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.

翻译：人工光源在现代城市中扮演着不可或缺的角色，显著提升了人类生产力和文明运行效率。然而，过度照明会引发光污染，对经济负担、生态系统和人类健康构成不容忽视的威胁。尽管光污染问题至关重要，但人工智能领域对其成因的探索仍相对有限，导致对光污染影响因素及可持续照明规划的认知尚不完整。为填补这一空白，我们提出了一种名为"因果感知生成对抗网络"（CAGAN）的创新框架。该方法旨在揭示城市光污染的根本驱动因素，并为可持续城市发展背景下优化照明资源分配提供智能化解决方案。我们首先对全球七座大都市33,593个居住区的光污染状况进行了分析，发现草地、商业中心和住宅建筑等多种建筑类型对光污染水平具有显著影响，特别是其中主要的贡献因素。这些发现的因果关联被无缝整合至生成建模框架中，用以指导针对不同居住区的光污染地图生成过程。大量实验表明，CAGAN能够为制定有效缓解光污染策略提供信息支撑与行动指引。我们的代码与数据已公开至 https://github.com/zhangyuuao/Light_Pollution_CAGAN。