An application of multivariate statistical techniques to partial equilibrium models outputs: The analysis of the NEEDS-TIMES Pan European model results

Sustainable development requires analytical tools to assess through a comprehensive approach the effectiveness of energy-environmental policies on medium-long term and their impact on the different macroeconomic sectors. Among the available tools, partial equilibrium models are particularly suited to represent and analyze complex energy systems with a high technology detail as well as to individuate the optimal energy-technology roadmaps that allows to fulfil multiple objectives (e.g. energy supply security, climate change mitigation and air quality improvement). In fact these models enable the users to perform a scenario analysis in order to explore the behaviour of energy system to boundary conditions variations. They are typically characterized by a huge amount of data which informative content is often not fully exploited. In this framework, multivariate statistical techniques (Cluster Analysis—CA and Principal Component Analysis—PCA) can represent a key tool to characterize data correlation structure and to point out the variables with the highest information content. The paper shows the methodological procedure utilized to characterize the NEEDS-TIMES Pan European model results, describing its usefulness to individuate homogeneous areas for the application of suited targets and to characterize from a statistical point of view energy systems behaviour in different scenario hypothesis.