Estimation of gridded population and GDP scenarios with spatially explicit statistical downscaling

TL;DR

This paper presents a spatially explicit statistical downscaling method for projecting population and GDP scenarios at a 0.5-degree grid level, considering urban dynamics and auxiliary geographic data.

Contribution

It introduces a novel downscaling approach that explicitly models spatial and socioeconomic interactions, utilizing auxiliary variables and a model ensemble to improve scenario accuracy.

Findings

Results align with SSP assumptions, showing concentration and dispersion patterns.

The method avoids over-smoothing in non-urban areas, capturing urban-rural differences more realistically.

Downscaled scenarios are consistent with scenario assumptions and improve spatial detail.

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 influence from each factor by a model ensemble approach; (iv) it allows us controlling 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 scenario tends to have overly-smoothed population distributions in non-urban areas, ours does not suffer from the problem, and captures difference in urban and non-urban areas in a more reasonable manner.