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Water consumption and water quality continue to be key factors affecting environmental sustainability in biofuel production. This review covers the findings from biofuel water analyses published over the past 2 years to underscore the progress made, and to highlight advancements in understanding the interactions among increased production and water demand, water resource availability, and potential changes in water quality. We focus on two key areas: water footprint assessment and watershed modeling.

Biomass power offers utilities a potential pathway to increase their renewable generation portfolios for compliance with renewable energy standards and to reduce greenhouse gas (GHG) emissions relative to current fossil-based technologies. To date, a large body of life-cycle assessment (LCA) literature assessing biopower’s life-cycle GHG emissions has been published.
 

EXECUTIVE SUMMARY: Life cycle assessment (LCA) is a powerful tool that may be used to quantify the environmental impacts of products and services. It includes all processes, from cradle-to-grave, along the supply chain of the product. When analysing energy systems, greenhouse gas (GHG) emissions (primarily CO2, CH4 and N2O) are the impact of primary concern. In using LCA to determine the climate change mitigation benefits of bioenergy, the life cycle emissions of the bioenergy system are compared with the emissions for a reference energy system.

Despite a rapid worldwide expansion of the biofuel industry, there is a lack of consensus within the scientific community about the potential of biofuels to reduce reliance on petroleum and decrease greenhouse gas (GHG) emissions. Although life cycle assessment provides a means to quantify these potential benefits and environmental impacts, existing methods limit direct comparison within and between different biofuel systems because of inconsistencies in performance metrics, system boundaries, and underlying parameter values.

Recent legislative mandates have been enacted at state and federal levels with the purpose of reducing life cycle greenhouse gas (GHG) emissions from transportation fuels. This legislation encourages the substitution of fossil fuels with ‘low-carbon’ fuels. The burden is put on regulatory agencies to determine the GHG-intensity of various fuels, and those agencies naturally look to science for guidance.

This study focuses on the simulation of a complete process for producing butanol via
acetone, butanol, and ethanol corn fermentation.

We assessed the life-cycle energy and greenhouse gas (GHG) emission impacts of the following three soybean-derived fuels by expanding, updating, and using Argonne National Laboratory’s Greenhouse Gases, Regulated Emissions, and Energy Use in Transportation (GREET) model: (1) biodiesel produced from soy oil transesterification, (2) renewable diesel produced from hydrogenation of soy oil by using two processes (renewable diesel I and II), and (3) renewable gasoline produced from catalytic cracking of soy oil.

We assessed current water consumption during liquid fuel production, evaluating major steps of fuel lifecycle for five fuel pathways: bioethanol from corn, bioethanol from cellulosic feedstocks, gasoline from U.S. conventional crude obtained from onshore wells, gasoline from Saudi Arabian crude, and gasoline from Canadian oil sands.

The United States shares with many other countries the goal of the United Nations Framework Convention on Climate Change “to achieve . . . stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic interference with the climate system.”1 The critical role of new technologies in achieving this goal is underscored by the fact that most anthropogenic greenhouse gases (GHGs) emitted over the next century will come from equipment and infrastructure that has not yet been built.

The model is a vehicle fuel-cycle model for transportation systems. The model provides a set of outcomes that would involve feedstock production, biorefinery production, storage and consumer demand as the complete fuel-cycle. The data is internal to the model, but might be adaptive to different biofuels specifications. This model was developed by the Energy Systems Division at Argonne National Laboratory.