Evaluation and Forecasting of Coordinated Development Level between Digital Economy and New-Type Urbanization Based on Machine Learning in China
Year: 2025
Type: conference-paper
Abstract: In the context of the accelerating convergence of the global digital economy and new urbanization, this study focuses on China and aims to address the challenges of its coordinated development. A review of extant studies reveals limitations in both theory and practice. The present study proposes an evaluation index system for the digital economy and new urbanization, employing the entropy weight method to calculate development indicators. The Coupled Coordination Degree (CCD) model is employed to assess their synergistic relationship, while recurrent neural network (RNN) and long short-term memory (LSTM) machine learning models are utilized for prediction. The findings indicate that from 2011 to 2023, China's digital economy development index and new urbanization construction level exhibited an upward trend, though notable regional variations were observed. The CCDL between the two shows a slight downward trend, indicating an imbalance and a transitional development stage. Preliminary analyses suggest that the CCDL will persist in demonstrating a downward trend. To promote coordinated development, it is imperative to optimize policies, reallocate resources, and innovate systems. This study provides a foundation for China to achieve high-quality urbanization and balanced regional development.
Authors: Zhenfeng Zhu, Xiaowen Huang, Yang Liu
Institution: Guangdong University of Science and Technology
Language: English
Subfield: Strategy and Management
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W4412789910