基于LMDI模型的能源系统碳排放影响因素分析

In the context of the "carbon peaking and carbon neutrality" goals, as the main source of carbon emissions in the whole society, carbon reduction in energy activities is particularly critical. In order to clarify the role and contribution of power system carbon emissions, energy consumption intensity, industrial structure and other factors to the carbon emissions of energy system. Firstly, the total energy consumption in China is predicted based on the gray GM(1, 1) model, and the model is tested to ensure the feasibility. Secondly, based on the intergovernmental panel on climate change (IPCC) carbon emission factor method, the carbon emissions of each industry from 2015—2019 are measured based on the data of China's energy balance sheet. On this basis, a carbon emission analysis model is constructed using the logarithmic mean Divisia index (LMDI) method, and the results are standardized to analyze the degree of influence of each effect on the carbon emission of the energy system. The calculation results prove that energy consumption intensity, population size, urbanization level and carbon emission of electricity promote the growth of carbon emission of energy system, while energy consumption intensity, rural population share and industrial structure play a suppressing role.

Keywords： carbon emissions ; energy activities ; gray prediction model ; logarithmic mean Divisia index (LMDI) method decomposition model ; effect analysis