Beyond case studies: Barriers to energy efficiency in commerce and the services sector

Available online at www.sciencedirect.com

Energy Economics 30 (2008) 449 – 464 www.elsevier.com/locate/eneco

Beyond case studies: Barriers to energy efficiency in commerce and the services sector Joachim Schleich a,b,⁎, Edelgard Gruber a a

b

Fraunhofer Institute for Systems and Innovation Research, Department of Energy Policy and Energy Systems, Breslauer Str. 48, 76139 Karlsruhe, Germany Virginia Polytechnic Institute and State University, Department of Agricultural and Applied Economics, Blacksburg, VA 24061-0401, USA Received 4 August 2006; accepted 6 August 2006 Available online 14 September 2006

Abstract To assess the empirical relevance of various barriers to the diffusion of energy-efficient measures, we conduct econometric analyses for 19 sub-sectors in the German commercial and services sectors. Results from estimating a separate Logit model for each sub-sector suggest that the most important barriers are the investor/user dilemma and the lack of information about energy consumption patterns. Typically, multiple types of barriers are found to be statistically significant, but they vary considerably across sub-sectors. Finally, we discuss policy implications for the most relevant barriers. © 2006 Elsevier B.V. All rights reserved. JEL classification: D20; Q48 Keywords: Energy efficiency; Organisational investment behaviour; Technology diffusion

1. Introduction The implementation of technologies and practices which reduce energy consumption at the level of private and public organisations or individual households is often hindered by obstacles. Barriers such as transaction costs, hidden costs, the investor/user dilemma, technological and financial risks, or organisational and behavioural constraints may prevent

⁎ Corresponding author. Fraunhofer Institute for Systems and Innovation Research, Department of Energy Policy and Energy Systems, Breslauer Str. 48, 76139 Karlsruhe, Germany. Tel.: +49 721 6809 203; fax: +49 729 6809 272. E-mail address: [email protected] (J. Schleich). 0140-9883/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.eneco.2006.08.004

450

J. Schleich, E. Gruber / Energy Economics 30 (2008) 449–464

energy-efficiency measures from being realised.1 Many of these measures are not applied even though they would be cost-effective from the company's or individual's perspective under prevailing economic conditions.2 In principle, policy interventions, promotional activities or organisational measures may help overcome such barriers and not only improve economic efficiency and competitiveness, but also help to achieve national and international greenhouse gas emission targets. Empirical analyses exploring the nature and relevance of barriers to energy efficiency provide crucial information for the design of effective policies. Typically, such analyses consist of theory-based case studies. That is, hypotheses about the nature of the barriers are developed based on theories, such as neo-classical economics, transaction cost economics, information economics, organisational theory, sociology, or psychology. The empirical basis for assessing the relevance of the various types of barriers and for policy recommendations is then based on (usually only a few) interviews in companies. These case studies tend to be carried out either for a specific technology, such as for electric motors (de Almeida, 1998; Ostertag, 2002), or for specific sectors with a comparatively low energy cost share, such as manufacturing, the food industry or the public sector (Wuppertal Institute et al., 1998; Ramesohl, 1998, 2002; Schleich et al., 2001; Sorrell, 2003; Sorrell et al., 2004).3 Case studies are well suited to gain insights into complex decision-making processes and structures within organisations, but their findings are usually limited to an analytical generalisation, where observed outcomes of decision-making processes are explained through the identification of relevant causal mechanisms (Yin, 1994, p. 45f). The basis for generalising in a statistical sense, however, is weak. An alternative approach to assessing the empirical relevance of the various barriers to energy efficiency is based on econometric estimations, in which variables reflecting investment behaviour in energy efficiency are regressed on variables reflecting socioeconomic information. The few econometric studies which have been carried out so far include Brechling and Smith (1994), Scott (1997), and de Groot et al. (2001). Brechling and Smith (1994) analyse the take-up of loft insulation, wall insulation and double glazing in the UK household sector. A similar study was conducted by Scott (1997) for attic insulation, hot water cylinder insulation, and low-energy light bulbs in Ireland. Their findings indicate that information costs, transaction costs, restricted access to capital, the investor/user dilemma, and small potential savings are relevant barriers to energy efficiency. For the industry sector, DeCanio (1998) analyses company-level data from the United States Environmental Protection Agency's Green Lights program. His findings suggest that, besides economic factors, a set of organisational and bureaucratic barriers determine companies' investment behaviour. de Groot et al. (2001) analyse to which extent barriers to the implementation of energy-saving technologies in Dutch companies vary across sectors and across firms' characteristics. To do so, de Groot et al. (2001) ran separate regressions for each potential barrier. However, the limited number of observations did not allow for sector-specific analyses.

1 The categorization of barriers is contested in the literature in the sense that different authors use different typologies. Likewise, there is substantial overlap between those categories. See, for example, Stern (1986); Howarth and Andersson (1993); Jaffe and Stavins (1994a,b); Howarth and Sanstad (1995). More recent overviews on the concepts explaining the various barriers are provided by Brown (2001), IPCC (2001) and Sorrell et al. (2004). 2 The IPCC (2001) estimates that between 10% and 20% of global emissions in the year 2020 can be reduced at no extra cost. According to UNDP/UNDESA/WEC (2000) this so-called no-regret potential ranges between 20% and 30%. 3 By “low energy cost share” we mean that the share of energy costs in total turnover is less than 3%.

J. Schleich, E. Gruber / Energy Economics 30 (2008) 449–464

451

Table 1 Description of sub-sectors Sector

NACE description

Small commercial businesses and trade Agriculture Agriculture and forestry Bakeries Bread, fresh pastry goods and cakes Butchers Processing and preserving of meat and meat products Car repair industry Maintenance and repair of motor vehicles Construction Construction Horticulture Horticulture Laundries and dry cleaners Washing and dry-cleaning Metal industry Basic metals and fabricated metal products Retail trade Retail trade Wholesale trade Wholesale trade Wood working and processing Wood and wood products Public and private services organisations Banks and insurance companies Financial intermediation Gastronomy Restaurants, bars, canteens and catering Hospitals Hospital activities Hotel industry Hotels Non-commercial organisations Non-commercial organisations, activities of membership organisations Public administrations Public organisations Schools Education Services Services: lawyers, architects, small private health services, private agencies etc.

