EU's green taxonomy analysis incorporated with energy-mix by machine learning for climate mitigations. (2nd June 2023)

• Record Type: Journal Article
• Authors: Lee, Sewon; Han, Changho; Baek, Chang Hyun; Woo, Tae Ho
• Is Part Of: International journal of global warming. Volume 30:Number 3(2023)
• Page Start: 296
• Page End: 308
• Publication Date: 2023-06-02
• Subjects: climate -- taxonomy -- energy-mix -- artificial intelligence -- AI -- machine learning -- ML; Global warming -- Periodicals; Global warming -- Statistics -- Periodicals; Global warming -- Economic aspects -- Periodicals; Climatic changes -- Periodicals; Climatic changes -- Economic aspects -- Periodicals; Climatic changes -- Environmental aspects -- Periodicals; 363.73874
• Languages: English
• ISSNs: 1758-2083

Abstract

This paper analyses the European Union (EU) 's green taxonomy incorporated with energy-mix by nonlinear algorithms where the uncertainties of the energy-mix are described by the artificial intelligence (AI). There are simulations of dynamical configuration of the EU's taxonomy incorporated with the energy-mix. The trends of precision, recall, specificity, and accuracy are converged after the 30th year. The comparisons are done using a new factor of energy-mix consideration. The figure shows the comparison with and without energy-mix. The differences between two values are bigger around the 20th and 30th years and it is not much near the 50th year. This analysis of the EU's green taxonomy could provide solutions for optimising the environment and energy production.