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Abstract

With the aggravation of the negative impact of carbon emissions, the research of carbon emissions has gradually become a hot topic. Accurate prediction of carbon emissions serves as a crucial foundation for attaining carbon neutrality and carbon peaking, and the prognosis of carbon emissions is of paramount significance. By collecting the data of nine major energy sources in Hebei Province from 2005 to 2019, this study adopts the Ridge Regression model to study the influencing factors of carbon emissions. The results show that coal consumption, electricity and GDP have a significant positive effect on carbon emissions, while natural gas has a significant negative effect on carbon emissions. Based on the heat conversion, the nine major energies were converted into carbon emission data. The grey prediction model was employed to prognosticate the carbon emissions in the subsequent five years, and conclusions and recommendations were presented in accordance with the research results.