The Energy Paradox Revisited: Analyzing the Role of ... · have addressed the role of...

ORIGINAL PAPER

The Energy Paradox Revisited: Analyzing the Roleof Individual Differences and Framing Effectsin Information Perception

Samdruk Dharshing1 & Stefanie Lena Hille1

Received: 15 December 2016 /Accepted: 8 September 2017 /Published online: 18 October 2017# Springer Science+Business Media, LLC 2017

Abstract In the ongoing debate about the Benergy paradox^, a recent stream of literaturehighlights the importance of behavioural anomalies such as bounded rationality and self-control problems. However, the role of individual-level factors in explaining the energyparadox is still not fully understood. Combining literature on behavioural anomalies andconsumer heterogeneity, the current paper analyses how individual differences influencethe perception of energy-related information and susceptibility to choice-framing effects.A choice-based conjoint experiment about energy-saving home improvements was con-ducted with 363 homeowners in Switzerland. Results show that numeracy and energyliteracy have no influence on how much attention individuals pay to energy cost savings.However, impulsivity and risk aversion are found to significantly impact homeowners’weighting of future energy cost savings. Further, it is found that impulsive homeownersare significantly more susceptible to energy cost-framing effects. A key implication forconsumer policy is that general educational programs targeted at enhancing citizens’knowledge and cognitive abilities are unlikely to increase energy conservation invest-ments. The findings further suggest that consumer policies and business models aimed atreducing impulsiveness and influencing risk perception might foster the uptake ofenergy-saving measures in the residential housing sector.

Keywords Household behaviour . Energy conservation . Numeracy . Energy literacy .

Time preferences . Cost framing . Consumer policy

J Consum Policy (2017) 40:485–508DOI 10.1007/s10603-017-9361-0

* Samdruk [email protected]

* Stefanie Lena [email protected]

1 University of St. Gallen, IWO-HSG, Tigerbergstr. 2, 9000 St. Gallen, Switzerland

http://crossmark.crossref.org/dialog/?doi=10.1007/s10603-017-9361-0&domain=pdf

mailto:[email protected]

mailto:[email protected]

Research in energy economics and policy has long debated the existence, potential reasons for,and consequences of the so-called energy paradox (Gillingham and Palmer 2014; Hausman1979; Jaffe and Stavins 1994). This well-known puzzle refers to consumers’ apparent Bunder-valuation^ of energy cost savings, leading to low adoption rates of energy conservation andefficiency measures, even when the net present value of future operating cost savings morethan offsets the initial investment (e.g., Bento et al. 2012; Jaffe and Stavins 1994). Tradition-ally, literature that has attempted to explain the divergence between the efficiency potentialidentified by technical-economic models and empirically observed adoption levels has focusedon analysing market failures, such as principal–agent problems, information failures, creditconstraints, or transaction costs (e.g., Brown 2001; Gillingham et al. 2009; Jaffe and Stavins1994; Koomey and Sanstad 1994; Levine et al. 1995; Linares and Labandeira 2010). Accord-ingly, the standard policy approach for addressing the energy paradox involves analysing andmitigating market failures (Ramos et al. 2015), for example, through Pigouvian taxation(Linares and Labandeira 2010) or the provision of information to consumers (Abrahamseet al. 2005; Geller et al. 2006). However, a more recent stream of literature suggests that theassumptions underlying the traditional model of market failure should be relaxed to accountfor behavioural anomalies and failures, such as bounded rationality or loss aversion(Gillingham et al. 2009; Gillingham and Palmer 2014; Howarth and Sanstad 1995; Ramoset al. 2015). Without diving further into the details of the vast literature on market andbehavioural failures, the current paper focuses on a specific stream of literature that analysesa set of behavioural anomalies most relevant to the provision and processing of information.While literature indicates that such behavioural anomalies are contingent on the characteristicsof the decision-maker (e.g., Parker and Fischhoff 2005; Stanovich andWest 1998), few studieshave addressed the role of individual-specific factors in consumers’ perceptions about energy-related information (for a notable exception, see, e.g., Blasch et al. 2016). The main contribu-tion of this paper, then, is to address this research gap by combining literature on consumerheterogeneity (e.g., Bento et al. 2012) with research on a number of selected behaviouralanomalies that are most relevant to the processing of energy-related information at the point ofdecision-making.

First, while energy policy measures often prescribe the provision of energyefficiency-related information to consumers, the theory of bounded rationality suggeststhat consumers tend to simplify complex decisions by selectively focusing on a subsetof information (Gabaix and Laibson 2005; Simon 1955). When evaluating alternativeoptions, Bdeliberation costs^ influence consumers’ decisions about allocating scarcecognitive resources (Conlisk 1996; Pingle 2006). Even if energy cost information isprovided, optimizing total life-cycle costs in investment decisions requires additionalinformation and computation with respect to, for example, future energy prices or theexpected intensity of usage (Blasch et al. 2016). To avoid the time and cognitiveeffort required to carry out such optimization, boundedly rational consumers mightpay less attention to energy-related information than to other, more salient productattributes and, as a consequence, might systematically under-value the cost savings ofenergy efficiency measures (Allcott and Greenstone 2012; Andor et al. 2016; Sallee2014; Turrentine and Kurani 2007). Similar to Blasch et al. (2016), we argue that theindividual-level deliberation cost of conducting a proper evaluation of energy efficien-cy depends on numeracy and energy literacy. Therefore, we expect that consumerswho lack numerical skills and energy literacy will be more prone to heuristicdecision-making and thus be less likely to take energy costs into consideration.

486 S. Dharshing, S. L. Hille

Second, in addition to bounded rationality, time-inconsistent preferences and self-control problems are another potential explanation for consumers’ apparent undervalua-tion of future energy cost savings (Gillingham and Palmer 2014). Decisions related toenergy conservation and efficiency measures usually involve a trade-off between thedisutility of investing a large sum today and the benefits of energy cost savings, whichonly materialize over time. Tsvetanov and Segerson (2013) suggest that self-controlproblems could explain why certain consumers systematically rank Btempting^ low pur-chase prices higher in importance in their choices than energy cost savings occurring in thefuture. Extending this line of research, we argue that Bimpulsive^ individuals incur higherself-control costs and thus focus less on energy cost savings in the future than lessimpulsive consumers.

Third, the materialization of future energy cost savings is characterized by technologicaluncertainty and energy price risks (Hassett and Metcalf 1993; Howarth and Sanstad 1995;Linares and Labandeira 2010). Thus, energy efficiency choices may be influenced not only bytime preferences and impulsivity but also by risk preferences (Qiu et al. 2014). In this context,it is important to note that risk aversion per se is compatible with expected utility theory,although some authors argue that consumers’ actual risk preferences are better explained bytheories about behavioural anomalies, such as loss aversion or mental accounting (for adiscussion, see, e.g., Rabin and Thaler 2001). Without further examining this theoreticaldebate, the current paper disentangles the impact of both time and risk on decision-makingby analysing whether risk-averse individuals are less likely to incorporate uncertain futureenergy costs into their choices than risk-seeking consumers.

Fourth, the theory of bounded self-interest (Mullainathan and Thaler 2000) is a behav-ioural economic concept that claims that motives unrelated to self-interest, such as pro-environmental values, can influence energy conservation and efficiency choices (e.g., Barret al. 2005). While a large number of studies have been conducted on how environmentalconcern affects energy conservation behaviour in general (e.g., Frederiks et al. 2015),relatively little research exists about the impact of such attitudes on the perception ofenergy-related information. Based on Bamberg (2003), we analyse whether environmen-tally concerned individuals are more likely to pay attention to energy savings thanconsumers that are less concerned about the environment.

Research shows that the effectiveness of providing energy-related information inclosing the energy efficiency gap depends not only on individual differences but alsoon the format in which such information is provided (Heinzle 2012; Kaenzig andWüstenhagen 2010). Such findings are mainly based on choice-framing theory, whichsuggests that non-material differences in the display of otherwise identical informationcan lead to preference changes (for a review of related studies, see, e.g., Kühberger 1998;Levin et al. 1998). In the context of energy efficiency, a popular intervention strategybased on choice framing is to enhance information disclosure by presenting lifetimeinstead of annual cost savings, a method known as Btemporal reframing^ (Deutsch 2010).While ample studies have shown that individual differences moderate susceptibility tochoice-framing effects (e.g., Lauriola and Levin 2001; Lauriola et al. 2005; Peters andLevin 2008; Shiloh et al. 2002), evidence from the field of energy efficiency is stillsparse (for notable exceptions, see, Blasch et al. 2016; Kaenzig and Wüstenhagen 2010).This paper aims to contribute to this stream of literature by analysing how consumerheterogeneity affects homeowners’ susceptibility to temporal framing effects at the pointof energy efficiency decisions.

