Abstract
The detection and attribution of spatial differentiation in coal resource security (CRS) is a prerequisite for energy governance and regional scientific decision-making. Here we constructed a 4-As framework for CRS evaluation and screened indicators, evaluated the CRS of 598 counties in the Yellow River Basin (YRB) in 2017 through the entropy-cloud model, analyzed the spatial autocorrelation of CRS, and quantified the effects of 4-As indicators on CRS using the GeoDetector method. We discovered that (1) the spatial pattern of CRS is highly consistent with the basic geographic pattern. The coal resource security indexes (CRSIs) have apparent spatial differentiation. CRS shows differences in coal endowment and circulation, supplemented by differences in economic activities and environmental impacts. (2) The CRSIs have spatial autocorrelation, and the overall agglomeration trend is apparent. Due to coal development and administrative attributes differences, the CRSI shows different agglomeration types in each county. (3) Degree in coal aggregation, etc., are the dominant indicators for spatial differentiation of CRS in the whole basin of the YRB. The factor driving forces are significantly different. The driving force of two-factor interaction is more vital than that of single-factor interaction, and the interaction type is mainly nonlinear enhancement.
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