Question: What is the source of uncertainty in LCA attributable to a lack of knowledge about the system, and how can it be revealed?

Ignorance, which involves omission of data or incorrect assumptions about processes or elementary flows. Because it is not handled by quantitative uncertainty assessment, it may be revealed by a qualified peer review.

## Epistemic Uncertainty in LCA

The source of uncertainty attributable to a **lack of knowledge** about the system is called **epistemic uncertainty** (also referred to as **knowledge uncertainty** or **model uncertainty**).

### Key Sources Include:
- Incomplete or missing inventory data
- Unknown system boundaries
- Uncertainty about which processes/flows are relevant
- Lack of knowledge about real-world system behavior
- Uncharacterized environmental mechanisms

### How It Can Be Revealed:

**Epistemic uncertainty** can be revealed through:

1. **Scenario analysis** – Testing alternative assumptions, system boundary choices, or modeling approaches to see how results change
2. **Sensitivity analysis** – Identifying which parameters/assumptions drive the results most, highlighting areas where knowledge gaps matter most
3. **Expert elicitation** – Gathering expert judgment to characterize what is unknown
4. **Data quality assessment** – Using pedigree matrices (e.g., Weidema & Wesnæs) to document and score knowledge gaps
5. **Model comparison** – Comparing different modeling choices (e.g., attributional vs. consequential) to expose structural uncertainties

### Distinction from Stochastic Uncertainty:
Unlike **aleatory (stochastic) uncertainty** — which is inherent variability that cannot be reduced — epistemic uncertainty **can in principle be reduced** by gathering more knowledge or better data.

> This distinction is important in LCA because Monte Carlo simulation addresses aleatory/parameter uncertainty, while epistemic uncertainty requires qualitative or scenario-based approaches to be revealed.

Based on the context provided, the source of uncertainty attributable to a lack of knowledge about the system is called **"Ignorance"** (also referred to as "epistemological uncertainty" in Huijbregts' classification).

According to the ILCD Handbook, ignorance leads to the **omission of data or incorrect assumptions about processes or elementary flows**. It is related to choice uncertainty in that it shows discrete behaviour, but since it is not realized, it cannot be handled through quantitative uncertainty assessment.

**Ignorance can be revealed through a qualified peer review.**

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.379)*

Based on the context, the source of uncertainty in LCA attributable to a lack of knowledge about the system is called **"Ignorance."**

As described in the ILCD Handbook, ignorance refers to "the error attributable to ignorance, i.e. the lack of knowledge about the system, leading to omission of data or incorrect assumptions about processes or elementary flows." It is related to choice uncertainty in that it shows discrete behaviour, but since it is not realized, it cannot be dealt with in the same way as choices are handled. It is **not handled by quantitative uncertainty assessment**, but rather **"may be revealed by a qualified peer review."**

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.379)*


Question: What unit is given as an example for measuring damage to human health at the endpoint level, and what unit is given for global warming potential at the midpoint level?

Damage to human health is measured in Disability Adjusted Life Years (DALYs), and global warming potential is expressed in kg CO2 equivalents per kg elementary flow.

At the **endpoint level**, damage to human health is typically measured in **DALYs** (Disability-Adjusted Life Years).

At the **midpoint level**, global warming potential is measured in **kg CO₂-equivalent** (kg CO₂-eq).

**Damage to Human Health (Endpoint Level):**
The unit used is **DALY (Disability-Adjusted Life Years)** expressed in **[years]**.
*(Hauschild et al., LCA: Theory and Practice — 10 Life Cycle Impact Assessment, p.186)*

**Global Warming Potential (Midpoint Level):**
The unit used is **kg CO₂-equivalents per kg of substance emitted** (kg CO₂-eq/kg).
*(Hauschild et al., LCA: Theory and Practice — 10 Life Cycle Impact Assessment, p.186; also supported by Guinée et al., Handbook on LCA, p.571)*

At the **endpoint level**, damage to human health is measured in **DALY (Disability-Adjusted Life Years)**, expressed as [years] per kg of substance emitted (Hauschild et al., *10 Life Cycle Impact Assessment*, p.186).

