6.3.1.1 Renewable energy technologies (R)

We begin by looking at a class of household behaviors that shows perhaps the greatest promise for reducing greenhouse gas emissions: adoption of renewable energy technologies (R). These technologies include, among others, solar thermal water heaters, solar PV, pellet heaters, geothermal systems, and microwind turbines. Although the amount of achievable greenhouse gas reductions varies by technology, each is designed to replace fossil fuels for major household energy uses such as water heating; space heating and cooling; electricity for appliances, lighting, and electronics; and when PV are used to power electric vehicles, transportation.

Renewable energy technologies typically involve high upfront costs and structural changes to homes. As such, their adoption is affected by contextual factors such as access to capital and tolerance for financial risk. Literature reviews find that adopters tend to be wealthier, have larger households or homes, and have achieved higher levels of education (Balcombe et al., 2013; Kastner & Stern, 2015). Other contextual factors such as the availability of supportive government incentives and policies also appear to be highly correlated with adoption. Uptake of residential solar PV, for example, dramatically increased in many countries after favorable incentives such as feed-in-tariffs, income tax credits, and net metering policies were introduced (Balcombe et al., 2013; Stern, Wittenberg, Wolske, & Kastner, 2018).

Interest in renewable energy technologies appears to be strongly determined by the specific beliefs and attitudes households have about these technologies (Kastner & Stern, 2015; Wolske et al., 2017). Perceived tradeoffs between upfront capital costs and financial benefits are especially influential. Perceptions that solar PV would lead to personal financial gains, for example, are among the strongest predictors of intentions to adopt or to contact an installer (Korcaj et al., 2015; Wolske et al., 2017). Among adopters and households who rejected renewable energy technologies, capital costs and long payback periods are often cited as the most significant barriers (Balcombe et al., 2013; Caird & Roy, 2010; Caird, Roy, & Herring, 2008; Jager, 2006). Some evidence indicates, however, that early adopters do not explicitly consider payback periods (Schelly, 2014b) but rather view these technologies as long-term investments that can help with retirement planning and rising energy costs (Rai, Reeves, & Margolis, 2016; Schelly, 2014a, 2014b).

Other beliefs besides financial ones may facilitate or hinder adoption. Common barriers include concerns about esthetic changes to the home, uncertainty about the reliability and performance of the technology, the inconvenience of making major modifications to the house or property, and the need for future maintenance (Balcombe et al., 2013; Caird & Roy, 2010; Caird et al., 2008; Claudy, Peterson, & O’Driscoll, 2013; Stern et al., 2018; Wolske et al., 2017). Motivations may include reducing climate change, greater independence from electricity suppliers, and the desire to promote a “green” self-image (Caird & Roy, 2010; Caird et al., 2008; Claudy et al., 2013; Korcaj et al., 2015; Leenheer, de Nooij, & Sheikh, 2011).

The extent to which individuals hold these beliefs may depend on underlying personal dispositions. Research on potential PV adopters in the United States indicates that individuals who are more innovative and have stronger proenvironmental norms are more likely to have favorable beliefs about PV, which in turn influence intention to contact an installer (Wolske et al., 2017). Research on early adopters of renewable energy systems shows that they are often characterized by both their enthusiasm for the technology (Labay & Kinnear, 1981; Schelly, 2014b) and their concern for the environment (Haas, Ornetzeder, Hametner, Wroblewski, & Hübner, 1999; Jager, 2006). While the ultimate decision to install a renewable energy system may depend mostly on economic factors, some have suggested that environmental concern motivates people to learn more about these technologies and help sustains their interest throughout the decision-making process (Jager, 2006; Keirstead, 2007).

