4.1 Description of the sample
The sample was drawn by quota sampling, taking the following factors into consideration: participating households’ monthly net income, the number of persons living in one household, and region. It was expected that region and town size could affect the decision for or against an offered tariff. Therefore, the German federal states were divided into four regions[4] based on the cardinal directions. The variable “town size” was divided into five groups, and ranged from “less than 5,000 residents” to “more than 500,000 residents” following the classifications of the German Federal Statistical Office (see Appendix C).
The participating consumers were aged between 18 and 78 years old, with a representative average of 44 years of age [78]. Individuals younger than 18 years of age were not included in the survey, since few people younger than 18 live in their own households and make decisions regarding their electricity tariffs. In the sample, females were slightly overrepresented compared to the general German population in 2015 (57% vs. 52%) [79]. However, since the focus of this investigation was rather on individuals who are responsible for household energy-related decisions, the gender distribution of surveyed decision-makers may differ from the general German population. A total of 24% of sampled individuals had obtained the general higher education entrance qualification and another 24% held a university degree as the highest qualification level.
Considering the electricity consumption data, participants used 2,750 kWh per year on average and paid about €750 for their annual electricity bill. The latter value is considerably lower than the annual average German average annual electricity bill, which amounted to €1,008 in 2016 [54]. This difference may be due to the assumption that an average household has an electricity consumption of 3,500 kWh per year, while in our sample, 69% of the respondents stated that they consume less. 35.3% receive electricity from green energy.[5] Furthermore, only 6.4% of all German households switched their energy provider in 2015, meaning that few people have benefited from a cost reduction in their electricity bill [63], whereas in our sample, a quarter of the individuals switched their provider or tariff within the past year.
Statements relevant to the hypotheses showed the following response frequencies: half of the sampled individuals stated that green electricity is only trustworthy if no plants which could alternatively be consumed as food or feed are used for its generation. A quarter of the participants agreed that the EEG levy is a good instrument to promote the expansion of renewable energies. However, 63% agreed that the demand for green energy could be increased through the elimination of the EEG levy for those who decide to receive pure green energy. 12% of the participants felt well-represented by the political platform of the Green Party, which was slightly more than in the 2017 general election [80]. 52% of individuals considered environmental concerns when they buy their groceries. Although two thirds of participants had a positive attitude towards green energy, 31% of the participants had never taken the initiative to switch. About 16% of the participants would be more motivated to switch if there was somebody who could do this for them for a fixed fee of 50 Euros. Full descriptive statistics are provided in Appendix C.
4.2 General findings of the GMNL model in WTP space
Table 4 presents a basic model (Model 1) which represents the WTP of the average consumer as well as a model that includes several participant-specific variables as interaction terms with different tariff attributes (Model 2). Both were estimated in WTP space as a specified form of the GMNL model [36, 37] by implementing the Stata module of Gu et al. [81] using 1,000 Halton draws. These interaction terms account for possible causes of the observed heterogeneity in the valuation of the random parameters “alternative-specific constant (ASC)”, “share of green energy”, “switching bonus”, and “price guarantee” which are characterized by the standard deviations of the random parameter distributions of Model 1. As suggested by Hensher et al. [82], interactions that were not significant were excluded from the estimation process as they could have had an effect on the other coefficients within the model. Therefore, other tested variables, such as the participants’ educational level or the size of the household, were not considered in the final model estimation since they lacked significance. In order to prove the explanatory power of the models, the pseudo‑R2 was used as a goodness-of-fit measure. The values show that Model 2, with a pseudo‑R2 of 0.32, is an improvement of Model 1. According to Hensher et al. [82], a pseudo‑R2 of at least 0.3 represents an appropriate model fit. The underlying STATA-code can be found in Appendix D.
