IIIEE Reports 2008 : 02

Choice determinants for the (non) adoption of energy efficiency technologies in

householdsA literature review

Elvira Moukhametshina

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Published in 2008 by IIIEE, Lund University, P.O. Box 196, S-221 00 LUND, Sweden,Tel: +46 – 46 222 02 00, Fax: +46 – 46 222 02 10, e-mail: iiiee@iiiee.lu.se

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ISSN 1650-1675

AcknowledgementsThis report was developed through the vast body of literature that had been evolving over the last four decades. I would like to extend my gratitude to Mses. Mithra Moezzi, Lisa Skumatz, Diana Uitdenbogerd and Françoise Bartiaux for being open and kindly advising me in the very beginning of this review. Not the least I appreciate their practical help in providing me with some key references and publications that helped to further structure this report.

Messrs. Harold Wilhite and Richard Wilk have also been very helpful in providing access to some of their key publications.

Particular thanks belong to Professor Lena Neij whose structured and clear vision as well as academic and professional experience made, first of all, the project possible in the time when it is of the essence. Her supervision allowed posing the right questions and looking for objective answers, and guided this report.

Finally, I would like to thank Luis Mundaca for contributing with revision and streamlining the finalisation.

Lund, October 2008

Elvira Moukhametshina.

Table of ContentsList of FiguresList of TablesList of Appendices

1. INTRODUCTION.................................................3

2. ANALYTICAL FRAMEWORKS TO APPROACH THE (NON) ADOPTION OF ENERGY EFFICIENCY TECHNOLOGIES.................................................3

2.1 CO- (OR NON-ENERGY) BENEFITS APPROACH...................32.2 INTERVENTION FACTORS APPROACH................................32.3 INNOVATION-RELATED STUDIES......................................32.4 IMPLICIT DISCOUNT RATES.............................................32.5 OTHERS APPROACHES....................................................3

3. DETERMINANTS INDUCED BY ENERGY EFFICIENT TECHNOLOGIES..............................3

3.1 PRICE...........................................................................33.2 OPERATING COSTS.........................................................33.3 TIME.............................................................................33.4 STATUS/VISIBILITY/APPEARANCE.....................................33.5 COMFORT......................................................................33.6 BRANDING / DESIGN.......................................................33.7 COMPATIBILITY..............................................................33.8 PERFORMANCE..............................................................33.9 OBSERVABILITY..............................................................33.10 COMPLEXITY..................................................................33.11 CHOICE/PROBLEM SOLVING............................................3

4. DEMOGRAPHICAL FACTORS..............................34.1 AGE..............................................................................34.2 HOUSEHOLD SIZE AND/OR COMPOSITION.........................34.3 GENDER........................................................................3

5. FACTORS RELATED TO BUYER’S CHARACTERISTICS............................................3

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5.1 INCOME........................................................................35.2 KNOWLEDGE/INFORMATION/AWARENESS.........................35.3 ATTITUDE/ENVIRONMENTAL CONSCIOUSNESS..................35.4 EDUCATION/OCCUPATION...............................................35.5 TYPE OF DWELLING........................................................35.6 LIFE STYLE....................................................................35.7 TIMING / APPROPRIATE MOMENT....................................3

6. CONTEXTUAL FACTORS.....................................36.1 OWNERSHIP/SPLIT INCENTIVE PROBLEM..........................36.2 INTERMEDIARIES...........................................................36.3 POLICY INCENTIVES.......................................................3

7. ENERGY (EFFICIENCY) TECHNOLOGIES...........37.1 SPACE CONDITIONING AND BUILDING ENVELOPE..............37.2 SANITARY HOT WATER....................................................37.3 RENEWABLE ENERGY TECHNOLOGIES..............................37.4 LIGHTING......................................................................37.5 CONSUMER APPLIANCES.................................................3

8. CONCLUDING REMARKS...................................3

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List of FiguresFigure 1. Comparison of U.S. and Thai consumer priorities.3

Figure 2. Main factors preventing increased saturation of CFLs in the households with CFLs. Northwest Pacific. 2006..........................................................3

Figure 3. Main factors preventing increased saturation in the households without CFLs. Northwest Pacific. 2006.......................................................................3

Figure 4. Distribution of the external influence in the adoption of consumer durable products..............3

List of TablesTable 1: Main drivers for adopting energy related

measures. Results of the on-line survey..............3

Table 2: Estimated NEBs within EnergyStar New Homes programme............................................................3

Table 3: Estimated NEBs within EnergyStar Homes Performance programme.....................................3

Table 4: Reasons to buy and not to buy energy-efficient appliances (in %)...................................................3

Table 5: Rank order of insulation by men and women..........3

Table 6: Demographic and attribute perception ratings......3

Table 7: Effect of market factors on CFL sales in California in 2005...................................................................3

Table 8: Ownership of CFLs according to social-demographic characteristics................................3

Table 9: Main reasons for obtaining a CFL among owners. Cultural survey......................................................3

Table 10: Non-energy benefits / impacts of household appliances. Percent of overall non-energy benefits..................................................................3

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Table 11: Overall effectiveness of energy labelling..............3

Table 12: Differences among adopter categories (vitroceramic hobs)...............................................3

Table 13: Effect of market factors on resource efficient washing machines sales in California (US)..........3

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List of Appendices

APPENDIX 1:......BIBLIOGRAPHY REVIEWED BUT NOT REFERENCED .........................................57

APPENDIX 2:...............BIBLIOGRAPHY NOT REVIEWED.................................................................73

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1. IntroductionThis study presents a literature review focused on energy efficiency and determinants of the (non) adoption of energy efficient technologies in the household sector. The key guiding question is what determinant should be taken into account when analysing future energy (service) demand and potentials of reducing future energy demand by the use of different energy policy instruments. Based on a literature review, the objective of this report is to identify (mostly) quantitative studies that look at the criteria that determine the (non) adoption of energy efficient technologies in the household sector.1 The report highlights rational economic considerations complemented with co-benefits and behavioural aspects.

Low investment in energy efficient technologies is often identified as a result of the ‘energy efficiency gap’ (see e.g. Jaffe and Stavins, 1994a; Stern and Aronson, 1984; Weber, 1997). This term attempts to capture the slow diffusion of profitable energy efficient technologies that fail to achieve market success. The energy efficiency gap is described in terms of market failures and barriers, indicating that the investors do not choose energy technologies although they are cost effective. In addition, it could be argued that consumer decisions do not respond to the model of rational choice behaviour. Early work done by Lutzenhiser (1992) spotted evidences of lack of economic rationality in consumer decisions to forego some obviously energy efficient measures. In fact, one can safely argue that the approach of economic rationality is inadequate to properly reflect technological consumer preferences. Investment costs are only part of a great variety of variables that frame and drive energy related consumer’s investment decisions. For instance, design, comfort, equipment’s brand, timing, functionality, reliability, learning, marketing, environmental awareness, etc., are likely do influence altogether the decision about an energy-technology choice/purchase. From the societal point of view e.g. environment, the investment outcome is likely to be unsatisfactory so there is a great need in public policy to better understand consumer investment decisions in the context of energy use.

The literature review presented here is an attempt to picture the whole spectrum of determinants and to highlight those playing the strongest roles in households’ adoption decision-making within a range of energy (efficient) technologies. The objective is to capture studies focusing on qualitative as well as quantitative aspects of investments determinants. The determinants have been described form different perspectives:

Determinants induced by energy efficient technologies

Demographic aspects that affect (non) adoption

Factors related to buyer’s characteristics that affect the determinants of investments

1 Note that extensive literature was reviewed but not cited in this report. For further details see Annex 1. Furthermore, also note numerous sources were identified but not possible to review for a number of reasons (e.g. language, order/paid basis, non-availability). See Annex 2.

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Contextual factors that affect the decision-making process of (non) adoption

The outline of this report is as follows. Chapter 2 is devoted to a short overview of earlier meta- studies and reviews in the field of investments determinants of energy efficiency technologies. This report highlights different analytical approaches to investigate determinants of the (non) adoption of energy efficiency technologies. In Chapter 3 the report addresses the identified criteria or determinants of (non) adoption decisions based features of technologies. Cases of determinants are provided in terms of geographical location of samples, observed results and methods by which the results were obtained. In Chapter 4, 5 and 6 the report describes the (non) adoption of efficient technologies in terms of demographic aspects, the characteristics of (potential) adopter/households and the contextual factors in which the (non) adoption process can take place. In Chapter 7, takes a different approach, as it looks into specific technologies and describe the most important determinants on a technology-basis; highlighting that different determinants will be of different importance for different technologies. Finally, in Chapter 8 some concluding remarks are drawn.

2. Analytical frameworks to approach the (non) adoption of energy efficiency technologiesResearch in the area of choice determinants for in the adoption of energy efficiency technologies is not novel. Hirst and Goeltz (1985:25) state that “the critical determinants of the household decisions to retrofit are assumed to be the capital and operating costs of the retrofit choices”. This is generally the case of conventional wisdom for various household appliances. However, the literature on several determinants affecting energy (efficiency) technologies in the household sector is vast. In fact, approaches to identify and/or quantify the choice determinants have been developed over the years. Several studies have been published as well as a number of reviews addressing this topic, in particular looking at determinants outside pure financial aspects (see e.g. Lutzenhiser, 1993; Stern, 1986; Wilhite et al., 2000; Uitdenbogerd, 2007).

2.1 Co- (or non-energy) benefits approachTo begin with, several research efforts are identified in the literature when it comes to co-benefits (e.g. improved housing comfort level, reduced noise, etc.) of energy efficiency technologies influencing their adoption. For instance, Mills and Rosenfeld (1996) note that many co-benefits (also called ‘non-energy benefits’) play critical role in consumer perception (and adoption) of energy-related technologies. Likewise, yet in the early 80s Stern and Aronson (1984:62) noted that “people do not usually weigh the potential value of the energy saved by one purchase against the pleasure, convenience or status achievable by alternative purchases”.

Stoecklein and Skumatz (2007) have weighted co-benefits for four various technologies associated with residential energy efficiency initiative (New Zealand, Zero and Low Energy Homes). Both positive and negative impacts (benefits/losses) have been reviewed. It is suggested, “residents place

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considerable value on the lifestyle benefits from energy-efficiency features of their homes, beyond benefits from energy savings”. The authors observed that energy related technologies have a potential to bring benefits other than energy saving (e.g. reduced noise, increased comfort, better energy bill control, etc.).2 Significant benefits may relate to lifestyle and natural environment.3 This type of non-energy impacts either becomes a “component of decision-making” or a “contributing reason for satisfaction”. Stoecklein and Skumatz (2007:1962) continue with noting that co-benefits are, indeed, market goods that influence the adoption of energy efficiency technologies as “they are purchased by consumers bundled with the energy-efficiency appliances that produce them”. The authors refer to ‘motivation’ factors that were classified earlier by Lutzenhiser (2006:90), namely: specific system/building concern; environmental health and energy costs; comfort level; and resource conservation. Amann (2006) stresses data collection and work to develop for a proper methodology for incorporating co-benefits in cost-benefit analysis. At earlier stage Skumatz (2002:307-316) compared use of three methods to estimate ‘hard-to-measure’ non-energy benefits of participants of low-income weatherisation programmes. Skumatz (2002) concluded that estimation of overall non-energy benefits in relation to energy benefits within weatherisation programmes could range from 80% to 100% or USD65-USD100.

Relative valuation. The following benefits have been reported as more valuable than energy savings according to this approach: Control of bills (by 52% of respondents); comfort (34%); environmental (17%); maintenance (16%); moving avoided (13%); change in number of sick days (8%); appearance (6%); added features (6%); noise reduction (4%).

Willingness-to-pay. This evaluation method approached what programme participants care about. The following results were obtained: comfort (76%); education/control (55%); features/options (30%); noise reduction (30%); appearance (29%). Yet, this resulted in overstatement of individual non-energy benefits that generated a bigger sum than participants would be willing to pay for overall benefits (including energy benefits).

