What shapes the support of renewable energy expansion? Public attitudes between policy goals and risk, time, and social preferences

Energy Policy xxx (xxxx) xxx

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What shapes the support of renewable energy expansion? Public attitudes between policy goals and risk, time, and social preferences €llendorff b Elke D. Groh a, *, Charlotte v. Mo a b

University of Kassel, Institute of Economics, Nora-Platiel-Str. 5, 34109, Kassel, Germany University of Oldenburg, Department of Business Administration, Ammerl€ ander Heerstr. 114-118, 26129, Oldenburg, Germany

A R T I C L E I N F O

A B S T R A C T

JEL Classification: Q42 Q48 Q54

Climate protection goals can only be achieved if the expansion of renewable energies is publicly supported. This paper analyzes the relevance of the perceived importance of policy goals and framework conditions as well as individual risk, time, and social preferences for the support of the German energy transition. Based on data from an online survey, our empirical analysis reveals that the perceived importance of climate protection, nuclear risks, changes in the landscape and economic effects are decisive for a strong support of the energy transition. In addition, it reveals that the importance of the policy goals is driven by political orientation but also by trust and a feeling of responsibility. Therefore, this study emphasizes the need to compensate for the negative consequences of the transition process, to take confidence-building measures, and to sensitize the public for their own stake in greenhouse gas emissions.

Keywords: Energy policy Energy transition Public attitude Policy support Univariate ordered and binary probit model Multivariate ordered probit model

1. Introduction Germany was once known as one of the leading actors in tackling issues of global warming. However, in 2020 it will most probably miss its self-imposed goal to reduce greenhouse gas emission by 40 percent compared to the year 1990 (e.g. BMWi, 2019a). The energy sector is one of the biggest greenhouse gas emitters in Germany due to fossil fuels burned for the provision of electricity and heat. Thus, in order to narrow the gap and reach subsequent climate goals such as a 55 percent reduction by 2030 and close to zero emissions by 2050, it is essential to phase out coal fired power plants and expand the renewable power generation infrastructure. This is challenging in light of the simulta­ neous nuclear phase-out until 2022. The necessity of phasing out fossil fuels and the associated further expansion of the renewable energy infrastructure makes the topic of social acceptance highly important. There are, for example, discussions on the affordability of energy (e.g. Frondel et al., 2017; Heindl et al., 2014), on the effects on energy prices (e.g. Ziegler, 2019), and on the fair allocation of the costs of policy measures (e.g. Groh and Ziegler, 2018). In addition, there are conflicts at the local level when it comes to the implementation of renewable energy projects (e.g. Jones and Eiser, 2010; Wüstenhagen et al., 2007). Despite the rather positive public attitudes on the energy transition reflected in

representative surveys (e.g. AEE, 2016; IASS, 2017), these discussions can lead to lacking support of the policy. This might constrain the further development of renewable energies and the fossil phase-out, thus blocking the achievement of climate goals. Therefore, it is important to study the determinants of public attitudes towards the energy transition and the perceived importance of its policy goals and framework conditions. Joas et al. (2016) observed that the goals of the energy transition in Germany are diversely defined by politicians and scientists. Often the goals are complementary, though some are in conflict with each other. Based on this observation, they map the goals of the German energy transition based on interviews with policy actors and find that climate protection is the highest ranked policy goal of the experts. However, they also find that the energy transition is important beyond only climate protection, for example with regard to security of supply, low electricity prices, the nuclear phase-out, job creation as well as energy import independence. Against the background of the ill-defined policy goals, we analyze the importance of selected policy goals and framework conditions from the perspective of German citizens in order to identify the most important policy goals for the public support of the energy transition. Their identification might be helpful to find trade-offs be­ tween interest groups and to direct policy campaigns in order to

* Corresponding author. E-mail addresses: [email protected] (E.D. Groh), [email protected] (C.v. M€ ollendorff). https://doi.org/10.1016/j.enpol.2019.111171 Received 5 February 2019; Received in revised form 18 November 2019; Accepted 5 December 2019 0301-4215/© 2019 Elsevier Ltd. All rights reserved.

Please cite this article as: Elke D. Groh, Charlotte v. Möllendorff, Energy Policy, https://doi.org/10.1016/j.enpol.2019.111171

E.D. Groh and C.v. M€ ollendorff

Energy Policy xxx (xxxx) xxx

maintain a stable public consensus necessary for the long-term policy goals. Studies analyzing the acceptance of climate policies and renewable energy technologies already considered a variety of variables related to the design of climate policy instruments. For example, a Canadian study by Rhodes et al. (2017) compares public support for different climate policies finding that subsidies, educational programs and soft efficiency regulations or standards receive considerably higher support compared to carbon taxes or a cap-and-trade policy. In addition, a general ten­ dency to support soft and less-coercive instruments as well as differences in perceived (cost) effectiveness and benefits can explain some variation in the support for climate policies (e.g. Drews and van den Bergh, 2016). Furthermore, acceptance varies with the type of technology and spatial proximity to energy facilities (e.g. Andor et al., 2016; Bertsch et al., 2016) such that support is highest for wind and photovoltaic and lowest for coal and nuclear power. As a result, a policy based on renewable energy might be more accepted than a policy based on nuclear power. This can be explained by different externalities associated with the various forms of energy production (for a review see e.g. Welsch, 2016). These studies have given important insights on what is shaping public attitudes towards climate policies and their implementation. Nonethe­ less, little attention has been paid to the importance of different goals and framework conditions of climate policies and the relevance of risk, time, and social preferences in this context. We base our empirical analysis on the descriptive results of Schubert et al. (2015) and Bertsch et al. (2016), who find that environmental aspects and security of supply are more important for the German population than economic aspects and social considerations. We contribute to this literature by studying how the importance of policy goals and framework conditions is correlated with the individual support of the energy transition. Furthermore, we are interested in the relative relevance of the perceived policy goals and framework conditions when considering socio-psychological variables such as time, risk, and social preferences. Drews and van den Bergh (2016) highlight the importance of the socio-psychological variables in their interdisciplinary literature review on the support of climate policy. Conducting an online survey, we asked 674 respondents, to what extent they support the German energy transition and to rate the importance of several policy goals and framework conditions regarding their personal attitude towards the energy transition. The queried items refer to environmental sustainability, economic sustainability, security of energy supply, and social sustainability. Risk, time, and social pref­ erences are used to explain the support of the energy transition as well as the perceived importance of the related policy goals and framework conditions. In order to take previous findings on determining factors into account, we use a broad set of control variables, e.g. political orienta­ tion, environmental awareness, and socio-economic variables. Our descriptive statistics reveal that 37 percent of our respondents rather support the energy transition and additionally 54 percent even totally support it. The policy goals and framework conditions that are stated to be important for the support of the energy transition by the highest share of respondents belong to the topic of environmental sustainability, namely climate protection and extent of pollutant emissions. In contrast, the share of respondents that perceive social sustainability as important for their political attitude is relatively low. Even so, the evaluation is very heterogeneous across respondents, pointing out the need to consider regional as well as individual differences in preferences and attitudes in the design of policies. Applying univariate ordered and bi­ nary probit models, we find that the perceived importance of policy goals and framework conditions is of higher relevance for the support of the energy transition than the underlying risk, time and social prefer­ ences. Perceiving nuclear risks and climate protection as important is positively related to strong policy support. On the contrary, perceived importance of changes in the landscape, the level of unemployment and the level of energy prices are negatively related to strong support of the energy transition. With the help of a multivariate ordered probit model,

Table 1 Comparison of sample (674 observations) and population statistics. Sample

German population

36.4% 63.7%

50.7% 49.3%

14.8% 46.6% 38.6%

31.8% 35.2% 33.0%

24.2% 30.7% 2.1% 43.0%

26.6% 28.6% – –

13.4% 18.1% 4.6% 2.1% 0.6% 2.2% 9.9% 0.5% 9.9% 21.8% 5.2% 1.2% 2.7% 2.1% 3.4% 2.4%

