Comparison of biofuel life-cycle GHG emissions assessment tools. The case study of Brazilian sugarcane ethanol

Antonio Bonomi, Coordinator, Brazilian Bioethanol Science and Technology Laboratory (CTBE)


Antonio Bonomi Chemical Engineer. PhD in Chemical Engineering by the University of Minnesota, USA, 1977. Coordinator of the Process Intelligence Division at CTBE – Bioethanol Science and Technology National Laboratory of CNPEM – BRAZIL, since November 2008. Senior researcher at IPT – Research Institute of the State of São Paulo, Brazil from 1983 to 2008, developing activities in different areas: biotechnology processes, mathematical modeling and simulation, metrology in chemistry, among others. Worked for more than 40 years in the development of bioethanol technology, having several published papers in the area. Director of Biofuels at AEA – Brazilian Automotive Association from 2005 to 2009. Manager for Fuel Quality at ANP – Brazilian National Oil, Natural Gas and Biofuels Agency from 2000 to 2003. Presently, member of IEA-Bioenergy – Task 39 as one of the Brazilian representatives. Adviser in academic graduate programs in Chemical Engineering and Biotechnology areas.

About the speech:

The application of certain life cycle assessment (LCA) models obtaining discrepant results, specifically for the calculation of GHG emissions, jeopardize the use of LCA in the policy context and discredits its compliance with established climate change targets.

Three regulatory models which are currently operational and publicly available – GREET from the US, GHGenius from Canada, and BioGrace from the EU, and an assessment platform particularly designed for the assessment of sugarcane ethanol in Brazil, Virtual Sugarcane Biorefinery or VSB – were utilized to calculate the GHG emissions associated with ethanol produced from sugarcane in Brazil,
The aim of this presentation is to provide an overview of the tools, and to identify and track the main reasons for the results obtained by each model, depicting the main differences and commonalities in methodological structures, calculation procedures, and assumptions made.