The goal of this study is to identify which processes contribute most to fuel CO(2)e emissions, energy use, and employment generation when driving a typical Brazilian flex fuel vehicle (FFV) model with bagasse-derived ethanol. A hybrid life cycle assessment (LCA) is used to report the associated impacts and a structural path analysis (SPA) is utilized to identify, quantify, and rank the highest carbon emissions, energy use, and employment generation input paths in the supply chain of bagasse-derived ethanol. Additionally, this study explores the integrated hybrid LCA approach as a tool to explain the economic impacts of bagasse-derived ethanol. Hybrid LCA links process-based and input-output-based analyses into a consistent mathematical framework, thereby allowing the incorporation of economic factors into an environmental intervention matrix. The functional unit used in this study is a travel of 500 km with hydrated bagasse-derived ethanol in a Brazilian average FFV 1.0. The total CO(2)e emission is 55.4 kg, with 40 \% of the emissions from lime and enzyme production and the sugarcane production for bagasse (agricultural phase). The sulfuric acid, lime, and the agricultural phase are identified as the most intensive energy consumers, representing approximately 50 \% of the total energy used. From the economic perspective, commerce and services, ethanol industrial, and agricultural phase generate the greatest number of jobs in this bagasse-derived ethanol life cycle. The focus has shifted from first-generation biofuels to more advanced technologies. The results illustrate the importance of a complete life cycle vision for assessing the sustainability of a biofuel. Agricultural biomass production is identified as relevant in all environmental and economic impact categories assessed here. Integrated hybrid LCA was used as an analytical tool capable of assessing the product's sustainability performance, based on the fact that its mathematical framework allows the incorporation of economic and environmental coefficients into a single intervention matrix using a combined database.