13.3% 15.7% 4.3% 3.0% 0.8% 2.2% 7.5% 2.0% 9.6% 21.7% 4.9% 1.2% 4.9% 2.7% 3.5% 2.6%

a

Gender Women Men Ageb 18–40 years 40–60 years 60 years and older Religious affiliationb Catholic Protestant Other affiliations and other religion No religion Federal statec Baden-Wuerttemberg Bavaria Berlin Brandenburg Bremen Hamburg Hesse Mecklenburg Western Pomerania Lower Saxony Northrhine-Westphalia Rhineland Palatinate Saarland Saxony Saxony-Anhalt Schleswig Holstein Thuringia

Destatis (2018);Destatis (2019a);Destatis (2019b).

we also find that political orientation is a main driver of the perceived importance of the policy goals, notwithstanding, that trust and a feeling of responsibility play a major role for the evaluation as well. The remainder of the paper is structured as follows: Section 2 de­ scribes the data and variables used in the econometric analysis. Section 3 reports descriptive statistics and discusses the estimation results. Section 4 concludes. 2. Data and variables The empirical analysis is based on data that was collected from a computer-based survey among citizens in Germany. The survey was carried out by the market research company SUZ (Sozialwissen­ schaftliches Umfragezentrum GmbH) between November 2016 and January 2017.1 The random selection of the respondents referred to the universe of all German citizens with a landline or mobile connection. Each respondent was contacted by an e-mail beforehand, which included the link for the participation in the online survey. In order to increase the sample size, we contacted a new group of randomly selected citizens, as well as citizens who participated in a previous telephone survey among randomly selected individuals with sufficient information about the energy consumption in their households (e.g. Groh and Zie­ gler, 2018). Among all 674 respondents, the median time to complete the questionnaire was about 26 minutes. Of these respondents, 152 are excluded since they failed a control task2 and further 55 respondents had to be excluded from the empirical analysis due to missing values for some variables, so that overall 467 observations are the basis of the

1 Prior to implementing the survey in the field, we pre-tested the question­ naire by means of expert reviews and a small online survey within the target population. As the questions used for our analysis have not been changed after the pretest, pretest observations are included in the final sample. 2 The control task was to tick the answer category “rather agree” in-between the questions on social norms. On average, the excluded sample is less educated compared to the estimation sample and has lower incomes.

2

E.D. Groh and C.v. M€ ollendorff

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Table 2 Questions on the importance of the 13 selected policy goals and framework conditions (short names). Targets

Survey question: What significance do the following aspects have for your personal attitude towards the energy transition?

Economic sustainability

(1.1) (1.2) (1.3) (1.4) (2.1) (2.2) (2.3) (2.4) (3.1) (3.2) (4.1) (4.2) (4.3)

Environmental sustainability

Security of energy supply Social sustainability

Distribution of the costs of the energy transition between private households and industry (cost distribution) Level of energy prices (energy prices) Competitiveness of economy (economy) Level of unemployment (unemployment) Climate protection (climate) Extent of pollutant emissions (emissions) Amount of nuclear waste (nuclear waste) Risk of nuclear accidents (nuclear accidents) Reliability of energy supply (reliability) Dependence on energy imports (energy imports) Changes in the landscape (landscape) Participation of the population in location decisions for power plants (codetermination) Possibilities of financial participation in power plants (financial participation)

empirical analysis. Table 1 compares some of the sample characteristics with the German population and shows that women and young people are underrepresented in our sample (e.g. Destatis, 2018; 2019a; 2019b). While this underrepresentation might possibly distort some descriptive statistics, this is not very problematic for our study that focuses on the econometric analysis, i.e. the underrepresentation should not lead to biased estimation results since we include gender and age as control variables besides the main interesting explanatory variables. The survey included questions on attitudes towards the energy transition, house­ hold characteristics, individual attitudes and preferences, a stated choice experiment on the design of energy policy measures, an artificial field experiment, and questions on socio-demographic characteristics. The main questions for the present analysis refer to the individual level of support of the energy transition and the individual evaluation of the importance of different policy goals and framework conditions. In the first question, respondents were asked how strongly they support the energy transition on a symmetric five-point scale, with the response options “totally oppose”, “rather oppose”, “undecided”, “rather sup­ port”, and “totally support”.3 For the econometric analysis, we consider the corresponding raw ordinal variable “support”. Additionally, we constructed an aggregated dummy variable “strong support”, which takes the value one if respondents “totally support” the energy transi­ tion. In the second question, respondents evaluated the importance of selected policy goals and framework conditions for their support of the energy transition. Based on previous studies and the public discussion on the energy transition we included 13 policy goals and framework con­ ditions in the questionnaire. The selection is mainly based on Schubert et al. (2015) who considered elements of the target triangle of the German energy transition consisting of the targets of economic sus­ tainability, security of energy supply, and environmental sustainability (e.g. BMWi, 2016). In addition, we take into account the literature on indicators for social sustainability (e.g. Schlomann et al., 2016). The final selection consists of 13 policy goals and framework conditions that fit the four policy targets as shown in Table 2. In line with Joas et al. (2016), Schubert et al. (2015), and Bertsch et al. (2016, 2017), we expect that the policy goals and framework conditions related to the target of environmental sustainability, and in particular the goal of climate protection, have the highest relevance for policy support. The policy goals and framework conditions expected to be from second highest relevance refer to the target of sustainability of energy supply, followed by economic sustainability and social sustainability. We examine the relationship based on respondents’ evaluation of the

importance of the 13 policy goals and framework conditions for their support of the energy transition. Respondents indicated the importance on a symmetric five-point scale ranging from “very low importance”, “rather low importance”, “undecided”, “rather high importance”, to “very high importance”.4 In the econometric analysis, we consider the raw ordinal variables as dependent variables to examine the drivers of the perceived importance of the policy goals and framework conditions. For the use as explanatory variables, we consider dummy variables for each of the 13 ratings that take the value one if the policy goal or framework condition is of “rather high importance” or “very high importance”. In addition to the evaluation of the 13 policy goals and framework conditions, respondents had the opportunity to name further goals or conditions that are of high importance for their personal attitude to­ wards the energy transition. 144 respondents used the opportunity. 27 statements aim at sustainability and intra- and intergenerational justice while 17 statements mention aspects relating to social compatibility with respect to the distribution of costs and benefits. Furthermore, 16 statements convey a preference of a decentralized structure of the en­ ergy market, which is based on public participation over a monopolistic energy supply with a few large utilities. Some respondents used the statements to reinforce their opposition towards nuclear power, their skepticism against or their support for the energy transition. Other statements aimed at transparency, future viability, network expansion, energy efficiency, research and technology, the global context, mobility, health, or the pioneering role of Germany in the energy transition. Most of these statements are directly related to the 13 selected policy goals and framework conditions. Thus, the most important topics are covered by our selection. A still ongoing discussion in economics is the effect of framing. Tversky and Kahneman (1981) define a decision frame as the deci­ sion-maker’s perception of a choice situation that is influenced by her individual characteristics, for example norms and habits, but also by the

3 To ensure that everybody knows what the energy transition is, we defined the term at the very beginning of the survey as the transfer to a sustainable energy supply by means of renewable energy. In order to avoid a central ten­ dency bias, we provided a “don’t know/no answer” option.

4 We also offered the opportunity to skip the question by selecting a “don’t know/no answer” option, that was treated as missing value. The order of the policy goals and framework conditions was random across respondents to avoid a bias due to the sequence.

Table 3 Frequencies of support of the energy transition (522 observations).

