ORBIT> Optimized Regulations and Behavior-driven Intelligent Technology for Tackling Co2 Emissions

[Ongoing Project] As global carbon emissions continue to rise, their environmental and health impacts require urgent attention. The transportation sector which are significant contributors to these emissions, pose complex challenges that traditional mitigation strategies often fail to address due to their limited ability to process real-world data and lack of interpretability. This paper introduces ORBIT (Optimized Regulation and Behavior-driven Intelligence Technology for Mitigating Carbon Emissions), an AI-driven framework designed to tackle carbon emissions. By combining the interpretability of symbolic reasoning with the adaptability of machine learning models, ORBIT creates a sus- tainable and actionable strategy for emissions management. The framework employs symbolic reasoning to model environmental regulations and emissions metrics, while machine learning algo- rithms predict, optimize, and simulate outcomes across various carbon-reduction scenarios. This integrated approach provides actionable insights for policy-making, urban planning, and indus- trial practices. In addition to its environmental benefits, ORBIT highlights the critical implications of emissions on human health, particularly in predicting pollutant exposure and mitigating related risks. Our findings show that this approach enhances interpretability, predictive accuracy, and transparency, helping stakeholders understand emissions patterns, assess health im- pacts, and align actions with sustainability and well-being goals. This research represents a crucial step in advancing AI-driven solutions for emissions management and bridging environmental and health-focused decision-making for a sustainable future.