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ClimActor, harmonized transnational data on climate network participation by city and regional governments.
Hsu, Angel; Yeo, Zhi Yi; Rauber, Ross; Sun, James; Kim, Yunsoo; Raghavan, Sowmya; Chin, Nicholas; Namdeo, Vasu; Weinfurter, Amy.
Afiliación
  • Hsu A; Yale-NUS College, 16 College Ave W, Singapore, 138609, Singapore. angel.hsu@yale-nus.edu.sg.
  • Yeo ZY; Yale-NUS College, 16 College Ave W, Singapore, 138609, Singapore.
  • Rauber R; University of Chicago, Department of Computer Science, 5730 S. Ellis Ave, Chicago, IL, 60637, USA.
  • Sun J; Yale College, Department of Statistics and Data Science, 24 Hillhouse Ave, New Haven, CT, 06511, USA.
  • Kim Y; Yale-NUS College, 16 College Ave W, Singapore, 138609, Singapore.
  • Raghavan S; Yale-NUS College, 16 College Ave W, Singapore, 138609, Singapore.
  • Chin N; Yale-NUS College, 16 College Ave W, Singapore, 138609, Singapore.
  • Namdeo V; Yale-NUS College, 16 College Ave W, Singapore, 138609, Singapore.
  • Weinfurter A; Yale-NUS College, 16 College Ave W, Singapore, 138609, Singapore.
Sci Data ; 7(1): 374, 2020 11 06.
Article en En | MEDLINE | ID: mdl-33159088
ABSTRACT
Cities and regions have become increasingly engaged in global climate change governance. They are pledging their own climate mitigation targets and participating in membership networks that typically are transnational in nature and engage thousands of subnational governments. Researching these growing trends in participation has been difficult due to the disparate and inconsistent nature of this self-reported data. To facilitate future analyses of these actors, we introduce ClimActor, the largest harmonized global dataset of more than 10,000 city and regional governments participating in networks like the Global Covenant of Mayors for Climate and Energy, C40 Cities for Climate Leadership, ICLEI Local Leaders for Sustainability, among others. We include key contextual information on each actor's population, geographic location, and administrative jurisdiction to facilitate disambiguation of potential overlaps in actions or emissions. We also provide a series of cleaning functions based on phonetic and fuzzy string matching algorithms within an open-source R package to make it easy for anyone to immediately use the ClimActor dataset with other relevant data.

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Data Año: 2020 Tipo del documento: Article País de afiliación: Singapur

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: Sci Data Año: 2020 Tipo del documento: Article País de afiliación: Singapur
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