Multi-Scenario Simulation of Low-Carbon Land Use Using an Integrated NSGA-III–PLUS Framework in Coastal Urban Agglomerations

Rapid urban expansion poses growing challenges for balancing carbon emissions (CE), economic development, and ecological protection, particularly in coastal urban agglomerations. Although optimization–simulation approaches have been widely applied, explicit consideration of low-carbon objectives remains limited. To address this gap, this study proposes an integrated non-dominated sorting genetic algorithm III (NSGA-III)–patch-generating land use simulation (PLUS) framework that combines multi-objective optimization with spatially explicit land-use simulation. Using multi-temporal land-use datasets (2000–2020) from the Guangdong–Hong Kong–Macao Greater Bay Area (GBA), this research examined spatiotemporal land-use transitions and their co-evolution with CE, ecosystem services value (ESV), and GDP under five development scenarios. The results show that construction land expanded by 78% from 2000 to 2020, largely through cropland conversion, which pushed CE upward to 335.4 Mt. For 2030, the Low Carbon Emission scenario reduces CE by 11.8 Mt compared with the natural development scenario. The Balanced Development scenario maintains economic growth while limiting CE increases and stabilizing ESV. Spatially, scenario differences are limited in extent. Over 93% of areas remain unchanged, and variations are mainly concentrated in peri-urban corridors around the Guangzhou–Foshan core. Overall, the NSGA-III–PLUS framework provides a structured approach for coordinating carbon mitigation and land-use planning in rapidly urbanizing coastal areas.