基于计量经济学的能源转型背景下全球碳排放预测分析方法

Forecasting and Analyzing Global Carbon Emissions Based on Econometrics Against the Background of Energy Transformation

建设全球能源互联网(Global Energy Interconnection,GEI),有助于推动能源体系由化石能源向低碳可再生能源转型。为确定GEI战略的实施对全球碳排放的影响,文章以计量经济学理论为基础,提出STIRPAT(stochastic impacts by regression on population, affluence and technology)模型和向量自回归(vector auto regressive, VAR)模型相结合的一种全球碳排放预测方法。研究首先利用Kaya等式和灰色关联分析方法,对与全球碳排放密切关联的驱动因素进行分析并排序;在此基础上,建立了考虑单位能耗碳排放量的拓展STIRPAT模型,实现对能源转型过程中全球碳排放进程的预测,并利用向量自回归方法明确各驱动因素之间的相互作用关系;最后,针对3种GEI建设情景,利用上述模型预测全球碳排放进程,并对结果进行分析。

The construction of the Global Energy Interconnection(GEI) helps to promote the consumption of renewable energies instead of fossil energy. Based on an environmental impact evaluation model which is called STIRPAT(stochastic impacts by regression on population, affluence, and technology) and a vector auto regression(VAR) model, a method is proposed in this paper to assess the impact of the construction process of the GEI on global carbon emissions. Firstly, several driving factors closely related to global carbon emission are analyzed and ranked using the Kaya equation and the grey relational analysis method. On this basis, an extended STIRPAT model considering carbon emissions per unit of energy consumption is built to predict the global carbon emissions in the process of energy transformation. In addition, the vector auto regression method is adopted to clarify the interactions between the driving factors. Finally, the predictions of global carbon emissions in different scenarios of GEI construction are made by using the model that we build, and the results is thoroughly analyzed.