7. Data Quality, Sources, Representativeness & Uncertainty
(Questions 79–90)
79. How do I determine whether primary data is sufficiently representative?
Primary data is considered representative when it reflects actual production conditions for the product, covers at least one full year of operation, and corresponds to the correct facility. It should include all major material and energy flows without large gaps. If data is outdated or incomplete, document why it still reasonably represents current production.
80. What data quality requirements apply to EPDs under A2 rules?
EN 15804+A2 requires primary data for the core processes (A3) and encourages primary data for A1 and A2 where feasible. Data must meet minimum quality indicators for precision, completeness, temporal, geographic, and technological representativeness. Missing or low-quality data must be justified transparently.
81. How do I handle missing data when a supplier does not provide full information?
Use industry averages, generic datasets, or similar materials when supplier data is missing. Always try to contact the supplier at least once to obtain primary information. Clearly state any assumptions and ensure they do not bias results toward lower impacts.
82. Can I use secondary datasets if primary data is incomplete?
Yes, secondary datasets are allowed for upstream processes or when primary data cannot be obtained. They must come from reputable LCA databases and align with the PCR. Use them sparingly for core manufacturing processes unless justified.
83. How should I estimate uncertainty when only partial data is available?
Identify which data points are estimated and assess their influence on the overall impact. Provide conservative estimates where possible, and document the reasoning clearly. Uncertainty does not invalidate an EPD as long as assumptions are transparent.
84. How do I evaluate the representativeness of datasets from non-local regions?
Compare the production technology, raw material sources, and energy mixes of the foreign dataset to your situation. If large differences exist, adjust your assumptions or choose a more suitable dataset. Document any limitations or deviations.
85. What is the expected maximum age of primary data?
Primary data should ideally be less than three years old and cannot exceed five years for most Program Operators. If production has changed significantly, update the data regardless of age. Temporal representativeness must be addressed in your documentation.
86. What should I do if two datasets for the same material differ significantly?
Choose the dataset that best matches your product’s actual supply chain, technology, and region. Differences often arise from methodological choices, system boundaries, or age. If uncertainty remains, select the more conservative dataset and explain why.
87. How do I document data assumptions transparently for verification?
State what information was missing, how you filled the gaps, and why the chosen approach is reasonable. Include sources, dates, and supporting evidence. Transparency is more important than precision for verifier approval.
88. Are supplier declarations acceptable if no LCA data exists?
Supplier declarations can support assumptions but cannot replace LCA datasets. You can use them to estimate recycled content, energy sources, or material composition. However, you must still use an appropriate background dataset to model the environmental impacts.
89. When should I average data across multiple facilities?
Average data when facilities use similar technologies and produce interchangeable products. Weight the averages by production volume for accuracy. If plants differ significantly, model them separately.
90. How do I determine whether a dataset meets minimum data quality indicators?
Check the dataset’s documentation for completeness, representativeness, methodology, and compliance with EN 15804+A2 or ISO standards. Use datasets with clear system boundaries, recent data, and transparent assumptions. Avoid outdated or poorly documented datasets unless no alternatives exist.
8. Software Workflow & Modeling Questions (One Click LCA)
(Questions 91–103)
91. How should I structure my model when a product has multiple production stages?
Create clear sub-processes or input groups that reflect each major production stage (e.g., mixing, forming, finishing). This makes verification easier and helps track where impacts originate. The model should mirror the real production flow without unnecessary complexity.
92. Should production waste be modeled as a negative input or a separate process?
Production waste should be modeled as a separate waste flow, not a negative material input. This ensures proper treatment in Module C and D, especially for materials that generate recycling credits. Internal scrap loops can be modeled with dedicated internal recycling flows.
93. How do I enter supplier-specific datasets in One Click LCA?
You can either search for supplier-specific datasets already included in the database or add them as custom datasets if allowed by the PCR and Program Operator. For custom datasets, upload supporting documentation to ensure transparency. Always confirm dataset eligibility before finalizing.
94. How should I model products made in multiple manufacturing locations?
Model each facility separately if their inputs or technologies differ, then combine results using production-weighted averages. If facilities are identical, you can use a single model with aggregated data. Maintain facility-level documentation for verification.
95. Can I import electricity datasets not already included in the software?
Yes, if your PCR allows custom datasets and the Program Operator accepts the source. Import datasets via the private data feature and attach all evidence, including metadata and system boundaries. If custom data is not allowed, select the closest built-in dataset.
96. How do I update outdated datasets in an existing model?
Use the dataset management tools to replace out-of-date materials, energy datasets, or transport datasets with the newest versions. Updating datasets may require revalidation of results. You should document when and why updates were made, especially if they affect impact trends.
