Individual and collective socio-psychological patterns of photovoltaic investment under diverging policy regimes of Austria and Italy
Introduction
Energy transition from the current, centralized, fossil fuel based, CO2 emitting energy system towards a decentralized, low CO2 emitting system based on renewable energy sources is a key challenge to mitigate climate change (Sarrica et al., 2016, Sovacool, 2016). According to Baum and Gross (2016), energy transition is largely perceived as being technology-driven, which is likely to be insufficient, unless it is complemented by changes in people’s behavior, thoughts and norms (Nyborg et al., 2016). Thus, a richer explication of the interaction between energy policy and individual determinants of people’s behavior and thoughts is needed (Schot et al., 2016). This is however not easy, as human behavior is complex (Eidelson, 1997, Lee and Brosziewski, 2009, Perez and Batten, 2006, Teixeira, 2007), with uncountable influencing determinants (Teixeira, 2007).
Environmental psychologists discuss socio-psychological patterns (e.g. values, worldviews, human-nature relationships [HNR], norms, beliefs, motivation, etc.) as determinants of human behavior (Bamberg and Möser, 2007, Turaga et al., 2010). Stephenson et al. (2010) developed the Energy Culture Framework to investigate how external factors (e.g. policies) may strengthen or weaken individual drivers of energy culture change. Hoff-Elimari et al. (2014) identified a likely association between governance and people’s underlying socio-psychological patterns of values. Similarly, Baum and Gross (2016) suggest that policies have not only the primary effect of encouraging behavioral change, but also secondary effects such as crowding effects. Besides the standard direct price effect, monetary incentives also have an indirect psychological effect (Gneezy et al., 2011), in terms of crowding-out specific groups (Nyborg and Rege, 2003) or of reducing intrinsic motivation to contribute to public goods permanently (Thøgersen, 2003). This article aims to contribute to these discourses and investigates primary and secondary effects (Baum and Gross, 2016) of photovoltaic (PV) policies. The basic assumption is that policies do not only encourage PV investments (primary effect), but also motivation crowding, such as crowding-in/out individuals with specific socio-psychological patterns. In the presence of external rewards such effects might be the undermining of altruistic values, ecocentric HNR or the reinforcement of pro-environmental, pro-social motivation (see Fig. 1).
Herein, we specifically look at the household adoption of PV. This technology does not only contribute to a low emission energy future (del Rio and Burguillo, 2008), but it also belongs to those renewable energy sources that can be adopted in a decentralized way by citizens, either individually or collectively. Moreover, a variety of energy policies have emerged in recent decades to support the diffusion of PV (De Boeck et al., 2016). We investigated crowding effects of policies in two comparative study cases with contrasting PV programs for investments at the household level: high financial support in the province of Bolzano/South Tyrol (Italy, IT) versus lower financial support in the province of Styria (Austria, AT). While policies for the diffusion of PV differ, both regions are similar in terms of geography, history, culture, language, and socio-economic aspects.
The empirical base of our study was a questionnaire survey conducted in 2014 and 2015, including participants and non-participants of diverging PV programs at the household level. We first compared crowding effects of the two PV policy regimes. We assumed that besides encouraging PV diffusion in the short term, PV policies implicitly triggered crowding in/out effects, such as undermining self-motivation or groups with interest in collective action. Second, we analyzed how individual and collective PV investors differ from non-investors in terms of demographics and social-psychological patterns. To our knowledge, this is the first study that compares individual and collective socio-psychological patterns of PV investment in diverging policy regimes. Ultimately, understanding secondary effects of policies for energy transition can help to assess the overall efficacy of policies (Rode et al., 2015).
Section snippets
Research framework and questions
To investigate secondary effects of PV policies, we addressed individual determinants of human behavior that are widely studied to grasp human behavioral decisions (Poortinga et al., 2012, Steg et al., 2014, Klöckner, 2013), or PV adoption in particular (Fleiß et al., 2017, van der Werff and Steg, 2016, Wolske et al., 2017). Universal frameworks to investigate human behavior (for an overview see Bamberg and Möser, 2007) are the Theory of Planned Behavior by Ajzen (1991), the Value-Belief-Norm
Study case description
To investigate our RQs, we selected the provinces of South Tyrol (Italy) and Styria (Austria) as our two study contexts. Both have a similar Alpine setting, but are located in different countries and thus belong to diverging policy contexts. South Tyrol, the northern province of Italy, was part of the Austro-Hungarian Monarchy until 1920, and shares a common historical and cultural development with Austria, and is mainly German speaking.
Italy introduced its financing program ‘Conto Energia’ to
Study objectives and hypotheses
This paper’s focus is on socio-psychological patterns of values and HNR of PV investors and non-investors, but also explores more traditional research patterns such as motivation and household characteristics (Fig. 1). Next, we describe the operationalization of our independent variables and the associated hypotheses (Table 2).
Methods
We conducted 22 exploratory interviews before the quantitative survey in summer 2013 and spring 2014. Interviewees represented a wide field of stakeholders with involvement in PV. Representatives from the local to regional government administration were interviewed on the PV policy, while motivation was discussed with private household residents and CSPP managers. Interviews were tape-recorded, transcribed verbatim, and analyzed according to the qualitative content analysis (Mayring, 2014).
Results
The sample consists of 579 respondents, 368 from Austria and 211 from Italy. We found Austrian respondents investing in PV until the end of data collection (2015), but only one Italian investor who invested after the termination of the state support in 2013. The majority of respondents was male (66.8%), over 50 years old (50.48%) and had an advanced level of education (60.4%). We see an unequal gender distribution between PV and nPV as the likely consequence of males generally being more
Discussion
In this paper we sought to better understand crowding effects of PV policies. In an ex-post quasi-experimental setting, we cross-sectional compared five different subsamples of PV investors and non-investors in two diverging PV regimes regarding their socio-psychological patterns and household characteristics. We first discuss crowding effects of PV policies with low and high incentives and afterwards socio-psychological patterns and household characteristics that support household decisions to
Conclusions and policy implications
This research sought to enhance our understanding of crowding effects of PV policies. In general, we recommend policies that support PV investments – whether individual or collective – balance economic and non-economic framings and suggest the following considerations.
We advise balancing monetary incentives to support PV investments in the long run. We do not suggest that economic incentives are dispensable or have negligible motivational power, but similar to Schelly (2014), we found that it
Acknowledgements
The research leading to this paper was conducted within the Doctoral School Sustainable Development (dokNE) at BOKU Vienna, jointly funded by BOKU University, the Austrian Federal Ministry of Science, Research and Economy, the Federal States of Vienna and Lower Austria, and BILLA. The authors want to thank Julian Wudy for his support in data collection, and Christina Roder for her support in data collection and proof reading.
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