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So far, you've been provided with all of the
inventory data you've used in your bottled soda LCA,
which is helping you focus on learning the basics
of inventory analysis and on building out your model.
In practice, however, you'll often encounter data
gaps when compiling a unit process inventory.
For example, you may find that important flow data
are missing from existing inventory sources, or even more commonly,
that an inventory does not even exist for one or more of your unit processes.
What do you do when this happens?
Of course, if you have direct access to the actual processes
you can try to collect primary data to fill your data gaps.
But in many LCA studies, we don't have access to the real world processes.
And so primary data collection isn't an option.
And even though today's commercial and public LCA databases contain a lot
of great inventory data, they do not come close to being
inclusive of many materials, processes and products you may like to study.
For example, perhaps you're studying a specialty
material that is not used widely, or
perhaps you're studying a new material or
unit process for which data don't yet exist.
Or, as is most common, you may simply be
studying one of the many thousands of materials and unit
processes that are in use in the real
world but haven't been subjected to an LCA yet.
As you've learned, a good life cycle
inventory takes time, planning and money to compile.
So, existing inventories are limited.
So, if you practice LCA, dealing with data gaps comes with the territory.
Today we'll discuss using analysis based
estimation methods to generate inventory data.
By analysis based, what I mean is that we used
reasoned data analysis or engineering methods to estimate inventory data.
In other words, we don't make estimates based on guess work
or intuition, which can seriously reduce the credibility of an analysis.
Let me give you three quick examples of analysis
based estimation which you'll also practice in your homework assignment.
The first is an example of
what some might call a black box inventory where we assemble
consistent inputs and outputs without explicitly
characterizing the functional relationships between them.
Such inventories can sometimes be constructed using environmental statistics
such as those issued by regional or national government agencies.
In my case I'll construct a black box inventory
for diesel fueled, light trucks in the United States using
national fleet, energy use and emissions data.
My data source is the US Department of
Energy's Transportation Energy Data Book, which is issued annually.
The year of my data is 2005,
and the geographical coverage is the United States.
The technology mix is the average of all light diesel trucks in operation.
Here are the data, which includes total passenger miles traveled,
diesel fuel use and emissions of
CO2, carbon monoxide, particulate matter, volatile organic
compounds, nitrogen oxides, and sulfur dioxide for the 2005 US light truck fleet.
To construct the inventory, I'll choose a product output of one passenger
mile, and normalize all my input and output data on this basis.
My estimated inventory then looks like this.
Such an approach typically only works for widespread technologies or
entire industries for which all relevant flow data are consistently tracked.
Note that when using this approach, you must be sure that it
is acceptable in light of the data quality requirements of your study.
For example, this sort of estimated inventory might be
acceptable as a background process in a LCA Model.
Note also
that we don't know the relationships between inputs and outputs.
For example, if we change the amount of fuel used per passenger mile we
don't know by how much emissions of VOC's per passenger mile should also change.
Hence, the name black box model.
Another important consideration is that one needs
to establish that all relevant flows are captured.
In this case I'm only capturing combustion
related air missions.
So I need to document that any other relevant flows such
as waste engine oil discarded per passenger mile are not considered.
The
second example is using verified emission factors for combustion
related unit processing such as furnaces, kilns or boilers.
Recall that I use this approach earlier in the course, when I
estimated the unit process inventory for operating a residential hot water heater.
I obtained air emission factors from the US environmental protection
agencies, AP 42 emission factor databases which contains emission factors from
many different combustion technologies based
on test data and engineering methods.
One can also find emission factors from
other environmental agencies and in the engineering literature.
Note that this approach typically only captures air emissions.
Which are the primary outputs of most combustion processes.
Other flows, such as disposal of recovered ash or
other pollutant abatement flows, would have to be estimated
from other sources.
Thus, like our black box model the emission factor approach applies
to generic technology populations and is thus best suited for background processes.
However, the emission factor approach does capture the
functional relationships between fuel inputs and air emission outputs.
And thus, it is more flexible than a black box approach.
The third example is using an engineering approach
to quantify the functional relationship between unit
process inputs and outputs, using a process model.
For example, let's say I want to construct a unit
process inventory for an industrial blower, used in a pneumatic conveyor.
From basic energy engineering, I know that the relationship between fan input power
and fan output power is HP equals CFM times PSI
divided by 229 times the efficiency of the fan.
Where HP is fan horse power.
CFM is the cubic feet per minute of air flow.
PSI is the pounds per square inch of
air pressure and 229 is a unit conversion factor.
If I have some understanding of the process I am analyzing I can
estimate the CFM and PSI and use a typical fan efficiency for the
application from the engineering literature.
I can then convert to kilowatt
hours of electricity required to provide pressurized
air at a given flow rate and pressure for a given amount of time.
This is an admittedly simple example.
In practice, engineering estimation can become
more complicated for more complex processes.
The point is that if one can characterize the underlying physics,
or chemistry of a process in a way that conserves mass, and energy it is
often possible to construct engineering based inventories
when no other sources of data are available.
However, with this method it's critically important
that the engineering functions reflect the real world
equipment characteristics including efficiency
losses Thus, engineering estimates
are best left to engineers with process knowledge.
Lastly,
I want to point out that all of these methods can
also be helpful for checking unit process inventory data throughout a study.
As I mentioned previously, data validation is an
important step in the life cycle inventory process.
It is always a good idea to double check all inventory data using logic,
comparing the black box data, comparing to
emission factors or comparing to engineering models.
For example, lets check the CO2 flow in
my unit process inventory for diesel fueled light trucks.
Using an air emission factor for diesel fuel combustion.
According to the U.S. EPA, on average, a liter of
diesel fuel will release 2.7 kilograms of CO2 during combustion.
If I apply that emission factor to the diesel
fuel and CO2 data in my black box model,
I can see that the CO2 flow value seems reasonable for this dated fuel input.
So we've now seen three ways we can
address data gaps using analysis based estimation approaches.
However, there are important caveats that I'll stress again.
First, all approaches to addressing data
gaps must be clearly documented and reproducible
so your audience can form their own
opinions about the credibility of your methods.
Second, compiling primary data is clearly
preferable to using estimation approaches, whenever feasible.
Third, any estimation method must meet the minimum data quality
requirements stated in the scope definition of an LCA study.
Otherwise, the goals of the study will not be met.
Despite these caveats, these methods can often
be helpful in the absence of better data.
In my experience,
using estimation to fill data gaps
is especially helpful for generic background processes.
And, for determining if a given unit process or flow qualifies for
exclusion from a study on the basis of stated cut off rules.
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