Multi-objective land use optimization based on integrated NSGA–II–PLUS model: Comprehensive consideration of economic development and ecosystem services value enhancement

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Urbanization and industrialization have resulted in an excessive intensity of land use, structural imbalances, and regional ecological deterioration. To achieve sustainable development, it is essential to optimize different land use types and enhance the ecosystem service value (ESV). However, previous studies rarely combined the optimization of land use quantitative structure with the simulation of land use spatial layout under the background of maximizing economic and ecological benefits at the same time, and this kind of research was rarely applied to state-level new areas. On the premise of maximizing economic and ecological benefits, this study develops an improved optimization method that integrates the non-dominated sorting genetic algorithm II (NSGA-II) with the patch generating land use simulation (PLUS) model. The approach optimizes land use while predicting ESV for a state-level new area-Liangjiang New Area (LJNA). The model operation takes into full account ecological spatial constraints, including ecological protection red line, prime farmland, and urban development land use suitability. Results show that LJNA encountered the most substantial increase in built-up land (392.31 km2) due to farmland occupation, leading to a continuous decrease in ESV (443.6 million yuan) from 2000 to 2020. Optimization results for 2030 indicate that the main difference between different scenarios is the area of forest and built-up land. Under ecological protection priority scenario, ESV will increase in 2030 but built-up land should be confined within 620 km2. We present multiple land use scenarios based on decision makers’ preferences, offering important implications for the balance and optimization of land resources under different policy priorities using coupled spatiotemporal simulation-quantitative allocation model. This study provides valuable references for future land use optimization research.