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This research aims to evaluate the effectiveness of different deep learning architectures in forecasting CO2 emissions.

January 6, 2026Open Access

Multivariate CO2 Emissions Forecasting Using Deep Neural Network Architectures

EAEman AlshehriImam Abdulrahman Bin Faisal University

Key Points

This research aims to evaluate the effectiveness of different deep learning architectures in forecasting CO2 emissions.
Assessed four deep learning architectures: LSTM, GRUs, CNN–LSTM, and DNNs.
Compared models using consistent training protocols and hyperparameters.
Analyzed performance across multiple prediction horizons.
LSTM networks excelled in capturing long-term temporal dependencies.
Architecture-specific performance degradation patterns were identified across forecasting horizons.
Empirical support for utilizing neural networks in climate modeling was provided.

Abstract

One major factor influencing the development of eco-friendly policies and the implementation of climate change mitigation strategies is the accurate projection of CO2 emissions. Traditional statistical models face significant limitations in capturing complex nonlinear interactions within high-dimensional emissions data. Advanced deep learning architectures offer new opportunities to overcome these computational challenges due to their strong pattern-recognition capabilities. This paper evaluates four distinct deep learning architectures for CO2 emissions forecasting: Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), Hybrid Convolutional–LSTM (CNN–LSTM) systems, and Dense Neural Networks (DNNs). A comprehensive comparison is conducted using consistent training protocols, hyperparameters, and performance metrics across five prediction horizons (1, 3, 6, 12, and 24 steps ahead) to reveal architecture-specific degradation patterns. Furthermore, analyzing emissions by category provides insight into the suitability of each architecture for varying levels of pattern complexity. LSTM-based models demonstrate particular strength in modeling long-term temporal dependencies, making them well-suited for integration into long-range environmental policy planning frameworks. Overall, this study provides empirical evidence supporting the use of neural networks in climate modeling and proposes criteria for selecting optimal architectures based on forecasting horizon and computational constraints.

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