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Artificial light plays an integral role in modern cities, significantlyenhancing human productivity and the efficiency of civilization. However,excessive illumination can lead to light pollution, posing non-negligiblethreats to economic burdens, ecosystems, and human health. Despite its criticalimportance, the exploration of its causes remains relatively limited within thefield of artificial intelligence, leaving an incomplete understanding of thefactors contributing to light pollution and sustainable illumination planningdistant. To address this gap, we introduce a novel framework named CausallyAware Generative Adversarial Networks (CAGAN). This innovative approach aims touncover the fundamental drivers of light pollution within cities and offerintelligent solutions for optimal illumination resource allocation in thecontext of sustainable urban development. We commence by examining lightpollution across 33,593 residential areas in seven global metropolises. Ourfindings reveal substantial influences on light pollution levels from variousbuilding types, notably grasslands, commercial centers and residentialbuildings as significant contributors. These discovered causal relationshipsare seamlessly integrated into the generative modeling framework, guiding theprocess of generating light pollution maps for diverse residential areas.Extensive experiments showcase CAGAN's potential to inform and guide theimplementation of effective strategies to mitigate light pollution. Our codeand data are publicly available athttps://github.com/zhangyuuao/Light_Pollution_CAGAN.

Causally Aware Generative Adversarial Networks for Light Pollution Control