Towards low-carbon cities: Patch-based multi-objective optimization of land use allocation using an improved non-dominated sorting genetic algorithm-II

[Display omitted] •We proposed a low carbon multi-objective land use allocation optimization model.•The model is solved with an improved non-dominated sorting genetic algorithm-II.•Resulting optimal schemes significantly outperform the original land use plan.•Vector patch-based optimized schemes better support low-carbon land use plan. The rational land use allocation is of great significance to the construction of low-carbon cities. The optimization model of land use allocation is an important tool that helps urban planners to quantitatively trade-off among the multi-objectives and achieve optimal land use schemes. For multi-objective optimization of low-carbon land use allocation, the models conducted by existing studies generally tend to be based on gridded data, lack of comprehensive consideration of quantitative and spatial objectives, and efficient algorithms to execute the optimization process. Therefore, this paper proposed a patch-based low carbon multi-objective land use allocation (LC-MLUA) optimization model involving both quantitative and spatial optimization targets. The LC-MLUA optimization model was solved with an improved non-dominated sorting genetic algorithm-II (NSGA-II), and the weighted-sum method was used to make the final selection under different preferences. The LC-MLUA optimization model was then applied to a case study of Changxing, a county-level city in east China, and there were three key results. (1) The LC-MLUA optimization model had a remarkable outperform of the land use allocation than the original land use plan, and the optimized values of economic benefit, emission reduction, and accessibility increased by 27.0%, 6.2% and 8.3%, respectively. (2) The LC-MLUA optimization model generated a series of optimal schemes to support suggestion-making for the low-carbon adjustment of the land use structure and spatial layout. (3) The LC-MLUA optimization model based on vector land patch data was proved more efficient as the unit number was reduced by 5 times than gridded data and better reflected the land use planning practice. (4) Compared with other algorithms, the improved NSGA-II had better performance in the number of solutions, target optimization rate, and comprehensive performance. Based on these results, it suggests that the patch-based LC-MLUA optimization model method can provide good technical support for low-carbon land use planning, and can be flexibly applied to other cities.