Local acceptance of solar farms: The impact of energy narratives.

This research explores how a new concept, support for energy transition narratives, and other commonly applied social licence factors influence local acceptance of large-scale solar farms. Producing renewable electricity requires deploying more large-scale renewable technology infrastructure and support of host communities will be paramount. There is a dearth of studies that investigate a comprehensive set of social licence factors that influence local support of solar farms. Moreover, there is a lack of research considering how broader environmental worldview and beliefs about the energy transition influence local level support for renewable energy infrastructure. Survey data from a large, representative sample of the Australian population is analysed using a network modelling approach to identify conditional associations between variables of interest. Results reveal that general support for renewables in the energy transition had relatively strong direct and indirect associations with local acceptance in addition to social licence factors. Conversely, beliefs about the energy transition beyond the role for renewables (i.e., support for the role of natural gas, support for exporting fossil fuels and willingness to make trade-offs for a faster energy transition) had relatively weak effects on local acceptance and other social licence factors. The findings suggest that individual alignment with broader energy transition narratives explains local solar farm support beyond social licence factors. Future social licence research should consider the role of broader public narratives when investigating the factors that facilitate or impede local acceptance of renewable energy projects. [Display omitted] • Environment worldviews relate to beliefs about the role for renewables. • Broader beliefs about the role of renewables influence local solar farm acceptance. • Beliefs about the role of fossil fuels are weakly associated with local acceptance. • Network models identified conditional associations between social licence factors. • Survey with a large, representative Australian sample (n = 3103). [ABSTRACT FROM AUTHOR]