Analysis on the Way and Potential of Economic Low-Carbon Development of China Based on Genetic Algorithm.

How a developing China can meet the challenges of the post-Kyoto era in the process of rapid industrialization is a hot issue in current academic research. Facing the pressure of the international community to reduce emissions and the energy and resource constraints under the development trend of the heavy chemical industry, China can only turn the pressure into a driving force and seek a low-carbon development path. This paper proposes a prediction model for China's low-carbon economic development based on the combined model of genetic algorithm (GA) and long short-term memory neural network (LSTM). The data are encoded with one-hot, embedding is used to reduce the dimension, and the genetic algorithm is used to obtain the optimal hyperparameters of the LSTM model to improve the accuracy of the model. The results show that the model accuracy remains above 90%. [ABSTRACT FROM AUTHOR]