Abstract
This study downscales the population and gross domestic product (GDP) scenarios given under Shared Socioeconomic Pathways (SSPs) into 0.5-degree grids. Our downscale approach has the following features. (i) It explicitly considers spatial and socioeconomic interactions among cities, (ii) it utilizes auxiliary variables, including road network and land cover, (iii) it endogenously estimates the influence from each factor by a model ensemble approach, and (iv) it allows us to control urban shrinkage/dispersion depending on SSPs. It is confirmed that our downscaling results are consistent with scenario assumptions (e.g., concentration in SSP1 and dispersion in SSP3). Besides, while existing grid-level scenarios tend to have overly-smoothed population distributions in nonurban areas, ours does not suffer from the problem, and captures the difference in urban and nonurban areas in a more reasonable manner. Our gridded dataset, including population counts and gross productivities by 0.5 degree grids by 10 years, are available from http://www.cger.nies.go.jp/gcp/population-and-gdp.html.
Highlights
Socioeconomic scenarios are needed to project carbon dioxide (CO2) emissions, disaster risks, and other factors affecting sustainability from a long-term perspective
The results suggest that population increases rapidly in cities with dense road network and good access to airports
Since SSP1-3 concerns globalization, business as usual (BAU), and fragmentation scenarios, respectively, different levels of socioeconomic interactions are assumed in each scenario
Summary
Socioeconomic scenarios are needed to project carbon dioxide (CO2) emissions, disaster risks, and other factors affecting sustainability from a long-term perspective. Regionalized spatially fine scenarios have been developed in the USA by [12,13,14], in Japan by [15], and in the Mediterranean costal area by [16] McKee et al.’s study of [13] is an exception, as it considers land use data, road network data, and so on Their target area is limited to the USA. The objective of this study is downscaling the country-level SSP1-3 scenarios into 0.5-degree grids while overcoming the two above limitations. [10] already published gridded SSP population scenarios, they apply a simple approach ignoring auxiliary variables. Our study considering (i) and (ii) would be beneficial to develop more sophisticated gridded scenarios
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