Evaluation of Provincial Economic Resilience in China Based on the TOPSIS-XGBoost-SHAP Model

• Published in: Journal of mathematics (Hidawi) Vol. 2023; pp. 1 - 12
• Main Author: Wu, Zhan
• Format: Journal Article
• Language: English
• Published: Cairo Hindawi 2023; Hindawi Limited
• Subjects: Algorithms; Artificial intelligence; Consumer goods; Consumption; Decision analysis; Economic analysis; Economic conditions; Economic development; Economic growth; Economic recovery; Economic statistics; Economic structure; Education; Efficiency; Entrepreneurship; Expenditures; Fuzzy sets; GDP; Gross Domestic Product; Growth rate; Infrastructure; Machine learning; Mathematics; Optimization; Per capita; Principal components analysis; Resilience; Retail sales; Social change; Technical services; Unemployment; Value added; China
• ISSN: 2314-4629; 2314-4785
• DOI: 10.1155/2023/6652800

The aim of this research is to propose a framework for measuring and analysing China’s economic resilience based on the XGBoost machine learning algorithm, using Bayesian optimization (BO) algorithm, extreme gradient-boosting (XGBoost) algorithm, and TOPSIS method to measure China’s economic resilience from 2007 to 2021. The nonlinear effects of its key drivers are also analysed in conjunction with the SHAP explainable model to explore the path of China’s economic resilience enhancement. The results show that the level of China’s economic resilience is improving, but the overall level is low; R&D expenditure and the number of patents granted are important factors affecting China’s economic resilience with a significant positive relationship. The BO-XGBoost model outperforms the benchmark machine learning algorithm and can provide stable technical support and scientific decision-making basis for China’s economic resilience measurement analysis and high-quality economic development.