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.