NACE number 01 (without 01.12) 15.81 15.1 50.20 45 01.12 93.01 27, 28 52 51 20

65, 66, 67 55.3, 55.4, 55.5 85.11 55.1 91 75, 80, 90 80 74.11, 74.20, 93.05

In this paper, we econometrically explore barriers to energy efficiency for the German commercial and services sectors.4 As can be seen from the overviews provided in Tables 1 and 2, this sector consists of small industrial enterprises, all public and private services as well as the agricultural and construction sectors. The data set used of more than 2000 observations is based on a survey of energy consumption in the commercial and services sectors (Geiger et al., 1999). Although the energy cost share of a typical organisation in these sectors is low, they are responsible for about 17% of final energy consumption (Federal Ministry of Economics and Labour, 2005) and for about 7% of direct CO2 emissions in Germany (Federal Ministry for the Environment, Nature Conservation and Nuclear Safety, 2004). Our approach differs from the existing literature in three aspects. First, we apply econometric analyses of barriers to energy efficiency to energy consumers who have been neglected in this type of study so far: small commercial businesses as well as private and public service organisations. Previous analyses of these sectors by Jochem and Gruber (1990) and Gruber and Brand (1991) are confined to the study of patterns of energy consumption and to measures to improve energy efficiency. Second, since we conduct econometric analyses for each sub-sector individually, we may assess the relevance of various barriers to energy efficiency on the level of individual sub-sectors. Third, our analyses focus on organisations' energy performance in

4

In the German energy balances, final energy consumption is partitioned into four end-use sectors: industry, private households, transportation, and the combined commercial and services sector.

452

J. Schleich, E. Gruber / Energy Economics 30 (2008) 449–464

Table 2 Overview of sub-sectors Number of observations

Average size a

Average annual energy use (MW h)

Active in energy efficiency (%)

Small commercial businesses and trade Agriculture 148 Bakeries 88 Butchers 76 Car repair industry 78 Construction 98 Horticulture 57 Laundries and dry cleaners 66 Metal industry 116 Retail trade 291 Wholesale trade 164 Wood working and processing 94

6 9 8 8 15 (2725) 19 9 29 37 8

208 378 151 339 89 478 1978 125 459 837 209

65.4 40.9 76.3 46.2 43.9 47.4 46.3 31.0 35.9 36.9 41.5

Public and private services organisations Banks and insurance companies 126 Gastronomy 102 Hospitals 79 Hotel industry 128 Non-commercial organisations 127 Public administrations 93 Schools 92 Services 76 Average b 110

162 10 (300) 17 29 69 (1561) 10 27

1622 216 8834 589 508 934 2455 48 931

47.3 34.0 59.5 64.3 39.4 37.5 57.6 29.5 44.32

a Usually, size stands for the number of employees. For horticulture, this represents greenhouse area in square meters, for hospitals the number of beds, and for schools the number of pupils. b Sectors horticulture, hospitals and schools are not used to calculate average size.

general, and–in contrast to Brechling and Smith (1994), Scott (1997) and DeCanio (1998)–not on particular technologies. The paper is organised as follows. In Section 2 we offer a brief summary of barriers to the diffusion of energy efficiency which are relevant for this study. Section 3 provides a description of the survey together with some descriptive statistics. The model is described in detail in Section 4. In Section 5, we present and interpret the estimation results. Finally, in Section 6, we provide general policy implications and recommendations for addressing the most relevant barriers to the diffusion of energy efficiency. 2. Barriers to energy efficiency Companies from energy-intensive industries like the power, the iron and steel or the mineral processing industries tend to be quite aware of the potential cost savings from investing in energy efficiency. The high energy cost share in these companies provides a strong economic incentive to find and realise efficiency potentials. Likewise, since investing in energy efficiency directly affects the core production processes in energy-intensive companies, energy use is automatically considered in investment decisions. By contrast, in the commercial and services sectors, the energy cost share is usually low, and investments in energy efficiency rarely affect the core production processes. In addition, since companies in this sector are usually rather small, the

J. Schleich, E. Gruber / Energy Economics 30 (2008) 449–464

453

indirect or hidden costs associated with investments in energy efficiency, such as overhead costs for energy management, or costs for training personnel are more likely to be prohibitive. Rather than reviewing the extensive literature on barriers, we briefly present the main concepts relevant to this study. 2.1. Information and other transaction costs Transaction costs include the costs of gathering, assessing and applying information about energy savings potentials and measures, as well as the costs associated with finding and negotiating the contracts with potential suppliers, consultants or installers, or the costs of reaching, monitoring and enforcing contracts (Coase, 1991). If the transaction costs for a particular measure are high, the investment may not be profitable. For example, since gathering information about energy-efficient measures or about the energy performance of particular technologies is costly, firms may not have sufficient information about the ways to save energy. Similarly, firms may not be aware of the savings potential because they do not–or for technical reasons cannot–measure energy consumption regularly. Even if energy consumption is measured regularly, it may not be at the level of individual buildings, rooms, or end-use equipment. Thus, if organisations do not have the relevant data on energy-efficient measures or energy use available, the savings potential from implementing energy-efficient technologies remains unknown and investments cannot be properly appraised. Empirical findings based on case studies for particular technologies imply that transaction costs vary considerably across technologies and agents. The findings about costs in relation to investment volume are ambiguous; some indicate that the share of transaction costs tends to decrease with the size of the investment, implying that transaction costs may be less serious or even negligible for large investments (Hein and Blok, 1995; Bieniek, 2000). Others deny a systematic correlation (e.g., Ostertag, 2002). 2.2. Bounded rationality When faced with a complex decision structure, agents may not be able to optimise because of lack of time, attention, or the ability to adequately process information. Instead, bounded rationality may result in using routines or rules of thumb (Simon, 1957, 1959; Gigerenzer and Selten, 2001). For example, small motor end-users tend to consider only delivery time or price instead of life-cycle costs when buying a new motor to replace an old one (de Almeida, 1998). Similarly, when making decisions about investment priorities, firms are likely to focus on the core production process rather than on ways to save energy costs. Likewise, in cases where investments in energy-efficient technologies are being considered, the same profitability or payback criteria may be required as for the core production technologies, although the economic risks associated with the former are much lower. 2.3. Capital constraints Restricted access to capital markets is often considered to be an important barrier to investing in energy efficiency. That is, investments may not be profitable because companies face a high price for capital. As a result, only investments yielding an expected return that exceeds this (high) hurdle rate will be realised. Since the price for capital also reflects the risk associated with the borrower, small and medium-sized companies often have to pay higher-than-average interest

