Abstract

This study is conducted in response to the increasingly prominent climate crisis in contemporary society. It aims to contribute to the growing body of research on the application of deep learning (DL) in environmental sciences and to provide practical guidance for model selection in similar predictive tasks. To this end, the study focuses on carbon dioxide (CO2) emissions prediction, employing Multilayer Perceptron (MLP) models to analyze multi-country panel data. By integrating MLP with explainable artificial intelligence (XAI) techniques, this research not only investigates the underlying mechanisms of various factors influencing CO2 emissions but also quantifies and visualizes the contribution of different driving factors to the prediction outcomes, providing decision support for climate governance strategies. Through an analysis of global panel data, we construct a model incorporating 14 driving factors spanning multiple dimensions, including economic, social, environmental, energy, and technology aspects. To optimize the MLP model, we employ a fivedimensional hyperparameter space comprising hidden layer structure, learning rate, batch size, dropout rate, and training epochs and apply Grid Search for parameter tuning. Experimental results indicate that the MLP model achieves R2 of 0.9951, demonstrating its strong capability in highprecision nonlinear fitting under complex policy scenarios. To further enhance the interpretability of neural networks in CO2 emissions prediction, we introduce SHapley Additive exPlanations (SHAP) to quantify the marginal contributions of different driving factors. This analysis reveals that energy-related features play a dominant role in emission predictions, laying the foundation for scenario analysis and emission reduction policy evaluation. Furthermore, this study incorporates scenario analysis to simulate potential trajectories of CO2 emissions under different policy scenarios, providing a quantitative reference for future emission reduction strategies and environmental governance policies.