Abstract
A growing literature highlights the presence of spatial differences in solar photovoltaic (PV) adoption patterns. Central to forward planning is an understanding of what affects PV growth, yet insights into the determinants of PV adoption in the literature are limited. What factors do drive the adoption at local level? Are the effects of these factors geographically uniform or are there nuances? What is the nature of these nuances? Existing studies so far use aggregate macro datasets with limited ability to capture the role of peer effects. This paper considers some established variables but also broadens the base of variables to try to identify new indicators relating to PV adoption. Specifically, it analyses domestic PV adoption in the UK at local level using data on the number of charities as a proxy to capture the opportunities to initiate social interactions and peer effects. A geographically weighted regression model that considers the spatially varying relationship between PV adoption and socio-economic explanatory variables reveals significantly more variability than the global regression. Our results show that charities and self-employment positively influence PV uptake while other socio-economic variables such as population density has bidirectional impacts.
Highlights
Using solar energy to produce electricity creates mitigation oppor tunities within energy security, climate change, and affordability (the so-called ‘energy trilemma’ (WEC, 2013))
This study aims to identify the effects of different factors on spatial patterns of PV adoption in the UK at local level whereby peer effects are captured by the intensity of subgroup membership
Global Regression Model estimates for Eq (4) are presented in Table 6, where R2 denotes the coefficient of determination and AIC denotes Akaike Information Criterion
Summary
Using solar energy to produce electricity creates mitigation oppor tunities within energy security, climate change, and affordability (the so-called ‘energy trilemma’ (WEC, 2013)). While greater insight can be invaluable in designing policies to utilize such differences, literature in this area is scarce (excepting peer effect studies). The use of such an aggregate variable does not distinguish the different levels through which peer effects might be realized: pairwise communication (micro), more intensive interactions within a subgroup (meso) and global in fluences such as social norms (macro) (Xiong et al, 2016). One example of meso-level interactions is solar community organizations which catalyze peer effects and foster PV adoption (Noll et al, 2014). This study aims to identify the effects of different factors on spatial patterns of PV adoption in the UK at local level whereby peer effects are captured by the intensity of subgroup membership.
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