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

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.

Innovative measurement, trade-off-synergy relationship and influencing factors for agricultural net carbon emissions and effective supply of agricultural products in China

“…Therefore, if Pearson parametric correlation test is applied, the trade-off or synergy relationship between these two, such as the relationship of one increase and one decrease contains the two opposite situations of the best and the worst, can not be accurately assessed. So, this paper uses the bivariate spatial auto-correlation analysis ( (8) , (9) ) [ 42 , 43 ] to present the trade-off-synergy relationships between ANCE and ESAP. According to the conclusion of Zhang et al (2022) [ 31 ] on the decoupling relationship between agricultural carbon emission reduction and agricultural product supply guarantee, the classification of area can be divided into low-low agglomeration trade-off zone, low-high agglomeration synergy zone, non-significant zone, high-low agglomeration non-trade-off-synergy zone and high-high agglomeration trade-off zone.…”

Interrelationships between Urbanization and Ecosystem Services in the Urban Agglomeration around Poyang Lake and Its Zoning Management at an Integrated Multi-Scale

“…The calculations were performed using GeoDa 1.20 software, and the geographic weight matrix used was based on the first-order queen or rook contiguity. The formulas [60][61][62][63] are as follows:…”

Influence of Natural and Social Economic Factors on Landscape Pattern Indices—The Case of the Yellow River Basin in Henan Province

“…Through spatial autocorrelation modeling, Huang et al [49] analyzed the relationship between influencing factors, such as the intensity of human activities in the Three Gorges Reservoir area, and the landscape pattern and found that the distance was the key influencing factor affecting the evolution of the ecosystem. Ding et al [50] used a hierarchical approach of bivariate spatial autocorrelation to study the spatial autocorrelation and spatial heterogeneity of the Liuxi River, effectively capturing the spatial explanatory power of the drivers. In Henan Province, the topography is complex, stretching from the rugged mountains and hills in the west to the expansive plains in the east.…”