The　aim　of　this　paper　is　to　investigate　the　effects　of　light　pollution　on　the　environment　and　human　health,　proposing　effective　intervention　measures.　A　widely　applicable　metric　is　developed　to　determine　light　pollution　risk　levels　in　different　regions,　forming　the　basis　for　a　reliable　assessment　model.　This　model,　integrating　Analytic　Hierarchy　Process　(AHP),　Technique　for　Order　of　Preference　by　Similarity　to　Ideal　Solution　(TOPSIS)　model,　and　multiple　regression　analysis　(MRA),　offers　a　comprehensive　understanding　of　light　pollution　risk　by　considering　regional　development,　light　usage,　and　geographical　factors.　The　assessment　model　demonstrates　high　accuracy　and　reliability　in　measuring　light　pollution　risk,　providing　valuable　insights　into　the　interaction　between　factors　such　as　light　intensity,　biodiversity　loss,　GDP,　and　population.　Empirical　analysis　reveals　regional　disparities　in　light　pollution　risk　levels,　underscoring　the　need　for　targeted　interventions　to　mitigate　its　adverse　impacts.　Overall,　the　developed　light　pollution　risk　assessment　model　serves　as　a　valuable　tool　for　policymakers,　facilitating　informed　decision-making　and　effective　intervention　strategies.　©　2024　SPIE.