Keywords
How to Cite
Accepted 2025-01-15
Published 2025-03-17
Abstract
The rapid advancement of low-carbon technologies, such as wind and nuclear power, introduces critical ethical challenges, including conflicts between environmental protection, land use, and community rights. This study presents a comprehensive framework to address these conflicts through data-driven optimization and ethical analysis. First, a robust data collection and modeling process is established to quantify energy demand and renewable adoption trends. Multi-objective optimization using the Multi-Objective Particle Swarm Optimization (MOPSO) and Mixed-Integer Programming (MIP) methods is then applied to balance conflicting objectives. The results reveal significant improvements in energy efficiency, carbon reduction, and stakeholder satisfaction, with MOPSO demonstrating superior performance. Ethical considerations are integrated through an impact vs. satisfaction analysis, which highlights the positive correlation between ecological benefits and public acceptance. Finally, a sensitivity analysis validates the robustness of the proposed solutions under varying conditions. The findings emphasize the potential of combining advanced algorithms with ethical frameworks to design sustainable and socially equitable low-carbon energy systems.
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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Copyright (c) 2025 Dongwen Luo, Qisen Cheng, Jinming Xing, Chang Xue, Xiaoran Yang (Author)