Common Pitfalls in LCA Reporting and How to Avoid Them
Learn about the most frequent mistakes in Life Cycle Assessment reporting and practical strategies to ensure your LCA results are accurate, credible, and defensible.
After reviewing hundreds of Life Cycle Assessment reports, certain mistakes appear repeatedly. These pitfalls can undermine the credibility of your LCA, lead to incorrect business decisions, and create problems during verification or stakeholder review. Here are the most common issues and how to avoid them.
Pitfall 1: Poorly Defined Goal and Scope
The foundation of any LCA is a clear statement of what you’re assessing and why. Yet this section is often treated as an afterthought.
Common Mistakes
- Vague functional unit: “1 product” instead of specific functionality delivered
- Unclear system boundaries: Not specifying what’s included and excluded
- Unstated assumptions: Key decisions buried or missing entirely
- Mismatched goal and scope: Study designed for internal decision-making but used for external marketing
How to Avoid
Define your functional unit precisely. For example, not “1 shirt” but “one garment providing 100 wears of upper body coverage, assuming average European consumer washing behavior.”
Create an explicit system boundary diagram. List exclusions and justify them (e.g., “capital equipment excluded as contribution <1% based on screening study”).
State your intended audience and application upfront. If you might want to use results publicly later, design the study accordingly from the start.
Pitfall 2: Data Quality Mismatch
Using data that doesn’t represent your actual situation is perhaps the most pervasive LCA error.
Common Mistakes
- Geographic mismatch: Using European electricity data for Asian manufacturing
- Temporal mismatch: Using 10-year-old data for current technology
- Technology mismatch: Using average industry data for your specific, more efficient process
- Scale mismatch: Using industrial-scale data for pilot production
How to Avoid
For each significant data point, document:
- Source
- Geographic coverage
- Time period
- Technology represented
- How well this matches your actual situation
Conduct a data quality assessment using ISO 14044 criteria. Be honest about gaps and their implications.
Prioritize primary data collection for processes that significantly influence results. A sensitivity analysis can identify where data quality matters most.
Pitfall 3: Inappropriate Comparisons
LCA is often used to compare products or scenarios, but invalid comparisons are common.
Common Mistakes
- Different functional units: Comparing products that deliver different functionality
- Different system boundaries: One product includes use phase, another doesn’t
- Inconsistent data quality: High-quality primary data vs. generic estimates
- Cherry-picked impact categories: Only showing categories where your product wins
How to Avoid
Ensure functional equivalence before comparing. Products must deliver the same function to the same quality level for the same duration.
Use identical system boundaries for compared products. If this isn’t possible, document differences and their implications.
Apply consistent data quality standards. If one product has supplier-specific data, attempt to get equivalent data for alternatives.
Report all relevant impact categories, not just favorable ones. If certain categories aren’t relevant, explain why in the methodology section.
Pitfall 4: Allocation Errors
Multi-functional processes require allocation of environmental burdens, and this is a frequent source of error.
Common Mistakes
- Ignoring the allocation hierarchy: Jumping to economic allocation without considering alternatives
- Inconsistent allocation: Using different approaches for similar processes
- Unrealistic co-product credits: Overstating benefits from by-products
- Missing sensitivity analysis: Not testing how allocation choices affect results
How to Avoid
Follow the ISO 14044 allocation hierarchy:
- Avoid allocation through sub-division or system expansion
- Allocate based on physical relationships
- Allocate based on other relationships (economic)
Document your allocation approach clearly. Explain why you couldn’t use higher-priority approaches.
Test sensitivity to allocation assumptions, especially for processes where allocation significantly affects results.
Pitfall 5: End-of-Life Assumption Errors
How products are disposed of—and what credits are claimed—causes many disputes.
Common Mistakes
- Optimistic recycling rates: Assuming higher recycling than actually occurs
- Ignoring regional variation: European end-of-life scenarios applied globally
- Double counting: Claiming recycling credits that should go to the next product cycle
- Static assumptions: Ignoring how end-of-life infrastructure is evolving
How to Avoid
Use location-specific end-of-life data where possible. National waste statistics are usually available.
