Document Type
Article
Publication Date
9-8-2020
Abstract
China is one of the largest carbon emitting countries in the world. Numerous strategies have been considered by the Chinese government to mitigate carbon emissions in recent years. Accurate and timely estimation of spatiotemporal variations of city-level carbon emissions is of vital importance for planning of low-carbon strategies. For an assessment of the spatiotemporal variations of city-level carbon emissions in China during the periods 2000–2017, we used nighttime light data as a proxy from two sources: Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) data and the Suomi National Polar-orbiting Partnership satellite’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). The results show that cities with low carbon emissions are located in the western and central parts of China. In contrast, cities with high carbon emissions are mainly located in the Beijing-Tianjin-Hebei region (BTH) and Yangtze River Delta (YRD). Half of the cities of China have been making e orts to reduce carbon emissions since 2012, and regional disparities among cities are steadily decreasing. Two clusters of high-emission cities located in the BTH and YRD followed two di erent paths of carbon emissions owing to the diverse political status and pillar industries. We conclude that carbon emissions in China have undergone a transformation to decline, but a very slow balancing between the spatial pattern of high-emission versus low-emission regions in China can be presumed.
Recommended Citation
Sun, Y.; Zheng, S.; Wu, Y.Z.; Schlink, U.; Singh, R.P. Spatiotemporal variations of city-level carbon emissions in China during 2000-2017 using nighttime light data. Remote Sens.<.em> 2020, 12, 2916. https://doi.org/10.3390/rs12182916
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The authors
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
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Comments
This article was originally published in Remote Sensing, volume 12, issue 18, in 2020. https://doi.org/10.3390/rs12182916