements;
- reduction of fugitive non-CO2 GHG emissions;
- switching from (unabated) fossil fuels with high specific GHG emissions (e.g., coal) to those with lower ones (e.g., natural gas);
- use of renewable energy; - use of nuclear energy;
- and carbon dioxide capture and storage (CCS).
In addition to these strategies that can be employed at the distribution and supply levels of the energy chain, reduction in CO2 emissions can be achieved at the consumer level in a
number of ways including but certainly not limited to: - reducing the use of personal transport (Wegener 1996);
- the design of smaller and more energy efficient households (Joelsson 2008);
- public-private-people partnerships (4P) when creating new developments or legislation (Kuronen et al. 2010);
- energy conservation behaviours such as learning to live with less air-conditioning, using less hot water, replacing high energy usage devices, and more; and
- providing effective energy consumption feedback beyond just simple energy bills (Mankoff et al. 2007; Ehrhardt-Martinez et al. 2010).
This research focuses on the final point regarding feedback, as we will see the most relevant examples in Games for Change are in this area. Human behaviour in terms of energy consumption can indeed vary based upon environmental and weather conditions, as well as location and socio-economic status (Parker et al. 2008), however studies have shown in cases where the environment is similar, major variation can still occur. For example, a study by Parker et al. (1996) on 10 different homes with identical appliances and equipment all within the same area found the standard deviation of average total electricity consumption was nearly half the mean value of 43 kWh/Day. This study shows human behaviour is a core part of energy consumption, not just the choice of appliances. If we can change behaviour, then there is the potential to have a massive impact on energy consumption.
There is a chance to dramatically reduce energy consumption in the residential area by up to 100 billion kilowatt-hours annually in the United States by 2030 by developing energy usage feedback solutions (Ehrhardt-Martinez et al. 2010).
According to Ehrhardt-Martinez et al. (2010), who performed a meta-analysis of 36 different studies that provided energy feedback to consumers, the level of feedback (direct or indirect) is a contributing factor to the amount of electricity savings reported (Figure 8). They found that 4–12% savings could be achieved using feedback, a result mirrored by another meta- analysis performed by Darby (2001) of 38 studies which found an average of 10% savings.
Figure 8. Average amount of electricity savings in residences based upon feedback type (Ehrhardt- Martinez et al. 2010).
Ehrhardt-Martinez et al. (2010) refers to two kinds of feedback: indirect and direct and this is further expanded upon by Darby (2001) who identifies a number of examples categorised by two variables: the immediacy of the information (immediate/frequent/real-time versus single- event/infrequent), and the control the user has over finding and using the information (whether the feedback initiated by the user, or from another source such as the energy provider). This is summarised in Figure 9.
Figure 9. Different forms of energy-related feedback varying between immediacy and control of information as identified by Darby (2001).
Ehrhardt-Martinez et al. (2010) however point out some issues with their findings from the meta-analysis. They note that many studies they examined suffered from small sample sizes and short durations. Additionally, they note the potential of some of the lower-ranked enhanced billing methods as being quite a cost-effective solution for the savings yielded compared to the real-time feedback that may require expensive equipment such as smart meters or in-home displays. The Darby (2001) study also highlights the variation in sample sizes (3–2,000), and other factors such as the duration of studies, and location.
The Ehrhardt-Martinez et al. (2010) study notes that it is not just the type of feedback provided that has impact on the amount of energy savings. They identify that tailored feedback, multiple sources of information, meaningful feedback, and motivational techniques such as goal setting, social norms, commitments, and social comparison are things that must be considered in the design of an effective feedback system in the energy domain. Also, it is interesting to note that studies which focussed on peak-usage or other cases where a specific time period was targeted only averaged a 3% decrease in consumption, compared to 10% for those that did not. Ehrhardt-Martinez et al. (2010) suggests that this is due to these methods prompting participants to move their energy usage to another time, rather than reducing it in general. This has its own merit since peak load is an area of interest to energy suppliers and retailers.
This section examines ways in which feedback can be provided in a residential setting. First, examples which do not rely on technology are presented, before moving on to examples which have been enabled by technology in the form of smart meters.
