Carbon Monitor, a near-real-time daily dataset of global CO2 emission from fossil fuel and cement production View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2020-11-09

AUTHORS

Zhu Liu, Philippe Ciais, Zhu Deng, Steven J. Davis, Bo Zheng, Yilong Wang, Duo Cui, Biqing Zhu, Xinyu Dou, Piyu Ke, Taochun Sun, Rui Guo, Haiwang Zhong, Olivier Boucher, François-Marie Bréon, Chenxi Lu, Runtao Guo, Jinjun Xue, Eulalie Boucher, Katsumasa Tanaka, Frédéric Chevallier

ABSTRACT

We constructed a near-real-time daily CO2 emission dataset, the Carbon Monitor, to monitor the variations in CO2 emissions from fossil fuel combustion and cement production since January 1, 2019, at the national level, with near-global coverage on a daily basis and the potential to be frequently updated. Daily CO2 emissions are estimated from a diverse range of activity data, including the hourly to daily electrical power generation data of 31 countries, monthly production data and production indices of industry processes of 62 countries/regions, and daily mobility data and mobility indices for the ground transportation of 416 cities worldwide. Individual flight location data and monthly data were utilized for aviation and maritime transportation sector estimates. In addition, monthly fuel consumption data corrected for the daily air temperature of 206 countries were used to estimate the emissions from commercial and residential buildings. This Carbon Monitor dataset manifests the dynamic nature of CO2 emissions through daily, weekly and seasonal variations as influenced by workdays and holidays, as well as by the unfolding impacts of the COVID-19 pandemic. The Carbon Monitor near-real-time CO2 emission dataset shows a 8.8% decline in CO2 emissions globally from January 1st to June 30th in 2020 when compared with the same period in 2019 and detects a regrowth of CO2 emissions by late April, which is mainly attributed to the recovery of economic activities in China and a partial easing of lockdowns in other countries. This daily updated CO2 emission dataset could offer a range of opportunities for related scientific research and policy making. More... »

PAGES

392

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41597-020-00708-7

DOI

http://dx.doi.org/10.1038/s41597-020-00708-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1132443302

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/33168822


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N-Triples is a line-based linked data format ideal for batch operations.

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Turtle is a human-readable linked data format.

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RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41597-020-00708-7'


 

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