基于2011—2022年中国内地30个省份数据，构建新质生产力指标体系并使用熵值法测度各省市以及八大综合经济区新质生产力水平，分析其动态演进趋势以及地区差异，识别新质生产力关键影响因素、逻辑关系及作用路径，并通过障碍度计算主要发展障碍因子，提出区域新质生产力提升路径。结果显示：①生产力发展水平高的地区包括广东、北京、江苏、浙江以及山东，按区域划分，东部沿海地区新质生产力发展水平最高；②各区域新质生产力水平呈上升态势但存在较大差异，高水平地区对低水平地区存在正向空间溢出效应；③技术合作流动、未来产业布局、数字经济发展和科技创新为影响地区新质生产力发展的主要障碍因子。研究丰富了新质生产力理论分析框架，为区域因地制宜发展新质生产力提供参考借鉴。

The intensification of anti-globalization and protectionism has led the world into a new period of turbulent transformation. At this stage, new quality productive forces is a key factor for China's high-quality development. New quality productive forces is characterized by high technology, efficiency, and quality, fundamentally transforming productivity through technology, resource allocation, and industrial transformation. Regional disparities exist in China in terms of economy and industry due to varying development conditions, necessitating tailored development strategies as a primary governmental task. It is of significant theoretical and practical importance to analyze the evolution and disparities in new quality productive forces across regions, study influencing factors, identify obstacles, and explore enhancement paths for driving high-quality development so as to promote industrial upgrading and transformation, and lead global economic development.
A new quality productive forces development level index system is constructed based on data from 30 Chinese provinces between 2011 and 2022. The entropy method is used to measure the current state of new quality productive forces development at the national level, in each province, and within the 8 major economic zones. Markov chains and Dagum's Gini coefficient are employed to analyze the dynamic trends and regional development disparities. The DEMATEL-ISM model is utilized to conduct a structural analysis of the influencing factors of new quality productive forces, identifying intrinsic key factors and the logical relationships and pathways between these factors. Main obstacles hindering the development of new quality productive forces are identified through obstacle degree calculations, followed by targeted path analysis for enhancing new quality productive forces in each region, integrating all analytical indicators.
The results show that (1) in terms of the development of new quality productive forces, China has experienced a consistent upward trajectory from 2011 to 2022. Provinces with high development levels include Guangdong, Beijing, Jiangsu, Zhejiang, and Shandong. When divided by region, the overall level of new quality productive forces is highest in the eastern coastal regions. (2) According to Markov chain and Dagum analysis, significant differences exist in the levels of new quality productive forces development across various regions in China. These differences primarily stem from disparities among the 8 major economic zones. The largest regional disparities are between the northwest region and the eastern coastal, southern coastal, and northern coastal regions, with the lowest disparities between the middle reaches of the Yellow River and the middle reaches of the Yangtze River. Within various regions, the southern coastal areas exhibit the greatest disparities, while the eastern coastal areas show lower and more balanced differences. Additionally, there are spatial spillover effects, where regions with high development levels can drive development in regions with lower levels without hindering the development of higher-level regions. (3) Utilizing the DEMATEL-ISM model and obstacle degree analysis, the study reveals that within the 15 primary indicators of new quality productive forces, 8 indicators are identified as root factors, which include aspects related to the human capital structure, while 7 indicators are classified as outcome factors, encompassing elements such as human capital investment. Root factors affecting the structure include levels of technological cooperation flow, development of emerging industries, and future industrial layout. Intermediate influencing factors include human capital investment, telecommunications infrastructure, and human capital structure, among 7 indicators. Surface influencing factors include human capital assurance, transportation infrastructure, and five other indicators. The main obstacles in most regions are inadequate development in technological cooperation flow, future industrial layout, structure of the digital economy, and level of technological innovation. (4)Through path analysis, the study identifies four strategic development paths to bolster the new quality productive forces. The first path is dedicated to surmounting core barriers and igniting the catalyst for their advancement. The second path is geared towards navigating intermediate obstacles and establishing a systematic framework for growth. The third path underscores the importance of regional collaboration to amplify joint development initiatives. The last path concentrates on the pivotal role of core leadership in fostering balanced development of new quality productive forces.