<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Russia’s military aggression against Ukraine has undermined the global energy system, leading to high energy prices and increased concerns about the EU’s energy security EU leaders have adopted a number of laws and developed the REPowerEU plan to reduce dependence on Russian energy imports by accelerating the transition to clean energy and creating a more sustainable energy system in Europe. The plan includes measures to save energy, diversify supplies and rapidly replace fossil fuels with clean energy sources, as well as prioritizes equity and solidarity, taking into account the energy balances of each EU member state. It builds on the Fit for 55 proposals and supports the ambitious goal of achieving at least -55% net greenhouse gas emissions by 2030 and climate neutrality by 2050.The aim of the article is to study the use of renewable energy in the European Union, the application of autoregressive models to predict the development of renewable energy.The results and conclusions. As a result of the study, based on the methodology of transients, a model of change in the volume of investment in wind energy was developed in the form of a differential equation. It was proved that the transition process is stable, even with time constraints or reduction of investment in the development of wind energy over time it will return to a stable growing trend (which was obtained by means of bagatofactor autoregressive models).</p>
</abstract>

Russia’s military aggression against Ukraine has undermined the global energy system, leading to high energy prices and increased concerns about the EU’s energy security EU leaders have adopted a number of laws and developed the REPowerEU plan to reduce dependence on Russian energy imports by accelerating the transition to clean energy and creating a more sustainable energy system in Europe. The plan includes measures to save energy, diversify supplies and rapidly replace fossil fuels with clean energy sources, as well as prioritizes equity and solidarity, taking into account the energy balances of each EU member state. It builds on the Fit for 55 proposals and supports the ambitious goal of achieving at least -55% net greenhouse gas emissions by 2030 and climate neutrality by 2050.The aim of the article is to study the use of renewable energy in the European Union, the application of autoregressive models to predict the development of renewable energy.The results and conclusions. As a result of the study, based on the methodology of transients, a model of change in the volume of investment in wind energy was developed in the form of a differential equation. It was proved that the transition process is stable, even with time constraints or reduction of investment in the development of wind energy over time it will return to a stable growing trend (which was obtained by means of bagatofactor autoregressive models).

Study of Best Practices of Green Energy Development in the EU Countries Based on Correlation and Bagatofactor Autoregressive Forecasting

Mykhailo Tuhan-Baranovskyi Donetsk National University of Economics and Trade

ECONOMICS

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Russia’s military aggression against Ukraine has undermined the global energy system, leading to high energy prices and increased concerns about the EU’s...

Study of Best Practices of Green Energy Development in the EU Countries Based on Correlation and Bagatofactor Autoregressive Forecasting

Pubblicato online: 20 lug 2023

Pagine: 183 - 197

Ricevuto: 17 mar 2023

Accettato: 16 mag 2023

DOI: https://doi.org/10.2478/eoik-2023-0029

Parole chiave
renewable energy sources, wind energy, autoregressive models, investment, transition process

© 2023 Oksana Yelisieieva et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Study of Best Practices of Green Energy Development in the EU Countries Based on Correlation and Bagatofactor Autoregressive Forecasting

Pubblicato online: 20 lug 2023

Pagine: 183 - 197

Ricevuto: 17 mar 2023

Accettato: 16 mag 2023

DOI: https://doi.org/10.2478/eoik-2023-0029

Parole chiaverenewable energy sources, wind energy, autoregressive models, investment, transition process

© 2023 Oksana Yelisieieva et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
renewable energy sources, wind energy, autoregressive models, investment, transition process