AI-Powered Integrated Modeling of Urban Energy Systems: Co-Benefits for Climate Mitigation and Cancer Risk Reduction

The energy system, climate change, air pollution, and their associated effects on human health are intricately interconnected. This research introduces an innovative AI-powered integrated model that combines optimization techniques for energy systems with machine learning approaches, including XGBoost for PM 2 . 5 prediction and Prophet models for forecasting weather data and energy demand. This hybrid approach enhances predictive accuracy and enables a comprehensive evaluation of the co-benefits of mitigating carcinogenic impact, climate change, and economic outcomes in urban energy transitions. Findings suggest that, although effective economic management can reduce PM 2 . 5 concentrations to comply with national and EPA regulations, achieving WHO’s more stringent guideline of 5 μg/m 3 by 2045 necessitates a net-zero emissions target. The ambitious "Cheetah" scenario attains the most significant decrease in PM 2 . 5 , nearly eradicating seasonal variations and potentially decreasing cancer-related fatalities by 92%. Conversely, less ambitious scenarios such as “Caracal” and “Hyena” forecast only incremental advancements, failing to meet WHO criteria by 2050 and indicating ongoing health hazards. Moreover, AI-driven forecast analyses, particularly using Prophet models, underscore the imperative for robust actions to stabilize PM 2 . 5 throughout seasonal and high-emission scenarios. Although the Cheetah scenario incurs 60% higher costs compared to the Hyena Scenario, it not only achieves the net-zero emissions target but also reduces the carcinogenic impact to 14% of that in the Hyena scenario. • AI-powered modeling integrates energy, emissions, and health to assess co-benefits. • PM2.5 predictions using XGBoost model for high accuracy with traffic and meteorological data. • Cancer risk assessment based on PM2.5 concentrations across socio-economic conditions.