Spatial Association and Quantitative Attribution of Regional Ecological Risk: A Case Study of Guangxi, China

Hui Wang ()
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Hui Wang: College of Geography and Planning, Nanning Normal University, Nanning 530001, China

Sustainability, 2025, vol. 17, issue 19, 1-20

Abstract: Regional ecological risk assessment (RERA) is a valuable tool for analyzing ecological risks at a broad-scale whose potential needs to be further exploited, especially when it comes to the in-depth mining of the final risk. Thus, in this research, based on RERA results acquired through land use function valuation and the ecological risk source-receptor-vulnerability framework, spatial autocorrelation analysis and geographical detector methods were employed to explore the spatial association features of regional ecological risk and its significant influencing factors in Guangxi, China. Next, a bivariate local spatial autocorrelation analysis tool was used to manifest the spatial impact directions of the important affecting factors on the final risk. The results of the study indicate that: (1) the north and west parts of Guangxi had a higher final ecological risk than that of the southeast; (2) from a percentage viewpoint, the low, medium, high, and very high levels of ecological risk accounted for 41.85%, 28.31%, 21.86%, and 7.98% of the total area, respectively; (3) the final regional ecological risk exhibited significant positive spatial correlation (Moran’s I = 0.466, p = 0.000) and the high-high association type was concentrated in the north and west parts of Guangxi while there was a low-low type in the southeast; (4) the most significant influencing factors for final risk consisted of lithology, land use ecology and production functions, slope, and soil; (5) compared with ecology and production functions, lithology, slope, and soil exhibited stronger positive influences on the final risk. Spatial association and quantitative attribution studies can increase the deepness of RERA and undoubtedly advance this field in the future. Moreover, based on the findings from the spatial quantitative attribution analysis, more explicit sustainable development countermeasures could be determined for the region.

Keywords: regional ecological risk assessment (RERA); production-living-ecology land use function; spatial autocorrelation; geographical detector; sustainable development; Guangxi (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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