Ecosystem Service Trade-Offs and Spatial Pattern Optimisation under Different Land Use Scenarios: A Case Study in Guanzhong Region, China

Abstract

Understanding the complex interactions (i.e., trade-offs and synergies) among ecosystem services (ESs) and exploring land use optimisation are important to realize regional ecological governance and sustainable development. This study examined Guanzhong Region, Shaanxi Province, as the research object. We established 12 future land use scenarios and projected the future land use patterns under the future climate change scenarios and local development policies. Next, we assessed the four main ecosystem services—carbon sequestration (CS), habitat quality (HQ), soil conservation (SC), and food supply (FS) by using related formulas and the InVEST model. Furthermore, the production possibility frontier (PPF) was used to measure trade-offs and synergistic relationships among ESs, and extract the optimal ES group under the different target needs. The results are as follows: (1) In the future 12 land use scenarios of 2050 in Guanzhong Region, forested land increased evidently in the RCP2.6 ecological protection scenario (18,483.64 km). In the RCP6.0 rapid urban development scenario, construction land showed evident expansion in the central and northeastern areas (4764.52 km2). (2) Compared with the ESs under the future multiple scenarios, CS and HQ achieved the maximum value in the RCP8.5 ecological protection scenario. In the RCP2.6 ecological protection scenario, the amount of SC was the largest (3.81 × 106 t). FS in the RCP2.6 business as usual scenario got the maximum value (18.53 × 106 t). (3) By drawing the optimal PPF curve of multiple scenarios in 2050, trade-off relationships were found between FS and CS, HQ, and SC, and synergistic relationships were found between CS, HQ, and SC. Next, the optimal ES groups under the fitted curve were selected by comparing with the ESs of 2018, and adjusting the land use areas and spatial pattern to finally optimise the relationships between ES and achieve the best land use spatial pattern.