A System of Systems for the Selection of Optimal Climate Change Decisions

Climate change mitigation is crucial for the health and sustainability of our planet. This paper presents a novel system utilizing the Model Predictive Control technique (MPC) to optimize greenhouse gas emissions reductions. Through a receding horizon approach, the system dynamically adjusts carbon dioxide, methane, and other greenhouse gases emissions to maintain temperature anomalies within safe limits. The system operates in two phases: first, applying MPC to minimize temperature anomalies by optimizing emissions from 2025 to 2100; second, embedding an artificial neural network trained to capture the relationship between policy decisions and greenhouse gas emissions into an optimization process that identifies the policy set required to achieve a desired emissions pathway. The proposed two-phase approach successfully constrains temperature anomalies near the 1.5 degrees C threshold by 2100, limits mid-century overshoot, and computes optimal emission pathways that can be translated into actionable policy measures. Consequently, the presented tool supports policymakers by providing data-driven, scientifically validated decisions, integrating advanced control strategies and neural network modeling to offer a comprehensive solution for sustainable climate change mitigation.