Your browser doesn't support javascript.
loading
Near-real-time daily estimates of fossil fuel CO2 emissions from major high-emission cities in China.
Huo, Da; Liu, Kai; Liu, Jianwu; Huang, Yingjian; Sun, Taochun; Sun, Yun; Si, Caomingzhe; Liu, Jinjie; Huang, Xiaoting; Qiu, Jian; Wang, Haijin; Cui, Duo; Zhu, Biqing; Deng, Zhu; Ke, Piyu; Shan, Yuli; Boucher, Olivier; Dannet, Grégoire; Liang, Gaoqi; Zhao, Junhua; Chen, Lei; Zhang, Qian; Ciais, Philippe; Zhou, Wenwen; Liu, Zhu.
Afiliación
  • Huo D; Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
  • Liu K; Department of Civil & Mineral Engineering, University of Toronto, Toronto, ON, M5S 1A1, Canada.
  • Liu J; Product and Solution and Website Business Unit, Alibaba Cloud, Hangzhou, Zhejiang, 311121, China.
  • Huang Y; Product and Solution and Website Business Unit, Alibaba Cloud, Hangzhou, Zhejiang, 311121, China.
  • Sun T; Product and Solution and Website Business Unit, Alibaba Cloud, Hangzhou, Zhejiang, 311121, China.
  • Sun Y; Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
  • Si C; School of Environmental Science and Engineering, Tianjin University, Tianjin, 300072, China.
  • Liu J; Department of Electrical Engineering, Tsinghua University, Beijing, 100084, China.
  • Huang X; Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
  • Qiu J; The Chinese University of Hongkong, Shenzhen, Guangdong, 518172, China.
  • Wang H; Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
  • Cui D; Product and Solution and Website Business Unit, Alibaba Cloud, Hangzhou, Zhejiang, 311121, China.
  • Zhu B; The Chinese University of Hongkong, Shenzhen, Guangdong, 518172, China.
  • Deng Z; The Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, Guangdong, 518172, China.
  • Ke P; Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
  • Shan Y; Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
  • Boucher O; Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
  • Dannet G; Department of Earth System Science, Tsinghua University, Beijing, 100084, China.
  • Liang G; School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, B15 2TT, UK.
  • Zhao J; Institute Pierre-Simon Laplace, Sorbonne Université/CNRS, Paris, France.
  • Chen L; Institute Pierre-Simon Laplace, Sorbonne Université/CNRS, Paris, France.
  • Zhang Q; The Chinese University of Hongkong, Shenzhen, Guangdong, 518172, China.
  • Ciais P; The Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, Guangdong, 518172, China.
  • Zhou W; The Chinese University of Hongkong, Shenzhen, Guangdong, 518172, China.
  • Liu Z; The Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen, Guangdong, 518172, China.
Sci Data ; 9(1): 684, 2022 Nov 10.
Article en En | MEDLINE | ID: mdl-36357411
ABSTRACT
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.
Asunto(s)

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Dióxido de Carbono / Combustibles Fósiles País/Región como asunto: Asia Idioma: En Revista: Sci Data Año: 2022 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Dióxido de Carbono / Combustibles Fósiles País/Región como asunto: Asia Idioma: En Revista: Sci Data Año: 2022 Tipo del documento: Article