Current Status and Future Forecast of Global CO2 Concentration Using Statistical and Deep Learning Time Series Methods

In this study, four models were developed and assessed, including Autoregressive Integrated Moving Average (ARIMA), Feedforward Neural Network (FNN), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM) to evaluate current status and future forecast of global CO2 concentration. A total of 554 global monthly datasets were employed to train and test the developed models, aimed at estimating future CO₂ concentrations. Then, each developed model was also utilized to estimate CO₂ concentrations for the future 110 months, from March 2025 to February 2035. Among all generated techniques, the LSTM model showed the highest estimation accuracy with an MAPE of 0.05%, an MAE of 0.2028 ppm, and an RMSE of 0.3216 ppm. Whereas GRU and FNN techniques also obtained good results with the same MAPE of 0.05%, their MAE and RMSE values were slightly higher. The four developed models (ARIMA, FNN, GRU, and LSTM) agree on a continuous rise in atmospheric CO2 level within the range between March 2025 and early 2035, and they typically show CO2 concentrations starting from approximately 425 ppm in early 2025 to 442-443 ppm by the end of 2034.