Unveiling land use-carbon Nexus: Spatial matrix-enhanced neural network for predicting commercial and residential carbon emissions

Carbon emissions play a pivotal role in driving global warming, with commercial and residential sectors ranking as the third-largest source after industrial and transportation sectors. Consequently, establishing a framework for spatial characterizing and predicting carbon emissions from these sectors is vital for effective carbon reduction planning. This study proposes an optimized Backpropagation Neural Network, incorporating a Spatial Weight Matrix, to model the relationship between land use and carbon emissions, taking spatial effects into account. Additionally, an optimized Random Forest model with variable search step lengths is developed for large-scale and multi-class land use prediction. Beijing, China serves as the study site, yielding the following findings: (1) The optimized Backpropagation Neural Network outperforms traditional models, achieving goodness of fit of 0.999 and 0.997 in training and testing datasets, respectively. (2) The optimized Random Forest model, while reducing prediction precision for ecological areas (Root Mean Square Error increases by 4.12 %), enhances overall model performance by 73.95 % in a single iteration. (3) By 2025, commercial and residential carbon emissions in Beijing are projected to reach 8.91 million tons. (4) The study characterizes the spatial patterns of commercial and residential carbon emissions at a 1 km resolution for the next 15 years, revealing statistical features. (5) Expansion of mixed commercial and residential land relies on service-oriented development, positively impacting regional carbon emission intensity control.