454

J. Schleich, E. Gruber / Energy Economics 30 (2008) 449–464

rates. Possible explanations include the smaller companies' limited ability to offer collateral, or potential lenders having to bear higher costs to assess the credit-worthiness of small and mediumsized companies. There is special situation in the public sector. On the one hand, since the probability of default is virtually zero, private banks provide capital at low interest rates to public organisations. On the other hand, budgeting laws may prevent organisations from borrowing capital. For many public administrations, access to the private capital market is either completely closed, or only feasible if operating revenues sufficiently exceed outlays. When access to the capital market is constrained, the allocation of funds within an organisation becomes even more important. In addition, internal decision-making and priority setting may not only depend on hard investment criteria such as the rate of return or payback time of an investment project, but also on soft factors such as the status of energy efficiency, reputation, or the relative power of those responsible for energy management within the organisation (Morgan, 1985; DeCanio, 1994; Sorrell et al., 2004). 2.4. Uncertainty and risk Even if organisations have easy access to capital at relatively low prices, the uncertainty associated with the returns from investments may be prohibitive. For investments in energy efficiency, the uncertainty is primarily caused by stochastic future energy prices. On the one hand, risk-averse investors will demand higher returns from assets with uncertain yields. On the other hand, investments in energy efficiency lower the energy bill and thus reduce the financial risks associated with energy price uncertainty (Howarth and Sanstad, 1995). That is, if risk-averse investors consider the effects of stochastic energy prices on the returns of the investment project only, they are expected to invest less. But if they take into account the effects on company costs and profits, they may actually invest more because overall company costs and profits become less volatile. On the other hand, such investments reduce companies' “risk exposure”, since less emissions need to be covered once energy-efficient technologies are implemented. The relative magnitude of both effects is company-specific and generally ambiguous.5 Finally, postponing irreversible investments in energy efficiency may be optimal if future energy prices are uncertain (Hassett and Metcalf, 1993; van Soest and Bulte, 2001). For example, investing in a more energy-efficient technology may turn out to be unprofitable if energy prices fall after the new technology has been implemented. Hence, there is an option value associated with postponing investments (McDonald and Siegel, 1986; Dixit and Pindyck, 1994).6 In general, the more risk-averse the investor, the higher will be the required risk premium. Since larger companies can diversify their portfolio at a lower cost, and since risk aversion is likely to decrease with wealth, smaller companies are expected to be more risk-averse and to demand higher returns from investments. 2.5. Investor/user dilemma If a company is renting office space, neither the landlord, nor the company (tenant) may have an incentive to invest in energy efficiency, because the investor cannot appropriate the energy cost savings. On the one hand, the landlord will not invest in energy efficiency if the investment costs 5

See Ben-David et al. (2000) for a similar argument in the context of emission trading systems. See Sanstad et al. (1995) for a critique of the ‘option value’ explanation for the neglect of investments in energy efficiency. 6

J. Schleich, E. Gruber / Energy Economics 30 (2008) 449–464

455

cannot be passed on to the tenant, who will benefit from the investment through lower energy costs. On the other hand, the tenant will not invest if he/she is likely to move out before fully benefiting from the energy cost savings. In principle, this so-called investor/user or landlord/ tenant dilemma could be avoided, if the investing party were able to credibly transmit the information about the benefits (i.e., future cost savings) arising from the investment, and to enter into a contract with those benefiting from the investment. Such a contract would have to secure the appropriation of the cost savings so that the investor can cover the costs for the investment. However, the costs associated with the verification of the energy cost savings, and the costs for the contractual arrangements are often prohibitive. Thus, asymmetric information and transaction costs are at the root of this investor/user dilemma problem (Jaffe and Stavins, 1994b). As pointed out earlier, results from multivariate, large-sample empirical studies for the household sector by Brechling and Smith (1994) for the UK and by Scott (1997) for Ireland indicate that the investor/ user problem is a significant barrier to energy efficiency. However, for the organisations considered in this study, it may be argued that the investor/user dilemma is less severe, since the duration of rental agreements for companies and public organisations are generally longer than for private households.7 3. The data The data used for the analyses presented in this paper are taken from a representative survey in the commercial and services sector in Germany (Geiger et al., 1999). A total of 2848 companies and public institutions were interviewed personally about economic and technical data on energy use, energy management, measures to improve energy performance and perceived obstacles to energy efficiency. Because of its heterogeneity, the sector was broken down into 23 fairly homogeneous splits which reflect the sub-sectoral structure in official statistics. To capture the differences within sub-sectors, some were further broken down into sub-splits.8 In the survey, interviewees were given a set of possible energy saving measures and asked which of those measures had already been implemented in the organization. The measures considered differed substantially across sub-sectors and generally include both technical measures and organisational measures. For the industrial sub-sectors, those measures referred to the specific production technologies. Similarly, in the sub-sectors dominated by space heating, the list of measures includes a set of technological and management options to reduce heat demand and to improve the energy performance of the heat supply system. The measures considered in the survey may be distinguished in two types. The first type are cross-cutting measures which may be applied in all sub-sectors and includes the following: thermal insulation of outside walls (exceeding legal thresholds by the ordinance on thermal insulation), thermal insulation of the roof and the basement ceiling, thermally insulated glass, heat recovery in refrigerators and freezers, waste heat use of discharged air, combined heat and power systems, outdoor temperature regulation of heating, automatic temperature drop during non-working hours and controlling 7

Other forms of split-incentive problems which are likely to be barriers to energy efficiency in the sectors studied but which were–for lack of data–not included in the subsequent analyses include: (i) bias towards projects with short payback periods which prove inferior in a full discounted cash flow analysis because managers remain in their post for relatively short periods of time; or (ii) lack of departmental accountability of energy costs; (iii) separate budgets for capital and operating costs without full transferability of funds between budgets (relevant in particular in the German public and semi-public sectors). See Sorrell et al. (2004, Ch. 2) for an overview. 8 Due to insufficient data, not all sub-sectors could be included in the econometric analyses presented in this paper. The numbers in Table 1 refer to the observations included in the econometric analyses.

