LCA Pitfalls and Best Practices

Even experienced LCA practitioners make methodological mistakes that can undermine the credibility and usefulness of their assessments. Research has identified systematic problems across published LCA studies, with critical issues appearing in each of the four ISO phases (Reap et al., 2008). This lesson covers the most common pitfalls and provides guidance for avoiding them.

Why LCA Quality Matters

Life Cycle Assessment influences major decisions—product design, procurement, policy, and investment. Flawed LCAs can lead to:

• Misallocated resources: Pursuing improvements that don't actually reduce impacts
• Greenwashing accusations: Claims that don't withstand scrutiny
• Policy mistakes: Regulations based on incorrect assumptions
• Loss of credibility: Undermining trust in LCA as a decision tool

The good news: most common mistakes are avoidable with awareness and careful methodology.

Goal and Scope Pitfalls

Pitfall 1: Product-Based Instead of Function-Based Comparisons

The mistake: Comparing "1 kg of Product A vs. 1 kg of Product B" when the products deliver different amounts of function per kilogram.

Why it matters: A concentrated detergent delivering 20 washes per kg shouldn't be compared to a dilute product delivering 5 washes per kg on a mass basis.

Best practice: Always define functional units based on the function delivered, not the physical product. Compare "delivering 100 clean washes" rather than "1 kg of detergent."

Pitfall 2: Inconsistent System Boundaries

The mistake: Including different life cycle stages for compared alternatives, or applying cut-off criteria inconsistently.

Examples:

• Including transport for Product A but not Product B
• Different end-of-life assumptions without justification
• Inconsistent infrastructure inclusion

Best practice: Document system boundaries clearly and apply them consistently to all alternatives. Any deviations should be justified and tested through sensitivity analysis.

Pitfall 3: Mismatched Goal and Methodology

The mistake: Using attributional methods when the goal requires consequential analysis, or vice versa.

Why it matters: A policy asking "what happens if we mandate biofuels?" needs consequential LCA that considers market effects. Using attributional (average) data misses the point entirely.

Best practice: Clearly state whether your study is attributional or consequential in the goal definition, and use appropriate data and methods throughout.

Inventory Analysis Pitfalls

Pitfall 4: Data Quality Mismatches

The mistake: Using high-quality data for some processes and poor-quality data for others, without acknowledging the implications.

Examples:

• Primary data for your process, outdated secondary data for suppliers
• European database values for Asian manufacturing
• Laboratory-scale data for industrial production

Best practice: Assess and document data quality systematically using pedigree matrices. Focus data improvement efforts on processes that contribute most to results.

Pitfall 5: Cut-off Errors

The mistake: Excluding flows that seem minor but actually contribute significantly to impacts.

Why it matters: A 1% mass contribution might be 30% of toxicity impacts if it's a hazardous material. Mass-based cut-offs can miss important environmental flows.

Best practice: Apply cut-offs based on environmental significance, not just mass or cost. Test cut-off sensitivity by including/excluding borderline flows.

Pitfall 6: Allocation Without Justification

The mistake: Choosing allocation methods (mass, economic, energy) without clear rationale, or switching methods within a study.

Why it matters: Allocation choices can swing results dramatically. Dairy LCAs allocate between milk and meat; the choice between mass and economic allocation changes milk's footprint by 30-50%.

Best practice: Follow the ISO allocation hierarchy—avoid allocation if possible through subdivision or system expansion. When allocation is necessary, justify your choice and test alternatives.

Impact Assessment Pitfalls

Pitfall 7: Carbon Tunnel Vision

The mistake: Focusing exclusively on Global Warming Potential while ignoring other impact categories.

Why it matters: A solution that reduces GWP might increase water use, toxicity, or land use. Single-indicator focus misses trade-offs.

Best practice: Report multiple impact categories appropriate to your goal. At minimum, include climate change, resource depletion, and one local impact category (acidification, eutrophication, or toxicity).

Pitfall 8: Spatial and Temporal Blindness

The mistake: Using global average characterization factors when impacts are highly location-dependent.

Why it matters: Water use in Sweden has different implications than water use in Egypt. Emissions in urban areas affect more people than rural emissions.

Best practice: Use regionalized impact assessment methods when available and when geographic specificity matters to your conclusions.

Pitfall 9: Misinterpreting Normalization and Weighting

The mistake: Treating normalized or weighted results as objective measures rather than value-laden interpretations.

Why it matters: Normalization reference values and weighting factors embed assumptions and values. Different choices yield different conclusions.

Best practice: Present characterized results first. If using normalization or weighting, clearly state the reference systems and weighting factors used, and test sensitivity to these choices.

Interpretation Pitfalls

Pitfall 10: Overinterpreting Small Differences

The mistake: Drawing strong conclusions from differences that are within the uncertainty range of the analysis.

Why it matters: If Product A has GWP of 10 ± 3 kg CO₂e and Product B has 11 ± 3 kg CO₂e, declaring A "better" is not supported by the evidence.

Best practice: Conduct uncertainty analysis. Report confidence intervals. Make probability statements ("A is likely better than B") rather than absolute claims when differences are small.

