Disentangling small-scale solar photovoltaic adoption: A spatial analysis of decision factors and localized interactions in Germany

• Neighborhood effects influence PV adoption rates • Large-scale PV installations boost residential PV adoption. • High-resolution data improves PV adoption analysis. • Study limitations include price and subsidy exclusions. The existence of spatial patterns in the adoption of small-scale solar photovoltaic (PV) systems is widely accepted in academic literature. The diffusion of these systems depends on the decisions of heterogenous units, often households, that form their decision based on unit characteristics and attitudes, the built environment, economic and physical factors, as well as peer effects. When including several of these factors, many studies use macro-level datasets, which have a limited ability to capture ‘real’ small-scale spatial patterns. Using data on a 1 km2 grid level for Germany, we identify spatial patterns of adoption while also controlling for highly localized explanatory variables. Spatial dependence is estimated and tested with spatial econometric models. Using this set of small-scale data, we show that spatial clustering affects the adoption of residential PV systems, which is increasing with larger neighborhood sizes. Further, the presence of large-scale PV installations has strong direct and also indirect, i.e., spillover effects on the adoption of rooftop PV installations. Finally, spillover effects from defined neighborhoods are found to become statistically insignificant with larger distances, promoting the use of such small-scale data.