The Influence of Consumer Heterogeneity on the Energy Paradox—Evidence... 487

Finally, this paper not only contributes to the theoretical literature about the energy efficiencygap but also has important practical implications: The empirical investigation described in thispaper focuses on residential energy efficiency measures. Studies suggest that the energy paradoxis particularly problematic in the residential housing sector, which accounts for around 18% oftotal global energy use (U.S. Energy Information Administration 2015). Conservation activitiesrelated to household energy consumption can be categorized into efficiency and curtailmentbehaviours. The former address one-off investments into conservation measures, on which thispaper focuses, while the latter refer to repetitive actions (Gardner and Stern 1996). Many scholarshave demonstrated the economic potential of residential efficiency investments (e.g., Jakob 2006).However, the empirically observed under-valuation of seemingly profitable energy-saving homeimprovements indicates that this potential is not being fully realized (e.g., Hausman 1979; Jaccardand Dennis 2006; Metcalf and Hassett 1999), suggesting that more research is required to gain abetter understanding of the energy paradox in the residential sector.

The remainder of the paper proceeds as follows: The second section provides a literaturereview, and the third section presents the methodology of the choice experiment. We presentthe results in the fourth section. The fifth section summarizes the main findings and concludesby describing implications for consumer policy.

Literature Review

Individual Differences and Information Processing

Numeracy

Most theoretical studies of energy conservation and efficiency assume that consumers calcu-late the net present value of energy cost savings or perform similar financial analyses tooptimize life-cycle costs (Sanstad et al. 1995). However, empirical studies based on the theoryof bounded rationality (e.g., Gabaix and Laibson 2005; Simon 1955) suggest that deliberationcosts induce consumers to avoid the cognitive effort of evaluating energy cost savings andfocus on other, more salient product attributes instead (Allcott 2011; Allcott and Greenstone2012; Andor et al. 2016; Sallee 2014; Turrentine and Kurani 2007). For example, Allcott(2011, p. 100) finds that 40% of American survey participants Bdid not think about fuel costs atall^ when making car purchase decisions. We extend this theory by arguing that consumers’level of numeracy determines their individual-level deliberation costs and hence the likelihoodthat they will incorporate energy cost information into their decision-making. BNumeracy^entails the ability to perform basic mathematical operations, understand ratio concepts (e.g.,fractions), and compare magnitudes of numbers (Dickert et al. 2011; Reyna et al. 2009).Research into numeracy suggests that a lack of mathematical ability leads to an inferiorprocessing of numerical information and a lower likelihood of well-thought-out decisions(Peters and Levin 2008; Peters et al. 2006). Closely related research into Bfinancial literacy^shows that a lack of financial knowledge results in suboptimal decisions, such as under-savingfor retirement (Lusardi and Mitchelli 2007b). In the field of energy efficiency, Blasch et al.(2016) find that individuals with high levels of investment literacy are less prone to boundedrationality in purchasing decisions related to energy-using durable goods. Building on thisresearch, we expect that numerical abilities positively influence homeowners’ attentiveness toenergy costs at the point of energy efficiency investment decisions.


Energy Literacy

Given that the evaluation of energy conservation measures requires a basic understanding oftechnical information, differences in Benergy literacy^ among consumers might be anotherfactor that influences perceptions of energy-related information. According to the US Depart-ment of Energy (2012), energy literacy is defined as Bunderstanding of the nature and role ofenergy in the universe and in our lives. Energy literacy is also the ability to apply thisunderstanding to answer questions and solve problems.^ DeWaters and Powers (2011) use abroader definition of energy literacy that encompasses not only knowledge but also attitudes,values, and behaviours in the fields of energy consumption and production. For the purpose ofthis study, we apply the narrower definition of energy literacy focused mainly on knowledgeand understanding. To date, few studies have examined the influence of energy literacy onchoices and behaviour related to energy conservation and efficiency. Brounen et al. (2013)demonstrate that energy literacy is low among Dutch households but is not correlated witheither attitude or household energy consumption. In contrast, Zografakis et al. (2008) find thatan energy education program in Greece leads to a significant increase in energy-efficientbehaviour among students and their parents. From a survey among US secondary students,DeWaters and Powers (2011) report that energy-related knowledge is less strongly correlatedwith energy consumption behaviour than attitudes and values. Lee et al. (2015) identify anBacceptable^ level of energy knowledge among students in Taiwan but find discrepanciesbetween affect and behaviour. Despite these pioneering empirical studies, the mechanism bywhich energy literacy influences energy efficiency choices is still not fully understood. In thiscontext, Blasch et al. (2016) suggest that possessing energy-related knowledge leads to a lowerpropensity to heuristic decision-making and thus increases the likelihood of accurate assess-ments of energy efficiency and lifetime energy costs. The authors show that an optimization-based decision strategy associated with higher levels of energy literacy positively influencesthe probability that consumers will choose energy-efficient appliances. Given the evidence thatenergy literacy facilitates the understanding and processing of energy-related information, wepredict that higher levels of literacy increase the likelihood that energy cost information will beincorporated into energy efficiency investment decisions.

Impulsivity

Evaluating the financial feasibility of energy retrofits brings the problem of intertemporalsubstitution to the fore, as the benefits of energy cost savings occur over a future time interval.

Based on the classical model of intertemporal choice, consumers are expected to discountthe costs and benefits associated with energy efficiency measures at market interest rates(Howarth and Sanstad 1995). Some authors argue that a lower Bsocial^ (or Benvironmental^)discount rate should be applied to the cost benefit analysis of environmental projects, althoughthe exact value of the discounting parameter is debated in the literature (Harrison 2010;Hausman 1979; Jaffe et al. 2004; Levine et al. 1995; Linares and Labandeira 2010;Weitzman 1998). However, the implicit discount rates for energy efficiency investments foundin a large number of empirical studies are much higher than any reasonably risk-adjusteddiscount rates described in theoretical literature, in some cases exceeding 100% (e.g., Hassettand Metcalf 1993; Hausman 1979; Howarth and Sanstad 1995; Min et al. 2014; Revelt andTrain 1998; Train 1985). One potential explanation for this phenomenon can be found inresearch from the fields of behavioural economics and psychology, which provides evidence


for a number of deviations from the standard assumptions of time-consistent preferences basedon exponential discount functions (e.g., Diamond and Köszegi 2003; Laibson 1997). Amongother factors, studies show that some consumers have time-inconsistent preferences (Strotz1955) characterized by a hyperbolic discount function, which leads to self-control problemsarising from conflicts between their present and future selves (Laibson 1997). Gul andPesendorfer (2001) propose an alternative model of self-control, in which decision-makerschoosing among sets of alternatives can resist temptation but incur disutility (self-control costs)if they do so. Research suggests that time-inconsistent preferences and self-control problemscontribute to under-investment in energy efficiency (Gillingham and Palmer 2014; Kuosmanen2005), although only a few empirical studies about the topic exist. Lillemo (2014), forexample, suggests that individuals with a greater tendency to procrastinate are less likely toengage in energy conservation activities. Relying on Gul and Pesendorfer’s (2001) model ofself-control, Tsvetanov and Segerson (2013) show that consumers are Btempted^ by the lowprices of less energy-efficient products. Building on this research, we expect that individualswith a propensity for self-control pay more attention to future energy costs, given that they aremore likely to resist the immediate Btemptation^ of low purchase prices. This paper focuses onthe underlying personality trait of impulsivity or the Bpredisposition toward rapid, unplannedreactions to internal or external stimuli without regard to the negative consequences of thesereactions to the impulsive individuals or to others^ (Moeller et al. 2001, p. 1784). Impulsivityis thus closely associated with a focus on immediate outcomes, which is expected to lead tolower levels of self-control (Wulfert et al. 2002).

Risk Preference

Some scholars suggest that the uncertainties and risks associated with the actual materialization offuture energy cost savings could provide an alternative explanation for consumers’ high implicitdiscount rates for energy efficiency investments (Hassett and Metcalf 1993). Risk and uncertaintystem from three main sources: First, the level of energy efficiency realized in actual use does notalways correspond to the performance predicted by technical analysis. Second, a technology’suseful lifetime cannot be predicted with absolute certainty (Greene 2011). Third, monetizedsavings are subject to the volatility of future energy prices (Alberini et al. 2013; Amstalden et al.2007). Given these risks, it could be expected that individual risk preferences affect decisionsrelated to large up-front investments in energy conservation measures. In the literature, two majordefinitions of risk preference can be identified: The expected utility framework defines riskpreference as the shape of a person’s utility function, which derives from a set of riskychoices—for instance, binary lottery tasks (Neumann and Morgenstern 1947).1 Another streamof literature describes risk preference as a psychometric construct that can be measured by directlyasking individuals about their agreement with a series of statements related to risks (Shapira 1995).While an individual’s standing on the continuum between risk averse and risk seeking is oftenconsidered an inherent personality trait (Fellner and Maciejovsky 2007), empirical research showsthat risk preference is domain specific (Weber et al. 2002). In the domain of energy conservationand efficiency, earlier empirical studies have shown that risk preferences influence decision-making. For example, Farsi (2010) finds that risk aversion affects willingness to pay for energyefficiency in rental apartments in Switzerland. Through use of a survey in Turkey, Erdem et al.(2010) show that risk-loving individuals are more likely to pay premiums for hybrid

1 For a discussion of alternative models of risk aversion, see e.g., Rabin and Thaler (2001).


vehicles. Using a sample of 432 homeowners from Arizona, Qiu et al. (2014) elicited riskpreferences through lottery tasks tailored to energy costs. Their key finding is that risk-averse consumers have a lower probability of adopting energy-efficient technologies thantheir risk-seeking counterparts. Building on this research, we expect that risk preferencesinfluence the degree to which individuals incorporate potentially uncertain future energycosts into their investment decisions.

