Estimating spatiotemporal variations of city-level energy-related CO2 emissions: An improved disaggregating model based on vegetation adjusted nighttime light data

Abstract In response to global warming, a spatiotemporal inventory of city-level CO2 emissions is of great importance for the developing countries, especially for China of which CO2 emissions increase rapidly but lack in energy statistics at urban scale. Currently the nightlight imagery from the Defense Meteorological Satellite Program's Operational Linescan System (DMSP-OLS) has been widely used as a promising data source for estimating the spatiotemporal distributions of CO2 emissions. However, most previous studies ignored the saturation problem of DMSP-OLS nightlight imagery which could result in more uncertainties in CO2 emission estimations. Moreover, little work was carried out to evaluate the important emission sources from a large population living in the unlit areas with the consideration of different emission levels. To address these issues, this study proposed an improved allocating model to map the city-level energy-related CO2 emissions of mainland China in 2000, 2005, 2010, and 2013 based on an enhanced vegetation index (EVI) adjusted nighttime light index (EANTLI) and LandScan population data. Through the comparison with the original nightlight images, the EANTLI is proved to increase inter-urban variability and alleviate DMSP-OLS saturation. Meanwhile, the accuracy assessment with the statistical data of CO2 emissions at the level of city units has also demonstrated that the proposed model is appropriate and reliable in estimating CO2 emissions not only in the lit areas but also in the unlit areas. The model outputs presented that cities with huge amounts of CO2 emissions mainly agglomerated in the southern and eastern coastal regions, while spatial distributions of cities with small amounts of CO2 emissions mainly appeared in western and central regions. These results could improve the understanding of regional discrepancies of spatiotemporal CO2 emission dynamics at urban scale, and provide a scientific basis for policymaking on viable CO2 emission mitigation policies.