Spatiotemporal Variations of City-Level Carbon Emissions in China during 2000–2017 Using Nighttime Light Data
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 p...
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MDPI AG
2020-09-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/18/2916 |
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| author | Yu Sun Sheng Zheng Yuzhe Wu Uwe Schlink Ramesh P. Singh |
| author_facet | Yu Sun Sheng Zheng Yuzhe Wu Uwe Schlink Ramesh P. Singh |
| author_sort | Yu Sun |
| collection | DOAJ |
| description | 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 efforts 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 different 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. |
| first_indexed | 2024-03-10T16:28:25Z |
| format | Article |
| id | doaj.art-2fbb20377dfe4ab99df07d8ac507537e |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T16:28:25Z |
| publishDate | 2020-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-2fbb20377dfe4ab99df07d8ac507537e2023-11-20T13:00:17ZengMDPI AGRemote Sensing2072-42922020-09-011218291610.3390/rs12182916Spatiotemporal Variations of City-Level Carbon Emissions in China during 2000–2017 Using Nighttime Light DataYu Sun0Sheng Zheng1Yuzhe Wu2Uwe Schlink3Ramesh P. Singh4Department of Land Management, Zhejiang University, Hangzhou 310058, ChinaDepartment of Land Management, Zhejiang University, Hangzhou 310058, ChinaDepartment of Land Management, Zhejiang University, Hangzhou 310058, ChinaDepartment of Urban and Environmental Sociology, Helmholtz Centre for Environmental Research-UFZ, Permoserstraße 15, D-04318 Leipzig, GermanySchool of Life and Environmental Sciences, Schmid College of Science and Technology, Chapman University, One University Drive, Orange, CA 92866, USAChina 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 efforts 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 different 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.https://www.mdpi.com/2072-4292/12/18/2916spatiotemporal variationscarbon emissionsDMSP-OLSNPP-VIIRS |
| spellingShingle | Yu Sun Sheng Zheng Yuzhe Wu Uwe Schlink Ramesh P. Singh Spatiotemporal Variations of City-Level Carbon Emissions in China during 2000–2017 Using Nighttime Light Data Remote Sensing spatiotemporal variations carbon emissions DMSP-OLS NPP-VIIRS |
| title | Spatiotemporal Variations of City-Level Carbon Emissions in China during 2000–2017 Using Nighttime Light Data |
| title_full | Spatiotemporal Variations of City-Level Carbon Emissions in China during 2000–2017 Using Nighttime Light Data |
| title_fullStr | Spatiotemporal Variations of City-Level Carbon Emissions in China during 2000–2017 Using Nighttime Light Data |
| title_full_unstemmed | Spatiotemporal Variations of City-Level Carbon Emissions in China during 2000–2017 Using Nighttime Light Data |
| title_short | Spatiotemporal Variations of City-Level Carbon Emissions in China during 2000–2017 Using Nighttime Light Data |
| title_sort | spatiotemporal variations of city level carbon emissions in china during 2000 2017 using nighttime light data |
| topic | spatiotemporal variations carbon emissions DMSP-OLS NPP-VIIRS |
| url | https://www.mdpi.com/2072-4292/12/18/2916 |
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