97. When modeling packaging, should I enter mass or volume?
Always enter mass, as environmental datasets are mass-based. If only volume is available, convert it using the material density. Packaging should be modeled separately from the product unless it remains part of the final delivered unit.
98. How do I choose between generic and manufacturer-specific datasets in the interface?
Use manufacturer-specific datasets when they are available, recent, and verifiable. Otherwise, select a generic dataset that closely matches your material type and region. Consistency matters—try not to mix fundamentally different data sources unless justified.
99. How should I document data sources within the software for verification?
Use the documentation fields to explain material origins, energy sources, assumptions, and any data substitutions. Upload evidence such as supplier declarations, meter readings, or process flow diagrams. Good documentation reduces questions during verification.
100. What is the correct way to model complex bill of materials imported from spreadsheets?
Prepare a clean spreadsheet with proper units, material names, and quantities before import. Map each line item to the correct dataset during import. After import, review all materials to ensure no mismatched datasets or incorrect unit conversions.
101. Should multi-layer materials (e.g., composites) be modeled as one input or multiple?
Model each layer separately using its own dataset to ensure accuracy, unless a composite dataset already exists that fully represents the product. Multi-layer materials can have very different environmental profiles across layers. Accurate modeling helps you capture these differences.
102. How can I model internal recycling loops in One Click LCA?
Use the internal recycling or “closed-loop” features to represent scrap recovery inside the factory. Assign the scrap to the correct recycling flow without granting Module D credits. Document scrap rates and yields to support verification.
103. How do I model renewable electricity supply contracts in the tool?
Select a market-based renewable electricity dataset if allowed by your PCR, and upload proof of the contract, RECs, or guarantees of origin. If no market-based dataset exists, use the standard grid mix and document the renewable electricity purchase separately. Transparency is essential for verifier acceptance.
9. Scenario Modeling & Assumptions
(Questions 104–111)
104. What default end-of-life scenarios can I rely on, and when do I need custom scenarios?
Use default end-of-life scenarios when provided by the PCR or Program Operator, as these take precedence. If no defaults exist, choose typical regional waste treatment routes based on industry practice (e.g., metals → recycling, plastics → incineration or landfill). Custom scenarios are required only when the product has a unique disposal pathway or the PCR mandates one.
105. How should I choose realistic recycling rates for different materials?
Use national or regional recycling statistics for each material type—many countries publish annual recycling performance data. If no local data exists, rely on established industry averages. Avoid unrealistic assumptions such as 100% recycling unless it is explicitly required or documented.
106. How do I justify scenario assumptions during verification?
Show the source of each assumption (national data, PCR guidance, municipal waste reports, or industry references). Explain why the chosen scenario matches expected real-world behavior. Clarity and documentation matter more than achieving perfect accuracy.
107. What assumptions should be used when real-world conditions vary widely?
Use weighted averages or a balanced “typical case” scenario that reflects the dominant disposal or use pattern. Avoid modeling extreme or best-case scenarios unless justified. When variability is very high, explain why the chosen assumption is reasonable and representative.
108. How do I model processes with incomplete operational data?
Use conservative estimates supported by industry benchmarks, similar facilities, or engineering calculations. Fill data gaps with documented assumptions and explain how they were derived. Highlight any data that may influence results significantly.
109. When is sensitivity analysis required?
Sensitivity analysis is needed when assumptions have a large impact on results, such as recycling rates, energy sources, or transport distances. PCRs sometimes require it explicitly—especially when modeling cradle-to-grave scenarios. Even when optional, sensitivity analysis strengthens your documentation.
110. What ranges of deviation are acceptable between modeled and actual production values?
Small deviations (±10–15%) are typically acceptable when variability is normal for the process. Larger deviations must be justified using operational data, production schedules, or explanations of seasonal fluctuations. Verifiers mainly want to see that the data reflects real production conditions.
111. How should I document scenario choices when multiple options are possible?
List each possible scenario, explain why you chose the selected one, and include sources or references. State any regional, regulatory, or technology-based reasoning. A simple, well-documented justification prevents verification challenges later.
10. Industry-Specific & Material-Specific Questions
(Questions 112–121)
112. How should I model steel production using different furnace technologies?
Choose datasets that reflect the correct steelmaking route, such as blast furnace/basic oxygen furnace (BF/BOF) for primary steel or electric arc furnace (EAF) for recycled steel. These routes have drastically different environmental profiles, so selecting the right one is essential. If the supplier uses a hybrid or intermediate technology, document it and choose the closest matching dataset.
113. How is aluminum production best represented when recycled content is high?
Use datasets that accurately represent the recycled content percentage, as secondary aluminum typically has much lower impacts than primary aluminum. Ensure that scrap sources, quality, and post-industrial vs. post-consumer distinctions are clear. High recycled content also affects Module D, where avoided burdens can be significant.
