Page 44 - 《华中农业大学学报）》2024年第6期

38 华 中 农 业 大 学 学 报 第 43 卷

Analyzing socio-economic spatial factors driving soil erosion in China
based on multi-scale geographically weighted regression model

1
2,3
LI Keke ,ZHOU Zhanhang ,WANG Zhen 1,3
1.College of Resources and Environment， Huazhong Agricultural University， Wuhan 430070， China；
2.College of Public Administration， Huazhong Agricultural University， Wuhan 430070， China；
3. Interdisciplinary Research Center for Territorial Spatial Governance and Green Development， Huazhong
Agricultural University， Wuhan 430070， China

Abstract Soil erosion poses a threat to food security and ecosystem services， and is one of the severe
environmental problems facing China. It is affected by both natural factors and human activities. At pres⁃
ent， a large number of studies in China have focused on the driving effect of socio-economic factors on soil
erosion， but there are still insufficient in studying the spatial non-stationary relationship between the two
and paying attention to the scale differences of affecting factors. 346 prefecture-level cities in China were
used to study the complex driving mechanisms of socio-economic activities on soil erosion. The soil erosion
prediction （RUSLE） model and multiscale geographically weighted regression （MGWR） model were used
to investigate the spatial heterogeneity of soil erosion in various cities in China using 2017 as a reference
year. The spatial driving effects of socio-economic factors on rates of soil erosion in prefecture-level cities
across China and the differences in the scale of action between factors were studied. Results showed that the
spatial distribution of the rate of soil erosion in various prefecture-level cities in China had a significant posi⁃
tive spatial correlation. Hotspots of soil erosion were mainly distributed in the west， northeast， the Yunnan-

Guizhou Plateau， Sichuan Basin and the Loess Plateau. Compared with models based on global regression
and traditional geographically weighted regression models， MGWR significantly improved the explanatory
power of socio-economic variables on the rate of soil erosion， with a model fitting goodness of 0.87. From
the perspective of driving factors， the direction of the effect of each driving factor on the rate of soil erosion
in various prefecture-level cities in China had structural differences with changes of spatial location except
for the factor of regional gross domestic product per capita. On average， population density was the factor
contributing the most to the rate of soil erosion in various prefecture-level cities in China. The rate of soil
erosion in prefecture level cities in China was more susceptible to the influence of multiple cropping indices
in the western region， while the driving mechanism of socio-economic factors on the rate of soil erosion in
the eastern region was more complex， and the spatial scale differences of different driving factors were
more obvious. It is indicated that decision makers should fully consider the spatial heterogeneity of the im⁃
pact of human activities on soil erosion to achieve the sustainable development of soil and water conserva⁃
tion.
Keywords soil erosion； soil erosion preparation model； revised universal soil loss equation；multiscale
geographically weighted regression model； socio-economic factors； patial non-stationarity

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