Forecasting share of renewables in primary energy consumption and CO2 emissions of China and the United States under Covid-19 pandemic using a novel fractional nonlinear grey model

Abstract

China and the United States (U.S.) are in the first two places among the countries that consume the most primary energy and emit CO2 emissions in the world. Considering the Sustainable Development Goal 7.2 and Paris Agreement's goals, forecasting of CO2 emissions and the share of renewables in primary energy consumption for China and the United States in the coming years will be a guide for energy policies. In this context, this study aims to forecast the share of renewables in primary energy consumption and CO2 emissions of China and the U.S. using a novel optimized fractional nonlinear grey Bernoulli model with rolling mechanism, briefly as ROFANGBM(1,1), under pandemic and non-pandemic scenarios. This study also analyzed the gap in the energy consumption and CO2 emissions for the year 2020 caused by the Covid-19 pandemic. The results showed that ROFANGBM(1,1) gave the highest prediction performance with having the lowest mean absolute percentage error (MAPE) value for all cases and the share of renewables in primary energy consumption in 2025 is forecasted as 12.3% for the U.S. and 16.6% for China by using ROFANGBM(1,1). Additionally, CO2 emissions of China and the U.S. are forecasted by using ROFANGBM(1,1) as 10112 Mt and 4583 Mt in 2025, respectively. It is believed that this study will provide new avenues for researchers to make more accurate predictions by addressing the fluctuations due to the Covid-19 pandemic thanks to the proposed novel grey prediction model.