Technical Report Comparison of Biofuel Life Cycle Analysis Tools Phase 2, Part 2: Biochemical 2G Ethanol Production and Distribution

by Antonio Bonomi, Otávio Cavalett, Bruno Colling Klein, Mateus Ferreira Chagas, and Nariê Rinke Dias Souza (Brazilian Biorenewables National Laboratory (LNBR)/ National Center for Research in Energy and Materials (CNPEM)/IEA Bioenergy) Second-generation (2G) or lignocellulosic biofuels are deemed advanced in view of the several advantages when compared to first generation biofuels, such as no need for land expansion or competition with food production, thus producing more fuel within the same area, as in the case of 2G sugarcane ethanol, besides low climate change impact. If deployed in large scale, lignocellulosic ethanol will be a great ally in helping the world meeting the long-term requirements for the reduction of greenhouse gases (GHG) emissions.

The present report is the continuation of the Technical Report - Comparison of Biofuel Life Cycle Analysis Tools - Phase 2, Part 1: FAME and HVO/HEFA. Its main motivation is still the comparison of different LCA models and the identification of the main differences and commonalities in methodological structures, calculation procedures, and assumptions to demonstrate the possibility of obtaining homogeneous results for similar production chains.

With the presented analysis, it was possible to evaluate four selected models, comparing the LCA differences from each production system. The main reasons for each identified difference were pinpointed on a case-by-case basis.

The scope of this study is restricted to second generation ethanol produced from either corn stover, wheat straw, sugarcane bagasse and/or straw, and forest residues. The four LCA models compared in this study were:
 GHGenius (Canada): available in https://www.ghgenius.ca/index.php/downloads;
 GREET (United States of America): available in:
https://greet.es.anl.gov/index.php?content=greetdotnet;
 New EC (European Community): available in http://data.jrc.ec.europa.eu/dataset/jrcalf-bio-biofuels_jrc_annexv_com2016-767_v1_july17;
 VSB (Brazil): not available to external users (Bonomi et al., 2016).

Three models are publicly available and serve regulatory purposes (GHGenius / GREET /New EC). The VSB is not publicly available, the model was initially developed by CNPEM is an organization supervised by Ministry of Science, Technology, Innovation and Communication (MCTIC).

LNBR/CNPEM to assess the sugarcane production chain, having further expanded its scope to several other feedstocks and conversion pathways within a biorefinery context.

The results presented in this report are limited to the GHG emissions determined by each model with the default conditions to which they were developed using both cradle-to-gate and cradle-to-pump boundaries. The cradle-to-gate approach considers the emissions of biofuel production from the feedstock production up to the gate of the biofuel producing unit, while the cradle-to-pump analysis includes additional impacts of biofuel distribution to fuel pumps.

In the case of wheat straw 2G ethanol, GHGenius presents higher emissions than New EC, mostly because GHGenius considers NPK replacement in the field due to straw removal, and higher energy inputs in the industrial phase. For corn stover 2G ethanol, GHGenius also presents higher emissions than GREET. This is due to higher energy inputs in the industrial phase, lower emissions displaced by co-products, no avoided emissions of N2O and NOx due to corn stover removal, and no avoided LUC emissions. In the case of forest residues 2G ethanol, GHGenius presents the highest emissions among the 3 models assessed, GREET presents the lowest emissions and VSB is in between. The emissions in the industrial processes are higher in GHGenius compared to the other models. The results for sugarcane
straw 2G ethanol obtained with VSB are close to the values presented for corn stover and forest residues 2G ethanol in GREET.

The harmonization procedure carried for the corn stover ethanol and forest residues ethanol pathways show that it is possible to harmonize the results issued by the models through a series of steps considering only few parameters/operations. The reported analysis found differences in the input data and methodological choices, some of which could be harmonized, such as the divergences between energy inputs among the studied models, or the
considered avoided emissions.

As in the Phase 2 Part 1, we emphasize that there is room for discussion and standardization of models in order to decrease the variation of input data and approaches and thus “preharmonize” all models. READ MORE