At the **midpoint level**, global warming potential is expressed in **kg CO₂-equivalents per kg of substance emitted** (Hauschild et al., *10 Life Cycle Impact Assessment*, p.186; also referenced in Guinée et al., p.571).


Question: When summing a lead emission to air of unknown quantity with a known emission of 0.00026 kg from another process during LCI result calculation, what problem arises?

The two cannot be meaningfully summed, because adding an unknown amount of kg to 0.00026 kg yields an undefined result (unknown kg plus 0.00026 kg = ?), illustrating the difficulty of combining partial data gaps with available information.

## Problem: Loss of Significant Figures (Floating Point Precision Issue)

When summing an **unknown quantity** (which in LCA software is often represented as **0** or a null/placeholder value) with a known value of **0.00026 kg**, the core problem that arises is:

### **Significant Figures / Numerical Precision Loss**

More specifically, if the "unknown" quantity is treated as **zero** or an **indefinite value**, the sum becomes misleading:

- **0 + 0.00026 = 0.00026 kg** — appears precise, but the result is **falsely certain**
- The unknown contribution is silently ignored, making the total **incomplete and potentially underestimated**

### **Key Problem: Propagation of Unknown/Missing Data**

The fundamental issue is that **you cannot meaningfully sum a known value with an unknown value** — the result is still **unknown** (or at best a **lower bound**):

> **Unknown + 0.00026 kg = Unknown** (not 0.00026 kg)

This violates the principle that LCI results should represent **complete and traceable** inventories.

---

### In Practice This Leads To:
- **Underestimation** of the total lead emission to air
- **False precision** in the LCI result
- Potential misrepresentation in **characterization** and **impact assessment** (e.g., human toxicity)
- Non-compliance with **data quality** requirements (ISO 14044)

The recommended approach is to flag the result as **incomplete/uncertain** or conduct **sensitivity analysis** on the unknown quantity.

When summing a lead emission to air of unknown quantity with a known emission of 0.00026 kg from another process during LCI result calculation, the problem that arises is that it is mathematically and practically impossible to combine the two values meaningfully — the result would be "Unknown kg + 0.00026 kg = ?". This highlights the difficulty of combining partial data gaps (qualitative or quantitative unknown amounts) with available quantitative information, as the unknown quantity cannot be properly aggregated with the known value, making the LCI result indeterminate.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p. 209)*

When summing an unknown quantity of lead emission to air with a known emission of 0.00026 kg from another process during LCI result calculation, the problem that arises is that it is mathematically and scientifically impossible to combine the two values meaningfully — as expressed by the equation: **"Unknown kg plus 0.00026 kg = ?"** The partial data gap (qualitative or quantitative) cannot simply be added to the known value, and an appropriate solution must be found for how to combine such incomplete information with available data, while avoiding combining highly uncertain information with more certain data.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), pp. 209)*


Question: In a municipal waste incinerator treating a mixture of household waste materials, on what basis could the emission of cadmium in the flue gas be allocated among the co-incinerated waste streams?

It could be allocated according to the cadmium content of each material in the co-incinerated waste streams.

## Allocation of Cadmium Emissions in Municipal Waste Incineration

Cadmium emissions in flue gas from a municipal waste incinerator treating mixed waste streams can be allocated among co-incinerated waste fractions on several defensible bases:

---

### 1. **Cadmium Content Basis (Mass of Cd Input)**
The most physically meaningful allocation: each waste stream is allocated a share of total Cd emissions proportional to its **cadmium mass contribution** to the input mixture.

> Allocation factor for stream *i* = Cd mass in stream *i* / Total Cd mass in all streams

This is the preferred approach in LCA (ISO 14044) when a causal physical relationship exists, since cadmium in the flue gas originates directly from cadmium in the waste feedstock.

