Interpretation
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Your own assistant knows your role, your industry and your products. Pick a concept and it opens a ready-made prompt that asks for an explanation with examples from your world. The prompt tells the assistant not to invent emission factors or dates, and to finish with a question to check you understood.
- Interpretation in LCAClaude (opens a new tab): explain Interpretation in LCAChatGPT (opens a new tab): explain Interpretation in LCA
- Contribution analysisClaude (opens a new tab): explain Contribution analysisChatGPT (opens a new tab): explain Contribution analysis
- Sensitivity analysisClaude (opens a new tab): explain Sensitivity analysisChatGPT (opens a new tab): explain Sensitivity analysis
- Consistency and completeness checksClaude (opens a new tab): explain Consistency and completeness checksChatGPT (opens a new tab): explain Consistency and completeness checks
- Writing limitationsClaude (opens a new tab): explain Writing limitationsChatGPT (opens a new tab): explain Writing limitations
“Copy prompt” works with any assistant, such as Gemini, Microsoft Copilot or your company’s own. Nothing is sent anywhere until you choose to.
Interpretation is where a result becomes a finding. ISO 14044 structures it in three parts.
1. Identify the significant issues
Section titled “1. Identify the significant issues”What drives the result? The standard tool is contribution analysis: the share of each life cycle stage, process or flow in each impact category. In most studies a few processes account for most of the impact. Those are your hotspots, and they tell you where data quality matters most.
2. Evaluate
Section titled “2. Evaluate”Three checks:
Completeness check. Is all the information needed for interpretation available? Are any excluded processes (cut-offs, data gaps) possibly significant?
Sensitivity check. How much do the results change when a key assumption changes? Test the choices that could flip a conclusion:
- the allocation rule for important co-products
- the electricity mix (e.g. national grid vs supplier-specific)
- transport distances and product lifetime
- the emission factor of the top hotspot
If a conclusion holds across plausible alternatives, it’s robust. If it doesn’t, say so.
Consistency check. Were assumptions, methods and data applied the same way throughout? Especially important in comparisons: product A and product B should use the same database version, the same allocation rules and the same LCIA method.
3. Conclusions, limitations and recommendations
Section titled “3. Conclusions, limitations and recommendations”Conclusions must follow from the results and the checks. Good limitations sections are specific:
Results use industry-average steel data (European, 2022). The supplier’s own electric-arc steel could reduce the climate result by up to 40% (sensitivity case S2). Only climate change was assessed; results say nothing about other impact categories.
Weak: “This study has some limitations regarding data quality.”
What you should now be able to do
Section titled “What you should now be able to do”- Run a contribution analysis and name the hotspots.
- Pick the three assumptions most worth a sensitivity test in a given study.
- Write a limitation that a reader could act on.
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