Climate change has become a global non-traditional security issue. As an important ecological security barrier in China, the comprehensive management of the Yellow River basin with carbon emission reduction as the core is of great significance to the high-quality development of the Yellow River basin. In this paper, by calculating the level of carbon total factor productivity in the Yellow River basin from 2000 to 2017, the spatial correlation analysis model and geographic detector model are used to analyze the spatio-temporal dynamic evolution characteristics of carbon productivity in the Yellow River basin. The results show that: 1) the average annual growth rate of county carbon total factor productivity in the Yellow River Basin from 2000 to 2017 is 14.98%. From the source of carbon total factor productivity growth, scale technological progress is the main driving force to promote the growth of county carbon total factor productivity in the Yellow River Basin. 2) from the perspective of spatio-temporal trend, the growth of carbon total factor productivity in counties in the Yellow River Basin from 2000 to 2017 showed obvious periodic fluctuations and spatial imbalance. 3) from the point of view of the decomposition items of carbon total factor productivity in each province, the driving situation of total carbon factor productivity in the counties of nine provinces in the Yellow River Basin is mainly divided into two kinds: the first situation is “two-track drive”. It mainly includes 8 provinces of Gansu, Henan, Inner Mongolia, Ningxia, Shandong, Shanxi, Shaanxi and Sichuan. The second case is “monorail drive”, which represents the province as Qinghai. 4) the regional gap of county total carbon factor productivity in the Yellow River basin shows a narrowing trend, has the characteristics of dynamic convergence, and its growth shows the phenomenon of overall disparity. From the distribution of each province, the convergence characteristic is obvious, but the level of carbon total factor productivity fluctuates greatly.
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