Case study

Lifecycle Assessment and CBios Emission Factor for biodiesel production with variation of the biomass and eligibility criteria

Abstract

The Brazilian National Biofuels Policy (RenovaBio) has as its main purpose to promote the growth of the biofuels chain in Brazil, from more efficient and sustainable production models, culminating in the reduction of greenhouse gas (GHG) emissions, as well as contributing to the fulfillment of the commitments assumed by the country at COP-21. RenovaCalc is a tool from RenovaBio that is capable of analyzing lifecycle inventory data of background processes, added to the technical parameters of agricultural and industrial production that is informed by the biofuel producer. The final product is given through an Energy-Environmental Efficiency Grade (NEEA). In this context, NEEA, together with raw material eligibility values, will serve as the basis for the calculation of decarbonization credits (CBios). Given this context, the present study sought to investigate three scenarios: S1: excluded agricultural phase; S2: 100% eligible soy combined with the use of beef tallow, and S3: production of biodiesel with 100% soybean oil. The study compared two biodiesel producing national industries to NEEA certified by the Brazilian National Office of Petroleum, Natural Gas and Biofuels (ANP). It can be concluded that NEEA does not have a direct influence on the quantity of CBios to be traded, that is, the highest ratio is given from the eligibility (%) of the raw material chosen for the production of the fuel. Thus, scenario 2, which relied on the use of waste, proved to be 10 times more profitable compared to the other scenarios, in both analyses, due to its high eligibility. However, in terms of NEEA, it was noted that the exclusion of the agricultural phase (scenario 1) was the one that was most efficient in terms of gCO₂ eq./MJ. It is relevant to emphasize the importance of proper handling and practices that guarantee the traceability of the grain so that the eligibility indexes are optimized.