Labelled Magnitude Scaling. This approach brought about the figure close to the first method. Non-energy benefits comprised 99% of energy benefits from weatherisation and house envelope improvement. In dollar terms value of non-energy benefits was computed to USD70-USD110.

Within the context of co-benefits, Knight et al. (2006) bring forward that motivational surveys suggest that customers incorporate “perceived non-energy benefits” into their decisions, which does not exclude their rational choice. The benefits that accompany retrofitting choices (insulation,

2 For further details see Stoecklein and Skumatz (2007:1964).3 They provide the results of co-benefits categories by weights in connection with four

energy-related kinds of investments: double-glazed windows; super insulation, solar water heating (SWH), and solar design. The weights of respective determinants are based on the results of contingent valuation and relative scaling. See Stoeckleine and Skumatz (2007) for further details.

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appliances, etc.) include increased comfort, reduced noise, improved health, safety, durability properties, and a sense of environmental citizenry, first-on-the-block status, long-term value, and overall peace in mind. They may have a dominating influence on decisions compared to the energy (operational) costs, use and conservation aspects as such. They conclude that based on their survey’s tentative results, “most homeowners appear to value the variety of non-energy benefits much more higher than the energy cost savings”. (Knight et al., 2006: 5-8).

While some authors did not place focus on decision-making phase, for others it seemed central. Knight et al. (2004) do highlight the existence of “full range of performance benefits” that helps homeowners to justify their (investment or purchase) decisions when “energy efficiency alone is insufficient”. (Knight et al., 2004: 7-162). They note that such other benefits would have their subjective utility being characteristic to individual homeowners. Such benefits like “family health, safety, comfort, or prestige” may be significant to become a factor in a purchase or investment decision. Further on the authors share their reservation on available dependable statistical data or studies underway to shed some light on measurable co-benefits effects or perceptions (though rather on the retrofitting part).4

2.2 Intervention factors approachAnother important systematic approach looking into energy efficiency intervention success factors was taken by Uitdenbogerd et al. (2007).5 Their review covered vast body of research in various energy efficiency measures beyond housing envelope and weatherisation. It aimed to identify the determinants influencing different components of end-use energy efficiency and present their significance. The approach was to estimate importance of determinants, including the investment behaviour of households with the help of Intervention Mapping Protocol (normally applied in healthcare). This evaluation is basically derived from the number of times respondents in the reviewed references were revealing statistical significance of one or another determinant in a specific context. The team has arrived at the estimations of importance of various criteria on household investment behaviour: They identified personal and external determinants, demographical and contextual factors and quantified their influence/strength with the help of Intervention Mapping Protocol.

Based on the results of their intervention success quantification attempt, the strongest degree of influence is attributed to knowledge, information / learning need / cost estimation, product use (see also Stern et al., 1986a; Spapen, 2003; Derijke and Uitzinger, 2004; Derijke et al., 2001). Uitdenbogerd et al. (2007) regarded a weaker degree of influence as factors that play a role. Here, a number of studies point several factors that play (a weak) role in investment behaviour, namely: appearance, status, visibility, symbolic value, leisure; optimising luxury, price-judgement/quality, problem

4 See Knight, et al. (2004: 7-163) for further details.5 It was done based on how often and in what connection these were mentioned in the

referred works. According to Cees Egmond (personal communication) the review by Uitdenbogerd et al. (2007), in which he also took part, the assessment of determinants’ role was strongly framed with expert judgements and no statistical confirmation has been found to contribute to assessment of their importance level for decision-making.

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solving, service, appropriate moment (see e.g. Stern et al., 1986a; Spapen, 2003; Derijke and Uitzinger, 2004; Black et al., 1985; Lutzenhiser, 1993; Bais et al., 1994; Leidelmeijer and Grieken, 2005). In addition, a contextual factor representing the reliability of vendor also plays a role in taking purchase decision (see Stern et al., 1986a; Görts et al., 2002).

Within this analytical approach, it is possible to identify a number of determinants that are evaluated as not so important (or the least significant) for household’s investment behaviour: social contacts (Derijke, E. et al., 2001); positive experience (Bais, J.M. et al., 1994; Derijke, E., Uitzinger, J., 2004); age, family age, becoming of age (Leidelmeijer, K. and van Grieken, P., 2005; Abrahamse, W. et al., 2005). Based also on a literature review, Uitdenbogerd et al. (2007:1849-1850) defined the following determinants affecting energy investments-behaviour, namely: knowledge, information about costs, information about additional characteristic (such as comfort, appearance, status, visibility and luxury), availability of choice possibilities, financial space and the ‘right’ moment.

2.3 Innovation-related studiesLaBay and Kinnear (1981) looked into framework for adoption and diffusion of innovations. The authors focused on solar energy technologies as major technological innovations. Their model included both adopters and non-adopters categories and was devoted particularly to the purchase decision process. Furthermore, multivariate techniques allowed calculating mean factor importance ratings among product attributes, consumers’ demographic and socio-economic characteristics (see LaBay, and Kinnear, 1981:276).

Weber et al. (1985) drew an analogy between the development of innovative housing and technological diffusion. Among the criteria they referred to were the following ones: relative advantage, risk, compatibility, complexity, and communicability. Within this context, it is found that Hall and Reed (1999) identified ‘relative advantage’ as an important attribute of innovation, which often includes energy efficiency technologies). They defined a relative advantage as a degree to which an innovation is perceived as a better alternative within number of factors. Among the latter there are such as: profits, costs, comfort, prestige, time savings, level of effort, immediacy of reward (Hall, N. and Reed, J., 1999).

2.4 Implicit discount rates There is compelling evidence that shows that households use implicitly high discount rates (e.g. up to 90% and even much higher) that hinder the adoption of efficient technologies; thus, setting greater hurdles than for conventional technologies (see Hausman, 1979; Gately, 1980; Train, 1985; Ruderman et al., 1987; Lutzenhiser, 1992; Jaffe and Stavins, 1994a, 1994b; Metcalf, 1994; Howarth and Sanstad, 1995). Depending on the income class (see below) and for the specific case of the household market behaviour, implicit discount rates have been analysed and estimated in a number of studies:

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Train (1985) found that average implicit discount rates in household purchase decisions for efficient equipments range between: i) 10 to 32% for insulation; ii) 4 to 36% for space heating, iii) 3 to 29% for air conditioning, and iv) 18 to 67% for other appliances (e.g. water heating, cooking).

Hausman (1979) found average implicit discount rate of 25% for air conditioners (range between 9 to 39%).

Gately (1980) estimated rather high implicit discount rates for efficient refrigerators, ranging from 45% up to 300%.

Dubin and McFadden (1984) estimated an average discount rate of 20% for water- and space-heating measures.

Sutherland (1991) notes that energy efficiency appliances appear to entail very high discount rates, say 50% or higher.

Within the frame of the studies mentioned above, what is of prime importance is to look at the determinants behind such high implicit discount rates identified in the reviewed literature. Although not exhaustive, various causes can explain the use of high implicit discount rates. According to a number of authors, potential causes within the household sector can be: a lack of information about cost and benefits of efficiency improvements; lack of knowledge about how to use available information; uncertainties about technical performance of investments; lack of sufficient capital to purchase efficient products (or capital market imperfections); income level; high transaction costs for obtaining reliable information; risks associated to investments; etc. (e.g. Ruderman et al., 1981; Train, 1985; Sutherland, 1991; Gates, 1993; Metcalf, 1994).In terms of some socio-economic explanations for high implicit discount rates, the ownership status is regarded as a relevant cause (Train, 1985). Hausman (1979) and Train (1985) also argue that implicit discount rates vary inversely with income class. Train (1985) argues that the relationship between low-income class and high implicit discount rates can be explained partly because low-income households have less access to capital markets and less liquid capital to invest than higher income class households. Thus, even in the presence of good information about investment returns, lower incomes households will still be unable to invest in efficient technologies if complementary economic instruments are not in place.

In all cases, note that it has long been debated whether the difference between social and private discount rates can be attributed to market imperfections (see, for instance, Reddy, 1991; Jaffe and Stavins, 1994a, 1994b; Sanstad and Howarth, 1994; Scheraga, 1994; Howarth and Sanstad, 1995; Anderson and Newell, 2002). For instance, it is argued that the difference exists not just because of market imperfections but also because consumer behaviour is hampered by institutional and regulatory structures (Scheraga, 1994). In fact, Sutherland (1991) argues that household investments in energy efficiency appliances might correctly imply high discount rates because these investments are illiquid, risky and face high transaction costs. However, Morgenstern and Al-Jurf (1999) conclude that information programmes positively affect the diffusion of efficient technologies. Despite the fact that high implicit discount rates have been the most common and mentioned evidence for inefficient consumer behaviour (Huntington, 1994), the debate still continues (see Anderson and Newell,

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2002). This surely indicates that much more research is needed on behavioural aspects driving choices about energy efficiency technologies.

2.5 Others approachesBlack et al. (1985) applied path analysis within a causal model. Their model considered demographic, economic, and structural variables as contextual, and attitudes, individual beliefs, values and norms, as personal. The model was used to analyse influences on different investment scales in residential energy efficiency. Major capital investments and low cost efficiency improvements were analysed to determine direct and indirect influence of contextual and attitudinal variables on constraining behavioural choice through causal chain.

Martinez et al. (1998) approached an innovative category of consumer durables and looked into combination of social-economic, demographic factors, as well as innovators’ attitude. This was done in order to find out what would determine which group consumer would likely belong to. They determined significance of some demographic and other buyer’s characteristics. In order to assign significance to the determinants they applied pairwise comparison. This was done among different adopters categories: (i) innovators and early adopters; (ii) early majority; and (iii) late majority. Different sets of variables (determinants) showed different statistical significance under different pair comparisons (see Martinez et al., 1998:333).

Vaage (2000) argued that the choice about an energy appliance is the first decision that relates to energy consumption. The consequent decision is the one about how the appliance is going to be utilised. Therefore, models that ignore interdependence of appliance choice and its use, risk building up biased assertions. Blumstein et al. (2001) look specifically into market transformation and state that markets are not less important than consumer behaviour to look at as far as energy related adoption decisions are concerned.

3. Determinants induced by energy efficient technologies

3.1 PriceAs a determinant, the price indicates the importance of initial costs in energy-efficiency related technologies (i.e. direct price effect). As noted by many authors, high initial costs often are characteristics of energy efficiency products and this particularly slows down their adoption. (see e.g. Hall and Reed, 1999). Kempton and Montgomery (1982) showed that immediate cost is given more focus compared to long-term savings.6 Boonekamp (2007) supports that referring to van Swichem (2000): people base their decisions to buy on actual prices and do not have a good knowledge about operational

6 As a part of explanation they brought the issue of implicit discount rates. See Kempton and Montgomery (1982) for further details.

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costs. Negative correlation in terms of higher price of and probability of purchase is observed.

The literature showing how the product price is an influential determinant is vast. For instance, in a study that included conjoint analysis of different attributes of cloth washers in the US (including five cities), the price shown to be the most important attribute both for the total sample and each demographic group (see Grover and Babiuch, 2000:144). The sample studied with this conjoint analysis allowed to determine that they are willing to pay additional USD 225 for a new cloth washer that would bring USD 50 per year savings in energy and water bills (Grover and Babiuch, 2000).

Using the Netherlands as a case study, Uitdenbogerd (2007:185-187) shows that product price is an important determinant that prevents the adoption of energy efficiency technologies. From a sample of 376 households 38% do not buy energy saving light bulbs due to their expensiveness; 18.8% do not buy water-saving shower heads due to their expensiveness; and 26.1% do not buy energy efficient appliances due to their expensiveness. Likewise, Rasmussen et al. (2007:1955-1957) shows that the price of CFLs represents a barrier to expanding CFL installations compared to incandescent bulbs. Using a sample of 224 households, 33% rejected CFLs purchase because they are seen too expensive.

