Spatial correlation among cultivated land intensive use and carbon emission efficiency: A case study in the Yellow River Basin, China

Considering the current global goal of carbon neutrality, the relationship between cultivated land intensive use (CLIU) and carbon emission efficiency (CEE) should be explored to address the global climate crisis and move toward a low-carbon future. However, previous work in this has been conducted at provincial/regional scales and few have identified the spatial correlation between CLIU and CEE at the scale of large river basins. Therefore, this study explored the spatiotemporal characteristics of CLIU, cultivated land carbon emissions (CLCE), and CEE, as well as the spatial correlation between CLIU and CEE in the Yellow River Basin (YRB), China. A comprehensive evaluation model, the Intergovernmental Panel on Climate Change (IPCC) coefficient methodology, existing data envelopment analysis model, and bivariate spatial autocorrelation models were used to analyze statistical data from 2005 to 2017. We found that the overall CLIU and CLCE values in the YRB exhibited a continuous increase; the average carbon emission total efficiency and carbon emission scale efficiency first decreased and then increased, and the average carbon emission pure technical efficiency gradually decreased. Areas of high CLCE were concentrated in eastern areas of the YRB, whereas those of high CLIU, carbon emission total efficiency, carbon emission scale efficiency, and carbon emission pure technical efficiency predominantly appeared in the eastern areas, followed by central and western areas of the YRB. Spatial analysis revealed a significant spatial dependence of CLIU on CEE. From a global perspective, the spatial correlations between CLIU and CEE changed from positive to negative with time. Moreover, the aggregation degree between CLIU and CEE gradually decreases with time, while the dispersion degree increases with time, and the spatial correlation gradually weakens. The local spatial autocorrelation further demonstrates that the number of high–low and low–high clusters between CLIU and CEE gradually increases over time, while the number of high–high and low–low clusters gradually decreased over time. Collectively, these findings can help policymakers formulate feasible low-carbon and efficient CLIU policies to promote win–win cooperation among regions.