China has actively formulated and implemented a series of policies to address climate change and actively promoted the green transformation of its economy. Therefore, this paper carries out adecomposition of energy carbon emission factors and realises scenario projections for Guangdong Province in order to promote scientific, efficient and targeted carbon emission reduction. Firstly, a measurement method proposed in IPCC(2006) is used to calculate Guangdong's energy consumption carbon emissions from 2000 to 2020. On this basis, based on the extended Kaya equation, the LMDI decomposition method is applied to quantitatively measure the carbon emissions of energy consumption in Guangdong, and to reveal the influence mechanism of each factor on energy consumption. In order to solve the problem of multiple co-linearity of factors in the STIRPAT prediction model, two types of regressions, Lasso regression and Ridge regression, we reused for fitting. Finally, through the analysis of the factors affecting the carbon emissions of China's energy system and the simulation of the scenarios, a decision basis is provided for the optimisation of the carbon emissions of China's energy system.

Guibin Li, Zhiyuan Wang, Zhanjie Wen. Decomposition of Energy Carbon Emission Factors and Scenario Prediction Based on Multiple Models. International Journal of Frontiers in Sociology (2024), Vol. 6, Issue 9: 84-90. https://doi.org/10.25236/IJFS.2024.060913.

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