Measurement and Prediction: Coupling Coordination of Finance and Air Environment

Abstract

This study finds that the comprehensive development degree (CDD) of the finance subsystem is less fluctuated than that of the air environment subsystem, and both subsystems share similarities in spatial distributions. The coupling coordination degrees (CCD) keep fluctuating with varied development directions and extents in different regions; besides, the eastern regions are higher than the western ones for the coupling coordination degrees. In the next years, the coordination degrees of the regions will have different tendencies: despite of the former fluctuation trends, regions in the coordination range will have upward trends, while those in the transition range will be likely to decline. The results are useful in proposing corresponding measures to promote the coordination development between finance and air environment.

Higher education and science popularization: Can they achieve coordinated growth?

“…In specific, the spatial distributions of the average overall performances of the two systems show slight differences: (1) for the higher education system, the generally believed regions with advantages in education (Sichuan, Hubei, Jiangsu, Shanghai, and Zhejiang) still maintained the competitiveness in higher education. These places enjoyed abundant population and traditionally paid great attention to education, therefore, they enjoy qualified local higher education institutes and college students with quantity, and performs better than other regions, which is supported by previous work [ 7 ]; (2) for the science popularization system, the coastal regions had better average overall performances (Jiangsu, Shanghai, and Zhejiang), mainly because of the convenient transportation and strong fiscal strength so that they were relatively much easier to attract more visitors, construct more science and technology museums, and organize more relevant activities, which is also supported by previous work [ 43 ].…”

“…Two categories of methods, which are objective category and subjective category, are usually used to assess the weight of indices when measuring the coordinated growth relationship between systems. For the objective category, including cluster analysis method, entropy weight analysis method, main component analysis method, Vlse Kriterijumska Optimizacija Kompromisno Resenje method (VIKOR), ranking of alternatives through functional mapping of criterion sub-intervals into a single interval method (RAFSI), criteria importance through inter-criteria correlation method (CRITIC), etc., the methods obtain weights of indices by calculating data objectively, therefore, the weights are much more objective with less bias or errors; however, some problems will be encountered if we use these objective methods; for instance, some important indices will be eliminated if main component analysis method is solely used; the accuracy is doubtful if cluster analysis method is used solely without combing other methods; VIKOR requires the accuracy of weight coefficients of the criterion, which in practice is somehow difficult to obtain; entropy, CRITIC, and RAFSI may not correct and may bias the weights in certain multi-criteria decision-making cases if they are used solely [ 43 – 47 ].…”

Measurement of sustainable higher education development: Evidence from China

“…The objective approach is used to determine the weight of indicators based on the factual data to avoid the deviation of factors and personal bias. The typical methods include cluster analysis [31], gray correlation analysis (GCA) [45], principal component analysis (PCA) [46], information entropy weight analysis (IEW) [47], and the technique for order preference by similarity to an ideal solution (TOPSIS) [47]. Previous research has proven the deficiencies of the methods above.…”

Coordinated Interactions of Sustainable Urbanization Dimensions: Case Study in Hunan, China

“…Based on this, an integrated measurement framework can be introduced to assess the coupling coordination mechanism between social urbanization and environmental urbanization. Based on previous research (Geng, Maimaituerxun, & Zhang, 2020; Geng & Tan, 2020; Liu et al, 2018), indicators of the integrated measurement framework are selected according to the following selection criteria: (a) select simple indicators to facilitate data collection and understanding, and to eliminate multicollinearity issues; (b) select indicators representing the connotation of sustainable urbanization; (c) select the indicators with more citations; (d) follow local policies or government strategies; (e) select indicators where the data include urban areas only and exclude rural areas. Thereafter, through expert reviews and quantitative analysis, the indicators are further screened and determined.…”