To address the disconnect between macro-quantity planning and micro-spatial allocation at the township level during rapid urbanization, this study developed a coupled model framework based on Multi-Objective Planning (MOP) and the Future Land-Use Simulation (FLUS) model, using Longhu Town as a case study. First, economic and ecological benefit coefficients were calibrated via the Grey Prediction Model and equivalent factor method to define three scenarios: Economic Priority (EPS), Ecological Protection (EcPS), and Balanced Development (BDS). Second, an Artificial Neural Network (ANN) was employed to quantify driving factors, coupled with self-adaptive Cellular Automata (CA) for spatial allocation in 2030. The results indicate that: (1) The model exhibits high reliability for small-scale simulation, with a Kappa coefficient of 0.95 and a Figure of Merit (FoM) of 0.29. (2) Strategic orientations lead to distinct spatial differentiation: under the EPS, urban–industrial land expands significantly northwestward (+16.60%), causing fragmented erosion of cropland; the EcPS achieves a 5.27% increase in forest land and ecological restoration through strict quantitative constraints; the BDS realizes the synergy of urban clustering and ecological enhancement with a marginal urban increase (0.72%). (3) The eastern urban sectors and northeastern cropland belts are identified as future land-use conflict hotspots. The “quantity-space” collaborative optimization path proposed in this study provides a scientific basis and dynamic simulation tool for refined territorial spatial management at the township scale.
Ma et al. (Wed,) studied this question.
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