Luojia 1–01 Data Outperform Suomi-NPP VIIRS Data in Estimating CO<sub>2</sub> Emissions in the Service, Industrial, and Urban Residential Sectors
Yuanzheng Cui, Hui Zha, Lei Jiang, Mi Zhang, Kaifang Shi
Abstract
Reducing carbon dioxide (CO2) emissions has been a global concern for urban development. In recent years, while the Suomi-National Polar-Orbiting Partnership Satellite–Visible Infrared Imaging Radiometer Suite (Suomi-NPP VIIRS) nighttime light (NTL) data have been widely used to estimate CO2 emissions, the Luojia 1–01 NTL data with finer spatial resolution have rarely been used for this purpose. Therefore, this letter estimated four types of sectoral CO2 emissions (i.e., urban residential, services, industrial, and transport) in Chinese cities by merging two sets of NTL data with functional urban zoning information. The results show that Luojia 1–01 data outperformed Suomi-NPP VIIRS data in estimating total CO2 emissions. Regarding the disaggregated estimation of CO2 emissions in the service, industrial, and urban residential sectors, Luojia 1–01 data surpassed Suomi-NPP VIIRS data. However, Suomi-NPP VIIRS data were better suitable for estimating the transport CO2 emissions than Luojia 1–01 data. We found linear regression more appropriate for estimating CO2 emissions in the service, transport, and urban residential sectors, but the power function regression was more suitable for estimating CO2 emissions in the industrial sector. Our results will help provide a scientific reference for selecting optimal NTL data as well as regression models to be used in estimating sectoral CO2 emissions, which are also essential for achieving China’s carbon emissions targets.