Toward Net-Zero Emissions: The Role of Smart City Technologies in Reducing Carbon Emissions in China

This paper examines how smart city technologies can help promote sustainability in China by cutting energy use and carbon footprint, as well as how smart city technologies can help achieve urban sustainability. With the help of Random Forest Regression (RFR), Extreme Gradient Boosting (XGBoost) approaches to machine learning (ML), Long Short-Term Memory (LSTM), graph neural networks (GNNs) and SHapley Additive exPlanations (SHAP) value analysis, we have predicted urban energy consumption and have revealed the most powerful emission drivers. The findings indicate that smart grids could decrease energy use by 15 percent and renewable energy integration decreases per capita emissions by about 12 percent. The predictive model’s outstanding performance (R2 = 0.996; RMSE = 13.63) confirms the reliability of the predictions. The major contributors to emissions, based on the SHAP analysis, are water heating and urban central heating systems, highlighting the critical significance of upgrading heating systems. Monte Carlo simulations and sensitivity analysis also illustrate that the possibility of optimization of heating infrastructure has the most significant potential of reducing the emissions. These results show that although renewable energy is needed, it is impossible to achieve a high level of de-carbonization without implementing ML-based prediction, smart grids, and building improvements on an integrated basis as part of urban development approaches.