Well before the recent fuss about increases in energy prices, the reduction of electricity use by households and businesses had already been identified as an important national policy goal, with benefits for the climate, the electricity supply sector, business costs and household budgets. However, despite increasing costs to both users and producers and warnings about the impacts of climate change, consumption of electricity continues to rise and is predicted to continue rising over the coming decades. This increased demand, and the need to shift away from fossil fuel sources, is driving costly investment in the electricity generation and distribution networks, further increasing the cost of power. These higher electricity prices, in turn, are causing heightened community sensitivity to price, and problems for some household budgets, particularly those of low income earners (although as a proportion of household budgets, power costs are not rising). While the probable effects on prices of the introduction of a price on carbon are being wildly exaggerated by the tabloid press and political opportunists (and the compensation overlooked), it is clear that helping households and businesses cut their electricity consumption would assist in reducing the impact of rising prices. And by all accounts, there is plenty of room to move without compromising current standards of convenience and comfort.
Since price increases alone do not appear to be moderating demand (people are apparently unaware that their use is increasing and believe they are already doing enough), other approaches to curtail household energy use are needed. To date, most of the attempts to reduce energy use have taken the form of mass media campaigns based on the assumption that information about how to reduce use and persuasive advertising will motivate the necessary behaviour change. However, systematic evaluation of such campaigns indicates that they are largely ineffective; and even when people become more aware and concerned about a particular problem, and express the intention to change, they do not necessarily act – the well-documented “attitude-behaviour” gap (Staats et al., 1996).
Evidence is accumulating that interventions based on social influence can result in similar effects on consumer behaviour to those achieved by large changes in relative prices (Bertrand, et al., 2010), but without the negative consumer reaction. Among the most promising and least costly of these interventions are those which employ comparative normative information (Cialdini et al., 2006).
An extensive literature in psychology shows that we are influenced by other people’s actions and opinions, even when we think we are not. People are very sensitive to information which suggests that they are different from the majority in any way. This information may cause them to feel uncomfortable and can motivate them to change their behaviour in order to align more with what others are doing. People have been shown to be influenced by information about what other people approve or disapprove of (injunctive norms); e.g., that littering is bad. They are also influenced by their perception of what others typically do in certain situations (descriptive norms); e.g., putting rubbish in the bin.
Numerous studies attest to the power of social norms in influencing environmental behaviour, including energy conservation. For example, Schultz (1999) found that normative feedback increased recycling rates in his participants, especially among those who were informed that others in the community recycled at higher rates. Similarly in a Swedish study of what influenced the intention to choose “green” energy (Ek & Soderholm, 2008), householders were presented with two scenarios of others’ behaviour – viz. others contribute little (8%) or others contribute a lot (75%). People were more likely to choose “green” energy when they believed that it was the majority decision.
Some energy reduction programs have specifically sought to harness the effects of descriptive social norms to decrease energy consumption. Typically, people are provided with data about their own behaviour relative to the average of members of their community as a whole or of a more narrowly defined group, such as near neighbours; for example, how much energy they use compared with the average, comparable household (the effect is stronger when the comparison is made to people who are similar and/or with whom identification is strong; Alick et al., 1995). To illustrate, an experimental field study by Nolan et al. (2006), in which householders were provided with energy tips and one of four appeals to reduce energy use, found that providing people with normative information about others’ energy use (“the majority of your neighbours conserve energy”) was significantly more effective in achieving reductions than appeals based on protecting the environment, benefitting society or saving money.
Further development of this approach has resulted in an extensive – and relatively low-cost – roll out of programs which provide feedback to customers about their electricity and gas use compared with their peers. One such program, conducted for an electricity utility in California, took the form of a randomized controlled field experiment. Fifty thousand households were assigned to the control group and 35,000 to the treatment group, which received “home energy reports” on a periodic basis. The reports contained energy efficiency advice with tips based on the household’s energy use pattern, housing and demographic characteristics; bar charts comparing the household’s recent and 12 month electricity use with that of average of comparable neighbours and “energy efficient” neighbours (the lowest 20%) as well as injunctive messages (emoticons) depending on whether households were above or below these groups; and charts comparing the household’s use in the current month with the same month from the previous year. The result was a significant drop in electricity consumption of the treatment households relative to the controls of 2.35%, a result confirmed in two independent analyses (Allcott, 2009; Ayers et al. 2009). Overall, 80% of households decreased their use and the highest energy users reduced their energy consumption the most – 7% more than high energy users in the control group. While the magnitude of change may be lower than that achieved in some smaller scale – and typically more expensive – interventions, it was still strong, and growing, after three years of the households receiving the reports.
To date, there has been no systematic assessment of the effects on electricity consumption of feedback about the level of energy savings people achieve over time compared with similar households. Such information may encourage greater emulation than data on averages which simply convey current use and say nothing directly about other people’s success in cutting energy use. There is some evidence that in group settings, such as workplaces and offices, the greatest savings are made when employees compare their energy savings with that of other groups, adding a competitive edge to energy savings. Siero et al. (1996) for example, found that workers in a metallurgical factory were more likely to turn off computers at night, turn off lights not in use, report compressed air leakages, and disconnect electrical appliances when their success in making cuts was compared with that of other similar factories. Similarly, in an office setting, Staats et al. (2000) were able to decrease natural gas use by providing graphic feedback to office workers on their performance relative to other offices. As the authors commented, a remarkable feature of these results was that “behavioural change took place with hardly any change in attitudes or intentions” (p. 235). Although they did not measure actual energy use, Gockeritz et al. (2010) found that the more people believed others were making efforts to conserve energy, the more likely they were to report intending to conserve energy themselves.
Several possible pathways have been suggested to explain why information about others’ energy use may affect electricity demand. It may be that some people gain satisfaction from being seen as more thrifty than their neighbours, from seeing themselves as playing a part in the public good or to avoid censure for failing to conserve. Research within behavioural economics on “conditional cooperation” shows that people are more likely to contribute to public goods when they are informed that others are doing their bit. It is also likely that such feedback increases people’s attention to and knowledge about the amount of energy they are actually consuming. It seems that normative feedback is particularly powerful in conditions of uncertainty, when people are more likely to attend and respond to information about what others are doing. Providing feedback about others’ behaviour may be particularly significant when people have imperfect information and are searching for clues about the right way to act. Electricity is a case in point – it is invisible – and most people have a relatively limited understanding of how much energy they are using or how to save energy. They usually make decisions about comfort and convenience, about which “energy services” such as heating, cooling, lighting, cleanliness and entertainment to use, not about electricity use per se. As Backhaus and Heiskanen (2009) observed, “because we rarely make a conscious decision to use energy, it is also difficult to make conscious decisions to save it” (p. 3). Knowing what others are doing provides some guidance for these decisions. Providing people with evidence of what others have done may also make it harder for them to construe themselves as exceptional; harder to justify their own inaction
Although much has been made of the need to reduce household electricity use, affordable interventions such as those described above, have not been trialled or evaluated in Australia, despite the potentially substantial savings to both household budgets and the business bottom line. Not to mention the effects on Co2 emissions.
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 Market research studies reported in The Prime Minister’s Task Force on Energy Efficiency, 2010, p 101.