Proposed Feature Dimensionality Reduction Method in a Predictive Model for Carbon Neutrality: A Hybrid Quantum-Classical Approach

As global carbon emissions continue to rise, achieving carbon neutrality has become a primary objective worldwide. Predictive modeling for carbon emissions has emerged as a crucial strategy, with numerous methods developed to forecast and manage emissions effectively. Traditional models, which predominantly employ classical computing-based metaheuristic optimization techniques, often face limitations in reaching global optima, impacting prediction accuracy. This study introduces a hybrid quantum-classical approach to enhance prediction models, particularly through feature dimensionality reduction, which is essential for improving model performance. Quantum algorithms, especially quantum annealing (QA), have shown advantages in addressing complex optimization problems by utilizing principles such as superposition and tunneling, thus offering significant benefits over classical methods. The integration of QA with classical dimensionality reduction techniques, such as principal component analysis (PCA), represents a novel advancement in managing high-dimensional data within predictive models. By optimizing feature selection, our approach aims to increase both the accuracy and computational efficiency of carbon neutrality models. This paper proposes hybrid quantum-classical optimization opportunities that can serve as a foundation for future studies, ultimately leading to more robust and accurate predictive models.