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Assessing the recent impact of COVID-19 on carbon emissions from China using domestic economic data
The outbreak of coronavirus disease 2019 (COVID-19) has caused tremendous loss to human life and economic decline in China and worldwide. It has significantly reduced gross domestic product (GDP), power generation, industrial activity and transport volume; thus, it has reduced fossil-related and cem...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier B.V.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7425766/ https://www.ncbi.nlm.nih.gov/pubmed/32835964 http://dx.doi.org/10.1016/j.scitotenv.2020.141688 |
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author | Han, Pengfei Cai, Qixiang Oda, Tomohiro Zeng, Ning Shan, Yuli Lin, Xiaohui Liu, Di |
author_facet | Han, Pengfei Cai, Qixiang Oda, Tomohiro Zeng, Ning Shan, Yuli Lin, Xiaohui Liu, Di |
author_sort | Han, Pengfei |
collection | PubMed |
description | The outbreak of coronavirus disease 2019 (COVID-19) has caused tremendous loss to human life and economic decline in China and worldwide. It has significantly reduced gross domestic product (GDP), power generation, industrial activity and transport volume; thus, it has reduced fossil-related and cement-induced carbon dioxide (CO(2)) emissions in China. Due to time delays in obtaining activity data, traditional emissions inventories generally involve a 2–3-year lag. However, a timely assessment of COVID-19's impact on provincial CO(2) emission reductions is crucial for accurately understanding the reduction and its implications for mitigation measures; furthermore, this information can provide constraints for modeling studies. Here, we used national and provincial GDP data and the China Emission Accounts and Datasets (CEADs) inventory to estimate the emission reductions in the first quarter (Q1) of 2020. We find a reduction of 257.7 Mt. CO(2) (11.0%) over Q1 2019. The secondary industry contributed 186.8 Mt. CO(2) (72.5%) to the total reduction, largely due to lower coal consumption and cement production. At the provincial level, Hubei contributed the most to the reductions (40.6 Mt) due to a notable decrease of 48.2% in the secondary industry. Moreover, transportation significantly contributed (65.1 Mt), with a change of −22.3% in freight transport and −59.1% in passenger transport compared with Q1 2019. We used a point, line and area sources (PLAS) method to test the GDP method, producing a close estimate (reduction of 10.6%). One policy implication is a change in people's working style and communication methods, realized by working from home and holding teleconferences, to reduce traffic emissions. Moreover, GDP is found to have potential merit in estimating emission changes when detailed energy activity data are unavailable. We provide provincial data that can serve as spatial disaggregation constraints for modeling studies and further support for both the carbon cycle community and policy makers. |
format | Online Article Text |
id | pubmed-7425766 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier B.V. |
record_format | MEDLINE/PubMed |
spelling | pubmed-74257662020-08-14 Assessing the recent impact of COVID-19 on carbon emissions from China using domestic economic data Han, Pengfei Cai, Qixiang Oda, Tomohiro Zeng, Ning Shan, Yuli Lin, Xiaohui Liu, Di Sci Total Environ Article The outbreak of coronavirus disease 2019 (COVID-19) has caused tremendous loss to human life and economic decline in China and worldwide. It has significantly reduced gross domestic product (GDP), power generation, industrial activity and transport volume; thus, it has reduced fossil-related and cement-induced carbon dioxide (CO(2)) emissions in China. Due to time delays in obtaining activity data, traditional emissions inventories generally involve a 2–3-year lag. However, a timely assessment of COVID-19's impact on provincial CO(2) emission reductions is crucial for accurately understanding the reduction and its implications for mitigation measures; furthermore, this information can provide constraints for modeling studies. Here, we used national and provincial GDP data and the China Emission Accounts and Datasets (CEADs) inventory to estimate the emission reductions in the first quarter (Q1) of 2020. We find a reduction of 257.7 Mt. CO(2) (11.0%) over Q1 2019. The secondary industry contributed 186.8 Mt. CO(2) (72.5%) to the total reduction, largely due to lower coal consumption and cement production. At the provincial level, Hubei contributed the most to the reductions (40.6 Mt) due to a notable decrease of 48.2% in the secondary industry. Moreover, transportation significantly contributed (65.1 Mt), with a change of −22.3% in freight transport and −59.1% in passenger transport compared with Q1 2019. We used a point, line and area sources (PLAS) method to test the GDP method, producing a close estimate (reduction of 10.6%). One policy implication is a change in people's working style and communication methods, realized by working from home and holding teleconferences, to reduce traffic emissions. Moreover, GDP is found to have potential merit in estimating emission changes when detailed energy activity data are unavailable. We provide provincial data that can serve as spatial disaggregation constraints for modeling studies and further support for both the carbon cycle community and policy makers. Elsevier B.V. 2021-01-01 2020-08-13 /pmc/articles/PMC7425766/ /pubmed/32835964 http://dx.doi.org/10.1016/j.scitotenv.2020.141688 Text en © 2020 Elsevier B.V. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Han, Pengfei Cai, Qixiang Oda, Tomohiro Zeng, Ning Shan, Yuli Lin, Xiaohui Liu, Di Assessing the recent impact of COVID-19 on carbon emissions from China using domestic economic data |
title | Assessing the recent impact of COVID-19 on carbon emissions from China using domestic economic data |
title_full | Assessing the recent impact of COVID-19 on carbon emissions from China using domestic economic data |
title_fullStr | Assessing the recent impact of COVID-19 on carbon emissions from China using domestic economic data |
title_full_unstemmed | Assessing the recent impact of COVID-19 on carbon emissions from China using domestic economic data |
title_short | Assessing the recent impact of COVID-19 on carbon emissions from China using domestic economic data |
title_sort | assessing the recent impact of covid-19 on carbon emissions from china using domestic economic data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7425766/ https://www.ncbi.nlm.nih.gov/pubmed/32835964 http://dx.doi.org/10.1016/j.scitotenv.2020.141688 |
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