Carbon Emission Trend Prediction and Green Sustainability Analysis Based on XGBoost Model

This paper focuses on the impact mechanism and trend prediction of AI on carbon emission efficiency in Chinese provinces and regions, and utilizes two-way fixed-effects model, panel threshold model, spatial Durbin model (SDM) and XGBoost machine learning model. The results show that there is significant regional heterogeneity in the promotion of AI on carbon emission efficiency, and there exists a dynamic evolution path of "single-double-three thresholds", which needs to cross the threshold to release the green dividend. Carbon emission efficiency shows a significant positive spatial correlation, and the indirect effect of AI technology spillover in geographically neighboring regions is 0.0379. The XGBoost model realizes high-precision prediction of carbon emission efficiency through data enhancement and parameter optimization, and Jiangsu and Beijing is expected to exceed 0.9 by 2027.The study provides a good basis for the study on the impact of regional differentiated emission reduction policies and development of the "dual carbon" target. The study provides theoretical and methodological support for the synergistic application of regional differentiated emission reduction policies and AI technology under the "dual-carbon" target.