---

### 2. **Mass of Waste Basis**
Allocation proportional to the **mass fraction** of each waste stream. Simpler but less accurate, as it ignores the highly variable Cd concentrations across waste types (e.g., batteries, pigments, plastics are disproportionate contributors).

---

### 3. **Economic/Market Value Basis**
Used when physical causality is weak or when incineration provides energy recovery services with differentiated tipping fees, but this is generally less appropriate for pollutant allocation.

---

### 4. **Energy Content Basis**
Proportional to the **calorific contribution** of each stream; relevant if the incineration is primarily an energy service, but poorly correlated with Cd release.

---

### Recommended Practice

The **cadmium content of each input stream** is the scientifically most defensible basis because:
- It reflects the **physical-chemical causality** (Cd in → Cd out, accounting for partitioning to bottom ash, fly ash, and flue gas)
- It aligns with the **polluter pays** principle
- It is consistent with LCA guidance (ISO 14044, ILCD Handbook) to use physical causality before economic allocation

In practice, this requires knowing (or estimating from literature) the **Cd concentration per unit mass** of each waste fraction (e.g., NiCd batteries, PVC stabilizers, phosphate fertilizer residues, etc.).

Based on the context provided, the emission of cadmium in the flue gas could be allocated among the co-incinerated waste streams **according to their contents of cadmium**.

This reflects a causal physical relationship approach, where cadmium emissions can be traced directly back to the cadmium content of specific waste inputs. As further explained, since cadmium cannot be formed in the waste incineration process itself, all cadmium emissions originate solely from whichever waste stream contains cadmium — meaning 100% of cadmium emissions would be allocated to that stream.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.265; Hauschild et al., LCA: Theory and Practice, Chapter 8, p.93)*

Based on the retrieved passages, the emission of cadmium in the flue gas could be allocated among the co-incinerated waste streams **according to their cadmium content**.

This is grounded in the causal physical relationship between the cadmium content of each waste stream and the resulting cadmium emissions. As further illustrated, if one waste stream contains no cadmium (e.g., plastic), 100% of the cadmium emission would be allocated to the stream that does contain cadmium (e.g., batteries), since cadmium cannot be formed in the incineration process itself.

> *"The emission of cadmium in the flue gas could be allocated between the materials in the co-incinerated waste streams according to their contents of cadmium"*
— **ILCD Handbook: General Guide for LCA (2010), p. 265**

> *"cadmium emissions can be allocated 100% to the batteries"* (in a case where only batteries contain cadmium)
— **Hauschild et al., LCA: Theory and Practice, Chapter 8 Scope Definition, p. 93**


Question: Under what conditions can data from one process be considered representative of a similar process operated elsewhere?

When the applied technologies, the way the process is operated, the abatement technologies, and the background system (such as the raw material route and waste treatment) are very similar or at least result in very similar inventory values.

## Representativeness of Process Data in LCA

Data from one process can be considered representative of a similar process operated elsewhere under the following conditions:

### Technological Similarity
- Same or equivalent production technology and process route
- Similar equipment age, efficiency, and configuration
- Comparable raw material inputs and their qualities
- Similar by-product and waste generation patterns

### Operational Similarity
- Comparable scale of production (capacity utilization rates)
- Similar operating conditions (temperature, pressure, throughput)
- Equivalent maintenance and operational practices

### Geographic/Contextual Factors
- Similar background energy systems (electricity grid mix)
- Comparable infrastructure and supply chain conditions
- Similar regulatory environment influencing emissions controls
- Equivalent fuel mixes and energy sources

### Temporal Relevance
- Data collected within an acceptable time window (typically ≤5–10 years, per ISO 14044)
- No significant technological changes have occurred in the interim

### Data Quality Considerations
- Sufficient precision and completeness in the source data
- Uncertainty ranges are documented and acceptable
- The data source is transparent about system boundaries and assumptions

### Formal Criteria (per ISO 14044 / ecoinvent)
- **Technological representativeness**: same function and technology
- **Geographical representativeness**: similar regional conditions
- **Temporal representativeness**: current and time-relevant
- **Completeness**: covers all relevant flows

### Practical Guidance
When direct data are unavailable, proxy data may be acceptable if **sensitivity analysis** confirms that differences between the proxy and actual process do not materially affect study conclusions, and **uncertainty is explicitly documented**.

