Carbon emission from energy consumption is the main factor leading to the greenhouse effect,and a reasonable prediction of carbon emission is conducive to the formulation of relevant emission reduction policies.In the prediction of carbon emissions,the total annual carbon emissions measurement method is constructed based on nine major energy consumption data,and the carbon emissions are predicted based on the gray GM(1,1)model,and the prediction results are corrected by combining with the weighted Markov model for the shortcomings of the model with low fitting accuracy.The main energy consumption of Hunan Province from 2006 to 2021 is taken as an example to predict carbon emissions from 2022 to 2026.The results show that the gray-weighted Markov model in predicting carbon emissions in Hunan Province in 2006-2021 compared with the gray GM(1,1)model its accuracy of carbon emissions prediction increased by 65.49％,and the next five years of energy consumption in Hunan Province,although the carbon emissions are in the continuous growth,but the growth rate is only 0.286 0％.The results of the study can provide a reference basis for the development of"carbon neutral"and"carbon peak"in Hunan Province.
energy consumptioncarbon emission forecastinggray GM(1,1)weighted Markov modelHunan Province
NETL
NSTL
万方数据
