Comparative analysis of the effectiveness of correlation-regression and neural network modeling in predicting energy emissions of carbon dioxide in Russia

Abstract

Effective national cap-and-trade system involves accurate projections of greenhouse gas emissions for the national economy as a whole and by industry. The main source of carbon dioxide emissions in most countries of the world (including Russia) is the energy sector with traditional fuels (coal, gas and oil). The objective of the paper is to forecast energy emissions of carbon dioxide in the Russian Federation by applying adequate economic and mathematical modelling methods. To achieve it, two hypotheses are consistently put forward and tested: the possibility of building a medium-term forecast of the indicator as a result of correlation and regression analysis and the one based on the formation of a Bayesian ensemble of artificial neural networks. Both hypotheses are confirmed in the empirical study. However, the second method provides a higher degree of accuracy in approximating statistical data. Therefore, within the framework of this article, the formation of medium-term forecasts of energy carbon dioxide emissions in Russia is made with the help of neural network modeling. Highly accurate forecasting provides a scientific basis for effective policymakers’ decisions in decarbonisation of the national economy.