Integrated approach for the identification of spatial patterns related to renewable energy potential in European territories

TL;DR

This paper uses cluster analysis on renewable energy potential data across European territories to identify spatial patterns, revealing heterogeneity within and among regions, aiding policy development.

Contribution

It introduces an integrated clustering approach combining renewable potential, socio-economic, and geographic data to classify European territories into meaningful groups.

Findings

17 distinct clusters identified across EU28 and Switzerland.

Heterogeneity observed within national borders and macro regions.

Results support targeted transnational renewable energy policies.

Abstract

The study presents an effort to classify the territories of a specific area, according to similarities in the estimated potential of their renewable sources, considering also their economic and sociodemographic structure and their geographic features. Specifically, the paper focuses on the area of EU28 and Switzerland and uses as basis for the analysis, data estimating the potential of renewable energy sources collected and elaborated in the framework of the project HotMaps (Horizon 2020). The method used to group the territorial units is cluster analysis, and specifically the k-means algorithm. The data present some interesting patterns and the territories of EU28 and Switzerland at NUTS3 level are classified into 17 clusters. The analysis shows the presence of heterogeneity within national borders and among territories comprised in the macro regions target of specific EU programmes, specifically the Adriatic-Ionian region, the Alpine region, the Baltic Sea region and the Danube region. The results of this research are meant to be used by European policy makers in developing more focused transnational renewable energy policies and strategies.