How to combine energy potential and social survey data in GIS for pro...

This poster shows how social-survey data on social acceptance of wind power plants can be integrated in GIS analytics to estimate renewable energy technology potentials, first results and prospective vision to improve sampling design of social acceptance (aspect of distance from turbines – acceptance, place attachment/identity/disruption, temporal aspect/effect of built, projected and not yet existent turbines), geographical parameters like distance from existing wind turbines and place disruption through urban/rural tourism/industry.

Our approach is bottom-up using actual public acceptance data for specific areas in Austria crossed with geospatial GIS-based parameters. Though GIS models usually rely on social acceptance components derived from proxies, our study will make use of surveys conducted 2015 and 2016.Technology potentials are calculated per postcode areas.

We found out that actual wind energy potential and social acceptance of wind turbines were not related. While social acceptance of wind turbines was rather high, wind energy potential was very heterogeneous across districts. It is emphasized the need to redesign sampling social acceptance strategies to be used more effectively with GIS analytics like in environmental psychology.