Although　bioethanol　is　viewed　as　a　promising　alternative　to　traditional　fossil　fuels,　its　commercialization　is　hindered　by　high　production　costs　and　uncertain　policies.　This　study　proposes　a　novel　integrated　framework　to　support　decision　making　in　the　bioethanol　supply-chain　system　under　complex　uncertainties.　By　considering　various　geographical,　economic,　and　environmental　factors,　potentially　suitable　sites　are　identified　using　a　geographic　information　systems-based　(GIS-based)　multi-criteria　decision-making　approach.　Then,　a　two-stage　stochastic,　fuzzy,　chance-constrained　programming　model　is　developed　to　determine　investment　decisions　(i.e.　site　and　size　of　biorefineries)　and　operational　strategies　(i.e.　production　schedule,　biomass　supply　network,　etc.)　in　the　face　of　uncertainties.　The　proposed　research　framework　is　tailored　for　a　case　study　in　Henan　Province,　China,　which　suggests　the　installation　of　six　biorefineries　with　a　total　capacity　of　679.11　million　liters　to　reduce　local　carbon　emissions　by　1.54　million　tons.　Decision　makers　with　different　risk　preferences　can　use　this　method　to　find　optimal　tradeoff　strategies　between　system　cost　and　risk　under　uncertainties.　Sensitivity　analysis　also　reveals　that　increasing　the　proportion　of　bioethanol　blending　targets　is　the　key　exogenous　variable　to　promote　the　use　of　local　biomass　resources.