基于MIDAS模型中国碳排放量的实时预报与短期预测

随着全球气候变暖问题不断加重,二氧化碳排放量预测成为各国制定碳减排措施的核心问题.传统的二氧化碳排放量预测模型是基于同频数据进行的,高频数据必需处理为低频数据,这样不仅忽略了高频数据携带的有效信息,还影响了模型预测的及时性,降低了模型预测的精度.本文将混频数据抽样模型(MIDAS)用于碳排放量预测研究,分析了高频季度GDP滞后阶数变化效应及其对低频二氧化碳排放量的影响效应.研究结果表明,季度GDP对碳排放量具有正负两种效应,该效应会持续6个季度且以正效应为主,碳排放量自身之间也存在着相互影响,该影响会持续4年之久,这与中国的经济运行状况相吻合,说明混频数据抽样模型(MIDAS)对二氧化碳排放量预测的合理性.此外,混频数据抽样模型(MIDAS)在中国二氧化碳排放量的短期预测方面具有较高的预测精度,在实时预报方面具有显著的可行性和时效性.

Real-time forecasting and short-term prediction of Chinese carbon dioxide emissions based on MIDAS model

As global warming increases significantly, the forecast of carbon dioxide emissions became the key point for worldwide carbon reductions. The traditional carbon emissions prediction model is based on co-frequency data. Transferring high-frequency data to low-frequency data not only ignores the effective information with it but also influences the timeliness and reduces the accuracy of prediction. This paper uses mixed-data sampling (MIDAS) to predict carbon emissions and analyzes the effect of high-frequency quarterly GDP hysteresis and low-frequency carbon emissions. According to the research, quarterly GDP has both positive and negative impacts on carbon emissions and positive effects dominate over six quarters. Besides that, there are mutual influences between carbon emissions and these influences will last about four years, which coincide with Chinese economy and illustrates the rationality of the mixed-data sampling (MIDAS) for carbon dioxide emissions. In addition, mixed-data sampling (MIDAS) has accurate short-term prediction in carbon dioxide emissions and has significant feasibility and timeliness in real-time forecasting.