Cultivated land use efficiency and its driving factors in the Yellow River Basin, China

Abstract

China is accelerating green and low-carbon transformations of cultivated land to achieve a carbon peak by 2030 and to actively respond to global climate change. Evaluating the cultivated land use efficiency (CLUE) and determining its driving factors help reduce cultivated land-use carbon emissions and promote green and low-carbon transformations of cultivated land. However, previous carbon emission-based studies of CLUE and its driving factors have been insufficient. Therefore, in this study, we explored the spatiotemporal characteristics and driving factors of CLUE in the Yellow River Basin (YRB), China, based on carbon emissions. A slacks-based model with undesirable (bad) outputs, spatial autocorrelation models, and geographic detector models was used for the analysis, with statistical data from 2005 to 2017 as inputs. The overall CLUE values in the YRB increased and subsequently decreased with time. The Huang-Huai-Hai Plain (HHHP) consistently had the highest values, followed by the northern arid and semi-arid regions (NASR), the Loess Plateau (LP), and the Qinghai-Tibet Plateau (QTP). According to the global spatial autocorrelation, the CLUE had a positive association with themselves. The major cluster types moved from high–high and low–low to high–high cluster types in eastern Henan and southern Shanxi provinces, according to local spatial autocorrelation. Per capita grain possession was the main driver of CLUE in the YRB, HHHP, LP, and NASR, and road network density was the key driver of CLUE in the QTP, according to Geodetector analysis. Furthermore, non-linear enhancement dominates the driving factors of CLUE in the YRB, with non-linear enhancement dominating the HHHP, LP, and NASR, and bi-factor enhancement dominating the QTP. The findings of this study can be used to assist policymakers in formulating feasible, green, and efficient cultivated land-use policies.