Beyond identification: ranking stable COz emission drivers in Malaysia via ML-Enhanced SDA for SDG 13
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Abstract
This study advances understanding of COz emission dynamics in Malaysia by integrating Structural Decomposition Analysis (SDA) with Machine Learning (ML) techniques to identify key emission drivers and evaluate their stability and predictability during 2010-2020. Using multi-year input-output tables and emissions data, an additive SDA framework decomposes changes in COz emissions into six effects: COz intensity, technology (production structure), household expenditure, investment, government expenditure, and exports. Four ML methods, namely Neural Networks with SHapley Additive exPlanations (SHAP), Lasso Regression, Elastic Net Regression, and Bayesian Regression, are applied to rank driver importance at aggregate and sectoral levels. Results show consistent findings across methods, with COz intensity emerging as the most influential and variable driver, followed by technology and household consumption. In contrast, government expenditure, investment, and exports contribute relatively little to emission changes. Sectoral analysis indicates that emissions fluctuations are primarily driven by electricity, gas and water supply, manufacturing, wholesale and retail trade, and transportation. SHAP results further confirm the significance of intensity improvements in the early decade, demand-driven growth in the middle years, and pandemic-related declines in 2019-2020. The findings provide evidence-based insights for climate policy, highlighting the importance of renewable energy adoption, technological upgrading, carbon taxation, and sustainable consumption in supporting Malaysia's net-zero ambitions and Sustainable Development Goal 13.
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