Table 4 – Generalized multinomial logit model in willingness-to-pay space a)
Variables
|
GMNL-WTP-space I
Basic Model
|
GMNL-WTP-space II Interaction Model
|
Coefficient (mean)
|
Coefficient (mean)
|
Random parameters
|
|
|
|
|
Alternative-specific constant (ASC) b)
|
21.649
|
***
|
27.983
|
***
|
Share of green energy
|
0.022
|
***
|
0.027
|
***
|
Switching bonus
|
0.004
|
**
|
-0.002
|
|
Price guarantee
|
0.148
|
***
|
0.063
|
**
|
Tariff price
|
-1[fixed]
|
|
-1[fixed]
|
|
Non-random Parameters c)
|
|
|
|
|
Green energy source: solar
|
0.211
|
**
|
0.188
|
**
|
Green energy source: wind
|
0.196
|
**
|
0.178
|
**
|
Green energy source: RE mix
|
0.059
|
|
0.067
|
|
Interaction variables
|
|
|
|
|
ASC x region: east.d)
|
|
|
0.502
|
**
|
ASC x region: south d)
|
|
|
-1.139
|
***
|
ASC x region: west d)
|
|
|
0.671
|
***
|
ASC x town size e)
|
|
|
-0.496
|
***
|
ASC x EEG levy acceptancef)
|
|
|
0.555
|
***
|
ASC x Green Party identification f)
|
|
|
-1.038
|
***
|
Share of green energy x Green Party identification f)
|
|
|
0.010
|
***
|
ASC x food or fuel f)
|
|
|
-0.646
|
***
|
ASC x environment is important when buying groceries f)
|
|
|
1.130
|
***
|
ASC x never switched before g)
|
|
|
-0.381
|
***
|
ASC x wish to outsource switching process f)
|
|
|
1.279
|
***
|
Standard deviations (SD) of parameter distributions
|
|
|
|
|
SD ASC
|
5.654
|
***
|
4.999
|
***
|
SD Share of green energy
|
0.023
|
***
|
0.020
|
***
|
SD Switching bonus
|
0.010
|
***
|
0.007
|
***
|
SD Price guarantee
|
0.088
|
***
|
0.116
|
***
|
Scale heterogeneity
|
|
|
|
|
Tau
|
1.014
|
***
|
1.137
|
***
|
Goodness of fit measures
|
|
|
Participants/observations
|
371/4,452
|
371/4,452
|
McFadden pseudo-R2
|
0.309
|
0.322
|
Log-Likelihood at convergence
|
-2,716.756
|
-2,670.03
|
Akaike information criterion
|
5,471.512
|
5,408.06
|
Source: Author’s calculations by means of the STATA-command “gmnl” in STATA 14 using 1,000 Halton draws.
Notes: a) * p < 0.1; ** p < 0.05; *** p < 0.001; randomized WTP coefficients with significant SD are assumed to be normally distributed and correlated; the price coefficient was normalized to be log-normal and constrained to -1.
b) Binary coded variable; reference: status-quo alternative “No switch.”
c) Effect coded; reference: “Energy source: biogas”.
d) Effect coded; reference: “Region: north”.
e) The variable “town size” was divided into five groups, and ranged from “less than 5,000 residents” to “more than 500,000 residents”. For a detailed structuring of the groups see Appendix C.
f) Effect coded; reference: “Participant does not support the queried statement”.
g) Effect coded; reference: “Participant switched the electricity tariff at least once before”.
The price coefficient was normalized to -1, and the other coefficients represent the WTP for each variable. The models include a dummy-coded ASC, which was valued at one for choosing one of the tariff alternatives and zero for the status-quo alternative “no switch”. The significant ASC of Model 1 implies that the average participant is willing to pay 21.6 euro cent kWh-1 for an offered green electricity tariff instead of choosing no offered tariff (status-quo alternative). This value reflects a general WTP for green electricity as all offered tariffs within the DCE contained green electricity. On average, German consumers paid about 28.8 euro cent kWh-1 for their electricity in 2016 [54], indicating that a tariff switch can be strongly motivated by a price reduction. However, this relatively high value arises from the fact that about one third of the consumers received electricity via basic tariffs, which are the most expensive way to obtain electricity [63]. Considering all available existing pure green energy tariffs in Germany, the average cost for one kilowatt hour was only 22 euro cent kWh-1 in 2016 [48]. Therefore, it can be assumed that the estimated WTP of 21.6 euro cent kWh-1 for switching to a green electricity tariff reflects a realistic amount.
