for Energy for Sustainable Development Manuscript ...

Elsevier Editorial System(tm) for Energy for Sustainable Development Manuscript Draft Manuscript Number: Title: Consumer preference for electricity products when the share of renewable energy increases Article Type: Full Length Article Keywords: consumer preference, renewable energy diversification, renewable energy portfolio Corresponding Author: Dr. Yingkui Yang, Ph.D. Corresponding Author's Institution: Department of Environmental and Business Economics, University of Southern Denmark First Author: Yingkui Yang, Ph.D. Order of Authors: Yingkui Yang, Ph.D.; Hans Stubbe Solgaard, Ph.D.; Wolfgang Haider, Ph.D. Abstract: While the share of renewable energy, especially wind power, increases in the energy mix, the risk of temporary energy shortage increases as well. Thus, it is important to understand consumers' preference for the renewable energy towards the continuous growing renewable energy society. We use a discrete choice experiment to infer consumers' preferences when the share of renewable energy increases. The study results indicate that consumers are willing to pay extra for an increasing share of renewable energy, but the renewable energy should come from different renewable energy sources. We also found that consumers prefer to trade with their current supplier rather than another wellknown supplier. This study contributes to the energy portfolio theories and the theory of energy diversification in a consumer perspective. The managerial implications of this study are also discussed. Suggested Reviewers:

Cover Letter

Consumer preference for electricity products when the share of renewable energy increases Yingkui Yang Department of Environmental and Business Economics, University of Southern Denmark, Niels Bohrs Vej 9, DK-6700 Esbjerg, Denmark. Tel. +45 6550 1527, E-mail: [email protected]

Hans Stubbe Solgaard Department of Environmental and Business Economics, University of Southern Denmark, Niels Bohrs Vej 9, DK-6700 Esbjerg, Denmark. Tel. +45 6550 1528, E-mail: [email protected]

Wolfgang Haider School of Resource and Environmental Management, Simon Fraser University 8888 University Drive, Burnaby, British Columbia, Canada. Tel. +1 778 782 3066, E-mail: [email protected]

Highlights (for review)

Highlights: 

This paper investigates consumer preference for electricity when the share of renewable energy increases in the energy mix.



Consumer prefers a high percentage of mixed renewable energy at an affordable price level when the share of renewable increases.



Current electricity supplier was found to be the most favorable supplier for consumers.



Results had implications on energy regulators/policy makers, electricity retailers and renewable energy investors.

Manuscript Click here to view linked References

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

Consumer preference for electricity products when the share of renewable energy increases Abstract While the share of renewable energy, especially wind power, increases in the energy mix, the risk of temporary energy shortage increases as well. Thus, it is important to understand consumers’ preference for the renewable energy towards the continuous growing renewable energy society. We use a discrete choice experiment to infer consumers’ preferences when the share of renewable energy increases. The study results indicate that consumers are willing to pay extra for an increasing share of renewable energy, but the renewable energy should come from different renewable energy sources. We also found that consumers prefer to trade with their current supplier rather than another well-known supplier. This study contributes to the energy portfolio theories and the theory of energy diversification in a consumer perspective. The managerial implications of this study are also discussed.

Contents

Introduction........................................................................................................................................................ 1 Theoretical framework ....................................................................................................................................... 4 The theory of energy portfolio ....................................................................................................................... 4 The choice model of electricity products ....................................................................................................... 6 Methodology ...................................................................................................................................................... 7 Discrete Choice experiment ........................................................................................................................... 7 Data collection ............................................................................................................................................... 8 Statistical estimation and results ...................................................................................................................... 10 The model specification ............................................................................................................................... 10 Conclusion and discussions ............................................................................................................................. 12 Acknowledgements .......................................................................................................................................... 14 References ........................................................................................................................................................ 15

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

Introduction

Denmark’s long-term energy goal is to become a fossil fuel independent nation by 2050 (Danish Goverment, 2011). This long-term goal is primarily driven by the current high CO2 emission per capita and the continuous decline of the oil and natural gas reserves over the last few decades (Lund & Mathiesen, 2009). Thus, there is a need for developing renewable energy alternatives. One of the important renewable energy sources is wind power. Today approximately one-third of electricity in Denmark is generated by wind. The current high share of wind power in the energy mix is a result of the Danish energy policy (Danish Energy Agency, 2012). Wind power is given the highest priority in the governmental energy plans in 1976 and 1981, and the feed-in tariff was used as one of the most important incentives to attract wind investments (Meyer, 2004). After supporting wind generated electricity for several decades, wind power has gradually penetrated the Danish electricity market as shown in Figure 1. [Insert Figure 1 here] Electricity that is generated from wind power has a number of advantages. Firstly, wind is a clean fuel source. There is no toxic gas emission from wind power generation. Thus, it is environmentally acceptable. Secondly, wind is a domestic source of energy. It is always available and accessible for energy producers thereby it minimizes the geopolitical risks. Thirdly, the cost of wind power generation is continuously decreasing due to economics of scale, which enhances its economic affordability for consumers. However, wind energy also has its downsides. One of the most critical drawbacks is that wind force is unpredictable and uncontrollable. Therefore, the production of wind power fluctuates highly over time. This is bad because the grid does not tolerate high fluctuation. On the one hand, fossil fuel fired (primarily coalfired) plants have to stand by to fill up the energy supply deficit on calm days to keep the grid being balanced (Gatermann, 2008). On the other hand, when there is strong wind, the fossil fuel fired plants then need to shut down to reduce its product in order to maintain a balanced grid. But the fossil fuel fired plants especially the coal fired plants are not quickly adjustable and have to remain generating electricity that may cause the grid to get overloaded. Since electricity is non-storable on a large scale and there is no domestic

