Synergistic Evolution of China’s Green Economy and Digital Economy Based on LSTM-GM and Grey Absolute Correlation

Guoteng Xu, Shuai Peng, Chengjiang Li () and Xia Chen
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Guoteng Xu: State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
Shuai Peng: State Key Laboratory of Public Big Data, Guizhou University, Guiyang 550025, China
Chengjiang Li: School of Management, Guizhou University, Guiyang 550025, China
Xia Chen: School of Modern Post, Beijing University of Posts and Telecommunications, Beijing 100876, China

Sustainability, 2023, vol. 15, issue 19, 1-29

Abstract: This study aims to understand the synergistic evolution of the green and digital economies towards sustainable development. Previous research lacked quantitative analysis, which hindered the development of a comprehensive understanding. An evaluation index system is established using the CRITIC and entropy weight combination methods. The TOPSIS model is utilized to evaluate indicators and derive a comprehensive development index for both economies. The LSTM-GM model is employed to predict the evolutionary trends for the next five years. The absolute grey correlation model is applied to analyze historical and future synergistic evolutionary trends. Findings show increasing levels of green and digital economic development. The digital economy promotes green economic development by enhancing efficiency through innovation and upgrades. The green economy facilitates the structural adjustment of the digital economy by reducing emissions and enhancing resource utilization. Predictions indicate a steady growth in both economies and an increasing synergistic evolution. Based on the analysis, policy recommendations are proposed to promote the integration and development of the digital and green economies, facilitating high-quality synergistic growth.

Keywords: green economy; digital economy; TOPSIS; grey relation analysis; long short-term memory neural network (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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