In this paper, we present a new lens to study harmonization of RES-E support schemes. We collect data from existing RES-E support scheme designs, break up RES-E policies into their design elements, and relate each design element or policy characteristic to policy objectives. Based on these relations, which are complex, we use the decision tree learning method in order to understand exactly how relevant each design element is to a certain policy objective. With this knowledge it is possible to understand which attributes of an RES-E scheme must be harmonized or co-ordinated at the EU level and which ones could be left to the discretion of the member states.
Keywords: Decision trees, design elements, harmonization, machine learning, renewable energy support schemes.
Published: May 2015.