1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

demand for the large power surplus, Danish wind power has often been sold to Germany at a very low price (CEPOS/Center for Political Studies, 2009). As a consequence, although the share of wind power has grown considerably over the past decades, the share of fossil fuels generated electricity has decreased rather moderately, see Figure 1. It is therefore not surprising that the growing share of wind power can be a major challenge for achieving the goal of being a fossil-fuel independent nation since there is little advancement in the electricity storage technology on a large scale and since there is a limited amount of renewable energy sources such as biomass to replace the current fossil fuels. As the share of renewable energy that is generated from wind power increases, the associated risk of blackouts will rise and hence the cost of maintaining a stable power supply will also increase. Therefore, the policy of renewable energy development to achieve a 100% renewable energy system indeed needs the support and backup from the energy consumers. Consumer acceptance of renewable energy has important influence on the diffusion of renewable energy (Painuly, 2001). Consumer attitudes and preferences are two important factors that influence consumer acceptance (Ajzen, 1991; Fishbein & Ajzen, 1975). Thus, consumer acceptance of renewable energy will have an influence on their willingness to adopt and use renewable energy. Furthermore, consumer acceptance can influence government and other organizations and help shape energy transformations (Stern, 2014). “As policy issues emerge and transform, views of energy technologies and policies may change too, and interested parties may engage in efforts to reframe them” (Stern, 2014). Given the considerable promotions of green marketing in the deregulated electricity market and the importance of triggering consumers’ volunteer demand for renewable energy, it is therefore important to understand the determinants that shape consumers’ preference for various types of electricity products. Consumers’ concerns about the electricity generation and the environment are important issues for energy policy as well as for the electricity retailers (also known as “electricity suppliers”). This is because consumers’ concerns reflect consumers’ preference for renewable energies and their desire to expand renewable energy generation capacity (Markard & Truffer, 2006). Eventually, consumer acceptance of renewable energy can help supporting government’s policies on promotion of renewable energies (Painuly, 2001). Therefore, consumers’ role should not be excluded in the renewable energy policy making. From a public policy point of view,

2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

consumer valuation of the service attributes of renewable energy is important in order to identify demanddriven market-based possibilities for improving the sustainability of the energy supply. Understanding consumer’ valuation of electricity product attributes is also important for the energy sector for marketing purposes. Electricity producers have the incentives to expand the renewable energy production when more and more consumers are demanding and are willing to pay for an increasing share of renewable energy. Another incentive for electricity producers is that the increasing production of renewable energy can help phase out the current fossil fuel fired production gradually and thereby reduce the CO2 emission, eventually it helps improve the environment, which in turn benefits the whole society. A growing number of research on consumers’ preference for green electricity (i.e., electricity that is generated from renewable energy sources) have emerged in recent years (Yang, 2013). This research indicates that there is a significant proportion of consumers preferring green electricity and are willing to pay extra for green electricity (Yang, 2013). Goett et al. (2000) found that households in the U.S. generally prefer hydroelectricity and wind power. Bergmann et al. (2006) found that households display different preference towards various renewable energy investments in Scotland. Navrud and Braten (2007) found that Norwegian consumers prefer wind power as compared to hydropower, natural gas fired power or continued import from coal-fired electricity. Ladenburg and Dubgaard (2007) discovered a relationship between consumers’ willingness to pay for wind energy and visual amenities from offshore wind farms in Denmark. So far, there is very little knowledge on consumers’ preference for electricity products when the share of renewable energy is increasing. Furthermore, although many established and new electricity retailers have begun to differentiate their electricity products in the form of service contracts to attract more customers after the deregulation of the electricity market, there has been very little interests in these products from the customers (Yang, 2013). This is an indication of poor marketing communication due to a lack of understanding regarding consumers’ preferences. This is bad because the government/energy regulator wants consumers to accept and actively shop for green electricity. Therefore, the purpose of this paper is to provide some insights into Danish consumers’ perceptions and preferences for electricity products that contain a

3

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

number of certain favorable environmental attributes. A second purpose is to assess the impact of the marketing programs on the specific product attributes. This study differs from earlier research in this area in several ways. Firstly, this study investigates consumers’ preference for electricity products when the share of renewable energy increases. Given the already high share of renewable energy, especially wind power, in Denmark, there is a need for understanding consumer attitudes towards renewable energy expansion in the future. Because consumer attitudes can influence consumers buying intention to support the expansion of renewable energy (Ajzen & Fishbein, 1977; Fishbein & Ajzen, 1975). Secondly, we use a hypothetical choice experiment to investigate consumer preferences, which can help gain knowledge on how consumers make trade-off decisions between attributes and attributes levels when the share of renewable energy is increasing. Thirdly, the results of this research are expected to improve the understandings of consumers’ preferences towards different types of renewable energy when the share of renewable energy in the current energy mix is increasing. Fourthly, the results of this research are also expected have implications for energy policy makers regarding portfoliobased planning for electricity generation. The paper is organized as follows. Section 2 presents the theoretical framework and hypothesis. Section 3 describes the experimental design and the data collection. Section 4 reports the study results. Section 5 includes conclusions and discussions.

