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Evaluating China's fossil-fuel CO2 emissions from a comprehensive dataset of nine inventories

  • Pengfei Han*
  • , Ning Zeng*
  • , Tom Oda
  • , Xiaohui Lin
  • , Monica Crippa
  • , Dabo Guan
  • , Greet Janssens-Maenhout
  • , Xiaolin Ma
  • , Zhu Liu
  • , Yuli Shan
  • , Shu Tao
  • , Haikun Wang
  • , Rong Wang
  • , Lin Wu
  • , Xiao Yun
  • , Qiang Zhang
  • , Fang Zhao
  • , Bo Zheng
  • *此作品的通讯作者
  • CAS - Institute of Atmospheric Physics
  • University of Maryland, College Park
  • Universities Space Research Association
  • European Commission
  • Tsinghua University
  • University of East Anglia
  • Nanjing University
  • University of Groningen
  • Peking University
  • Fudan University
  • Université Paris-Saclay

科研成果: 期刊稿件文章同行评审

摘要

China s fossil-fuel CO2 (FFCO2) emissions accounted for approximately 28% of the global total FFCO2 in 2016. An accurate estimate of China s FFCO2 emissions is a prerequisite for global and regional carbon budget analyses and the monitoring of carbon emission reduction efforts. However, significant uncertainties and discrepancies exist in estimations of China s FFCO2 emissions due to a lack of detailed traceable emission factors (EFs) and multiple statistical data sources. Here, we evaluated China s FFCO2 emissions from nine published global and regional emission datasets. These datasets show that the total emissions increased from 3.4 (3.0 3.7) in 2000 to 9.8 (9.2 10.4) Gt CO2 yr-1 in 2016. The variations in these estimates were largely due to the different EF (0.491 0.746 t C per t of coal) and activity data. The large-scale patterns of gridded emissions showed a reasonable agreement, with high emissions being concentrated in major city clusters, and the standard deviation mostly ranged from 10% to 40% at the provincial level. However, patterns beyond the provincial scale varied significantly, with the top 5% of the grid level accounting for 50 % 90% of total emissions in these datasets. Our findings highlight the significance of using locally measured EF for Chinese coal. To reduce uncertainty, we recommend using physical CO2 measurements and use these values for dataset validation, key input data sharing (e.g., point sources), and finer-resolution validations at various levels.

源语言英语
文章编号2020
页(从-至)11371-11385
页数15
期刊Atmospheric Chemistry and Physics
20
19
DOI
出版状态已出版 - 6 10月 2020

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