Abstract: Light pollution in urban locations is a complex, serious problem, but researchers have paid more attention to light pollution on natural, non-urban environments. Understanding the sources of artificial light radiance intensity is the first step in minimizing damage from light pollution in urban areas. The purpose of this study is to quantitatively examine the relationship between light pollution and urban built environments. We developed databases for a series of urban–built environment data with composite Visible Infrared Imaging Radiometer Suite day-night band (VIIRS-DNB) data from the Earth Observation Group of the United States National Oceanic Atmospheric Administration’s National Geophysical Data Center to apply regression models (production functions) with grid cells at a spatial resolution of 15 arc seconds. Based on the results, we identified urban-development and land-use characteristics and built-environment factors that caused high levels of light emissions in a city. First, high levels of light emissions are associated with urban spatial-development patterns, such as roads, office buildings, commercial facilities, higher proportions of “station influence areas,” and urban-development intensity. Second, more seriously, the infiltration of commercial facilities into urban residential areas makes them brighter and increases the risk of exposure to light pollution. Therefore, the development of commercial areas and commercial facilities that emit light should be properly managed, especially for areas mixed with residential land use. Third, our quantitative model with intra-city-level analyses can estimate a high level of the baseline light-emission propensity in Seoul, which indicates that a city’s light-emission intensity can be highly associated with its sociocultural and institutional characteristics for lighting and light uses.

Abstract: A satellite survey of the cumulative radiant emissions from electric lighting across China reveals a large radiance decline in lighting from December 2019 to February 2020—the peak of the lockdown established to suppress the spread of COVID-19 infections. To illustrate the changes, an analysis was also conducted on a reference set from a year prior to the pandemic. In the reference period, the majority (62%) of China’s population lived in administrative units that became brighter in March 2019 relative to December 2018. The situation reversed in February 2020, when 82% of the population lived in administrative units where lighting dimmed as a result of the pandemic. The dimming has also been demonstrated with difference images for the reference and pandemic image pairs, scattergrams, and a nightly temporal profile. The results indicate that it should be feasible to monitor declines and recovery in economic activity levels using nighttime lighting as a proxy.