456

J. Schleich, E. Gruber / Energy Economics 30 (2008) 449–464

brightness of lighting. The second type of measures varied across sub-sectors. For example, for laundries, the following measures were considered: heat recovery from flue gas or condensate return to preheat the boiler, heat recovery from the air discharged by the drier, heat recovery from the mangle outlet air to preheat the mangle rollers or the intake air, heat recovery with condensation to heat the tumbler, heat recovery from mangle outlet air to preheat the drier intake air, heat recovery from the compressor waste heat to preheat the burner intake air and for hot water production, heat recovery from warm washing water to heat the rinsing or fresh water reducing the liquor ratio, thermal insulation of pipes, valves and condensate pans, use of flash steam, regulating amount of air discharged at mangles, isolating vats, use of mangle covers, full use of total capacity, switching off energy supply during longer downtimes and further use of washing or rinsing water.9 Interviewees had the opportunity to indicate whether some of the suggested measures were not feasible for their organisation, for example, for technical reasons. Since interviewees were asked to judge the relevance of potential barriers to energy efficiency within their company or organisation, the survey also allowed for an assessment of the determinants and of the barriers to energy efficiency.10 4. The model In the survey, questions on perceived barriers to energy efficiency within the organisation were not directed at specific technologies, but rather at energy-efficient investments in general. Thus, unlike in Brechling and Smith (1994), Scott (1997), or DeCanio (1998), there was no technologyspecific information available on the barriers that could be used in an econometric model. Instead, our analyses focus on organisations' energy performance in general. Organisations were split in two groups depending on their take-up of energy-efficiency measures. An organisation was termed ‘active’ if it had adopted at least 50% of the set of energyefficiency measures which–as described in Section 3–were deemed feasible for the individual organisation.11 For example, if a particular laundry had already adopted 6 of, say 10 measures which were feasible to it this laundry was termed ‘active’. The portion of active organisations for each sub-sector is displayed in Table 2. Then, to empirically assess the relevance of the various barriers on the take-up of energyefficiency measures in general, a separate Logit model was estimated for each sub-sector. The dependent variable is dichotomous and takes the value of one if the organisation is ‘active’. For ‘inactive’ organisations, the dependent variable is zero. The set of independent variables consists of EKNOWN: Split of final energy consumption into thermal energy and electricity consumption is unknown INFO: Lack of information about energy-efficiency measures TIME: Lack of time to analyse potentials for energy efficiency

9

For a similar list of measures for the other sub-sectors see Geiger et al. (1999). It may be argued that the interviewees geared their answers to justify their own (lack of) actions with regard to energy efficiency. Scott (1997) contends that this could be a serious problem for household surveys. However, for the organisations included in this study, this bias is expected to be less important, since the interview partners were usually not directly responsible for the actual investment decisions. 11 Using a 50% threshold appears natural, but is, of course, arbitrary. Additional Logit estimations which used 40% and 60% as the threshold showed that the results presented in Section 5 are robust to variations in this share. 10

J. Schleich, E. Gruber / Energy Economics 30 (2008) 449–464

457

PRIORITY: Other investment priorities UNCERT: Energy costs may vary in the future RENTED: Organisation space is rented PURCH: Organisation automatically considers energy efficiency of new equipment ENERGY: Total annual specific energy consumption in kW h12 SIZE: Size of the organisation. The dummy variables for the barriers are EKNOWN, INFO, TIME, PRIORITY UNCERT and RENTED. They assume the value of one if the associated statement was judged to be true for the organisation, and zero otherwise. Whether the organisation knows the split of final energy consumption is used as a proxy for lack of information about energy consumption. Poor information on energy consumption patterns prevents energy conservation measures to be identified and evaluated properly. Thus, EKNOWN is expected to have a negative impact on ‘active’. The dummy variables INFO and TIME serve as proxies for information costs, transaction costs and possibly bounded rationality. The sign of the parameter estimates is expected to be negative for both dummies. Investments in energy efficiency may be crowded out by other investments which are higher on the decision-makers' list of priorities. This may be particularly valid for organisations in which access to capital is restricted. Thus, the sign for the parameter estimate associated with PRIORITY is expected to be negative. As explained above, the expected impact of stochastic energy prices on investments in energy efficiency is generally ambiguous. However, since interviewees were asked to assess the relevance of potential barriers to energy efficiency in the survey, the expected sign for UNCERT is negative. The dummy variable RENTED captures the investor/user dilemma discussed above and takes the value one if office space is rented and zero otherwise. Thus, the sign of the parameter estimate associated with RENTED is expected to be negative. PURCH is also a dummy variable and provides information on whether energy efficiency is–at least to some extent–an integral part of the culture and organisational procedures of an organisation. In this case PURCH assumes the value of one and is expected to have a positive effect on the take-up of energy-efficiency measures. The variable ENERGY reflects the importance of energy consumption and energy costs to the organisation. Since the economic incentives to save energy increase with energy consumption, ENERGY is expected to have a positive impact on the take-up of energy-efficiency measures within sub-sectors. To control for size effects, specific values are used rather than the actual levels of fuel consumption. To generate ENERGY, the total annual fuel and electricity consumption were added up and divided by the number of employees.13 For three sub-sectors, it was considered more useful to relate energy consumption to other variables and not to the number of employees. For horticulture, greenhouse area in square meters was used, for hospitals, the number of beds, and for schools, the number of pupils. Since–as argued earlier–larger organisations are more able than smaller ones to deal with information and other transaction costs, bounded rationality, credit constraints, or uncertainty, SIZE is expected to have a positive impact on

12

In the specification for the econometric estimations, the natural log of specific energy consumption is used. The energy cost share would have been a better measure than energy consumption per employee. However, since 27% of the respondents did not answer the question on the energy cost share, using this measure would have resulted in the loss of a substantial number of observations and degrees of freedom in the econometric analyses. 13