Theoretical framework The theory of energy portfolio Fossil fuel fired energy production has been considered an important contributor to the climate change. Therefore, expanding renewable energy in the current energy mix becomes more and more important in many countries around the world (Lund, 2007). However, many renewable energy sources have some pitfalls. For example, the production of wind, hydro and solar power are all weather-driven and climate dependent. Thus, the production output can be highly variable. As a consequence, the variability will increase when the

4

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

share of renewable energy increases. Yet, the electric grid does not tolerate variability in the energy supply. Furthermore, variability will also bring an increased cost of energy, e.g. price increases might occur when the demand is much higher than the supply. Therefore, balancing the energy market is crucial especially for the deregulated electricity market (Möller et al., 2011). In order to maintain the stability of energy supply with a growing share of renewable energy, there is a need for diversifying the energy fuels. Portfolio theory is an often used means for the quantitative determination of the diversification (Delarue et al., 2011). Bar-Lev and Katz (1976) are among the first to apply portfolio theory from finance in the electricity sector using the mean-variance approach. In the same vein, Roques et al. (2008) applied the mean-variance portfolio theory to assess the fuel mix diversification incentives in liberalized electricity markets. The basis of portfolio theory is that by diversifying a portfolio of assets, the overall risks of the portfolio will be lower than the risk of a single asset. Eventually, portfolio theory helps energy investors balance the risk and return. The energy portfolio theory that derives from the portfolio theory in finance is used to help energy investors to manage risk and maximize the portfolio performance, and eventually help enhance energy security (Awerbuch, 2006; Delarue et al., 2011; Galvani & Plourde, 2010). Energy security refers to the availability of adequate energy resources and services at affordable prices (Knox-Hayes et al., 2013). In addition, a diversified energy portfolio can also help reduce the greenhouse gas emission by reducing the share of fossil fuel generation (Awerbuch et al., 2006). Awerbuch (2006) pointed out that the affordability of renewable energy use, fossil fuel independence and enhanced energy security are important motivating factors for using portfolio-based planning for electricity generation. Thus, portfolio-based planning is widely used for electricity generation (De Jonghe et al., 2011; Delarue et al., 2011; Galvani & Plourde, 2010; Meunier, 2013; Möller et al., 2011). Given all the benefits from energy source diversification using the energy portfolio theory, our hypothesis is that consumers will also prefer mixed renewable energy sources rather than a dominant single renewable energy source when the share of renewable energy is increasing.

5

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

The choice model for electricity products The underlying theoretical framework is random utility theory (McFadden, 1974; Train, 2009). The random utility theory assumes that an individual chooses the alternative that providers her/him with the highest utility (Train, 2009), which can be expressed as: Pr , , ∀ ∈ Pr , , ∀ ∈ where is the utility that an arbitrary individual i will assign to an alternative j, is a deterministic component and is a stochastic component. The conditional logit model was used to model how consumers make their choices based upon the characteristics of the available green electricity products. The probability that an individual i choose alternative j is derived from the utility of alternative j as compared to the utility of all other alternatives in the choice sets. The logit model is based on the assumption that the stochastic component of utility is extreme value distributed and can be written as follows (Greene, 2012; McFadden, 1974):

exp ∑!" exp

where is the deterministic part of the utility function ( ), is the vector of the exogenous

levels of the attributes of green electricity, and is the vector of the coefficients for the attributes. The logit model has been widely applied in marketing and consumer research to describe consumer choice behavior (Alberini et al., 2006). There are three reasons for using of this model: 1) conceptual appeal being grounded in economic theory; 2) analytical tractability and ease of econometric estimation, and 3) excellent empirical performances as measured by model fit and other criteria. In the next section, the choice experiment for the survey is described.

6

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

Methodology Discrete Choice experiment The process of selecting the attributes and attribute levels used to describe the electricity products follows the guidelines from (Bergmann et al., 2006; Hensher et al., 2005). The attributes and attribute levels from previous choice modeling studies (Borchers et al., 2007; Goett et al., 2000; Navrud & Braten, 2007; Roe et al., 2001; Wood et al., 1995) and from current products in the market were presented and discussed in focus group interviews. Finally, we updated the five selected attributes with different levels, as shown in Table 1. The first four attributes measure households’ preference towards the contents of the electricity product (i.e., service contract), which are manageable by the electricity suppliers. These attributes provides information on consumers’ preference towards renewable energy and price, which can reflect the public opinions about the renewable energy expansion. The last attribute, electricity supplier, also provides information on household’s willingness to switch supplier rather than stick to their current. This is an important issue because the electricity market is deregulated and both industry and government desire to know about the demand for such services by many suppliers, which also actually reflects the actual market situation. All attributes were specified to include various levels, as shown in Table 1. The lowest level for “percentage of renewable energy” and “price pr. kWh” were chosen to be similar to current market conditions. This is done to introduce market realism increasing face validity of the research (Louviere et al., 2000). The pilot study indicated that all attributes and attribute levels appeared to be common sense for the respondents. [Insert Table 1 here] The experimental design An efficient experimental design was used when creating the choice tasks (Kuhfeld et al., 1994, 2010; Lazari & Anderson, 1994). The statistical software, SAS, suggests a solution with 48 choice sets, where each choice set consists of 3 alternatives, which gives a total of 144 product profiles/concepts (Kuhfeld et al., 1994). However, we detected 64 unrealistic or implausible concepts from this design. The