Environmentalism

The theory of Bbounded self-interest^ (Mullainathan and Thaler 2000) suggests that energyconservation and efficiency decisions are influenced not only by financial motives but also byvalues and attitudes—in particular, environmentalism. Environmentalism is generally considered aworldview that describes an individual’s orientation towards nature (Dunlap et al. 2000) andbroadly encompasses a Bperson’s tendency to be concerned about the natural environment^(Bissing-Olson et al. 2013, p. 160). A wealth of empirical research has analysed the impact ofenvironmental attitude on subsequent behaviour (for a comprehensivemeta-analysis, see Kollmussand Agyeman 2002; Osbaldiston and Schott 2012). The current paper focuses on a subset ofstudies that aremost relevant to the field of energy conservation and efficiency. Earlier research thathas examined the influence of pro-environmental values, beliefs, and attitudes on energy con-sumption and conservation behaviour yields mixed results (for an extensive literature review, seeFrederiks et al. 2015). Frederiks et al. (2015, p. 598) identify a Bsizable discrepancy between ‘goodintentions’ and actual behaviour,^ but they also note that evidence is highly domain specific withrespect to the energy-related practices in question. In this context, Barr et al. (2005) suggest that adistinction needs to be made between Bhabitual actions^ concerning everyday energy usage andpurchasing activities related to one-off technology choices. In the domain of Bhabitual^ actions,studies have shown that energy use is more strongly determined by sociodemographic factors thanby environmentalism or similar psychological constructs (e.g., Gatersleben et al. 2002; Poortingaet al. 2004). Nevertheless, some authors find that environmental values and attitudes positivelyinfluence energy conservation behaviour (Gadenne et al. 2011; Martinsson et al. 2011; Sapci andConsidine 2014) and on changes in energy usage (Abrahamse and Steg 2009). Similarly, studies inthe domain of purchasing activities show that environmental concern leads to a higher likelihood ofpurchasing energy efficient appliances (Barr et al. 2005) and implementing residential energyefficiencymeasures (Sütterlin et al. 2011).While most of these studies focus on identifying a directrelationship between environmental concern and behaviour, literature about the influence ofenvironmental concern on the perception of energy-related information is scant. In this context,Bamberg (2003) suggests that environmental concern as a general attitude influences specificbehaviour not directly but indirectly through changes in the perception and evaluation of asituation. In line with this theory, Alberini et al. (2013), in a choice experiment about residentialenergy efficiency measures, find that individuals who are concerned about climate change putsignificantly more decision weight on energy cost savings than other participants. Similarly, weargue that that environmentally concerned individuals will focus more strongly on energy savingsat the point of decision-making than less environmentally concerned people.

Information Framing Effects

A large stream of literature has analysed behavioural intervention strategies for influencingconsumer behaviour related to energy conservation and efficiency (for a review, see


Abrahamse et al. 2005). A popular intervention strategy designed to enhance the disclosure ofenergy efficiency information is to display lifetime instead of annual energy cost savings(Deutsch 2010). Empirical evidence shows that this tactic significantly increases willingness topay for energy-efficient products (Bull 2012; Kaenzig and Wüstenhagen 2010), though theeffectiveness of the strategy seems to depend on the product in question (Kallbekken et al.2013). This temporal reframing approach is based on choice-framing theory, which suggeststhat individuals change their preferences when otherwise identical information is presenteddifferently (for a review of related studies, see, e.g., Kühberger 1998; Levin et al. 1998). Afurther explanation for the impact of displaying lifetime costs on consumers’ choices ofenergy-using durables is the Bpeanuts^ or Bpennies-a-day^ effect (Gourville 2003;Markowitz 1952), according to which individuals fail to consider how small amounts ofmoney add up over time. Extending prior literature, we investigate whether individualdifferences among consumers explain discrepancies in reactions to temporal reframing.

Earlier research indicates that having strong mathematical abilities leads to superior pro-cessing of numerical information and reduced susceptibility to framing effects (Peters andLevin 2008; Peters et al. 2006). The main explanation for this finding is that highly numerateindividuals are more likely to be able to process, transform, and compare numbers presented indifferent frames, while the less numerate rely more strongly on the salient informationprovided by a single frame (Peters and Levin 2008). While individuals who lack numericalcapabilities may not pay attention to annual energy costs due to the cognitive effort required toperform calculations (Blasch et al. 2016), we expect that providing Bready-made^ life-cyclecost information through temporal reframing will increase their attentiveness towards energycosts. Therefore, we argue that less numerate individuals are susceptible to temporal reframingeffects when the same energy costs are presented as lifetime costs. However, we predict thatthis change in information display will have no impact on the choices of numerate consumers,who are able to assess the total life-cycle costs in both cases.

Presenting total life-cycle costs instead of annual cost savings increases the magnitude of themonetary amounts displayed. In this context, the magnitude bias describes the tendency ofindividuals to be more patient in the hope of obtaining a larger reward—a well-documentedphenomenon in empirical research (e.g., Benzion et al. 1989; Chapman and Winquist 1998;Green et al. 1997; Green et al. 2003; Hardisty et al. 2013; Loewenstein and Prelec 1992). Weexpect that displaying the increased Bmagnitude^ of lifetime cost savings will help impulsiveindividuals better understand the Btrue^ economic costs of a decision and broaden their timehorizon. Research shows that imagining the economic costs of decisions is one of the mostimportant self-control mechanisms for decreasing impulsive behaviour (Hoch and Loewenstein1991). Thus, we argue that impulsive individuals are likely to paymore attention to energy costswhen they are displayed as lifetime cost savings. In contrast, we expect that temporal reframingwill have no influence on the information perception of non-impulsive individuals.

Methodology

Procedure and Experimental Design

The object of the analysis was to assess decisions of homeowners regarding investing in anenergy efficiency home renovation that was expected to generate future energy cost savings.The study used a between-subjects design with two experimental groups.


Procedure

Before starting the questionnaire, all participants were required to read through a detaileddescription of the experiment. A computer-based selection system randomly sorted participantsinto two experimental groups. The design of the two experimental groups differed only in thedisplay of Benergy cost savings,^ with no changes in the set of other attributes and levels.

Experimental Design

The methodology chosen for this study was a choice-based conjoint analysis carried outthrough an online experiment. The basic nature of choice experiments is to Bunbundle^products into their individual components (Green and Srinivasan 1990). As not all Bproduct^characteristics will be optimal in any given choice, individuals need to weigh one attributeagainst another (Moore et al. 1999). The key advantage of choice experiments is that they elicitpreferences indirectly by enabling participants to select among different attributes and levels.Multiple studies in the environmental field have also used choice experiments (e.g., Alberiniet al. 2013; Heinzle and Wüstenhagen 2012; Ölander and Thøgersen 2014; Tabi et al. 2014).

To test the research questions in a straightforward way, we chose a simplified decisionenvironment that focused on the key attributes of interest. Careful definition of attributes andlevels is crucial for the design of a choice experiment. To identify attributes and levels that areboth realistic and relevant, we conducted two interviews with experts from municipal energyagencies. After setting up an initial version of the experiment, we administered two testexperiments (with 10 and 30 participants, respectively). The feedback from the test participantsserved as input for further refinement of the experiment. The inputs from experts and testexperiment participants revealed that four key attributes were most relevant to homeowners’decision-making (Binvestment costs,^ Bgovernment subsidy,^ Benergy cost savings,^ andBthermal comfort^). Therefore, we used these four attributes in the study, each with three levelscorresponding to a realistic investment environment. The up-front investment costs were set at55 000, 70 000, or 85 000 Swiss Francs (CHF). The attribute government subsidy denoted thesize of benefit provided by the government to incentivize the modernization of building stockand equalled 5000, 10 000, or 15 000 CHF. The attribute energy cost savings was set at 1000,1500, or 2000 CHF per year for experimental group 1 and at 20 000, 30 000, or 40 000 CHFover a period of 20 years for experimental group 2. Participants in experimental group 1 alsoreceived the information that the measures were expected to lead to savings over an averagelifetime of 20 years. The final attribute of this choice experiment was the expected improvementin thermal comfort, which entailed either a Bslight increase^ or a Blarge increase.^

After defining the attributes and the levels of the choice experiment, we bundled all factorsinto a set of choice tasks. In every choice task, participants had to choose among three differenthome improvement projects, all of which were associated with different investment costs,government subsidies, energy cost savings, and levels of thermal comfort. Participants wererequired to make a choice between the different projects, assuming that the options differedonly in the specified attributes, with all other characteristics of the renovation projects beingidentical. Use of a full factorial design (one that includes all potential combinations of theaforementioned attributes) was not feasible for reasons of practicality, including the risk ofrespondent fatigue and time constraints. We therefore opted for a fractional factorial designwith 13 choice tasks and three concepts shown per choice task to reduce the cognitive burdenon participants. The randomized choice tasks were based on the principles of minimal overlap/


complete enumeration. In our case, this meant that each attribute level appeared exactly once inevery given choice task (Chrzan and Orme 2000). The principle of Blevel balance^ ensuredthat across all 13 choice tasks, all attribute levels were presented with equal frequency. Weconstructed six different versions of the questionnaire to rule out the possibility that the orderof the choice tasks might influence the responses. A t test revealed that the version of thequestionnaire did not have any significant influence on participants’ preferences.

