Reconciling landscape multifunctionality and population-based spatial equity under future land-use scenarios in Jingning County

Rapid land-use change is reshaping both the supply of ecosystem services (ESs) and the spatial distribution of human demand, creating new challenges for equitable landscape management in ecologically sensitive regions. However, how alternative land-management strategies jointly affect landscape multifunctionality (LMF) and population-based spatial equity remains insufficiently understood. This study integrated the Patch-generating Land Use Simulation (PLUS) model with ES assessment models to evaluate changes in landscape multifunctionality and population-weighted inequality in Jingning County, China. We calculated the LMF index using a preference-weighted sum approach, with weights empirically derived from a stakeholder survey, to integrate biophysical ES supply with social demand. Population-based spatial equity was quantified using the Gini coefficient of LMF relative to resident population. The results showed that preference-weighted LMF decreased from 0.60 in 2000 to 0.37 in 2020, while the Gini coefficient increased from 0.13 to 0.19, indicating that multifunctionality became less evenly distributed relative to the resident population. Future scenarios revealed distinct trade-offs between overall multifunctionality and its population-weighted distribution. Single-objective scenarios improved selected outcomes but failed to simultaneously optimize both LMF and population-weighted equality. In contrast, the optimization scenario, which incorporates fine-scale forest structure adjustment, achieved relatively higher multifunctionality and a lower Gini coefficient, suggesting that spatially targeted landscape management may help reduce multifunctionality–distribution trade-offs. These findings provide case-based evidence for balancing ecological conservation, multifunctional landscape performance, and the population-weighted distribution of landscape benefits under future land-use change.