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Adoption of new technologies necessitates changes in behaviours, attitudes, and social norms that cannot be sufficiently explained by a single predictor or group of predictors, particularly in heterogeneous populations. Aiming to add a greater understanding of intention to adopt Rooftop Photovoltaic (PV), this paper utilised an established population segmentation tool, the Sinus-Milieus and the Theory of Planned Behaviour (TPB) to account for population heterogeneity of the sample of German homeowners (n = 1800). By employing partial least squares structural equation modelling, we confirmed that attitudes, social norms, and financial evaluation significantly influence adoption intention at the population level. A multi-group analysis revealed noteworthy variations in the relative impact of attitudes and social norms on adoption intention across different milieugroups. Notably, even milieugroups sharing the same broad social status and/or values, as defined by the Sinus-Milieu tool, exhibited significant differences in the influence of attitudes and social norms on adoption intention. Moreover, a Kruskal–Wallis H-test indicated significant differences in the reported level of adoption intention among milieugroups, with the Leading milieus showing the highest intention and the Deprived the lowest. Differences were also observed for the determinants of social norms, attitudes, environmental concern, and novelty-seeking behaviour. Our findings confirm the relevance of considering potential adopters as a heterogeneous group in terms of overall lifestyle characteristics. Tailoring communication strategies to target the underlying motivations of specific milieus can lead to more effective interventions.
Although there is a clear indication that stages of residential decision making are characterized by their own stakeholders, activities, and outcomes, many studies on residential low-carbon technology adoption only implicitly address stage-specific dynamics. This paper explores stakeholder influences on residential photovoltaic adoption from a procedural perspective, so-called stakeholder dynamics. The major objective is the understanding of underlying mechanisms to better exploit the potential for residential photovoltaic uptake. Four focus groups have been conducted in close collaboration with the independent institute for social science research SINUS Markt- und Sozialforschung in East Germany. By applying a qualitative content analysis, major influence dynamics within three decision stages are synthesized with the help of egocentric network maps from the perspective of residential decision-makers. Results indicate that actors closest in terms of emotional and spatial proximity such as members of the social network represent the major influence on residential PV decision-making throughout the stages. Furthermore, decision-makers with a higher level of knowledge are more likely to move on to the subsequent stage. A shift from passive exposure to proactive search takes place through the process, but this shift is less pronounced among risk-averse decision-makers who continuously request proactive influences. The discussions revealed largely unexploited potential regarding the stakeholders local utilities and local governments who are perceived as independent, trustworthy and credible stakeholders. Public stakeholders must fulfill their responsibility in achieving climate goals by advising, assisting, and financing services for low-carbon technology adoption at the local level. Supporting community initiatives through political frameworks appears to be another promising step.
This paper addresses two issues in spatial research on residential PV adoption: First, spatial analyses typically do not differentiate between households who have decision power over their rooftop, and households who do not, possibly causing indistinct results. Second, household characteristics like environmental attitudes and innovativeness are not accounted for sufficiently, despite their proven relevance for adoption decisions. We performed a statistical analysis with an innovative spatial data set of Saxony (Germany) in which households of (semi-) detached houses were analyzed separated from the remainder, and household characteristics were included through the Sinus-milieu model. PV saturation in subgroups of the sample was compared using the Mann-Whitney U test. Results show that PV saturation is high in affluent zip-codes with high shares of (semi-) detached houses, and is higher in rural than in urban areas. The presence of environmentally concerned households (Post-Materialists) is positively, and the presence of innovative households (Leaders) is negatively associated with PV saturation. Our results show that household characteristics beyond socio-economic measures are relevant to understand differences in PV saturation in the Saxonian zip-codes. The merits of distinguishing between decisive households and the remainder are less clear.
The adoption of residential photovoltaic systems (PV) is seen as an important part of the sustainable energy transition. To facilitate this process, it is crucial to identify the determinants of solar adoption. This paper follows a meta-analytical structural equation modeling approach, presenting a meta-analysis of studies on residential PV adoption intention, and assessing four behavioral models based on the theory of planned behavior to advance theory development. Of 653 initially identified studies, 110 remained for full-text screening. Only eight studies were sufficiently homogeneous, provided bivariate correlations, and could thus be integrated into the meta-analysis. The pooled correlations across primary studies revealed medium to large correlations between environmental concern, novelty seeking, perceived benefits, subjective norm and intention to adopt a residential PV system, whereas socio-demographic variables were uncorrelated with intention. Meta-analytical structural equation modeling revealed a model (N = 1,714) in which adoption intention was predicted by benefits and perceived behavioral control (R2 = .280), and benefits in turn could be explained by environmental concern, novelty seeking, and subjective norm (R2 = .641). Our results imply that measures should primarily focus on enhancing the perception of benefits. Based on obstacles we encountered within the analysis, we suggest guidelines to facilitate the future aggregation of scientific evidence, such as the systematic inclusion of key variables and reporting of bivariate correlations.
Although single empirical studies provide important insights into who adopts a specific LCT for what reason, fundamental questions concerning the relations between decision subject (= who decides), decision object (= what is decided upon) and context (= when and where it is decided) remain unanswered. In this paper, this research gap is addressed by deriving a decision framework for residential decision-making, suggesting that traits of decision subject and object are determinants of financial, environmental, symbolic, normative, effort and technical considerations preceding adoption. Thereafter, the decision framework is initially verified by employing literature on the adoption of photovoltaic systems, energy efficient appliances and green tariffs. Of the six proposed relations, two could be confirmed (financial and environmental), one could be rejected (effort), and three could neither be confirmed nor rejected due to lacking evidence. Future research on LCT adoption could use the decision framework as guidepost to establish a more coordinated and integrated approach, ultimately allowing to address fundamental questions.
PVactVal: A Validation Approach for Agent-based Modeling of Residential Photovoltaic Adoption
(2022)
Agent-based simulation models are an important tool to study the effectiveness of policy interventions on the uptake of residential photovoltaic systems by households, a cornerstone of sustainable energy system transition. In order for these models to be trustworthy, they require rigorous validation.However, the canonical approach of validating emulation models through calibration with parameters that minimize the difference of model results and reference data fails when the model is subject to many stochastic influences. The residential photovoltaic diffusion model PVact features numerous stochastic influences that prevent straightforward optimization-driven calibration.From the analysis of the results of a case-study on the cities Dresden and Leipzig (Germany) based on three error metrics (mean average error, root mean square error and cumulative average error), this research identifies a parameter range where stochastic fluctuations exceed differences between results of different parameterization and a minimization-based calibration approach fails.Based on this observation, an approach is developed that aggregates model behavior across multiple simulation runs and parameter combinations to compare results between scenarios representing different future developments or policy interventions of interest.