Interpreting Life Cycle Assessment (LCA) results is the final phase of the LCA process. This phase helps you analyze your results for meaningful environmental insights, guides you toward potential improvements, and leads to a robust LCA model you can share with confidence. This article answered the following questions to help you interpret your results:
What is the LCA interpretation phase?
How to analyze your LCA results for environmental impact hotspots?
How to interpret LCA results?
How to make conclusions and recommendations?
Feel like you're missing information? This article builds upon the following articles, check them out if you want to learn more:
On the LCA interpretation phase: Explained: Interpretation phase
What is the LCA interpretation phase?
The interpretation phase is crucial because it links to all three previous LCA phases. You'll analyze the impact assessment results (obtained in phase 3) based on the LCA’s goal and scope (defined in phase 1). This process may reveal errors, missing data, or the need for better quality input data, prompting revisions to your inventory analysis (phase 2) and recalculations (phase 3). The interpretation process may also lead you back to phase 1 to adapt the Goal and Scope of your LCA. LCA is iterative, meaning each iteration refines your results, providing more reliable and relevant insights.
What are examples of the interpretation phase in practice?
What are examples of the interpretation phase in practice?
For example, suppose you're assessing two paints in your portfolio and discover during the interpretation phase that the application methods differ significantly (e.g., one is applied with a brush and the other with a spray can). In that case, you might need to revise the product unit from "cradle-to-gate" (set in Phase 1) to include the application phase. This adjustment ensures a fairer comparison. Similarly, if the paints come in different quantities (e.g., one in 100-liter buckets and the other in 10-liter buckets), adjusting the product unit to a standard measure, like 10 liters or 1 m² of applied paint, might be necessary.
The interpretation phase may affect Phase 2 if comparable paints show unexpectedly different impacts, it might indicate an error, such as using an incorrect dataset. Correcting this data ensures accuracy in your LCA. In Phase 3, if a comparison with a competitor reveals you're using an outdated LCIA method (e.g., EF 3.0 instead of EF 3.1), updating to the latest method is crucial for maintaining relevance and comparability in your assessment.
How to analyze your LCA results for environmental impact hotspots?
Conduct a contribution analysis (aka hotspot analysis)
Conduct a contribution analysis (aka hotspot analysis)
Assess which processes, materials, life cycle stages, or emissions drive the biggest impacts. These are defined as impact hotspots in your model. Mobius and Helix both offer great visualization tools to help identify environmental hotspots in your model.
In Mobius: Use the visualization tools available.
In Helix: View results in the Product overview and Statistics pages
Double-check surprising data for mistakes and uncertainties (sensitivity analysis).
How to compare LCA results?
How to compare LCA results?
Comparing LCA results is a common practice for companies seeking to benchmark their environmental performance (against competitors), make informed product choices, or demonstrate sustainability commitments. However, it's important to approach these comparisons with a clear understanding of the inherent assumptions and variability in LCA methodologies. LCAs provide impact scores that translate complex environmental data into a more understandable set of metrics but do not prescribe how to conduct the assessments. The guidelines for performing LCAs come from standards and Product Category Rules (PCRs), not from the LCA methods. This distinction is important when interpreting and comparing results.
Caution - LCA comparison considerations: When comparing LCAs, consider the following points:
Understanding impact categories and factors: Impact categories represent different types of environmental impacts, such as global warming potential or water scarcity. Impact factors are the specific metrics used to quantify these impacts. Different LCIA methods may emphasize various categories and use different factors, so it's essential to understand what each category represents and how the scores are calculated.
Assumptions in LCA results: LCA results are built on numerous assumptions, from data sources to impact assessment methods. These assumptions can vary significantly between studies, even when the same standards or guidelines are followed. Recognize that these differences can affect the comparability of results.
Comparability limits: Direct comparisons of LCA results should be approached with caution. Results can be influenced by differing goals, scopes, data quality, and methodological choices. For meaningful comparisons, ensure that LCAs are aligned in these areas and use the same or compatible standards.
If you want to compare your LCA results to another LCA, use the following guidance to do so properly:
Determine the scope and modules: Ensure that the LCAs or EPDs you are comparing cover the same life cycle stages. For instance, if your LCA only includes cradle-to-gate (modules A1-A3), it wouldn't be fair to compare it with an LCA that includes cradle-to-grave (A1-D). Always align the scope of comparison to ensure accuracy.
Check the LCA method and framework: Different LCAs may use different standards and frameworks, such as EN15804+A2 versus EN15804+A1. These frameworks may have different methods for calculating impact categories, like Global Warming Potential (CO2 eq.). Ensure that the methods are compatible before making comparisons.
Pay attention to database versions: The version of the database used in LCA calculations can significantly affect the results. For example, results from Ecoinvent 3.4 may differ from those of Ecoinvent 3.8. Always verify that the databases and their versions are the same or adjust for differences.
Standardize the unit of measurement: LCA results can be reported in different units (e.g., per ton, per m²). Make sure to standardize the unit of measurement across all LCAs being compared. This may require converting data so that all results are on the same basis, facilitating a fair comparison.
Consider multiple impact categories: While CO2 equivalent emissions are a commonly compared metric, it's important to consider a range of impact categories, such as eutrophication or water usage. A comprehensive view provides a more nuanced understanding of environmental impacts.
Pro tip - Interpreting other LCAs: If something in another LCA is unclear, contacting the contact person listed in the Environmental Product Declaration (EPD) is recommended. This practice enhances the accuracy and depth of your environmental assessments.
How to interpret LCA results?
