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How can you reduce uncertainty in LCA?

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Life cycle assessment (LCA) is a powerful tool for measuring the environmental impacts of products and services. However, LCA results are often affected by uncertainty, which can limit their reliability and usefulness. Uncertainty can arise from various sources, such as data gaps, variability, assumptions, and methodological choices. How can you reduce uncertainty in LCA and improve the quality of your analysis? Here are some tips to help you.

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1 Define the goal and scope

The first step in any LCA is to define the goal and scope of the study, which includes the functional unit, the system boundaries, the impact categories, and the data requirements. A clear and consistent goal and scope can help you avoid unnecessary uncertainty and focus on the most relevant aspects of your study. You should also document and justify any assumptions and limitations that affect your scope definition.

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Dr. Pranjal Kumar Phukan

Supply Chain & Operations Management Expert
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Data Quality Assurance: Ensure the accuracy and reliability of data by employing high-quality sources. Prioritize data from reputable databases, peer-reviewed literature, and reliable industry reports. Conduct thorough data validation and verification processes. Sensitivity Analysis: Perform sensitivity analyses to identify influential factors and parameters in the LCA. By assessing the impact of variations in key inputs, practitioners can pinpoint areas of high uncertainty and focus efforts on refining those specific aspects. Scenario Analysis:

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2 Choose reliable data sources

The quality of your LCA data is crucial for reducing uncertainty. You should choose data sources that are relevant, representative, complete, and transparent. Ideally, you should use primary data from your own or your suppliers' processes, as they reflect the actual conditions and performance of your system. However, if primary data are not available or feasible, you can use secondary data from databases, literature, or other sources. In that case, you should check the quality and suitability of the secondary data and adjust them if necessary.

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Destiny B.

Climate Solutions & Business Operations Specialist | Leveraging Data, Innovation & Automation for Sustainability and Efficiency
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Choosing reliable data sources is akin to laying the groundwork for a sustainable future. Optimal Life Cycle Assessment (LCA) hinges on data that not only reflects reality but also stands up to scrutiny. While primary data offers a direct window into your system's performance, the absence of such data necessitates careful consideration of secondary sources. The key is to not just accept them at face value—vet their quality and relevance, adjusting as needed. This diligence ensures the integrity of your LCA, contributing not just to reduced uncertainty but also to informed and impactful sustainability decisions.

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3 Use appropriate methods and tools

The methods and tools you use for your LCA can also introduce uncertainty, especially if they are not compatible with your goal and scope. You should select methods and tools that are scientifically sound, widely accepted, and transparent. For example, you should use consistent and comprehensive impact assessment methods that cover the impact categories of your interest. You should also use software and models that are reliable, up-to-date, and user-friendly.

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4 Perform sensitivity and uncertainty analysis

One of the best ways to reduce uncertainty in LCA is to perform sensitivity and uncertainty analysis. Sensitivity analysis helps you identify the parameters that have the most influence on your results and test how they change under different scenarios or assumptions. Uncertainty analysis helps you quantify the range and distribution of your results and express them with confidence intervals or error bars. These analyses can help you improve your data quality, validate your methods and tools, and communicate your results more clearly.

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Julien Nizri

Directeur Général chez AFNOR Certification, tiers de confiance engagé pour votre performance durable
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Il peut être intéressant pour effectuer ces analyses de sensibilité et d'incertitude d'utiliser une simulation aléatoire de type Monté Carlo

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5 Review and verify your LCA

The final step to reduce uncertainty in LCA is to review and verify your study. You should check your data, methods, tools, and results for any errors, inconsistencies, or gaps. You should also compare your results with other studies or benchmarks to assess their validity and relevance. Moreover, you should seek feedback from experts, peers, or stakeholders who can review and verify your study from different perspectives and provide suggestions for improvement.

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Julien Nizri

Directeur Général chez AFNOR Certification, tiers de confiance engagé pour votre performance durable
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Au regard de l'importance des ACV pour orienter certains choix politiques ou industriels, il peut être souhaitable de garantir l'impartialité et l'absence de conflit d'intérêt de la part des reviewers et donc de faire aussi certifier la méthode de calcul et les choix opérés par une tierce partie indépendante.

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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?

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How can you reduce uncertainty in LCA?

1

2

3

4

5

6

1 Define the goal and scope

2 Choose reliable data sources

3 Use appropriate methods and tools

4 Perform sensitivity and uncertainty analysis

5 Review and verify your LCA

6 Here’s what else to consider

Sustainability

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How can you reduce uncertainty in LCA?

1

2

3

4

5

6

1 Define the goal and scope

2 Choose reliable data sources

3 Use appropriate methods and tools

4 Perform sensitivity and uncertainty analysis

5 Review and verify your LCA

6 Here’s what else to consider

Sustainability

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