2.2.2.1 Non-Technology-Based Strategies
The main school of thought in trying to bring about energy conservation (at least before the introduction of smart meters) has been through the use of social norms, a concept covered by Social Normative Theory (Perkins & Berkowitz 1986).
Social Normative Theory suggests that our behaviour is governed at least in part by social norms (Perkins & Berkowitz 1986). The Cialdini & Trost (1998, p.152) definition of social norms states that they “are rules and standards that are understood by members of a group, and that guide and/or constrain social behaviour without the force of laws”. They go on to describe four main kinds of social norms:
- injunctive norms: societal expectations of behaviour;
- descriptive norms: expectations of our behaviour based upon our observations of others behaviour;
- subjective norms: expectations of valued others for our behaviour; and - personal norms: expectations of ourselves for our behaviour.
Put more simply, Cialdini et al. (1991) state that descriptive norms refer to that of what is (the typicality of a specific behaviour) whereas injunctive norms refer to what ought to be (whether significant others approve of a specific behaviour).
Social norms provide the basis for a common way of trying to bring about energy conservation behaviours, providing normative feedback.
Normativefeedbackallows an individual to monitor their behaviour in reference to what is the norm. The concept has been used in a variety of areas including: driver safety (Feng & Donmez 2013), recycling (Schultz 1999), alcohol consumption (Collins et al. 2002; Lewis & Neighbors 2007), drug use (Donaldson et al. 1994), gambling (Larimer & Neighbors 2003), littering (Cialdini et al. 1990), and—as mentioned previously—energy conservation (Schultz et al. 2007; Foster et al. 2010; Laskey & Kavazovic 2010).
As Torriti (2012) points out, normative feedback (in the form of “consumption data sharing”) has been used in the area of energy conservation in a number of cases. One example of this is a study conducted by Schultz et al. (2007) who provided normative feedback to participants on their energy usage behaviours. They compared two kinds of feedback. The first kind of
feedback showed the participants their energy consumption for previous weeks, alongside a descriptive norm that showed participants the average energy consumption of other people in their area (i.e. they were shown what the norm for energy consumption by similar others was). For this kind of feedback, the Schultz et al. (2007) study provided the expected kind of results for descriptive normative feedback—as the authors put it, the feedback was both constructive and destructive depending on who was receiving it. That is, participants who initially used an above-average amount of energy reduced their energy consumption by an average of 1.22kWh—showing the constructive power of social norms—whereas participants who initially used a below-average amount of energy increased their energy consumption by an average of 0.89kWh. This phenomenon is referred to as the boomerang effect, where the average figure shown in the descriptive norm tends to act as a magnet—positively affecting those doing poorly to improve their behaviour and negatively affecting those already doing well. Torriti (2012) refers to the boomerang effect in the energy conservation space as the zero conservation effect, and according to Fischer (2008) is the main contributor to poor performance by normative feedback methods. A similar effect was also found by Elliott (2012).
The second kind of feedback Schultz et al. (2007) tried was the same descriptive norm plus an additional injunctive norm in the form of a simple hand drawn smiley face () or sad face () depending on whether or not the household had consumed less or more than the average consumption for their area—indicating the approval or disapproval of the household’s energy consumption. Schultz et al. hypothesised that the inclusion of this injunctive norm would remove the boomerang effect based upon Focus Theory (Cialdini et al. 1991), which suggests that if only a descriptive norm or an injunctive norm are present in an individual’s consciousness (and not both) then that norm will have the strongest influence on the individual’s behaviour. Their predictions were correct and found that with this second kind of feedback; the boomerang effect was not present.
This kind of normative feedback has been put into practice by electricity company OPOWER who place a smiley face (injunctive norm) and comparison feedback (descriptive norm) on customer bills. OPOWER have reported a 2—5% aggregate increase in energy savings using this method (Laskey & Kavazovic 2010; Allcott & Rogers 2014; Allcott 2011), which is considerable given the large sample size of their customer base. According to Fischer (2008), electricity companies in Denmark are legally required to provide normative feedback on electricity bills.
