Bioethanol　has　emerged　as　a　promising　alternative　to　fossil　fuels,　but　its　commercialization　is　hindered　by　high　costs　and　uncertainties　surrounding　feedstock　supply　and　policies.　To　address　these　challenges,　a　two-stage　stochastic　robust　programming　model　is　developed　for　regional　biorefineries　planning　and　supply　chain　management　with　various　technological　choices　and　uncertainty　fusions.　The　model　maximizes　total　profits　by　optimizing　site　selection,　alternative　technology　combinations,　feedstock　inventories,　and　accounting　for　uncertain　and　seasonally　varying　biomass　supply.　This　method　helps　decision-makers　achieve　risk-aversion　robust　optimal　solutions,　tailored　for　a　case　study　in　Guangdong,　China.　The　results　suggest　that　installing　8　biorefineries　using　the　LHW-SSF-A　technique　combination　would　be　optimal.　The　desired　feedstock　warehouse　capacity　would　be　358.48　ktons,　with　low　utilization　closely　tied　to　seasonal　crop　harvests.　Risk-averse　decision　makers　would　take　conservative　production　strategies,　potentially　reducing　investment　in　biorefineries,　while　market　prices　and　subsidies　could　encourage　fewer　conservative　investors.