Atmospheric CO2 concentration prediction based on bidirectional long short-term memory

IntroductionAccurate prediction of atmospheric CO2 concentration is essential for evaluating local emission dynamics, supporting regional carbon management, and promoting carbon neutrality goals. However, CO2 variations are highly influenced by complex interactions between meteorological conditions and anthropogenic activities, leading to highly nonlinear and time-dependent behavior that challenges conventional prediction methods.MethodsTo address this issue, a time series prediction framework b