Based on the context provided, data from one process can be considered representative of a similar process operated elsewhere under the following conditions:

Data can be considered representative when there are **similar production conditions** between the areas. The sources identify a hierarchy of geographical and technical correlation for representativeness (Guinée et al., 2002, p.5):

**Geographical correlation** (in descending order of representativeness):
- Data from the area under study
- Average data from a larger area that includes the area under study
- Data from an area with similar production conditions
- Data from an area with slightly similar production conditions
- Data from an unknown area or area with very different production conditions

**Technical correlation** (in descending order of representativeness):
- Data from enterprises, processes and materials under study
- Data on processes and materials under study but from different enterprises
- Data on processes and materials under study but with different technology
- Data on related processes or materials but with same technology
- Data on related processes/materials but with different technology

Additionally, the ILCD Handbook (p.199) notes that a process with **limited representativeness** can still be considered appropriate if the specific technology route used in the data set matches exactly what is needed for the product system being modeled, which should be verified through data set documentation.

Based on the retrieved sources, data from one process can be considered representative of a similar process operated elsewhere under the following conditions:

**Technological and Operational Similarity**

According to Guinée et al. (2002, p.5), geographical and technical correlation criteria determine representativeness. Data can be considered representative when it comes from:
- "data from area with similar production conditions" (geographical correlation)
- "data on processes and materials under study but from different enterprises" or "data on processes and materials under study but with different technology" (technical correlation)

**Operational Conditions Coverage**

Per the ILCD Handbook (2010, p.201), the data must account for the **full cycle of the process**, including not just actual operation but also "start, closure, and eventual stand-by times," as a large share of emissions may occur under these special operating conditions.

**Appropriateness and Representativeness Assessment**

The ILCD Handbook (2010, p.199) further clarifies that representativeness must be judged for the **specific given case**, noting that "a process with limited representativeness is actually very appropriate" if, for example, the specific technology route used in the product system matches the data set — even if that data set does not fully represent a broader country-mix. This is to be verified through data set documentation.


Question: In the recycling formula described, what does the variable r represent and what two efficiencies does it incorporate?

r is the average recycling rate (a value from 0 up to but not including 1), and it incorporates both collection efficiencies and processing efficiencies.

Without knowing which specific "recycling formula" you're referring to, I can offer the most commonly referenced interpretation in LCA and material flow contexts:

In many standard recycling/end-of-life formulas (such as those used in PAS 2050, EN 15804, or the widely cited **Circular Footprint Formula (CFF)** used in the EU Product Environmental Footprint methodology):

**r** typically represents the **recycling rate** or **recycled content fraction** — that is, the proportion of a material that is actually recycled at end of life (or sourced from recycling at the input stage).

The two efficiencies it commonly incorporates are:

1. **Collection efficiency** — the fraction of material that is actually collected for recycling (as opposed to being landfilled or otherwise lost)
2. **Recycling/reprocessing efficiency** — the fraction of collected material that is successfully recycled into usable secondary material (accounting for losses during the recycling process)

So **r = collection efficiency × recycling process efficiency**, representing the net fraction of material that successfully completes the recycling loop.

---

If you are referring to a **specific formula** from a particular source (e.g., a textbook, standard, or paper), please share more context and I can give a more precise answer.