7

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

main problem for these concepts was the mismatch between attributes and levels, e.g., the lowest price for the highest renewably percentage and for the longest contract period, and vice versa. We therefore decided to make small adjustments for these 64 concepts with some restrictions together with an experienced discrete choice experiment expert (Sandor & Wedel, 2001). For Alternative A, the highest price level were raised from 2.35 Kr./kWh to 2.45kWh. For alternative C, the percent of renewable energy was fixed to 100% and the corresponding price level was set to 2.35 Kr./kWh. It was expected that consumers would go for the cheap alternative given the same percentage of renewable energy. In order to avoid overstated willingness to pay, we thus decided to increase the highest price level of 2.35 Kr./kWh with 0.1 Kr.in the original design. Finally, we included a common choice set that reflected a worst and best hypothetical scenario. The goodness of fit indicator of the revised design, the D-efficiency, was re-estimated to 77.37% indicating that the revised design is acceptable. The 48 statistically generated choice sets were blocked into eight survey versions. Each respondent was asked to complete seven choice sets or tasks. The first six choice sets/tasks were randomly chosen from the eight survey versions, the last choice set/task is the common choice task. Exhibit 1 presents an example of the choice task and the common choice task. [Insert Exhibit 1 here] A pilot estimation of the model using the revised design and based on data from 100 respondents provided parameter estimates all having the expected sign indicating that the revised design needs no further changes. Data collection

The survey was a self-administrated online questionnaire, using a commercial marketing research firm based in Denmark and the time for completing the questionnaires was about 10 to 15 minutes. The target respondents were chosen from the Internet-panel administrated and maintained by the professional

8

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

marketing research firm. The Internet panel includes 7000+ Danish consumers reflecting the Danish population 15 to 65 years of age. The entire questionnaire consists of four main sections. In the first section, questions concerning consumers’ knowledge about household electricity consumption and the deregulated electricity market are asked. The second section includes questions such as consumers actual buying of green electricity, and consumers’ attitudes towards adoption of green electricity. The third section is the choice tasks, which contain six different choice sets randomly chosen from the 48 generated choice sets plus one common choice set. The final section is questions about consumers’ socio-demographic profiles. General respondent characteristics There are 1012 usable respondents after closing the questionnaire. Generally, the sociodemographic background reflects the whole population in Denmark in terms of gender, age, and the number of children at home. The sample is slightly skewed towards higher educated people with a higher level of income. This bias may be attributed to the sampling method that only respondents who have access to Internet were recruited. Of the 1012 respondents, 84.4% knew the possibility of switching electricity suppliers. Overall, approximately 51 % of the respondents are either satisfied or very satisfied with their current electricity suppliers, see Table 2. Regarding the electricity consumption, about 42% of the respondents have annual electricity consumption between 2000 and 6000 kWh, but 29% have no clues about their annual electricity consumption. This maybe an indication of very low consumer awareness on electricity consumption due to electricity is a basic necessary good. [Insert Table 2 here] Table 3 presents the public opinion about renewable energy expansion. The majority of the respondents believe that the government has the primary responsibility for the expansion of renewable energy in Denmark whilst approximately one-fourth of the respondents believe that the electricity suppliers

9

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

have the main responsibility. Surprisingly, about 6.7% of the respondents believe that consumers should take the primary responsibility. In this survey, over 40% of the respondents prefer offshore wind farms. The majority of the respondents want new built wind farms kept at a distance so not to disturb people. Given the geographic nature of Denmark, there is little space for setting up new wind farms that will not influence local residents; the study results signal a strong not in my backyard effect (Wolsink, 2007). [Insert Table 3 here] A total of 7084 choice sets were completed in the survey. It should be noted that there was no dominant preference for any particular choice options appearing from the distribution of choices observed in the data. Regarding the common choice set, 44% of the respondents have selected the least green alternative (i.e., Product A) with the lowest price, 36.1% have chosen the greenest alternative (i.e., Product C) with a high price, and 20% have chosen Product B, which contains a relatively highly percentage of renewable with high price. These results may show that most respondents prefers greener electricity product and they are ready to pay extra for the increased share of green electricity.

