Bringing knowledge closer to practice: an inferential analysis of EU climate change policies and measures

In the context of climate change, European Member States are committed to developing policies and taking corresponding adaptation measures. In this direction, every two years, the European Environment Agency (EEA) publishes an extensive dataset related to climate policies and measures (PaMs) reported in Europe and generated by European research projects, with the aim of improving and disseminating the information covering all actions aimed at reducing GHG emissions. In this study, an inferential data analysis is conducted on the PaMs dataset, setting as the variable of interest the reported quantified GHG emissions savings of each PaM and inferring its variance through a set of related explanatory qualitative factors (i.e., type of measure, sector of policy, related entities, implementation period etc.) together with their higher-level interactions. This is achieved by employing a number of widely used statistical techniques for the analysis of multi-factor data, such as regression analysis, hypothesis testing, influence diagnostics and variable selection methods to (a) investigate the significance and effect of the factors in relation to GHG emissions and (b) model the relationships between the variables of interest. The resulting analysis aims to obtain practical insights from a retrospective view of a wide number of PaMs and generalize their response in a descriptive and explicable way. This will allow the interested parties to gain interpretable feedback from existing measures applied in practice and subsequently ‘feed back’ new knowledge on climate adaptation decision making.

This work is supported by IMPETUS research project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 101037084.