The attribute “share of green energy” was measured in percentage and described the proportion of green energy sources in the tariff for an annual electricity consumption of 3,500 kWh. Model 1 shows that on average, the WTP increased by 0.022 euro cent kWh-1 if the share of green energy increased by 1%. For instance, the lowest offered green energy share in the tariffs was 40%, resulting in an additional WTP of 0.88 Eurocent kWh-1 (0.022*40), meaning that participants would agree to pay 2.2 euro cent kWh-1 (0.022*100) more for a pure green energy tariff if they decided to switch their tariff. In terms of the annual electricity bill, this means a sum of €77 (0.022*100*3,500). The influence of the “switching bonus” was also significant if the participants were willing to opt for a new tariff. Model 1 reveals that for a one Euro increase in the bonus payment, participants would pay 0.004 euro cent kWh-1. Thus, in order to receive the maximum offered switching bonus of €120, the average participant was willing to spend €16.80 € (0.004*120*3,500) more on the annual electricity bill. The “price guarantee” was given in months and led to a relatively high WTP, as shown in Model 1. If the average participant decided to switch their tariff, they were willing to pay 0.15 euro cent kWh-1 for every additional month the guarantee is extended. In other words, regarding an annual electricity consumption of 3,500 kWh, a 12-month guarantee was valued by the average participant at €63. The variable “energy source” was effect coded, meaning that “biogas” acted as a reference for the other energy sources. The coefficient for biogas was then calculated as suggested by Hensher et al. [82] using the following equation: WTPbiogas = – (WTPsolar + WTPwind). Thus, the coefficient was -0.407 (-0.407= – (0.211 + 0.196)), as it can be understood from Model 1. This suggests that participants had a WTP for a tariff including solar or wind energy but not for a tariff with biogas energy. Furthermore, no significant WTP for a renewable electricity mix was found.
4.3 Hypotheses testing
Hypothesis 1 – Different preferences regarding RES
The results of Model 1 reflecting the average consumer’s preferences were used for testing Hypothesis 1 since no preference heterogeneity was determined for the coefficients of the energy sources “solar”, “wind”, and “RE mix”. The green energy source “biogas” acted as a reference for the other energy sources. The results revealed that consumers have a marginally higher WTP for solar energy than wind energy (coefficients: 0.211 vs. 0.196) if “biogas” is understood as the reference. Furthermore, a renewable electricity mix does not motivate participants to pay more for biogas in a potential new tariff, as the coefficient was not significant. If consumers have the choice between the various energy sources presented in this study, neither biogas nor a RE mix are energy sources that facilitate an increased rate when switching the tariff. This is contrary to Burkhalter et al. [35], who reported that a green electricity mix is more appreciated by consumers than green electricity from a single source. However, if consumers have a negative perception of biogas production and more specifically, of RES that can alternatively serve as feed or food [44, 47], it seems plausible that a green electricity mix containing energy of this origin is more likely to be rejected. This assumption was confirmed by the negative coefficient of the interaction term “ASC x food or fuel” (Model 2: -0.646). Without accounting for specific tariff arrangements, it was shown that if a participant does not want to support an energy source that can either serve as food or fuel, their WTP decreases by 0,646 euro cent kWh¬1. Consequently, the results corroborate other scientific studies that also found that if consumers consider switching to green energy tariffs, they have a general WTP for green electricity products, but that this varies over different energy sources [16, 20, 28, 44-47]. In light of these results, H1: the consumer prefers electricity from solar and wind over electricity from biogas can be confirmed.