Social influence has also been shown to have a positive influence on renewable energy technology adoption, especially solar PV. Several studies have demonstrated that the more concentrated PV installations are in a region, the greater the likelihood that additional installations will occur (Bollinger & Gillingham, 2012; Graziano & Gillingham, 2015; Müller & Rode, 2013). Peer effects appear to operate both passively through observation of nearby installations and actively through direct engagement with existing adopters. In the United States, seeing nearby PV installations and talking with adopters has been found to shorten the decision period for adoption and spark initial interest in the technology (Rai et al., 2016; Rai & Robinson, 2013). In Sweden, Palm (2017) found that nearby installations were only influential if potential adopters also spoke to an acquaintance with PV. The effect of talking to existing PV adopters was not to generate interest in the technology but rather to confirm its benefits, reduce uncertainties, and get information about incentives and installers. Other trusted sources of information may also fulfill this role, including renewable energy advocacy groups (Schelly, 2014b), local electricity utilities (Palm, 2016), and solar community organizations (Noll, Dawes, & Rai, 2014).

Households increasingly have opportunities to adopt renewable energy systems without directly making capital investments or changing the physical conditions of their homes. For example, Germany has offered incentives for households to participate in community solar PV projects that can be constructed away from the homes of those involved. This enables homes that are poorly situated for PV to switch to solar power. In many countries, and in some US jurisdictions, electricity customers are now allowed to choose among electricity suppliers and thus pay for renewable power if they want, again without making physical changes to their homes. Even renters in many places have this opportunity. These options considerably increase the potential penetration of renewable energy.

6.3.1.2 Energy-efficient and alternative-fuel vehicles

Passenger cars, sport utility vehicles, light-duty trucks, and minivans account for approximately 16% of US greenhouse gas emissions (Center for Sustainable Systems, 2016; US EPA, 2016). Energy-efficient vehicles, which use less petroleum per mile driven, and alternative fuel vehicles, which replace petroleum with less carbon-intensive fuels, are two strategies for reducing these emissions. Alternative fuel vehicles include both vehicles with traditional combustion engines that burn fuels with smaller climate impacts (e.g., biodiesel or natural gas) as well as electric vehicles. Electric vehicles can further be divided into several classes: hybrid electric vehicles that pair traditional internal combustion engines with regenerative braking, plug-in hybrid electric vehicles that extend the range of the battery by allowing it to be recharged, and plug-in battery electric vehicles that run entirely on electricity and must be recharged more frequently than hybrids. Of the various vehicle types, plug-in hybrids and battery electric vehicles have considerable potential to reduce carbon emissions¹ (McLaren et al. 2016), but, as we discuss below, some of the biggest barriers to adoption.

Most research on alternative fuel vehicle purchasing has focused on the economic and contextual factors that influence adoption. Analyses based on purchasing data of alternative fuel vehicles indicate that their adoption is driven primarily by rising fuel costs, government incentives that reduce upfront costs (Diamond, 2009; Gallagher & Muehlegger, 2011), and in the case of electric vehicles, the number of charging stations available (Sierzchula, Bakker, Maat, & van Wee, 2014). The form of the incentive also matters. In the United States, state sales tax waivers have a stronger impact on hybrid purchasing than income tax credits—even when the magnitude of the waiver is smaller—suggesting that consumers are more attentive to incentives that have immediate and transparent effects on the purchase price (Gallagher & Muehlegger, 2011). (In the United States, income tax credits offer their benefits in the year after the purchase.) Other evidence confirms that the timing of alternative fuel vehicle purchases aligns with the availability of incentives (Sallee, 2011), with sales dropping once incentives are removed (Tabuchi, 2017).