In a similar study, DuPont (1998) found that both US and Thai consumers rank the price as an important determinant for the adoption of the energy efficiency appliances, with ‘efficiency’ as such having a much lower ranking (see Figure 1).

Figure 1. Comparison of U.S. and Thai consumer priorities.

Source: Du Pont (1998:6-11).

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3.2 Operating costsThis determinant is expected to have negative correlation in terms of more likely adoption if comparatively lower operating costs are anticipated. Wilk and Wilhite (1984b) analysed why people in Santa Cruz, California, do or do not weatherise their homes. They found that despite clearly economic benefits brought by weatherisation, it is a very unpopular measure.

Referring to Booij’s research (1992), Boonekamp (2007:151) mentions that there was found no “influence of energy prices on the ownership rate of basic appliances”. Only for ownership of such appliances like dishwasher and clothes dryer it was observed an energy price effect. In addition, it is pointed that there was found 1-2 years delayed reaction to change in energy prices. Mainly this is due to delivery of final energy bills up to a year after price changes occur. This increases the possibility that “purchasing of more energy efficient systems or appliances becomes part of the short-term price elasticity”. (Boonekamp, 2007:151).

Quantitative studies about operational costs as a choice-determinant were found in the literature. For instance, Uitdenbogerd (2007:185) found operational costs to be a critical determinant (first ranked) for energy-saving light bulbs, as adopters do it for “saving money during use”. Likewise, buyers of water-saving shower heads ranked operational costs as a second determinant, and buyers of energy-efficient appliances, expecting to save money during use, ranked operational costs as first choice determinant. Grover and Babiuch (2000:144) found that the youngest demographic group (18-24 years old) placed the greatest importance on operation costs, ranked in second place when buying cloth washers. In all cases, lack of information/awareness also prevents adopters to fully understand or take into account the importance of this determinant. For instance DuPont (1998:6-12) found that most of the US consumers (80%) stated that they did not know annual operating costs for appliances they purchased recently.

3.3 TimeThis determinant brings different connotations. Those referring to appliances may have time as a constraint only in terms of effort and information collection and processing time-costs (c.f. Uitdenbogerd, 2007). The other side, more related to renovation, weatherisation, retrofitting measures, is linked to estimation of the physical requirement of time for implementing these measures. For instance, Komor and Wiggins (1988) link time consumption and efforts into a hassle factor. Hassle factor indicates “perceived time investment necessary to install or operate the conservation device”. Effort associated with installing a conservation action can represent a significant barrier. It is argued that measures that do not require self-installation seem more attractive to households. (Komor, Wiggins, 1988:636, 644). The other connotation refers to time pressure. As far as time in relation to search for information is concerned, Uitdenbogerd (2007: 186, 187) found that 11.4% of households in The Netherlands did not buy water-saving shower heads because of time and effort that was needed to find information, evaluate it and judge the payback time. Similarly, it is found that 14.2% of households do not buy energy-efficient appliances due to the burden originated in the search of useful and reliable information

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No particular mentioning of saved time considerations were found as determinants. However, cloth dryer seems to belong to the kind of appliances that have this dimension, but as well often it is seen as a luxury compared to line drying and rather reducing physical efforts. Hall and Reed and Martinez et al. provide an indication of time-saving as one of the relative advantages of energy-related technologies. In both cases it is mentioned as a feature of innovative products. (Martinez et al., 1998; Hall and Reed, 1999).

3.4 Status/visibility/appearanceThis determinant would vary with categories of products and them being public/private necessity/luxury. It is rather generally applicable statement that products define a social status. (Solomon, 1983). Among the energy technologies both more and less costly are associated with different image creation. Gram-Hanssen et al. (2007), researching on residential energy consumption in Belgium and Denmark formulated a general condition prerequisite for implementation of more efficient household energy technologies. According to them, “Social support and social recognition as well as consistency between several sources of information” are crucial in household energy efficiency improvement (Gram-Hanssen et al., 2007:2885).

Certain features would concern different categories of households. Solar panels represent a luxury purchase that would be publicly visible. While refrigerators and light bulbs are rather a private necessity. CFLs are seen as an innovative product and therefore possibly as a private luxury. Relatively efficient refrigerators are almost indistinguishable from other alternatives. Banks (1999:3) suggested that the stronger recognition of a product as a luxury is, the more important friends, peers and ‘significant others’ will be in terms of influencing its adoption. Farhar et al. (2002) found that homeowners of zero-energy houses in USA were especially proud of their energy efficiency appliances. (Farhar, B. et al., 2002:50). Diamond and Moezzi (2000) witnessed an example of a household in US installing solar panels on the north side of the roof, rather for visible effect. Social conformity was noted by Guerin et al. (2000) as one of the occupant predictors for motivation to save energy.

Martinez et al. (1998) marked that in Spain keeping up with neighbours or being ahead of them was an important driver for purchases of energy efficiency technologies. Wilk and Wilhite, (1984a) show that perceived status, hatred of utility companies and home improvements are given greater importance in case of solar panels installation in Santa Cruz, the US. Condelli et al. (1984) considered social network to have proven playing an influential role in diffusion of innovations (more than mass media). In making energy decisions people are moved “in part because of social influence, e.g. a friend has a solar water heater and it works” (Condelli et al., 1984:490).

Despite there is no specific quantification identified for this determinant, yet in early 80s Lutzenhiser, referring to Monnier (1983), pointed that household represents “a symbolic realm of energy use”. Referring to Hackett and Lutzenhiser (1990) – “appliances must conform to status expectations”. Further notion shedding more light on the energy use, its symbolic nature and link to social status that social actors would see relative energy efficiency only as one of many salient factors, came from Gordon and

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Dethman (1990). Lutzenhiser (1993:264) highlighted that “buildings, energy improvements, and alternative energy sources such as solar collectors must fit into the symbolic realm of families and communities”.

Ethnographic research from the US reported on some samples where “energy use and conservation were incorporated by households into their larger ‘home improvement’ plans, and conservation measures were chosen on the basis of visibility to neighbours and visitors (although the investments selected were often among the least effective)”.(Wilk and Wilhite, 1984). For instance Wilk and Wilhite, (1984a:250) show that that solar panels are eagerly shown and discussed with pride in Santa Cruz, the US, while weatherstripping is “passed over and ignored”. In the cases when renovation and weatherisation are concerned, the aesthetic appearance is considered. Gram-Hanssen et al. (2007) tracked in Belgium and Denmark aesthetic views, showing that tastes prevented some households from renovating their houses for energy efficiency improvement, including roof insulation and single versus double glazed windows.

3.5 ComfortComfort could be the strongest of determinants associated with adoption of an energy technology in particular related to space-heating or insulation/weatherisation. It can be a driving factor with positive influence. Looking into Belgian and Danish cases, Gram-Hanssen et al. (2007) found that in house renovation, for instance, payback time is not always a leading criterion but comfort is prioritised. Martinez et al. (1998:342) looked into a case of new consumer durables in Spain from the innovations diffusion point of view. They stated that functional advantage is important for adoption of new consumer durables, but so is the impression of modern attitude consumers might practice through it.

Households can experience comfort or discomfort in various cases and be influenced by these notions to a different extent. Gram-Hanssen et al. (2007) mention additional effort in building’s (attic wall) insulation to cause inconvenience in a household or ‘discomfort’ and prevent from implementation. On the contrary, those measures that do not require substantial financial and temporal investments were found as ‘convenient and easy to implement’. (Gram-Hanssen, K. et al., p. 2885). Another dimension of comfort is related to expectations of better indoor climate, for instance as a result of replacement to energy windows. (Gram-Hanssen et al., 2007, Bartiaux et al., 2007).

Occupants’ perception of co-benefits of weatherisation is given as a comfort improvement in comparison with control-group. Berry et al. (1997) looked into USA weatherisation projects and found that the weatherised group reported highly significant and positive change. Stoecklein and Skumatz (2007) show that comfort plays an important role as a determinant when purchasing double glazing and solar water heaters. Herring et al. (2007:1887-1888) show that 58% of those who applied loft insulation pointed at having a warmer house as a main benefit. For 77% - warmer house is the main reason to adopt loft insulation. Increase comfort/warmth/retain heat was mentioned as a driver for implementing the three of four investigated energy efficient technologies: new or extra loft insulation – 77%; heating

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controls – timer (37%) and thermostatic radiator valves (32%); condensing boiler – 35%. See also Table 1.

Table 1: Main drivers for adopting energy related measures. Results of the on-line survey.

Main driver

New or extra loft insulatio

n(250 mm or more)

Heating controlsCondensing boiler

Energy efficient

bulbsTimer

programmer

Thermostatic Radiator

Valves (TRVs)

CFLs LEDs

Save energy / reduce fuel

consumption84% 78% 59% 77% 91% 57%

Reduce fuel bills / save

money81% 74% 57% 69% 82% 34%

Increase comfort / warmth /

retain heat77% 37% 32% 35% n/a n/a

Concern for environment /

global warming /

reduce emissions

68% 57% 45% 60% 82% 11%

Data source: Herring et al. (2007:1888).

Following on quantitative studies addressing comfort as key determinant, Barr et al. (2005:1438) found that over 60% of non-environmentalists mentioned feeling comfortable at home as a significant issue. Fewer than 20% of committed environmentalists considered comfort as important (over 60% were ready to sacrifice it for the sake of energy saving). Analysing the EnergyStar - New Homes and Home Performance programme - in the US, Fuchs et al. (2004: 2-84) found that co-benefits such as personal satisfaction and comfort received the highest percentage as a category of benefits when calculated per participating building. In the New Homes programme comfort got high value by 50% of the respondents. When estimated as a share of co-benefits per building, comfort as determinant got 11% and is 2nd by rank. In the Home Performance programme the results for net co-benefits indicated that comfort is a ‘valuable’ and ‘very valuable’ choice determinant by 65% of the respondents. See Table 2 and Table 3 below.

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Table 2: Estimated NEBs within EnergyStar New Homes programme.

NEB categoryAverage valuation (1 min -5 max)

Those who valued 4-5, %

% of a NEB category per participating building

Personal satisfaction 3.2 57 12

Comfort 3.0 50 11Doing good for environment 3.1 56 10Ease of selling home 2.9 53 10

Data source: Fuchs et al. (2004:2-84).

Table 3: Estimated NEBs within EnergyStar Homes Performance programme.

NEB categoryAverage valuation (1 min -5 max)

Those who valued 4-5, %

% of a NEB category per participant

Personal satisfaction 3.5 70 11

Comfort 3.2 65 10Doing good for environment 3.1 62 10Ease of selling home 3.1 53 10

Data source: Fuchs et al. (2004:2-84).

3.6 Branding / designBrand or design have both quality and aesthetic dimensions. Ashdown et al. (2004) recognised importance of branding for Solid-State Lighting in the case of US households. Further, concerning the lighting issue and fixtures, Feldman and Mast (2001) concluded that style (along with operating costs) is referred to as major fixture selection criteria. Those segments that are likely to select style among their top decision factors are less likely to see operating costs as important factor. Oxera (2007) report shows the importance of brand as a determinant when purchasing TV-sets, and screen size was noted to be especially important in the case of wide-screen TV-sets purchasers.

