How to Evaluate the Level of Green Development Based on Entropy Weight TOPSIS: Evidence from China

Abstract

Evaluating the level of green development is of great significance to better implement the concept of green development. By constructing an evaluation index system for green development, this paper comprehensively uses the entropy weight Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method and coefficient of variation method to evaluate the green development level of 30 provinces in China from 2010 to 2019 and analyzes the regional differences of green development in China. The research findings are as follows: First, the level of green development in China is low but shows a slow rise trend, from 2010 to 2019; China’s green development level rises from 0.274 to 0.317, an increase of 15.7%. Secondly, regional differences of green development in China are obvious, with the level ranking from high to low as eastern, western, and central regions. Third, regional differences in China’s green development first widen and then narrow, with the variation coefficient of green development in 30 provinces and eastern, central, and western regions of China showing an inverted U-shaped trend of first increasing and then decreasing. Fourth, the regional difference of green development in eastern China is largest, followed by western China, and the smallest is central China. Finally, based on research findings, relevant policy recommendations are put forward.

Green Development Level Evaluation of Urban Engineering Construction in the Mid-Low Reaches of Yangtze River, China

“…The first is the subjective weighting method, which includes the Delphi method, analytic hierarchy process, or fuzzy comprehensive evaluation. The second is the objective weighting method, which includes principal component analysis, entropy weight method, or comprehensive index method [33]. While the subjective weighting method requires collecting initial data through questionnaire surveys, an excessive number of indicators can complicate questionnaire design and question selection.…”

Spatio–Temporal Evolutionary Features and Drivers of Green Competitiveness of Cities Surrounding the Yellow River

“…Wang et al (2022) analyzed the spatial and temporal characteristics and drivers of China's rural green competitiveness through spatial autocorrelation (SA) analysis and GeoDetector and found that China's rural green development presents a spatial differentiation feature that is high in the eastern region and low in the central and western regions [14]. Ma et al (2023) analyzed regional differences in China's green competitiveness through methods such as coefficient of variation analysis and found that the coefficient of variation in green competitiveness of China's 30 provinces and the eastern, central, and western regions show an inverted U-shape trend of first increasing and then decreasing [19]. explored the changing characteristics of the green development level in Hunan Province based on the DPSIR model and proposed that there are significant spatial differences in the level of green development in counties in Hunan Province, showing a pattern of "strong in the east and weak in the central and western parts of the country" [20].…”