458

J. Schleich, E. Gruber / Energy Economics 30 (2008) 449–464

adopting energy-efficient measures.14 For each sub-sector, an individual Logit regression equation is specified and estimated. 5. Results The estimation results for the Logit models appear in Table 3. The parameter estimates are reported together with their standard errors (in parentheses). The coefficient of determination (‘Pseudo’ R2), which measures the percentage of variation in the dependent variable explained by the estimation (goodness of fit), varies across sectors and ranges from 13% for the sub-sector agriculture to 39% for laundries and dry cleaners. Thus, most estimated regression equations explain a relatively high percentage of the variation in the dichotomous dependent variable, in particular, when taking into consideration that the data are cross-sectional and that organisations within one sub-sector may still be fairly heterogeneous. Also, estimation results are generally consistent with the hypotheses developed in Section 2. With one exception, the statistically significant15 parameter estimates show the expected signs.16 The results also indicate that the barriers to energy efficiency vary considerably across subsectors. For most sub-sectors, two or three of the variables reflecting various types of barriers are statistically significant, but no clear pattern as far as combinations of barriers appears to exist. The only exception to this rule may the relation between RENTED and EKNOWN: organisations with rented buildings and office space also tend to know less about energy consumption patterns. As expected, those sub-sectors with the fewest statistically significant barriers tend to be quite energy intensive, such as hospitals, the hotel industry, laundries and dry cleaners, or horticulture. In these sectors, economic incentives to improve the energy performance are higher. On the other hand, estimation results indicate that barriers impede the energy performance of sub-sectors with public or quasi-public ownership to a more than average extent. These sub-sectors include noncommercial organisations, public administrations and hospitals. Analysing the relevance of individual factors, the lack of information about energy consumption patterns–as captured by the variable EKNOWN–constitutes a statistically significant barrier with the expected negative sign within seven of the sub-sectors examined. These sub-sectors tend to exhibit low absolute energy costs, so that the costs for installations to measure energy consumption are more likely to be prohibitive. For public administrations and schools incentives to meter energy use are particularly low. For example, there are typically no arrangements for the decentralised accountability of energy costs. Instead, these costs are usually paid out of the administrations' general budget for operational costs. In addition, because of public budgeting laws energy cost savings cannot easily be used for other purposes. The estimation results further suggest that lack of information about energy-efficiency measures appears to be a barrier within about a third of the sectors, mainly in the private and public services sector such as banks and insurance companies, public administration and schools. A possible explanation may be that employees in these sectors tend to have little technical expertise. Lack of

14 Following, for example, Scott (1997), the variables may also be grouped in two sets, one containing rather objective information (ENERGY, SIZE, EKNOWN, RENTED) and the other containing more subjective information (INFO, TIME, PRIORITY, UNCERT). 15 ‘Statistically significant’ as used in this paper means significant at least at the 10% level, i.e., the P-values (not reported here) associated with the parameter estimates are no greater than 0.1. 16 Only the sign of the parameter estimate associated with INFO in the equation for wholesale trade is not consistent with our hypotheses.

Table 3 Logit estimation results EKNOWN

CONSTANT

N

‘Pseudo’ R2

(0.22) (0.31) (0.47) (0.31) (0.24) (0.41) (0.41)

0.03 (0.02) 0.11 a (0.05) 0.07 (0.08) 0.14 a (0.06) 0.02 (0.02) 0.00 (0.00) 0.06 a (0.03)

− 4.73 a (2.59) − 0.18 (3.39) 1.12 (5.09) − 0.65 (3.28) − 0.37 (2.20) − 2.16 (2.76) − 0.09 (4.53)

148 88 76 78 98 57 66

0.13 0.22 0.22 0.28 0.18 0.38 0.39

0.36 (0.77) 0.79 (0.58) 0.65 (0.81) 0.39 (0.78)

0.20 (0.26) 0.27 a (0.17) 0.18 (0.19) 0.06 (0.25)

0.03 (0.04) 0.00 a (0.00) 0.00 (0.00) 0.05 (0.05)

− 3.64 (2.68) − 3.61 a (1.72) − 2.90 a (2.05) − 1.00 (2.68)

116 291 164 94

0.21 0.29 0.31 0.18

− 1.49 b (0.50)

− 0.48 (0.61)

− 0.02 (0.23)

0.00 (0.00)

1.15 (2.13)

126

0.26

−0.05 (0.51) −0.03 (0.54) 0.61 (0.44) −0.73 a (0.42)

− 1.29 a (0.54) −0.41 (1.44) − 1.13 a (0.57) −1.15 (0.76)

0.11 (1.27) − 0.14 (0.93) 0.80 (0.74)

− 0.17 0.41 0.09 0.14

(0.32) (0.45) (0.29) (0.25)

0.03 (0.02) 0.00 (0.00) 0.01 (0.01) 0.02 b (0.01)

0.59 (3.36) − 1.77 (4.81) − 1.45 (3.02) − 1.62 (2.46)

102 79 128 126

0.27 0.26 0.14 0.36

0.29 (0.51) −0.23 (0.51) 0.08 (0.61)

− 1.76 a (0.86) 1.36 (0.96) − 2.06 b (0.66)

0.27 (0.88) 2.51 b (0.98) 0.42 (0.81)

0.00 (0.29) 0.49 a (0.29) − 0.20 (0.44)

0.00 (0.00) 0.00 a (0.00) 0.03 (0.03)

− 0.25 (2.84) − 4.99 a (2.46) 0.72 (4.04)

93 92 76

0.32 0.33 0.31

TIME

PRIORITIY

0.23 (0.42) −0.58 (0.55) − 0.62 (0.69) 0.73 (0.61) − 0.16 (0.58) − 1.65 a (0.85) − 0.65 (0.77)

− 0.16 (0.39) − 0.86 (0.57) − 0.31 (0.81) 0.37 (0.59) − 0.70 (0.49) − 0.71 (0.72) − 1.34 a (0.73)

− 0.66 (0.43) 0.34 (0.55) − 1.50 a (0.76) 0.42 (0.56) − 0.27 (0.47) − 0.34 (0.77) − 0.51 (0.69)

0.56 −0.40 1.04 −0.66 0.25 1.24 −0.26

0.69 (0.56) 0.37 (0.34) 1.08 a (0.50) − 0.03 (0.57)

0.09 (0.56) − 0.23 (0.34) − 0.76 (0.49) − 0.69 (0.50)

− 0.33 (0.49) − 0.27 (0.31) 0.41 (0.45) − 1.05 a (0.51)