It might be a discussion issue as to what extent conclusions about brand and design related to common goods can be applied to energy technologies and appliances, however it is suggested here that they can be used. With durables, including appliances, Brucks et al. (2000) learned that in US brand serves as one of quality dimensions. The authors highlight a complex nature of dimensions playing a role when purchasing durables. Brand and price are searched by consumers to evaluate prestige of the durable goods and both determinants brand and price seem to be important indicators of quality. Nowlis and Simonson (1997:8) noted that brand name as an attribute has a tendency to receive a greater weight “when preferences are formed on the basis of separate evaluations of individual options”. Bringing an example of television sets, toasters and/or imaginary products the authors saw that high-perceived quality brand names “are preferred more in ratings of purchase

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likelihood than in choice”.. As shown in Figure 1, DuPont (1998) indicates that brand can play a different role when purchasing household appliances in the US and Thailand. In the latter, brand appears to be the most important determinant. On the contrary, US consumers place it in a less relevant role – still high relatively speaking, though. Uitdenbogerd (2007:187) found that whereas 8.4% of households do not buy energy efficiency appliances due to their “preferences for brand or type”, 33.6% of households buy energy-efficient appliances because of brand or type preferences, see Table 4.

Table 4: Reasons to buy and not to buy energy-efficient appliances (in %)

Reason Not to buy Not important To buy Total

Expensive to buy 26.1 54.9 19 100Saves money during use 0.6 7.6 91.9 100Time and effort to find info and evaluate payback time

14.2 76 9.8 100

Preferences for brand or type

8.4 58 33.6 100

Opinion of household members

3.1 76.3 20.6 100

Better for the environment

1.4 10.1 88.5 100

Data source: Uitdenbogerd (2007:187).

3.7 CompatibilityThe compatibility determinant comprises at least two dimensions relevant to different energy technologies. First, it mostly refers to the size of existing fixtures and more energy efficient replacements concerned. Size incompatibility with existing fittings negatively influences adaptation of an energy technology. Calwell et al. (2002) found that in the US product size and ability to fit into existing fixtures was named as the major market barriers for CFLs that were successfully overcome and facilitated further sales. According to Uitdenbogerd (2007:185) 61.7% of non-adopters refer to energy-saving bulbs being “not suitable for all fittings”. DuPont (1998:6-9) reveals that for US consumers size is as an important screening criterion. This is mainly due to existing limitations of kitchen rooms. According to the author, in Thailand smaller refrigerators are seldom a problem to fit into doorways or into a space between counters.

3.8 PerformancePerformance as a determinant embraces functionality, as well as qualitative and quantitative dimension of technology/product output. However, it is argued that is hard for consumers to evaluate the performance of products. In fact, consumers’ estimates are based on observable product characteristics (Sanstad and Howarth, 1994a). In relation to innovative products, performance can relate to functional advantage in comparison to existing technologies. Dunphy and Herbig (1995) note that new products are purchased at first due to their conceptual appeal. According to the authors, repurchases seem to occur on the basis of tangible satisfaction. Addressing

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the performance of CFL in the US, Rasmussen et al. (2007:1955-1956) show that the colour of light, brightness, and delayed lighting were critical issues preventing a purchase.

3.9 ObservabilityThis determinant implies having an energy technology not only present in the market but in use, so that it becomes visible. Observing the use of technology by others may facilitate certain household categories in taking adoption decision. Faiers et al. (2007a) noted in the case of the UK that among different groups of customers considering adoption of domestic solar system, innovators don’t have a particular regard for ‘observability’, while pragmatists were inclined to assess a product for observability and trialabilty.

3.10 ComplexityComplexity represents a negative attribute that prevents an energy technology from adoption. It was not possible to identify quantitative studies available for this determinant in the literature. Faiers et al. (2007), regarding adoption of domestic solar system, note that pragmatists did view complexity as a limiting factor. The other group of consumers, innovators, were “balanced as to whether they view complexity as a limiting factor” (Faiers et al., 2007a:3421). As earlier noted by Hall and Reed (1999), among the attributes that are found with energy efficiency products, complexity and/or unfamiliarity can prevent their faster adoption. Higgins and Shanklin (1992) observed that technical complexity shown as the most widespread concern for using/purchasing high-tech technologies by moderate to light users of high-tech. But it may serve as a stimulus for innovators. Findings show that technical sophistication of products draws consumers away from them.

3.11 Choice/problem solvingThis determinant implies that an energy efficient technology is more likely adopted when it is present among available choices, and when it helps to solve an existing household problem. Possibility of choice and problem solving is playing a role in households’ investment behaviour. (Uitdenbogerd, D., 2007).

4. Demographical factorsProducts and markets, potentially building a part of households’ decision-making process, pass through a demographical context of a household. The following determinants, found in the literature, seem to play a role when energy related decisions by households are in question.

4.1 AgeAge may bring about certain patterns related to energy technologies, since human behaviour is affected by changing of the life stages. For instance in the UK, adopters of solar water heaters (renewable energy technology) contained a larger proportion of retired people (45%) (see Herring et al.,

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2007:1890). In Belgium, energy saving lighting is rather equally spread. According to Bartiaux et al. (2006:34-35), youngest cohort (18-29 years old) indicates 70.5% of saving bulbs’ usage. 50-69 years old also have high rate – 68.6%. 18-29 years old and especially 30-49 are the most likely age groups installing A or B labelled housework appliances (67.9% and 69.1%). For the oldest age cohort 70-89 years old it is not likely to have A/B appliances.

4.2 Household size and/or compositionAnother demographic determinant relates to how large and of who does the household consist. Boonekamp (2007) referring to Energiened (2000) states that ownership rates of heavy appliances are determined by household composition, lifestyle and job situation. Herring et al. (2007:1889) report from a 2006 survey that the households who were adopters of renewable technologies were typically coming from “two-person adult households”. About a quarter of those had children under 16 years old.

Wilk and Wilhite (1985:626) report from a weatherisation programme that ran in Santa Cruz, California, that mostly unmarried couples or married couples with children under 12 make investments (over USD 50) in house weatherisation. This is explained with special circumstances of moving into a new house (Wilk and Wilhite, 1985:626). Curtis et al. (1984:453) found that 2-4 people households were more active with energy conservation actions compared to other sizes and households consisting of two adults reported the most number of saving activities. In Belgium, Bartiaux et al. (2006:34-35) show that two adults with children, as well as 4 persons households are the most likely users of energy saving lighting (66% and 68.2% of households of this composition and size). In addition, the authors also show that households of 3 and 4 persons are the most likely to have at least one A or B labelled housework appliances (74.3% and 73.6% correspondingly). It is argued that while 1 person household is quite below the average likelihood (46.64%). As well, this is similar for 1 adult without children (46%) and on the contrary, 2 adults with children will appear at the higher end (71.6%) (Bartiaux et al., 2006:34-35).

4.3 GenderWithout many available evidences gender is sometimes suggested to influence investment decisions. Though, it might be irrelevant to some and relevant to other energy technologies. In the following examples a slight difference is present. For instance Martinez et al. (1998), with the help of a survey in Zaragoza, Spain, found that wife’s age, employment status, education and household income are the factors that can distinguish the early adopters of two of the household appliances studied in the survey. These appliances are a dishwasher and vitroceramic hob. The authors concluded that with increase of wife’s age the probability of household to be among the early adopters diminishes (see Martinez et al., 1998). In Belgium, the study done by Bartiaux et al. (2006:34-35) shows that energy efficient lighting is used almost equally by female and male representatives of households (65.1% - male, 63.8% – female). Housework appliances: To the surprise of some, females are more frequent installers of A/B labelled appliances than men (64.7% versus 58.6% with average of 62.2%).

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In Sweden, Palmborg (1986), addressing social habits and energy consumer behaviour in single-family homes, provides a rank order for insulation of a house among six product categories by men and women. As shown in Table5, results show slight difference:

Table 5: Rank order of insulation by men and women

Rank position n Men, % n Women, %1st 33 50.0 32 46.42nd 13 19.7 10 14.53rd 3 4.5 8 11.6

Data source: Palmborg (1986:78).

5. Factors related to buyer’s characteristicsLutzenhiser (1992:53) pointed that social science research shows that likelihood of individuals to aim at conservation and understand energy issues and related technology will systematically vary among groups “identified on the basis of social class, ethnicity, life cycle stage, gender, education, geographical location, local culture”). Besides demographic differences, households have a number of determinants constructing quality of their lives. It is an initial assumption that these determinants as well contribute to decision-making that is in focus of the current report. According to Hirst (1984:428) household that tend to “have higher incomes, more education, larger homes, and live in a single-family houses of their own” are keener on participation in such programmes as residential conservation service in the US and going through energy audits.

Palmborg (1986) investigated residential energy-related investment behaviour in Sweden in 1981. Purchase decisions influence the household overall consumer behaviour. According to the author, the decisions are socially defined and “lead to social rewards” for households. Decisions like: 1 – concentrate resources, 2 – nice vs. energy efficient, 3 – brand, comfort. It is argued that restrictions, decisions, priorities, demands, brand-choices “are developed within a specific part of social system”. “In total, 58% of the variance in households’ energy consumer behaviour is explained by the sociological variables.” “Presence at home, disposable income, demand for insulation, attitude to energy consumption and priority of housing – explain 50% of the total variation in households’ energy consumer behaviour” (Palmborg, 1986: 23, 24, 81, 82).

5.1 IncomeIncome can have ambiguous implication. In some cases it might stimulate purchasing of products with energy-efficiency features, while in other, it may give a feeling that a household can afford energy costs they have to pay, or have no importance when a related-decision will have to be made.

Costanzo et al. (1986) note that disposable income plays an important role in decision about adoption of energy-conserving technologies. For instance a Canadian study from 1982 shows significant relationship between income

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and energy conservation actions (see Curtis, 1984:453). Lutzenhiser (1993:63) referring to Dillman et al. (1983) mentioned that in the USA, when faced with energy price increases, higher income households “took advantage of tax credits and incentives programs to invest in building and equipment energy efficiency”. Likewise, Black et al. (1985) reported that income has indirect influence on the capital investment in energy efficiency in Massachusetts (US) in the 1980’s. Herring et al. (2007) found a certain class affiliation and somewhat professional category attributed to adoption of energy related technologies. Investigating the UK as a case study, a sample shows that adopters of energy efficient technologies to be represented by a middle class household with a main earner who is a professional/senior or middle management, or retired. Stern et al. (1986) looked into the homes weatherisation features and provide percentage of homes having weatherisation features in relation to their incomes. Analysing domestic solar system among energy-conscious households in the UK split into 2 groups, innovators and pragmatists., Faiers et al. (2007:3419-3420) found that a larger portion of innovators have income less than £30 k per year, whereas higher proportion of pragmatists have over £50 k per year. Bartiaux et al. (2006:35) show that higher income inclines households to have A/B labelled housework appliances: 70.8% among those with an income € 2200-3300 per month, and 68.5% among those with a monthly income above € 3300 per month.

On the contrary, there is also a body of literature that shows no or low correlation between income and adoption of efficient technologies. Ruderman (1987), referring to a study by Reid mentioned no (or low) correlation between presence of energy efficient appliances in a family to their income. However, there was found a stronger influence of home ownership on purchase of efficiency appliances (see Ruderman, 1987:118). Barr et al. (2005) suggest that the committed environmentalists were the more likely purchasers of energy saving equipment. Nevertheless both extreme groups of non-environmentalists and committed environmentalists fell under the lower income category. Therefore Barr et al. (2005) do not see income as a strong predictor for further analysis. Ürge-Vorsatz and Hauff (2001) noted that the use of CFL bulbs is in slight correlation with income groups (lower usage in poor households, and highest in the richest). However, for the middle class income group the situation changes: the higher income share of the middle class is using CFLs in fewer cases than a lower income part of the middle class. Further these authors refer to a Philips survey that demonstrated no direct correlation between adoption of efficient lighting technology and level of wealth (see Ürge-Vorsatz and Hauff, 2001: 291).

