Machine Learning-Driven Carbon Reduction Optimization with Adaptive Portfolio Coordination

This study proposes a machine learning-driven carbon reduction optimization algorithm with adaptive portfolio coordination. By integrating a random forest prediction model and the NSGA-II multi-objective optimization framework, we construct a dynamic decision-making model that simultaneously addresses carbon reduction effectiveness, system efficiency, and stability maintenance. The algorithm innovatively introduces a policy-sensitive weight adjustment mechanism, combined with mutual information feature selection and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$K$</tex>-nearest neighbor imputation to resolve dynamic policy constraints and data heterogeneity. Experimental results demonstrate 22.2% and 15.7% improvements in carbon reduction performance and system stability compared to traditional genetic algorithms, validating the effectiveness of multi-dimensional collaborative optimization. The research provides an extensible technical pathway for green resource allocation and emission reduction quantification, advancing the deep integration of machine learning and environmental governance decision-making.