0.29 −0.18 −0.55 0.75

− 0.47 (0.44)

Public and private services organisations Banks and insurance −0.48 (0.44) − 1.02 a (0.48) companies Gastronomy − 1.16 a (0.54) − 1.16 a (0.65) Hospitals −0.31 (0.57) − 1.17 (0.85) Hotel industry 0.27 (0.40) 0.05 (0.47) Non-commercial −0.53 (0.41) 0.01 (0.56) organisations Public administration −0.70 (0.52) − 2.09 a (0.92) Schools − 1.11 a (0.54) − 1.76 b (0.66) Services −0.78 (0.65) − 0.56 (0.72)

UNCERT

RENTED

PURCH

ENERGY

(0.40) (0.55) (0.76) (0.57) (0.47) (0.83) (0.71)

−0.97 (1.21) − 1.07 a (0.68) − 1.09 a (0.67) −0.86 (0.66) − 1.24 a (0.75) 0.13 (1.49) −0.59 (0.87)

1.34 (0.97) − 1.16 (1.06) 0.18 (1.08) − 0.53 (1.06)

0.37 0.11 − 0.01 − 0.03 − 0.03 0.05 − 0.03

(0.49) (0.30) (0.43) (0.49)

− 1.58 (0.57) − 1.91 b (0.33) − 2.54 b (0.61) −0.13 (0.66)

− 0.03 (0.50)

0.88 a (0.42)

0.49 (0.59) − 0.05 (0.55) − 0.51 (0.44) − 0.87 a (0.49)

− 0.29 (0.51) − 1.51 a (0.66) 0.10 (0.43) − 0.22 (0.42)

− 0.00 (0.63) 0.54 (0.57) − 0.45 (0.62)

− 1.11 a (0.51) − 0.49 (0.52) 0.38 (0.60)

b

a

J. Schleich, E. Gruber / Energy Economics 30 (2008) 449–464

Small commercial businesses and trade Agriculture −0.52 (0.38) Bakeries −0.37 (0.56) Butchers −0.13 (0.63) Car repair industry −0.80 (0.54) Construction − 0.98 a (0.48) Horticulture − 2.21 a (0.90) Laundries and −0.51 (0.68) dry cleaners Metal industry − 0.93 a (0.51) Retail trade − 0.69 a (0.29) Wholesale trade − 0.91 a (0.40) Wood working −0.07 (0.49) and processing

SIZE

INFO

‘Pseudo’ R2 is the Nagelkerke coefficient of determination. a b

Individually statistically significant at least at 10% level. Individually statistically significant at least at 1% level.

459

460

J. Schleich, E. Gruber / Energy Economics 30 (2008) 449–464

TIME to analyse potentials to improve energy efficiency is found to be a statistically significant barrier in just two sectors, i.e., in the hotel industry and in non-commercial organisations.17 Subsectors where internal PRIORITY setting appears to matter include butchers and wood working and processing, hospitals and public administrations. A likely explanation for the latter organisations lies in the tight federal, state and local budgets, and public budgeting laws. These laws effectively restrict access to the capital market for public administrations as well as for hospitals which often operate under a public legal form because they are owned by a public administration. The parameter associated with stochastic energy prices, UNCERT, is statistically significant in two sub-sectors: with a negative sign for non-commercial organisations and with a positive sign for banks and insurance companies. One explanation for the rather unexpected positive sign may be the interviewees' understanding of price uncertainty. Uncertainty may imply variations around a constant mean. In the same way, there may be uncertainty about the time path of the mean. Since the survey interviews were conducted just before the liberalisation of the electricity and gas markets in Germany in the years 1998 and 2000, respectively, organisations may have (correctly) expected energy prices to (at least temporarily) in the wake of the liberalised energy markets, rendering any investments in energy efficiency less profitable. Since RENTED is found to be statistically significant in more than half the sub-sectors analysed, the large sample estimation results clearly support the view that the investor/user dilemma is a barrier to the take-up of energy-efficient measures in the commerce and services sectors. Sub-sectors where RENTED is not significant are primarily those where renting buildings or office space is not very common, such as agriculture, horticulture or hospitals. Consequently, in these sectors, the variable RENTED is almost always zero, providing only little variation (and information) which could be exploited statistically. According to Table 3, integrating energy consumption into purchasing procedures positively affects the diffusion of energy efficiency in only two sub-sectors, wholesale trade and schools. Since for most sub-sectors, more than 90% of the organisations integrated energy consumption into the purchasing of new equipment, PURCH exhibits only little variation.18 Thus, PURCH may only be a very crude measure for capturing cultural and organisational procedures in organisations with respect to energy efficiency. Variation in (specific) energy consumption within sub-sectors turns out to be statistically significant for agriculture, retail trade, and schools. As can be seen from Table 2, of these subsectors only schools are among the top energy consumers. In general, we would have expected variations in energy consumption to matter more in sub-sectors where consumption levels are high, because in these cases economic incentives to improve energy performance are also high.

17 To some extent, INFO and TIME may capture the same concept, that is, organisations may exhibit lack of information on energy efficiency opportunities, because they lack the time to investigate those opportunities. From a statistical point of view, such a relation would result in a correlation between the “explanatory” variables INFO and TIME, but the associated parameter estimates remain unbiased. For the sub-sectors analyzed collinearity between INFO and TIME does not appear to be serious. The corresponding correlation coefficient is positive for all sectors, but usually well below 0.5. It is highest for the sub-sector metal (0.506). 18 For the sub-sectors laundries and dry cleaners, construction, and non-commercial services and for horticulture, all or almost all companies had integrated energy efficiency into purchasing procedures. Since a constant is included, PURCH was dropped from the equation to prevent the regressor matrix from becoming (near) singular.