114. What is the correct approach for modeling plastics with complex polymer blends?
Model each major polymer component separately if available, and use blend-specific datasets only if they closely match the composition. Add fillers, additives, or reinforcements as individual inputs when they meaningfully affect environmental impacts. Avoid using a generic plastic dataset unless no alternatives exist.
115. How do I model timber products that store biogenic carbon?
Use datasets that correctly account for biogenic carbon flows according to EN 15804+A2. Wood products often show negative CO₂ flows in A1–A3 due to carbon storage, which is later balanced in end-of-life modules. Ensure that moisture content, density, and treatment processes are correctly modeled.
116. How should mineral wool or insulation be modeled when density varies?
Choose the dataset that matches the correct density and binder technology, as these have strong influence on impacts. If density varies by product variant, calculate impacts per declared unit (e.g., per m² of installed insulation) rather than per kilogram. Avoid mixing densities unless averaging is justified.
117. What is the best way to model paints, adhesives, and liquid materials?
Use mass-based inputs that reflect solids content and chemical composition. If only volume is provided, convert to mass using density. Liquids often have high impacts per kilogram, so accurate mass inputs are important.
118. How do I model products with highly variable batch compositions?
Use representative or average batch compositions based on historical production data. If the variation is large and affects environmental performance, consider modeling multiple product variants or providing ranges. Document how averages were created for verification.
119. How do ceramic products differ in their end-of-life modeling?
Ceramics are typically inert and end up in construction waste landfills, making their end-of-life impacts relatively low. They do not degrade or release significant emissions. Use inert landfill datasets and avoid assigning recycling credits unless genuine reuse pathways exist.
120. What are the correct datasets for concrete with SCMs (fly ash, slag)?
Choose concrete datasets that include supplementary cementitious materials (SCMs) in the correct proportions. If none exist, model cement and SCM components separately and create a custom mix. Ensure that SCM sourcing is documented, particularly when using industrial by-products.
121. How do I treat products with embedded electronics?
Model the electronics using datasets for circuit boards, components, batteries, or wiring as appropriate. Electronic components often have very high impacts per kilogram, so include them even when their mass is small. Use specialized waste treatment datasets for end-of-life handling.
11. Interpretation of Results & Impact Categories
(Questions 122–129)
122. Why do A1–A3 impacts dominate most product EPDs?
A1–A3 includes raw material extraction, upstream processing, and manufacturing—typically the most energy- and emissions-intensive stages for construction products. Many materials (e.g., metals, cement, plastics) have large embodied impacts before they ever leave the factory. As a result, A1–A3 often accounts for the majority of the total GWP unless the product has significant use-phase emissions.
123. Why do some materials show negative GWP in Module D?
Negative values in Module D occur because recycling or energy recovery can avoid virgin production or reduce fossil fuel use. For example, recycled metals replace primary metal production, which is carbon-intensive. Module D is designed to reward circularity by showing these benefits beyond the system boundary.
124. Why can primary energy demand increase even when GWP decreases?
Different processes have different environmental profiles across impact categories. A material with lower carbon emissions may require more total energy, especially renewable or non-fossil energy. This shows why evaluating multiple categories—not just GWP—is essential for a balanced assessment.
125. How should I interpret very high results in ADPe or ADPf categories?
High Abiotic Depletion Potential values (elements or fossils) often reflect materials that rely heavily on scarce minerals or fossil resources. Metals, electronics, and petrochemicals often score high in these categories. It does not necessarily mean the product is “bad,” but it highlights resource dependency.
126. Why might two datasets for the same material differ significantly in GWP values?
Differences may arise from varying energy mixes, manufacturing technologies, scrap content, allocation rules, or dataset age. Even two steel mills using the same technology can have different energy sources and efficiencies. Always choose the dataset that matches your supply chain most closely.
127. How should I compare two EPDs when they use different end-of-life scenarios?
Comparisons are only meaningful when the same boundaries and assumptions are used. If two EPDs use different end-of-life scenarios, compare A1–A3 directly, then evaluate C and D separately. Always check declared unit, scenarios, and PCR alignment before drawing conclusions.
128. Why do biogenic carbon flows sometimes appear confusing or counterintuitive?
Biogenic carbon accounting involves carbon storage, release, and sometimes delayed emissions. Wood products may show negative emissions in A1–A3 but positive emissions in C, reflecting stored carbon that is later released. EN 15804+A2 handles these flows systematically, but interpreting them requires understanding both storage and release phases.
