Stratton1
LC-GHG emissions for the alternative diesel fuel pathways in the study. Uncertainty bars represent the variability captured by the low emissions, baseline, and high emissions scenarios. Note the different scales for the top and bottom portions of the figure. Credit: ACS, Stratton et al. Click to enlarge.

In a paper published in the ACS journal Environmental Science & Technology, researchers from MIT conclude that it is “paramount” that decision makers and the general public be given the range of
LC-GHG emissions that could result from the production and use of a given bio or synthetic fuel, due to high variability within pathways. Among their findings in the study are that subjective choices such as coproduct usage and allocation methodology can be more important sources of variability in the life cycle greenhouse gas (LC-GHG) inventory of a fuel option than the process and energy use of fuel production.

Variability in lifecycle analysis (LCA), Stratton et al. say, must be distinguished from uncertainty. Variability—which is inherent due to both inexact LCA procedures and variation of numerical inputs—is a dispersion of discrete results, each of which has been measured or calculated with an inherent uncertainty in the result. The authors group the sources of variability into three categories: pathway-specific variability; coproduct usage and allocation; and land use change (LUC).

In their paper, they used specific examples from the production of diesel and jet fuels from 14
different feedstocks to demonstrate general trends in the types and magnitudes of variability present in life cycle greenhouse
gas (LC-GHG) inventories of middle distillate fuels.

To understand how variability impacts LC-GHG inventories
of transportation fuels, a new methodological approach was
developed using screening level LCAs. Screening level analyses
provide preliminary assessments of technology alternatives with
the intent of informing research funding and decision makers.

A requirement of screening level LCAs is to identify the pivotal factors defining the LC-GHG emission profiles of fuel production for each LC step and each feedstock. Optimistic, nominal,and pessimistic sets of these key parameters were developed to
formulate corresponding low LC-GHG emissions, baseline or nominal LC-GHG emissions, and high LC-GHG emissions scenarios for each feedstock-to-fuel pathway; hence, results for each feedstock-to-fuel pathway are a range of possible LC-GHG
inventories intended to demonstrate variability in fuel production
processes.

A requirement of screening level LCAs is to identify the pivotal factors defining the LC-GHG emission profiles of fuel production for each LC step and each feedstock. Optimistic, nominal,
and pessimistic sets of these key parameters were developed to
formulate corresponding low LC-GHG emissions, baseline or
nominal LC-GHG emissions, and high LC-GHG emissions
scenarios for each feedstock-to-fuel pathway; hence, results for
each feedstock-to-fuel pathway are a range of possible LC-GHG
inventories intended to demonstrate variability in fuel production
processes.

—Stratton et al.

Transportation fuel pathways often result in coproducts; to allocate emissions among
products, a usage must first be defined for the coproduct. In the study, the team examined four allocation methods to assign
LC-GHG emissions between the primary fuel product and any
coproducts: mass allocation; energy allocation; market-value allocation; and displacement (a.k.a., system expansion).

The choice of coproduct usage and allocation method may
significantly affect the final results of the LCA. Several studies in
the literature have acknowledged the variability introduced to
LCA by different allocation methods. Three examples
were chosen herein to demonstrate the variability introduced by
coproduct treatment: (1) the oil and biomass coproduct system
of soybeans where the biomass has an existing market as an
animal feed; (2) the oil and biomass coproduct system of
jatropha capsules where the biomass coproducts have a variety
of potential uses; and (3) the liquid fuel product slate from a coal
and biomass fed F-T facility. All three examples show a general
shortcoming in the displacement approach. When the coproduct
creation is large relative to the primary product, the LC-GHG
inventory of the primary product depends more strongly on the
LC-GHG inventory of the displaced product than the processes
and energy flows of the product being examined.

—Stratton et al.

Stratton2
As an example, sensitivity of LC-GHG emissions from jatropha oil HRD to coproduct usage and allocation assumptions are shown. Baseline value indicates the chosen combination to represent HRD production from jatropha oil. Single usage and allocation entries indicate uniform application across all coproducts. Scenario 3 assumes meal is detoxified and used for animal feed with allocation by economic value, while all other coproducts are used for electricity with allocation by displacement of average grid electricity.
Credit: ACS, Stratton et al. Click to enlarge.

The team found that all of the biofuel options examined in their study could either
potentially be produced with lower LC-GHG emissions than conventional diesel, or with LC-GHG emissions that exceed those of conventional
diesel. The difference is due to the LC-GHG intensity
of the processes and the emissions resulting from LUC.

For this
reason, it is critical to emphasize that the use of renewable
resources as feedstock does not guarantee an environmentally
beneficial fuel. Knowledge of specific production details is
required for any definitive conclusions to be drawn. This constitutes
a strong argument for LC-GHG inventories of transportation
fuels to be presented as a range.

…Three key
conclusions can be drawn from the potentially dominating
influence of variability due to coproduct usage and allocation
and LUC assumptions: 1) minimizing variability across LCA
results by maximizing methodological consistency is essential to
making useful comparisons between fuel options; 2) the absolute
result from attributional LCAs have a diluted physical meaning
and are most effectively used as a comparative tool, given the
condition from the first key conclusion; and 3) it is paramount
that decision makers and the general public be given the range of
LC-GHG emissions that could result from the production and
use of these fuels.

Such an approach emphasizes the importance of understanding the key aspects that determine the LC-GHG
emissions from fuel production and use. Furthermore, it can do
so before any production actually occurs. Such knowledge would
help to develop technologies and policies that diversify our
energy supplies and stimulate economic development while
mitigating the LC-GHG emissions from transportation.

—Stratton et al.

Resources

  • Russell W. Stratton, Hsin Min Wong, James I. Hileman (2011) Quantifying Variability in Life Cycle Greenhouse Gas Inventories of Alternative Middle Distillate Transportation Fuels. Environmental Science & Technology Article ASAP doi: 10.1021/es102597f


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