Lack of knowledge and understanding about life cycle costs has been cited as a significant barrier to purchasing of fuel-efficient vehicles (Lane & Potter, 2007; Rezvani, Jansson, & Bodin, 2015). While consumers tend to be knowledgeable about fuel costs at the pump, research shows that few people take into consideration the full costs of owning, operating, and maintaining a vehicle when comparison shopping (Allcott, 2011a; Lane & Potter, 2007; Rezvani et al., 2015; Sovacool & Hirsh, 2009). As Lane and Potter (2007) suggest, this may be because people assume that cars of the same class (e.g., four-door sedans) have similar fuel economy. US studies indicate that people systematically misinterpret miles-per-gallon (MPG) ratings (Larrick & Soll, 2008). Most people incorrectly believe that MPG scale linearly such that a difference of 1 MPG between two inefficient vehicles (e.g., getting 10 or 11 MPG) has the same impact on fuel consumption as a 1-MPG difference between two highly efficient cars (e.g., 40 vs 41 MPG). In fact, MPG have a curvilinear relationship, such that the impact of a 1-MPG difference decreases as fuel efficiency increases. Coined the “MPG Illusion,”² this bias may lead consumers to overlook small differences in MPG among vehicles with lower fuel efficiency ratings (e.g., vans and trucks), when in fact those differences are substantial—and likewise overvalue cars at the high end of the spectrum, where small gains in MPG matter less (Allcott, 2011a).

Even when consumers are provided with a knowledge-related decision aid at the point of purchase, information about total operating costs may not affect purchasing behavior. In an experimental study by Allcott and Knittel (2017), car shoppers were approached at the dealership and shown the annual and lifetime fuel costs of the customer’s current car as compared to the three vehicles the person was considering buying. To make the comparisons concrete, the lifetime fuel savings associated with the most efficient vehicle were compared to other purchases such as the number of iPads or trips to Hawaii that could be purchased. When the researchers contacted shoppers months later, no differences were found between the treatment and control conditions in terms of the average fuel efficiency of the cars purchased. The results suggest that lifetime cost considerations were outweighed by other factors.

Several studies have looked more specifically at the psychological determinants of alternative fuel vehicle adoption, particularly plug-in electric vehicles. Literature reviews by Rezvani et al (2015) and Lane and Potter (2007) identify several categories of beliefs that influence the decision (or intention) to adopt electric vehicles: financial considerations, inconvenience, concerns about performance, perceived environmental benefits, and symbolic attributes. As with renewable energy technologies, the high purchase price and long payback period associated with electric vehicles is a deterrent to many. Practical concerns about the limited range of all-electric battery electric vehicles, the time needed for batteries to recharge, as well as safety concerns about slow acceleration when driving are barriers. Some consumers may delay considering electric vehicles for fear that currently available technologies will quickly become obsolete. Evidence about the role of perceived environmental benefits is mixed, with some adopters describing environmental concerns as a motivation for their purchase, and other consumers expressing doubts about electric vehicles’ environmental benefits (Rezvani et al., 2015). Consumers may also be deterred by the small size or style of some electric vehicles or be concerned about their slow operation (Graham-Rowe et al., 2012). In general, believing energy-efficient vehicles involve sacrificing performance, comfort, or pleasure has been shown to decrease intentions to adopt (Nayum & Klöckner, 2014).

Personal dispositions may shape consumers’ beliefs about alternative fuel vehicles, as well as the attributes they pay attention to when shopping. Some studies have linked alternative fuel vehicle adoption to consumer innovativeness (Heffner, Kurani, & Turrentine, 2007; Jansson, 2011). Adopters in Sweden were found to rate alternative fuel vehicles as more compatible with their needs, less complex, and more advantageous than nonadopters did (Jansson, 2011). Other work finds that individuals with stronger proenvironmental norms perceive the functional attributes of electric vehicles more favorably (Schuitema, Anable, Skippon, & Kinnear, 2013). These views, in turn, positively predict beliefs about the symbolic and hedonic benefits of electric vehicles, which predict intentions to buy. Jansson, Marell, & Nordlund (2010, 2011) found that VBN variables, especially proenvironmental norms, had predictive value in explaining past adoption of alternative fuel vehicles as well as willingness to adopt them in the future. Intentions to adopt electric vehicles are also correlated with beliefs that adoption will enhance environmental identity and social status (Noppers et al., 2014).

Different attitudes may be important to understanding why people do not buy battery electric vehicles. Using a market segmentation approach, Nayum, Klöckner, and Mehmetoglu (2016) compared battery electric vehicle adopters in Norway with conventional car owners. While battery electric vehicle owners had higher ratings on personal norms and beliefs about the benefits of these vehicles, these variables had less discriminatory power than attitudes about performance and convenience, with owners of larger cars rating these as particularly important to their decision-making. In a survey of potential electric vehicle adopters, Egbue and Long (2012) similarly found that while environmental considerations were important, beliefs about cost and performance mattered more.

