Quantification and spatially explicit driving forces of the incoordination between ecosystem service supply and social demand at a regional scale

Accurate spatio-temporal quantifying of ecosystem services (ESs) supply and social demand and their coordination dynamics, and determining which factors affect such coordination and the spatial distribution patterns of these effects, is essential to guide targeted ecosystem management decisions. Unfortunately, quantitative data demonstrating the explicit spatial relationships between the biophysical supply and social demand for ESs, as well as identifying the spatial non-stationary response of such relationships to influencing factors, is currently inadequate. To this end, this study employed China's Loess Plateau (LP) as an example to quantify ES supply and social demand in 2000 and 2015, during which the society and ecology in this region have undergone tremendous changes. Then, the coupling coordination degree model was used to explore the coordination relationships between ES supply and social demand. Also, we analyzed the spatial correlations between such coordination relationships and various natural factors, policy factors and socio-economic factors, using geographically weighted regression. The results showed that ES supply and social demand had great spatiotemporal variations, and both the ES supply and social demand increased from 2000 to 2015. Although the incoordination between ES supply and social demand was great, such incoordination did improve from 2000 to 2015 with positive transitions from the lower coordination grades to the higher coordination grades. The occurrence of coordination between ES supply and social demand was highly correlated with vegetation cover driven by the "Grain for Green" Program, followed by slope (a natural factor) and socio-economic factors. This study helps deepen our understanding of the incoordination between the ES supply and social demand of the LP and the spatially explicit drivers of such incoordination, and could support auxiliary decision-making for regional ecosystem management.