Low-Carbon Power Technology Innovation: Addressing Environmental Protection, Land Use, and Community Rights DOI Creative Commons

Dongwen Luo,

Qing Cheng,

Jinming Xing

et al.

Published: March 17, 2025

The rapid advancement of low-carbon technologies, such as wind and nuclear power, introduces critical ethical challenges, including conflicts between environmental protection, land use, community rights. This study presents a comprehensive framework to address these through data-driven optimization analysis. First, robust data collection modeling process is established quantify energy demand renewable adoption trends. Multi-objective using the Multi-Objective Particle Swarm Optimization (MOPSO) Mixed-Integer Programming (MIP) methods then applied balance conflicting objectives. results reveal significant improvements in efficiency, carbon reduction, stakeholder satisfaction, with MOPSO demonstrating superior performance. Ethical considerations are integrated an impact vs. satisfaction analysis, which highlights positive correlation ecological benefits public acceptance. Finally, sensitivity analysis validates robustness proposed solutions under varying conditions. findings emphasize potential combining advanced algorithms frameworks design sustainable socially equitable systems.

Language: Английский

Image-driven prediction system: Automatic extraction of aggregate gradation of pavement core samples integrating deep learning and interactive image processing framework DOI
Han-Cheng Dan, Zheying Huang, Bingjie Lu

et al.

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 453, P. 139056 - 139056

Published: Nov. 1, 2024

Language: Английский

Citations

8
Low-Carbon Power Technology Innovation: Addressing Environmental Protection, Land Use, and Community Rights DOI Creative Commons

Dongwen Luo,

Qing Cheng,

Jinming Xing

et al.

Published: March 17, 2025

The rapid advancement of low-carbon technologies, such as wind and nuclear power, introduces critical ethical challenges, including conflicts between environmental protection, land use, community rights. This study presents a comprehensive framework to address these through data-driven optimization analysis. First, robust data collection modeling process is established quantify energy demand renewable adoption trends. Multi-objective using the Multi-Objective Particle Swarm Optimization (MOPSO) Mixed-Integer Programming (MIP) methods then applied balance conflicting objectives. results reveal significant improvements in efficiency, carbon reduction, stakeholder satisfaction, with MOPSO demonstrating superior performance. Ethical considerations are integrated an impact vs. satisfaction analysis, which highlights positive correlation ecological benefits public acceptance. Finally, sensitivity analysis validates robustness proposed solutions under varying conditions. findings emphasize potential combining advanced algorithms frameworks design sustainable socially equitable systems.

Language: Английский

Citations

0