## 2 Materials and Methods
### Methods
#### 2.3.3 The PLUS Model  
The PLUS model is utilized to predict LUCs and is primarily composed of the Land Expansion Analysis Strategy (LEAS) and the Multi-Type Random Seed CA Model (CARS) [43]. The LEAS module identifies and samples the expanded areas within each land cover category between two land use datasets. It employs the random forest method to determine the development likelihood for each land cover type, the CARS module to simulate the formation of patches in time and space, and the PLUS model optimizes simulation strategies for land use dynamics, and it incorporates future planning policies as a driving factor affecting LUCs [52]. Therefore, this study employs this model to predict the land use in the study area in 2035; thus, it subsequently simulates the spatiotemporal changes in the ESV of the area in 2035. Before conducting land use simulation, it is necessary to select the driving factors; the selection of driving factors should be comprehensive and systematic. The driving factors selected in this study include socioeconomic factors, climate and environmental factors, and geographical factors. Socioeconomic factors include GDP, Pop, distance to roads, and residential areas. Climate and environmental data include annual average temperature and annual average precipitation. Geographical factors include distance to mountains, distance to major water bodies, elevation, slope, and aspect. When simulating land use, it is necessary to set the neighborhood weight parameters. The neighborhood weight parameter indicates the expansion intensity of each land class, with values closer to 1 suggesting that the land class is less likely to transform into other land classes. Based on the LUCs in the study area, this study combines the research of multiple scholars and repeated tests and optimizations to obtain the optimal neighborhood weight results [31; 42; 46], as shown in Table 4 below. In the PLUS model, the _Kappa_ coefficient greater than 0.75 indicates a higher level of simulation accuracy and better simulation outcomes. In this study, the _Kappa_ coefficient is 0.89; this indicates high simulation accuracy and suggests that this model is suitable for predicting future spatial patterns of land use. Finally, the PLUS model can perform multi-scenario simulations based on different focal points to predict spatial distribution under various development paths, thereby predicting changes in the regional ecological environment. This research leverages the methodologies and outcomes of multiple scholars to modify the likelihoods of land use transitions across different categories [42; 43; 53]. It then combines these findings with regional policy requirements to establish different scenarios for simulating regional land use. The scenario settings include NDS, FPS, EPS, and CDS. The NDS is formulated by considering the empirical land use development trajectories within the study area spanning two decades. No restrictive conditions are set, and the evolution follows the existing expansion patterns of land use. In the FPS, land that has been continuously used for agriculture for five years is designated as permanent basic farmland, while land with a slope of less than 6\({}^{\circ}\) is designated as high-quality farmland. These two types of farmlands are classified as restricted areas. The likelihood of transforming farmland into urban areas is decreased by 30%, whereas the chances of converting forest, grassland, built-up land, and unused land into farmland are augmented by 20%, 20%, 50%, and 70%, respectively. In the EPS, there is a focus on the development of forests, water, and grasslands. In \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Land Use** & **Farmland** & **Forestland** & **Grassland** & **Water Area** & **Unused Land** & **Built-Up Land** \\ \hline Neighborhood & 0.18 & 0.07 & 0.03 & 0.2 & 0.4 & 0.12 \\ weight & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Neighborhood weight settings.