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Exploring long-run CO2 emission patterns and the environmental kuznets curve with machine learning methods

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  • Han, Thi Thanh Tam
  • Lin, Ching-Yang

Abstract

This study utilizes machine learning methods to forecast long-term CO2 emission trends and employs interpretable machine learning techniques to reexamine the Environmental Kuznets Curve (EKC) hypothesis. All methods we experimented with, including Random Forest, XGBoost, Support Vector Regression, and Artificial Neural Networks (ANN), demonstrated remarkable forecasting power, with ANN outperforming all others. Using SHapley Additive exPlanations (SHAP), we identified an EKC relationship within the ANN model, consistent with linear regression findings. Moreover, long-run forecasts for the next decade indicate that CO2 emissions growth in high-income economies tends to approach zero. In contrast, low-income countries may maintain relatively high emissions, but such predictions exhibit uncertainty due to significant discrepancies among the algorithms.

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  • Han, Thi Thanh Tam & Lin, Ching-Yang, 2025. "Exploring long-run CO2 emission patterns and the environmental kuznets curve with machine learning methods," Innovation and Green Development, Elsevier, vol. 4(1).
    • Handle: RePEc:eee:ingrde:v:4:y:2025:i:1:s2949753124000729
    DOI: 10.1016/j.igd.2024.100195
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    Keywords

    Machine learning; SHAP; EKC; CO2;
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