3

Totally oppose

Rather oppose

Undecided

Rather support

Totally support

Don’t know/no answer

4 (0.77%)

17 (3.26%)

22 (4.21%)

193 (36.97%)

284 (54.41%)

2 (0.38%)

E.D. Groh and C.v. M€ ollendorff

Energy Policy xxx (xxxx) xxx

Table 4 Frequencies of strength of importance of the 13 policy goals and framework conditions (522 observations). Policy goals/ framework conditions

Very low importance

Rather low importance

Undecided

Rather high importance

Very high importance

Don’t know/no answer

(2.1) Climate (2.2) Emissions (3.1) Reliability (2.3) Nuclear waste (2.4) Nuclear accidents (1.1) Cost distribution (3.2) Energy imports (1.3) Economy (1.2) Energy prices (4.2) Codetermination

4 (0.77%) 1 (0.19%) 8 (1.53%) 10 (1.92%) 10 (1.92%) 5 (0.96%) 5 (0.96%) 12 (2.30%) 15 (2.87%) 24 (4.60%)

21 (4.02%) 14 (2.68%) 18 (3.45%) 29 (5.56%) 44 (8.43%) 43 (8.24%) 72 (13.79%) 78 (14.94%) 103 (19.73%) 99 (18.97%)

155 (29.69%) 196 (37.55%) 195 (37.36%) 108 (20.69%) 103 (19.73%) 207 (39.66%) 191 (36.59%) 231 (44.25%) 185 (35.44%) 173 (33.14%)

330 (63.22%) 287 (54.98%) 271 (51.92%) 349 (66.86%) 339 (64.94%) 208 (39.85%) 163 (31.23%) 117 (22.41%) 147 (28.16%) 106 (20.31%)

1 (0.19%) 4 (0.77%) 3 (0.57%) 3 (0.57%) 4 (0.77%) 7 (1.34%) 6 (1.15%) 6 (1.15%) 4 (0.77%) 3 (0.57%)

(4.1) Landscape (1.4) Unemployment (4.3) Financial participation

23 (4.41%) 42 (8.05%) 67 (12.84%)

158 (30.27%) 142 (27.20%) 176 (33.72%)

11 (2.11%) 20 (3.83%) 27 (5.17%) 23 (4.41%) 22 (4.21%) 52 (9.96%) 85 (16.28%) 78 (14.94%) 68 (13.03%) 117 (22.41%) 93 (17.82%) 91 (17.43%) 116 (22.22%)

151 (28.93%) 141 (27.01%) 105 (20.11%)

94 (18.01%) 92 (17.62%) 39 (7.47%)

3 (0.57%) 14 (2.68%) 19 (3.64%)

Note: The policy goals and framework conditions are sorted by the sum of respondents selecting “rather high importance” and “very high importance”. For each policy goal and every framework condition, the mode of the strength of importance is highlighted in bold. Table 5 Spearman’s rank correlation coefficients (p-values) of the policy goals and framework conditions (469 observations) Policy target

Policy goal/ framework condition

(1.1)

Economic sustainability

(1.1) Cost distribution (1.2) Energy prices

1

(1.3) Economy

Environmental sustainability

(1.4) Unemployment (2.1) Climate (2.2) Emissions (2.3) Nuclear waste

Security of energy supply Social sustainability

(2.4) Nuclear accidents (3.1) Reliability (3.2) Imports (4.1) Landscape (4.2) Codetermination (4.3) Financial participation

0.30 (0.00) 0.11 (0.02) 0.18 (0.00) 0.10 (0.03) 0.17 (0.00) 0.13 (0.00) 0.14 (0.00) 0.27 (0.00) 0.14 (0.00) 0.12 (0.01) 0.21 (0.00) 0.11 (0.02)

(1.2)

(1.3)

(1.4)

(2.1)

(2.2)

(2.3)

(2.4)

(3.1)

(3.2)

(4.1)

(4.2)

(4.3)

1 0.27 (0.00) 0.28 (0.00) -0.11 (0.02) -0.02 (0.70) -0.11 (0.01) -0.07 (0.15) 0.35 (0.00) 0.19 (0.00) 0.27 (0.00) 0.18 (0.00) 0.08 (0.10)

1 032 (0.00) 0.08 (0.10) -0.00 (0.97) -0.00 (0.98) -0.00 (0.93) 0.33 (0.00) 0.32 (0.00) 0.12 (0.01) 0.07 (0.13) 0.09 (0.06)

1 0.03 (0.59) 0.09 (0.06) 0.12 (0.01) 0.19 (0.00) 0.25 (0.00) 0.19 (0.00) 0.22 (0.00) 0.23 (0.00) 0.19 (0.00)

1 0.35 (0.00) 0.40 (0.00) 0.33 (0.00) -0.03 (0.46) 0.14 (0.00) -0.05 (0.26) 0.11 (0.02) 0.14 (0.00)

1 0.35 (0.00) 0.36 (0.00) 0.05 (0.29) 0.17 (0.00) 0.05 (0.26) 0.08 (0.07) 0.15 (0.00)

1 0.62 (0.00) -0.03 (0.57) 0.14 (0.00) 0.05 (0.29) 0.17 (0.00) 0.15 (0.00)

1 0.03 (0.54) 0.18 (0.00) 0.05 (0.26) 0.17 (0.00) 0.15 (0.00)

1 0.22 (0.00) 0.17 (0.00) 0.10 (0.04) -0.01 (0.82)

1 0.17 (0.00) 0.09 (0.04) 0.19 (0.00)

1 0.32 (0.00) 0.10 (0.03)

1 0.19 (0.00)

1

Note: The correlations of the policy goals and framework conditions belonging to the same policy target are highlighted in bold.

formulation of the decision problem. There are several studies exam­ ining effects of the detailed formulation of the decision problem. Those studies show that framing is also influencing decisions in the public goods context (e.g. Andreoni, 1995). However, there are also studies showing that this might not be the case in a world of strategic consid­ erations and rivalry (e.g. Isaksen et al., 2019). In order to find out if the question on the general support of the energy transition is influencing the evaluation of the policy goals and framework conditions and vice versa by a different framing of the decision problem, we randomized the order of our two main questions. We expect that the stated support of the energy transition is biased through the frame of the initial evaluation of the importance of the 13 aspects and vice versa. Therefore, one-half of the respondents indicated their support of the energy transition first while the other half of respondents indicated the importance of the policy goals and framework conditions first. In the econometric analysis, we thus included a dummy variable that takes the value one if

respondents belong to the group that received the importance of policy goals and framework conditions question before the question on the support of the energy transition. In order to compare the effects of the importance of the different energy policy goals and framework conditions, we set them into relation with socio-psychological variables often claimed to influence contribu­ tions to public goods (e.g. Fehr-Duda and Fehr, 2016). Trust plays an important role in the support of climate policies. For example, Drews and van den Bergh (2016) identified trust in politicians and perceived policy support of others to be important for the support of climate pol­ icies. In a same manner, Jagers et al. (2010) find that the agreement with the introduction of a personal carbon allowance scheme is higher if people generally trust in politicians. Other studies show that people who feel responsible or get a warm glow from contributing to environmental protection rather agree with environmental policies (e.g. Stern et al., 1999; Menges et al., 2005) while the fear that other individuals or 4

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Energy Policy xxx (xxxx) xxx

