An interval fuzzy chance-constrained programming model for sustainable urban land-use planning and land use policy analysis

•An interval-fuzzy-chance-constraint programming method is proposed for land-use planning.•Model can tackle uncertainties described as intervals, probabilities and fuzzy sets.•The model systematically considers economic/environmental/social/ecological factors.•IFCCP is applied to a real case study of land-use planning in Nanjing.•Results can analyze the tradeoff between socio-economic and eco-environment. In this study, an interval fuzzy chance-constrained land-use allocation (IFCC-LUA) model is developed for sustainable urban land-use planning management and land use policy analysis under uncertainty. This method is based on an integration of interval parameter programming (IPP), fuzzy flexible linear programming (FFLP) and chance-constrained programming (CCP) techniques. Complexities in land-use planning management system can be systematically reflected, thus applicability of the modeling process can be highly enhanced. The developed method is applied to planning land-use allocation practice in Nanjing city, China. The objective of the IFCC-LUA is maximizing net benefit from LUA system and the main constraints include investment constraints, land suitability constraints, water/power consumption constraints and wastewater/solid waste capacity constraints. Modeling results indicate that desired system benefit will be between [1.34, 1.74]×1012 yuan under the minimum violating probabilities; the optimized areas of commercial land, industrial land, agricultural land, transportation land, residential land, water land, green land, landfill land and unused land will be [290, 393]hm2, [176, 238]hm2, [3245, 4390]hm2, [126, 170]hm2, [49, 66]hm2, [1241, 1679]hm2, [102, 138]hm2, [7, 10]hm2 and [178, 241]hm2. They can be used for generating decision alternatives and thus help decision makers identify desired land use policies under various system-reliability constraints of economic development requirement and environmental capacity of pollutant. Tradeoffs between system benefits and constraint violation risks can also be tackled.