Applied Mathematics and Nonlinear Sciences, Год журнала: 2025, Номер 10(1)
Опубликована: Янв. 1, 2025
Abstract The increasing urgency of addressing climate change and transitioning to sustainable development has highlighted the need for effective methods balance environmental financial objectives. This paper proposes a novel low-carbon index construction framework that integrates multi-objective optimization advanced computational techniques. is designed evaluate trade-offs between carbon emissions performance, providing robust decision-making tool policymakers industry stakeholders. proposed method employs hybrid algorithm combining genetic algorithms global exploration gradient-based local refinement, ensuring both solution diversity precision. Key metrics such as intensity, renewable energy adoption rates, economic growth indicators are integrated into index, with weights determined using an analytic hierarchy process. Sensitivity analysis validates robustness under varying weight scenarios, demonstrating its adaptability different sustainability priorities. experimental results showcase framework's ability generate comprehensive Pareto-optimal front, highlighting enabling informed decision-making. Furthermore, achieves superior efficiency compared traditional methods, faster convergence reduced runtime. study provides practical scalable constructing indices, contributing advancement green finance.
Язык: Английский