Conduct a sensitivity analysis
What is a sensitivity analysis?
What is a sensitivity analysis?
Sensitivity analysis evaluates how changes in assumptions, data, or methods impact the overall results. It assesses the robustness and reliability of an LCA study, by identifying which factors most significantly influence outcomes. By systematically varying these factors, sensitivity analysis helps validate results, manage uncertainties, and improve decision-making, ensuring transparency and credibility in the findings. Sensitivity analysis is a required aspect of the LCA reporting process, and methods may vary depending on the LCA standard (e.g., Bepalingsmethode) you are following.
How to conduct a sensitivity analysis?
How to conduct a sensitivity analysis?
Step 1: Identify uncertain data: Focus on uncertain or low-quality data. This includes data from weak sources, data ranges, and assumptions made during the LCA.
Weak data: Data from less credible sources or poorly researched data.
Example: One of your components is manufactured through an innovative production process. The process is new, so there are few studies on it, and ecoinvent does not have a dataset for this manufacturing process. The manufacturer does not want to share its sensitive production data so you estimated the production processes based on a theoretical approach. It is an approximation, but your results are uncertain and not confirmed.
Data range: When data is known to be variable or lie within a range of values (e.g., variations in efficiency lead to a range of impact values).
Example: One of your components varies in dimensions. The dimensions impact the waste percentage during production (imagine: weird shape, more waste). You used an average dimension to approximate the general waste % for this component.
Assumptions: Qualitative choices made during LCA.
Example: You assumed that your client cleans certain components during the use phase but in an interview, you conclude that nobody does this and it affects the lifetime of your product.
Alternative Options: Related to checking assumptions.
Example: You selected the first ecoinvent dataset that looked like it might be a good fit for one of your components and never thought about it again. You haven’t checked if ecoinvent provides any datasets that are more representative of your component.
Step 2: Determine your strategy for the sensitivity analysis: Once data has been deemed uncertain, you can think about your sensitivity analysis strategy. This involves changing input parameters. The following are some recommendations for how to test the sensitivity of your data:
For generally uncertain data, evaluate the impact of selecting different production routes, materials, or allocation methods on your results.
For data ranges more specifically, test best- and worst-case scenarios. If changing or adjusting the data to account for this range affects results, try finding more exact, credible data.
For data assumptions, test how changing assumptions affects results.
Step 3: Perform the sensitivity analysis: Conduct the sensitivity analysis using one of Ecochain’s software solutions - Helix or Mobius. Do this by creating a copy of your product model (via scenarios in Mobius, or by copying a production year in Helix), adjusting parameters, and comparing percent changes in results side-by-side.
Compare the results of your sensitivity analysis to your original or baseline model. If adapting the data leads to significant changes in your model’s impact, those parameters should be considered sensitive. The exact percentage change that constitutes sensitive data depends on the standard you’re following. For example, ISO 21930 defines data as sensitive if it impacts results by over 10%.
Step 4: Improve Sensitive Data: If you have sensitive data, data that is significantly affected by changing certain parameters, you should:
Focus on parameters that significantly impact results. Return to the LCI phase (phase 2) of your LCA and improve the quality of this data, and find more credible sources.
If data cannot be improved, report how the uncertain parameter affects your conclusions transparently.
Conduct a future scenario analysis
What is a scenario analysis?
What is a scenario analysis?
Scenario analysis explores how changing specific variables affects the overall environmental impact of your product system. Unlike sensitivity analysis, which tests the robustness of the model by varying assumptions and data, scenario analysis looks forward, creating hypothetical future situations to understand potential outcomes under different conditions.
How to conduct a scenario analysis?
How to conduct a scenario analysis?
Identify variables: Begin by selecting the key factors that could influence the environmental impact of your product. Start with hotspots identified in your hotspot analysis, for example, the most impactful raw material (Mobius) or the product you produce most (Helix).
Define scenarios: Develop scenarios to alter these key variables. For example, one scenario might test switching to renewable energy sources another might be to report the situation to the R&D department and look for alternative materials/production processes.
Model the scenarios: Use your LCA model to simulate each scenario. This involves adjusting the relevant inputs and assumptions according to the scenario definitions.
Analyze and compare results: Assess the environmental impacts under each scenario, comparing them to the baseline or current state. This comparison helps identify which scenarios result in significant changes and highlights areas where improvements or adaptations may be necessary.
In Mobius: Use the Scenario and Comparison features.
In Helix: Copy a production year and make adjustments to the copy.
How to make conclusions and recommendations?
Based on your analyses, identify areas for improvement and potential changes in materials or processes.
Consider the impact on stakeholders and outline steps for further investigation and improvement.
Use insights gained to make recommendations for enhancing product sustainability.
Pro tip - Using cost implications alongside environmental considerations: To persuade stakeholders of the benefits of an environmental improvement strategy (e.g., switching to solar electricity - identified in your scenario analysis), calculate and present the business implications alongside the environmental benefits, such as the reduction in kg CO2 eq. This approach ensures a balanced consideration of both sustainability and organizational factors.
Did you know that Helix offers a cost functionality? If you fill in all your expenditures connected to your LCA model inputs, you can analyze this in combination with environmental impact to gain deeper decision-making insights!
Next steps
Interpreting LCA results is key to gaining actionable environmental insights. By conducting thorough contribution, sensitivity, and scenario analyses, you ensure the reliability of your results and identify impactful areas for improvement. These analyses are also an essential part of the LCA reporting process. Continue exploring Mobius' and Helix’s features through our help center articles to maximize your ecodesign potential. Our support team is always ready to assist if you need further guidance.