Measures

Numeracy

The first three numeracy questions tested the ability of participants to calculate compoundinterest and inflation rates, similar to previous studies (e.g., INFE 2011; Lusardi and Mitchell2011; Lusardi et al. 2010; Van Rooij et al. 2011). A further numerical/financial question wasadded that tested participants’ ability to calculate the amortization of an energy-using product.We added up the test scores per respondent (one point for each correct answer) to form anumeracy scale, which ranged from 0 (no right answers) to 4 (all answers correct). AppendixTable 3 provides a detailed description of the items used.

Energy Literacy

We used a self-developed energy literacy scale because many questions related to energyproduction and usage are highly country specific (see Appendix Table 4). Three of thequestions used in the energy literacy scale were connected to the theme of household energyconsumption, and the fourth question addressed the more general topic of the Swiss electricitymix. We added up the number of correct answers to form a scale that ranged from 0 to 4.

Impulsivity

The Barratt Impulsivity Scale is the most widely used psychometric measure of impulsivity as apersonality trait (Patton and Stanford 1995). While the original scale includes 30 items, it wasnot possible to conduct the full test because of time constraints. We chose five items related tothe sub-scale of Bnon-planning impulsivity,^ which involves careful, future-oriented planning(or lack thereof). Appendix Table 5 provides a detailed description of the items used. This typeof impulsivity can be important in the evaluation of future energy costs. Participants reportedtheir agreement with statements that described their own impulsivity using a five-point Likertscale ranging from 1 (Bstrongly disagree^) to 5 (Bstrongly agree^). We added up the scores ofeach respondent to form a scale.

Risk Preference

Following the approach of Weber et al. (2013), we measured risk preference with three self-reported questions related to financial risk-taking, which form a psychometric scale. Weberet al. (2013) argue that this scale of investors’ attitudes can predict actual risk-taking behaviour.All questions were answered on a five-point Likert scale with the endpoints 1 (stronglydisagree) and 5 (strongly agree). We added up the scores of each respondent to form a scale(see Appendix Table 6).


We split the Likert scales for each of the constructs into three equal-sized categories, andthen, following the approach suggested by methodological studies, omitted the Bmedium^-level groups (e.g., Hernández et al. 2004). The main rationale is that Bmiddle-ranking^responses, such as Bundecided,^ Bit depends,^ or Bneither,^ cannot readily be interpretedcompared with more extreme responses. Inclusion of only the groups of participants with thelowest and highest manifestations of each characteristic (e.g., high impulsivity vs. lowimpulsivity) helps create a clear-cut division between participants and eases the interpretationof results (Gelman and Park 2012).

Environmentalism

Based on the approach of Heinzle (2012), the questionnaire included a question about theparticipants’ self-assessed level of environmental friendliness on a scale from 1 (Bvery low^) to5 (Bvery high^). For a methodological discussion of the advantages and disadvantages ofsingle- vs. multi-item measures of constructs, see Bergkvist and Rossiter (2007).

Control Variables

The analysis included data on participants’ age, gender, education, household size, andgross household income to control for individual demographic characteristic. We alsocontrolled for building age and size, as well as the type of heating system installed athome. A dummy variable indicated whether the house was owner-occupied. Furthermore,we included a question about participants’ expectations regarding the development offuture heating oil prices.2

Sample Selection and Representativeness

The process of contacting potential participants by e-mail was handled by Intervista, aprofessional Swiss market research agency. Intervista is certified by ISO 26362, which ensuresthe implementation of high-quality standards in panel maintenance and survey methods.Intervista randomly draws samples from a panel of more than 55 000 actively recruitedregistered panellists across all regions of Switzerland (i.e., self-registration for the panel orfor individual surveys is not possible).

Data quality issues caused by incentive payments are a methodological concern in onlineexperiments conducted by commercial institutions. To mitigate this problem, an additionalquality check was implemented to identify Bfraudulent^ participants.3 This check led to 37potential participants being eliminated from the experiment.

Intervista contacted randomly chosen panellists by e-mail to ask them to participate in theonline experiment. Two screening questions were implemented to select the final sample: Aninitial screening question at the beginning of the questionnaire identified owners of single- ormulti-family homes in Switzerland; non-homeowners were screened out. Participants were

2 The survey included another question on expectations of future gas prices. However, we do not include this inthe analysis because of multicollinearity concerns (significant bivariate correlation coefficient).3 To identify such fraudulent participants, the questionnaire contained a simple check of participants’ attention inthe form of the following statement: BThis is a little test to check whether you are paying attention. Please selectthe word ‘energy’ from the following list.^ Four response options were given (Benergy ,̂ Benvironment^,Bbuilding^, and Bpolicy^).


asked in a second screening question whether they would consider implementing a renovationproject within the next 10 years. Forty-six homeowners who stated that they would definitelynot consider any renovation were thereby eliminated. The main reason for focusing exclu-sively on homeowners planning to undertake a thermal retrofit of their house within the nextten years was to make the questions related to investment decisions about energy-saving homeimprovements more realistic. The final sample consisted of 363 homeowners.

The key strength of our sample is its focus on real homeowners who expressed a genuineinterest in energy efficiency measures, which ensures a realistic decision environment. How-ever, given that we used an online experiment, the sample may not be representative of theoverall population of homeowners in Switzerland who plan to undertake a retrofit within thenext 10 years. Nevertheless, for the purpose of eliciting the preferences of our target group,representativeness is not a crucial issue.

Table 1 includes the descriptive statistics of the sample, including a comparison with theofficial demographic statistics of Switzerland (when applicable). The home ownership rate inSwitzerland is relatively low compared with that of other European countries (under 40% in20154), and therefore the demographic profile of homeowners differs from the averagepopulation. This explains the relatively high average age, as well as the substantial proportionof male participants and the over-representation of higher-income quintiles in the sample. Thedemographic profile of the study is similar to the sample characteristics in the work of Alberiniet al. (2013), which also involved an experiment among Swiss homeowners.

Pearson correlation analysis tested for multicollinearity. Household size was significantlycorrelated with age in our sample and therefore excluded to mitigate multicollinearity. Therewere no multicollinearity issues for the other variables.5

Results

Two-way factorial analyses of covariance (ANCOVAs) served to analyse the interactioneffects between the framing of energy cost information and consumer characteristics. Thedependent variable in each regression procedure was the importance score for energy costsavings. This score measured the relative weight that participants award to specific attributes asdecision criterion compared with the other attributes. It can thus be interpreted as a measure ofconsumers’ attentiveness towards energy cost savings in energy efficiency investments. Wecalculated the importance scores for the attributes using the Hierarchical Bayes (HB) method,based on 13 choice tasks per respondent. The HB method allows individual-level estimationsof part-worth utilities to be made, unlike aggregate estimation methods such as multinomiallogit (for an in-depth discussion of the HB methodology, see Allenby and Rossi 2003; Rossiand Allenby 2003).

We included the consumer characteristics under investigation (namely, numeracy, energyliteracy, impulsivity, and risk preference) as independent variables in order to analyse whetherthey affect the individual decision-makers’ weighting of future energy cost savings in retrofitinvestment decisions. The second objective of the regression analyses was to investigatewhether temporal reframing influences the perception of future energy costs, and if so, whetherthis effect depends on impulsivity or numeracy. Therefore, the framing of the energy cost

4 https://www.bfs.admin.ch/bfs/de/home/statistiken/bau-wohnungswesen/.5 The multi-collinearity table is not included here for reasons of brevity.


https://www.bfs.admin.ch/bfs/de/home/statistiken/bau-wohnungswesen

Table 1 Descriptive statistics of the sample

Sample characteristics Official Swiss statistics

Mean Std.dev.

Frequency In%

Importance scoreenergy cost savings

23.1 10

Gender Male 247 68.0 49.5Female 116 32.0 50.5

Age 56.6 11.5 41.99Education No information 1 .3

Elementary school 1 .3Secondary school,

vocational training,or equiv.

111 30.6

High school or equiv. 52 14.3University of applied

sciences or equiv.110 30.3

University or equiv. 55 15.2Doctorate/PhD/MBA 33 9.1

Gross householdincome

0 = no information 48 13.2Below 4880 CHF 14 3.9 Categories correspond

to the quintiles ofgross householdincome in Switzerland

4880–7173 49 13.57174–9702 CHF 80 22.09703–13170 CHF 105 28.9Over 13 170 CHF 67 18.5

Building size 187.7 134.6Building age 29.6 20.9Heating system Oil 120 33.1

Wood 24 6.6Heat pump 98 27.0Electricity 23 6.3Gas 69 19.0Teleheating 3 .8Solar thermal 4 1.1Other 21 5.8Don’t know 1 .3

Energy priceexpectation

Strong decrease 5 1.4Slight decrease 38 10.5Same as today 54 14.9Slight increase 154 42.4Strong increase 112 30.9

Owner-occupied Yes 346 95.3No 17 3.9

Numeracy Low 138 38.0Medium 122 33.6High 103 28.4

Energy literacy Low 175 48.2Medium 112 30.9High 76 20.9

Impulsivity Low 110 30.3Medium 124 34.2High 129 35.5

Risk preference Low 98 27.0Medium 161 44.4High 104 28.7

Environmentalscale

Low 12 3.3Average 97 26.7High 225 62.0Very high 29 8.0


savings (either per year or over 20 years) was incorporated as the first fixed factor in each ofthe regressions. In addition, either impulsivity or numeracy was included as the second factor.Furthermore, the covariates mentioned in the methodology section (e.g., demographic andbuilding variables) were included in the analyses.

