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

• Published in: Annals of operations research Vol. 345; no. 2; pp. 665 - 683
• Main Authors: Magazzino, Cosimo, Mele, Marco
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2025; Springer Nature B.V
• Subjects: Algorithms; Business and Management; Carbon dioxide; Combinatorics; Economic development; Economic growth; Energy consumption; Machine learning; Natural gas; Operations Research/Decision Theory; Original Research; Qualitative analysis; Theory of Computation; Q43; B22; Energy use; C32; Economic growth; Machine learning; Time-series; CO; emissions; D2C algorithm; Russia; N55
• ISSN: 0254-5330; 1572-9338; 1572-9338
• DOI: 10.1007/s10479-022-04787-0

The aim of this study is to explore the nexus among CO 2 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 CO 2 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.