Integrating communication networks with reinforcement learning and big data analytics for optimizing carbon capture and utilization strategies

In recent years, the escalating impact of climate change has brought increasing attention to carbon-neutral strategies as a critical component of global environmental protection efforts. These strategies demand a comprehensive understanding of carbon emissions, which are influenced by a myriad of factors, including external conditions like seasonality and weather, as well as internal dynamics such as production and energy consumption. However, existing approaches often fail to account for these complex, dynamic interactions, resulting in suboptimal outcomes. To address these challenges, we propose an integrated model combining Autoformer, Deep Q-Network (DQN), and Deep Forest. This model is designed to dynamically respond to environmental changes using advanced time-series forecasting, adaptive decision-making, and robust feature extraction. Extensive experiments across multiple datasets reveal that our model significantly enhances carbon capture efficiency and accuracy, outperforming conventional methods. By providing a scalable and intelligent solution for carbon capture and utilization, this research not only supports the advancement of carbon-neutral strategies but also contributes to the broader goals of sustainable development and climate change mitigation.