一种基于大气CO<sub>2</sub>浓度时空特征的碳排放分区估算方法

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Abstract Aiming at the non-normal distribution of anthropogenic carbon emissions due to the large spatial difference of magnitude, a neural network estimation model for anthropogenic carbon emissions was proposed in this study based on the clustering of spatial-temporal variation characteristics of satellite XCO₂. Using the spatial and temporal variations of satellite XCO₂ (2010~2021), which are strongly correlated with anthropogenic carbon emissions, for clustering and partitioning, and utilizing satellite-observed SIF, nighttime lighting, as well as population density and anthropogenic emission inventory(EDGAR) as training data, neural network models for Carbon emission estimation were built respectively in each cluster region, and the anthropogenic carbon emissions of the study area were estimated for the year 2021. Cross-verification results with EDGAR show that compared with the unified modeling method based on the data of the whole study area, the estimation result of the partition modeling proposed in this study increased the correlation coefficient (R²) from 0.43 to 0.82, with more reasonable spatial distribution, and the mean deviation decreased from 0.039Mt CO₂ to 0.018Mt CO₂. The study shows that the estimation of anthropogenic carbon emissions using neural network training with multi-source data can provide data support for the characterization of regional carbon emissions and the assessment of uncertainty in emission inventories.

Key words： anthropogenic carbon emission neural network multi-source data machine learning

Received: 09 March 2023

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	ZHANG Shao-qing
	LEI Li-ping
	SONG Hao
	GUO Kai-yuan
	JI Zhang-hui
	SHENG Meng-ya

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ZHANG Shao-qing,LEI Li-ping,SONG Hao等. A neural network partitioning method for carbon emission estimation based on spatial-temporal clustering of atmospheric CO₂ concentration[J]. CHINA ENVIRONMENTAL SCIENCECE, 2023, 43(10): 5604-5613.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2023/V43/I10/5604

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