With financial and performance considerations being of primary importance to many shoppers, car dealerships may play a vital role in shaping consumer perceptions. However, the technology may have advanced faster than the ability of dealerships to market it. A study by Matthews, Lynes, Riemer, Del Matto, and Cloet (2017) found that in visits to 24-certified electric vehicle dealerships in Ontario, Canada, only 13 had an electric vehicle on the lot available to test drive, and in those dealerships, between a quarter to one-third of sales associates provided inaccurate information about available subsidies.

6.3.2.1 Home weatherization (W)

Improving the building envelope of one’s home and upgrading to more efficient heating and cooling systems are among the most effective strategies a household can take to reduce its climate change impact (see Table 6.1). Compared to energy-related behaviors with lower potential climate impact, however, they have received much less research attention from psychologists.

Kastner and Stern (2015) examined 26 empirical studies to identify the determinants of major energy-related investments that involve physically altering residential homes, including both retrofit measures (e.g., added insulation) and renewable energy systems. With the exception of wood pellet heaters, few differences were found between the determinants of different types of investments. In line with past research (Black, Stern, & Elworth, 1985), demographic variables and personal dispositions of the decision-maker, including environmental attitudes, were found to be less important than the perceived consequences of investing. Beliefs about investment costs and energy savings, increases in thermal comfort, and benefits to the environment were most commonly associated with decisions to invest in insulation measures. These findings complement earlier work by Caird et al. (2008). In their study of UK residents, concerns about capital costs and slow payback periods were the most common barriers to home efficiency improvements; increasing comfort and warmth while saving money and energy were cited as the primary motivations for adoption.

Other contextual factors may influence the timing of weatherization upgrades. Several studies suggest that homeowners are more likely to upgrade heating and cooling systems, appliances, and home insulation when undergoing other renovations (Judson & Maller, 2014; Noonan, Hsieh, & Matisoff, 2015) or after moving (Caird et al., 2008). Ethnographic research from Australia suggests, however, that even among households who self-identify as pursuing “green renovations,” energy efficiency is often secondary to concerns about improving thermal comfort, reducing future costs, or changing the layout of a home to accommodate other needs (Judson & Maller, 2014).

6.3.2.2 Efficient appliances (E)

Appliances are responsible for approximately 18% of household electricity consumption in the United States (US EIA, 2017). While upgrading to more efficient models is a relatively straightforward way for households to reduce their climate change impacts and save money, energy consumption does not appear to be top of mind for most when shopping (Gaspar & Antunes, 2011). In a study of UK consumers, for example, price, brand, and reliability were reported as primary considerations; only 19% of respondents listed energy efficiency as a leading factor in their decision making (Yohanis, 2012). In the United States, Zhao, Bell, Horner, Sulik, and Zhang (2012) investigated what factors might lead consumers to consider purchasing energy-efficient and renewable energy goods such as Energy Star appliances, efficient heating and cooling systems, and solar panels. Initial costs and potential financial savings were ranked as having the greatest influence on decision-making followed by the availability of tax credits. Environmental benefits, cutting-edge technology, and access to low-interest financing were seen as much less important. Some evidence suggests that energy-efficient purchasing may be more likely among individuals who already engage in energy curtailment behaviors (Gaspar & Antunes, 2011) or who have purchased efficient appliances in the past (Nguyen, Lobo, & Greenland, 2016).

