Evaluating Light Pollution: An IES Model for Intervention Strategies

  •  Ruixi Su    
  •  Yi Chen    
  •  Zibin Huang    


There is an increasing urgency to address how the light pollution risk level can be accurately and comprehensively measured and evaluated. Based on current research and data, this paper proposes a model concerning light pollution risk levels applicable to various regions. Optimized intervention strategies are then provided to reduce the effect of light pollution. For one thing, this paper establishes an Illumination-Environment-Society Evaluation (IES) model to evaluate a region’s light pollution risk level. Primary indicators of the model involve three dimensions, each quantified by 2 to 5 secondary indicators, with sufficient data analysis conducted, including data rasterization of satellite remote sensing images, K-means clustering analysis, Principal Component Analysis (PCA), Entropy Weight Method (EWM), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Analytic Hierarchy Process (AHP), and other assistant algorithms. In this regard, the present study obtains and grades some regions’ light pollution risk levels. For another, this paper determines three possible intervention strategies for light pollution based on the IES model after interpreting the results. Non-linear programming methods are also employed to optimize these three strategies. The present study aims to exploit a new avenue for relevant environmental research, providing references for light pollution measurement and intervention.

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