Carbon emission prediction in China considering new quality productive forces using a deep and cross learning modeling framework
Gongquan Zhang
New quality productive forces (NQPF), digital economy development, and artificial intelligence (AI) are increasingly discussed as potential drivers of sustainable urban development. This study develops a Multi-head Attention Deep and Cross Network (MADCN) framework to predict urban carbon emissions and examine the relative associations of technological factors with those emissions. The framework integrates feature-interaction modeling, attention mechanisms, and SHapley Additive exPlanations (SHA