How can you ensure consistent LCA results for different products or processes?

1 Define the goal and scope

The first step in any LCA is to define the goal and scope of the study. This includes specifying the functional unit, the system boundaries, the impact categories, the data sources, and the methods for handling uncertainty and variability. The goal and scope should be clear, relevant, and consistent with the intended application and audience of the study. You should also document and justify any deviations from the standard LCA guidelines or frameworks.

2 Choose appropriate data and models

The quality and representativeness of the data and models used in the LCA are crucial for obtaining accurate and meaningful results. You should select data and models that match the temporal, geographical, and technological aspects of your system. You should also avoid data gaps, outliers, and inconsistencies by using reliable and verified sources, applying data quality indicators, and performing sensitivity and uncertainty analyses. If possible, you should use primary data from your own measurements or surveys, or secondary data from reputable databases or literature.

3 Apply consistent allocation and cut-off rules

Allocation and cut-off are two techniques for dealing with multi-functional processes or systems that have more than one input or output. Allocation is the process of assigning the environmental impacts of a process or system to its different functions or products, based on some criteria such as mass, energy, or economic value. Cut-off is the process of excluding some inputs or outputs from the system boundary, based on some threshold such as mass, energy, or environmental significance. You should apply consistent allocation and cut-off rules across your system and across different products or processes, and report their effects on the results.

4 Compare similar scenarios and alternatives

One of the main purposes of LCA is to compare the environmental impacts of different products or processes, and to identify opportunities for improvement. However, to make a fair and meaningful comparison, you should ensure that the scenarios and alternatives are similar in terms of the functional unit, the system boundary, the impact categories, and the data and models. You should also consider the uncertainty and variability of the results, and use appropriate statistical methods to test the significance and robustness of the differences.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?