Innovative Climate Solutions: Simulating the Future with En-Roads

This study analyzes the climate change system using the En-ROADS tool to prioritize key variables and assess global policy effectiveness. Employing multiple linear regression, LASSO, Ridge, and Random Forest regression methods, the analysis found Random Forest to be the most reliable based on RMSE. The most impactful variables identified were Carbon Pricing, Methane and Other Gases, and Afforestation. Current policy analysis indicates a global focus on transitioning to renewable energy and fostering international cooperation. Despite the potential benefits, carbon pricing is not prioritized due to its economic implications, indicating that global climate change mitigation efforts are still in the early stages.