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Machine learning approaches reveal highly heterogeneous air quality co-benefits of the energy transition.
Zhang, Da; Wang, Qingyi; Song, Shaojie; Chen, Simiao; Li, Mingwei; Shen, Lu; Zheng, Siqi; Cai, Bofeng; Wang, Shenhao; Zheng, Haotian.
Afiliação
  • Zhang D; Institute of Energy, Economy, and Environment, Tsinghua University, Beijing, China.
  • Wang Q; Joint Program on the Science and Policy of Global Change, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Song S; Department of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
  • Chen S; State Environmental Protection Key Laboratory of Urban Ambient Air Particulate Matter Pollution Prevention and Control & Tianjin Key Laboratory of Urban Transport Emission Research, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China.
  • Li M; CMA-NKU Cooperative Laboratory for Atmospheric Environment Health Research, Tianjin 300350, China.
  • Shen L; Harvard-China on Energy, Economy, and Environment, Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.
  • Zheng S; Heidelberg Institute of Global Health, Faculty of Medicine and University Hospital, Heidelberg University, Heidelberg, Germany.
  • Cai B; Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Wang S; Institute of Energy, Economy, and Environment, Tsinghua University, Beijing, China.
  • Zheng H; Center for Policy Research on Energy and the Environment, Princeton University, Princeton, NJ, USA.
iScience ; 26(9): 107652, 2023 Sep 15.
Article em En | MEDLINE | ID: mdl-37680462
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: IScience Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China País de publicação: Estados Unidos
Texto completo
Adicionar na Minha BVS
Imprimir
XML
PubMed Links
Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: IScience Ano de publicação: 2023 Tipo de documento: Article País de afiliação: China País de publicação: Estados Unidos