Performance Comparison of Deep Learning Models for CO2 Prediction: Analyzing Carbon Footprint with Advanced Trackers

Climate change and global warming demand immediate global action to protect future generations. Although deep learning is crucial for solving complex tasks, its high energy consumption and carbon emissions from extensive training processes necessitate improvements in efficiency to reduce its environmental impact. This research addresses the need for accurate carbon emission tracking to mitigate climate change and promote sustainable development. We evaluated deep learning models—specifically, the Temporal Fusion Transformer (TFT), Attention-based LSTM (Att-LSTM), and standard LSTM—in predicting CO2 emissions using datasets from World Bank Indicators (WBI) and Our World in Data (OWID). The results highlight TFT’s superiority, achieving the lowest mean squared error (MSE) in validation (0.0029) compared to Att-LSTM (0.0070) and LSTM (0.0072). Additionally, TFT demonstrates a lower root mean squared error (RMSE) in validation (0.0539) than both Att-LSTM (0.0712) and LSTM (0.0909), showcasing its enhanced predictive accuracy across evaluation tools like CarbonTracker, eco2AI, and CodeCarbon. Furthermore, TFT consistently outperforms LSTM in computational efficiency and carbon emissions, despite LSTM’s lower power usage but higher emissions in specific scenarios. These findings underscore TFT's pivotal role in formulating effective climate policies and promoting renewable energy sources, emphasizing its potential to significantly contribute to global warming mitigation and sustainability.