Accounting for internal migration in spatial population projections—a gravity-based modeling approach using the Shared Socioeconomic Pathways

Lena Reimann,Athanasios T Vafeidis,Bryan Jones,Theodore Nikoletopoulos

doi:10.1088/1748-9326/ac0b66

Abstract

Gridded population projections constitute an essential input for climate change impacts, adaptation, and vulnerability (IAV) assessments as they allow for exploring how future changes in the spatial distribution of population drive climate change impacts. We develop such spatial population projections, using a gravity-based modeling approach that accounts for rural-urban and inland-coastal migration as well as for spatial development patterns (i.e. urban sprawl). We calibrate the model (called CONCLUDE) to the socioeconomically diverse Mediterranean region, additionally considering differences in socioeconomic development in two geographical regions: the northern Mediterranean and the southern and eastern Mediterranean. We produce high-resolution population projections (approximately 1 km) for 2020–2100 that are consistent with the Shared Socioeconomic Pathways (SSPs), both in terms of qualitative narrative assumptions as well as national-level projections. We find that future spatial population patterns differ considerably under all SSPs, with four to eight times higher urban population densities and three to 16 times higher coastal populations in southern and eastern Mediterranean countries compared to northern Mediterranean countries in 2100. In the South and East, the highest urban density (8000 people km−2) and coastal population (107 million) are projected under SSP3, while in the North, the highest urban density (1500 people km−2) is projected under SSP1 and the highest coastal population (15.2 million) under SSP5. As these projections account for internal migration processes and spatial development patterns, they can provide new insights in a wide range of IAV assessments. Furthermore, CONCLUDE can be extended to other continental or global scales due to its modest data requirements based on freely available global datasets.

Highlights

The future impacts of climate change will be driven by physical changes in climatic conditions as well as by changes in socioeconomic development (Field et al 2014)
These spatial population patterns reflect the national-level population (Kc and Lutz 2017) and urbanization projections (Jiang and O’Neill 2017) (SM8) as well as the qualitative assumptions regarding spatial development patterns described in each Shared Socioeconomic Pathways (SSPs) (O’Neill et al 2017)
SSP5 is characterized by urbanization levels similar to those in SSP1, combined with rapid population growth in northern countries and the Middle East, which leads to considerable urban sprawl, in northern parts of the region

Summary

Introduction

The future impacts of climate change will be driven by physical changes in climatic conditions as well as by changes in socioeconomic development (Field et al 2014). Recent studies found that socioeconomic development can be the dominant factor in driving impacts, in particular in the first half of the 21st century when climatic changes still take place at a slower pace (Marsha et al 2018, Rohat et al 2019c) and in regions with rapid population growth (Brown et al 2018, Jones et al 2018, Monaghan et al 2018, Rohat et al 2019a). To assess future impacts in a comprehensive manner, it is important to explore the range of uncertainty regarding changes in socioeconomic conditions in locations that are exposed to climate hazards (Moss et al 2010, Ebi et al 2014). Five global-scale SSPs describe plausible alternative trends in socioeconomic development in the course of the 21st century based on societal challenges to climate change mitigation and adaptation. Each SSP has an underlying narrative that describes the socioeconomic developments of the SSP in qualitative terms (O’Neill et al 2017; SM1); the narratives have been quantified to produce national-level projections of key variables in impacts, adaptation, and vulnerability (IAV) research (Van Ruijven et al 2014) such as population (Kc and Lutz 2017), urbanization (Jiang and O’Neill 2017), and gross domestic product (Crespo Cuaresma 2017, Dellink et al 2017, Leimbach et al 2017)

Methods

Results

Discussion

Conclusion