The accurate measurement of CO2 emissions is helpful for realizing the goals of "carbon neutralization" and "carbon peak". However, most current research on CO2 emission measurements utilizes the traditional energy balance coefficient and top-down methods. The data granularity is large, and most studies are concentrated at the national, provincial, municipal, or district/county administrative unit scale. As an important part of the Guangdong-Hong Kong-Macao Greater Bay Area of China, the Pearl River Delta region has good nighttime light vitality and faces huge carbon emission pressure. Using the Pearl River Delta as the research area, this study constructed an optimized pixel-scale regression model based on NPP-VIIRS (The Visible Infrared Imaging Radiometer Suite on the Suomi National Polar-Orbiting Partnership spacecraft) nighttime light data and CO2 emissions data at the district and county levels for 2017. In addition, the spatial pattern of CO2 emissions in the Pearl River Delta was analyzed based on the predicted CO2 emission status. The results showed that the spatial pattern of CO2 emissions in the Pearl River Delta had the distinct characteristics of the "center-edge" effect, the spatial spillover effect, and high-value aggregation, which should be considered when making related social or public decisions.
Keywords: CO2 emissions; Pearl River Delta; night light data; optimized regression model; spatial pattern.