responsibility, and the perception of freeriding are taken from Schwir­ plies and Ziegler (2015). They are considered as dummy variables in our econometric analysis taking the value one if respondents rather or totally agree to the corresponding statement (social norms: “my social environment (friends, family, colleagues) contributes to environmental protection“, responsibility: “I feel responsible to make a contribution to environmental protection”, warm glow: “it makes me feel good to contribute to environmental protection”, freeriding: “others, who themselves do not contribute to the protection of the environment, profit from my contribution”). Risk preferences are measured by a self-assessed rating on the general willingness to take risks, which has been validated with an incentivized risk lottery by Dohmen et al. (2011). Time preferences are measured as the self-assessed rating on the general patience of oneself, which has been validated by Vischer et al. (2013). In the econometric analysis, we consider the dummy variables “risk pref­ erence” and “time preference”. The variables take the value one, if a respondent states to be rather or very willing to take risks or to be rather or very patient, respectively. Previous studies also showed that environmental awareness, politi­ cal orientation, religious beliefs, and socio-economic variables are important for individual attitudes towards climate policies (e.g. Born­ stein and Lanz, 2008; Drews and van den Bergh, 2016). Therefore, we include a large set of control variables in our analysis. In general, higher environmental awareness as well as left and green political orientation is associated with more favorable attitudes towards climate policies (e.g. Attari et al., 2009; Drews and van den Bergh, 2016; Ziegler, 2019). Regarding Christian religious values, results are ambiguous. For instance, Martin and Bateman (2014) find no effect of religious values on individual green behavior in the United Stated of America, while Cui et al. (2015) show that firms located in US counties with high shares of Christians, behave less environmentally friendly. In order to measure these individual attitudes and values we considered the following approaches: Our measure for environmental awareness refers to a frequently applied instrument, namely the New Ecological Paradigm (NEP) scale (Dunlap et al., 2000). We use a short version of the NEP scale in line with Whitmarsh (2008, 2011). The indicator is based on the stated agreement on a five-point scale to the six statements: “humans have the right to modify the natural environment to suit their needs”, “humans are severely abusing the planet”, “plants and animals have the same right to exist as humans”, “nature is strong enough to cope with the impacts of modern industrial nations”, “humans are meant to rule over the rest of nature”, and “the balance of nature is very delicate and easily upset”. The indicator adds up the ordinal variables of the positively keying statements and the reversed ordinal variables of the negatively keying statements. It ranges from 6 to 30 and higher values represent higher levels of environmental awareness. For the econometric analysis, we consider the dummy variable “NEP” that takes the value one if the NEP scale of a respondent is equal to or larger than the median value of 25. Our measures for political orientations are based on four statements related to a conservative, liberal, social, and ecological political orien­ tation in line with Ziegler (2017), who introduced the approach in order to take account of the German political landscape. In line with Ziegler (2017) we consider dummy variables that take the value one if the respondent rather or totally agrees to the statements “I identify myself with conservatively/ liberally/ socially/ ecologically oriented politics”. Therefore, we consider the four dummy variables “conservative”, “lib­ eral”, “social”, and “ecological” in the econometric analysis. The measure for Christian religious beliefs is based on the official religious affiliation of the respondents. We restrict our analysis to Catholic and Protestant affiliations as only a small minority in Germany belongs to other Christian movements or to other religions. The corre­ sponding dummy variable “Catholic” (“Protestant”) takes the value one if the respondents state to belong to the Catholic (Protestant) church. The base category for the interpretation of the two dummy variables is the group of respondents who are not affiliated with the Catholic or

Table 6 Descriptive statistics of the estimation sample (467 observations). Variable Support of the energy transition Strong support Importance of policy goals and framework conditions (1.1) Cost distribution (1.2) Energy prices (1.3) Economy (1.4) Unemployment (2.1) Climate (2.2) Emissions (2.3) Nuclear waste (2.4) Nuclear accidents (3.1) Reliability (3.2) Energy imports (4.1) Landscape (4.2) Codetermination (4.3) Financial participation Other explanatory variables Reversed order Trust Social norms Responsibility Warm glow Freeriding Risk preferences Time preferences NEP Conservative Liberal Social Ecological Catholic Protestant Sex Age Education Income Kids Eastern Germany

Mean

Standard deviation

0.56

0.50

0.80 0.63 0.68 0.46 0.93 0.93 0.88 0.86 0.89 0.69 0.46 0.53 0.28

0.40 0.48 0.47 0.50 0.26 0.25 0.32 0.34 0.32 0.46 0.50 0.50 0.45

0.51 0.56 0.59 0.89 0.87 0.61 0.30 0.55 0.59 0.24 0.39 0.78 0.73 0.24 0.31 0.35 53.95 0.50 0.53 0.21 0.14

0.50 0.50 0.49 0.31 0.33 0.49 0.46 0.50 0.49 0.43 0.49 0.41 0.45 0.43 0.46 0.48 13.09 0.50 0.50 0.41 0.35

countries behave as free riders acts as a barrier towards climate action (e.g. Lorenzoni et al., 2007). In addition to social preferences, also risk and time preferences have been shown to influence contributions to public goods (e.g. Fehr-Duda and Fehr, 2016). Some studies found that climate policies receive higher support if the perceived risks from air pollution (e.g. Lubell et al., 2006), or climate change impacts (e.g. Zahran et al., 2006; Frondel et al., 2017) are rated high. In a similar vein, Huhtala and Remes (2017) find that a high perceived risk of nuclear accidents negatively affects the probability to support nuclear power. Trust is measured by individuals’ perception on the trustworthiness of others, which has been validated through an incentivized trust game by Fehr et al. (2003).5 The indicator for trust is based on the stated agreement on a five-point scale to the following statements: “in general, people can be trusted”, “nowadays you cannot rely on anyone”, “if you are dealing with strangers, it is better to be careful before trusting them”. The indicator adds up the ordinal variables of the positively keying statements and the reversed ordinal variables of the negatively keying statements. It ranges from 3 to 15 and higher values of the indicator represent higher levels of trust. For the econometric analysis we consider the dummy variable “trust” that takes the value one if the level of trust is equal to or higher than the median level of trust which is 10. The statements for social norms, warm glow, the feeling of

5 The described survey questions on trust, risk preferences, and time prefer­ ences are frequently applied in large scale surveys such as SOEP and the Gen­ eral Social Survey and analyzed in various empirical studies (e.g. Dohmen et al., 2008; Fischbacher et al., 2015; J€ ager et al., 2010).

5

0.25 (1.62) 0.35** ( 2.49) 0.05 (0.38) 0.37*** ( 2.90)

0.79*** (3.34)

0.14 ( 0.66)

(1.1) Cost distribution

(2.1) Climate

(2.2) Emissions

0.33 ( 1.36) 0.09 (0.74) 0.53*** ( 4.04)

0.00 (0.00) 0.20 (1.33) 0.22* (1.79) 0.07 (0.56) 0.12 (0.90) 0.38** (2.00)

0.03 ( 0.15)

0.02 (0.17) 0.03 (0.19) 0.19 ( 1.50) 0.19 (1.46) 0.47*** ( 3.17)

(3.1) Reliability (3.2) Energy imports

(4.2) Codetermination

6

Warm glow

Freeriding

0.24 (1.54) 0.13 (0.89) 0.05 (0.37)

0.00 ( 0.34)

Catholic

Protestant

Age

Gender

0.10 ( 0.83) 0.06 ( 0.38) 0.57*** (3.73)

Liberal Social Ecological

Conservative

Time preferences NEP

Risk preferences

Responsibility

Social norms

Trust

(4.3) Financial participation Reversed order

(4.1) Landscape

0.81*** (4.17)

(2.4) Nuclear accidents

(1.4) Unemployment

(1.2) Energy prices (1.3) Economy

Estimated parameters

Explanatory variables

Model

0.00 (0.33)

0.00 ( 0.38)

0.00 ( 0.92)

0.00 (0.80) 0.00 (0.37) 0.01** ( 2.01) 0.00 ( 1.51)

0.01* (1.88)

0.00 (1.41) 0.00 ( 1.33)

0.00 ( 0.20)

0.00 ( 0.17)

0.00 (0.15)

0.01 ( 1.38)

0.00 ( 0.88)

0.00 (0.34)

0.00 ( 0.37)

0.01 ( 0.91)

0.00 (0.79) 0.00 (0.39) 0.03*** ( 2.94) 0.01 ( 1.55)

0.02** (2.48)

0.01 (1.46) 0.01 ( 1.37)

0.00 ( 0.19)

0.00 ( 0.17)

0.00 (0.15)

0.02* ( 1.71)

0.01 ( 0.88)

0.00 ( 0.55)

0.01* ( 1.76)

0.00 ( 1.40) 0.00 ( 0.54)

0.01 ( 1.35)

0.00 ( 0.00)

0.02*** (3.09)

0.05*** ( 2.61) 0.01 (1.51) 0.00 ( 0.71)

0.01 (0.71)

0.05** ( 2.37)

0.02** (2.42)

0.01 ( 1.44) 0.01** (2.46) 0.00 ( 0.38)

Rather oppose

0.00 ( 1.21)

0.00 ( 0.00)

0.01** (2.31)

0.01** ( 2.12) 0.00 (1.49) 0.00 ( 0.70)

0.00 (0.63)

