Spatial rationalization of location value and use conversion cost in multiobjective land use optimization: Coupling hedonics pricing model and genetic algorithm

Abstract

This study critiques global-based land use optimization methods and introduces a spatially rationalized framework to address their limitations. Global optimization often oversimplifies land value changes and use conversion costs by relying on averaged data, leading to unrealistic projections and inefficiencies. By contrast, the proposed spatial rationalization optimization framework integrates NSGA-II with a Ricardian-hedonic valuation model, providing a more accurate representation of local dynamics. Results of a case study, Mek’ele City in Ethiopia, illustrates that the global optimization outputs false signals on location value and use conversion cost whose policy implications are serious. The global optimization version false signals the land use conversion cost is 390.9 per square meter while the spatial rationalization version indicates the net change is a gain of 11.8. The spatial rationalization also maintains more areas with their original use indicating lower fiscal effect of implementing the optimal plan. The spatial allocation of uses aligns more closely with real-world conditions and enhances the effectiveness of land valuation strategies, such as land value capture. The findings highlight the need for spatially informed optimization in land use planning, advocating for policies that reflect true value changes and improve implementation accuracy. The study’s insights emphasize the importance of moving beyond global optimization frameworks.