Increasing total factor carbon productivity (TFCP) is crucial to mitigate global climate change and achieve carbon neutrality target. The Yellow River Basin is a critical energy area in China, but its TFCP is relatively low, which results in particularly prominent environmental problems. This paper investigates TFCP using MCPI, Global Moran’s I and kernel density estimation based on panel data of the 9 provinces along this vast basin in 2007–2017. The results demonstrate that: the average value of TFCP fluctuates around 1 and overall TFCP evolution exhibits significant spatial aggregation effect, and technological progress is the dominant impetus for TFCP growth. At regional level, regional heterogeneities of TFCP change and its dynamics exactly exist, with Qinghai the lowest performance and Shandong the highest performance. Moreover, global Moran’s I index reflects there is a significant positive spatial correlation between provincial TFCP, and cumulative TFCP takes on a certain degree of club convergence features. Furthermore, specific and targeted recommendations have drawn from this paper, in particular for the Yellow River Basin, to increase TFCP and achieve sustainable development in the long run.
Developing forest carbon sinks (FCS) is significant for China to achieve carbon neutrality. The Yellow River Basin is a principal area for China’s energy consumption, and the forest resource distribution of this vast basin is spatially dependent, determining that the development of FCS cannot be separated geographically. Based on the spatial panel data of 69 prefecture-level cities in the Yellow River basin from 1988 to 2018, we used ESDA to reveal the spatial–temporal characteristics of FCS, and we established a spatial econometric model to investigate the transregional spillover effects of FCS. The results showed that: ① the overall FCS showed a gradually increasing trend, with a general distribution characteristic of “upstream > midstream > downstream” from 1988 to 2018. ②FCS presented an agglomeration distribution pattern with significant spatial spillover effects, and the degree of uneven spatial agglomeration varied across the years. ③Urbanization rate, forestry fixed assets investment, labor input, and afforestation management level directly promote FCS growth, whereas forest harvesting, precipitation, and temperature decrease FCS. ④Urbanization rate, forest harvesting, forestry fixed assets investment, labor input, and afforestation management level have positive spillover effects on FCS, while precipitation and temperature have adverse spillover effects on FCS. Hence, pertinent policy suggestions are put out to serve as a guide for increasing FCS.
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