Statistical estimation and results The model specification Although there is no dominating choice alternative in the experiment, it can be noted that for Alternative C, the level for “price” and “percentage of renewable energy” are constant. Therefore, only the independent variables, i.e., attribute “percentage of renewable energy” and “price”, have been specified to Alternative A and B in the analysis. The utility that individual i assigns to choice alternative j is specified as: V$% ∙ ASC" ∙ ASC* ∙ ASC+ " ∙ PER./ * ∙ SRW./ + ∙ PRC./ 1 ∙ CNT./ 4 ∙ SUP./ where ASC" , ASC* and ASC+ are three alternative specific variables, PERAB represents percent of renewable energy for product A and B, SRW represents source of renewable,

10

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

PRCAB represents price for product A and B, CNT represents contract length, SUP represents electricity supplier for product,

The dependent variable is the individual’s choice. The independent variables entered into the model are the given attributes of green electricity. For all attributes with numeric levels, we coded the variables with linear and quadratic coding respectively (Louviere et al., 2000). The categorical attributes and associated levels were coded as dummy variables. Table 4 presents the parameter estimations. All parameter estimates followed a priori expectations. The changes of part-worth utilities for the attributes also appear to be consistent with the expectations. The Pseduo R2 measures how well the estimated model predicts the choices (Vermunt & Magidson, 2005). The results in Table 4 indicate that the model estimation is reasonable and acceptable (Breffle & Rowe, 2002). [Insert Table 4 here] The parameter indicates that price has an important influence on consumers’ preference. It can be noted that consumers prefer a higher percentage of renewable energy delivered to their homes with various renewable energy sources. Besides, consumers prefer to trade with their current electricity suppliers. This may be attributed to several factors. First is the high consumers’ satisfaction with current electricity suppliers. Traditional local monopolies have established a solid consumer relationship through stable supply over decades, so there is a solid switching inertia (Gärling et al., 2008). Secondly, electricity is a necessity good, so consumers do not want to invest much effort in searching for new offers or consumers believe that it is not worthwhile to search for new offers. Thirdly, electricity is homogenous at the point of use, it is therefore difficult for consumers to articulate the difference between green electricity and non-green electricity physically. Finally, there is a lack of market communication promoting supplier switching. In the survey, only about 22 % have heard of Elpristalvn.dk, a website for comparing all available electricity products and prices in Denmark. And only 12% of the respondents have tried to switch their electricity suppliers at the point of survey.

11

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

Based on the estimation results, we can derive the marginal willingness to pay for each of the attributes. The marginal willingness to pay can be computed as the inverse value of “the coefficient of the attribute divided by the price coefficient” (Bergmann et al., 2006). Table 5.2 presents the marginal willingness to pay for the various product attributes. [Insert Table 5 here] It is clear that consumers are willing to pay extra for an increasing share of renewable energy from mixed renewable energy sources. The positive marginal willingness to pay for current suppliers also indicate that consumers prefer to trade with the current supplier.

Conclusion and discussions Understanding the tastes of residential energy customers is important for electricity retailers to identify marketing opportunities and design products that are attractive to specific consumer groups, and it is also an essential component of renewable energy management for energy policy makers. By allowing respondents to trade-off different product profiles/concepts under different hypothetical scenarios, the discrete choice study provides comprehensive assessments and insightful knowledge about consumer preference in the light of an increasing share of renewable energy. Consumers in this study display obvious trade-offs between electricity attributes. Price and the percentage of renewable are two important attributes when consumers choose the product. Beside, source or renewable energy, contract length and electricity suppliers are also found to be significant for consumer choice. The results indicate that affordability and the problem of diversifying the renewable energy mix are important when the share of renewable energy increases. Although an increasing share of renewable energy is preferred, it should be noted that the expansion of renewable energy only is acceptable if it has no or only few impacts for local residents. This study has a number of managerial implications. First is that import of renewable energy to raise the share of renewable energy should not be regarded as an option. This is because electricity is a basic

12

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

necessity, and the government/policy-maker may lose control over price if they rely too much on importing electricity. Consumers’ apprehension for importing renewable energy may partly originate from the experiences/memories of the oil crises in 1970s (Danish Energy Agency, 2012). Because Denmark was heavily dependent on imported fuels, the Danish electricity industry was very vulnerable. In addition, hydro power sold in the current Danish market is generally from Norway, indicating that there is a lack of supplier diversification for renewable energy. Thus, the basic good – electricity, should not only be sustainable, but also be locally available in order to have control over energy price. The second implication is that diversifying the renewable energy mix is needed for Denmark to become a fossil fuel free nation. Given the fact that Denmark has already a high share of wind power, the emphasis on wind energy may need to be re-considered. Consumers are generally concerned about the stability of the energy supply when the shares of renewable energy especially wind power increases. Thus, a dominant wind energy in the energy mix will increase the risk for an imbalanced grid. However, this also leaves business opportunity for developing new technologies for storage renewable energy. The implication of importing renewable energy and diversifying renewable energy confirms earlier theories about energy diversifications, namely, that the diversification of energy sources and suppliers are equally important in order to maintain a nation’s energy security (Konoplyanik, 2005; Stringer, 2008; Vivoda, 2009). These two implications also contribute to the portfolio theory. Consumer preferences support the diversification of renewable energy mix, but also the diversification of renewable energy (source) suppliers. This finding confirms our hypothesis. For the renewable energy investors and marketers, there is a market for the increasing share of mixed renewable energy in Denmark. Thirdly, although the retail electricity market has been deregulated since 2003, consumers appear to prefer trading with the current suppliers. Although the alternative offered by another supplier that may be cheaper than the current supplier’s offer, consumers still want to stay with their current supplier. This may be an indication of an endowment effect (Pindyck & Rubinfeld, 2012). This is because for those consumers who are familiar and feel safe to trade with current suppliers, it can be difficult to enter into a new contract with

