Preprints
https://doi.org/10.5194/essd-2021-10
https://doi.org/10.5194/essd-2021-10

  26 Jan 2021

26 Jan 2021

Review status: this preprint was under review for the journal ESSD but the revision was not accepted.

Spatially explicit global gross domestic product (GDP) data set consistent with the Shared Socioeconomic Pathways

Tingting Wang1 and Fubao Sun1,2,3,4 Tingting Wang and Fubao Sun
  • 1Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China
  • 2State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, China
  • 3Akesu National Station of Observation and Research for Oasis Agro-ecosystem, Akesu, China
  • 4College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China

Abstract. The increasing demand of ScenarioMIP is calling for GDP projections of high resolution for the future Shared Socioeconomic Pathways (SSPs) in both socioeconomic development and in climate change of adaption and mitigation research. While to date the global GDP projections for five SSPs are mainly provided at national scales, and the gridded data set are very limited. Meanwhile, the historical GDP can be disaggregated using nighttime light (NTL) images but the results are not open accessed, making it cumbersome in climate change impact and socioeconomic risk assessments across research disciplines. To this end, we produce a set of spatially explicit global Gross Domestic Product (GDP) that presents substantial long-term changes of economic activities for both historical period (2005 as representative) and for future projections under all five SSPs with a spatial resolution of 30 arc-seconds. Chinese population in SSP database were first replaced by the projections under the two-children policy implemented since 2016 and then used to spatialize global GDP using NTL images and gridded population together as fixed base map, which outperformed at subnational scales. The GDP data are consistent with projections from the SSPs and are freely available at http://doi.org/10.5281/zenodo.4350027 (Wang and Sun, 2020). We also provide another set of spatially explicit GDP using the global LandScan population as fixed base map, which is recommended at county or even smaller scales where NTL images are limited. Our results highlight the necessity and availability of using gridded GDP projections with high resolution for scenario-based climate change research and socioeconomic development that are consistent with all five SSPs.

Tingting Wang and Fubao Sun

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-10', Anonymous Referee #1, 02 Mar 2021
    • AC1: 'Reply on RC1', Tingting Wang, 27 May 2021
  • RC2: 'Comment on essd-2021-10', Anonymous Referee #2, 21 Apr 2021
    • AC2: 'Reply on RC2', Tingting Wang, 27 May 2021

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2021-10', Anonymous Referee #1, 02 Mar 2021
    • AC1: 'Reply on RC1', Tingting Wang, 27 May 2021
  • RC2: 'Comment on essd-2021-10', Anonymous Referee #2, 21 Apr 2021
    • AC2: 'Reply on RC2', Tingting Wang, 27 May 2021

Tingting Wang and Fubao Sun

Tingting Wang and Fubao Sun

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Short summary
We produce a set of spatially explicit global GDP with consideration of two-children policy in China that presents substantial long-term changes for both historical period and for future projections under five SSPs to face the increasing demand of ScenarioMIP of high resolution for future socioeconomic development and climate change of adaption and mitigation research.