Rizal Purnawan 

In this paper, we analyze historical greenhouse gas (GHG) emissions worldwide, leveraging a dataset provided by (Gütschow and Pflüger, 2023). Employing mathematical modeling, a formal theory is developed, describing the growth trajectories of GHG emissions over time. The theory introduces several key concepts, including historical upper bound emission (HUBE), historical peak emission (HPE), and others, pinpointing pivotal periods and expected growth rates. Additionally, machine learning algorithms and computer algebra systems are also utilized in the computational implementation of the theory, enhancing the robustness and efficiency. Our findings reveal valuable insights into historical GHG emissions and offer a novel approach to their analysis, bridging a gap between formal mathematics and environmental science.