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

Authors: Jinpei Ou*, Sun Yat-sen University, Xiaoping Liu, Sun Yat-sen University
Topics: Remote Sensing, Geographic Information Science and Systems, Environment
Keywords: City-level CO2 emissions, Nighttime light imagery, Spatiotemporal variations, China
Session Type: Paper
Day: 4/4/2019
Start / End Time: 1:10 PM / 2:50 PM
Room: Roosevelt 5, Marriott, Exhibition Level
Presentation File: No File Uploaded

A spatiotemporal inventory of city-level CO2 emissions is of great importance 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 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. This study proposed an improved allocating model to map the city-level energy-related CO2 emissions of mainland China based on an enhanced vegetation index 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 results can 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.

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