Inferring local social cost from renewable zoning decisions. Evidence from Lower Austria's wind power zoning.

Deploying renewable electricity generators comes with significant benefits but can also bring about social costs, for example, through interference with landscapes or the biosphere. 
We develop a novel methodology for quantifying the local social costs of renewable energy technologies. Through zoning decisions, authorities implicitly value spatial characteristics by trading-off different traits, such as wind resource quality or distance to settlements. 
We develop a simple theoretical model of renewable zoning and implement a corresponding discrete choice model that allows us to estimate the implied valuation from observed data in high spatial resolution. The wind power zoning in the federal state of Lower Austria, home to Austria's most significant wind resources, serves as our case study. 
According to our preliminary estimates, local social costs are non-negligible and significantly affect wind turbines' socially optimal spatial distribution. These results can inform optimal capacity choice in power system models and support wind turbine siting. Moreover, spatially highly resolved assessments of social cost are a significant improvement over conventional potential assessments based on binary exclusion criteria.