Clearly state your recycling allocation approach (e.g., cut-off, avoided burden) and be consistent.
Be conservative with recycling credits unless you have specific evidence. Include scenarios with different end-of-life assumptions.
For long-lived products, consider how waste infrastructure might change over the product lifetime.
Pitfall 6: Uncertainty Ignored
All LCA results have uncertainty, but reports often present single-point numbers as if they were precise.
Common Mistakes
- No uncertainty analysis: Results presented without any indication of confidence
- Precision mismatch: Reporting results to many decimal places when uncertainty is ±30%
- Missing sensitivity testing: Not exploring how key assumptions affect results
- Overconfident conclusions: Strong claims based on differences within uncertainty range
How to Avoid
Include at minimum a qualitative discussion of major uncertainty sources.
For key conclusions, conduct sensitivity analysis on influential parameters. Report ranges, not just central estimates.
Match reporting precision to actual confidence. If uncertainty is ±20%, reporting 12.847 kg CO₂eq is misleading.
Avoid definitive comparative claims when differences are within uncertainty ranges. “Product A appears to have lower impacts, though results are sensitive to electricity grid assumptions” is more honest than “Product A is better.”
Pitfall 7: Poor Documentation
Insufficient documentation undermines reproducibility and verification.
Common Mistakes
- Missing data sources: Results impossible to trace back to origins
- Undocumented assumptions: Key choices made but not recorded
- Version confusion: Unclear which database or software versions were used
- Lost expertise: When the practitioner leaves, no one can update the study
How to Avoid
Maintain a detailed background report, even if the main report is summarized. Include:
- All data sources with specific references
- All assumptions with rationale
- Software and database versions
- Calculation details or model files
Store LCA model files alongside reports. Consider them part of the deliverable.
Document methodology in enough detail that another practitioner could reproduce the study.
Pitfall 8: Misleading Communication
How results are communicated can undermine an otherwise solid study.
Common Mistakes
- Selective presentation: Only showing favorable results
- Missing context: Percentage improvements without absolute numbers
- Comparison confusion: Comparing to outdated baselines
- Unqualified claims: Marketing statements that overstate LCA conclusions
How to Avoid
Present complete results, including unfavorable findings. Acknowledge trade-offs between impact categories.
Provide context for claims. “30% reduction” is meaningless without knowing the baseline, scope, and absolute impact levels.
Use clear, accurate language. “Lower carbon footprint for the production phase” is more precise than “eco-friendly.”
Have marketing claims reviewed against the actual LCA. Ensure communications are defensible.
Pitfall 9: Ignoring Reviewer Feedback
Whether internal review, critical review, or verification, feedback is often inadequately addressed.
Common Mistakes
- Dismissing concerns: Not seriously considering reviewer questions
- Surface changes: Making cosmetic edits without addressing underlying issues
- Missing responses: Not documenting how feedback was addressed
- Arguing with verifiers: Treating verification as negotiation rather than quality assurance
How to Avoid
Take all reviewer comments seriously. If you disagree, provide documented technical justification.
Track feedback and responses. Create a response matrix showing each comment and how it was addressed.
View verification as adding value. Verifiers catch issues that could undermine your study’s credibility later.
Building Quality Into Your Process
The best way to avoid these pitfalls is building quality in from the start:
Plan thoroughly: Invest in goal and scope definition before data collection.
Document continuously: Record decisions and rationale as you go, not retrospectively.
Review iteratively: Don’t wait until the end for quality checks.
Seek expertise: Complex methodological decisions benefit from experienced guidance.
Learn from feedback: Each review is an opportunity to improve future studies.
How QuaLCA Can Help
Avoiding these pitfalls requires experience and systematic approaches. QuaLCA offers:
- Study design review: Ensuring your goal, scope, and methodology are robust before you invest in data collection
- Data quality assessment: Identifying gaps and recommending appropriate data sources
- Pre-verification review: Catching issues before formal verification
- Training: Building internal capability to produce quality LCAs
Our focus on data quality and methodological rigor helps ensure your LCA results are credible and defensible.
Want a review of your LCA approach or results? Contact QuaLCA for an independent assessment and improvement recommendations.
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