Gross domestic product (GDP) downscaling: a global gridded dataset consistent with the Shared Socioeconomic Pathways

• Main Authors: Wang, Tingting, Sun, Fubao
• Format: Other/Unknown Material
• Language: unknown
• Published: Zenodo 2022
• Subjects: GDP; SSP; downscaling; Antarc*; Antarctica
• Online Access: https://doi.org/10.5281/zenodo.5880037

We developed and presenteda set of comparable spatially explicit global gridded gross domestic product (GDP) for both historical period (2005 as representative) and for future projections from 2030 to 2100 at a ten-year interval for all five SSPs. The DMSP-OLS nighttime light(NTL) images and the LandScan Global Population database were used to generate LitPop map, whichreduces the limitationsof saturation problemof using NTL images alone or theassumption of even GDP per capita within an administrative boundary of gridded data set in GDP disaggregation. We used the LitPop maps to disaggregate national GDP and over 800 provincial gross regionalproduct(GRP, in 2005 PPP USD) across the globe in 2005and to downscaled to a spatial resolution of 30 arc-seconds (~1 km at equator). National and supranational GDP growth rate projections in 2030-2100 under five SSPs were then downscaled to 1-km grids based on the LitPop approach, which usedNPP-VIIRS product as fixed NTL imagein 2015 and the population projections of 0.125 arc-degreee (Jones and O'Neill, 2016), which are downscaled to 1-km based onLandScan population distribution patternin 2015.We then upscaled this gridded GDP dataset to0.25 arc-degree and provided here. There are 41 tif files (2005 and2030 - 2100 at a ten-year interval for five SSPs) for each spatial resolution. The gridded GDP are distributed over land with value of zero filled in the Antarctica, oceans andsome desert or wilderness areas (non-illuminated and depopulated zones). The spatial extents are 60S - 90N and 180E - 180W in standard WGS84 coordinate system. For more details, please refer to thecorresponding article:Global gridded GDP data set consistent with the shared socioeconomic pathways by Wang and Sun (2022).