Future Scenarios of Global Urban Expansion and Carbon Emissions with National Heterogeneity

Urban Nighttime Light (NTL) Data under SSP-RCP Scenarios (2017-2053)

Content

Use of the dataset and full description

Before using this dataset, please read the accompanying article describing the methodology, especially the "Discussion and limitations" section.

The article is available at: https://www.mdpi.com/2072-4292/17/18/3251

When using this dataset, please cite both the journal article and the dataset DOI:

Support

If you encounter possible errors or need support using this dataset, please contact [u003159e[at]ecs.osaka-u.ac.jp].

Abstract

Cities play a pivotal role in environmental transformation and climate change mitigation. Urban expansion has substantial impacts on socioeconomic development and carbon emissions. This study develops a predictive model for future urban expansion and CO₂ emissions based on nighttime light (NTL) data, under five SSP-RCP scenarios projected to 2053.

This study introduces three key improvements from previous literature: (1) a mixed-effects model to capture cross-national and regional differences in urban expansion patterns; (2) incorporation of grid-level random effects to reflect inter-city growth heterogeneity; and (3) integration of SSP-RCP scenarios to incorporate the influence of emission efficiency and socioeconomic policies. Using this improved framework, we estimate future urban expansion and carbon emissions for 555 global cities.

Files included in the dataset

The dataset contains predicted NTL images for 555 global cities under five SSP-RCP scenarios:

• SSP1-RCP3PD: A low-emission, sustainability-focused pathway featuring high equity, low population growth, and a shift to clean energy
• SSP2-RCP4.5: A "middle of the road" scenario with moderate challenges to mitigation and adaptation, reflecting historical development trends
• SSP3-RCP6.0: A scenario of regional rivalry with low international co-operation, high population growth, and slow technological development
• SSP4-RCP6.0: A highly unequal world with disparities in access to resources, where elites benefit from clean technologies while others are left behind
• SSP5-RCP8.5: A fossil fuel-intensive development pathway characterized by rapid economic growth, high energy demand, and high emissions

Folder Structure

The data is organized in the following directory structure on OUKA platform:

File Naming Convention

Each TIFF file follows this naming pattern:

Where:

• ISO3: 3-letter country code (ISO 3166-1 alpha-3)
• Index: 4-digit unique city identifier (padded with leading zeros)
• Urban.Agglomeration: Name of the urban agglomeration
• Year: Prediction year (2017, 2021, 2025, ..., 2053)

Data Specifications

• Format: GeoTIFF (.tif)
• Projection: WGS84 (EPSG:4326)
• Resolution: [X] arc-seconds (consistent with input NTL data)
• Value range: 0-300 (DN values clipped at 300)
• NoData value: [Specify if applicable]

Temporal Coverage

Each scenario includes predictions for the following years:

Data sources

This dataset was generated using the following input data:

• Historical NTL data from [source]
• SSP socioeconomic projections from [source]
• RCP emission scenarios from [source]
• GDP and population data from [source]

Changelog

Version 1.0 (2025-09-20): Initial release of the dataset

Data Access

The complete dataset is available on the OUKA platform: https://ir.library.osaka-u.ac.jp/repo/ouka/all/102574/

DOI: https://doi.org/10.60574/102574