Cargando…

Research on the impact of COVID-19 on the spatiotemporal distribution of carbon dioxide emissions in China

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, mathematic...

Descripción completa

Detalles Bibliográficos
Autores principales: Guo, Li, Bai, Lifang, Liu, Yixuan, Yang, Yuzheng, Guo, Xianhua
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9951609/
https://www.ncbi.nlm.nih.gov/pubmed/36855647
http://dx.doi.org/10.1016/j.heliyon.2023.e13963
_version_ 1784893423687303168
author Guo, Li
Bai, Lifang
Liu, Yixuan
Yang, Yuzheng
Guo, Xianhua
author_facet Guo, Li
Bai, Lifang
Liu, Yixuan
Yang, Yuzheng
Guo, Xianhua
author_sort Guo, Li
collection PubMed
description 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.
format Online
Article
Text
id pubmed-9951609
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher Elsevier
record_format MEDLINE/PubMed
spelling pubmed-99516092023-02-24 Research on the impact of COVID-19 on the spatiotemporal distribution of carbon dioxide emissions in China Guo, Li Bai, Lifang Liu, Yixuan Yang, Yuzheng Guo, Xianhua Heliyon Research Article 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. Elsevier 2023-02-24 /pmc/articles/PMC9951609/ /pubmed/36855647 http://dx.doi.org/10.1016/j.heliyon.2023.e13963 Text en © 2023 The Authors. Published by Elsevier Ltd. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Research Article
Guo, Li
Bai, Lifang
Liu, Yixuan
Yang, Yuzheng
Guo, Xianhua
Research on the impact of COVID-19 on the spatiotemporal distribution of carbon dioxide emissions in China
title Research on the impact of COVID-19 on the spatiotemporal distribution of carbon dioxide emissions in China
title_full Research on the impact of COVID-19 on the spatiotemporal distribution of carbon dioxide emissions in China
title_fullStr Research on the impact of COVID-19 on the spatiotemporal distribution of carbon dioxide emissions in China
title_full_unstemmed Research on the impact of COVID-19 on the spatiotemporal distribution of carbon dioxide emissions in China
title_short Research on the impact of COVID-19 on the spatiotemporal distribution of carbon dioxide emissions in China
title_sort research on the impact of covid-19 on the spatiotemporal distribution of carbon dioxide emissions in china
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9951609/
https://www.ncbi.nlm.nih.gov/pubmed/36855647
http://dx.doi.org/10.1016/j.heliyon.2023.e13963
work_keys_str_mv AT guoli researchontheimpactofcovid19onthespatiotemporaldistributionofcarbondioxideemissionsinchina
AT bailifang researchontheimpactofcovid19onthespatiotemporaldistributionofcarbondioxideemissionsinchina
AT liuyixuan researchontheimpactofcovid19onthespatiotemporaldistributionofcarbondioxideemissionsinchina
AT yangyuzheng researchontheimpactofcovid19onthespatiotemporaldistributionofcarbondioxideemissionsinchina
AT guoxianhua researchontheimpactofcovid19onthespatiotemporaldistributionofcarbondioxideemissionsinchina