A Practical Guide to LCA interpretation

An essential step when performing a Life Cycle Assessment (LCA) is the development of the Life Cycle Inventory (LCI). The LCI is the inventory of inputs and outputs that are involved in the production of a product.

Such inputs and outputs can also be grouped per unit processes (or activity), such as industrial processes, raw material extraction, use of products and end-of-life. 

For clothing, unit processes can be activities like cotton cultivation, weaving, and dyeing in manufacturing, in-use stage considerations processes such as washing and ironing, and end-of-life processes like recycling or incineration.

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How to build the LCI backbone

Each unit process is characterised by an input and an output flow. The former is made of product, material or energy flow that enters a unit process. The latter is made of product, material or energy flow that leaves a unit process. Products and materials include raw materials, intermediate products and co-products.

Input and output flows can be classified as follows:

1. Elementary flows: all material or energy entering the system being studied that has been drawn from the environment without previous human transformation, or material or energy leaving the system being studied that is released into the environment without subsequent human transformation. Elementary flows include, for example, resources taken from nature or emissions into air, water, soil that are directly linked to the characterization factors of the EF impact categories.

2. Intermediate flows: product, material or energy flow occurring between unit processes of the product system being studied. An example of intermediate flows could be electricity, heat or chemical products being used in a unit process.

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Life Cycle Inventory Assessment (LCIA) explained on Environmental Flows

The Life Cycle Inventory Assessment (LCIA) is done by taking these environmental flows and classifying them on how much each flow contributes to a certain impact indicator, this is called characterization factors.

Here is an example in the IPCC 2021 method of some environmental flows:

Using the IPCC methodology where the unit is kg CO2 eq, the above factors mean that if we for example emit 1 kg of methane this is equal to 29.8 kg CO2 eq, if we emit 1 kg propane this is equal to 0.02 kg CO2 eq.

All flows in the LCI are converted into impacts through these factors. Therefore it is essential to carefully model the LCI to accurately build a proxy for the activity. This means paying close attention to elements such as energy mix, the nature of chemicals and in general the amount of inputs and outputs.

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How LCI and Impact Categories correlate in a typical LCA

As measuring the environmental impact, beyond carbon, of products and services is becoming a practice integrated with other business functions, we thought it could be of good use, to provide some elements to better understand the correlation between the LCI and the impact categories usually included in an LCA study.

A key aspect, as one can imagine, is that not all inputs and outputs have the same contribution towards the different impact categories.

Inputs such as chemicals are usually related to indicators that have to do with toxicity such as ecotoxicity and human toxicity and inputs in the form of energy usually greatly affect indicators such as global warming, fossil resource scarcity or ozone formation. Read more about this here.

Here is a small general mapping of some resources and impact categories often associated with them:

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Interpretation of LCA Results

When interpreting the LCA results for a unit process, a material or an entire product, it’s important to factor into the interpretation the intrinsic characteristics of the object of the study.

This means that depending on what type of inputs we have to study the impact categories in relation to this and understand the result by knowing the relationship between inputs and impacts.

Using batch dyeing of fabric as an example, we have built a correlation matrix between the main process inputs and some of the EF 3.1 impact categories.

We can clearly see how such mapping could help better understand impact drivers. However, this should be only the first step towards a more comprehensive analysis of the LCA results. 

In fact, data quality can greatly impact the output of the study and therefore mislead the practitioner.  An example is Ecotoxicity, which has an equal contribution from chemicals and heat.

However, this could be the result of a poorly modeled dataset. In this specific case, a very generic chemical organicproxy is used for the dyes. The use of this proxy leads to an underestimation of certain impacts in some cases which are very specific to dyes.

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Data quality and results sensitivity

Below we can see how results change if, for the same unit process, we replace the chemical organic proxy with our own modeled dye. The distribution of ecotoxicity among the inputs drastically changes and chemicals become the most relevant impact driver.

This means that, if possible, we should ensure that the quality of data available is fit for purpose. This could be achieved by having access to primary data or performing a sensitivity analysis which could provide a better understanding of how results would change by changing certain parameters.

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The Influence of Country and Electricity Mix

If we focus on climate change, resource use or fossils we see that heat and electricity are the main drivers of impact for these categories. However, as you can see below, the change in energy mix can have a relevant impact on the climate change category.

Usually, the mix of countries using a lot of coal and oil-based technologies (like China) will have a larger impact in this category while the mix of countries using more renewables (like Italy) will have a lower impact.

Thus, depending on the mix of energy used by the country or the company-specific energy mix, electricity and heat generation can become the main drivers of impact for other categories.

For example, Electricity generated from nuclear power is usually connected to a high impact in the category of ionizing radiation which measures harmful radiation that can damage human and animal cells.

France is one of the countries with the highest use of nuclear electricity compared to Italy which is relatively low in comparison to its use of nuclear electricity.

Below we have the example again with batch dyeing seeing how the impacts change depending on the electricity mix chosen:

This illustrates the importance of knowing each resource you put into your unit process. Different modeling choices can give widely different final results for the product or service under evaluation.

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Conclusion

This is why in the end an LCA study can be of better or worse quality and a practitioner should always aim to choose the data that best represent the reality. For example, picking the right chemical or electricity mix.

‍Ready to conduct your LCA with us? Get in touch with our Sustainability Success team here!

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