5.2 Knowledge/information/awarenessThis determinant refers to the knowledge of a product/technology possessed by a household. There is compelling evidence that consumers often lack information and expertise in processing and applying the information (see e.g. Sanstad and Howarth, 1994a). Studies vary in conclusions regarding the role of this determinant. Lack of knowledge prevents customers from making decisions on energy investments. On the contrary, positive relationship between knowledge build-up, action and planned action is found in the literature. For instance Darby (2006:2934) reports that the percentage of households planning or owning solar water heating panels increases with

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more of knowledge built-up, arguing that “around one third of all energy-efficient measures … could be accounted as direct consequence of taking part and receiving advice.” The author found that the mean number of energy related alterations to home in previous 6 years shows strong increase with the growing number of energy information sources. Likewise, Rasmussen et al. (2007) found that CFL awareness and purchase rates have been reported positively correlated in California and Northwest Pacific region. The authors found that in California during 1998-2006, the purchase rate rose from 17% in 1998 to 69% in 2006, with awareness gradually increasing from 59% in 1998 to about 94% in 2006 (Rasmussen et al., 2007:1952, 1957). In the UK, it is argued that the perception gap (i.e. lack of knowledge about the costs and benefits) is present concerning insulation measures. This barrier is an important “factor affecting the ability of consumers to make decisions on the take-up of LI (Loft insulation) or CWI (cavity wall insulation) (Oxera, 2006:22). Herring et al. (2007:1887) shows that 40% of those who implemented loft insulation in the UK (both in on-line survey and interviews) became more concerned about saving energy after the measure was actually implemented.

However, it has been also argued that that the presence of knowledge would lead to non-adoption of energy-efficient technology (e.g. Banks, 1992). Yavas et al. (1986) made a study in Finland where consumers were grouped based on cognition level (low; moderate; high knowledge about energy conservation). Analysis of number of energy efficient products they purchased (and derived product purchase index) showed no link between level of cognition and differentiated purchase patterns. In addition, as studied by Farhar et al. (2002), there are cases (e.g. USA) when new Zero Energy Home buyers “do not appear to know enough about energy efficiency and solar features to ‘demand’ them… yet… once they moved in, they accept, tolerate and even enjoy those features”. It is also argued that a lack of awareness is a barrier but not a major. Rasmussen et al. (2007) reported that even in the regions in the US with very active awareness rising campaigning, 10% of households didn’t install CFLs due to not having heard of them.

5.3 Attitude/environmental consciousnessThere can be an overstatement of own environmental awareness and its role in the studied context. Not the least is a latent wish of human beings to bring up some features that they reckon as positive. Numerous examples are found in the literature about the positive correlation between environmental awareness and adoption of energy efficiency technologies. Barr et al. (2005) noted that committed environmentalists are more likely to purchase energy efficient lighting and look for efficient appliances. Further they found that in the UK mainstream environmentalists show similar tendency. The major difference in purchasing is seen compared to the two other categories – occasional environmentalists and non-environmentalists (see Barr et al., 2005: 1430). According to Darby (2006:2936), those who saw themselves as ‘strongly energy-conscious’ were “far more likely to … plan/own solar water heaters”. It is found that there is a significant positive relationship between self-assessed consciousness and the installation of energy-efficiency measures by the householders. This was mainly due to the total number of improvements in the house, which included energy efficiency measures (see Darby, 2006).

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According to Banks (1999), analysis suggests that people with “security” and “environmental” value orientation will be most likely to choose efficient appliances. Likewise, Palmborg (1986), looking into energy behaviour of Swedish consumers, concludes that for major energy conservation decisions energy consciousness played stronger role than an economic motive. Uitdenbogerd (2007:185, 187) found that 89.2% of Dutch households purchasing energy-saving light bulbs did it because it is “better for environment”. Similarly, 91% of buyers of water-saving shower heads also did it because of the same reason. The same study shows that that 88.5% of households with high environmental awareness buy for this reason energy-efficient appliances. For the UK, Herring et al. (2007:1887) report that over 80% of adopters and 70% of non-adopters of energy efficiency measures were “fairly or slightly concerned about reducing their environmental impacts”. In fact, most adopters and non-adopters tend to save energy, recycle household waste, economise water and car use. In the same study, concern for environment was mentioned as one of main drivers for the following measures: new or extra loft insulation – 68%; heating controls – timer (57%) and Thermostatic Radiator Valves (45%); condensing boiler – 60%; energy efficient lighting – CFLs (82%) and LED (11%). 83 to 90% of households who adopted renewable energy technologies were “fairly or very concerned about reducing their environmental impact” (Herring et al., 2007:1890).

On the contrary, several authors (see e.g. Vringer et al, 2007; Gatersleben et al., 2002; Hezemans, 2005) looking into residential energy requirements found that pro-environmental features of households do not necessarily lower their energy requirements or create motivation to save energy. In Sweden, environmental attitudes showed to be the weakest explanatory variable in relation to explaining energy behaviour in households (see Carlsson-Kanyama, 2005:251). In the study of residential energy consumers, Ester (in Lutzenhiser, 1992a:5) found that energy attitudes “explained only 30% of the variance in the intention to conserve energy”. This lead to his conclusion that households’ intentions on energy-saving are determined in a complex way and hard to predict.

5.4 Education/occupationRegarding this particular determinant, the volume of literature seems to be much less abundant. Ürge-Vorsatz and Hauff (2001) show that in two surveys carried out in Hungary (1997 and 1999) a strong correlation between the level of education and ownership of CFLs was found and confirmed. On the contrary, Curtis et al. (1984:453) argue that in Canada, “the occupation and education of both the person interviewed and the main wage earner of the family had no significant influence on the number of household energy conservation actions”.

5.5 Type of dwellingIt is argued that the type of dwelling is one of the indirect determinants of energy related technologies. It is the dwelling choice that precedes energy-related choices and may further limit them. For instance, Aune et al. (1995:1) analysed energy concerns in the process of choosing dwelling in Norway. They conducted the survey in 1991 and found considerable variation in energy consumption in different dwellings: “type of dwelling is in many ways

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indicative of the lifestyle of a family which affects energy consumption”. Similarly, Vaage (2000) attempted building a model of discrete choice for heating appliances under four different heating systems (electricity, wood, oil and combination) based on actual cross-sectional data available for Norway. In the model estimates, both the type and age of building shown to have a significant influence on choice of appliances (see Vaage, 2000:661). In the UK, adopters of energy efficient technologies were mostly occupying semi-detached or detached houses with three-four bed-rooms (Herring et al., 2007). According to the authors, the same characteristics belong to the households who adopted renewable energy technologies. On the contrary, and the case of Belgium, Bartiaux et al. (2006:35) found that households accommodated in apartments are less inclined to purchase A or B labelled housework appliances (51.1% compared to the 62.2% average).

5.6 Life styleLife style has been introduced in the energy related behavioural research in attempts to find explanation to strongly varied energy behaviour. Aune et al. (1995) refer to life style as to a cultural factor. They agree that along with habits it plays very important explanatory role within energy consumption. Nevertheless, as admitted by Jensen (2001), there is no agreement among academic scholars as what is meant by lifestyle.

Lutzenhiser (1992:55; referring to Schipper et al., 1989) argued that life-style energy-use differences “can be accounted for by reference to varying household time budgets, travel practices, and residential activity patterns”. Further on Lutzenhiser and Gossard (2000) pointed that by early 90s there were data collected to distinct across subgroups of consumers their differing energy use patterns and efficiency choices. Shove and Wilhite (1999) denoted that a Dutch programme on Sustainable Homes recognised that household choices were not determined only by demographic, technological or economic factors. It lead them to looking into life-style choices. Life-style approach with consumer choices (in energy-related purchases and use) as one of its components was employed by Bin and Dowlatabadi (2005:198-199) to “quantify the relationship between consumer choices and consequences at a macro level”.

Interesting to also note is that, analysing residential solar equipment in the US - California, the life-style of voluntary simplicity is seen by Leonard-Barton (1982) as having potentially strong implications on energy-consumption patterns. This concept appeared in 1936 conceived by Richard Gregg and can be briefly explained as avoidance of possessions that are irrelevant to the main purpose of life. The degree of simplicity will be an individual matter and not the economic necessity. The index of voluntary simplicity behaviour was the second strongest predictor to purchase residential solar equipment (see Leonard-Barton, 1982:249).

Finally, it is also found in the literature that household mobility has a major effect on home improvement and investment strategies. According to Wilk and Wilhite (1984a:455-456) more often moving households are “reluctant to invest in retrofits, though they may compensate by seeking to buy a home which is already energy efficient”.

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5.7 Timing / Appropriate momentThis determinant, as meant by Uitdenbogerd et al. (2007), implies a connection of a technology adoption to such events as house refurbishing or retrofitting (see also Stern et al., 1986a; Lutzenhiser, 1993; Jaffe and Stavins, 1994b; Wilson and Dowlatabadi, 2007).

According to Stern and Aronson (1984:62) important energy decisions in households are made “when furnaces, water heaters, refrigerators, and other equipment wear out”. Holak et al. (1987) argue that timing of purchase is influenced by expectations from innovative technology. As noted by Banks (2001), the most of household decision-making on infrastructure improvement will take place when moving to a new home. In her survey Darby (2006:2934) marked that in the UK replacing of most/all windows with double-glazing appeared to be the most ‘popular’ house alteration undertaken since moving into a home. This finding seems to support the appropriate moment hypothesis noted above – changing windows when moving in. In the US, DuPont (1998:A-5) shows that more than 40% of appliance purchases are connected to replacement and moving to a new residence, while laundry appliances and microwaves are named as exceptions. Likewise, it is found that weatherisation of homes in Santa Cruz (California, US) was more frequently taking place under special circumstances, such as moving into a new house (see Wilk and Wilhite, 1984). However, the intention of staying shorter or longer can also be influential. Sanstad and Howarth (1994) mentioned that tenants would not be willing to retrofit, since they may move out before cost savings (see below split incentive problem).

6. Contextual factorsIn this report, contextual factors refer or include those factors that go beyond product, demographic or buyer’s characteristic. It relates to various actors or conditions that are important in implementing energy efficient solutions.

6.1 Ownership/split incentive problemIn a study of homeowners’ buying patterns by Reid (1982; referred to in Ruderman et al., 1987:118), 50% to 100% of central heating, refrigerators, water heating appliances purchased by homeowners had more of energy-saving features than those installed in rented units.. This is further supported by Guerin et al. (2000:62) who noted that “consumers who own their homes are likely to have more energy conservation features in their homes because of the personal benefits from the investment, and they are more responsive to long-term capital investment.” Costanzo et al. (1986) strengthened this line confirming that home ownership plays important role in adoption of energy conservation technologies.

For instance when it comes to capital investments in energy efficiency, Black et al. (1985:11) found that, among three significant direct paths that explain 10% of variance on the investment scale, the strongest direct effect is played by home ownership. Referring to the same study, Gardner and Stern (1996) argue that attitudes and beliefs were becoming less important (relative to ownership) as energy-saving activities were getting more difficult and costly

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(insulation, major furnace repairs). On the other hand, a Canadian study didn’t assert the same conclusion. Curtis et al. (1984) found that formal conditions for occupying a dwelling were not strongly associated with reported number of energy conservation measures. Still, the authors confirmed that ownership status slightly increases the number of actions compared to renters (perhaps, due to more responsibility and ability to make changes in the house) (see Curtis et al., 1984:453).

The ownership problem is sometimes known as the principal – agent problem. In the latter, purchase decisions related to energy efficiency technologies are made by other those who pay the energy bills (Jaffe and Stavins, 1994a). As noted by Blumstein (1980) in the landlord-tenant relationship, when landlord pays the bill, the tenant loses incentive to behave energy-efficiently. When the bill responsibility is shifted to a tenant, a landlord loses incentives for energy conservation improvements. Quantification of the principal-agent consequences for various residential energy end-uses (water heating, space heating, and lighting) can be found in Murtishaw and Sathaye (2006)

6.2 Intermediaries This is an important contextual determinant, as not in all cases consumers have to make their own choice on energy technologies (e.g. principal agent problem). In addition, purchase decisions can be strongly affected by marketing techniques applied by commercial organisations or intermediaries. In fact, Wilhite, and Shove (1998:327) argue that by focusing on individual; rather than on intermediaries, any research “blocks out both the other numerous institutions which contribute to the formation of energy consumption patterns, and their links to individual consumers”.

