Construction and Application of Carbon Emission Prediction Model for China's Textile and Garment Industry Based on Improved WOA-LSTM.

Carbon emissions prediction in the textile and garment industry makes the industry' s carbon reduction policies more practical. In order to predict the industry's carbon emissions more accurately, this paper proposed a prediction model based on Long-Short Term Memory neural network (LSTM), which was optimized by an improved Whale Optimization Algorithm (WOA). The machine learning method was introduced to provide a basis for exploring the industry's carbon reduction path. Firstly, the LSTM model used WOA to optimize the key parameters, and chaotic mapping initialization population and adaptive weights were taken to improve the algorithm. At the same time, an improved WOA-LSTM model was constructed. Secondly, the carbon emissions of the textile and garment industry were calculated from 1990 to 2020, and the STIRPAT model was used to screen the influencing factors of carbon emissions in the industry. Comparative analysis was used to confirm the model's performance, and several scenarios were used to predict the industry's carbon emissions trend. Experiments show that the prediction accuracy is significantly improved, and the MAE, RMSE, and MAPE values for the model test set are 4.868, 4.984, and 0.024, respectively. Meanwhile, the industry will primarily rely on technological innovation to achieve carbon neutrality. This study offers new perspectives to research on carbon emissions prediction in industrial sectors. [ABSTRACT FROM AUTHOR]