期刊
SCIENTIFIC REPORTS
卷 11, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41598-021-81754-y
关键词
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资金
- National Key Natural Science Foundation of China [71934001]
- National Natural Science Foundation of China [71471001, 41771568, 71533004, 71503001]
- National Key Research and Development Program of China [2016YFA0602500]
- Sichuan Province Social Science High Level Research Team Building Program
- Program for Major Projects in Philosophy and Social Science Research under China's Ministry of Education [14JZD031]
This study estimates the CO2 emissions of 334 prefecture-level cities in China from 1992 to 2017 using satellite data, revealing that the eastern and coastal regions had higher carbon emissions, but their carbon intensity decreased more rapidly. The research provides the most extensive and long-term CO2 dataset to date, which will be valuable for future CO2 research and emission reduction strategies in China.
Accurate, long-term, full-coverage carbon dioxide (CO2) data in units of prefecture-level cities are necessary for evaluations of CO2 emission reductions in China, which has become one of the world's largest carbon-emitting countries. This study develops a novel method to match satellite-based Defense Meteorological Satellite Program's Operational Landscan System (DMSP/OLS) and Suomi National Polar-orbiting Partnership's Visible Infrared Imaging Radiometer Suite (NPP/VIIRS) nighttime light data, and estimates the CO2 emissions of 334 prefecture-level cities in China from 1992 to 2017. Results indicated that the eastern and coastal regions had higher carbon emissions, but their carbon intensity decreased more rapidly than other regions. Compared to previous studies, we provide the most extensive and long-term CO2 dataset to date, and these data will be of great value for further socioeconomic research. Specifically, this dataset provides a foundational data source for China's future CO2 research and emission reduction strategies. Additionally, the methodology can be applied to other regions around the world.
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