Multi-scenario optimization of land use structure and prediction of ecosystem service value in Guanzhong Plain urban...

Rapid economic development has led to significant changes in land use in the Guanzhong Plain urban agglomeration, which alters regional ecosystem service value (ESV). Based on the land use and driver data of the Guanzhong Plain urban agglomeration, we used the system dynamics (SD) model coupled with the mixed-cell cellular automata (MCCA) model to predict the subtle spatial and temporal changes of ESV within the land use unit in 2040 under the scenarios of natural development, economic development, ecological protection, and arable land conservation, to reveal the responses of ESV to the socio-economic evolution. We examined the impacts of land use change on ESV by using the sensitivity index. The results showed that land use transformation between 2000 and 2020 in the study area was mainly the conversion between arable land, forest, grassland, and the conversion of arable land to construction land. Due to increased forest and water, ESV increased slightly during this period. In 2040, compared with the ecological protection scenario, the proportion of forest in the economic development scenario decreased by 1.8%, and the construction land increased by 1.3%. During 2020-2040, under the economic development scenario, ESV showed a downward trend in the central and eastern regions but an upward trend under the arable land conservation scenario, with hydrological and climatic regulation contributing the most to ESV. Total ESV showed a decreasing trend except for the ecological conservation scenario. In the ecological protection scenario, land use change positively impacted ESV. In contrast, ESV had a negative response to land use change in other scenarios, with the greatest reduction in the economic development scenario. The research could provide new methods for multi-scenario land use simulation and ESV prediction and have scientific and practical significance for optimizing land space layout, land resource planning management, and sustainable development path strategy of urban agglomerations.