Land use and cover change(LUCC) is one of the important causes of the Earth’s carbon cycle imbalances resulting from failure in optimizing land use. The solution to this problem has been the hotspot of research in land and environmental science. We took ''''low carbon'''', ''''energy saving'''' and ''''high-efficiency'''' as the goals of land use optimization,and integrated Markov-CA(Cellular Automaton),the Grid-Fractal model and GIS,in order to study carbon emission objective function,to establish a simulation method for land use spatial allocation optimization,to evaluate the effect of the method on carbon emissions. Regulation policy on three types of land use spatial allocation was proposed,including ''''low-carbon type'''', ''''low-carbon-economic type'''' and ''''economic type''''. We applied the method to analyze the land use spatial allocation in Taixing City of the ''''Yangtze River Delta'''' regions in China,and obtained the following results:(i) The three optimization types would improve carbon emissions by 3. 21%,1. 80% and 0. 36% respectively in 2020,compared with 2010;(ii) The actual planning for 2020 was close to the ''''low-carbon-economic type'''';(iii) The optimization method and regulation policy,combining local optimization and global control,could meet the sustainable multi-objective requirements for low-carbon constraints of land use spatial allocation. The result of this research could also serve as a reference for exploration into patterns of regional low-carbon land use and measures for energy saving and emission reduction.
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