ISETS Working Papers

ISETS Working Paper No. 24-0001

A Study on the Efficiency of Sustainable Development of Green Economy in Chinese Cities in the Context of Carbon Neutrality Based on Deep Learning

Ying Zhang, Keyi Ju

Abstract: While the global economy is developing, the problem of environmental pollution is also becoming more and more serious. “Carbon peaking and carbon neutrality” are important goals for China’s green and low-carbon economic development, which China has successively proposed. In order to reflect China’s major decision and social responsibility to cope with global warming, it is necessary to incorporate the concept of “carbon peak and carbon neutral” into China’s second centur goal, so as to truly demonstrate China’s power and improve its international status. Therefore, in order to solve the problems in the process of achieving “peak carbon and c  arbon neutrality”, this paper analyzes China’s green low – carbon economic developmen t model, mainly discusses the scientific planning of low carbon environmental protection  in China, considers how to reduce energy emissions and control the total amount of carbon emissions. In the context of deep learning, this paper proposes a long and short- term memory model (LSTM) for predicting carbon emissions. The model uses a genetic    algorithm to search for optimality of the parameters of the LSTM network, and the LSTM   network is supplemented with empirical modal decomposition and structural optimality search. Experiments on the carbon emission data set show that this method can accurately and effectively predict the carbon emission trend because the genetic algorithm ensures the goodness of the network and the empirical modal decomposition removes some noise.