We present the estimation results of the two-way factorial ANCOVAwith the framing effect (peryear/over 20 years) as the first factor and impulsivity as the second factor (low/high) in Table 2.

The variables numeracy and energy literacy are not significant at the 5% level. Thus,individuals with strong numerical skills or more energy-related knowledge are not more likelyto attach value to energy cost savings than other participants. The variable Bimpulsivity^ issignificant (F(1, 218) = 4.672, p = .032), which provides support for the hypothesis that lessimpulsive homeowners value future energy cost savings more highly than their more impul-sive counterparts. Risk preference has a significant influence (F(1, 218) = 10.645, p = .001) onhow much importance the participants attach to future energy cost savings. In agreement withthe findings of Alberini et al. (2013), we find that expectations of future energy costssignificantly alter the perceived importance of energy cost savings (F(1, 218) = 3.966,p = .048).

Furthermore, the results in Table 2 suggest that demographic variables, as well as buildingcharacteristics, have no significant influence on the importance awarded to annual energy costsavings in thermal retrofit investment decisions (p > .05). Similarly, the type of heating systemis not statistically significant at the 5% level. Finally, environmentally concerned citizens arenot more likely to pay attention to the attribute energy cost savings than individuals who careless about the environment.

We find no statistically significant average effect for temporal reframing. In the analysis, theenergy frame itself has no influence on the relative importance of energy cost savings as anattribute (F(1, 218) = 1.291, p = .257). At first glance, the findings seem to contradict theresults of previous studies that suggest that displaying life-cycle energy costs induces con-sumers to opt for the more energy-efficient option (e.g., Deutsch 2010; Kaenzig andWüstenhagen 2010). However, the results show that the interaction between the energy frameand impulsivity is statistically significant (F(1, 218) = 4.959, p = .027). Therefore, whether theframing has a significant effect on the importance awarded to energy cost savings depends onthe individuals’ degree of impulsivity: The perception change induced by changing the timehorizon of the information display is stronger for impulsive than for less impulsive individuals.

Figure 1 depicts the estimated marginal means of energy cost savings for the significantvariable impulsivity. More specifically, the figure shows the mean of the importancescores awarded to future energy cost savings (i.e., how much attention each group paidto this specific attribute). In addition, the graph shows how impulsivity interacts with thetime-framing effect: For participants with low levels of impulsivity, changing how energycosts are displayed does not alter decision making. However, highly impulsive individualspay significantly more attention to future energy cost savings if they are presented with atime horizon of 20 years.

Similar to the interaction between cost framing and impulsivity in Table 2, we posited thatthere might be significant interaction between numeracy and the energy cost frame. To test forthis effect, we carried out a separate two-way factorial ANCOVA, which included the framingeffect as the first factor and numeracy as the second factor. Results show that there is nostatistically significant interaction between this construct and the energy cost frame.6

6 Results not reported here for reasons of brevity.


Discussion and Conclusions

The energy paradox denotes consumers’ disproportionate focus on the up-front investment costs ofenergy conservation measures, even if future operating cost savings more than offset higher initialinvestments. This paper extends previous research on the energy paradox by analysing the impactof individual consumer characteristics and framing effects on the perception of energy-relatedinformation. The study yields three key findings and implications, which we summarize next.

First, we find no influence of general numeracy and energy literacy on how much attentionhomeowners pay to future energy savings. This suggests that consumers’ low weighting ofenergy cost savings is not influenced by their ability to perform basic calculations or by a lackof knowledge about energy-related issues. In a similar vein, we find that self-identifiedenvironmentalists are not more likely to pay attention to the attribute energy cost savings thanindividuals who care less about the environment. These findings contribute evidence thatsupports the existence of the empirically observed gap among knowledge, favourable attitudes,and actual decision-making in energy conservation and efficiency decisions.

Second, the results suggest that impulsive consumers pay significantly less attention tofuture energy savings than less impulsive individuals. This finding could contribute toexplaining the aforementioned knowledge–behaviour gap, given that even well-educatedindividuals may neglect future energy-operating costs in their investment decisions becauseof a preference for immediate gains (i.e., low up-front costs). The analysis also finds that re-framing the time horizon by presenting total life-cycle instead of annual cost savings

Table 2 Results of two-way ANCOVA—between-subject effects for the impact of individual-level differencesand cost framing on the perception of energy cost savings

Sum of Squares df Mean square F Sig.

Corrected model 3352.066a 16 209.504 2.213 .006Intercept 1032.771 1 1032.771 10.908 .001 ***Energy cost framing 122.235 1 122.235 1.291 .257Energy cost framing ∗ impulsivity 469.512 1 469.512 4.959 .027 **Gender 200.508 1 200.508 2.118 .147Age .109 1 .109 .001 .973Education 3.048 1 3.048 .032 .858Income 28.842 1 28.842 .305 .582Building size 9.175 1 9.175 .097 .756Building age 158.272 1 158.272 1.672 .197Heating system 117.942 1 117.942 1.246 .266Price expectation 375.525 1 375.525 3.966 .048 **Owner-occupied 266.847 1 266.847 2.818 .095Numeracy 311.998 1 311.998 3.295 .071Energy literacy 4.294 1 4.294 .045 .832Impulsivity 442.347 1 442.347 4.672 .032 **Risk preference 1007.845 1 1007.845 10.645 .001 ***Environmental scale 90.956 1 90.956 .961 .328Error 20,640.356 218 94.681Total 146,590.687 235Corrected total 23,992.422 234

Dependent variable: importance score of future energy cost savingsaR2 = .140

***Significant at 1% level

**Significant at 5% level


significantly increases the weight that impulsive individuals award future energy costs.Therefore, increasing the magnitude of the presented costs seems to reduce impulsivity andincrease the likelihood of opting for the more energy-efficient option.

Third, the study suggests that risk-averse participants award significantly lessimportance to future energy cost savings than their risk-seeking counterparts, andthus, are less inclined to trade off higher up-front costs for lower energy operatingcosts. This result may be explained by the understanding that future rewards alwaysinvolve a degree of risk and uncertainty that stems from, for example, fluctuatingenergy prices or technological issues.

In terms of implications for the broader academic literature on consumer behaviour andenergy efficiency, it is important to note that the purpose of this study was not to argue whetherthe potential under-valuation of energy cost savings by consumers is rational. Rather, theresults suggest that ignoring consumer heterogeneity might lead to a biased view of the energyefficiency gap.

The findings of this study also have significant practical implications, both for con-sumer policy and for business. Many policy programs focus on educating consumers aboutenergy conservation based on the implicit assumption that a higher level of knowledge andfavourable attitudes lead to behaviour changes. However, considering the knowledge–behaviour gap, as well as the attitude–behaviour gap found in our study, it seems unlikelythat general education programs will lead to the diffusion of energy-saving technologies.Therefore, in line with Abrahamse et al. (2005), we recommend that consumer policymove away from applying generic information strategies and focus on more targetedbehavioural interventions that rest on a thorough problem diagnosis. The results of thisstudy show that consumer preferences for immediate gains rather than long-term costsavings might be impeding the adoption of energy-efficient technologies. This finding

Fig. 1 Estimated marginal means for the importance scores of energy cost savings by impulsivity andexperimental condition


suggests that consumer policy should focus on helping consumers to widen their timehorizon and on implementing measures to reduce impulsivity at the point of decision-making. For example, policy-makers and businesses could collaborate to develop im-proved energy labels and building certification schemes that magnify either the futurecosts of present-biased actions or the benefits of waiting for future rewards. Anotherpossible intervention could be to encourage homeowners to engage in episodic futurethinking, for example, through visualization tasks, in which they envision the futurebenefits of energy cost savings (Peters and Büchel 2010). We leave the analysis of suchbehavioural interventions to future research in the field of consumer policy. In addition tobehavioural strategies, another policy idea for overcoming impulsivity is to promoteBpay-as-you-save^ schemes, in which energy retrofits are (partly) financed throughinterest-free public loans that are repaid by the homeowners through energy bills(Bioregional 2011). Such schemes eliminate up-front investment, which appears to bea key barrier to improving energy efficiency among impulsive homeowners, althoughempirical evidence of the effectiveness of such programs has been inconclusive (Pettiforet al. 2015).

This study suggests that, in addition to time preference, risk preference may be anotherbarrier to investment in profitable energy conservation measures. In terms of practical impli-cations, this finding suggests that there is market potential for performance-contractingbusinesses that typically eliminate uncertainty by guaranteeing contractually agreed-on costsavings. For example, energy service companies (ESCOs) that finance, develop, install, andoperate energy efficiency projects could not only mitigate issues related to energy price andtechnology risks but also reduce the up-front cost of energy conservation measures (Vine2005). However, most ESCOs have not yet targeted the residential household sector, mainlybecause of the difficulty of attracting private investors for small-scale projects. Consumerpolicy could play an important role in facilitating the growth of the ESCO sector, for exampleby setting up private–public partnerships or establishing quality control and certificationschemes (Marino et al. 2011). We leave the analysis of potential growth strategies and policysupport schemes for ESCOs and similar performance-contracting schemes in the residentialsector to future research.

