Integrating Linear Programming and CLUE-S Modeling for Scenario-Based Land Use Optimization Under Eco-Economic Trade-Offs in Rapidly Urbanizing Regions

Rapid urbanization has profoundly reshaped land use patterns and intensified the conflict between economic development and ecological sustainability, particularly in fast-growing cities like Guangzhou, a core hub of the Guangdong–Hong Kong–Macao Greater Bay Area. To address these trade-offs, this study integrates a linear programming (LP) model with the CLUE-S model to simulate sustainable land use in 2035 under four policy-oriented scenarios: Ecological Protection (EPS), Cultivated Protection (CPS), Economic Development (EDS), and Balanced Development (BDS). Results reveal significant variation across objectives. Under the ecological objective, EPS increased ecosystem service value (ESV) by 9.85% and enhanced forest connectivity, though with reduced agricultural land. CPS preserved agricultural land but compromised ecological gains. Under the economic objective, EDS maximized economic benefit (20.29%) but caused landscape fragmentation. BDS under economic goals delivered strong economic gains (18.39%) but only marginal ESV improvement (0.87%), reflecting limited ecological effectiveness. Notably, EPS under economic goals and BDS under ecological goals emerged as the most sustainable pathways, balancing growth and conservation. The integrated LP–CLUE-S framework effectively captures land use dynamics and supports scenario-based spatial planning, offering practical guidance for optimizing land resource allocation in rapidly urbanizing regions.