Balancing economic development and forest ecosystem services: Multi-scale spatial land use optimization modeling for Guiyang, China

The imbalance of land use and the degradation of ecosystem services have become key issues constraining the region's sustainable development. Existing studies still lack forest ecosystem service valuation and spatial allocation optimization. Therefore, this study develops a multi-scale optimization framework by integrating the NSGA-II and SLSQP algorithms with the PLUS model employed for land use planning. Taking Guiyang City as an example, it also constructs four scenarios, namely, inertial development (ID), economic development priority (EDP), Ecological Protection Priority (EPP), and ecological-economic balance Development (EEB). In all, this study proposes an optimization strategy for land use in Guiyang City. It is found that the optimal allocation of forest coverage (66.3%) and the proportion of arable land (25.1%) under the EEB scenario creates a spatial pattern of “centralized construction in the south, eco-agriculture in the center, and eco-conservation in the north.” The analysis shows that GDP and population density are the main driving forces for expanding construction land. Sensitivity analysis identifies forest coverage rate and population carrying capacity as the most critical regulatory parameters (RSI = 1.49 and 0.91, respectively). This study reveals the optimal spatial pattern of regional land use and the underlying driving mechanism. This is achieved by constructing a multi-scale coupling framework that integrates “total optimization” with “spatial allocation.” The framework offers a novel methodology for synergizing economic development and forest ecosystem services. • Tree species heterogeneity integrated into forest ecosystem service valuation. • Multi-scale framework couples macro land allocation with tree species optimization. • Optimal scenario retains 87.5% economic and 93.3% ecological benefits. • Population capacity and woodland coverage identified as key regulatory indices.