0.02 ( 1.54)

0.01* (1.95)

0.00 ( 1.30) 0.00** (1.96) 0.00 ( 0.37)

Totally oppose

0.00 (0.34)

0.00 ( 0.37)

0.00 ( 0.85)

0.00 (0.82) 0.00 (0.38) 0.02** ( 2.51) 0.01 ( 1.40)

0.02** (2.23)

0.01 (1.38) 0.01 ( 1.35)

0.00 ( 0.19)

0.00 ( 0.17)

0.00 (0.15)

0.01 ( 1.61)

0.00 ( 0.85)

0.00 ( 0.54)

0.01* ( 1.72)

0.01 ( 1.40)

0.00 ( 0.00)

0.02*** (2.84)

0.04** ( 2.56) 0.01 (1.44) 0.00 (-0.72)

0.04** ( 2.30) 0.00 (0.70)

0.01** (2.48)

0.01 ( 1.48) 0.01** (2.24) 0.00 ( 0.38)

Undecided

0.00 (0.34)

0.01 ( 0.37)

0.03 ( 0.89)

0.02 (0.83) 0.01 (0.39) 0.12*** ( 3.66) 0.05 ( 1.55)

0.10*** (3.31)

0.04 (1.50) 0.04 ( 1.43)

0.01 ( 0.19)

0.00 ( 0.17)

0.01 (0.15)

0.07** ( 2.11)

0.02 ( 0.89)

0.01 ( 0.56)

0.04* ( 1.80)

0.04 ( 1.30)

0.00 ( 0.00)

0.11*** (3.94)

0.15*** ( 5.03) 0.07 (1.33) 0.02 ( 0.74)

0.13*** ( 4.51) 0.03 (0.67)

0.08*** (2.90)

0.05* ( 1.67) 0.08** (2.35) 0.01 ( 0.39)

Rather support

Estimated average discrete and marginal probability effects

Ordered probit model (dependent variable: support)

Table 7 Maximum likelihood estimates (robust z statistics) in univariate ordered and binary probit models (467 observations).

0.00 ( 0.34)

0.02 (0.37)

0.04 (0.90)

0.07 (1.57)

0.15*** ( 3.16) 0.03 ( 0.83) 0.02 ( 0.39) 0.18*** (3.66)

0.06 ( 1.51) 0.06 (1.44)

0.01 (0.19)

0.01 (0.17)

0.01 ( 0.15)

0.11** (1.98)

0.04 (0.89)

0.02 (0.56)

0.07* (1.82)

0.06 (1.34)

0.16*** ( 4.06) 0.00 (0.00)

0.09 ( 1.39) 0.03 (0.74)

0.25*** (4.30)

0.04 ( 0.68)

0.11*** ( 2.93) 0.24*** (3.48)

0.07 (1.62) 0.11** ( 2.49) 0.02 (0.38)

Totally support

0.00 ( 0.53)

0.03 (0.20)

0.03 (0.18)

0.08 (0.45)

0.11 ( 0.81) 0.17 (0.94) 0.58*** (3.45)

0.24 ( 1.48)

0.20 ( 1.48) 0.28** (1.98)

0.23 (1.52)

0.03 (0.22)

0.06 (0.25)

0.28 (1.15)

0.15 (1.04)

0.03 ( 0.18)

0.28** (2.07)

0.35** (2.20)

0.04 ( 0.28)

0.36 ( 1.42) 0.13 (0.87) 0.47*** ( 3.20)

0.62*** (2.95)

0.29 (1.00)

0.70** (2.37)

0.32* (1.85) 0.43*** ( 2.74) 0.14 (0.87) 0.38** ( 2.57)

(continued on next page)

0.02 (0.45) 0.01 (0.18) 0.01 (0.20) 0.00 ( 0.53)

0.03 ( 0.81) 0.05 (0.93) 0.18*** (3.36)

0.04 (1.04) 0.08 (1.14) 0.02 (0.25) 0.01 (0.22) 0.07 (1.53) 0.06 ( 1.50) 0.09* (1.95) 0.07 ( 1.46)

0.01 ( 0.18)

0.08** (2.10)

0.10** (2.24)

0.01 ( 0.28)

0.10 ( 1.47) 0.04 (0.87) 0.14*** ( 3.20)

0.09 (0.99) 0.19*** (2.96)

0.21** (2.42)

0.11*** ( 2.59)

0.10* (1.85) 0.13*** ( 2.74) 0.04 (0.88)

Estimated average discrete and marginal probability effects

Binary probit model (dependent variable: strong support) Estimated parameter

E.D. Groh and C.v. M€ ollendorff

Energy Policy xxx (xxxx) xxx

0.12** ( 1.97)

–

0.40** ( 1.97)

3.1. Descriptive results

1.70*** ( 3.06)

Note: * (**, ***) means that the appropriate parameter is different from zero at the 10% (5%, 1%) significance level, respectively.

– – – – – Constant

0.00 (1.24) Eastern Germany

0.01 (1.40)

0.01 (1.42) –

0.05 (1.62)

0.08 ( 1.57)

0.08 ( 0.59) 0.12 ( 0.66) 0.00 ( 0.07) 0.00 ( 0.06)

0.01 (0.07) 0.01 (0.06) 0.26 ( 1.59) Income Kids

0.00 (0.08) 0.01 ( 0.08)

0.00 ( 0.07) 0.00 ( 0.06)

0.00 ( 0.07) 0.00 ( 0.06)

0.00 ( 0.07) 0.00 ( 0.06)

0.00 (0.07) 0.00 (0.06)

0.01 (0.26) 0.02 ( 0.59) 0.04 ( 0.66) 0.04 (0.26)

Protestant churches also including very few respondents with another religious affiliation such as Muslims or Jews. With respect to socio-demographic variables, studies tend to show that education, gender, age and income can influence levels of support for environmental or climate policies, for instance, more educated, fe­ male and wealthier respondents show greater policy support. The effect of age is not so clear-cut (e.g. Klineberg et al., 1998; Dietz et al., 2007). Hence, we include a set of control variables. The dummy variable “gender” takes the value one if the respondent is a woman, the contin­ uous variable “age” measures the age of the respondent in years, and the dummy variable “education” takes the value one if the respondent has a university degree. We also consider the dummy variable “income” that takes the value one if the household’s equivalent net income is above the sample median of about 2120 Euros and the dummy variable “kids” that takes the value one if at least one child younger than 14 years old is living in the respondent’s household. Finally, we control for regional differences through the dummy variable “eastern Germany” that takes the value one if the respondent lives in the eastern federal states of Germany and zero if the respondent lives in the western federal states of Germany. It is common to control for the differences in attitudes be­ tween federal states of the former German Democratic Republic and the rest of Germany.6 3. Empirical analysis

Education

0.00 (0.08)

0.00 (0.08)

0.00 (0.08)

0.00 ( 0.08)

Estimated average discrete and marginal probability effects Totally support Rather support Undecided Rather oppose Totally oppose

Estimated average discrete and marginal probability effects

Ordered probit model (dependent variable: support)

Estimated parameters Model

Explanatory variables

Table 7 (continued )

Energy Policy xxx (xxxx) xxx

Estimated parameter

Binary probit model (dependent variable: strong support)

E.D. Groh and C.v. M€ ollendorff

Table 3 shows the detailed frequencies of the support of the energy transition. Of the respondents, 37 percent stated to rather support and 54 percent to totally support the energy transition. The broad public support of the energy transition is in line with previous studies on the acceptance and support of the energy transition (e.g. AEE, 2016; IASS, 2017). Table 4 reports the detailed frequencies of the importance of the 13 policy goals and framework conditions for the support of the energy transition. It shows that the policy goals and framework conditions related to the target of environmental sustainability and the goal of reliability have the highest importance for the attitudes towards the energy transition. More specifically, climate protection, the extent of pollutant emissions, and the reliability of energy supply are stated to be important for attitudes towards the energy transition by around 90 percent of the respondents. This is in line with the study of Joas et al. (2016) who interviewed policy experts and find that the precise political goals of the energy transition in Germany are not well defined, but that climate protection is one of the top-level goals. Regarding the remaining policy goals and framework conditions, the evaluation of the respondents is more diverse. The amount of nuclear waste, the risk of nuclear accidents, as well as the distribution of costs between households and the industry is considered to be important by a large majority. However, a non-negligible share of respondents is un­ decided or state that these policy goals and framework conditions are of rather low importance for their political attitude. This pattern applies in particular to the remaining policy goals and framework conditions which are related to the targets of economic and social sustainability. Concerning this matter, we need to acknowledge that the link between some of them (e.g. unemployment level) and the energy transition might be too complex for some of the respondents. In addition, changes in the landscape and participation options are related to the local expansion of renewable energies such that the confrontation with these topics differs across regions. It is therefore interesting to have a closer look on the