13

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

another electricity service provider. This may also have some implications for the deregulation advocates in Denmark and probably in Europe. First is to redesign the current policy in order to trigger more consumers switching. Secondly, there may be a need for re-estimating the cost and benefits from consumer switching. Preferring the current supplier has implications for the electricity marketers. There are marketing opportunities for current electricity suppliers to earn a profit by selling a high share of mixed renewable energy. In addition to the price, electricity suppliers maybe can supply a contract that is longer than the one in the hypothetical experiment in this study to attract new consumers. A major limitations of this paper lies in the model assumption that individuals within a group have homogeneous preferences. Thus, further analysis such as latent class modelling to identify and explain the degree of preference heterogeneity among residential energy consumers may be useful. Nonetheless, we hope that the presentation of this generic choice model will provide information on the public perceptions and preferences about the expansion of renewable energy. Furthermore, the use of consumers’ role for expanding the renewable energy should be careful because the majority of respondents in this study still believes that the government or the electricity retailers have the primary responsibility for expanding renewable energy in Denmark. But this study indicates that there is a trend generally that most consumers are willing to pay extra for an increasing share of renewable energy.

Acknowledgements We thank the financial support from the “Energi på havet” (“Energy at sea”) project, Offshore Center Denmark, the Growth Forum for Southern Denmark, and the European Regional Development Fund.

14

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

References

Danish Goverment. (2011). Energy Strategy 2050 - from coal, oil and gas to green energy Copenhagen The Danish Ministry of Climate and Energy. Lund, H., & Mathiesen, B. V. (2009). Energy system analysis of 100% renewable energy systems—The case of Denmark in years 2030 and 2050. Energy, 34(5), 524-531. doi: http://dx.doi.org/10.1016/j.energy.2008.04.003 Danish Energy Agency. (2012). Energy Policy in Denmark. Copenhagen: Danish Minister for Climate, Energy and Building. Meyer, N. I. (2004). Renewable energy policy in Denmark. Energy for Sustainable Development, 8(1), 25-35. doi: http://dx.doi.org/10.1016/S0973-0826(08)60388-9 Gatermann, R. (2008). Danish coal trumps wind. European Energy Review(November / December), 17. CEPOS/Center for Political Studies. (2009). Wind Energy: the case of Denmark. Copenhage: CEPOS/Center for Politiske studier/Center for Political Studies. Painuly, J. P. (2001). Barriers to renewable energy penetration; a framework for analysis. Renewable Energy, 24(1), 73-89. doi: http://dx.doi.org/10.1016/S0960-1481(00)00186-5 Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Process, 50, 179-211. Fishbein, M., & Ajzen, I. (1975). Beliefs, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Reading, MA: Addison-Wesley. Stern, P. C. (2014). Individual and household interactions with energy systems: Toward integrated understanding. Energy Research & Social Science, 1(0), 41-48. doi: http://dx.doi.org/10.1016/j.erss.2014.03.003 Markard, J., & Truffer, B. (2006). The promotional impacts of green power products on renewable energy sources: direct and indirect eco-effects. Energy Policy, 34(3), 306-321. doi: http://dx.doi.org/10.1016/j.enpol.2004.08.005 Yang, Y. (2013). Perspectives on Marketing of Green Electricity. Ph.D dissertation, Faculty of Social Science, University of Southern Denmark: University Press of Southern Denmark. Goett, A. A., Hudson, K., & Train, K. E. (2000). Customers' choice among retail energy suppliers: The willingness-to-pay for service attributes. Energ J, 21(4), 1-28. Bergmann, A., Hanley, N., & Wright, R. (2006). Valuing the attributes of renewable energy investments. Energy Policy, 34(9), 1004-1014. doi: http://dx.doi.org/10.1016/j.enpol.2004.08.035 Navrud, S., & Braten, K. G. (2007). Consumers' preferences for green and brown electricity: A choice modelling approach. Rev Econ Polit, 117(5), 795-811. Ladenburg, J., & Dubgaard, A. (2007). Willingness to pay for reduced visual disamenities from offshore wind farms in Denmark. Energy Policy, 35(8), 4059-4071. doi: http://dx.doi.org/10.1016/j.enpol.2007.01.023 Ajzen, I., & Fishbein, M. (1977). Attitude-behavior relations: a theoretical analysis and review of empirical research. Psychological Bulletin, 84(5), 818-918. Lund, H. (2007). Renewable energy strategies for sustainable development. Energy, 32(6), 912-919. doi: http://dx.doi.org/10.1016/j.energy.2006.10.017 Möller, C., Rachev, S. T., & Fabozzi, F. J. (2011). Balancing energy strategies in electricity portfolio management. Energy Economics, 33(1), 2-11. doi: http://dx.doi.org/10.1016/j.eneco.2010.04.004 Delarue, E., De Jonghe, C., Belmans, R., & D'Haeseleer, W. (2011). Applying portfolio theory to the electricity sector: Energy versus power. Energy Economics, 33(1), 12-23. doi: http://dx.doi.org/10.1016/j.eneco.2010.05.003 Bar-Lev, D., & Katz, S. (1976). A Portfolio Approach to Fossil Fuel Procurement in the Electric Utility Industry. The Journal of Finance, 31(3), 933-947. doi: 10.2307/2326437