Quantitative information available at present is rather limited, but (mostly qualitative) references point out to this determinant as an important one. For instance institutions’ influence on consumers is described by Lutzenhiser (1993). The author acknowledges the role of intermediaries or ‘infrastructure players’7 (referenced to Lutzenhiser’s personal communication with T. Henneberger). Further on he concludes that “intermediaries’ incentives to pursue energy efficiency are few, while their disincentives are many” (Lutzenhiser, 1993:276) referring to a number of publications (e.g. Stern, and Aronson, 1984; Blumstein et al., 1980; Ling and Wilhite, 1992; Hewitt and Palermini, 1989; Gordon and Dethman, 1990). Along these lines, Stern and Aronson (1984) underline that energy users are not the only investors. Intermediaries, such as developers, construction companies, take many important decisions influencing further energy end-use. According to the Swedish Energy Agency, who commissioned survey on heat pumps in Swedish households, installation company seemed to have an influence over decision of the heat pump model (Energimyndigheten, 2005). Brown (2001:1999) highlights that “involvement of intermediaries in the purchase of energy technology limits the ultimate consumer’s role in decision making and leads to an underemphasise on life-cycle costs.”

7 Lutzenhiser names the following actors falling under this category: builders, code officials, heating contractors, automobile dealers, utility company representatives, architects, appliance salesmen.

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Intermediaries can also become influential through how they form their presence in the market. According to Uitdenbogerd et al. (2007), reliability of vendors plays a role (middle ranking) in determining the adoption of energy efficiency technologies in households (see also Stern and Aronson et al., 1986a; Görts et al., 2002). A case-study from the US done by Lawrence and Jankins (2000) showed that HVAC contractors and purchasers have different emphasis. Salesperson strongly stresses the efficiency, while consumers, efficiency and comfort (especially for adding equipment). According to the authors, “contractors’ view of the customer purchase decision-making process is not consistent with customer reported purchase behaviours. Most contractors believe that they must compete on cost” and response time, while customers discuss mostly such issues as: comfort improvement, noise reduction, indoors air quality improvement. (Lawrence and Jenkins, 2000:252-253).

Finally, utilities supplying energy to the residential sector can as well be a part of contextual condition. Condelli (1984) noted that utilities are in the right position to question, learn and understand the processes of end-user’s energy decisions. Likewise, Vine and Fielding (2006) have found that utilities can influence on supply and demand characteristics (e.g. CFLs). According to the authors, they can help improved availability, accessibility and affordability.

6.3 Policy incentivesThere is extensive literature and compelling evidence (e.g. ex post evaluation studies) about the effects of policy instruments (e.g. tax, rebates, soft loans, subsidies) and their outcomes in terms of the adoption of energy efficient technologies by target groups (e.g. household sector) (for details see e.g. Gillingham et al., 2006; Vreuls et al., 2005). For instance, Hassett and Metcalf (1995) found that having tax incentives in place is very likely to increase the number of conservation investments. Stern (1985:140) show that policy incentives such as soft loans seem to speed up the pace of purchasing or investing: 78% of programme participants stated that availability of zero-interest loans influenced their decision to install energy conservation equipment. Furthermore, the study also concludes that the conservation equipment wouldn’t have been installed at that period, if zero-interest loan were not available.

7. Energy (efficiency) technologiesThis section was necessary, as the major part of the reviewed publications are devoted to particular devices or technologies (e.g. space conditioning, hot water supply, renewable energy in residential use, lighting and consumer appliances).8 These are meant to comprise all the categories of energy efficiency-related products that consumers make their choices about. In the following sections the starting point is a particular technology, followed by the determinants found related to them, come from available researched and

8 Renovation and weatherisation are not exactly products but they do represent activities that imply decision-making and clear energy efficiency outcomes. Therefore, they should be regarded as a part of energy-efficiency decisions in the category of energy technologies.

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reported cases. The aim of this section is also to identify the case in which determinants and/or their importance differ from various product categories.

7.1 Space conditioning and building envelopeVentilation and both heating and cooling related-devices and technologies are in the focus of this section.

Kempton et al. (1992) note that user behaviour in the use of air-conditioning in the case of US had been influenced by a number of non-economic factors. Those reasons limiting use of air-conditioners were found to be related to: health, comfort, safety, energy waste avoidance, among others (see Kempton et al., 1992:189). Some of them would as well seem plausible to be a part of decision-making at a purchase stage. Mebane and Presutto (2001) in the cases of Italy and Spain found that in general, responses on importance of choice influencing factors had the following sequence: advice; performance characteristics; economic factors; and marketing factors. Brands showed slightly more importance in Spain. Model loyalty was shown to be present in both countries (see Mebane and Presutto, 2001:482, 486).

For the specific case of heating, ventilation, and air-conditioning systems (HVAC) installations, several cases were found in the literature. Bensch (2005:41) found that among the factors leading to specific model selection, the following determinant contributed the most to consumer choice: price (25%); recommendation (16%); efficiency/cooling capacity (12%). Lawrence and Jenkins (2000) surveyed sales volumes of furnace, central air conditioning, heat pump and evaporative cooler installations. The authors found that the following considerations were important for those who replace HVAC, namely: lower energy costs (35%); reliability/durability (18%); comfort (16%); timing (8%); proper sizing (8%); while, cost and safety are important to only 7%. Among those who add pieces of HVAC equipment the following matters: comfort (31%); lower energy costs (23%); proper sizing (12%); reliability/durability (11%), and 11% consider the costs (see Lawrence and Jenkins, 2000:250-251). In addition to the determinant presented above, it is interesting to note that Jakob (2007) states that building envelope renovation is more affected by technical characteristics of buildings and housing related activities, e.g. building extensions, and to a lesser degree by socio-economic variables characterising households.

InsulationWhen it comes to insulation, different research efforts are found in the literature. For instance DEFRA (2006) commissioned investigation on additional insulation of U.K. houses through loft insulation and cavity wall insulation and concluded there was perception gap (i.e. lack of information about costs and benefits) among potential adopters (already noted in previous sections). Also in the UK, Herring et al. (2007:1887-1888) address the reasons behind the adoption of new or extra loft insulation. The authors conclude that households carried out the implementation due to (i) to save energy and reduce fuel consumption– 84% in the on-line survey and 43% in the interviews; (ii) to reduce fuel bill and save money – 81% in the on-line survey and 71% in the interviews; (iii) to increase comfort/warmth/retain heat – 77% in the on-line survey and 71% in the interviews; and (iv) to reduce emissions, due to concern about environment/global warming – 68% in the

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on-line survey and 21% in the interviews. In addition, few households mentioned the cooling function of insulation. A small portion (4%) used the efficiency gained through insulation to increase room temperatures, heating larger space or for longer periods. An insulation of 250 mm was under survey and promoted through special programmes. Among reasons not to perform it was a substantial loss of storage space in the loft (37%) (see Herring et al., 2007:1887-1888). For the EU-15, Stead (2005) shows that different levels of actions with home insulation depend on age, education, and gender. General trend for males shows that later completion of education, as well as older age make the house insulation more likely to have occurred and reported. 49%-50% for the age above 65 years old, finished education at 16-19 or over 20 years old. The female group figures are not easy to draw a conclusion upon (Stead, 2005:1214).

For Sweden, Palmborg (1986) found five variables, (priority of saving, attitude to energy consumption, education, household size, and presence at home) that explain 20% of variation in households’ demand for insulation. Except for one (priority of saving), they are negatively correlated with the demand, which is the dependent variable (see Palmborg, 1986).

Space heatingRehdanz (2007) found that ownership is likely to influence installation of energy-efficient heating and hot water supply systems in residential houses compared to the status of renting accommodation.

In Sweden, Linden et al. (2006:1923-1924) found that 55% do not want more than 20oC, however in apartment houses it is often 22 oC. The explanation lies in the monthly rent that includes heating charge, unlike with detached households paying their heating costs directly to energy supplier. The authors also mention that 38% of those who have possibility to lower indoor temperature at night do not use it. Those who lower temperature at night do it for comfort. Furthermore, 23% cannot do it; covering windows at night (approx. 50%); and airing during winter time (40% daily during winter season). According to Bartiaux et al. (2006:28), addressing the case of Belgium, the practice to regulate temperature in wintertime or when airing is more common for smaller income households or single family dwellings (not apartments).

For the UK, Herring et al. (2007) analysed the installation of controls (timer/programmer) for central heating systems. The study reveals numerous reasons as drivers for the adoption of installations. Timers were said in the online-survey to be installed by 78% in order to save energy/reduce fuel consumption; by 74% - to reduce bill/save money; 37% did it for heat retain and comfort increase; and 57% - because of their environmental concerns. Among non-adopters, 26% did not expect to gain as much as invested, and 17% considered having “too much trouble to install” (Herring et al., 2007:1888-1889). Also addressing non-adopters in the US, Meier and Eide (2007:1869) found that 46-48% of residential space heating is hampered by the principal agent problem.

WindowsStoeckleine and Skumatz (2007) performed an evaluation of non-energy benefits on New Zealand’s zero and low-energy homes. They estimated that

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non-energy benefits associated with double glazing make almost a quarter in relation to energy savings benefits. Analysing adoption patterns in Switzerland, Banfi et al. (2008:513-514) arrive to the conclusion that there is a “significant willingness-to-pay (WTP) for energy-efficient attributes of rental apartments and of purchased houses”. The marginal WTP for new windows as compared to medium old ones is 13% of a rental price for both, flat-renters and single-house purchasers. Medium-old windows as compared to very old ones get marginal WTP of 10% and 8% correspondingly for renters and purchasers. For the case of Sweden, research targeting attitudes of consumers to energy-efficient windows found that 90% of those who installed more energy efficient windows were driven by expecting lower energy costs and reduced cold air inflow (see NUTEK, 1995:6-7). A similar percentage considers noise reduction as a motive. The same study mentions that 100% of the surveyed consumers agreed that they save energy by replacing windows for more energy efficient. Furthermore, environmentally friendly image was an important motive in nearly 75% of cases during reconstruction process that was studied. Against (non-adopters): almost 85% of those who rejected such windows stated high initial costs as the major obstacle and for 75% it was the decisive factor not to invest. Almost 50% of those who rejected (non-adopters) were convinced that energy-efficient windows are tight (do not allow airflow) (NUTEK, 1995:6-7).

Thermostatic Radiator Valves (TRVs)By definition this is the energy saving technology as it allows setting a required temperature level and leaving for good a human control over residential space temperature. Herring et al. (2007:1888-1889) found that the installation of TRVs was driven by expectations of energy and fuel consumption reduction (59% - online survey, 29% - interview); monetary savings (57% - online survey, 29% - interview); 45% did it for environmental reasons and 32% to increase comfort. The same reasons as above were mentioned to prevent the adoption of this type of central heating regulation in the course of on-line survey: 47% found it to be complicated to install, and 20% - “savings not worth the costs”.

BoilerFor the specific case of condensing central heating boiler, Herring et al. (2007:1888) found that the strongest driver for adoption appeared to be saving energy and reducing consumption (77%), following were financial savings and reduction of fuel bills (69%), environmental concern stipulated 60% of installations, and improved comfort – 35%. Among the criteria preventing adoption, the study identified that a large proportion of household found the boiler type too expensive; another believed that it has unreliable reputation; and others didn’t expect sufficient savings.

7.2 Sanitary hot waterAnalysing water heaters in the US, Meier and Eide (2007:1868) found the principal agent problem. The authors estimate that only 21% of US households select water heater and pay the energy bills (i.e. the principal and the agent coincide). For the rest, 78% remain in the situation when the principal and agent do not coincide and either efficiency or usage problem arises.