Pitfall 11: Conclusions Exceeding the Scope

The mistake: Making universal claims based on a study with limited geographic, temporal, or technological scope.

Examples:

• "Electric vehicles are better for the environment" (based on one European country's grid)
• "Organic farming has lower impacts" (based on one crop in one region)

Best practice: Be explicit about the conditions under which your conclusions hold. Acknowledge limitations clearly.

Pitfall 12: Missing Sensitivity Analysis

The mistake: Presenting results without testing how key assumptions affect conclusions.

Why it matters: Results may depend heavily on assumptions that could reasonably be different. Stakeholders need to know which findings are robust.

Best practice: Identify key assumptions (energy sources, transport distances, end-of-life scenarios) and systematically test their influence on conclusions.

Terminology Confusions

Several terms in LCA are commonly confused or misused. Clear terminology is essential for credible communication.

"Mass Balance" vs. "Mass Balance Approach"

These terms mean very different things:

Mass balance (in LCA): A fundamental principle of inventory analysis ensuring that mass is conserved across process inputs and outputs. Inputs = outputs + accumulation. This is a quality check for inventory data.

Mass balance approach (chain of custody): A certification and accounting method used in supply chains (especially chemicals and plastics) to track sustainable/recycled content when materials are physically mixed. This is about claims and traceability, not LCA methodology.

"Cut-off" vs. "Cut-off Approach"

Another easily confused pair:

Cut-off criteria (in inventory): Rules for excluding minor flows from the LCI based on mass, energy, or environmental significance thresholds. Every LCA uses cut-off criteria.

Cut-off approach (allocation/system model): A specific system model (used in ecoinvent "cut-off by classification") where secondary materials enter product systems burden-free, and end-of-life treatment is allocated to the waste producer.

The cut-off approach is one of three ecoinvent system models (alongside APOS and consequential). Cut-off criteria apply to all LCAs regardless of system model.

"Cradle-to-Gate" vs. "Cradle-to-Grave"

Cradle-to-gate: System boundary from raw material extraction through production, ending when the product leaves the factory. Does not include use or end-of-life.

Cradle-to-grave: Complete life cycle from raw materials through use and final disposal.

Common mistake: Comparing cradle-to-gate results to cradle-to-grave results without acknowledgment. A product with low production impacts might have high use-phase impacts (or vice versa).

"Carbon Neutral" vs. "Net Zero" vs. "Climate Positive"

These marketing terms are often misused:

Carbon neutral: Emissions are offset, typically through purchased credits. The product/organization still emits.

Net zero: Emissions are reduced as far as possible; residual emissions are neutralized. Implies actual emission reduction, not just offsetting.

Climate positive: Removing more carbon than emitted—going beyond net zero. Requires verified carbon removal, not just offsets.

LCA can support these claims, but the terms themselves are not LCA methodology.

Quality Assurance Best Practices

Use Checklists

Before finalizing any LCA study, verify:

• Functional unit describes function, not product
• System boundaries are clearly defined and consistently applied
• Data sources are documented with quality assessments
• Allocation methods are justified
• Multiple impact categories are reported
• Sensitivity analysis covers key assumptions
• Uncertainty is acknowledged and, where possible, quantified
• Conclusions don't exceed the study's scope
• Limitations are clearly stated

Seek Peer Review

For important decisions or public claims, independent review adds credibility:

• Internal review by someone not involved in the study
• External review by independent LCA experts
• For comparative assertions: panel review as required by ISO 14044

Document Everything

Reproducibility requires thorough documentation:

• Software and version used
• Database names and versions
• All assumptions and their rationale
• Calculation methods
• Data sources for each process

Key Takeaways

1. Functional unit errors are the most critical and common mistake—always define function, not product
2. Consistency across alternatives is essential for valid comparisons
3. Carbon tunnel vision misses important trade-offs; report multiple categories
4. Small differences require uncertainty analysis; don't overinterpret results
5. Terminology precision matters—mass balance, cut-off, and other terms have specific meanings
6. Document assumptions and test their influence through sensitivity analysis

Practice Exercise

You're reviewing an LCA comparing paper cups to ceramic mugs. The study concludes that "paper cups are more environmentally friendly." Identify potential methodological issues you would check:

1. What would you examine about the functional unit?
2. What system boundary questions would you ask?
3. What impact categories would you expect to see?
4. What sensitivity analyses would strengthen the conclusions?

---

References

Reap, J., Roman, F., Duncan, S., & Bras, B. (2008). A survey of unresolved problems in life cycle assessment. Part 1: Goal and scope and inventory analysis. The International Journal of Life Cycle Assessment, 13(4), 290-300.

Reap, J., Roman, F., Duncan, S., & Bras, B. (2008). A survey of unresolved problems in life cycle assessment. Part 2: Impact assessment and interpretation. The International Journal of Life Cycle Assessment, 13(5), 374-388.

Finkbeiner, M., Inaba, A., Tan, R., Christiansen, K., & Klüppel, H. J. (2006). The new international standards for life cycle assessment: ISO 14040 and ISO 14044. The International Journal of Life Cycle Assessment, 11(2), 80-85.