Future Scenarios of Global Urban Expansion and Carbon Emissions with National Heterogeneity: A Mixed-Effects Model Based on Urban Nighttime Lights

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 (SSP1–2.6, SSP2–4.5, SSP3–6.0, SSP4–6.0, and SSP5–8.5) 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. The results show that the sensitivity of urban expansion to GDP and population growth varies across countries, leading to diverse urban expansion trajectories. Nonetheless, urban areas are projected to increase under all scenarios. Meanwhile, improvements in emission efficiency under the SSP-RCP scenarios are expected to curb future emission trajectories. This study enhances urban scenario modeling and contributes to a better understanding of regional differences in global urban growth and CO2 emissions.

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Publication Details

Journal
Remote Sensing
Published
2025-09-20
DOI
https://doi.org/10.3390/rs17183251
Citations
1
Primary Topic
Impact of Light on Environment and Health
Type
article
Field-Weighted Citation Impact
1.11

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article

Future Scenarios of Global Urban Expansion and Carbon Emissions with National Heterogeneity: A Mixed-Effects Model Based on Urban Nighttime Lights

Chun-Chen Chou, Junrui Xu, Masanobu Kii, Yasunori Okano
1 citations
Remote Sensing
Impact of Light on Environment and Health
1.11
article

Future Scenarios of Global Urban Expansion and Carbon Emissions with National Heterogeneity: A Mixed-Effects Model Based on Urban Nighttime Lights

Chun-Chen Chou, Junrui Xu, Masanobu Kii, Yasunori Okano
article en
1 citations

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 (SSP1–2.6, SSP2–4.5, SSP3–6.0, SSP4–6.0, and SSP5–8.5) 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. The results show that the sensitivity of urban expansion to GDP and population growth varies across countries, leading to diverse urban expansion trajectories. Nonetheless, urban areas are projected to increase under all scenarios. Meanwhile, improvements in emission efficiency under the SSP-RCP scenarios are expected to curb future emission trajectories. This study enhances urban scenario modeling and contributes to a better understanding of regional differences in global urban growth and CO2 emissions.

Remote SensingVol. 17(18)
The University of Osaka (JP)
Japan Science and Technology Agency, Strategic International Collaborative Research Program
Openalex Percentile: Top 19%
Impact of Light on Environment and Health
1.11
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Future Scenarios of Global Urban Expansion and Carbon Emissions with National Heterogeneity: A Mixed-Effects Model Based on Urban Nighttime Lights — Chun-Chen Chou, Junrui Xu, et al. · Remote Sensing (2025) | TGRS Research Map | TGRS