Multi-Objective Optimization of Industrial Productivity and Renewable Energy Allocation Based on NSGA-II for Carbon Reduction and Cost Efficiency: Case Study of China

This study develops a novel framework for optimizing regional power generation structures in support of China’s “dual carbon” goals. The framework introduces three main innovations. First, it formulates a comprehensive optimization paradigm that simultaneously balances industrial output, carbon emissions, and electricity costs, thereby directly addressing the trade-offs in regional energy planning. Second, it enhances data reliability and representativeness through systematic augmentation strategies, improving the robustness of optimization under data scarcity. Third, it incorporates an intelligent multi-objective search mechanism that yields a practical, three-dimensional decision space for policymakers. Beyond its methodological contributions, this research provides significant scientific value by offering a replicable framework for accelerating low-carbon energy transitions in rapidly industrializing economies. The proposed approach directly supports global sustainability targets and aligns with the United Nations Sustainable Development Goals (SDG 7, SDG 9, and SDG 13) by promoting clean energy adoption, fostering industrial innovation, and contributing to climate action. Together, these contributions provide a scalable and actionable pathway for adjusting regional power structures, aligning with national policies and accelerating the achievement of the carbon peak target by 2030.