A new machine learning algorithm to explore the CO2 emissions-energy use-economic growth trilemma
利用俄罗斯1970-2017年数据，通过时间序列分析和新提出的D2C机器学习算法，发现经济增长是能源使用和二氧化碳排放的原因，并建议将增长用于发展替代能源以减少污染。
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.