Based on Source 1 (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.2):

The variable **r** represents the **average recycling rate**, with a value range of [0...1), and it incorporates both:

1. **Collection efficiencies**
2. **Processing efficiencies**

Based on Source 1 (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.2), the variable **r** represents the **average recycling rate**, which falls within the range [0...1), and it incorporates both:

1. **Collection efficiencies**
2. **Processing efficiencies**

As stated directly in the source: "r average recycling rate [0...1), incorporating both collection efficiencies and processing efficiencies."


Question: In allocating land transformation emissions, why are emissions occurring in year 0 shared linearly while emissions occurring over a longer period are shared in a triangular shape that favors the land use directly following the transformation?

Because the year-0 "peak" emissions are equivalent to a one-time production inventory (like producing a refillable bottle), whereas the longer-period emissions, though related to the transformation, depend on the specific land use in a given year (e.g., whether soil organic carbon loss is stopped by better management), making them operational emissions (like washing a refillable bottle) that must be inventoried as and when they occur, thus giving a higher share to the land use directly following the transformation.

## Land Transformation Emission Allocation in LCA

This allocation approach reflects two distinct physical/temporal realities:

### Year 0 Emissions (Linear Sharing)
Emissions occurring **instantaneously at transformation** (e.g., burning, immediate decomposition of removed biomass) have no inherent temporal association with any particular land use within the occupation period. Since there is no logical basis to favor one year over another, they are distributed **equally (linearly)** across all years of the subsequent land use.

### Longer-Period Emissions (Triangular/Decreasing Shape)
Emissions that occur **gradually over time** following transformation (e.g., slow decomposition of soil organic matter, decaying root systems) are physically **concentrated at the beginning** of the occupation period — the processes are most active immediately after transformation and diminish over time.

The triangular shape mathematically reflects this: the land use **directly following transformation** receives the **highest allocation** because it temporally coincides with the highest actual emission rates. Subsequent land uses receive progressively less because by the time they occupy the land, the transformation-driven processes have largely run their course.

### Underlying Principle
Both approaches follow the **"Weidema & Lindeijer" / ecoinvent convention**: allocate emissions to land uses in proportion to their **temporal proximity to and overlap with the actual emission profile**. The shape of allocation should mirror the shape of the actual emission curve over time.

This avoids arbitrarily burdening distant future land uses with emissions that physically occurred (or were driven by processes occurring) much earlier.

The emissions occurring in year 0 are shared linearly because they represent a "peak" inventory of transformation, equivalent to a production inventory (such as that of a refillable bottle) — they are a one-time, discrete event tied directly to the transformation itself.

In contrast, emissions occurring over a longer period (such as ongoing soil organic carbon loss) are shared in a triangular shape — giving a higher share to the land use directly following the transformation — because these longer-term emissions are still related to the transformation, but their continuation depends on the specific land use in any given year. For example, whether soil organic carbon loss is stopped depends on whether better land management is adopted in subsequent years. This makes these ongoing emissions more akin to **operational emissions** (similar to washing a refillable bottle), and they are therefore inventoried as and when they occur, with the greatest burden assigned to the earliest land uses that are most closely linked to the transformation decision.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p. 236, footnote 160)*

Based on the ILCD Handbook, the reasoning is as follows:

The emissions that occur in **year 0** are treated like a "peak" inventory of transformation, which is equivalent to a **production inventory** (analogous to, for example, the production of a refillable bottle). These are one-time, upfront emissions directly tied to the act of transformation itself, so they are shared **linearly** across the following land uses over several years.

In contrast, emissions that occur **over a longer period** (such as ongoing soil organic carbon loss) are still related to the transformation, but their magnitude depends on the **specific land use in a given year** — for instance, whether better land management stops the soil organic carbon loss. This makes them more akin to **operational emissions** (similar to washing a refillable bottle). Because these emissions are more closely linked to the decision to convert the land in the early years, and because they are inventoried as and when they physically occur, a **triangular allocation pattern** is used, giving a **higher share to the land use directly following the transformation** and tapering off over subsequent years.

