[MUSIC]
Today, we'll continue our discussion of data
validation in the life cycle inventory analysis step.
If you recall, a few lectures ago, we
discussed analysis based estimation methods for addressing data gaps.
I'd like to stress again, that these methods
can also be used as part of data validation.
More specifically, these estimation methods can provide independent
reference points against which to test whether inventory
data makes sense.
For example, I used an air emissions factor for CO2
emissions from diesel fuel combustion to check if the CO2 emissions
estimated from my black box unit process model for light trucks
were consistent with the amount of diesel fuel in the inventory.
Additional ways of validating data include checking to ensure unit
process inventory conserves both mass and energy between its inputs and
outputs, checking your inventory data for anomalies or gaps,
by comparing them to similar inventories, or taking process measurements.
Today, we'll focus on data quality assessment, which
is the process of ensuring our life cycle inventory
data have met the data quality requirements that
we specify during the scope definition of our study.
We'll use my plastic bag LCA model as an example when discussing
this concept.
Note that it is good practice to evaluate data quality
throughout a life cycle inventory, as we've done by documenting the
time related, geographical, and technology coverage for each unit process, as
well as important explanatory notes,
describing any allocation and agrication decisions.
Thus, the goal of data quality assessment should not
be to alert you to major quality issues after
you've completed your entire inventory.
If that happens, it means you probably weren't paying close enough attention
to your own data quality requirements when you were compiling your inventory data.
To the contrary, the data quality assessment really serves two purposes.
First, it forces one to think about if data requirements were met in a
consistent manner for the inventory as a
whole, and with respect to key data characteristics.
Second, it forces one to document a structured and intuitive summary of data
quality for the study's audience, which aids
in their interpretation of the study's results.
There is no standard method for data quality
assessment in LCA, rather different methods have been
proposed by different authors, some of which I
will refer you to in the lecture notes.
I'd like to use the data pedigree mix proposed by Wydama and Wasnes in 1996,
because it is easy to apply and intuitive to interpret.
This data pedigree matrix has five data
quality indicators, which I'll discuss one by one.
The reliability indicator relates to the sources, acquistion
methods and verification procedures used to obtain the data.
The completeness indicator relates to the statistical properties of the data.
How representative is the sample?
Does the sample include a sufficient number of data?
And is the period adequate to even normal fluctuations?
Now, the next three data quality indicator should look very familiar to you,
because we specified them in our data
quality requirements in the scope definition step.
We've also been tracking them carefully for each unit process inventory.
The temporal correlation indicator represents the correlation
between the stated analysis years of the
study and the years of the obtained inventory data.
Recall that I specified that, if possible, my inventory data for
the plastic bag life cycle should be from the last five years.
The geographical correlation indicator represents the
correlation between the stated geographical focus
of a study and the geographical
characteristics of the obtained inventory data.
Recall that I specified
the United States as the geographical focus of my
plastic bag life cycle and, finally, the technological correlation indicator.
This represents the correlation between the
technologies and/or technology mix specified for a
study and the technologies and, or technology
mix represented by the obtained inventory data.
For my plastic bag life cycle model, I specify that a US average
technology mix for my target time period would be acceptable.
For each of these five data quality indicators, we
assign a score for our studies data on a
scale of one to five with one indicating the
highest data quality and five indicating the lowest data quality.
The resulting data pedigree matrix looks like this, which allows
one to see at a glance, how well the study with
respect to each data quality indicator, and with respect to data quality overall.
What you're seeing here, is the data pedigree table
that I completed for my plastic bag LCA study.
So, how did I determine what numerical score
to give my study for each data quality indicator?
Well, the data pedigree matrix comes with guidelines for how to assign scores.
For example, when scoring temporal correlation,
the guidance states that I should assign a 1 if my data are less that 3
years old, or a 2 if my data are less than 6 years old, and so on.
Let's take a look at the scoring guidance for geographical correlation which
states that I should assign a one if my data are from the
area under study at the other end of the spectrum I should assign
a five if the data are from an unknown area or an area
with completely different production conditions.
Since, all of the inventory data in my
plastic bag LCA model are from the Unidted States.
I have assigned a score of one
in my data quality assessment for geographical correlation.
Note also that I provided an explanation for
my chosen score for each data quality indicator.
This gives the audience some insight into my rational
Now they may or may not agree with my rationale,
but clearly documenting it is critical for
ensuring my study is transparent in every respect.
We don't have time to go through the guidelines for each category.
So I've provided the full scoring guidance table in the lecture notes.
You'll use the same scoring guidance for
assessing data quality in your bottled soda LCA.
As I mentioned earlier, in addition to
providing important documentation for your study's audience,
the data quality assessment also forces us to take a step back
and carefully review just how well we did with respect to data quality.
While hopefully you won't identify major data issues at
this point You might often find that, in retrospect, the
data you compiled did not perfectly satisfy the data quality
requirements that you laid out during the scope definition step.
If this happens, what should you do? Well,
one option is to go back and try to compile better
data to strengthen quality with respect to one or more indicators.
But, this isn't always possible.
Another option is to go back and adjust
your goal and scope definition so that your stated
application, purpose, and audience are still valid, in
light of your data quality limitations, whatever the action.
Hopefully, you can now see the value in data quality assessment, to the analyst
And to the audience. I'll see you next time.