Carbon emission prediction of 275 cities in China considering artificial intelligence effects and feature interaction: A heterogeneous deep learning modeling framework

Abstract

High technology and artificial intelligence (AI) are crucial for achieving urban Dual Carbon Goals. This study proposes a heterogeneous deep learning framework with analysis and prediction phases to explore AI technology's impact on urban carbon emissions. In the analysis phase, fixed effect models address differences in AI development and time heterogeneity among cities. In the prediction phase, an Attention Deep & Cross Network (ADCN) model leveraging feature interactions is proposed to enhance prediction precision and robustness. The Shapley Additive Explanations (SHAP) method quantifies each feature's contribution to ADCN's predictions, elucidating factors' impacts on carbon emissions. This study investigates AI development levels and other variables across 275 Chinese cities to test model performance and uncover the AI-carbon emissions relationship. Results show that fixed effects models significantly improve prediction accuracy, with ADCN outperforming statistical and machine learning models (RMSE: 646.262, MAE: 474.818, R²: 0.993). SHAP analysis reveals that AI technology level (11.85 %), smart city (12.35 %), energy consumption (11.60 %), population (9.38 %), urbanization rate (8.89 %), and GDP (8.40 %) significantly influence carbon emissions. Especially, the interaction between AI technology and smart city or intelligent manufacturing proportion increases their carbon reduction by 1.059 × 10²¹ or 4.992 × 10¹⁹ tons. AI technology moderates the impact of increasing energy consumption and urbanization, reducing their potential emissions by 20 % and 1 %. The framework offers high accuracy and scalability, providing valuable insights for strategy development.