Spatio-temporal evolution and trend prediction of urban ecosystem service value based on CLUE-S and GM (1,1) compound model

Abstract

Ecosystem service value (ESV) is a significant indicator related to regional ecological well-being. Evaluating ESV premised on continuous time series land benefit data can provide an accurate reference for regional ecological civilization construction and sustainable development. Taking Shijiazhuang, the capital city of Hebei Province as an example, the study analyzed land use changes based on the land use data of the continuous time series from 2000 to 2020 and introduced a socio-economic adjustment factor and biomass factor adjustment factor to construct a dynamic assessment model of ecosystem service value. The spatiotemporal changes of the ecosystem service value in Shijiazhuang City were evaluated, and the dynamic prediction of the ecosystem service value was made using the CLUE-S model and the GM (1,1) model. (1) The changes in the overall ESV and spatial pattern in Shijiazhuang are strongly linked to the change in land use, and the contribution of cultivated land, woodland, and grassland to ecosystem service value exceeds 90%. (2) Between 2000 and 2020, the value of ecosystem services illustrated a dynamic change and gradually declined, with the total amount falling from 28.003 to 19.513 billion yuan. Among individual ecosystem services, the value of regulation services suffered the most serious loss. (3) CLUE-S and GM (1,1) perform well in the prediction of ESV. The prediction outcomes illustrate that the ecosystem service value of Shijiazhuang will continue to decline by 2025, and the ecosystem value will drop to 16.771 billion yuan. This research may offer a reference for the dynamic assessment of ESV of the continuous sequence and help to promote regional ecological protection and sustainable development.

Characteristics Analysis and Prediction of Land Use Evolution in the Source Region of the Yangtze River and Yellow River Based on Improved FLUS Model

“…The simulation model of land use change is an effective tool to predict the future land use situation in the region [17]. Currently, widely utilized models include the Markov Model [18], Cellular Automata Model (CA) [19], Conversion of Land Use and its Effects at Small Regional Extent Model (CLUE-S) [20], the Agent-Based Model (ABM) [21], the Future Land Use Simulation Model (FLUS) [22], and their coupled variants [23][24][25]. The FLUS model stands out for its computational efficiency and extensive simulation capabilities, making it a popular choice for land use simulation [26,27].…”