Prediction of Carbon Emissions in China’s Power Industry Based on the Mixed-Data Sampling (MIDAS) Regression Model
Xiaoxiang Xu, Mingqiu Liao
Abstract
China is currently the country with the largest carbon emissions in the world, to which, the power industry contributes the greatest share. To reduce carbon emissions, reliable and timely forecasting measures are important and necessary. By using different frequency variables, in this study, we used the mixed-data sampling (MIDAS) regression model to forecast the annual carbon emissions of China’s power industry compared with a benchmark model. It was found that the MIDAS model had a higher prediction accuracy than models such as the autoregressive distributed lag (ARDL) model. Moreover, our results showed that the MIDAS model could conduct timely nowcasting, which is useful when the data have some releasing lag. Through this prediction method, the results also demonstrated that the carbon emissions of the power industry have a significant relationship with GDP and thermal power generation, and that the value of carbon emissions would keep increasing in the years of 2021 and 2022.