J. Schleich, E. Gruber / Energy Economics 30 (2008) 449–464

461

Finally, differences in size within sub-sectors are statistically significant in six sub-sectors, with a higher share in the sub-sectors of small commercial businesses and trade. 6. Conclusions and policy implications The results of the econometric estimations have significant implications for policies addressing barriers to energy efficiency in the commercial and services sectors. Assessing the relevance of potential barriers to energy efficiency based on estimates from a large sample complements the findings from case studies, and improves the statistical basis for policy recommendations. Since the results provide information at a relatively low level of aggregation, they may be used to identify organisations where policy interventions are likely to be more effective. Our estimation results also suggest that since barriers vary across sub-sectors, such policies have to take these differences into account. Further, since we usually found multiple types of barriers to be significant, a mix of policies appears to be more appropriate than single policy instruments. According to our findings, organisations with public or quasi-public ownership structure (i.e., who are not profit oriented) exhibit the most barriers, and those with high energy consumption levels exhibit the least. But within sub-sectors, variations in energy costs appear to matter in only a few sub-sectors. These findings are basically in line with DeCanio (1998). Looking at individual barriers across sub-sectors, the investor/user dilemma arising from rented office space turned out to be a significant barrier in more than half the sub-sectors analysed. This suggests that the investor/user dilemma is not only a barrier to energy efficiency in private households, but also in the commercial as well as in most private and some public services sectors. Effective policies to address this dilemma need to reduce investors' transaction costs to appropriate the benefits. For example, at the EU level, the EU Directive on the Energy Performance of Buildings (European Parliament and Council, 2003) requires an energy pass for the existing building stock from 2006 on when buildings or flats are rented or sold in the public, commercial and private sector. This energy pass documents all the characteristic key data obtained from calculating the annual primary energy demand of the building and must be made accessible to clients and tenants. Other measures to address the investor/user problem include rent control legislation to facilitate passing on the investment costs to tenants. Similarly, if tenants decided to make investments but moved out before they had time to appropriate the full benefits, they would have to be guaranteed adequate compensation. But accounting for compensation in lease contracts is likely to be associated with high transaction costs.19 Lack of information about the pattern of energy consumption was prevalent within a third of the sub-sectors examined. However, the survey data does not allow any inferences about whether this is due to missing metering devices or organisational deficiencies. For some sub-sectors with low energy costs, such as the metal industry, the car repair industry or the wood working and processing industry, costs for installing meters may be prohibitive. Organisational measures include assigning clear responsibilities for energy management and costs and devolved budgeting. These organisational measures are particularly relevant for organisations in the public and quasi-public sectors. Our results imply that lack of information about energyefficiency measures is a barrier primarily in the public and private services sectors. Hence, information campaigns on energy-saving technologies should concentrate on these sectors. In principle, energy audits may also help to overcome missing information about energy 19

For further discussion, see Sorrell et al. (2004, p. 42).

462

J. Schleich, E. Gruber / Energy Economics 30 (2008) 449–464

consumption patterns and energy saving measures (Schleich, 2004). But, unlike large and energyintensive organisations, small and medium-sized organisations do not usually have skilled personnel available and external consultants are often considered too costly compared to the expected energy cost savings. Thus, subsidy programmes for low-cost energy audits in small private and public organisations, are likely to be effective even if–as Anderson and Newell (2004) point out–energy audits per se are unlikely to lower the hurdle rate for investments. Likewise, benchmarks for energy consumption at the level of products or processes are useful for providing low-cost information about energy savings' potentials. According to our estimation results, internal priority setting is a statistically relevant barrier within wood working and processing, butchers, public administrations and hospitals, possibly as a consequence of lack of capital. In principle, contract energy management via energy services companies may be an effective means to address lack of capital and risk as well as other barriers to energy efficiency such as lack of time and staff for energy management, bounded rationality, or costs for gathering information about energy consumption and energy saving technologies (Chesshire, 2000; Mills, 2003; Sorrell et al., 2004). In practice, however, energy services companies (ESCOs) are often reluctant to do business in the commercial sector. In Germany, they have primarily targeted the public sector, where the financial risks for the ESCO are much lower than in small private companies. Also, since transaction costs are generally high, ESCOs prefer projects which are larger in size than those typical for the commercial sector. Promising strategies to improve the market share of ESCOs in the commercial sector include the standardisation of products and concentrating on cross-cutting technologies with a large market volume, such as lighting, boilers, or small cogeneration plants for electricity- and heat-intensive enterprises such as, e.g., laundries or restaurants. Finally, our findings for organisation size indicate that the proposed policy measures should not only target smaller organisations within sub-sectors. However, a particular focus on smaller organisations may be particularly useful for agriculture, bakeries, butchers, the car repair industry, laundries and dry cleaners, retailers, non-commercial organisations and schools. Acknowledgements The authors thank Katrin Ostertag for her valuable comments on earlier versions of this paper as well as two anonymous reviewers for their critical review and their helpful comments. Part of this research was carried out while the first author was Professeur Invité at Université Louis Pasteur, Faculté des Sciences Economiques et de Gestion, Strasbourg, France. References Anderson, S.T., Newell, R.G., 2004. Information programs for technology adoption: the case of energy-efficiency audits. Resource and Energy Economics 26, 27–50. Ben-David, S., Brookshire, D., Burness, S., McKee, M., Schmidt, C., 2000. Attitudes toward risk and compliance emission permit markets. Land Economics 76, 590–600. Bieniek, K., 2000. Life-cycle costs of industrial electric drives in the process industry. Energy consumption and economics of electric drives. In: Bertoldi, P., de Almeida, A.T., Falkner, H. (Eds.), Energy Efficiency Improvements in Electric Motors and Drives. Springer, Berlin, pp. 529–539. Brechling, V., Smith, S., 1994. Household energy efficiency in the UK. Fiscal Studies 15, 44–56. Brown, M.A., 2001. Market failures and barriers as a basis for clean energy policies. Energy Policy 29, 1197–1207. Chesshire, J.H., 2000. From electricity supply to energy services. Prospects for active energy services in the EU. Report prepared for the European Commission (Directorate General for Energy and Transport) and the Union of the Electricity Industry (EURELECTRIC). European Commission, EURELECTRIC, Brussels.

