  Artificial light plays an integral role in modern cities, significantly
enhancing human productivity and the efficiency of civilization. However,
excessive illumination can lead to light pollution, posing non-negligible
threats to economic burdens, ecosystems, and human health. Despite its critical
importance, the exploration of its causes remains relatively limited within the
field of artificial intelligence, leaving an incomplete understanding of the
factors contributing to light pollution and sustainable illumination planning
distant. To address this gap, we introduce a novel framework named Causally
Aware Generative Adversarial Networks (CAGAN). This innovative approach aims to
uncover the fundamental drivers of light pollution within cities and offer
intelligent solutions for optimal illumination resource allocation in the
context of sustainable urban development. We commence by examining light
pollution across 33,593 residential areas in seven global metropolises. Our
findings reveal substantial influences on light pollution levels from various
building types, notably grasslands, commercial centers and residential
buildings as significant contributors. These discovered causal relationships
are seamlessly integrated into the generative modeling framework, guiding the
process of generating light pollution maps for diverse residential areas.
Extensive experiments showcase CAGAN's potential to inform and guide the
implementation of effective strategies to mitigate light pollution. Our code
and data are publicly available at
https://github.com/zhangyuuao/Light_Pollution_CAGAN.
