Gridded GDP Projections Compatible With the Five SSPs (Shared Socioeconomic Pathways)

Daisuke Murakami, Takahiro Yoshida, Yoshiki Yamagata

Research output: Contribution to journalArticlepeer-review

30 Citations (Scopus)


Historical and future spatially explicit population and gross domestic product (GDP) data are essential for the analysis of future climate risks. Unlike population projections that are generally available, GDP projections—particularly for scenarios compatible with shared socioeconomic pathways (SSPs)—are limited. Our objective is to perform a high-resolution and long-term GDP estimation under SSPs utilizing a wide variety of geographic auxiliary information. We estimated the GDP in a 1/12-degree grid scale. The estimation is done through downscaling of historical GDP data for 1850–2010 and SSP future scenario data for 2010–2100. In the downscaling, we first modeled the spatial and economic interactions among cities and projected different future urban growth patterns according to the SSPs. Subsequently, the projected patterns and other auxiliary geographic data were used to estimate the gridded GDP distributions. Finally, the GDP projections were visualized via three-dimensional mapping to enhance the clarity for multiple stakeholders. Our results suggest that the spatial pattern of urban and peri-urban GDP depends considerably on the SSPs; the GDP of the existing major cities grew rapidly under SSP1, moderately grew under SSP 2 and SSP4, slowly grew under SSP3, and dispersed growth under SSP5.

Original languageEnglish
Article number760306
JournalFrontiers in Built Environment
Publication statusPublished - 2021 Oct 22


  • 1/12-degree grid scale
  • downscale
  • gross domestic product
  • shared socioeconomic pathways
  • spatial econometrics

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Building and Construction
  • Urban Studies


Dive into the research topics of 'Gridded GDP Projections Compatible With the Five SSPs (Shared Socioeconomic Pathways)'. Together they form a unique fingerprint.

Cite this