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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

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 CO2eq 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 CO2eq 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 calculationsmore » 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.« less

Authors:
 [1];  [2];  [3];  [4];  [4];  [4]
  1. Brazilian Center of Research in Energy and Materials, Campinas (Brazil); Univ. of Toronto, ON (Canada)
  2. Brazilian Center of Research in Energy and Materials, Campinas (Brazil)
  3. Brazilian Center of Research in Energy and Materials, Campinas (Brazil); Univ. of Campinas (UNICAMP), Sao Paulo (Brazil)
  4. National Renewable Energy Lab. (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Bioenergy Technologies Office; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
OSTI Identifier:
1512665
Alternate Identifier(s):
OSTI ID: 1642291
Report Number(s):
NREL/JA-2A00-68651
Journal ID: ISSN 1364-0321
Grant/Contract Number:  
AC36-08GO28308
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Renewable and Sustainable Energy Reviews
Additional Journal Information:
Journal Volume: 110; Journal Issue: C; Journal ID: ISSN 1364-0321
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
09 BIOMASS FUELS; LCA; GREET; GHGenius; BioGrace; greenhouse gas emissions; commercial ethanol; harmonization

Citation Formats

Pereira, Lucas G., Cavalett, Otavio, Bonomi, Antonio, Zhang, Yi Min, Warner, Ethan, and Chum, Helena L. Comparison of biofuel life-cycle GHG emissions assessment tools: The case studies of ethanol produced from sugarcane, corn, and wheat. United States: N. p., 2019. Web. doi:10.1016/j.rser.2019.04.043.
Pereira, Lucas G., Cavalett, Otavio, Bonomi, Antonio, Zhang, Yi Min, Warner, Ethan, & Chum, Helena L. Comparison of biofuel life-cycle GHG emissions assessment tools: The case studies of ethanol produced from sugarcane, corn, and wheat. United States. https://doi.org/10.1016/j.rser.2019.04.043
Pereira, Lucas G., Cavalett, Otavio, Bonomi, Antonio, Zhang, Yi Min, Warner, Ethan, and Chum, Helena L. 2019. "Comparison of biofuel life-cycle GHG emissions assessment tools: The case studies of ethanol produced from sugarcane, corn, and wheat". United States. https://doi.org/10.1016/j.rser.2019.04.043. https://www.osti.gov/servlets/purl/1512665.
@article{osti_1512665,
title = {Comparison of biofuel life-cycle GHG emissions assessment tools: The case studies of ethanol produced from sugarcane, corn, and wheat},
author = {Pereira, Lucas G. and Cavalett, Otavio and Bonomi, Antonio and Zhang, Yi Min and Warner, Ethan and Chum, Helena L.},
abstractNote = {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 CO2eq 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 CO2eq 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.},
doi = {10.1016/j.rser.2019.04.043},
url = {https://www.osti.gov/biblio/1512665}, journal = {Renewable and Sustainable Energy Reviews},
issn = {1364-0321},
number = C,
volume = 110,
place = {United States},
year = {2019},
month = {4}
}

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Works referencing / citing this record:

Socio-environmental and land-use impacts of double-cropped maize ethanol in Brazil
journal, January 2020