Influencing Factors Analysis and Optimization of Land Use Allocation: Combining MAS with MOPSO Procedure

Jingjie Liu and Min Xia ()
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Jingjie Liu: College of Geographic Information and Tourism, Chuzhou University, Chuzhou 239000, China
Min Xia: College of Land Management, Nanjing Agricultural University, Nanjing 210095, China

Sustainability, 2023, vol. 15, issue 2, 1-17

Abstract: The rural land use preferences of multiple agents are crucial for optimizing land-use allocation. Taking Guanlin Town, Yixing City, China as an example, this study analyzed the factors by agents effecting rural land use conversion probability, identified the objectives and the constraints within the optimization of rural land-use allocation, and simulated the optimal land-use allocation for 2030 by combining MAS with an MOPSO procedure. The results showed that the preferences and decisions of main actors effected the optimal land-use allocation. The Government determined the conversion between land-use types. The preferences of the entrepreneurs resulted in the distribution of industrial land. Town residents made a high contribution to the configuration of the town residential land by considering some factors. Rural families influenced land-use allocation by considering the quality of cultivated soils, and the optimal spatial location of aquaculture systems. Four optimization objectives were identified. The most relevant constraints were the upper and lower limits of each land-use type. The land-use types in Guanlin town in 2015 had a low intensification and an unreasonable structure. The modeling results indicated a tendency for concentrated spatial distributions of rural land. The results of the present study can provide useful support for decision-making within land planning and consequent management.

Keywords: land planning; optimal land-use allocation; Multi Agent System (MAS); Multi Objective Particle Swarm Optimization (MOPSO) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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