15

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

Roques, F. A., Newbery, D. M., & Nuttall, W. J. (2008). Fuel mix diversification incentives in liberalized electricity markets: A Mean–Variance Portfolio theory approach. Energy Economics, 30(4), 18311849. doi: http://dx.doi.org/10.1016/j.eneco.2007.11.008 Awerbuch, S. (2006). Portfolio-Based Electricity Generation Planning: Policy Implications For Renewables And Energy Security. Mitig Adapt Strat Glob Change, 11(3), 693-710. doi: 10.1007/s11027-0064754-4 Galvani, V., & Plourde, A. (2010). Portfolio diversification in energy markets. Energy Economics, 32(2), 257268. doi: http://dx.doi.org/10.1016/j.eneco.2009.05.015 Knox-Hayes, J., Brown, M. A., Sovacool, B. K., & Wang, Y. (2013). Understanding attitudes toward energy security: Results of a cross-national survey. Global Environmental Change, 23(3), 609-622. doi: http://dx.doi.org/10.1016/j.gloenvcha.2013.02.003 Awerbuch, S., Stirling, A., Jansen, J. C., & Beurskens, L. W. M. (2006). Chapter 13 - Full-Spectrum Portfolio and Diversity Analysis of Energy Technologies. In K. B. Leggio, D. L. Bodde & M. L. Taylor (Eds.), Managing Enterprise Risk (pp. 202-222). Oxford: Elsevier Science Ltd. De Jonghe, C., Delarue, E., Belmans, R., & D’haeseleer, W. (2011). Determining optimal electricity technology mix with high level of wind power penetration. Appl Energ, 88(6), 2231-2238. doi: http://dx.doi.org/10.1016/j.apenergy.2010.12.046 Meunier, G. (2013). Risk aversion and technology mix in an electricity market. Energy Economics, 40(0), 866-874. doi: http://dx.doi.org/10.1016/j.eneco.2013.10.010 McFadden, D. (1974). Conditional Logit Analysis of Qualitative Choice Behaviour. In P. Zarembka (Ed.), Frontiers in Econometrics (pp. 105-142). New York: Academic Press. Train, K. E. (2009). Discrete Choice Methods with Simulation (2nd ed.). Cambridge: Cambridge University Press. Greene, W. H. (2012). Econometrics Analysis (7th ed.). Boston and etc.: Prentice Hall. Alberini, A., Longo, A., & Veronesi, M. (2006). Basic statistical models for stated choice studies. In B. J. Kannien (Ed.), Valuing environmental amenities using stated choice studies (pp. 203-227). Dordrecht, The Netherlands: Springer. Hensher, D. A., Rose, J. M., & Greene, W. H. (2005). Applied choice analysis: a primer: Cambridge University Press. Borchers, A. M., Duke, J. M., & Parsons, G. R. (2007). Does willingness to pay for green electricity differ by source. Energy Policy, 35(6), 3327-3334. Roe, B., Teisl, M. F., Levy, A., & Russell, M. (2001). US consumers’ willingness to pay for green electricity. Energy policy, 29(11), 917-925. Wood, L. L., Kenyon, A. E., Desvousges, W. H., & Morander, L. K. (1995). How much are customers willing to pay for improvements in health and environmental quality? The electricity journal, 8(4), 70-77. Louviere, J. J., Hensher, D. A., & Swait, J. D. (2000). Stated Choice Methods — Analysis and Application. Cambridge, UK: Cambridge University Press. Kuhfeld, W. F., Tobias, R. D., & Garratt, M. (1994). Efficient Experimental-Design with Marketing-Research Applications. Journal of Marketing Research, 31(4), 545-557. doi: Doi 10.2307/3151882 Kuhfeld, W. F., Tobias, R. D., & Garratt, M. (2010). Efficient experimental design with marketing research applications Marketing research methods in SAS - experimental design, choice, conjoint, and graphical techniques (pp. 243-265): SAS Institute Inc. Lazari, A. G., & Anderson, D. A. (1994). Designs of Discrete-Choice Set Experiments for Estimating Both Attribute and Availability Cross Effects. J Marketing Res, 31(3), 375-383. doi: Doi 10.2307/3152224 Sandor, Z., & Wedel, M. (2001). Designing conjoint choice experiments using managers' prior beliefs. J Marketing Res, 38(4), 430-444. doi: DOI 10.1509/jmkr.38.4.430.18904 Wolsink, M. (2007). Wind power implementation: The nature of public attitudes: Equity and fairness instead of ‘backyard motives’. Renewable and Sustainable Energy Reviews, 11(6), 1188-1207. doi: http://dx.doi.org/10.1016/j.rser.2005.10.005