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7.3 Renewable energy technologiesRenewable energy technologies are becoming more and more commercialised and available for residential use. The publications available on the subject in focus covered only solar energy and heat pumps’ residential applications.

SolarSolar technologies have been tried for residential purposes as long as ago as almost thirty years ago in U.S.A. LaBay and Kinnear (1981:275) reported their observations from Maine, that adopters seem to be “younger, more highly educated, higher in income, earlier in the family life cycle, and higher in occupational status than the general population”. The authors examined purchase decision process for solar energy technology through a survey. This process was perceived as a major technological innovation at that time. The authors focused on both: adopters and non-adopters, as well as on their demographic, socio-economic characteristics in combination with product characteristics. Several attribute perceptions were found. See Table 6.

Table 6: Demographic and attribute perception ratings

Source: LaBay and Kinnear (1981:275).

Faiers and Neame (2007:3422), who studied the U.K. case much later found that affordability and economic capabilities may become a more important factor for adopting an innovative technology than the attributes of the innovation technology per se. “Among other factors, this is possibly due to the greater purchasing freedoms that higher earners potentially have.”

Isaksson (2005) looked in the case of new low-energy houses with solar collectors and well insulated building construction in Sweden. The study found that for the majority of the residents the low-energy properties were not the major reason to purchase such house. Location, high standard (quality) and comparatively low price (against other houses in the area) were the leading factors in the house purchase. For many, the energy side became a bonus, as they considered this possibility for saving on energy as positive.

In Germany, Rehdanz (2007:172, 178) found that less than 1% of the households included in a survey use solar energy as additional source. The

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results show that using solar energy for heating, while energy prices grow, helps decreasing heating expenditures by about 14%.

In the UK, Herring et al. (2007:1889-1893) found that the implementation of solar water heating (SWH) relied on three most frequently given reasons: (i) reducing fuel bills (83%9 - online survey, 47% - interview), (ii) concern for environment (79% - online survey, 73% - interview), and (iii) monetary savings (77% - online survey, 53% - interview). Then, the study also addressed the implementation of solar photovoltaics (PV). The results showed that environmental concern was the main driver for installing PV (56%). Having funds available explained 43% of installations, and energy/fuel saving expectations – 31%. Among the main reasons for non-adoption of solar renewable technology, Herring et al. (2007) point out to: (i) “high capital costs” (73% - for SWH, 85% - PV), (ii) “fuel savings do not worth the costs” (35% - SWH, 40% - PV), and (iii) lifespan of technology “will not likely be enough to pay back the investments” (24% - SWH, 28% - PV). The other reasons to reject seem scoring in parallel for both solar renewable technologies: new technology with uncertain performance and reliability (23% - SWH, 19% - PV), difficulty finding space or suitable location for unit (17% - SWH, 16% - PV), getting planning permission (13% in both SWH and PV cases). Among the interviewees, 2/3 of those who adopted SWH, were influenced by their friends who already owned an SWH system. According to the study carried out by Herring et al. (2007), the main benefit mentioned by adopters of SWH, PV, and other renewable technologies was “the pleasure they got from using renewable energy”.

Heat pumpsAshdown et al. (2004:2, 17) carried participant survey comparing conventional water heater with the heat pump water heating (HPWH). They inform that in the US house owners considered the following about heat pump for water heating: initial costs, operating costs, fitting in available place, dehumidification, and product reliability. Energy efficiency was only a secondary benefit.

According to the Swedish Energy Agency (2005:9) the purchase of heat pump installations by households has mainly been explained by economic motives (74.8%) in Sweden, with more than a half installed it due to replacement of an old heating installation (57.2%). The study mentions that 34.4% named environmental reasons as a motivation for decision; and for 19.6% comfort was important. Also in Sweden, Sundberg et al. (1995:10, 15, 17) found that the motives for the adoption and installation of heat pumps are (i) energy savings (66%); (ii) reduction of environmental impact (62%); (iii) reduction of operating costs (60%). Interesting to note in this study is that short payback time was mentioned as one of the last reasons - by less than 30% of small houseowners. Among those who did not install, Sundberg et al. (1995) found that the most important reasons were: (i) expensive (47%); (ii) don’t expect to obtain the promised effects (40%); (iii) not proven technology (39%).

7.4 LightingEnergy efficient lighting has attracted substantial attention in studying consumer preferences and understanding why adoption or rejection decisions 9 Among those who adopted SWH.

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were taking place. This section covers the cases with compact fluorescent light (CFL) bulbs and light emitting diodes (LED). The former show substantially broader coverage in reflecting the adoption/foregoing decision-making, compared with the latter.

As noted by Meier and Eide (2007:1869) in one of the US cases, residential lighting is the kind of energy use least influenced by the principal agent problem: approximately 2% of households are faced by it. Cames (1999), studying a Swiss case stated that “The use of energy saving bulbs increases with social class and decreases with age” referring to Arend (1997:14).

Stokes et al. (2006) reported that style appeared to be the most frequent driver when selecting the fittings and, consequently, bulbs. Design strongly contributes to the choice of lighting. In the study, it is mentioned that, for instance, the Carbon Reduction in Buildings (CaRB) project showed that lighting is intimately linked to mood and well-being (see Stokes et al., 2006:10). To confirm this observation, the study carried out by Ecos Consulting (2002) in the US also found similar aspects driving the implementation of the efficient lighting systems. It is found that style is causing 51% of indoor lighting fixture replacement. “Style or aesthetics is the most important factor” for households when buying indoor lighting. For outdoors – safety, security and durability are the most important. Within this context, energy efficiency as such is ranked the last of considered attributes (see Ecos Consulting, 2002:14, 17).

In the UK, Oxera/DEFRA (2006:26) report that statistically significant factors influencing likelihood of purchasing efficient light bulbs are the following ones: (i) important – price, attitude to labelling, life-time; (ii) minor – receipt of advice; (iii) insignificant – cost savings. Within the same study, a sensitivity analysis was carried out for these drivers. For the price, results show that if the perceived cost of energy efficient bulb increases by 10%, the probability that households will purchase them decreases by 1 % (from 0.70 to 0.69). Analysing the hypothetical weights on CFL’s attributes’ importance, Turiel et al. (1997:404) also found similar drivers when buying CFLs in the US, namely price (29%); operating costs (25%); life of bulb (24%).

In Sweden, Linden et al. (2006) found that energy efficient light bulbs are not very common as one could expect. The study shows that 50% of households do not use them and that only 17% turn off lights when leaving rooms. The rest leave the lights on because of “cosiness, safety, and the habit of moving” (Linden et al., 2006:1924).

Hobart and Wilson (2007:428-429) found that in the UK the cost of CFLs relative to incandescent bulbs is a barrier to their use. In the course of the study they saw no obvious awareness among people about the potential reduction in energy use and longer life-time. At the same time around 90% were concerned about global warming issue and 85% believed that lower electricity use could help the issue. 100% satisfaction with the energy saving bulbs (and intention to continue using the provided CFLs) was reported in Hobart and Wilson’s study. Nevertheless, almost a half (45%) would stop using CFLs in some rooms. This was in all cases related to performance (slow start-up, low light intensity) or compatibility dissatisfaction (doesn’t fir existing fixtures). Also in the UK, Herring et al. (2007:1888, 1889) found that

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91% of those who installed CFLs, did it to save energy; 82% were expecting monetary savings, and 82% expressed their environmental concerns. Generally, longer operational life of CFLs acted as a driver. However, with experiencing shorter lifespan (1-3 years), it turned to be considered a barrier by households. With 10% of users a rebound effect was noticeable – leaving the bulbs on longer, install additional CFLs. About 40% of those who did not adopt this technology because the CFLs, the authors found that CFL were “ugly and/or too large” (42%); too expensive (33%); “don’t fit existing light fittings” (33%); “unpleasant or unsuitable colour of light” (33%). In addition, 17% noted lack of the CFLs wide presence on the stores shelves being an obstacle.

The Northwest Energy Efficiency Alliance (2000:20) reported the following primary barriers in the US: high first costs, unavailability, lack of awareness, incompatibility, performance problems. Sathaye and Murtishaw (2004:20) suggested that the key factor (other than incompatible fixtures sizes) is the lack of information. Rasmussen et al. (2007:1952), analysed residential lighting programme in California and the Pacific North-West. The study shows that the awareness about CFLs was at 58% when the programme was launched. Further the awareness was gradually increased thanks to more extensive information resources. It reached 94% in 2006. Consequently, the purchase rate was following the dynamics of awareness rate: from 17% in 1998 to 69% in 2006. This reflected broader consumer concern on energy saving. According to the study, in the Pacific Northwest the trends were similar. In 2006, CFL awareness increased up to 86% of consumers, while 67% were reported buying them. Various reasons that prevent households with CFLs and without CFLs from starting or continuing further to purchase them are provided in the See Figure 2 and Figure 3 below.

Figure 2. Main factors preventing increased saturation of CFLs in the households with CFLs. Northwest Pacific. 2006.

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Figure 3. Main factors preventing increased saturation in the households without CFLs. Northwest Pacific. 2006.

Source: Rasmussen et al. (2007:1995).

Following on the US, according to Grover and French (2004:135-137), market barriers to purchasing CFLs in the US: (i) strongest – high cost to purchase; type/style/size not available; (ii) don’t like the light/colour. Within this study, over a half of respondents replaced CFLs with incandescent bulbs. For this behaviour, the following reasons were given: (i) ‘did not fit the fixtures’ – 34%; (ii) ‘too expensive’ – 29%; (iii) ‘not bright enough’ – 25%. Based on a market transformation survey, Grover et al. (2002:106) found that that reasons for dissatisfaction with CFLs include10: ‘not as bright’; ‘light quality’, and ‘doesn’t fit fixtures’. Sathaye and Murtishaw (2004:19) found that CFLs sales reduction effect in the US is due to various factors, including the split incentive problem, lifetime uncertainty, product information costs, etc. (see Table 7 below). It is concluded that, as a whole, these factors reduce the penetration of CFL by 95% approximately.

10 The figures differ for 1st and 2nd waves of the survey under Residential Energy Star® Lighting Program.

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Table 7: Effect of market factors on CFL sales in California in 2005.

Source: Sathaye and Murtishaw (2004:19).

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Bartiaux et al. (2006:33-34) found that “saving lamps or CFLs appear to be equally widespread” in Belgium. According to the authors, households in detached houses have a higher ‘participation’ (70.9%) compared to households in apartments (54.6%). Highest proportion of CFLs is with the younger group of population (70.5% at 18-29 years old). Close to that is at 50-69 years old. Around 60% of those of age 30-49 and 70-89 use energy saving lamps. The percentage of CFLs increases with household size (from 58.6% with 1 person till 68.2% with 4 persons, while then drops to 58.5 for over 5 persons household). Highest rate of CFL utilisation is found in households of two adults with children (66%). However difference is not substantial compared to the 1 adult without children – 61-1%. Income does not seem to affect strongly application of CFL’s. For monthly incomes of Euro<1510; Euro 1510-2260; Euro 2260-3380 the percentage of households with energy saving lighting is slightly above 65%. This proportion drops by 5% with income exceeding Euro 3300 approx.

In Hungary, Ürge-Vorsatz and Hauff (2001:292) looked upon ownership of CFLs and socio-demographic factors driving the adoption of this technology. They found that “high” adopters can be characterised as follows: Age – 28% within 40-49; education – college/University – 44%; Urban domicile – 26%; 26% in the highest quarter; size – 27% with 4 people in a household. See Table 8 below.

Table 8: Ownership of CFLs according to social-demographic characteristics.

Age Ownership rate, %18-29 2130-39 17%40-49 28%

50-59% 21%Older than 60 11%

Level of education Ownership rate, %35

Less than 8 grades 6%Primary school with 8

grades14%

Secondary school 28%College, university 44%

Income group Ownership rate, %Lowest 25% 13%

25-50% 19%50-75% 15%

Highest quarter 26%Size of household Ownership rate, %

1 person 11%2 persons 14%3 persons 22%4 persons 27%

More than 5 persons 16%Data source: Ürge-Vorsatzand and Hauff (2001:292).

