A Multi-Objective Optimization Framework for Low-Carbon Index Construction and Application in Green Finance

The increasing urgency of addressing climate change and transitioning to sustainable development has highlighted the need for effective methods to balance environmental and financial objectives. This paper proposes a novel low-carbon index construction framework that integrates multi-objective optimization and advanced computational techniques. The framework is designed to evaluate the trade-offs between carbon emissions and financial performance, providing a robust decision-making tool for policymakers and industry stakeholders. The proposed method employs a hybrid algorithm combining genetic algorithms for global exploration and gradient-based methods for local refinement, ensuring both solution diversity and precision. Key metrics such as carbon intensity, renewable energy adoption rates, and economic growth indicators are integrated into the index, with weights determined using an analytic hierarchy process. Sensitivity analysis validates the robustness of the index under varying weight scenarios, demonstrating its adaptability to different sustainability priorities. The experimental results showcase the framework's ability to generate a comprehensive Pareto-optimal front, highlighting trade-offs and enabling informed decision-making. Furthermore, the hybrid algorithm achieves superior computational efficiency compared to traditional methods, with faster convergence and reduced runtime. This study provides a practical and scalable solution for constructing low-carbon indices, contributing to the advancement of sustainable development and green finance.