129. Why do some EPDs report zero values for categories that definitely have impacts?
Zero values may occur when data is missing, below detection limits, or outside the dataset’s scope. It may also indicate that the impact category was not modeled or included in the underlying database. Always read the technical background report to understand whether zeros are actual results or data limitations.
12. Verification, Documentation & Compliance
(Questions 130–137)
130. What documentation should be prepared for verification of primary data?
Provide annual energy bills, metered data, production records, material purchase invoices, and waste disposal data. Include a clear bill of materials and evidence of recycled content, if applicable. Verifiers mainly look for consistency between documentation and the modeled data.
131. How should assumptions be explained to the verifier?
List each assumption, explain why it was necessary, and provide supporting evidence or rationale. Make the explanation concise but transparent. Verifiers value clear reasoning over trying to hide uncertainty.
132. How do I justify the chosen allocation method during verification?
Reference the PCR and explain how the method reflects real material or economic relationships. Describe why mass, economic, or system expansion was selected. If another method was possible, briefly state why it was not chosen.
133. What evidence must be provided for renewable electricity use?
You need certificates, contracts, Guarantees of Origin, or RECs that match the correct production period and facility. Supplier declarations alone are insufficient. The verifier must be able to confirm both volume and validity.
134. When can a verifier request recalculation of scenarios?
Recalculations may be required if assumptions are unrealistic, inconsistent with the PCR, or poorly documented. Verifiers may also request recalculation if input data is incomplete or if allocation rules are applied incorrectly. Their goal is to ensure methodological correctness, not to achieve specific results.
135. What is required for internal consistency checks before submission?
Check that declared units match the model, that mass flows balance, and that datasets follow the correct geographic and technological representativeness. Ensure no outdated datasets remain in the model. A quick sanity check of each module helps prevent delays in verification.
136. Do I need permission to use supplier-provided LCA datasets?
Yes, if the dataset is proprietary or not publicly available. Suppliers typically provide explicit permission or a statement allowing use for EPD creation. Program Operators require confirmation to avoid intellectual property issues.
137. How should confidential data be handled during verification?
Provide detailed data to the verifier but mask sensitive values in the public EPD if necessary (e.g., using ranges or aggregated values). Verifiers are bound by confidentiality agreements. Transparency with the verifier is required, even if the published EPD uses simplified values.
13. Additional Technical or Edge-Case Questions
(Questions 138–147)
138. How should extremely low-volume materials be treated?
If a material accounts for less than 1% of product mass and has negligible environmental impact, it may be excluded using the cut-off rule. However, if it is environmentally intensive (e.g., catalysts, pigments), include it even in small amounts. Document the decision clearly for verification.
139. How do I model products with variable operational lifetimes?
Use a declared unit instead of a functional unit when lifetime varies widely or is application-dependent. Only include use-phase assumptions if required by the PCR. Keeping lifetime assumptions minimal helps avoid uncertainty and verification challenges.
140. Should emissions from employee travel or office energy ever be included?
No. These emissions fall outside the product system boundary and are not included in A1–A3 or other life cycle modules. Only emissions directly tied to product manufacturing should be included.
141. How do I assign impacts when a material’s composition is confidential?
Model materials using aggregated or representative datasets, and provide full composition details only to the verifier under confidentiality. Public EPDs can show simplified or grouped categories. Clear communication with the verifier avoids delays.
142. Can I model temporary carbon storage as a benefit?
Only if the PCR explicitly allows it and the storage period is long and stable enough to be meaningful (typically for bio-based products). EN 15804+A2 includes biogenic carbon flows but does not provide credit for temporary storage. Any storage benefit must follow the PCR rules strictly.
143. How should I handle missing emission factors for additives or pigments?
Use the closest chemical or material dataset in the LCA database, or select a generic dataset that approximates the additive’s environmental profile. When no good match exists, use conservative assumptions. Document how and why the substitution was made.
144. Are there recommended benchmarks for judging if results are “reasonable”?
Benchmarks can come from industry-average EPDs, trade associations, or similar products already published. Large deviations do not necessarily mean errors, but they require explanation. Always compare to materials with similar technology and region.
145. Should I include impacts from maintenance of production machinery?
No. Routine maintenance, machine replacement, and factory infrastructure fall outside the product system boundary. Only materials and energy directly consumed by manufacturing the product should be included.
146. How do I split impacts between multiple product lines sharing the same equipment?
Allocate shared impacts (e.g., energy, auxiliary materials) using production volume, mass, or process time—whichever best reflects the relationship between the products. Consistency across all product lines is important. Provide justification for the allocation basis used.
147. How do I handle products that generate recyclable waste during installation?
Model installation waste in A5, applying the correct waste treatment routes. Include the waste quantity and assign recycling or disposal impacts as appropriate. If recycling provides benefits, these appear in Module D.