A Review of Current Trends in Greenhouse Gas Emission Prediction through Machine Intelligence Learning Techniques and Future Challenges

Increased GHG emissions from human activities have severe environmental and health impacts, making accurate predictions essential for effective mitigation strategies. This review explores various models for GHG emission prediction, focusing on ML DL techniques. It examines the contributions of sectors such as transportation, agriculture, and energy production, and discusses challenges in estimating emissions from sources like soils, livestock, and hydroelectric power. Prediction accuracy has significantly increased thanks to developments in ML and DL algorithms, such as RNNs, CNNs, and artificial neural networks (ANNs). Case studies highlight the effectiveness of these models in capturing complex emission patterns and informing emission reduction decisions. Integrating ML and DL with traditional environmental data analysis offers a comprehensive approach to GHG emission prediction, supporting sustainable development and climate change mitigation. Despite significant progress, further research is needed to enhance model interpretability, data integration, and real-time prediction capabilities to address the dynamic nature of GHG emissions and their global impact.