An important aspect of providing normative feedback is the reference group used as the source of the norm. Abrahamse et al. (2005) state that “by giving comparative feedback, a feeling of competition, social comparison, or social pressure may be evoked, which may be especially effective when important or relevant others are used as a reference group” (p. 279). Loock et al. (2012) found evidence for this effect, finding that reference groups that are more local were more effective than groups that were more distant, and Lewis & Neighbors (2007) found that gender could also play a role.
A strong example of the effect of locality of the reference group is shown in a study by Goldstein et al. (2008) on towel reuse in a hotel. They tested normative messages to hotel guests with various level of reference group locality by leaving a message saying messages along the lines of “N% of people in X reuse their towel” where X referred to one of: people (in general); people who stayed at the hotel; and people who stayed in that room number. They found that those with the most local reference group (given a normative message about those who stayed in the same room) were more likely to reuse towels. This is an interesting example of the effectiveness of reference group closeness since the closeness of people staying in the same hotel room seems rather unimportant.
Normative feedback has been selfdom integrated in to technology-based solutions in this space, a notable exception being—the Wattsup application (Foster et al. 2010) which provided social comparison of energy usage powered by Facebook. They found a significant reduction in energy consumption for participants given the social comparison condition compared to a control group who were not given it.
Despite some positive results, some academics have identified a number of limitations to the use of normative feedback in the energy conservation domain, and these limitations are explored in the following section.
2.2.2.1.1 Limitations
Some studies have suggested that the preferences of individuals may affect the efficacy of normative feedback (Abrahamse et al. 2005; Costa & Kahn 2013; Allcott 2011). Costa & Kahn (2013) found different responses in energy conservation to the same normative feedback between those with pro-environmental/liberal preferences and those with republican preferences.
Fischer (2008) states that feedback (in their case, on energy consumption comparisons) is useless without the precondition that the individual is motivated to conserve in the first place. Fischer’s comments are based upon a meta-review of a number of studies using just
comparative, descriptive norm-based feedback, and not those that have employed an injunctive norm-based form of feedback such as shown by Schultz et al. (2007). It is arguable that the injunctive norm itself provides motivation by triggering the individual’s urge to avoid social sanctions since the injunctive norm indicates that their behaviour of failing to conserve energy is not socially approved.
Cialdini (2003) notes that some normative feedback information—such as ones presented through television or news—are too far removed from the opportunities to perform the change in behaviour that the feedback promotes. In the case of the stealing of wood example provided by Cialdini, the opportunity to perform the socially acceptable behaviour of not stealing wood was provided immediately after the feedback (signage placed at the park where wood is stolen). Whereas if that same information were provided through a means such as a television public service announcement (PSA), then the opportunity to not steal wood would not arise until the next time the person visited the park.
2.2.2.1.2 Summary of Non-Technology-Based Strategies
Non-technology-based strategies in energy conservation primarily use Social Normative Theory as a basis. From Caildini & Trost’s definition, we can see that social norms impact our behaviour, and we have already seen a number of examples where their application through normative feedback has brought about behaviour change. Surprisingly, the designs of the technology-based solutions in the following section do not make use of this particular theory, and later in this chapter we will see that this is also true in terms of the design of Games for Change in this area.
With an understanding of the methods used for non-technology-based strategies for energy conservation, the following section explores how the introduction of technologies such as smart meters and in-home displays have brought about new strategies for bringing about energy conservation.
2.2.2.2 Technology-Based Strategies
Recently, a number of solutions that aim to provide better feedback to users have been developed that make use of improved technology in the energy sector. In order to understand the ways in which improved feedback can be provided to consumers, it is important to first examine the technology that enables such information to be collected (smart meters) and displayed (in-home displays and web portals)—the foci of the following sections.
2.2.2.2.1 Smart Meters and Advanced Metering Infrastructure (AMI)
In the 1970s, electricity meters were developed which were able to remotely send energy usage to utilities using technology called Automated Meter Reading (AMR). These early devices exhibited characteristics now common in smart meters we see today, and provided benefits to utility companies such as reducing the labour required to make meter readings (employees no longer need to directly visit each meter), and improving reading accuracy.
