Identifying the spatial disparities and determinants of ecosystem service balance and their implications on land use optimization

Abstract

Ecosystem services (ESs) are increasingly affected by human interventions, and the ES balance between supply and demand plays a vital role in guaranteeing the expected efficacy of ecosystem restoration projects. However, the spatial disparities of ES balance, along with its determinants and spillover effects, remain unclear, constraining effective ES management and landscape planning. The aim of this study was to fill these gaps by quantifying the ES balance in the restoring Loess Plateau using an expert-based ES matrix approach and by examining the spatial associations between ES balance and driving factors via an integrated regression approach. The results showed that the county-scale ES balance was closely related to the land-use composition and population density. Geographic locations of counties with ES surplus were mainly concentrated in mountainous areas with high proportions of woodland and grassland, while urbanized land and a high population density resulted in an ES deficit. Forest and grass regeneration, due to revegetation practices, alleviated ES deficits, while rapid urbanization and population growth aggravated ES imbalance. The integrated regression approach demonstrated that the ES balance and its dependencies (i.e., landscape metrics and population density) had remarkable spatial heterogeneity and spillover effects, which should be practically considered in localized ES management and landscape optimization. Excessive agricultural reclamation and urban expansion improved grain productivity and economic profits but deteriorated landscape fragmentation, further aggravating the ES deficit. In contrast, excessive revegetation practices promoted ecosystem restoration and improved ES surplus but threatened food security. Therefore, an appropriate balanced state should be maintained for sustainable ecosystem restoration through timely and efficient policy interventions and landscape optimization.

Keywords: Deficit; Determinant; Integrated regression approach; Loess Plateau; Surplus.