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A dataset to measure global climate physical risk.
Guo, Kun; Ji, Qiang; Zhang, Dayong.
Affiliation
  • Guo K; School of Economics and Management, University of Chinese Academy of Sciences, Beijing 100190, China.
  • Ji Q; Research Center on Fictitious Economy & Data Science, Chinese Academy of Sciences, Beijing 100190, China.
  • Zhang D; Institutes of Science and Development, Chinese Academy of Sciences, Beijing 100190, China.
Data Brief ; 54: 110502, 2024 Jun.
Article de En | MEDLINE | ID: mdl-38774240
ABSTRACT
Extreme climate events have become more frequent and have had serious impacts on the global community. Consequently, the risk associated with climate change has gained increasing attention and has been considered as a new source of risk factors. To understand the socio-economic impacts of this new risk, systematically measuring risk around the world is critical for researchers and policymakers. Building on daily observations from meteorological stations, a Climate Physical Risk Index (CPRI) dataset is constructed for 170 countries, paying special attention to four extreme climate events extreme low temperature (LTD), extreme high temperature (HTD), extreme rainfall (ERD), and extreme drought (EDD). A comprehensive index of climate physical risk for each country has also been constructed, covering the period from 1993 to 2023. The dataset will be updated regularly. Subnational indices or more detailed regional indices are available upon request.
Mots clés

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Langue: En Journal: Data Brief Année: 2024 Type de document: Article Pays d'affiliation: Chine Pays de publication: HOLANDA / HOLLAND / NETHERLANDS / NL / PAISES BAJOS / THE NETHERLANDS

Texte intégral: 1 Collection: 01-internacional Base de données: MEDLINE Langue: En Journal: Data Brief Année: 2024 Type de document: Article Pays d'affiliation: Chine Pays de publication: HOLANDA / HOLLAND / NETHERLANDS / NL / PAISES BAJOS / THE NETHERLANDS