Introduction to Life-Cycle Assessment and Decision Making Applied to Forest Biomaterials 159
secondary data more useful, it is helpful to relate all the flows to one unit, 1 kg, of output
so that in later processes this can be scaled to meet the needs of one product. In this
example, all the numbers in the “Amount” column are divided by 23 to get the inputs
and outputs per one unit of product.
5.3.2.6 Relating Data to the Functional Unit
The next step of LCI is similar to relating to unit process step, but instead this time the
data are related to the functional unit defined in the goal and scope. For instance, if the
functional unit of the LCA was a rustic chair, that chair might require several kilograms
of wood as well as other materials. In relating the data to the functional units, all the
inputs and outputs are scaled to the quantity of material/product that is required to
fulfill the requirements of the functional unit; this flow is called the reference flow. This
step can be performed in Microsoft Excel worksheet or in an LCA software package.
The results after this step may include numbers such as energy use per functional unit or
CO 2 emission per functional unit. Elementary, waste, and product flows may be listed
at this point; however, they would all be listed in relation to the required amount per
functional unit.
5.3.2.7 Data Aggregation
When performing an LCI, many calculations are required for the different life-cycle
stages (remember: product production, product use, end of life) that may be useful to
analyze separately before combining. Often, the final results of both LCIs and LCAs are
reported by life-cycle stages as well as the total impacts. Since the final total number
is required, the LCI data are summed across all the life-cycle stages. For instance, if
electricity was used by five different processes, the total electric usage may be summed
for all these processes and reported.
5.3.2.8 LCI Data Interpretation
Inventory data interpretation is an important step within the larger interpretation of
the whole study. Throughout the LCI development process, some level of interpretation
must be performed. For example, when collecting data, the practitioner must interpret
the available data and make a judgment call on the quality and relevance to the goal and
scope. Often when performing an LCI, it will become clear that the goal and scope are
at times not appropriate given the availability of data, time, and resources available to
complete the study. Developing a high-quality LCI is the most time-consuming part of
an LCA, which often experiences hang-ups and delays that are in many cases beyond
the control of the practitioner. In these cases, where data are just not available, the goal
and scope can be adjusted so that the available data can support the goal and scope and
eventually the overall study conclusions.
Another aspect of interpretation is uncertainty in data and modeling assumptions.
Though the use of a sensitivity analysis, a variety of study assumptions, and data
can be tested to determine the influence on the overall LCI results. For instance, an
assumption on a process yield where incoming material is converted to a product
material can be varied depending on incoming material composition that varies with
time. To determine how this yield that can often change influences the energy or other
LCI parameters, the yield could be adjusted up or down a set percentage, for example,
