In recent years, the awareness of light pollution's hazards has grown. This article aims to develop metrics to assess light pollution, propose intervention strategies, and analyze their effectiveness. Key indicators such as population density, GDP, urban electricity consumption, high-rise buildings, vehicle ownership rate, AOD index, and remote sensing brightness were evaluated. Using entropy weight and Topsis methods, a Light Pollution Index (LPI) was derived. A multivariate regression model provided the regression equation for LPI. K-means++ clustering categorized LPI into five levels, delineating light pollution degrees in different regions. This framework aids urban planning assessments and establishes a comprehensive model for light pollution assessment. Through data analysis and evaluation, the feasibility of light pollution reduction measures was demonstrated.
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Yang et al. (2024) studied this question.
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