Hotspots of disagreement across global urban land projections until 2100

Abstract

Projections of future urban land change are essential for a range of sustainability assessments, including those related to biodiversity loss, carbon emissions, and agricultural land conversion. However, to what extent and where current projections agree or disagree remains unknown. Here, we systematically compare existing global projections that are consistent with the Shared Socioeconomic Pathways. We find that the total global urban land area is expected to increase by 112% between 2020 and 2100 (averaged across all projections), with a coefficient of variation of 0.81. This variation is mostly caused by the selection of the underlying drivers that are included in the different models. Regionally, the highest average growth rates are found in sub-Saharan Africa (+679% to +730%), while this region also has the highest variation across projections (coefficient of variation ranging from 2.02 to 2.18). When ranking scenarios within a study from the highest to the lowest projected increase in urban land, rankings are relatively similar for regions in the Global North, but not for regions in the Global South. The large disagreement across projections can lead to high uncertainties in assessments of future urban land change impacts, which can undermine the effectiveness of long-term planning, policymaking, and resource management decisions.

Keywords

Global / Urban / Built-up / Projections / SSPs / Model comparison

Data availability

All projections were derived from the published studies or, when not directly available, provided by the corresponding authors of these studies. The results of our analyses as well as the STATA script for the dominance analysis are available from doi: 10.34894/ZZUNXV.

CRediT authorship contribution statement

Jasper van Vliet: Writing - original draft, Methodology, Investigation, Formal analysis, Conceptualization. Hang Yang: Writing - review & editing, Formal analysis, Data curation. Nathalie Benz: Writing - review & editing, Data curation. Changxiu Cheng: Writing - review & editing, Resources. Jonathan Doelman: Writing - review & editing, Resources. Jing Gao: Writing - review & editing, Resources. Qingxu Huang: Writing - review & editing, Resources. Eric Koomen: Writing - review & editing, Resources. Xuecao Li: Writing - review & editing, Resources. Lu Niu: Writing - review & editing, Methodology, Formal analysis. Elizabeth A. Schrammeijer: Writing - review & editing, Resources. Yuyu Zhou: Writing - review & editing, Resources.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

JvV was supported by the Netherlands Organization for Scientific Research NWO in the form of a VIDI grant (Grant No. VI.Vidi.198.008). We thank two anonymous reviewers for their helpful suggestions.

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.geosus.2025.100403.

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