Ecological conservation- and economic development-based multiobjective land-use optimization: Case study of a rapidly developing city in central China

Ecological conservation has long been a hot topic in land-use planning. However, ecological conservation conflicts with economic development in the process of urbanization, which has been noted in a great many studies. In existing studies of land-use planning, a sumweighted method (SWM) has usually been used to combine several objectives into one objective, and only one solution generated. However, with the SWM, the trade-offs between conflicting objectives are ignored. In this paper, faced with the shortcomings of the existing approaches, a genetic algorithm-based multiobjective optimization (MOO) approach is proposed to search for the Pareto solutions of the land-use structure, followed by a cellular automaton model to represent the spatial land-use distribution. A rapidly developing city in central China, Wuhan, was selected as the case study area. Maximizing the gross domestic product (GDP) value generated by the land use and maximizing the ecosystem service value (ESV) were taken as the multiple objectives for land-use planning inWuhan. The Pareto solutions are compared with the solutions of three different single objectives: one, maximizing ESV; another, maximizing the sum of GDP and ESV; and the last one, maximizing GDP. It is concluded that the Pareto solutions can reflect the potential possible values of GDP and ESV. Moreover, the Pareto solutions can represent a trade-off between economic development and ecological conservation.