Portrait of China’s common prosperity level based on GRA-TOPSIS and deep learning

Abstract

We studied China’s Common Prosperity process by assessing and comparing the level of Common Prosperity in different regions of China and made some beneficial recommendations to government departments. The research data comes from the China Statistical Yearbook, which includes data from 31 provinces and cities from 2015 to 2020. According to the relevant research, eleven evaluation indicators were selected. We combined GRA with the TOPSIS method for scoring and the K-means clustering algorithm for dividing the GRA-TOPSIS scoring results into three evaluation levels. Then, the convolutional neural network model was used to predict and simulate the level of common prosperity. Taking 2020 as an example, the results show: (1) From 2015 to 2020, China’s Common Prosperity level reached its highest point in 2020. Due to the impact of COVID-19 in 2019, the scores of 31 regions are generally lower than in the previous four years. The situation changed in 2020; (2) In terms of regional distribution, the economic development of Beijing, Shanghai, and other eastern regions is relatively good, with a higher degree of Common Prosperity than that of other regions; (3) The average prediction accuracy is high in our model. It can be close to 100%, indicating that the model has a good prediction effect. In addition, we made recommendations based on the research results, which have good references for actively promoting common prosperity.

Spatial Network Analysis of Coupling Coordination between Digital Financial Inclusion and Common Prosperity in the Yangtze River Delta Urban Agglomeration

“…The CPI was subsequently computed by combining comprehensive weights with the MARCOS method. A comparison of the method proposed in this paper with Entropy-TOPSIS [23], PCA-TOPSIS [24], and GRA-TOPSIS [25] revealed a high degree of consistency in the ranking outcomes, alongside superior evaluation differentiation. This confirms the efficacy and stability of the proposed method.…”

“…To enhance the comparability of common prosperity development levels among regions, Xie et al [22] and Cheng et al [23] combined the entropy weighting method with the TOPSIS method to calculate the CPI for Chinese provinces and rural China, respectively. Meanwhile, MCDA methods that integrate Principal Component Analysis (PCA) [24] and Grey Relational Analysis (GRA) [25] with TOPSIS have also begun to be applied in the field of measuring the CPI. Entropy, PCA, and GRA are common objective weighting methods that may not fully reflect the decision-making environment's actual situation due to their reliance on objective weighting, which lacks subjective initiative.…”