One reason energy efficiency may be overlooked is that people fail to consider lifetime costs when comparison shopping. The price premium associated with more efficient goods can often be recouped over time through energy savings, but this fact may not be obvious to consumers in the store. Considerable research has consequently focused on strategies for making lifetime costs more transparent to consumers, usually through on-product labels. While hypothetical discrete choice experiments suggest that presenting information about total operating costs may be effective (Deutsch, 2010; e.g., Heinzle, 2012; Newell & Siikamäki, 2014), evidence from field studies is less clear. Though not focused on appliances, Allcott and Taubinsky (2015) tested the effects of presenting the lifetime costs for incandescent and compact fluorescent light bulbs to customers of a hardware store; the information had no effect on purchasing decisions. In a similar study, Kallbekken, Sælen, and Hermansen (2013) used a factorial design to experimentally test the effects of presenting lifetime energy costs on refrigerators and tumble driers and training sales staff about the energy consumption of those products. No treatment effects were found for refrigerators, perhaps because of the small difference in lifetime costs between efficient and less efficient models. For tumble driers, only the combined treatment of label plus trained staff led to more efficient purchasing, suggesting that the sales staff helped to make the information more salient.

Other contextual factors may influence the decision-making process. Appliance shopping most often occurs in response to other events: the old appliance breaks or the household moves and must equip a new home (Gaspar & Antunes, 2011). Under these circumstances, people may have limited time and attention to research efficient options. Young, Hwang, McDonald, and Oates (2010) found this to be true even among self-identified “green consumers.” “Green” shoppers also struggled to make efficient choices in the face of price constraints and the desire to factor in other criteria such as brand, size, appearance, and reliability.

6.3.3.1 Eco-friendly driving (D) and vehicle maintenance (M)

Small changes in the way people drive and maintain their motor vehicles can have significant, cumulative impacts on transportation-related emissions (see Table 6.1). Eco-friendly driving behaviors include chaining trips to reduce miles driven and driving within the speed limit to optimize fuel efficiency. Drivers can also maximize fuel efficiency by maintaining tire pressure, changing air filters as needed, and investing in low-rolling resistance tires. Psychological research on these behaviors, however, is quite sparse.

The evidence available indicates that people often lack accurate knowledge of the potential energy savings associated with these behaviors (Attari et al., 2010). In an experimental study in the Netherlands, Dogan, Bolderdijk, and Steg (2014) tested the effectiveness of different informational messages on intentions to engage in six eco-driving behaviors. Participants were told about either the carbon savings or the financial benefits of each behavior. As compared to a control group, both messages were equally effective at increasing intentions. However, when asked to rate how worthwhile eco-driving seemed, those exposed to the environmental frame had significantly higher ratings than the financial frame. A related study found that people perceived they would feel better about themselves for complying with an environmental appeal to check tire pressure than a financial one (Bolderdijk, Steg, Geller, Lehman, & Postmes, 2013). In a follow-up field experiment, customers at a fueling station saw one of four signs encouraging them to get a free tire pressure check, three of which described either the environmental, financial, or safety reasons for doing so. Customers exposed to the environmental sign were significantly more likely to respond. Collectively, these studies indicate that, unlike energy-related investment behaviors, the populations studied do not think about eco-driving or minor car maintenance with a financial mindset; behavior change is more likely if interventions tap motivations related to an individual’s self-concept.

6.3.3.2 Travel mode choice (D)

Another way households can reduce their travel-related emissions is to choose less carbon intensive modes of travel such as walking, biking, carpooling, or using public transportation instead of traveling alone in private motor vehicles. Given the diversity of these behaviors, a comprehensive review of their determinants is beyond the scope of this chapter. We focus here on key insights related to decisions to reduce car use and use public transportation. Most psychological research in this domain uses cross-sectional surveys to examine correlates of existing car use (or nonuse) and/or intentions to reduce driving. In a metaanalysis of 23 studies that examined car use as a function of TPB variables and habit, Gardner and Abraham (2008) found intentions to drive and past habit had the strongest relationships to car use, followed by perceptions about the difficulty of noncar travel (perceived behavioral control). Subjective norms to reduce car use and attitudes about the environmental impacts of different travel modes had significant but smaller effects on both intentions and behavior. More recent studies have confirmed the importance of attitudinal factors, perceived behavioral control, and past habit to travel mode choice (e.g., Abrahamse, Steg, Gifford, & Vlek, 2009; Galdames, Tudela, & Carrasco, 2011; Thøgersen, 2006).