As with any study, the current analysis is subject to some limitations that can be thestarting point for further research. The main limitation of the study is that the empiricalresearch is based on stated-preference analysis. By selecting relevant attributes from in-depth expert interviews and focusing on a sample of real homeowners, we have attemptedto provide a realistic setting for the experiment. Nevertheless, it would be worthwhileconducting research on revealed preferences. While the current study controlled forincome and also screened out participants who would be unable to afford a homeimprovement project, the debt levels of homeowners were not considered in the analysis.This is another potential limitation, given that high levels of borrowing may generate anaversion to efficiency investments or might be correlated with impulsivity. In addition tothis, the measurement of the psychometric constructs (in particular, the measures forimpulsivity, risk preference, and environmentalism) is based on participants’ self-assess-ments. To mitigate concerns about construct reliability, robustness, and validity, we onlyused scales that have been previously tested and published in peer-reviewed articles, suchas the Barratt Impulsiveness Scale. Nevertheless, it is important to acknowledge that thesescales are subject to the usual methodological limits of self-reported measures. Anotherwell-known challenge with the measurement of time and risk preferences is that both


concepts are difficult to disentangle, given that decisions concerning the future are ofteninherently uncertain. Therefore, a promising area for future work would be to replicate ourstudy using different constructs and item measures.

Moreover, some methodological limitations apply to the sample. We recruited participantsvia an online access panel and therefore, the sample might not be fully representative ofhomeowners in Switzerland. Further, we focused on homeowners who were planning to investin a thermal retrofit within the next 10 years. The rationale for focusing on this particular samplewas based on the research objective of investigating consumers’ perceptions of energy costinformation at the point of decision-making. An obvious follow-up would involve conducting asimilar study among a broader sample of homeowners, or even non-homeowners, and analysingdifferent types of energy efficiency measures to explore the energy paradox from a differentperspective. While the current paper focuses on behavioural anomalies, research suggests thatother barriers—in particular, market failures—could also be contributing to the existence of theenergy paradox (Linares and Labandeira 2010). Exploring such barriers, though beyond thescope of this paper, would be another worthwhile avenue for future research.

Acknowledgements We thank two anonymous reviewers for their constructive feedback on an earlier versionof this paper.

Funding Information The authors thank the Swiss Federal Office of Energy for funding the survey describedin this research article. The research is part of the activities of SCCER CREST (Swiss Competence Center forEnergy Research), which is financially supported by the Swiss Commission for Technology and Innovation(CTI) under Grant No. 466 KTI.2014.0114.

Appendix A

Table 3 Numeracy questionnaire

Question Options Correct response

Suppose you had 200 CHF in a savings accountand the interest rate was 10% per year. After2 years, how much would you have in thisaccount in total (in CHF)?

242

Suppose you had 100 CHF in a savings accountand the interest rate was 2% per year. After10 years, how much do you think you wouldhave in the account if you left the money toaccumulate?

More than 110 CHF/exactly110 CHF/less than 110CHF/do not know

More than 110 CHF

Imagine that the interest rate on your savingsaccount was 1% per year and inflation was2% per year. After 1 year, how much wouldyou be able to buy with the money in thisaccount?

More than today/exactly thesame/less than today/donot know

Less than today

Imagine that you replaced your current heatingsystem with a more energy-efficient heatingsystem that saved you 100 CHF per year atcurrent energy prices. Now suppose that theenergy prices increased by 20%. How wouldthat influence the payback period of yournew, more energy-efficient heating system?

Shorter/remains thesame/longer/do not know

Shorter


Appendix B

Appendix C

Appendix D

Table 4 Energy literacy questionnaire

Question Options Correct response

Which end use constitutes the largestpart of an average Swiss households’energy consumption?

Warm water/space heating/electronicappliances/lighting/do not know

Space heating

In which unit is household electricityconsumption usually measured?

Kilowatt kW/kilowatt hourskWh/volt V/horse power PS/donot know

kWh

How much energy does an averageperson use during a 5-min shower?

1.6 kWh/20.1 kWh/100.5 kWh/donot know

1.6 kWh

Which source of electricity contributesthe most to overall Swiss electricityproduction?

Nuclear energy/natural gas/solarenergy/hydropower/biomass/wind/oil/coal/do not know

Hydropower

Table 5 Impulsivity

Scale Wording in English

5-point Likert scale (1 = Bstrongly disagree,^ 5 = Bstrongly agree^) I plan tasks carefully.a

I save regularly.a

I say things without thinking.I get easily bored when solving t

hought problems.I am more interested in the present

than the future.

a Reverse coded

Table 6 Risk preference

Scale Wording in English

5-point Likert scale (1 = Bstrongly disagree,^ 5 = Bstrongly agree^) BIt is likely I would invest a significantsum in a high-risk investment.^

BI am a financial risk taker.^BEven if I experienced a significant loss

from an investment, I would stillconsider making risky investments.^

a Reverse coded


References

Abrahamse, W., & Steg, L. (2009). How do socio-demographic and psychological factors relate to households’direct and indirect energy use and savings? Journal of Economic Psychology, 30(5), 711–720.

Abrahamse, W., Steg, L., Vlek, C., & Rothengatter, T. (2005). A review of intervention studies aimed athousehold energy conservation. Journal of Environmental Psychology, 25(3), 273–291.

Alberini, A., Banfi, S., & Ramseier, C. (2013). Energy efficiency investments in the home: Swiss homeownersand expectations about future energy prices. Energy Journal, 34(1), 49–86.

Allcott, H. (2011). Consumers’ perceptions and misperceptions of energy costs. The American Economic Review,101(3), 98–104.

Allcott, H., & Greenstone, M. (2012). Is there an energy efficiency gap? The Journal of Economic Perspectives,26(1), 3–28.

Allenby, G. M., & Rossi, P. E. (2003). Perspectives based on 10 years of HB in marketing research. Paperpresented at the Sawtooth Software Conference Proceedings.

Amstalden, R. W., Kost, M., Nathani, C., & Imboden, D. M. (2007). Economic potential of energy-efficientretrofitting in the Swiss residential building sector: the effects of policy instruments and energy priceexpectations. Energy Policy, 35(3), 1819–1829.

Andor, M., Gerster, A., & Sommer, S. (2016). Consumer inattention, heuristic thinking and the role of energy labels.Retrieved from https://EconPapers.repec.org/. Accessed 06 Dec 2016.

Bamberg, S. (2003). How does environmental concern influence specific environmentally related behaviors? Anew answer to an old question. Journal of Environmental Psychology, 23(1), 21–32.

Barr, S., Gilg, A. W., & Ford, N. (2005). The household energy gap: Examining the divide between habitual-andpurchase-related conservation behaviours. Energy Policy, 33(11), 1425–1444.

Bento, A. M., Li, S., & Roth, K. (2012). Is there an energy paradox in fuel economy? A note on the role ofconsumer heterogeneity and sorting bias. Economics Letters, 115(1), 44–48.

Benzion, U., Rapoport, A., & Yagil, J. (1989). Discount rates inferred from decisions: An experimental study.Management Science, 35(3), 270–284.

Bergkvist, L., & Rossiter, J. R. (2007). The predictive validity of multiple-item versus single-item measures ofthe same constructs. Journal of Marketing Research, 44(2), 175–184.

Bioregional. (2011). Helping to inform the green deal green shoots from pay as you save, August 2011. London:Bioregional.

Bissing-Olson, M. J., Iyer, A., Fielding, K. S., & Zacher, H. (2013). Relationships between daily affect and pro-environmental behavior at work: The moderating role of pro-environmental attitude. Journal ofOrganizational Behavior, 34(2), 156–175.

Blasch, J., Filippini, M., & Kumar, N. (2016). Boundedly rational consumers, energy and investment literacy, andthe display of information on household appliances. CER-ETH-Center of Economic Research at ETHZurich, Working Paper, 16, 24.

Brounen, D., Kok, N., & Quigley, J. M. (2013). Energy literacy, awareness, and conservation behavior ofresidential households. Energy Economics, 38, 42–50.

Brown, M. A. (2001). Market failures and barriers as a basis for clean energy policies. Energy Policy, 29(14),1197–1207.

Bull, J. (2012). Loads of green washing—Can behavioural economics increase willingness-to-pay for efficientwashing machines in the UK? Energy Policy, 50, 242–252.

Chapman, G. B., & Winquist, J. R. (1998). The magnitude effect: Temporal discount rates and restaurant tips.Psychonomic Bulletin & Review, 5(1), 119–123.

Chrzan, K., & Orme, B. (2000). An overview and comparison of design strategies for choice-based conjointanalysis. Sawtooth software research paper series.

