ArticleScientific reports2025
Spatial correlation network characteristics and driving factors of carbon emissions from cultivated land use in the yellow river basin.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The study aimed to investigate the spatial correlation network of carbon emissions from cultivated land use in the Yellow River Basin(YRB) and its driving factors, which is conducive to promoting collaborative carbon emission reduction in cultivated land use across the region. The study utilized the carbon emission coefficient method to calculate carbon emissions from cultivated land use in the YRB between 2008 and 2022. A modified gravity model and social network analysis method were then employed to analyze the characteristics of the spatial correlation network of carbon emissions from cultivated land use in the YRB. Additionally, the Quadratic Assignment Procedure (QAP) method was utilized to identify driving factors. The research findings indicate that: (1) Between 2008 and 2022, the overall carbon emissions from cultivated land use in the YRB increased by 27.6%. The proportion of carbon emissions from different sources, from largest to smallest, was as follows: straw burning (over 60%), agricultural inputs (over 20%), and crop cultivation (around 13%). (2) According to the overall network characteristics, the spatial correlation network underwent an evolution from dispersion to concentration and then to adjustment. The network connectivity was good, efficiency fluctuated and rose to stability, centrality showed an inverted "U" shaped change, with Shandong and Henan remaining at the core of the network throughout. (3)Based on individual network characteristics, the network structure showed a core-periphery hierarchy, with Gansu and Inner Mongolia identified as the core provinces. (4) QAP analysis indicated that factors such as geographical proximity, urbanization levels, and agricultural machinery input intensity positively influenced the spatial correlation network, while factors like agricultural economic levels, farmer income levels, and environmental regulations had negative effects. Based on the findings of this study, feasible pathways are proposed from aspects including the three sources of carbon emissions, regional cooperation within the YRB, and policy optimization.
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