Personal norms may also be a factor in travel mode choice (Abrahamse et al., 2009; Bamberg, Hunecke, & Blöbaum, 2007; Nordlund & Garvill, 2003), especially among individuals who have already committed to driving less or using public transportation (e.g., Lind et al., 2015). In a market segmentation study, Anable (2005) found strong proenvironmental norms were a defining characteristic both of individuals who had intentionally given up their cars and “aspiring environmentalists” who had strong intentions to use alternative modes. A large segment of “complacent car addicts”—people who were dependent on their cars but recognized it was possible to use alternative modes—were distinguished by their lack of moral obligation and awareness of consequences; they did not perceive barriers to changing transit modes but lacked the motivation to do so.

Other evidence points to the importance of contextual factors such as distance between home and work and the availability of high quality transportation alternatives. For example, in a metaanalysis of 22 studies, Neoh, Chipulu, and Marshall (2017) found that the strongest predictors of carpooling behavior were situational factors such as employer size (which increases the pool of potential car sharers), transportation costs, reserved carpool parking, and the availability of high-occupancy vehicle lanes. In a longitudinal analysis of UK households, Clark, Chatterjee, and Melia (2016) found that car commuting patterns were fairly stable over time; commute mode changes primarily occurred when changing jobs or residences resulted in a shorter commute or improved access to public transport. Under these circumstances, individuals with stronger environmental attitudes were more likely to switch modes of transportation. Verplanken, Walker, Davis, and Jurasek (2008, p. 125) suggest that such contextual changes “can activate ecological values and beliefs, which thus guide the process of (re)negotiating proenvironmental behaviors.” More research is needed to understand whether travel mode preferences factor into larger life decisions such as choice of job or residence, or whether travel behavior simply follows from them.

6.3.4.1 Household curtailment behaviors

As there have been several previous reviews of the determinants of household energy use reductions and the types of interventions that are effective at promoting them without technological change (e.g., Abrahamse et al., 2005; Delmas et al., 2013; Steg, 2008; Stern, 1992), this review is brief. Research suggests that individuals typically lack accurate knowledge of how much energy different activities in their homes use (Kempton & Montgomery, 1982; Mizobuchi & Takeuchi, 2013). When asked to estimate the energy consumption associated with different actions, most people overestimate the benefits of highly visible behaviors such as turning off lights while underestimating the impact of appliances, electronics, and hot water usage (Attari et al., 2010). Many interventions have consequently focused on making energy use more salient and transparent. In particular, providing feedback on total household energy consumption or savings has proved to be an effective strategy, delivering 5%–12% in energy savings depending on how the feedback is given (Fischer, 2008; Karlin et al., 2015). When provided frequently (e.g., real-time, daily, or weekly), feedback on energy usage can make individuals more aware of their consumption, prompt conservation behavior, and help them learn the relative impact of different actions (Abrahamse et al., 2005; Darby, 2001; Faruqui, Sergici, & Sharif, 2010; Fischer, 2008; Grønhøj & Thøgersen, 2011; Tiefenbeck et al., 2016; Winett, Neale, & Grier, 1979). The effects on energy consumption may be amplified if the feedback is given in combination with a conservation goal (Abrahamse, Steg, Vlek, & Rothengatter, 2007; Becker, 1978; McCalley & Midden, 2002) or price signals related to electricity price changes (Newsham & Bowker, 2010). Home energy reports that compare a household’s energy consumption to its neighbors are also generally effective in US studies (Allcott, 2011b; Ayres et al., 2013), though more so for political liberals than conservatives (Costa & Kahn, 2013b). Two metaanalyses suggest, however, that comparative feedback results in lower savings compared to other types of feedback (Karlin et al., 2015) or information (Delmas et al., 2013).

