Forecasting 2030 CO2 reduction targets for Russia as a major emitter using different estimation scenarios

Abstract

ABSTRACTThis study firstly analyzes the impact of energy intensities and income on CO2 emissions in Russia, applying different estimation methods to the data period from 1990 to 2020. In addition, the study forecasts CO2 emissions considering 2030 targets under different assumptions and assesses the achievability of the set target. The estimation results concluded that the GDP and fossil fuel intensities of GDP have a statistically positive impact on CO2 emissions. Also, we found that the forecasted value for 2030, for the business-as-usual case, is 1750 MtCO2, with 95% confidence interval values of 1703 MtCO2 and 1796 MtCO2. This result shows that Russia needs to undergo substantial policy interventions to achieve its targets to reduce CO2 emissions. As the fifth biggest emitter, Russia missing its emissions targets will have undesirable implications for the rest of the world. Based on the projection results, the paper discusses some potential policy interventions.

Keywords

• CO2 emissions
• sustainable development
• machine learning algorithm
• forecasting
• Russia