基于改进障碍度模型的新质生产力内在驱动力识别研究OACHSSCD

This paper constructs an improved obstacle degree model,and optimizes the method in terms of contribution orien-tation,data adaptability and result interpretability.Based on the panel data of 30 provinces in China from 2015 to 2023,the paper also uses kernel density,convergence model,Gini coefficient decomposition,coupling coordination degree and path analysis meth-ods to identify the internal driving forces of new quality productivity and investigate its spatio-temporal evolution pattern,regional differences and driving paths.The research findings are as follows:First,the improved obstacle degree model has a good effect in identifying driving factors,providing methodological support for the study of new quality productivity.Second,there are obvious re-gional differences in driving factors.East China presents a"dual-wheel drive"of innovation and productivity,while the other re-gions are dominated by productivity factors;digital infrastructure,new industries,new energy and new technologies can all signifi-cantly promote the development of new quality productivity in various regions.Third,the development of new quality productivity in China presents spatial agglomeration,and the overall regional differences tend to converge;the differences in new quality pro-ductivity among the seven major regions mainly originate from inter-regional differences.