Table 3 Explanatory variables used in the model estimation for testing the hypotheses

H1—in comparison to other RES, there is no additional WTP for energy from biogas

“Food or fuel”: green electricity is only trustworthy if no plants which could alternatively be consumed as food or feed are used for its generation

Effect coded: 1 = agreement; -1 = disagreement

H2a/b—the participant’s WTP for a green electricity tariff is dependent on the region and the town size in which they live

“Region: east, south, west, north”: In which of the following regions do you live?

“Town size”: How many people live in the place (village, town, city) of your primary residence?

Effect coded: 1 = east, south, west; -1 = north

1 =  < 5,000 residents

2 = 5,000–19,999 residents

3 = 20,000–99,999 residents

4 = 100,000–499,999 residents

5 =  ≥ 500,000 residents

H3—the willingness to switch to a green electricity tariff depends on the acceptance of the EEG levy.^a)

“EEG levy acceptance”: The EEG levy of costs to all citizens is a good instrument to promote the expansion of renewable energies

Effect coded:

1 = agreement; -1 = disagreement

H4—an environmentally conscious way of life leads to a higher WTP for green electricity

“Green Party identification”: I feel best represented by the political platform of the Green Party

“Environment is important when buying groceries”: I consider environmental concerns when I buy my groceries for the week

Effect coded: 1 = agreement; -1 = disagreement

Effect coded: 1 = agreement; -1 = disagreement

H5—the number of tariff switches would increase if consumers could outsource the switching process to someone else

“Never switched before”: Have you ever actively (not moving) switched your electricity tariff?

“Wish to outsource switching process”: I would be more motivated to switch if there was somebody who could do this for me for a fixed fee of 50 Euros

Effect coded: 1 = yes; -1 = no

Effect coded: 1 = agreement; -1 = disagreement