To promote the public housing development on private land based on the Land Sharing Pilot Scheme (LSPS) in Hong Kong, this study develops a multi-objective optimization (MOO) model to optimize the land use allocation for increasing public housing supply.
The study uses the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to solve the MOO model to obtain optimal plot ratio increase (a) and public housing allocation ratio (β). The data of a real LSPS application is used for validation.
The model effectively identifies the Pareto front for LSPS parameters. For the empirical case, the optimal plot ratio increase (a) is 2.57 and the public housing gross floor area (GFA) allocation ratio (β) is 0.7, suggesting that under current parameters: (1) the optimal plot ratio demanded by LSPS applicants (i.e. developers) exists under LSPS framework; (2) the GFA allocation ratio for public housing should adhere to the government's minimum requirement. Policy implications are provided from the government and developer perspectives.
Unlike macro-scale land use studies, this article focuses on micro-scale residential land allocation within a real policy framework. It provides the first theoretical and quantitative decision-making tool for LSPS, bridging the gap between theory and practice. This study can be extended to a larger area and broader scope in terms of a replicable methodological framework and extension to PPP projects.
