Level measurement, regional differences and dynamic evolution of new quality productivity in the Yellow River Basin
Weixian Xue, Yanzhi JIN
Xi'an University of Technology
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摘要与影响
New quality productivity (NQP) is an important driving force for China's high-quality development and an important support for promoting ecological civilization in the Yellow River Basin (YRB). Based on the characteristics of "high technology, high quality and high efficiency" of the NQP, a "three productivity" measurement index system is constructed, and the global principal component - entropy weight method is used to measure the NQP of the YRB from 2009 to 2022, and the evolution law of NQP is discussed from multiple dimensions. The results show that: (1) The overall development index fluctuates greatly, but the regional imbalance. The downstream index is higher than the upstream index. Shandong, Inner Mongolia and Shaanxi ranked the top three, with central cities such as Jinan and Lanzhou ranking high. (2) The overall Gini coefficient fluctuated between 0.015 and 0.035, and the regional differences narrowed in the later period, but the growth rate slowed down. The average annual decline in the upper, middle and lower reaches is 4.3% to 5.8%, of which the lower reaches have the largest decline. Supervariable density (contributing up to 61.3%) is the primary disparity source, while inter-group differences rise to 7.58%. Markov chain analysis reveals "high-low polarization" in state stability, with increasing leapfrog potential. σ-convergence is observed basin-wide, most notably in midstream regions. (3) A gradient coordination mechanism is proposed to enhance downstream-upstream synergy. Priorities include strengthening tech innovation, optimizing resource-manufacturing-market industrial chains, and improving factor allocation through talent sharing and digital infrastructure. These measures aim to bolster NQP’s coordinated development across the YRB.
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经济 / 管理Regional Economic and Spatial Analysis
Economic Growth and Productivity · Efficiency Analysis Using DEA
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