An Explainable Comparative Study of Statistical, Machine Learning, Deep Learning, and Hybrid Models for CO2 Emissions Forecasting in Australia
- Publisher:
- Elsevier
- Publication Type:
- Journal Article
- Citation:
- Array, 2025, 29, pp. 100639
- Issue Date:
- 2025-12
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Accurate forecasting of national CO2 emissions is critical for evidence-based climate policy and for meeting commitments such as Australia’s 2050 net-zero target and the United Nations Sustainable Development Goal 13 (Climate Action). This study implements and evaluates thirteen forecasting approaches, including statistical models (ARIMA), machine learning methods (random forest, XGBoost, SVR), kernel methods (GPR), hybrid approaches (ELM, ISSA-ELM), deep learning networks (MLP, LSTM, GRU, RNN), and two ensemble models (stacking regressor and enhanced stacking regressor), using annual Australian data from 1982–2022 within a reproducible pipeline. Thirty random seeds ensured robustness for stochastic learners. Ensemble tree methods delivered the most accurate and stable predictions: Random Forest achieved mean cross-validation R2 ≈ 0.989 ± 0.003 and RMSE ≈ 0.018 ± 0.002 and generalized well to unseen 2016–2022 data (R2 ≈ 0.96; RMSE ≈ 2.43 Mt CO2). Pairwise significance testing confirmed that Random Forest and stacking significantly outperformed most individual learners (p < 0.01). SHAP analysis identified energy productivity, total GHG excluding land-use change, total energy consumption, and population as dominant drivers. Scenario experiments show that deterministic adjustments yield only modest 2050 reductions (−0.49% to −2.68%), with population shifts treated as exogenous sensitivities, underscoring the need for system-level action to achieve net-zero. Limitations include reliance on annual data and exclusion of policy and trade factors. Future work could extend this framework through causal inference and hybrid physics-informed machine learning. Building on global advances in emissions forecasting, this study contributes a localized, interpretable comparative framework tailored to Australia’s emissions profile, addressing a notable gap in national-level forecasting research. This transparent and reproducible approach provides evidence-based guidance for model selection and supports policy-relevant discussions on national CO2 forecasting.
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