Analysing several EU countries, Palmer and Boardman (1998) found that electricity monetary saving is the most important reasons for purchasing CFLs. In addition, longer operational life; energy saving (environmental) are critical drivers.. See Table 9 below. On the contrary, main reasons for non-adoption are numerous and differ across these countries: too expensive (Germany – 31%; Sweden – 48%; UK – 33%; Spain – 34%); never considered a purchase (Sweden – 29%; UK – 30%; Spain – 16%); unattractive (Germany – 31%); non-compatible (Germany – 19%). (see Palmer and Boardman: 1998:40).

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Table 9: Main reasons for obtaining a CFL among owners. Cultural survey.

Reason for owning Germany Sweden UK SpainI like the quality of light

- - - 40%

Electricity saving (money)

66% 53% 33% 33%

They last longer 24% 31% 31% 20%

Money saving in general

47% 24% 36% 15%

Electricity saving (environment)

42% 12% 17% -

Data source: Palmer and Boardman (1998:40).

For the specific case of light emitting diodes (LED), Herring et al. (2007:1888-1889) found that only about 7% of those who responded an on-line survey purchased LED lighting in the UK. The study reports 57% of those who implemented LEDs, did it to save energy; 34% were expecting monetary savings, and 11% explained it with concern for environment. The authors found that 40% of non-adopters mention limited availability as a major reason. Incompatibility with existing fittings and high costs is a barrier for 39%. 19% have a perception that savings will not be worth costs. 14% are not expecting performance they require.

7.5 Consumer appliancesThis section outlines findings of the research that covered all what falls into the household appliances use

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for various purposes, including practical (white goods) and entertainment/information devices.

Martinez et al. (1998), analysed the reasons for adoption of consumer durables such as refrigerator, washing machine, dishwasher, oven, vitroceramic hob, and microwave oven. The authors pointed that assistance in performing household tasks is one of the main characteristics of these appliances. These were suggested to be time-saving products. In the study, a survey with a random sample of 600 households was carried out in Zaragoza, Spain, in 1995. One of the observations from their survey was that external influence (publicity) is very important during first years, throughout commercialisation and affects the adoption. Microwave oven and vitro-ceramic hob (more advantageous than a traditional cooker) indicated much less dependence on the external influence, and the stronger role of the ‘word of mouth’, or internal influence (see Martinez et al., 1998:331, 332).

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Figure 4. Distribution of the external influence in the adoption of consumer durable products.

Source: Martinez et al. (1998:332).

Fuchs et al. (2004) carried out a comprehensive survey of non-energy impacts for six categories of household energy-related devices. Their survey concerned four operational residential Energy-Star related programmes. The NEBs results indicated what benefits or impacts households experienced from Energy Star measures as well as from purchasing Energy Star appliances. They can be seen in Table 10 below. NEB multiplier represents their value in relation to the energy savings.

Table 10: Non-energy benefits / impacts of household appliances. Percent of overall non-energy benefits.

Source: Fuchs et al. (2004:2-82).

According to Shorey and Eckman (2000:13), factors influencing purchase of energy efficiency appliances 39

include performance/quality; capacity; reliability; cost; reputation/brand; operating costs; efficiency; rebate availability. According to their study, these factors were mentioned as ‘most important’ by customers at the highest by somewhat 75% down to 20% in relation to the mentioned sequence.

Cold appliancesIn the US, Meier and Eide (2007:1869) found that 5% of residential refrigerators are facing principal agent problem. In the UK, Oxera Consulting (2006:27) analysed the factors that influence take-up of energy efficiency fridge-freezer. Statistically significant drivers of consumers’ choice were found to be: (i) price – very important; (ii) brand - minor; (iii) frost-free - insignificant. Interestingly, according to the study, energy labels reflecting annual energy savings of refrigerators/freezers were not shown to have great influence over choice, nevertheless, results of regression analysis showed that a greater value was attached to A-labelled appliances than to B-labelled ones.

In the UK, Schiellerup et al. (1998:11-12) analysed labelling schemes through an ‘in-the-home’ survey. Table11 below provides overview of labelling effectiveness in eleven EU countries. Influence of label on purchase is provided in the last column. As one can observe, the share of consumers who stated that energy labels on cold appliances influenced their purchase choices was as high as 56% in Denmark, 45% in the Netherlands, and 39% in Austria and Sweden. This is given in terms of the proportion of population who mention energy (efficiency) as a leading factor. Importance of energy efficiency, measured at three degrees depending on what part of consumers recognizes it (>70%; 50-70%; <50%) is also provided by country.

Table 11: Overall effectiveness of energy labelling.40

Source: Schiellerup et al. (1998:12).

In the US, the DECADE survey showed that the most important considerations prior to purchase were: for 35% - purchase price; for 20% - performance (“how well it would work”); 15% of the fridge purchasers mentioned concern of energy use and 15% environmental friendliness as a decision-influencing factor. According to this study, 37% of respondents reported purchasing refrigerator with energy label. More detailed analysis of qualitative interviews gave the following split-up: 15% - a great deal; 19% - quite a lot; 32% - somewhat influenced; 35% - not at all. Rating of the kind of information that was considered the most important (see Boardman et al., 1995:163-165).

Food provisionIn Spain, Martinez et al. (1998:323-334) considered vitroceramic hob (a cooking surface of a stove) rather innovative during the survey due to the short time of its presence at the market by then. The study highlights that not all innovative consumer durables entering the market shall be equally diffused. They attempted to analyse at what stage and what influences households’ adoption of

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household appliances. Data on adoption are available in Table 12 below.

Table 12: Differences among adopter categories (vitroceramic hobs).

Source: Martinez et al. (1998:334).

Washing and dryingIn Sweden, Linden et al. (2006:1923-1924) found that 80% of households, especially the younger ones, use washing machines only when load is full. This is explained with “changed standards for cleanliness,

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information11”. The study points that half of household use washing machines frequently – 4 times per week or more. Changed norms for cleanliness and product development are given as reasons for this. Furthermore, 70% of households never remove stains manually and instead do the laundry.

Sathaye and Murtishaw (2004:27) studied the impact of market factors and consumer preferences on reduction in penetration of resource efficient cloths washers in California. Quantitative estimates are given in Table 13 below. All factors affecting market situation of cloth washers, their impact on the cost of conserved energy and reduction of adoption of resource efficient washers are given in the first column. As one can observe, (search) costs related to product information and lifetime uncertainty have the greatest impacts.

11 Within this study the ‘information’ includes “national information campaigns promoting lowering energy use during the days of oil crisis” as well as “great number of repeated information campaigns”. (p. 1923)

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Table 13: Effect of market factors on resource efficient washing machines sales in California (US).

FactorParame

ter affecte

d

Cost of conser

ved energy (CCE) effect

Cumulative CCE

Penetration

reduction effect

Cumulative

reduction

Initial stock N/A N/A USD

0.043 N/A N/A

Baseline share

Eligible stock N/A USD

0.043 12% 12%

Split incentive

Eligible stock N/A USD

0.043 23% 32%

Product availability

Eligible stock N/A USD

0.043 24% 48%

Access to capital

Eligible stock N/A USD

0.043 12% 54%Energy consumption uncertainty

Cost USD 0.007

USD 0.051 14% 61%

Life-time uncertainty

Life-time

USD 0.023

USD 0.074 28% 72%

Product information cost

Capital cost

USD 0.034

USD 0.110 35% 82%

Vendor information cost

Capital cost

USD 0.026

USD 0.137 26% 86%

Consumer preferences, features

Capital cost

USD 0.026

USD 0.165 24% 90%

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Data source: Sathaye and Murtishaw (2004:27-28).

Based on conjoint analysis of a focus group, Turiel et al. (1997:400) found six attributes of cloth wash as important for adoption in the US. The three most important: price (26%); load size adjustment (25%); washing temperature (18%). Savings follow these with 14% relative importance level. Also in the US, Grover and Babiuch (2000:139, 146) found 12 attributes for cloth washer as important determinants. The study provide the following quantitative estimates: Price – 83%; Capacity (operating costs and performance according to the classification suggested above) – 81%; Energy and water costs (operating costs) – 72%; Load size options (operating costs) – 68%; Durability – 60%; Water temperature options (performance) – 60%; Door placement (design; space) – 42%; Quiet operation (noise, comfort) – 40%; Wash time (time) – 38%; warranty – 37%; Multiple wash cycles options (performance) – 33%; Horizontal/vertical axis (design/operating costs) – 28%. The probability of purchasing a higher efficiency cloth washer is stronger for ‘low income’ and ‘younger’ cohorts.

DishwasherLinden et al. (2006:1923) found that 71% of households in Sweden, especially younger households, use dishwashers only when full. This is explained with information and habit. In Spain, Martinez et al. (1998:333), analysed dishwashers. According to the study, household size and income; woman’s job and status, education and age, as well as man’s employment status and age will define what adopters’ category consumer belongs to. The three main categories are distinguished: early adopters; early majority, and late majority.

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Entertainment, information, leisureThe reviewed literature shows, for instance, that television set-top boxes are 100% affected by principal agent problem in the US (Meier and Eide, 2007:1869). In the UK, and for the case of televisions, statistically significant drivers of choice are price and recommendations (Oxera/DEFRA, 2006). In addition, annual costs of energy consumed during user phase have a very weak effect on the choice of TV-sets. Brand and screen size (for those consumers who purchase wide-screen panels) determine choice, notwithstanding the price determinant (Oxera/DEFRA, 2006:27).

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8. Concluding remarksThis report identified what factors and determinants are influencing the (non) adoption of energy efficiency technologies in the household sector. These determinants were grouped with a clear distinction of what is induced by energy product itself, what has socio-demographic origin, and what is derived from the context. The report also looks into different household energy technologies. The main goal was to highlight quantification of the determinants in order to understand what stands behind household energy investment decisions. Undoubtedly, the number of factors influencing households’ energy efficiency technology decisions is extensive.

The question that laid the basis for this report appeared to concern various dimensions. Among the determinants we find those supporting rational economic explanations; behavioural aspects, and not the least innovation aspects of the energy technologies. Which of them are more evident in guiding consumer decisions, is the matter of looking into quantitative comparisons in the cases of adoption or foregoing. Certainly, consumers’ behaviour provides a broad field for investigation.

Although many researchers earlier pointed at the lack of rationality expressed by consumers when making energy-related decisions, initial cost proves to exert quite important influence on household investment decisions. Many other factors came into picture in connection with various energy technologies. As it was presented, various characteristics play different roles, not the least will decisions get influenced by external incentives, or circumstances. The role and influence of the determinants can be direct/indirect/stronger/weaker and quite case/context specific. Operating costs, especially when reduction can be anticipated add to the line of rational determinants. Nevertheless, as the abundant

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discount rate studies show, for different technologies the ‘folk discount rate’ will vary widely and possibly show very high figures in many cases preventing adoption. While one group at one market will speculate about longer than acceptable payback period of one energy technology, another group may (due to other stronger determinants) be adopting this technology and experience value of some non-energy benefits that initially might not have been cognitively considered.

The quantitative evidences from reviewed publications suggest that different determinants will influence consumers’ investment decisions in different markets, under different contextual circumstance and with different energy technologies. Moreover, all these determinants will require special actions to turn them into direction stimulating investment decisions and wider adoption of more efficient energy technologies. The literature reviewed clearly suggest that policy instruments addressing increased energy efficiency need to be developed with considerations of different characteristics of households, technology features, and contextual factors inherent to any technology or market.

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Elvira Moukhametshina

Choice determinants for the (non) adoption of energy efficiency technologies in

householdsA literature review

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IIIEE Reports 2008:02The International Institute for Industrial Environmental EconomicsLund University, SwedenISSN 1650-1675

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