Quantification and driving force analysis of ecosystem services supply, demand and balance in China

Sci Total Environ. 2019 Feb 20:652:1375-1386. doi: 10.1016/j.scitotenv.2018.10.329. Epub 2018 Oct 26.

Abstract

Spatially quantifying ecosystem services (ES) supply, demand and balance dynamics and exploring their relations with socio-economic factors are very significant for regional sustainability. In this study, land use/land cover (LULC) matrix model was used to quantify the relevant capacity of the ES supply, demand and balance in China. Also, we explored the spatial-temporal characteristics of ES at three scales (national, provincial and city scale). The results revealed that the ES supply, demand and balance in China had strong spatial heterogeneity and showed different time-varying characteristics on different scales. For the provinces with ES deficit, linear optimization model was then applied to achieve the theoretical ES balance through land cover conversion. For the provinces with negative regulating ES, farmland should be significantly reduced while desert and grassland should be converted to farmland and forest for the provinces with negative provisioning ES. In addition, the key driving factors of ES dynamics were selected through ordination analysis of 109 cities at city scale. The results showed that forest proportion was the most important influencing factor of ecosystem services supply while ES supply management can be carried out by adjusting the output values of agriculture, forestry and animal husbandry. On the other hand, ES demand can be adjusted by per capita GDP, energy consumption per unit of GDP and permanent population. The results can provide targeted information with ES management and this method can be applied at a smaller scale considering data availability. This study provides a convenient and propagable method for ES quantification and a quantitative support for regional ES management decisions.

Keywords: Driving force analysis; Ecosystem services supply and demand; LULC matrix; Socio-economic factors; Time-varying analysis.