Associate Professor  Alexey  Mikhaylov
Department of Science, Financial Faculty, Financial University under the Government of the Russian Federation, Moscow,  Russia

Title: Revisit energy consumption, economic growth and carbon dioxide emissions links in transition countries using a new developed Quantile_on_Quantile approach

Abstract:

We investigate the links between energy consumption, carbon dioxide (henceforth, CO2) emissions, and economic growth in 6 transition countries for the period 1970–2021 by applying novel Quantile_on_Quantile approach. We aim to address the potential key factor, hidden in the general EKC theory. Three pairwise linkages, including the CO2 emissions and economic growth, CO2 emissions and energy consumption, and economic growth and energy consumption, are examined across several quantiles. Our empirical results show that energy consumption positively significantly affects both CO2 emissions and economic growth. Meanwhile, economic growth positively significantly influences CO2 emissions. These findings have important policy implications for the 6 transition countries under study.

Biography:

Alexey Mikhaylov is currently Associate Professor with the Financial Markets and Financial Engineering Department, Financial University under the Government of Russian Federation, Moscow. Author of over 200 scientific publications and conference proceedings indexed in Scopus and Web of Science and author of 8 scientific monographs. As of July 2025, he has the highest Hirsch index among Russian economists: according to Web of Science - 30, according to Scopus - 52, according to Google Scholar - 58. He is in the top 2 % of the most cited scientists (as of August 2024), is among the top cited young scientists according to MDPI (2024). Together with his team, he formed the theory of crypt asset prices and developed the theory of general artificial intelligence. With Y. N. Sotskov, he contributed to the development of the theory of schedules within the framework of the RNF project. He formed a database of renewable energy generation sources and carbon dioxide emissions used for analysis by artificial intelligence and fuzzy logic methods.