As urban expansion continues, the intensifying land surface temperature (LST) underscores the critical need for accurate predictions of future thermal environments. However, no study has investigated which method can most effectively and consistently predict the future LST. To address these gaps, our study employed four methods—the multiple linear regression (MLR), geographically weighted regression (GWR), random forest (RF), and artificial neural network (ANN) approach—to establish relationships between land use/cover and LST. Subsequently, we utilized these relationships established in 2006 to predict the LST for the years 2012 and 2018, validating these predictions against the observed data. Our results indicate that, in terms of fitting performance (R² and RMSE), the methods rank as follows: RF > GWR > ANN > MLR. However, in terms of temporal stability, we observed a significant variation in predictive accuracy, with MLR > GWR > RF > ANN for the years 2012 and 2018. The predictions using MLR indicate that the future LST in 2050, under the SSP2 and SSP5 scenarios, is expected to increase by 1.8 ± 1.4 K and 2.1 ± 1.6 K, respectively, compared to 2018. This study emphasizes the importance of the MLR method in predicting the future LST and provides potential instructions for future heat mitigation.

Reduction planning has been implemented to mitigate urban sprawl in Chinese megacities. However, few studies have focused on the spatial implementation of urban land consolidation, and tools to estimate and simulate urban land consolidation are still lacking. To address the critical gap in urban land consolidation modeling, this study develops a bidirectional cellular automata model as a novel geospatial tool for quantifying consolidation potential and simulating future construction land dynamics under reduction planning. Our framework uniquely integrates high-resolution (30 m) simulation of simultaneous construction land expansion and reduction, overcoming limitations of conventional unidirectional models like the Future Land Use Simulation (FLUS) system, with validation confirming better accuracy. Using Beijing as a case study, the model identifies hotspots for land consolidation and, based on neighborhood-scale land transformation probabilities, delineates the spatial distribution of expansion and reduction zones, highlighting priority areas for consolidation. This provides the first operational tool for evidence-based urban reduction planning and land consolidation, offering a transferable methodology for optimizing land use efficiency and curbing disorderly expansion in megacities globally.