This research introduces an innovative agricultural carbon accounting approach for straw burning that combines stochastic process modeling with LSTM neural networks. Traditional methods face limitatio
First, this data complies CO2 emissions, final energy consumption, GDP, share of zero emitting electricity for Annex B countries of Kyoto Protocol and east state in U.S. Second, this data also complie
Based on current CO2 emission accounting methods and results, this study employed nighttime lights (NTL) remote sensing data, coupled with the XGBoost model and a multi-task learning model, to develop
