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Global gridded GDP data set consistent with the shared socioeconomic pathways.

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This study introduces high-resolution global gridded Gross Domestic Product (GDP) projections, crucial for climate change research. The novel NTL-population approach enhances socioeconomic data accuracy for adaptation and mitigation strategies.

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Area of Science:

  • Climate Science
  • Socioeconomics
  • Geographic Information Systems (GIS)

Background:

  • Climate change adaptation and mitigation research requires high-resolution gridded Gross Domestic Product (GDP) data.
  • Existing global socioeconomic projections are often at the national level, and downscaling methods using nighttime light (NTL) or population data introduce uncertainties.
  • Accurate socioeconomic data is vital for understanding vulnerability, exposure, and resilience to climate extremes.

Purpose of the Study:

  • To develop a novel, high-resolution, spatially explicit global gridded GDP projection dataset.
  • To improve the accuracy of socioeconomic disaggregation for climate change research.
  • To provide essential data for assessing the impacts of climate change on economic activities.

Main Methods:

  • Utilized a nighttime light (NTL)-population-based approach for enhanced socioeconomic disaggregation.
  • Incorporated provincial Gross Regional Product (GRP) data for over 800 provinces, covering more than 60% of the global land surface and over 80% of global GDP in 2005.
  • Accounted for the two-child policy in China within the projections.

Main Results:

  • Generated the first comparable set of global gridded GDP projections with fine spatial resolutions (30 arc-seconds and 0.25 arc-degrees).
  • Provided projections for the historical period of 2005 and future intervals from 2030 to 2100 (10-year intervals).
  • Included projections under five Shared Socioeconomic Pathways (SSPs).

Conclusions:

  • The developed gridded GDP projection dataset significantly enhances the applicability of GDP data for research.
  • This dataset is essential for socioeconomic and climate change research, particularly in adaptation and mitigation efforts.
  • The improved spatial resolution and accuracy of the data will broaden its use in various scientific domains.