Estimation of Anthropogenic Carbon Dioxide Emissions in China: Remote Sensing with Generalized Regression Neural Network and Partition Modeling Strategy

Accurate estimation of anthropogenic CO2 emissions is crucial for effective climate change mitigation policies. This study aims to improve CO2 emission estimates in China using remote sensing measurements of column-averaged dry air mole fractions of CO2 (XCO2) and a neural network approach. We evaluated XCO2 anomalies derived from three background XCO2 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 CO2 emissions based on XCO2 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 CO2 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/m2/day, while incorporating population data led to a decrease in MAE values between 0.036 and 0.269 gC/m2/day for different partitions. The present methods and findings offer critical insights for supporting government policy-making and target-setting.