6

With respect to the support of the energy transition, the regional differences in the expansion of renewable energies such as wind farms might also be important. However, if we include dummy variables for the federal states of Germany instead of the dummy variable for eastern Germany, results remain qualitatively stable. 7

E.D. Groh and C.v. M€ ollendorff

Energy Policy xxx (xxxx) xxx

Table 8 Estimated average probabilities for the strength of support in the univariate ordered and binary probit model (467 observations). Model Level of support Estimated average probability

Ordered probit model Totally oppose 0.7%

Rather oppose 3.2%

Undecided 2.9%

determinants of the perceived importance of the policy goals and framework conditions in addition to the determinants of policy support. Table 5 reports the Spearman’s rank correlation coefficients of the 13 policy goals and framework conditions. It shows that the goals and framework conditions that belong to the same policy target are highly positively correlated with each other, particularly in the case of envi­ ronmental sustainability. Furthermore, it shows that the policy goals and framework conditions related to the economic sustainability target are highly positively correlated with the policy goal of reliability. The top part of Table 6 reports the means and standard deviations for the dummy specifications of the support of the energy transition variable and the importance of policy goals and framework conditions variables. The bottom part of Table 6 presents the descriptive statistics for all other explanatory variables.

Binary probit model Rather support 37.4%

Totally support 55.7%

Strong support 55.7%

estimated average probability effects of all explanatory variables changes between the fourth answer category (rather support) and the fifth answer category (totally support) of the dependent variable. This pattern might be related to the choice of the five point-scale. The number of respondents in the first three answer categories is rather low so that the scale only discriminates between “rather support” and “totally support”, implying that there could be a monotonic relationship if a larger scale is considered. The two coloumns on the right side of Table 7 report the estimated parameters and average probability effects in the univariate binary probit model. The results are mainly in line with the univariate ordered probit model but also show a positive relation­ ship of strong support of the energy transition with the distribution of costs and financial participation, similar in size to the discrete proba­ bility effects of energy prices and unemployment. The socio-psychological explanatory variables such as risk, time, or social preferences are not statistically different from zero in the uni­ variate ordered probit model with the exception of the feeling of re­ sponsibility. The probability to totally support the energy transition is 11 percentage points higher for respondents, who feel responsible for environmental protection, compared to respondents, who do not feel responsible. Therefore, the estimated discrete probability effect of the feeling of responsibility is from the same magnitude as the estimated discrete probability effects of the importance of the level of energy prices and unemployment. However, the feeling of responsibility is not statistically significant in the binary probit model. Considering the control variables, there is a highly significant and negative effect of identifying with conservative politics and a highly significant and pos­ itive effect of identifying with ecological politics. The effect size is comparable with the effect of the importance of changes in the land­ scape, pointing at the political significance of the energy transition. In the binary probit model, a conservative political orientation is not sta­ tistically significant while the estimated parameter for environmental awareness is significantly positive and the estimated parameter for the eastern federal states of Germany is significantly negative. For the joint analysis of the support of the energy transition and the importance of the 13 policy goals and framework conditions, we consider a multivariate approach that allows for potential correlations between the 14 ordinal dependent variables in the error terms of the underlying latent variables.9 Table 9 reports the corresponding estima­ tion results in the multivariate ordered probit model that can be inter­ preted in the same manner as the estimated parameters in the univariate ordered probit model. Table 9 reveals that there is no evidence for an influence of the order of the question on the strength of support of the energy transition and on the level of importance of the different policy goals and framework conditions. While risk, time, and social preferences are not statistically significantly related to support of the energy tran­ sition with the exception of the feeling of responsibility, they are rele­ vant with respect to the importance of the policy goals and framework

3.2. Econometric analysis Table 7 reports the estimated parameters of the econometric analysis in univariate ordered and binary probit models for the support of the energy transition as well as the corresponding estimated average prob­ ability effects.7,8 In the univariate ordered probit model a statistically significant and positive estimated parameter indicates that the proba­ bility to totally support (totally oppose) the energy transition is higher (lower) if the policy goal or framework condition is perceived to be rather or very important. The estimated average discrete and marginal probability effects indicate the direction and size of the relationship for the five answer categories. The table reveals that respondents, who perceive the risk of nuclear accidents (climate protection) to be impor­ tant, have a higher probability to totally support the energy transition. In this case, the probability that a respondent totally supports the energy transition is 25 (24) percentage points, and thus more than 40 percent, higher than the estimated average probability across all respondents to totally support the energy transition, which is 56 percent as reported in Table 8. In contrast, respondents who perceive the level of energy prices, the level of unemployment and particularly changes in the landscape to be important, have a 11–16 percentage points and therefore up to 29 percent lower probability to totally support the energy transition. With regard to the potential framing effect, Table 7 reveals that there is a weakly significant and positive effect that is, however, relatively small in magnitude compared to the estimated average probability effects of the importance of the policy goals and framework conditions. Re­ spondents belonging to the group that evaluated the importance of as­ pects first have a 7 percentage points higher probability to totally support the energy transition compared to the group of respondents that stated how strongly they support the energy transition first. We focus on the effects on the highest level of support as the direction of the 7

All estimations were conducted with the statistical software package Stata. Due to the very high correlation between the importance of the risks of nuclear accidents and the amount of nuclear waste, we excluded the variable “amount of nuclear waste” from the empirical analysis of the support of the energy transition. If the variable for the importance of the amount of nuclear waste is included in the econometric analysis instead of the variable for the importance of the risk of nuclear accidents, the variable is highly significant and the results are qualitatively very similar. If the variable is added to the reported model it is statistically not significant, which might be explained by multicollinearity. 8

9 We apply simulated maximum likelihood methods (SML) using the GewekeHajivassiliou-Keane (GHK) simulator (B€ orsch-Supan and Hajivassiliou, 1993; Geweke et al., 1994; Keane, 1994). In this respect, we use 223 random draws in the GHK simulator. Furthermore, we always consider robust estimations of the standard deviations of the parameter estimates. For the SML estimation of the multivariate ordered probit model we used the Stata module “CMP” according to Roodman (2011).

