Addressing the issues faced by climate change necessitates appropriate methodologies for evaluating climate policies, particularly when discussing long-term and real-world scenarios. While large language models (LLMs) have altered artificial intelligence, they ultimately fall short of connecting historical data with future estimates. We propose an agentic LLM system that would address this gap by considering and analyzing the probable outcomes of the user-specified climate policy inside the practical settings. Further, we propose using knowledge graphs to model the existing data about the impact of climate policies along with allowing our system to access the data about future climate predictions. Done this way, the model can peek into the past (previous policies) and the future (climate scenarios forecast), paving the way for agencies to evaluate and design strategies and plans for climate change more effectively.