Conlisk, J. (1996). Why bounded rationality? Journal of Economic Literature, 34(2), 669–700.Deutsch, M. (2010). Life cycle cost disclosure, consumer behavior, and business implications. Journal of

Industrial Ecology, 14(1), 103–120.DeWaters, J. E., & Powers, S. E. (2011). Energy literacy of secondary students in New York State (USA): A

measure of knowledge, affect, and behavior. Energy Policy, 39(3), 1699–1710.Diamond, P., & Köszegi, B. (2003). Quasi-hyperbolic discounting and retirement. Journal of Public Economics,

87(9), 1839–1872.Dickert, S., Kleber, J., Peters, E., & Slovic, P. (2011). Numeracy as a precursor to pro-social behavior: The impact

of numeracy and presentation format on the cognitive mechanisms underlying donation decisions. Judgmentand Decision making, 6(7), 638.

Dunlap, R. E., Van Liere, K. D., Mertig, A. G., & Jones, R. E. (2000). New trends in measuring environmentalattitudes: Measuring endorsement of the new ecological paradigm: a revised NEP scale. Journal of SocialIssues, 56(3), 425–442.


https://EconPapers.repec.org/

Erdem, C., Şentürk, İ., & Şimşek, T. (2010). Identifying the factors affecting the willingness to pay for fuel-efficient vehicles in Turkey: A case of hybrids. Energy Policy, 38(6), 3038–3043.

Farsi, M. (2010). Risk aversion and willingness to pay for energy efficient systems in rental apartments. EnergyPolicy, 38(6), 3078–3088.

Fellner, G., & Maciejovsky, B. (2007). Risk attitude and market behavior: Evidence from experimental assetmarkets. Journal of Economic Psychology, 28(3), 338–350.

Frederiks, E. R., Stenner, K., & Hobman, E. V. (2015). The socio-demographic and psychological predictors ofresidential energy consumption: A comprehensive review. Energies, 8(1), 573–609.

Gabaix, X., & Laibson, D. (2005). Bounded rationality and directed cognition. Harvard University. Retrievedfrom https://scholar.harvard.edu/xgabaix/publications/bounded-rationality-and-directed-cognition-working-paper. Accessed 10 Dec 2016.

Gadenne, D., Sharma, B., Kerr, D., & Smith, T. (2011). The influence of consumers’ environmental beliefs andattitudes on energy saving behaviours. Energy Policy, 39(12), 7684–7694.

Gardner, G. T., & Stern, P. C. (1996). Environmental problems and human behavior. Boston: Allyn & Bacon.Gatersleben, B., Steg, L., & Vlek, C. (2002). Measurement and determinants of environmentally significant

consumer behavior. Environment and Behavior, 34(3), 335–362.Geller, H., Harrington, P., Rosenfeld, A. H., Tanishima, S., & Unander, F. (2006). Polices for increasing energy

efficiency: Thirty years of experience in OECD countries. Energy Policy, 34(5), 556–573.Gelman, A., & Park, D. K. (2012). Splitting a predictor at the upper quarter or third and the lower quarter or third.

The American Statistician.Gillingham, K., Newell, R. G., & Palmer, K. (2009). Energy efficiency economics and policy. Annual Review of

Resource Economics, 1(1), 597–620.Gillingham, K., & Palmer, K. (2014). Bridging the energy efficiency gap: policy insights from economic theory

and empirical evidence. Review of Environmental Economics and Policy, 8(1), 18–38.Gourville, J. T. (2003). The effects of monetary magnitude and level of aggregation on the temporal framing of

price. Marketing Letters, 14(2), 125–135.Green, L., Myerson, J., & McFadden, E. (1997). Rate of temporal discounting decreases with amount of reward.

Memory & Cognition, 25(5), 715–723.Green, L., Myerson, J., & Schneider, R. (2003). Is there a magnitude effect in tipping? Psychonomic Bulletin &

Review, 10(2), 381–386.Green, P. E., & Srinivasan, V. (1990). Conjoint analysis in marketing: new developments with implications for

research and practice. The Journal of Marketing, 3–19.Greene, D. L. (2011). Uncertainty, loss aversion, and markets for energy efficiency. Energy Economics, 33(4), 608–616.Gul, F., & Pesendorfer, W. (2001). Temptation and self-control. Econometrica, 69(6), 1403–1435.Hardisty, D. J., Appelt, K. C., & Weber, E. U. (2013). Good or bad, we want it now: Fixed-cost present bias for

gains and losses explains magnitude asymmetries in intertemporal choice. Journal of Behavioral DecisionMaking, 26(4), 348–361.

Harrison, M. (2010). Valuing the future: The social discount rate in cost-benefit analysis. Available at SSRN:https://ssrn.com/abstract=1599963. Accessed 2 April 2016.

Hassett, K. A., & Metcalf, G. E. (1993). Energy conservation investment: Do consumers discount the futurecorrectly? Energy Policy, 21(6), 710–716.

Hausman, J. A. (1979). Individual discount rates and the purchase and utilization of energy-using durables. TheBell Journal of Economics, 10, 33–54.

Heinzle, S. L. (2012). Disclosure of energy operating cost information: A silver bullet for overcoming the energy-efficiency gap? Journal of Consumer Policy, 35(1), 43–64.

Heinzle, S. L., &Wüstenhagen, R. (2012). Dynamic adjustment of eco-labeling schemes and consumer choice—Therevision of the EU energy label as a missed opportunity? Business Strategy and the Environment, 21(1), 60–70.

Hernández, A., Drasgow, F., & González-Romá, V. (2004). Investigating the functioning of a middle category bymeans of a mixed-measurement model. Journal of Applied Psychology, 89(4), 687.

Hoch, S. J., & Loewenstein, G. F. (1991). Time-inconsistent preferences and consumer self-control. Journal ofConsumer Research, 492–507.

Howarth, R. B., & Sanstad, A. H. (1995). Discount rates and energy efficiency. Contemporary Economic Policy,13(3), 101–109.

INFE. (2011). Measuring financial literacy: questionnaire and guidance notes for conducting an internationallycomparable survey of financial literacy. Periodical measuring financial literacy: questionnaire and guidancenotes for conducting an internationally comparable survey of financial literacy.

Jaccard, M., & Dennis, M. (2006). Estimating home energy decision parameters for a hybrid energy—Economypolicy model. Environmental Modeling & Assessment, 11(2), 91–100.

Jaffe, A. B., Newell, R. G., & Stavins, R. N. (2004). Economics of energy efficiency. Encyclopedia ofEnergy, 2, 79–90.


https://scholar.harvard.edu/xgabaix/publications/bounded-rationality-and-directed-cognition-working-paper

https://scholar.harvard.edu/xgabaix/publications/bounded-rationality-and-directed-cognition-working-paper

https://ssrn.com/abstract=1599963

Jaffe, A. B., & Stavins, R. N. (1994). The energy-efficiency gap: What does it mean? Energy Policy, 22(10),804–810.

Jakob, M. (2006). Marginal costs and co-benefits of energy efficiency investments: The case of the Swissresidential sector. Energy Policy, 34(2), 172–187.

Kaenzig, J., & Wüstenhagen, R. (2010). The effect of life cycle cost information on consumer investmentdecisions regarding eco-innovation. Journal of Industrial Ecology, 14(1), 121–136.

Kallbekken, S., Sælen, H., & Hermansen, E. A. (2013). Bridging the energy efficiency gap: A field experimenton lifetime energy costs and household appliances. Journal of Consumer Policy, 36(1), 1–16.

Kollmuss, A., & Agyeman, J. (2002). Mind the gap: why do people act environmentally and what are the barriersto pro-environmental behavior? Environmental education research, 8(3), 239–260.

Koomey, J. G., & Sanstad, A. H. (1994). Technical evidence for assessing the performance of markets affectingenergy efficiency. Energy Policy, 22(10), 826–832.

Kühberger, A. (1998). The influence of framing on risky decisions: A meta-analysis. Organizational Behaviorand Human Decision Processes, 75(1), 23–55.

Kuosmanen, T. (2005). Measurement and analysis of eco-efficiency: An economist’s perspective. Journal ofIndustrial Ecology, 9(4), 15–18.

Laibson, D. (1997). Golden eggs and hyperbolic discounting. The Quarterly Journal of Economics, 112(2), 443–477.

Lauriola, M., & Levin, I. P. (2001). Personality traits and risky decision-making in a controlled experimental task:An exploratory study. Personality and Individual Differences, 31(2), 215–226.

Lauriola, M., Russo, P. M., Lucidi, F., Violani, C., & Levin, I. P. (2005). The role of personality in positively andnegatively framed risky health decisions. Personality and Individual Differences, 38(1), 45–59.

Lee, L.-S., Lee, Y.-F., Altschuld, J. W., & Pan, Y.-J. (2015). Energy literacy: Evaluating knowledge, affect, andbehavior of students in Taiwan. Energy Policy, 76, 98–106.

Levin, I. P., Schneider, S. L., & Gaeth, G. J. (1998). All frames are not created equal: A typology and criticalanalysis of framing effects. Organizational Behavior and Human Decision Processes, 76(2), 149–188.

Levine, M. D., Koomey, J. G., McMahon, J. E., Sanstad, A. H., & Hirst, E. (1995). Energy efficiency policy andmarket failures. Annual Review of Energy and the Environment, 20(1), 535–555.