> *"The 'peak' inventory of transformation is equivalent to a production inventory... The emissions that occur over a longer period are still related to the transformation, but it depends on the specific land use in a given year, whether the e.g. soil organic carbon loss is stopped by e.g. better land management, i.e. is an operational emission (similar to washing a refillable bottle). Hence it is to be inventoried as and when it occurs."*

— ILCD Handbook: General Guide for LCA (2010), **p. 236**


Question: Why is using a piece of electrical equipment's specified maximum power consumption (e.g., "10 kW") as its average consumption considered an error in LCA data collection?

Because it ignores that the equipment is not running all the time and that, when it does run, it typically operates at less than maximum load.

## The Error: Confusing Rated Capacity with Actual Consumption

Using nameplate/maximum rated power as average consumption is a significant LCA data quality error for the following reasons:

### 1. **Rated Power Represents a Ceiling, Not an Average**
Maximum power consumption describes the equipment's capacity under **full-load, worst-case conditions**. In practice, most equipment operates at partial load most of the time, meaning actual consumption is systematically lower.

### 2. **Utilization Rate Is Rarely 100%**
Equipment experiences:
- **Idle periods** (standby, warm-up, downtime)
- **Partial-load operation** (e.g., a motor rarely runs at full torque)
- **Duty cycles** that average well below maximum

A 10 kW rated machine running at 60% average load with 70% uptime consumes ~4.2 kW on average — less than half the rated value.

### 3. **Consequent Overestimation of Environmental Impacts**
Using maximum power systematically **inflates energy consumption** in the inventory, leading to:
- Overestimated electricity use (kWh)
- Overestimated associated emissions (CO₂, etc.)
- Potentially wrong conclusions in impact assessment or hotspot analysis

### 4. **It Violates the Principle of Representativeness**
LCA requires data that are **representative of actual operating conditions**. Manufacturer specifications describe *potential*, not *reality*.

### Correct Approach
Collect **measured consumption data** (e.g., from energy meters) or use **manufacturer efficiency curves** combined with actual load profiles and operating hours to calculate realistic average power draw.

Using a piece of electrical equipment's specified maximum power consumption as its average consumption is considered an error in LCA data collection because it fails to account for two key real-world operational factors:

1. **The equipment is not running all the time**, and
2. **When it does run, it typically operates below maximum load**.

This results in an overestimation of actual energy consumption, as the theoretical maximum rating does not reflect real operational performance.

This is explicitly identified as a "frequent error" stemming from modeling a process based on theory rather than verifying it with data from the process in real operation.

*(ILCD Handbook: General Guide for LCA, European Commission – JRC, 2010, p. 203)*

Using a piece of electrical equipment's specified maximum power consumption as its average consumption is considered a common and difficult-to-detect error in LCA data collection because it fails to account for two key realities: **the equipment is not running all the time, and when it does run, it typically does not operate at maximum load**.

This means that modelling a process based on theoretical specifications rather than verifying with real operational data leads to an overestimation of actual energy consumption, resulting in inaccurate inventory data.

This is explicitly identified as a "frequent error" in the ILCD Handbook:

> *"A very common error, which is difficult to detect in review, is to model the performance of a process based on some theory on how it operates and not verifying this with data from the process in real operation. For electrical equipment, sometimes the specified maximum power consumption (e.g. '10 kW') is used, implicitly assuming to be the average consumption. This does not consider that the equipment is not running all the time and that when it runs it typically is running not on maximum load."*

**(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p. 203)**

This point is further reinforced at **p. 65** of the same handbook, which notes the "direct use of maximum electricity uptake information on energy-using devices instead of the actual consumption" as an inappropriate use of technical standards in LCA.


Question: In consequential modelling, which domains of expertise should be involved in identifying and modelling large-scale consequences?

Technology development forecasting (e.g. learning curves, experience curves), scenario development, market cost and market forecasting, technology cost modelling, and general-equilibrium and partial-equilibrium modelling.

