Evaluation and prediction of high-quality development in China: A time-spatial analysis from Hubei province

Abstract

High-quality sustainable development is the common goal pursued by all countries in the world. China’s high-quality development (HQD) includes five concepts of “innovation, coordination, green, opening-up, and sharing”. In this context, we established an evaluation system that included these five fundamental characteristics, used the comprehensive entropy method and BP neural network to evaluate and predict the high-quality development of Hubei Province in China, and conducted a spatiotemporal deductive analysis. The study found that: 1) Economic growth still has an important impact on HQD, for all the five main indicators, “opening-up” and “innovation” have the highest impact weights, which are 0.379 and 0.278, respectively, while the proportions of coordination and sharing are both less than 0.1. 2) There are huge differences in the level of high-quality development between regions in Hubei Province. From 2010 to 2020, the average comprehensive index of Wuhan City was greater than 0.5, which is 7 times that of the second Xiangyang City, and 46 times that of the last Shennongjia district. 3) In the past few years, the overall high-quality development of Hubei Province has shown a fluctuating upward trend. However, due to the impact of COVID-19, during the following years, its comprehensive development index will decline by an average of 5% annually, but starting from 2022, it will gradually increase. As a result, tailored and coordinated sustainable environmental policies of integrating institutional and open-market measures should be provided.

A Study on the Spatial-Temporal Evolution and Problem Area Identification of High-Quality Urban Development in the Central Region

“…Second is a substitute for high-quality development through the construction of a comprehensive indicator system. Among them, the literature is mostly constructed based on the five development concepts and growth quality index [14][15][16][17]. Some studies do not construct indicators from the above dimensions but still implement the new development concept to some extent through the content of their indicators.…”

Path Optimization of Technological Innovation Efficiency Improvement in China’s High-Tech Industries Based on QCA and GA-PSO-BP Neural Network

“…where s, m, and n are the number of neurons in the hidden layer, input layer, and output layer, respectively, and are taken as integers between [1,10]. In the model, m = 7, n = 1, and s takes values in the range of [4,13]. The other parameters and structures in the network are kept constant, and the model with the number of neurons in hidden layers 4-13 is tested for attention.…”

“…Therefore, technological innovation is a multi-input and multi-output process by using internal and external Strengthens international cooperation and increasing financial support. Can contribute to the innovation efficiency of high-technology enterprises Huang et al (2022) [13] Comprehensive entropy method and BP neural network…”