A new machine learning algorithm to explore the CO2 emissions-energy use-economic growth trilemma
TLDR
In this article , the authors explore the nexus among CO 2 emissions, energy use, and GDP in Russia using annual data ranging from 1970 to 2017, and demonstrate the existence of causal links in sub-permanent states among these variables.Abstract:
Abstract 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. read more
Citations
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The trilemma among CO2 emissions, energy use, and economic growth in Russia
TL;DR: In this paper , the relationship among CO2 emissions, energy use, and GDP in Russia using annual data ranging from 1990 to 2020 was examined, showing that energy conservation measures should not adversely affect the economic growth path of the country.
References
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TL;DR: The relationship between co-integration and error correction models, first suggested in Granger (1981), is here extended and used to develop estimation procedures, tests, and empirical examples.
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Søren Johansen,Katarina Juselius +1 more
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The contribution of outdoor air pollution sources to premature mortality on a global scale
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