AMR technology improved to provide further benefits, such as energy profiling and demand prediction, and ultimately evolved into the beginnings of Advanced Metering Infrastructure (AMI, Ehrhardt-Martinez et al. (2010)). AMI, and subsequently smart meters, came about in response to electricity deregulation and the introduction of market-driven pricing whereby energy production companies were free to sell on their energy with fewer restrictions. Utilities were faced with a need to match the amount of energy they bought from producers with the amount of energy consumed by their customers. Smart meters help combat these problems and provide a range of benefits to all stakeholders in the electricity market, including producers, utilities, and consumers.
Smart meters are generally installed in residences, replacing existing electricity meters, and interfacing with other meters such as gas or water, and they communicate this information with utilities using a communication technology such as an ADSL internet connection, wireless network (such as GSM or GPRS), or Power Line Carrier (PLC) (van Gerwen et al. 2006). It is this communication that makes the meter smart, and enables their key features of remote meter reading, remote service usage limitation and cancellation by the utility, and smart grid functionality where homes can feed electricity back in to the grid using their renewable energy sources such as solar panels. Additionally, the collection of data over time, and the ability to query this data over various intervals (such as yearly, quarterly, daily, hourly, or sub-hourly) differentiate smart meters from traditional meters. An overview of the main components of a smart meter system is provided in Figure 10.
Figure 10. Schematic overview of a typical smart meter configuration, taken from van Gerwen et al. (2006). Key features are the communication with different meters, in-home displays, and the outside
world using a modem.
Smart meters can provide the consumer, retailer, and distributor feedback at a per-household level at various levels of granularity without any major change in infrastructure other than the installation of the meter itself.
Smart meters are being identified internationally as a powerful way of not only implementing Smart Grid technologies, but also as a way of providing important (in some cases real-time) feedback to energy users. Navigant Research (2013) estimates that up to 130 million new smart meters will be installed internationally each year until 2022, and that by the end of 2022 80% of residential premises in Europe will use a smart meter (EYGM 2013).
While the potential for conservation through improved and more immediate feedback is great (see the next section on in-home displays), there are some ethical concerns in the community about smart meters. McKenna et al. (2012) review these concerns, the greatest of which is the potential to infer private activities in a dwelling from the smart meter data. This has the potential to aid in burglaries, through establishing any routines of absence, or as low or no consumption can indicate the vacancy of a household (Lisovich et al. 2010; McKenna et al. 2012). Similarly, stalking could be aided by the knowledge that a person is present in the household. Other concerns include use of information by advertising companies for targeted
advertising (Lisovich et al. 2010), law enforcement to detect illegal activity (Quinn 2009), and household members “spying” on other household members (Hargreaves et al. 2010). These concerns are having an impact on smart meter rollouts globally, with many rollouts now being voluntary rather than mandatory (McKenna et al. 2012).
In the Australian state of Victoria, smart meters are now the standard type of meter in homes and businesses, and according to the State Government Victoria (2016), the rollout there is complete, with over 2.75 million meters installed. Benefits are already being seen in the state, with smart meters enabling metering (and rewarding) of solar panels feeding electricity back into the grid, electricity retailers actively using remote connect/disconnect to transfer services, and in-home displays being installed.
The Victorian rollout has been engineering-based and therefore mandatory, and as such has received considerable criticism given the concerns over privacy of smart meter data mentioned previously. Other Australian states such as New South Wales (State Government New South Wales 2014) and Tasmania (State Government Tasmania 2015) have learnt from this and are performing consumer-based rollouts which allow for competitive pricing, and consumer choice (including the choice to not install a smart meter).
In New Zealand, over 1.2 million smart meters are estimated to have been installed (Block 2015), up from 971,109 in 2013 (Beatty 2013). This is more than 50% of all residences. According to the latest statistics provided by the Department of Energy, Climate Change United Kingdom (2016) over 2.32 million smart meters have been deployed in residential properties