In consequential LCA modelling, identifying and modelling large-scale consequences requires input from multiple domains of expertise. The key domains typically include:

## Core Domains of Expertise

**1. Market and Economic Analysis**
- Understanding market dynamics, supply/demand relationships
- Identifying marginal suppliers and constrained markets
- Economic modelling of price effects and market responses

**2. Technology Assessment**
- Identifying which technologies are affected at the margin
- Understanding technology trends and substitution patterns
- Capacity constraints and expansion possibilities

**3. Energy Systems Modelling**
- Electricity grid dynamics and marginal generation
- Energy market interactions
- Transition scenarios

**4. Agricultural/Land Use Science**
- Land use change dynamics
- Crop substitution and displacement effects
- Indirect land use change (iLUC) modelling

**5. Life Cycle Assessment Methodology**
- System expansion principles
- Defining the decision context
- Handling co-products and allocation avoidance

**6. Statistics and Econometrics**
- Quantifying causal relationships
- Uncertainty analysis
- Data interpretation

## Why Multiple Domains Matter

Consequential modelling requires understanding **systemic responses** to decisions — including:
- Which suppliers/technologies are actually affected (marginal vs. average)
- Second-order economic and environmental effects
- Long-run vs. short-run consequences

The interdisciplinary nature reflects that consequential LCA is fundamentally about **predicting real-world change**, which extends beyond traditional LCA boundaries into economics, policy analysis, and systems modelling.

Based on the context provided, the following domains of expertise **should** be involved in identifying and modelling large-scale consequences in consequential modelling:

1. **Technology development forecasting** (e.g. learning curves, experience curves)
2. **Scenario development**
3. **Market cost and market forecasting**
4. **Technology cost modelling**
5. **General-equilibrium and partial-equilibrium modelling**

Additionally, **policy scenario experts** are recommended when policy scenarios are explicitly analysed in the study, particularly regarding their function in setting constraints.

*(Source 1: Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.1003; Source 3: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.176)*

Based on the context, in consequential modelling, the following domains of expertise **should** be involved in identifying and modelling large-scale consequences:

1. **Technology development forecasting** (e.g. learning curves, experience curves)
2. **Scenario development**
3. **Market cost and market forecasting**
4. **Technology cost modelling**
5. **General-equilibrium and partial-equilibrium modelling**

Additionally, **policy scenario experts** are recommended where policy scenarios are explicitly analysed in the study.

*(Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.1003)*


Question: When defining types, quality, and sources of required data and information, what two aspects related to data reliability are especially highlighted?

The required precision and the maximum permitted uncertainties.

When defining types, quality, and sources of required data and information in LCA (as outlined in ISO 14044 and related guidance), the two aspects related to data reliability that are especially highlighted are:

1. **Precision/Uncertainty** – the degree of variability or spread in the data values (e.g., how well measured or estimated the data are).

2. **Completeness** – the degree to which the data cover the relevant processes, flows, and geographical/temporal scope (i.e., whether all significant flows and processes are represented).

These are often cited alongside other data quality indicators (such as representativeness, consistency, and reproducibility), but **uncertainty** (or precision) and **completeness** are particularly emphasized in the context of data reliability for LCA studies.

Based on the context provided, when defining types, quality, and sources of required data and information, the two aspects related to data reliability that are especially highlighted are **representativeness** and **completeness** (along with precision for newly collected LCI data).

Specifically, from the ILCD Handbook, it states:

> "Regarding newly collected LCI data this means the needs for **representativeness**, **completeness**, and precision."

— European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.139

Additionally, from p.136, the overall data quality is identified as "the key measure" when identifying data and information needs and suitable sources.

— European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.136

Based on the retrieved passages, when defining types, quality, and sources of required data and information, the two aspects related to data reliability especially highlighted are **representativeness** and **completeness** (along with **precision**) for newly collected LCI data.

Specifically, from the ILCD Handbook (p. 139):

> "Regarding newly collected LCI data this means the needs for **representativeness, completeness, and precision**."

