A methodology for linking the Energy-related Policies of the European Green Deal to the 17 SDGs using Machine Learning

Phoebe Koundouri (), Nicolaos Theodossiou, Charalampos Stavridis, Stathis Devves and Angelos Plataniotis

No 2202, DEOS Working Papers from Athens University of Economics and Business

Abstract: The European Green Deal (EGD) was published in December 2019 with the ambition of being Europe's new growth strategy, making it climate neutral by 2050, and ensuring its citizens a sustainable, prosperous, and inclusive future. The energy sector is central to this ambition, as the European Commission's objectives are, among others, to increase the efficiency of energy production by establishing a fully integrated, interconnected, and digitalized EU energy market. The EGD was the starting point for the publication of a large number of Policy and Strategy documents for achieving Sustainability in Europe. One of the first attempts to systematically correlate the policy areas of the European Green Deal with the 17 Sustainable Development Goals (SDGs) was made in the first report of the UN SDSN's Senior Working Group for the Joint Implementation of the SDGs and the EGD, which was published in February 2021, where the EGD framework was linked to each of the 17 SDGs using textual analysis. Building on this methodology, in this chapter we extend the manual linkage of policy texts to SDGs, by using Natural Language Processing and Machine Learning techniques to automate it, focusing on Energy-related documents derived by the EGD.

Keywords: European Green Deal; Policies; Sustainable Development Goals; Deep Learning; Natural Language Processing; Semantics. (search for similar items in EconPapers)
Date: 2022-01-17
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ene and nep-env
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Working Paper: A methodology for linking the Energy-related Policies of the European Green Deal to the 17 SDGs using Machine Learning (2022)
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