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.
Highlights
The development of socioeconomic projection scenarios plays a key role in the assessment of climate change impact and socioeconomic risks for the coming decades (O’Neill et al, 2014; Wilbanks and Ebi, 2014)
The comparisons showed that the accuracy of three disaggregated Gross Domestic Product (GDP) decreases accompanied by the changes of their spatial scales, and GDPNTL-Pop is superior to global LandScan population only as base map (GDPPop) and GDPNTL at national, state, and county levels with clear advantages evaluated by their R2 and RMSE
The GDPLit-Pop is recommended for global, state and county scales disaggregation, and GDPPop can be used as an alternative and especially at county or even smaller scales where nighttime light (NTL) images are limited in very rural regions
Summary
The development of socioeconomic projection scenarios plays a key role in the assessment of climate change impact and socioeconomic risks for the coming decades (O’Neill et al, 2014; Wilbanks and Ebi, 2014). More research on reduction in exposure and vulnerability and increase in resilience to climate extremes can benefit from a spatially explicit GDP data set with increasing precision of NTL image products and population count at grid level (Chen et al, 2017; Chen et al., 2020; Wang et al, 2019; Wilbanks and Ebi, 2014). The objective of this study is to present a set of spatially explicit global GDP that presents substantial long-term changes of GDP for both historical period (2005 as representative) and for future projections under all five SSPs by incorporating various data sources and methods. In the following were the inputs, assumptions, methodologies, and results that we use to spatialize GDP data into a fine-scale, providing an alternative choice for scenario-based climate change research and socioeconomic development pathways
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