Network Algorithm-Based Implications of Nuclear Energy Capacity Expansion for Climate Mitigation

This study aims to develop an advanced, AI-driven System Dynamics model utilizing the Internet of Everything (IoE) to evaluate the impact of tripling nuclear energy capacity on climate change mitigation in alignment with the 28th Conference of the Parties (COP28) objectives. The Internet of Things (IoT), a network of interconnected devices, has been employed for computer system communication. The IoE extends this concept to encompass a more interconnected world, integrating processes, data, objects, and individuals. A dynamic simulation of 100 months was conducted, examining the interactions of Things, People, Data, and Processes. While the classification of Things increased steadily, the People category exhibited two distinct peaks at the 30th and 55th months, with false positives (FP) outnumbering Ttue positives (TP), false negatives (FN), and true negatives (TN). FN and TN demonstrated comparable trends. The Data category increased gradually across all classifications, while Processes declined with sporadic drops. A comparison of Recall (RC) and Precision (PC) shows consistently higher RC values, with People achieving the highest overall. These findings offer actionable insights for large-scale IoT and IoE systems in energy, manufacturing, transportation, and smart cities. Trends like steady growth in Things and Data, human-system volatility, and the importance of recalibration help organizations optimize system design, reduce errors, and improve smart infrastructure adaptability.