J. Schleich, E. Gruber / Energy Economics 30 (2008) 449–464

463

Coase, R., 1991. The nature of the firm. In: Williamson, O.E., Winter, S. (Eds.), The Nature of the Firm. Origins, Evolution, and Development. Oxford Univ. Press, New York, pp. 18–33. de Almeida, E.L.F., 1998. Energy efficiency and the limits of market forces: the example of the electric motor market in France. Energy Policy 26, 643–653. DeCanio, S.J., 1994. Agency and control problems in US corporations: The case of energy efficient investment projects. Journal of the Economics of Business 1, 105–123. DeCanio, S.J., 1998. The efficiency paradox: bureaucratic and organisational barriers to profitable energy saving investments. Energy Policy 26, 441–454. de Groot, H.L.F, Verhoef, E.T., Nijkamp, P., 2001. Energy savings by firms: decision-making, barriers and policies. Energy Economics 23, 717–740. Dixit, A.K., Pindyck, R.S., 1994. Investment under Uncertainty. Princeton University Press, Princeton. European Parliament and Council, 2003. Directive 2002/91/EC of the European Parliament and of the Council of 16 December 2002 on the energy performance of buildings, Official Journal of the European Communities L1/65-71. Federal Ministry for the Environment, Nature Conservation and Nuclear Safety, 2004. National Allocation Plan for the Federal Republic of Germany 2005–2007. Berlin. Federal Ministry of Economics and Labour, 2005. Energiedaten–Nationale und Internationale Entwicklung. Bonn. Geiger, B., Gruber, E., Megele, W., 1999. Energieverbrauch und Energieeinsparung in Gewerbe, Handel und Dienstleistung. Physica, Heidelberg. Gigerenzer, G., Selten, R., 2001. Rethinking rationality. In: Gigerenzer, G., Selten, R. (Eds.), Bounded Rationality: The Adaptive Toolbox. MIT-Press, Cambridge, London, pp. 1–12. Gruber, E., Brand, M., 1991. Promoting energy conservation in small and medium-sized companies. Energy Policy 19, 279–287. Hassett, K.A., Metcalf, G.E., 1993. Energy conservation investment: do consumers discount the future correctly? Energy Policy 21, 710–716. Hein, L.G., Blok, K., 1995. Transaction costs of energy efficiency improvements. In: European Council for an EnergyEfficient Economy (Ed.), Proceedings of the ECEEE Summer Study. ADEME, Paris. Howarth, R.B., Andersson, B., 1993. Market barriers to energy efficiency. Energy Economics 15, 262–272. Howarth, R.B., Sanstad, A.H., 1995. Discount rates and energy efficiency. Contemporary Economic Policy 13, 101–109. Intergovernmental Panel on Climate Change (IPCC), 2001. Third assessment report. IPCC: Geneva. Jaffe, A.B., Stavins, R.N., 1994a. Energy-efficiency investments and public policy. Energy Journal 15, 43–65. Jaffe, A.B., Stavins, R.N., 1994b. The energy-efficiency gap: what does it mean? Energy Policy 22, 804–810. Jochem, E., Gruber, E., 1990. Obstacles to rational electricity use and measures to alleviate them. Energy Policy 18, 340–350. McDonald, R., Siegel, D., 1986. The value of waiting to invest. Quarterly Journal of Economics 101, 707–727. Mills, E., 2003. Risk transfer via energy-savings insurance. Energy Policy 31, 273–281. Morgan, G., 1985. Images of Organisation. Sage, London. Ostertag, K., 2002. No-Regret Potentials in Energy Conservation: An Analysis of Their Relevance, Size and Determinants. Physica, Heidelberg. Ramesohl, S., 1998. Successful implementation of energy efficiency in light industry. In: Lawrence Berkeley National Laboratory (Ed.), Proceedings of the International Workshop on Industrial Energy Efficiency Policies: Understanding Success and Failure, International Network for Energy Demand Analysis in the Industrial Sector (INEDIS). Ramesohl, S., 2002. Social interactions and conditions for change in energy-related decision-making in SMCs—an empirical socio-economic analysis. In: Jochem, E., Sathaye, J., Bouille, D. (Eds.), Society, Behaviour, and Climate Change Mitigation. Advances in Global Change Research: 8. Kluwer Academic Publishers, Dordrecht, pp. 207–226. Sanstad, A.H., Blumstein, C., Stoft, S.E., 1995. How high are option values in energy efficiency investments? Energy Policy 23, 739–743. Schleich, J., Böde, U., Köwener, D., Radgen, P., 2001. Chances and barriers for energy services companies? A comparative analysis for the German brewery and university sectors. In: European Council for an Energy-Efficient Economy (Ed.), Proceedings of the 2001 ECEEE summer study. ADEME, Paris. Schleich, J., 2004. Do energy audits help reduce barriers to energy efficiency? An empirical analysis for Germany. International Journal of Energy Technology and Policy 2, 226–239. Scott, S., 1997. Household energy efficiency in Ireland: a replication study of ownership of energy saving items. Energy Economics 19, 187–208. Simon, H.A., 1957. Models of Man. Wiley, London. Simon, H.A., 1959. Theories of decision-making in economics and behavioural science. The American Economic Review 49, 253–283.

464

J. Schleich, E. Gruber / Energy Economics 30 (2008) 449–464

Sorrell, S, 2003. Making the link: climate policy and the reform of the UK construction industry. Energy Policy 31, 865–878. Sorrell, S., O'Malley, E., Schleich, J., Scott, S., 2004. The Economics of Energy Efficiency—Barriers to Cost-Effective Investment. Edward Elgar, Cheltenham. Stern, P.C., 1986. Blind spots in policy analysis: What economics doesn't say about energy use. Journal of Policy Analysis and Management 5, 200–227. United Nations Development Programme (UNDP), 2000. United Nations Department of Economic and Social Affairs (UNDESA), World Energy Council (WEC). World energy assessment: World energy and the challenge of sustainability, United Nations Development Programme. United Nations Department of Economic and Social Affairs and the World Energy Council. United Nations Publications: New York. van Soest, D.P., Bulte, E.H., 2001. Does the energy-efficiency paradox exist? Technological progress and uncertainty. Environmental and Resource Economics 18, 101–112. Wuppertal Institute, AKF-Institute for Local Government Studies, Energieverwertungsagentur, Fraunhofer Institute for Systems and Innovation Research, Institute for Psychology at the University of Kiel, Amstein and Walthert, Bush Energie. InterSEE (Interdisciplinary analysis of successful implementation of energy efficiency in industry, service, and commerce), Final report in 5 volumes. Wuppertal, Copenhagen, Vienna, Karlsruhe, Kiel, Zurich, Felsberg, 1998. Yin, R.K., 1994. Case Study Research: Design and Methods. Sage Publications, London.