Near-real-time daily estimates of fossil fuel CO2 emissions from major high-emission cities in China

Da Huo, Kai Liu, Jianwu Liu, Yingjian Huang, Taochun Sun, Yun Sun, Caomingzhe Si, Jinjie Liu, Xiaoting Huang, Jian Qiu, Haijin Wang, Duo Cui, Biqing Zhu, Zhu Deng, Piyu Ke, Yuli Shan, Olivier Boucher, Grégoire Dannet, Gaoqi Liang, Junhua ZhaoLei Chen, Qian Zhang, Philippe Ciais, Wenwen Zhou*, Zhu Liu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Cities in China are on the frontline of low-carbon transition which requires monitoring city-level emissions with low-latency to support timely climate actions. Most existing CO2 emission inventories lag reality by more than one year and only provide annual totals. To improve the timeliness and temporal resolution of city-level emission inventories, we present Carbon Monitor Cities-China (CMCC), a near-real-time dataset of daily CO2 emissions from fossil fuel and cement production for 48 major high-emission cities in China. This dataset provides territory-based emission estimates from 2020-01-01 to 2021-12-31 for five sectors: power generation, residential (buildings and services), industry, ground transportation, and aviation. CMCC is developed based on an innovative framework that integrates bottom-up inventory construction and daily emission estimates from sectoral activities and models. Annual emissions show reasonable agreement with other datasets, and uncertainty ranges are estimated for each city and sector. CMCC provides valuable daily emission estimates that enable low-latency mitigation monitoring for cities in China.

Original languageEnglish
Article number684
Number of pages21
JournalScientific Data
Issue number1
Publication statusPublished - 10 Nov 2022

Bibliographical note

Funding Information:
Authors acknowledges support from Dr. Bofeng Cai at the Center for Climate Change and Environmental Policy, Chinese Academy for Environmental Planning. ZL acknowledge the National Natural Science Foundation of China (grant 71874097, 41921005, and 72140002), Beijing Natural Science Foundation (JQ19032), and the Qiu Shi Science & Technologies Foundation.

Publisher Copyright:
© 2022, The Author(s).

ASJC Scopus subject areas

  • Statistics and Probability
  • Information Systems
  • Education
  • Computer Science Applications
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences


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