8

Explanatory variables

Dependent variables Support

(1.1) Cost distribution

(1.2) Energy prices

(1.3) Economy

Reversed order

0.14 (1.21)

0.10 (0.99)

0.13 (1.27)

0.01 ( 0.11)

Trust

0.08 (0.68)

0.19* ( 1.79)

Social norms

0.19 (1.59)

0.14 (1.36)

0.35*** ( 3.21) 0.05 (0.47)

0.26** (2.38)

Responsibility

0.45** (2.40)

0.37* ( 1.77)

0.08 ( 0.46)

0.11 ( 0.56)

Warm glow Freeriding

0.09 (0.48) 0.00 ( 0.04)

0.02 ( 0.09) 0.07 (0.66)

0.02 ( 0.13) 0.02 (0.24)

0.21 (1.45) 0.09 (0.82)

0.01 ( 0.05) 0.26** (2.46)

Risk preferences

0.06 ( 0.46)

0.02 ( 0.21)

0.13 (1.19)

0.24** (2.04)

Time preferences

0.18 ( 1.49)

0.00 (0.02)

0.06 ( 0.61)

0.05 ( 0.47)

0.31*** (2.88)

0.04 ( 0.37)

0.08 ( 0.82)

0.15 ( 1.09)

0.01 ( 0.09)

0.39*** (3.03)

0.04 (0.33)

0.14 (1.33)

0.05 ( 0.47)

0.13 (0.92)

0.02 (0.15)

0.02 ( 0.17)

0.06 (0.48)

0.26** ( 2.07) 0.02 ( 0.12) 0.14 (1.14)

NEP Conservative Liberal

9

Social Ecological

0.31** (2.55) 0.59*** ( 4.00) 0.20* ( 1.67) 0.02 (0.14)

(2.1) Climate

(2.2) Emissions

0.03 (0.32)

0.01 ( 0.08) 0.13 ( 1.03) 0.23* (1.78)

0.09 (0.76)

0.02 ( 0.13)

0.26** ( 2.12) 0.02 (0.19)

0.15 (1.29)

0.55*** (2.94) 0.22 (1.19) 0.04 ( 0.30) 0.06 (0.48)

0.09 ( 0.86)

0.00 (0.01)

0.16 (1.45)

0.47*** (3.85) 0.28* ( 1.85) 0.21* ( 1.77) 0.02 (0.11)

0.04 ( 0.37) 0.32*** (2.72) 0.03 ( 0.21) 0.14 ( 1.18) 0.32** (2.24)

0.25** ( 2.34) 0.19* (1.77) 0.02 ( 0.13)

0.07 ( 0.62) 0.24** ( 2.27) 0.12 (0.87)

Catholic

0.19 (1.27)

0.20 ( 1.52)

0.43*** ( 3.29) 0.03 ( 0.24)

Protestant

0.11 (0.82)

0.04 ( 0.34)

0.03 (0.24)

Gender

0.06 ( 0.45)

0.11 ( 0.93)

0.09 ( 0.80)

0.15 ( 1.38)

0.34*** (3.03)

0.11 (0.82)

Age

0.00 ( 0.80)

0.01*** (2.66)

0.02*** (3.99)

0.01*** (3.34)

0.02*** (3.82)

0.01* (1.82)

Education

0.00 ( 0.02)

0.28** ( 2.47)

Income

0.04 (0.36)

Kids Eastern Germany

0.72*** (4.96)

0.02 ( 0.19)

(1.4) Unemployment

0.48*** ( 3.78) 0.29** (2.36)

0.37*** (2.69) 0.08 (0.55)

0.18 (1.47)

0.07 (0.46)

0.34*** ( 3.04) 0.07 (0.69)

0.19* ( 1.84)

0.06 (0.47)

0.04 ( 0.35)

0.30*** ( 2.72) 0.17 ( 1.59)

0.01 ( 0.08)

0.09 (0.59)

0.06 ( 0.38)

0.02 ( 0.12)

0.00 ( 0.02)

0.20 (1.53)

0.10 ( 0.80) 0.07 (0.46)

0.30* ( 1.90)

0.11 (0.76)

0.12 (0.77)

0.06 ( 0.41)

0.13 ( 0.86)

0.25 ( 1.48)

(3.1) Reliability

(3.2) Energy imports

(4.1) Landscape

0.13 ( 1.11)

0.15 (1.37)

0.08 ( 0.76)

0.08 (0.79)

0.04 ( 0.39)

0.16 ( 1.58)

0.07 (0.59)

0.15 ( 1.26)

0.13 ( 1.26)

0.64*** (3.57) 0.14 (0.79) 0.06 (0.56)

0.42** (2.25)

0.36** (1.99)

0.31* (1.70)

0.38** (2.03) 0.05 (0.46)

0.16 (0.96) 0.01 ( 0.09)

0.19 ( 0.98) 0.00 (0.03) 0.02 (0.18)

0.19* ( 1.82) 0.26** ( 2.42) 0.18 (0.97)

0.30*** ( 2.87)

0.23* (1.88)

0.23** ( 1.99) 0.17 (1.46)

0.19* ( 1.73)

0.05 (0.38)

0.22 (1.34) 0.27*** (2.58)

0.39** (2.24) 0.11 (1.07)

0.29* (1.72) 0.13 (1.23)

0.14 (0.89) 0.34*** (3.28)

0.10 (0.73)

0.23* ( 1.84)

0.14 ( 1.08)

0.22* (1.86)

0.04 (0.36)

0.53*** (4.28)

0.29** (2.35)

0.29** (2.19) 0.23 ( 1.58) 0.01 ( 0.06) 0.19 (1.53)

(2.3) Nuclear waste

(2.4) Nuclear accidents

0.07 (0.62)

0.05 (0.47)

0.12 (1.01)

0.01 ( 0.13)

0.16 ( 1.61)

0.15 (1.45)

0.11 (1.00)

0.00 ( 0.02)

0.17 (1.28)

0.29** (2.20)

0.35*** ( 2.79)

0.35*** ( 2.65)

0.05 ( 0.50)

0.05 ( 0.47)

0.04 (0.30)

0.07 ( 0.54)

0.03 (0.20)

0.12 ( 0.96)

0.20 ( 1.51)

0.09 ( 0.87) 0.14 ( 0.97) 0.17 ( 1.31) 0.14 (1.07)

0.04 ( 0.30)

0.27** (2.20)

0.08 (0.66)

0.01 (0.05)

0.15 ( 1.31)

0.04 (0.31)

0.08 (0.71)

0.02 ( 0.21)

0.32** (2.28)

0.07 (0.45)

0.07 (0.49)

0.43*** (3.14)

0.41*** (2.86)

0.33** ( 2.46) 0.14 ( 1.00) 0.16 ( 1.26) 0.16 ( 1.33) 0.01** (2.47)

0.18 (1.40)

0.40*** (3.02)

0.02*** (3.78)

0.02*** (3.75)

0.30** ( 2.39) 0.02 ( 0.16)

0.23* ( 1.90)

0.03 ( 0.23) 0.26 ( 1.47)

0.54*** (3.57) 0.40** ( 2.36)

0.14 ( 1.12)

0.01 (1.41)

0.35*** (3.20) 0.01** (2.53)

0.01 (0.10)

0.00 (0.00)

0.10 (0.79)

0.12 (1.08)

0.18 (1.57)

0.36** (2.30)

0.03 ( 0.20) 0.14 (0.93)

0.11 ( 0.74)

0.11 ( 0.60)

0.26 (1.48)

0.15 ( 1.45) 0.08 (0.80)

0.28** ( 2.28)

0.00 ( 0.14) 0.10 ( 0.83) 0.01 (0.10)

0.06 ( 0.33)

0.12 (1.07)

0.03 (0.19)

0.20 ( 1.44)

0.10 (0.89)

0.09 ( 0.81)

0.37*** ( 2.65)

0.01 (0.08)

0.17 (1.56)

0.12 ( 1.03)

0.36*** ( 2.59) 0.17 ( 1.44)

0.00 (0.01)

(4.3) Financ-ial participation

0.02 ( 0.12) 0.20* (1.77) 0.03 ( 0.28) 0.19 (1.34)

0.08 ( 0.52)

(4.2) Codetermination

0.17 ( 1.49)

0.23 ( 1.46)

0.22** (2.02)

0.01 (0.12)

0.02*** (5.21)

0.01** (2.23)

0.11 (0.98)

0.13 ( 1.19)

0.16 ( 1.48)

0.20* ( 1.87) 0.06 ( 0.41) 0.00 (0.03)

0.17 ( 1.63)

0.04 ( 0.40)

0.05 ( 0.36)

0.02 (0.17)

0.12 ( 0.76)

0.02 ( 0.12)

E.D. Groh and C.v. M€ ollendorff

Table 9 Simulated maximum likelihood estimates (robust z statistics) in the multivariate ordered probit model (467 observations).

Note: * (**, ***) means that the appropriate parameter is different from zero at the 10% (5%, 1%) significance level, respectively.

