Since the outbreak of COVID-19 at the end of 2019, the Chinese government has imposed strict control measures on affected cities, which may have impacted the spatial and temporal pattern of carbon dioxide emissions. This paper follows the quantitative analysis method, experimental method, mathematical method, etc., and quantitatively studies the impact of the epidemic on China's carbon emissions. The combination model of ARIMA and BP neural network is used to predict the actual impact of epidemic situation on China's carbon emissions in 2020, and the spatial autocorrelation analysis method is used to analyze the spatial characteristics of China's provincial carbon emissions, which indicate that China's carbon emissions have consistently maintained a growth trend, from 2.05 billion tons in 2005 to 3.89 billion tons in 2019. Furthermore, the growth rate of carbon emissions and the changing trend of the emission intensity are the same, dropping from 12% in 2005 to 3% in 2019. The emission intensity also dropped from 1.1 in 2005 to 0.6 in 2019, indicating that the trend of increasing carbon emissions in northern provinces and Xinjiang changed significantly from 2005 to 2019. The overall carbon emissions of the 30 provinces in 2020 are predicted to be 4.068 billion tons, while the actual energy carbon emissions will be 3.921 billion tons, suggesting that the pandemic significantly reduced carbon emissions. Among affected provinces, carbon emissions from Hubei, Jiangsu, Shandong, Shanghai, and other places changed significantly, from 0.99, 0.25, 0.43, and 76 million tons in 2019 to 0.88, 0.24, 0.42, and 72 million tons in 2020, respectively. The results show a positive spatial correlation between China's provincial carbon emissions; the high-high and bottom-high agglomeration are mainly among the provinces, mainly distributed in North China and East China. Although the pandemic seriously impacts China's carbon emissions, each province's spatial relationship has not changed significantly.
Heliyon, Volume 9, Issue 3, March 2023, e13963,