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|Title:||Comparison of biofuel life-cycle GHG emissions assessment tools : the case studies of ethanol produced from sugarcane, corn, and wheat|
|Author:||Pereira, L. G.|
Chum, H. L.
|Abstract:||The use of alternative fuels, particularly bio-based fuels, has been an important strategy to achieve greenhouse gas (GHG) emission reductions compared to petroleum-based fuels. However, discrepancies between results obtained by using different attributional life-cycle assessment (LCA) tools have challenged the credibility of the individual assessments, and as result, the progress towards or compliance with GHG mitigation targets. The objective of this study was to identify the main differences and commonalities in methodological structures, calculation procedures, and assumptions for the major commercial biofuel, ethanol, across three public LCA tools, BioGrace (EU), GHGenius (Canada), and GREET (U.S.), and a research-oriented fourth, the Virtual Sugarcane Biorefinery (VSB), a Brazilian platform for sugarcane ethanol assessments. The calculated emissions across models ranged from 16 to 45 for sugarcane, 43-62 for corn, and 45-68 g CO(2)eq MJ(-1) for wheat ethanol. Harmonizing the three public models with VSB assumptions for sugarcane ethanol produced in Brazil, the range was reduced to 16-17 g CO(2)eq MJ(-1) for sugarcane ethanol. Agricultural production (e.g., N2O emissions from fertilizers; energy and fuel use; straw field-burning; and limestone application) and ethanol shipping were found to be the major causes for variations for differences calculated for sugarcane ethanol. Similarly, harmonizing BioGrace and GHGenius calculations using GREET assumptions for U.S. corn ethanol generated nearly identical results (models varied within a 3% range). The coproduct treatment method was found to be the most influential parameter in the variations calculated for both corn and wheat ethanol. The application of the tools as part of GHG emissions accounting requirements is often defined via regulations and differences and/or conflicting assumptions set-forth in these models lead to most differences observed. Our study provides recommendations for promoting transparency in LCA calculations and assumptions across the tools used in research and development or for regulatory tools regarding biofuels|
|Appears in Collections:||FEQ - Artigos e Outros Documentos|
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