Carbon Emission Prediction Model Based on LSTM Enhanced by Attention Mechanism and Elman Neural Network

China, now the global leader in energy production and consumption, has made significant strides towards its dual-carbon goals as of 2021. This underlines the crucial need for accurate regional carbon emission forecasting. Traditional back propagation neural networks face challenges with local minima and slow convergence. To overcome these, our study proposes an innovative method that combines LSTM-Attention, enriched with self-attention mechanisms, and Elman neural networks with the traditional BP neural model for regional carbon emission prediction. Through the development of a predictive model leveraging LSTM-Attention and Elman neural networks, and applying it to regional carbon emission forecasts, our findings reveal that this model achieves higher accuracy than conventional BP neural networks and substantially reduces the need for gas sensors, thus lowering costs in the statistical process.