Estimation of Anthropogenic Carbon Dioxide Emissions in China: Remote Sensing with Generalized Regression Neural Network and Partition Modeling Strategy
Accurate estimation of anthropogenic CO<sub>2</sub> emissions is crucial for effective climate change mitigation policies. This study aims to improve CO<sub>2</sub> emission estimates in China using remote sensing measurements of column-averaged dry air mole fractions of CO&l...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
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| Series: | Atmosphere |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2073-4433/16/6/631 |
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| Summary: | Accurate estimation of anthropogenic CO<sub>2</sub> emissions is crucial for effective climate change mitigation policies. This study aims to improve CO<sub>2</sub> emission estimates in China using remote sensing measurements of column-averaged dry air mole fractions of CO<sub>2</sub> (XCO<sub>2</sub>) and a neural network approach. We evaluated XCO<sub>2</sub> anomalies derived from three background XCO<sub>2</sub> concentration approaches: CHN (national median), LAT (10-degree latitudinal median), and NE (N-nearest non-emission grids average). We then applied the Generalized Regression Neural Network model, combined with a partition modeling strategy using the K-means clustering algorithm, to estimate CO<sub>2</sub> emissions based on XCO<sub>2</sub> anomalies, net primary productivity, and population data. The results indicate that the NE method either outperformed or was at least comparable to the LAT method, while the CHN method performed the worst. The partition modeling strategy and inclusion of population data effectively improved CO<sub>2</sub> emission estimates. Specifically, increasing the number of partitions from 1 to 30 using the NE method resulted in mean absolute error (MAE) values decreasing from 0.254 to 0.122 gC/m<sup>2</sup>/day, while incorporating population data led to a decrease in MAE values between 0.036 and 0.269 gC/m<sup>2</sup>/day for different partitions. The present methods and findings offer critical insights for supporting government policy-making and target-setting. |
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| ISSN: | 2073-4433 |