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

Zhan Wu and Ding-Xuan Zhou

Journal of Mathematics, 2023, vol. 2023, 1-12

Abstract: 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.

Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://downloads.hindawi.com/journals/jmath/2023/6652800.pdf (application/pdf)
http://downloads.hindawi.com/journals/jmath/2023/6652800.xml (application/xml)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:hin:jjmath:6652800

DOI: 10.1155/2023/6652800

Access Statistics for this article

More articles in Journal of Mathematics from Hindawi
Bibliographic data for series maintained by Mohamed Abdelhakeem ().