Earth Science: Environmental Challenges and Solutions
Research Article Volume: 1 & Issue: 1
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Abstract
This study presents a machine learning-based framework for enhancing the prediction and optimization of CO₂ and CH₄ emission reduction potential using multi-sectoral and socio-economic data, aligned with Sustainable Development and climate action goals. Leveraging Random Forest Regression, the model achieved exceptional predictive performance (R² ≈ 0.997, RMSE ≈ 53.69), with predicted emissions closely matching observed values and minimal systematic bias. Feature importance analysis identified oil production, coal-related emissions, and other CO₂ sources as the dominant contributors, while GDP and cement production exhibited moderate influence. Correlation analysis revealed strong interdependencies between greenhouse gas emissions and factors such as population, N₂O emissions, and fossil fuel consumption, underscoring the interconnected nature of emission drivers. The novelty of this approach lies in integrating high-resolution data with advanced predictive modeling to not only forecast emissions accurately but also pinpoint priority areas for targeted mitigation strategies. The findings provide a scalable, evidence-based decision-support tool for policymakers, enabling them to design effective interventions that accelerate decarbonization, methane reduction, and broader Sustainable Development objectives.
Keywords: Machine Learning, CO₂ Emissions, CH₄ Emissions, Feature Importance, Predictive Modeling
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