气候变化领域集成服务门户(CCPortal): Calculation of CO2 Emissions from China at Regional Scales Using Remote Sensing Data

CCPortal

DOI	10.3390/rs16030544
	Calculation of CO2 Emissions from China at Regional Scales Using Remote Sensing Data
	Li, Yaqian; Chen, Yile; Cai, Qi; Zhu, Liujun
发表日期	2024
EISSN	2072-4292
起始页码	16
结束页码	3
卷号	16 期号:3
英文摘要	Since industrialization, global carbon dioxide (CO2) emissions have been rising substantially, playing an increasingly important role in global warming and climate change. As the largest CO2 emitter, China has proposed an ambitious reduction plan of peaking before 2030 and achieving carbon neutrality by 2060. Calculation of CO2 emissions inventories at regional scales (e.g., city and county) has great significance in terms of China's regional carbon policies as well as in achieving the national targets. However, most of the existing emissions data were calculated based on fossil fuel consumptions and were thus limited to the provinces in China, making it challenging to compare and analyze the CO2 emissions of different cities and counties within a province. Machine learning methods provided a promising alternative but were still suffering from the lack of availability of training samples at city or county scales. Accordingly, this study proposed to use the energy consumption per unit GDP (ECpGDP) and GDP to calculate the effective CO2 emissions, which are the CO2 emissions if all consumed energy was generated by standard coal. Random forest models were then trained to establish relationships between the remote sensing night-light data and effective CO2 emissions. A total of eight predictor variables were used, including the night-light data, the urbanization ratio, the population density, the type of sensors and administrative divisions, latitude, longitude, and the area of each city or county. Meanwhile, the mean value of the five-fold cross-validation model was used as the estimated effective CO2 emissions in order to avoid overfitting. The evaluation showed a root mean square error (RMSE) of 10.972 million tons and an overall Pearson's correlation coefficient (R) of 0.952, with satisfactory spatial and temporal consistency. The effective CO2 emissions of 349 cities and 2843 counties in China during 1992-2021 were obtained, providing a promising dataset for CO2-emission-related applications.
英文关键词	carbon dioxide emissions; remote sensing; machine learning; night-light data
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001159258500001
来源期刊	REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/290133
作者单位	China Geological Survey; Institute of Karst Geology, Chinese Academy of Geological Sciences; Hohai University; Hohai University; Hohai University; Monash University
推荐引用方式 GB/T 7714	Li, Yaqian,Chen, Yile,Cai, Qi,et al. Calculation of CO2 Emissions from China at Regional Scales Using Remote Sensing Data[J],2024,16(3).
APA	Li, Yaqian,Chen, Yile,Cai, Qi,&Zhu, Liujun.(2024).Calculation of CO2 Emissions from China at Regional Scales Using Remote Sensing Data.REMOTE SENSING,16(3).
MLA	Li, Yaqian,et al."Calculation of CO2 Emissions from China at Regional Scales Using Remote Sensing Data".REMOTE SENSING 16.3(2024).