A novel deep ensemble-based model with outlier removal and order-invariant ranking for carbon dioxide emission prediction.

Excessive carbon dioxide ( ) emissions pose a formidable challenge, driving global climate change and necessitating urgent attention. Striking a balance between curbing emissions and fostering economic growth hinges upon the ability to reliably forecast emissions. Such forecasts are indispensable for policymakers as they endeavor to make informed decisions and proactively implement mitigation measures. In this research, we introduce an innovative deep ensemble prediction model for emissions. This model is constructed around four parallel Long Short-Term Memory (LSTM) neural networks, complemented by a novel Multi-Layer Perception (MLP)-based ensemble framework, equipped with an outlier detection mechanism and an order-invariant ranking module. To enhance prediction accuracy and stability, a k-nearest neighbor (KNN)-based outlier detection module is employed to identify non-outliers and reasonable predictions for the ensemble models. Additionally, a novel feature ranking module is proposed to mitigate prediction fluctuations. The performance evaluation of our model is conducted using historical emission data spanning from 1971 to 2021, encompassing six representative countries. Our findings demonstrate that the proposed methodology outperforms existing approaches across various evaluation metrics, offering considerably reduced prediction variances and greater stability. Moreover, long-term emission predictions for the corresponding six countries have been provided, which might offer policymakers some basis for making decisions.