During the last few decades, the European Union has made several proposals to stimulate the creation of clean energy inside its country members (Kyoto protocol, Paris Agreement, European Green Deal, Clean Energy for all Europeans, 2030 Climate and Energy EU objectives…) with the common objective of having a total renewable energy future. Several projects have been carried out trying to predict whether these objectives are plausible in the near future. A nonparametric approach seemed us like an interesting path which could help analyze the effort made regarding this cause since it permits to obtain consistent results under few conditions. We focused our attention on the European Green Deal (2020) that European Commission President Ursula von der Leyen considered “like man landing on the moon” for Europe. In fact this pact would make Europe the first continent to achieve Climate Neutrality by 2050 and in particular it enhances Paris objective with the new target of 55% cut of greenhouse gas emissions by 2030 respect to 1990. Given the great importance for Europe that this pact would have if it were respected we have set ourselves the goal of predicting whether this Green Deal target of a 55% cut in greenhouse gas emissions (Ghg) by 2030 respect to 1990 levels is feasible. To do so we analyzed the European renewable transition and forecasted future renewable energies and Ghg values, taking advantage of current and past knowledge of data about production, consumptions and greenhouse gases. Since all energy related information for every EU member is open to the public in the EU’s Eurostat portal, and all of its datasets contain clean and reliable information, we deemed this project very interesting and useful to draw further attention to these issues while using the nonparametric techniques learnt. We worked on a variety of time series datasets about European energy consumption and of Ghg emissions in the time window from 1990 to the present (see References). To properly predict future renewable energy and Ghg values and to make our models more robust we also used population and Gross Domestic Product (GDP) reliable predictions (see References). By means of nonparametric prediction tools, we highlighted the non-feasibility of the Green Deal target, obtaining a 32% cut in Ghg emissions. It is therefore clear that much more effort is needed, in order to properly tackle this crucial challenge. Our work wants to be yet another raised alarm, to be added to the already long list of scientific researches on this subject.
The project is part of the 2021-22 course 055702 - NONPARAMETRIC STATISTICS, by Politecnico di Milano university.