Consumer survey

Survey as a research strategy allows for the collection of potentially large amounts of quantitative data (Saunders et al. 2009). Survey data was used as the main data source of appended Articles III, IV, and V. The main objective of the survey was to better understand consumer decision-making concerning RET adoption, policy influence, and interest in innovation activities in a broad sense. A quantitative approach was selected to obtain a better representation of the population.

The survey was designed by a research group including researchers from the Tampere University and CERN. The questionnaire included 79 questions inquiring about different aspects of RET adoption and use. The questionnaire design was based on literature review on energy prosumers and theoretical concepts related to e.g. diffusion of innovations, attitudes toward adopting RET, co-creation and user-centric innovations, and policy instruments related to RET. The questions were designed as statements focusing on the decision-making criteria and motivations for RES investment, environmental attitudes, interests on co-creation activities, and preferences for incentives and other support for RES adoption. A seven-point Likert scale was used to rate the responses. In addition, various demographic data were collected, including housing type, income level, education, and age. Subsets of this broad questionnaire were used in the appended articles.

The data were initially collected in four European countries in the summer of 2016: France, Germany, Italy, and Switzerland. Data were collected in Finland a few months later at end of 2016 and early 2017. Countries selected for the consumer survey feature typical yet diverse cases (Gerring 2013). They are typical in being high-income, coordinated market economies with parliamentary political systems. They are all integrated in the European energy market and all except Finland are situated in the heart of the central European markets. Four out of the five countries, namely Finland, France, Germany and Italy, share common commitment to the EU’s 40%

target for GHG emission cuts by 2030, while they also have common targets for reaching near zero emission energy systems by 2050. Switzerland, while not a member state of EU, has adopted similar targets for the Swiss energy system and is aiming to achieve zero net carbon emission by 2050 (“Federal Council aims for a climate-neutral Switzerland by 2050” 2019). The selected countries also have clear differences. Geography of the countries vary remarkably, which explains partly their distinctive energy mixes. Italy and Germany are still heavily relying on coal. Finland, France and Switzerland have nuclear plants. Finland and Switzerland also have hydro power. Germany and Italy especially are investing in solar energy.

The countries are also different from the socio-technical point of view e.g. in their energy policies, regulation, and energy production as well as public attitudes toward renewable energy (Valta 2017). Germany supports solar generation and batteries but has been slow to introduce smart metering. Italy is Europe’s second biggest market of solar PV but aggregation and demand response are not as developed. Finland has good market conditions and advanced smart metering infrastructure but does not incentivize solar energy. France has established regulation for demand response and incentives for microgeneration and EVs. Switzerland has a dispersed policy landscape as cantons’ role is emphasized. It is a frontrunner in microgeneration and demand response but lags behind in smart metering.(FinSolar 2019; Rosen and Madlener 2016; Valles et al. 2016; Valta 2017; S. Zhou and Brown 2017).

In order to improve the quality of the survey, native speakers translated the questionnaire from English into French, German, Italian, and Finnish. The questionnaire was first tested with the translators to prevent any statements that might cause confusion or be easily misunderstood. Slight modifications were made after this piloting round.

The data collection took place in face-to-face setting in Tours, Toulouse, and Saint-Genis Pouilly in France; Freiburg, Aachen, and Munich in Germany; Napoli, Firenze, and Moncalvo in Italy; Geneva and Spietz in Switzerland; and Tampere in Finland. In addition, the questionnaire was made available online: the respondents who were unable to participate at the time were given an opportunity to answer the questions later online. Questionnaires are efficient when respondents have similar understanding of questions (Saunders et al. 2009); the researchers clarified some statements that raised questions as needed.

The data sample of this particular survey is 197 respondents. The intention was to collect a sample that would characterize the distinctions between the surveyed countries as well as detect key differences between prosumers (RES owners) and consumers. Owing to the explorative nature of the study as well as the limited availability of resources, the sample size was restricted to approximately 30 per country. Non-probability purposive sampling was used in order to ensure both prosumers and non-prosumers in the sample. In total, 75 of the respondents were prosumers and 122 were consumers. Respondents that indicated having “Solar panels,” “Wind,” “Geothermal,” and “EV” at their disposal were considered as prosumers and the others were considered normal consumers.

Data collection predominantly took place in public spaces, such as airports, railway stations, and city parks. In order to fulfil the target of reaching a sufficient

number of prosumers, i.e., RET owners, the questionnaire was delivered door-to-door in residential areas that had solar panel installations on rooftops. These neighborhoods were found using Google Maps’ satellite images. Selecting residential areas may have created bias as inhabitants’ demographic features, such as income and educational background, may be relatively homogenous in neighborhoods.

Young adults (25-40) were overrepresented and middle-aged adults (41-55) were underrepresented in the samples from Germany and Switzerland. In French and Italian samples the age distribution was closer to actual situation but older adults (55-) were slightly over-represented (CIA 2016(55-). Demographic information (average(55-) of the survey respondents is presented in the Table 5.

Table 5. Average demographic summary of the survey respondents

Characteristics Description %

Age group 18-24 18.4

25-40 40.3

41-55 15.3

>55 26.0

Education Primary school 4.7

Secondary school 28.1

Bachelor’s degree 19.3

Master’s degree 47.9

Income <3000 € 38.5

3000-6000 € 37.0

>6000 € 24.5

The response rate of the survey was 30,0%. Quantitative methods were applied to analyze the data. The methods included statistical analysis, principal component analysis (PCA), and partial least squares structural equation modeling (PLS-SEM).

The data analysis method is discussed in more detail in the respective articles. PLS-SEM was used in Articles IV and V as it fits well to research exhibiting small sample size, explorative research, and the desire to model the causal effects of certain parameters (Hair et al. 2014; Sarstedt et al. 2017). The analysis results are discussed in the article summaries in the next chapter. It should be noted, that the Article III uses data set from France, Germany, Italy and Switzerland as the results from the Finnish respondents were not ready, due to later data collection than in the central European states, at the time of writing the article.