Hypotheses 2a/b – Influence of where the participant lives
The northern states of Germany served as the reference for the estimations in Model 2, since consumers pay an average value for green electricity compared to the other regions [48]. The results showed that compared to the north, the south has the significantly lowest WTP for switching to green electricity (ASC x region: south: -1.139 Eurocent kWh-1), whereas households in the east or west would pay significantly more than households in the north for switching to green electricity (ASC x region: east: 0.502 Eurocent kWh-1, ASC x region: west: 0.671 Eurocent kWh-1). One possibility to explain these regional differences is to take the different network charges that consumers have to pay depending on the network operator into account. In general, it can be stated that households in the east pay significantly more for their electricity than households in the west [48, 49, 83]. This results from higher costs for the network expansion in the east, since here lots of renewable energy is produced which needs to be then fed in the grid and distributed. However, the study did not find that households in the east want a discount compared to the reference households in the north. Therefore, another path to explain the findings could be that even if different WTP values for switching to green energy are found in different regions, these values are not different because of the region and the average electricity price in each region. The household size, the income situation, as well as the tariff availability depending on where a participant lives, could have influenced the participants’ WTP in the observed regions.
The coefficient “ASC x town size” was significantly negative (-0.496). The coefficient can be interpreted as follows: the bigger the town a person lives in, the lower the WTP for a green electricity tariff switch. In other words, participants who live in very large cities with more than 500,000 residents have five times lower WTP (-2.48 euro cent kWh-1). In terms of the annual electricity bill, this means that these participants want to pay about €86.80 less (5*(-0.496)*3,500). This is an interesting finding, as on the one hand, it is conceivable that people who live in rural areas (represented by the smallest town unit) are more impacted by negative effects of renewable energy production, and therefore it could be expected that these participants would have the lowest WTP. On the other hand, and this is what the results suggest, it can be assumed that these participants are probably closer to nature and more involved in renewable energy production, and therefore have the highest WTP. This is in line with findings of Liebe et al. [50] and Meyerhoff [51] who showed in the context of wind power generation that respondents who lived further away from turbines were more likely to be opponent to wind power generation, whereas respondents who already had turbines in their vicinity were more likely to accept new ones. However, since this is probably the first study that considered the influence of where a person lives on whether a person wants to switch to green energy or not, further studies could analyze why consumers in towns want to pay less. Nevertheless, it becomes evident that H2: the participant’s WTP for a green electricity tariff is dependent on the region and the town size cannot be rejected.
Hypothesis 3 – Influence of a person’s attitude towards the EEG levy
The survey included the question of whether the participants perceived the EEG levy of costs to all citizens as a good instrument to promote the expansion of renewable energies. About 26% of the sample agreed with this. For those who supported this statement, the WTP increased significantly (by 0,56 euro cent kWh-1) if they decide to switch their tariff (“ASC x EEG levy: likely instrument”). However, the WTP decreased by the same amount for individuals who disagreed with this statement. In terms of the annual electricity bill, this amounts to €19.60 and shows that participants were (not) willing to pay more. As the EEG levy, in reality, costs consumers €216 per year at a consumption level of 3,500 kWh [52], the findings indicate that the WTP of participants who agreed (disagreed) with the EEG levy was €236 (€196). Thus, H3: the willingness to switch to a green electricity tariff depends on the acceptance of the EEG levy cannot be rejected, even if the influence of a person’s attitude is rather modest in terms of concrete figures. However, to explain why the majority of the participants want to reach a tariff price discount by reducing the amount of the EEG, it may be helpful to know that currently only 42% of the EEG levy is used to promote the expansion of renewable energies [84]. If participants have knowledge of this, it is conceivable that they consider the EEG levy to be an inappropriate mechanism. This assumption was additionally supported by 63% of participants, who stated in the survey that the demand for green energy could be increased through the elimination of the EEG levy for those who decide to receive pure green energy. For policy makers, this could be an interesting approach to motivate consumers to buy pure green energy. Consumers who decide to opt for a pure green energy tariff could be rewarded with a discount in the amount of the EEG levy, whereas all other groups of electricity customers who do not support the energy transition by purchasing green energy might be charged a penalty.
