Abstract

Understanding ecosystem service trade-offs and synergies is the foundation for achieving the efficient management of the ecosystem and improving human well-being. Therefore, in this paper, multi-scale trade-offs and synergies among eleven secondary ecosystem service (ES) types of four ecosystem service categories in the mountainous areas of North China in 2015 are assessed using statistical methods and spatial analysis, and their driving factors are analyzed, including natural factors and socioeconomic factors. The results show that for the study area, only the raw material production service and nutrient cycle maintenance service, water supply service and hydrological regulation service, environmental purification service and biodiversity maintenance service, environmental purification service and aesthetic landscape service, and biodiversity maintenance service and aesthetic landscape service show extremely strong synergistic correlations at four spatial scales. The spatial autocorrelation among services at different scales is basically consistent with the statistical correlation, but the degree of correlation varies. Unlike the grid, township, and county scales, all service pairs are spatially autocorrelated across the study area at the land use type scale, and the clustering characteristics are more obvious and similar. All service pairs are synergistic with low–low values at the mountain–plain junction in the Taihang Mountain (THM) and in the northern part of the Bashang region (BSR). The spatial trade-offs and synergies of the regulating and maintenance services in the study area are closely related to the spatial distribution of land use types. The main natural influence on the synergistic trade-offs of ecosystem services (ESs) at the township scale is elevation, while socioeconomics are mainly influenced by population and GDP. This study can contribute to strengthening decision makers’ understanding of the spatial scales of ES relationships in mountain areas and the extent to which different natural and socioeconomic factors influence them.

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