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E.D. Groh and C.v. M€ ollendorff

Energy Policy xxx (xxxx) xxx

conditions. Most of the policy goals and framework conditions related to the target of economic sustainability, security of energy supply and so­ cial sustainability are significantly and negatively correlated with trust. In addition, the policy goals and framework conditions related to the target of environmental sustainability are significantly and positively correlated with the feeling of responsibility. Also social norms, warm glow and the freeriding rational are correlated with a selection of the policy goals and framework conditions. Regarding risk and time pref­ erences, Table 9 reveals that risk aversion and patience are only relevant for the importance of a few policy goals and framework conditions. Risk averse respondents are more likely to state that the amount of nuclear waste is very important and less likely to state that the competitiveness of the economy is very important. Patient respondents are more likely to state that nuclear waste and reliability of energy supply is very important. The main finding of Table 9 is, however, the high relevance of in­ dividual values, in particular of political orientation, for the support of the energy transition and for the importance of almost all policy goals and framework conditions. A conservative person is less likely to support the energy transition, and to state that the policy goals and framework conditions related to the target of environmental sustainability and to participation opportunities are very important while more likely to consider the competitiveness of the economy, and changes of in the landscape as important. This is in contrast to a person with an ecological orientation, who is more likely to support the energy transition and to state that the policy goals and framework conditions that belong to the environmental sustainability target are very important but less likely to state that the policy goals and framework conditions that belong to the economic sustainability target and to reliability of energy supply are very important. The high relevance of environmental values is further emphasized by the highly significant and positive correlation of envi­ ronmental awareness and support of the energy transition as well as the importance of the policy goals and framework conditions that belong to the target of environmental sustainability and the importance of the cost distribution. Liberal or social political orientations are correlated with a few of the evaluations related to the environmental and economic sus­ tainability targets. Religious values and norms are from minor relevance in the study context. Regarding the socio-economic variables, Table 9 shows that being female is positively related with the importance of the level of unemployment, the risk of nuclear accidents, changes in the landscape and codetermination opportunities. Older respondents give a higher level of importance to almost all policy goals and framework conditions. Respondents with a university degree are less likely to perceive the policy goals and framework conditions related to the eco­ nomic sustainability target but also to nuclear risks as very important for their attitude for the energy transition compared to respondents without a university degree. Respondents with an above medium equivalent household income are less likely to state that changes in the landscape are very important. The correlations with education and income could result from the fact that highly educated individuals do not suffer from higher levels of unemployment to the same degree as less educated in­ dividuals. The estimated parameter for having children is also signifi­ cantly positive for the importance of nuclear risks. Respondents from the eastern federal states of Germany are less likely to totally support the energy transition and to perceive the amount of nuclear waste to be very important compared to respondents from the western federal states of Germany. In a nutshell, our econometric analysis reveals, in line with the previous literature, that the target of environmental sustainability is the most important target for the support of the energy transition. In particular, the importance of climate protection and the risk of nuclear accidents are decisive for policy support. The importance of the target of economic and social sustainability is negatively influencing support of the energy transition. The magnitude of this effect is comparable to the influence of political orientations, and a feeling of responsibility. The econometric analysis of the determinants of the importance of the policy

goals and framework conditions further emphasizes that the energy transition is a politically significant topic. While the importance of environmental policy goals are driven by political orientations, a feeling of responsibility, and environmental awareness, the importance of the policy goals targeting economic sustainability are driven by political orientations too, but also by trust. The reported regression models are, however, relatively naïve. The coefficients measure the effects of the importance of the policy goals on the stated support of the energy transition. However, as shown in the descriptive statistics, the impor­ tance of the policy goals and framework conditions are highly correlated with each other such that multicollinearity could be a potential problem. In addition, it is not satisfactory to treat these variables as exogenous due to simultaneity. In order to target these shortcomings, we run several recursive binary probit models treating either strong support or importance of policy goals as endogenous.10 Therefore, we created an importance-index using the same mechanism as used for the NEP and trust indicator. Thus, we added up the ordinal variables of all impor­ tance variables and defined a dummy variable that takes the value one if the index is above the medium. The corresponding estimation results reveal that strong support of the energy transition is significantly and positively (negatively) correlated with the overall importance-index, environmental awareness and an ecological (conservative) political orientation. The overall importance index is driven by social preferences and socio-demographic characteristics. The results support our implicit assumption that the support of the energy transition is rather based on an underlying evaluation of its policy goals and framework conditions than vice versa. As we have several realms in the policy goals and framework conditions, we also created four sub-indices, one index for each of the four policy targets. The estimated parameters in the recursive bivariate binary probit models, each including one of the sub-indices as endogenous variables, support our findings. 4. Conclusion and policy implications We study how the perceived importance of policy goals and frame­ work conditions as well as socio-psychological characteristics influence the support of the German energy transition. Our descriptive results reveal that the energy transition is rather supported by 37 percent of the respondents and totally supported by additional 54 percent of the re­ spondents. The policy goals and framework conditions perceived to be of very high importance by the largest share of the respondents refer to the pursued goals of the energy transition, namely climate protection, pollutant emissions, reliability of energy supply, and nuclear safety. The perceived importance of the other policy goals and framework condi­ tions is heterogeneous across respondents, pointing at the various levels of affectedness by specific policy measures. According to our econo­ metric analysis, the perceived importance of climate protection and nuclear risks is positively related to a strong support of the energy transition, while the importance of changes in the landscape, the level of unemployment, and the level of energy prices is negatively related. In addition, a conservative (ecological) political orientation is negatively (positively) related to strong support. Furthermore, feeling responsible for environmental protection is positively related with strong support for the energy transition. The relevance of political orientation in the context of public support is emphasized by the econometric analysis of the determinants of the importance of the 13 policy goals and frame­ work conditions. This analysis further reveals that the feeling of re­ sponsibility drives the importance of environmental sustainability, while trust mainly drives the importance of economic sustainability, security of energy supply and social sustainability. For further research, it would be interesting to consider the degree to which respondents are exposed to changes in their environment due to the expansion of 10 Detailed estimation results are not reported due to brevity but are available upon request.

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Energy Policy xxx (xxxx) xxx

renewable energies, e.g. regional experiences such as the proximity to different types of energy facilities or regional employment effects from the energy transition. The positive influence of the pursued goals of the energy transition, namely climate protection and nuclear phase-out, depict the broad consensus on the necessity of the energy transition in Germany. How­ ever, the results also imply that changes in the landscape, the level of unemployment, and the level of energy prices adversely affect re­ spondents’ attitudes towards the energy transition. This is problematic considering the initiated measures to reach the climate protection goals. For instance, in order to meet the government’s target of 65 percent of renewables in the energy sector until 2030 it is necessary to specify additional areas for wind energy usage but also to extend the grid infrastructure (e.g. BMWi, 2019a). Despite repowering efforts, public acceptance is already missing on the local level. In addition, it was recently decided to phase out coal-fired power plants in Germany, by 2038 at the latest, in order to reduce greenhouse gas emissions. Despite the promise to have fair transition conditions for the workforce in the coal sector (e.g. BMWi, 2019b), local protests can be expected as the lignite industry is a major employer in the affected regions. Closing down the coal-fired power plants will not only lead to the loss of job opportunities but also to the loss of training positions. Furthermore, projections for the year 2020 show that the energy levy as well as the stock market price for electricity will increase such that households will face slightly higher energy prices (e.g. Agora Energiewende, 2019). Although it is projected that the energy levy will diminish after reaching its maximum in the next few years, this is problematic in the short run. On top of these developments, it is important to note that one of the most important policy goals, namely the nuclear phase-out, is planned to be reached by 2022. The question, whether support of the energy transition will remain high afterwards, remains open. To this end, it is urgent to take action on sustaining high levels of support. There are various measures that could be taken, for example repowering, compensations for victims of the phase-out of coal, or alternative financing options that are not reflected in electricity prices such as a publicly financed €pfer (2015). In doing so, it is EEG-fund as proposed by Matschoss and To of particular importance to implement the measures in a reliable and transparent way and to take confidence-building measures as indicated by the importance of individuals trust in this context. Furthermore, in­ formation campaigns to sensitize the public for their own stake in greenhouse gas emissions could help to keep support of the energy transition high.

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