Hypothesis 4 – Environmental awareness and personal lifestyle
It seems obvious that people with a high awareness regarding environmental and sustainability issues are more likely to be interested in buying green electricity [55, 58, 59]. One way to gain information about consumer awareness is to ask whether participants are Green Party supporters [60]. In this study, the question was raised whether participants feel represented by the political platform of the Green Party. Those who identified with the Green Party showed a significantly reduced WTP for a switch to the offered green energy tariffs (“ASC x Green Party identification” = -1.038). This might be due to the fact that from the viewpoint of Green Party supporters, the offered tariffs could have included unfavorable energy sources, such as biogas. Interestingly, it was evident that the same participants had a rising WTP for each percentage increase in the share of green energy in the offered tariff (“share of green energy x Green Party identification” = 0.010). Therefore, it is conceivable that participants who felt represented by the Green Party considered switching to a green energy tariff only if this tariff consisted of pure green energy sources. If this is true, other tariffs that comprise of lower shares of green energy, including the electricity-mix currently offered in Germany, might not be a successful way to encourage this consumer group to switch to “greener” energy tariffs.
The influence of awareness of environmental issues on the participants’ decision to switch tariffs was also shown by the significant coefficient of the interaction term “ASC x environment is important when buying groceries” = 1.130. This result indicates that consumers who consider environmental issues in their daily life, e.g. when doing the weekly grocery shopping, have a higher WTP for switching to a green energy tariff. It is also conceivable that consumers who aspire to lead an environmentally-friendly lifestyle are more likely to switch their energy tariff to a green energy tariff since this kind of energy contributes to their desired way of life. Consequently, H4: an environmentally consciousness way of life leads to a higher WTP for green electricity cannot be rejected.
Hypothesis 5 – Influence of the participant’s desire to avoid transaction costs
There are several reasons why consumers do not switch their electricity tariffs, even if switching results in a financial benefit [25, 26, 27]. It was revealed that if a participant had never switched their tariff before, then they had a significantly lower WTP regarding a switch to a green energy tariff (“ASC x never switched before” = -0.381). This result confirms that certain obstacles exist for consumers when they switch their tariff. Thus, participants were asked in the survey whether they were more motivated to switch if they could outsource the switching process to someone else. The significant coefficient of the interaction term “ASC x wish to outsource the switching process” = 1.279 shows that participants who want to outsource the switching process demonstrate their appreciation of this assistance with an increased WTP. In light of these results, H5: the number of tariff switches would increase if consumers could outsource the switching process to someone else is supported and cannot be rejected. Therefore, offering a “full-service switch” or to work together with switching assistants could be one way to increase green energy adoption rates. In most cases, the termination of the current electricity tariff is already handled by the new provider. However, the consumer must cancel the tariff in some cases e.g. in the case of moving house. Therefore the results reveal that consumers evaluate this service positively and providers should continue and extend the possibility of a “full-service switch”.
Footnotes:
[4] The distribution of the federal states to the regions was as follows: north (Bremen, Hamburg, Lower Saxony, Schleswig-Holstein), east (Brandenburg, Mecklenburg-Western Pomerania, Saxony, Saxony-Anhalt, Thuringia, Berlin), west (North-Rhine Westphalia, Saarland), south (Bavaria, Baden-Wuerttemberg, Hesse, Rhineland-Palatinate).
[5] As it was assumed that many consumers did not know the share of green energy in their tariffs this information was not collected. However, this could be important information in future studies.