Other research has examined the underlying motivations for engaging in curtailment behaviors. Using voter registration and utility data, Costa and Kahn (2013a) found evidence that California households with liberal political party affiliations use less energy than conservatives living in comparable homes. This finding may reflect the tendency of liberals to have stronger proenvironmental values and attitudes (McCright, 2011). Strong proenvironmental norms have been found to predict self-reported curtailment behaviors such as shorter shower times (van der Werff & Steg, 2015) and lower hot water temperatures (Black et al., 1985). Other evidence suggests, however, that the effects are indirect: Environmental norms and beliefs do not explain added variance in household energy consumption if attitude measures from TPB are controlled (Abrahamse & Steg, 2009).

Several studies suggest that interventions are more effective at encouraging energy conservation if they call on prosocial motives rather than financial ones. In a series of multi-month field experiments, Asensio and Delmas (2015, 2016) demonstrated that providing feedback about the environmental and public health consequences associated with a household’s energy use was more effective at reducing consumption than messages about potential monetary savings. Households in the prosocial treatment conditions reduced energy consumption by 8%–10% compared to control groups, whereas the monetary framings did not result in significant savings. A recent meta-analysis suggests that monetary information may even be detrimental, causing households to increase energy consumption (Delmas et al., 2013).

6.3.4.2 Thermostat settings

Past research suggests that consumers underestimate the energy savings achievable through thermostat setbacks (Attari et al., 2010). Misconceptions about how home heating and cooling systems operate may also keep households from conditioning their homes efficiently (Peffer, Pritoni, Meier, Aragon, & Perry, 2011; Pritoni et al., 2015). Confirming earlier work by Kempton (1986), Pritoni et al. (2015) found that people may waste energy because they mistakenly think thermostats control indoor temperatures like the knob on a gas stove: By setting the temperature higher, they expect their homes to warm up faster. Other misconceptions include believing that the thermostat sets the temperature of the air coming out of the system, and that it takes more energy to bring a home back to a desired temperature after a setback period than to heat it at a constant temperature.

Confusion about heating controls themselves may also be a barrier to effective action. Programmable thermostats—which were introduced to the market to help automate setbacks—have grown increasingly complex, allowing users to set schedules for different days of the week and for multiple times of the day. Evidence indicates that programing features are often underutilized or overridden, as many do not know how to change settings or are afraid to do so for fear of overheating or overcooling their homes (US EIA, 2013a; Nevius & Pigg, 2000; Peffer et al., 2011; Pritoni et al., 2015; Sachs et al., 2012). Research in the United Kingdom suggests that households with radiators face similar frustrations as the settings on thermostatic radiator valves are not calibrated to actual temperatures (Caird et al., 2008).

Among households who report regularly setting back their thermostats, a sense of moral obligation appears to be a primary motivator (Black et al., 1985; Wolske, 2011). Studies have also examined beliefs about the benefits and consequences of setting back thermostats. Not surprisingly, households are less likely to adjust their thermostats if concerned about thermal comfort (Becker, Seligman, Fazio, & Darley, 1981; Pedersen, 2008; Wolske, 2011). The inconvenience of remembering to adjust the thermostat or learning to use its programing features may also be a deterrent, though this may be less of a barrier for individuals who have favorable attitudes toward energy conservation (Nevius & Pigg, 2000). While evidence is scarce, believing that thermostat adjustments could save money does not appear to prompt changes in behavior (Black et al., 1985).

A number of contextual variables may influence heating and cooling choices. Several economic studies have shown, for example, that renters use more energy for space conditioning when utilities are included in their rent than when they pay for utilities directly (Gillingham, Harding, & Rapson, 2012). In rentals and owner-occupied homes alike, households whose members have different schedules and comfort preferences may struggle to maintain a setback routine or to find an agreeable temperature (Karjalainen, 2007; Pritoni et al., 2015).

Though few interventions have specifically targeted space-conditioning behaviors, some studies indicate that encouraging households to set specific conservation goals and providing detailed information about potential energy savings or emissions reductions could encourage greater setback behavior (McCalley & Midden, 2003; Wolske, 2011). Additional research is needed to understand the long-term efficacy of these strategies.