A new machine learning algorithm to explore the CO2 emissions-energy use-economic growth trilemma

Cosimo Magazzino () and Marco Mele ()
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Cosimo Magazzino: Roma Tre University
Marco Mele: Roma Tre University

Annals of Operations Research, 2025, vol. 345, issue 2, No 5, 665-683

Abstract: Abstract The aim of this study is to explore the nexus among CO2 emissions, energy use, and GDP in Russia using annual data ranging from 1970 to 2017. We first conduct time-series analyses (stationarity, structural breaks, and cointegration tests). Then, we present a new D2C algorithm, and we run a Machine Learning experiment. Comparing the results of the two approaches, we conclude that economic growth causes energy use and CO2 emissions. However, the critical analysis underlines how the variance decomposition justifies the qualitative approach of using economic growth to immediately implement expenses for the use of alternative energies able to reduce polluting emissions. Finally, robustness checks to validate the results through a new D2C algorithm are performed. In essence, we demonstrate the existence of causal links in sub-permanent states among these variables.

Keywords: CO2 emissions; Energy use; Economic growth; Machine learning; D2C algorithm; Time-series; Russia (search for similar items in EconPapers)
JEL-codes: B22 C32 N55 Q43 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10479-022-04787-0

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