16

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

Vermunt, J. K., & Magidson, J. (2005). Latent GOLD® Choice 4.0 User's Manual Statistical Innovations Inc., Belmont, MA. Breffle, W. S., & Rowe, R. D. (2002). Comparing choice question formats for evaluating natural resource tradeoffs. Land Econ, 78(2), 298-314. doi: Doi 10.2307/3147275 Gärling, T., Gamble, A., & Juliusson, E. A. (2008). Consumers' switching inertia in a fictitious electricity market. International Journal of Consumer Studies, 32(6), 613-618. doi: 10.1111/j.14706431.2008.00728.x Konoplyanik, A. (2005). The View from Brussels. In H. McPherson, W. Duncan Wood & D. Robinson (Eds.), Emerging Threats to Energy Security and Stability (pp. 79-86): Springer Netherlands. Stringer, K. D. (2008). Energy security: Applying a portfolio approach. Baltic Security & Defence Review, 10(1), 121-142. Vivoda, V. (2009). Diversification of oil import sources and energy security: A key strategy or an elusive objective? Energy Policy, 37(11), 4615-4623. doi: http://dx.doi.org/10.1016/j.enpol.2009.06.007 Pindyck, R., & Rubinfeld, D. (2012). Microeconomics (8th ed.). Upper Saddle River, NJ: Prentice Hall/Pearson Higher Education. Energistyrelsen /Danish Energy Agency. (2014). Årlig energistatistik 2012 (Annual Energy Statistics 2012). Retrieved March 18, 2014, from http://www.ens.dk/info/tal-kort/statistik-nogletal/arligenergistatistik

17

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

Figure 1. Distribution of energy resources in electricity generation

Source: Energistyrelsen /Danish Energy Agency (2014).

18

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

Table 1. The electricity product choice experiment: attributes and levels Attribute % of renewable energy

Source of renwable energy

Price pr. kWh*

Contractual length

Electricity supplier

Levels 1. 2. 3. 4. 1. 2. 3. 1. 2. 3. 4. 5. 1. 2. 3. 4. 1. 2. 3.

25% 50% 75% 100% Mostly Wind Mostly Hydro(Water) Mixed incl. wind, hydro(water), biomass,solar and geothermal 2.05 Kr. 2.15 Kr. 2.25 Kr. 2.35 Kr. 2.45 Kr. ½ year 1 year 2 year 3 year Your current supplier Another well-known supplier Another unfamiliar supplier

* Note: Price level 2.45 Kr./kWh was only included in the final revision of the experimental design. 1 Danish Kr. ≈ 0.13 €

19

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

Exhibit 1. Choice set example and the common choice set If these were the only products available to you, which one will you choose? Product A

Product B

Product C

% of renewable energy

50%

75%

100%


Mixed incl. wind, hydro(water), biomass,solar and geothermal

Mostly Wind

Mostly Hydro (water)

Price pr. kWh

2.45 kr.

2.25 kr.

2.35 kr.

Contract Length

½ Year

2 Year

3 Year


Another wellknown supplier

Your current supplier

Another unfamiliar supplier

Your Choice

If these were the only products available to you, which one will you choose? Product A

Product B

Product C

% of renewable energy

25%

75%

100%


Mixed incl. wind, hydro(water), biomass,solar and geothermal

Mostly Hydro (water)

Mostly Wind

Price pr. kWh

2.05 kr.

2.25 kr.

2.35 kr.

Contract Length

½ Year

1 Year

3 Year


Your current supplier

Another well-known supplier

Your currenct supplier

Your Choice

20

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

Table 2. Consumers’ satisfaction and electricity consumption

n

%

Consumer satisfaction Very satisfied Satisfied Neither …. Nor… Dissatisfied Very dissatisfied

120 395 451 37 9

11.9 39.0 44.5 3.7 .9

Annual electricity consumption < 2000 kWh 2000 – 3999 kWh 4000 – 5999 kWh > 6000 kWh Don’t know

185 242 182 109 292

18.3 23.9 18.2 10.8 28.9

21

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

Table 3. Consumers’ opinions about renewable energy expansion

…has the most responsibility for the expansion of renewable energy generation in Denmark? The government The consumers The electricity suppliers Don’t know … where do you prefer most that the newest wind farms should be set up in Denmark? On land Offshore Either on land or offshore, but it should keep enough distance not to disturb people Don’t know

22

n

%

598 68 367 79

59.1 6.7 26.4 7.8

14 428 545

1.4 42.8 53.9

25

2.5

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

Table 4: Parameter estimations Coefficients Constants ASC1 ASC2 ASC3

- .02(.03) -.05(.02)* .06(.03) *

Percentage of renewable energy (PERAB)

1.26 (.06)*

Source of renewable (SRW)

Wind Hydro (water) Mixed sources

-.06(.02)* -.24(.02)* .30(.02)*

Price(PRCAB)

-2.22 (.07) *

Contract length (CNT)

.19(.04)*

Electricity suppliers (SUP)

Your current Another well-known Another unfamiliar

.29(.02)* - .09(.02)* -.20(.02)*

Model Fit Index Log-likelihood (LL) Number of parameters(Npar) Degree of freedom (df) BIC (L2) Pseudo R2 (overall)

-5744.38 9 1003 3783.76 .14

23

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

Table 5: Marginal willingness to pay for product attributes Percentage of renewable energy (PERAB)

.57

Source of renewable (SRW)

Wind Hydro (water) Mixed sources Contract length (CNT)

-.03 -.11 .14 .09

Electricity suppliers (SUP)

Your current Another well-known Another unfamiliar

.13 -.04 -.09

24