Lillemo, S. C. (2014). Measuring the effect of procrastination and environmental awareness on households’energy-saving behaviours: An empirical approach. Energy Policy, 66, 249–256.

Linares, P., & Labandeira, X. (2010). Energy efficiency: Economics and policy. Journal of Economic Surveys,24(3), 573–592.

Loewenstein, G., & Prelec, D. (1992). Anomalies in intertemporal choice: Evidence and an interpretation. TheQuarterly Journal of Economics, 107, 573–597.

Lusardi, A., & Mitchell, O. S. (2011). Financial literacy around the world: An overview. Journal of PensionEconomics and Finance, 10(04), 497–508.

Lusardi, A., Mitchell, O. S., & Curto, V. (2010). Financial literacy among the young. Journal of ConsumerAffairs, 44(2), 358–380.

Lusardi, A., & Mitchelli, O. (2007). Financial literacy and retirement preparedness: Evidence and implicationsfor financial education. Business Economics, 42(1), 35–44.

Marino, A., Bertoldi, P., Rezessy, S., & Boza-Kiss, B. (2011). A snapshot of the European energy service marketin 2010 and policy recommendations to foster a further market development. Energy Policy, 39(10), 6190–6198.

Markowitz, H. (1952). The utility of wealth. The Journal of Political Economy, 151–158.Martinsson, J., Lundqvist, L. J., & Sundström, A. (2011). Energy saving in Swedish households. The (relative)

importance of environmental attitudes. Energy Policy, 39(9), 5182–5191.Metcalf, G. E., & Hassett, K. A. (1999). Measuring the energy savings from home improvement investments:

Evidence from monthly billing data. Review of Economics and Statistics, 81(3), 516–528.Min, J., Azevedo, I. L., Michalek, J., & de Bruin, W. B. (2014). Labeling energy cost on light bulbs lowers

implicit discount rates. Ecological Economics, 97, 42–50.Moeller, F. G., Barratt, E. S., Dougherty, D. M., Schmitz, J. M., & Swann, A. C. (2001). Psychiatric aspects of

impulsivity. American Journal of Psychiatry, 158(11), 1783–1793.Moore, W. L., Louviere, J. J., & Verma, R. (1999). Using conjoint analysis to help design product platforms.

Journal of Product Innovation Management, 16(1), 27–39.Mullainathan, S., & Thaler, R. H. (2000). Behavioral economics (NBERWorking Papers: 7948). Retrieved from

http://www.nber.org/Neumann, L. J., & Morgenstern, O. (1947). Theory of games and economic behavior (vol. 60). Princeton:

Princeton University Press.Ölander, F., & Thøgersen, J. (2014). Informing versus nudging in environmental policy. Journal of Consumer

Policy, 37(3), 341–356.


http://www.nber.org/

Osbaldiston, R., & Schott, J. P. (2012). Environmental sustainability and behavioral science: Meta-analysis ofproenvironmental behavior experiments. Environment and Behavior, 44(2), 257–299.

Parker, A. M., & Fischhoff, B. (2005). Decision-making competence: External validation through an individual-differences approach. Journal of Behavioral Decision Making, 18(1), 1–27.

Patton, J. H., & Stanford, M. S. (1995). Factor structure of the Barratt impulsiveness scale. Journal of ClinicalPsychology, 51(6), 768–774.

Peters, E., & Levin, I. P. (2008). Dissecting the risky-choice framing effect: Numeracy as an individual-differencefactor in weighting risky and riskless options. Judgment and Decision Making, 3(6), 435.

Peters, E., Västfjäll, D., Slovic, P., Mertz, C., Mazzocco, K., & Dickert, S. (2006). Numeracy and decisionmaking. Psychological Science, 17(5), 407–413.

Peters, J., & Büchel, C. (2010). Episodic future thinking reduces reward delay discounting through an enhance-ment of prefrontal-mediotemporal interactions. Neuron, 66(1), 138–148.

Pettifor, H., Wilson, C., & Chryssochoidis, G. (2015). The appeal of the green deal: empirical evidence for theinfluence of energy efficiency policy on renovating homeowners. Energy Policy, 79, 161–176.

Pingle, M. (2006). Deliberation cost as a foundation for behavioral economics. In M. Altman (Ed.), Handbook ofcontemporary behavioral economics: Foundations and developments (pp. 340–355). New York: Routledge.

Poortinga, W., Steg, L., & Vlek, C. (2004). Values, environmental concern, and environmental behavior a studyinto household energy use. Environment and Behavior, 36(1), 70–93.

Qiu, Y., Colson, G., & Grebitus, C. (2014). Risk preferences and purchase of energy-efficient technologies in theresidential sector. Ecological Economics, 107, 216–229.

Rabin, M., & Thaler, R. H. (2001). Anomalies: Risk aversion. The Journal of Economic Perspectives, 15(1),219–232.

Ramos, A., Gago, A., Labandeira, X., & Linares, P. (2015). The role of information for energy efficiency in theresidential sector. Energy Economics, 52, S17–S29.

Revelt, D., & Train, K. (1998). Mixed logit with repeated choices: Households’ choices of appliance efficiencylevel. Review of Economics and Statistics, 80(4), 647–657.

Reyna, V. F., Nelson, W. L., Han, P. K., & Dieckmann, N. F. (2009). How numeracy influences riskcomprehension and medical decision making. Psychological Bulletin, 135(6), 943.

Rossi, P. E., & Allenby, G. M. (2003). Bayesian statistics and marketing. Marketing Science, 22(3), 304–328.Sallee, J. M. (2014). Rational inattention and energy efficiency. The Journal of Law and Economics, 57(3),

781–820.Sanstad, A. H., Blumstein, C., & Stoft, S. E. (1995). How high are option values in energy-efficiency

investments? Energy Policy, 23(9), 739–743.Sapci, O., & Considine, T. (2014). The link between environmental attitudes and energy consumption behavior.

Journal of Behavioral and Experimental Economics, 52, 29–34.Shapira, Z. (1995). Risk taking: A managerial perspective. New York: Russell Sage Foundation.Shiloh, S., Salton, E., & Sharabi, D. (2002). Individual differences in rational and intuitive thinking styles

as predictors of heuristic responses and framing effects. Personality and Individual Differences, 32(3),415–429.

Simon, H. A. (1955). A behavioral model of rational choice. The Quarterly Journal of Economics, 69(1),99–118.

Stanovich, K. E., & West, R. F. (1998). Individual differences in rational thought. Journal of ExperimentalPsychology: General, 127(2), 161.

Strotz, R. H. (1955). Myopia and inconsistency in dynamic utility maximization. The Review of EconomicStudies, 165–180.

Sütterlin, B., Brunner, T. A., & Siegrist, M. (2011). Who puts the most energy into energy conservation? Asegmentation of energy consumers based on energy-related behavioral characteristics. Energy Policy, 39(12),8137–8152.

Tabi, A., Hille, S. L., & Wüstenhagen, R. (2014). What makes people seal the green power deal?—Customersegmentation based on choice experiment in Germany. Ecological Economics, 107, 206–215.

Train, K. (1985). Discount rates in consumers’ energy-related decisions: A review of the literature. Energy,10(12), 1243–1253.

Tsvetanov, T., & Segerson, K. (2013). Re-evaluating the role of energy efficiency standards: A behavioraleconomics approach. Journal of Environmental Economics and Management, 66(2), 347–363.

Turrentine, T. S., & Kurani, K. S. (2007). Car buyers and fuel economy? Energy Policy, 35(2), 1213–1223.U.S. Energy Information Administration. (2015). World energy consumption by end-use sector (quadrillion Btu)

and shares of total energy use, 2011. Available at www.eia.govUS Department of Energy. (2012). Essential principles and fundamental concepts for energy education.

Retrieved from Washington: US Department of Energy.


http://www.eia.gov/

Van Rooij, M. C., Lusardi, A., & Alessie, R. J. (2011). Financial literacy and retirement planning in theNetherlands. Journal of Economic Psychology, 32(4), 593–608.

Vine, E. (2005). An international survey of the energy service company (ESCO) industry. Energy Policy, 33(5),691–704.

Weber, E. U., Blais, A. R., & Betz, N. E. (2002). A domain-specific risk-attitude scale: Measuring riskperceptions and risk behaviors. Journal of Behavioral Decision Making, 15(4), 263–290.

Weber, M., Weber, E. U., & Nosić, A. (2013). Who takes risks when and why: Determinants of changes ininvestor risk taking*. Review of Finance, 17(3), 847–883.

Weitzman, M. L. (1998). Why the far-distant future should be discounted at its lowest possible rate. Journal ofEnvironmental Economics and Management, 36(3), 201–208.

Wulfert, E., Block, J. A., Santa Ana, E., Rodriguez, M. L., & Colsman, M. (2002). Delay of gratification: Impulsivechoices and problem behaviors in early and late adolescence. Journal of Personality, 70(4), 533–552.

Zografakis, N., Menegaki, A. N., & Tsagarakis, K. P. (2008). Effective education for energy efficiency. EnergyPolicy, 36(8), 3226–3232.


Date post:	26-Jun-2018
Category:	Documents
Upload:	lythuy
View:	215 times
Download:	0 times

The Energy Paradox Revisited: Analyzing the Role of ... · have addressed the role of...

Documents