Identifying Driving Factors of Basin Ecosystem Service Value Based on Local Bivariate Spatial Correlation Patterns

Xue Ding, Yuqin Shu (), Xianzhe Tang and Jingwen Ma
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Xue Ding: School of Geography, South China Normal University, Guangzhou 510631, China
Yuqin Shu: School of Geography, South China Normal University, Guangzhou 510631, China
Xianzhe Tang: Guangdong Province Key Laboratory for Land Use and Consolidation, South China Agricultural University, Guangzhou 510631, China
Jingwen Ma: School of Geography, South China Normal University, Guangzhou 510631, China

Land, 2022, vol. 11, issue 10, 1-19

Abstract: Ecosystem service value (ESV) is a crucial indicator for evaluating ecosystem health, and identifying its spatial driving factors will help to provide scientific decision support for ecological protection and restoration. This study took the Liuxi River Basin in China as the research object and used the value equivalent method to estimate regional ESV. In the process of using the Geodetector model (GDM), the study area was spatially stratified by using the local bivariate spatial correlation pattern to mine the potential driving factors of ESV. The results show that: (1) From 2005 to 2018, the total value of ecosystem services in the Liuxi River Basin showed a fluctuating and increasing trend. ESV had high-value aggregation in the northeastern mountainous areas with high green space coverage and high river distance accessibility and low-value aggregation in the central and southwestern urban areas with frequent human activities. Its spatial heterogeneity and aggregation patterns were of statistical significance. (2) The spatial distribution characteristics of ESV were affected by various driving factors to varying degrees. The order of their degree of influence on ESV was per capita green area > slope > the proportion of urban and rural human settlements > river distance accessibility > population. (3) Compared to the previous study, the stratification method employing the local bivariate spatial correlation pattern more fully considers spatial autocorrelation and spatial heterogeneity. It effectively captured the spatial explanatory power of driving factors. This study can provide new ideas for capturing the driving mechanisms of ESV and insights into the sustainable development of the ecological environment in other regions with similar characteristics worldwide.

Keywords: ecosystem service value; spatial correlation pattern; spatial heterogeneity; driving factor (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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