And from the same source (p. 136), the required overall data quality is identified as "the key measure" for identifying data and information needs and suitable sources.

Additionally, from Guinée et al. (p. 491), data quality requirements should be set for **temporal coverage**, **geographical coverage**, and **technology coverage**, with data quality characterized in both **quantitative and qualitative terms** with due reference to the methods used to collect and integrate the data.

The two aspects most prominently highlighted as related to data reliability are **representativeness** and **completeness**, as cited in the ILCD Handbook: General Guide for LCA (2010), p. 139.


Question: In a comparative LCA study, what must be discussed in the interpretation phase regarding generic LCIA methods, and what is the consequence if further differentiation can be argued to lead to significantly different results?

The appropriateness of generic LCIA methods must be discussed in the interpretation phase. If further differentiation can be argued or approximated to lead to significantly different results, this finding may limit the conclusions and recommendations that can be drawn from the study.

## Interpretation Phase Requirements for Generic LCIA Methods in Comparative LCA

### What Must Be Discussed

In a comparative LCA study, the interpretation phase **must explicitly discuss the appropriateness of generic (site-generic) LCIA characterization methods** relative to the specific conditions of the study. This includes:

- Whether the generic/average characterization factors adequately represent the actual spatial, temporal, and technological conditions relevant to the compared systems
- The potential limitations of using spatially averaged or globally/regionally aggregated characterization factors when site-specific conditions may differ significantly between the compared systems

### Consequence if Further Differentiation Would Lead to Significantly Different Results

If it can be **argued that further differentiation** (e.g., site-specific, spatially differentiated, or temporally differentiated characterization factors) **would lead to significantly different results**, then:

> **The study must use such differentiated methods** — i.e., the more refined/differentiated LCIA approach becomes **mandatory** rather than optional.

This means the study cannot simply acknowledge the limitation and proceed with generic methods; it is obligated to apply the more appropriate differentiated characterization factors to ensure the comparative conclusions are valid and not misleading.

### Underlying Rationale

This requirement exists because comparative assertions—especially those disclosed to the public—carry the risk of leading to incorrect decisions if the LCIA methodology systematically favors one system over another due to inappropriate spatial or temporal averaging.

*(This reflects requirements consistent with ISO 14044 and related LCIA guidance documents.)*

Based on the provided sources, in a comparative LCA study, **the appropriateness of generic LCIA methods shall be discussed in the interpretation phase** of the study.

The consequence if further differentiation (especially spatial or temporal differentiation) can be argued to lead to substantially/significantly different results is that **this finding may limit the conclusions and recommendations that can be drawn from the study**.

Additionally, **LCIA results calculated from non-generic LCIA methods are to be presented separately from the generic/default ones and discussed jointly**.

*(Sources 1, p.993; Source 3, p.113)*

In a comparative LCA study, the **appropriateness of generic LCIA methods** must be discussed in the interpretation phase. If a further differentiation (especially spatial or temporal) can be argued or approximated to lead to **significantly different results**, this finding **may limit the conclusions and recommendations** that can be drawn from the study. Additionally, LCIA results calculated from non-generic LCIA methods are to be **presented separately** from the generic ones and **discussed jointly**.

*(Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.993; European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.113)*


Question: What does a reference flow represent in relation to the functional unit?

The reference flow is the amount of product(s) of the system that provide the function as specified in the functional unit.

A **reference flow** represents the **quantified amount of a product or service required to fulfill the functional unit**.

In other words, it is the measured quantity of inputs or outputs from a specific process (or product system) needed to deliver the function described by the functional unit.

**Example:**
- **Functional unit:** Painting 1 m² of wall surface with a defined coverage and durability
- **Reference flow:** 0.15 kg of paint (the specific amount of that particular paint needed to achieve the functional unit)

The reference flow thus serves as the **link between the functional unit and the process data/inventory**, allowing different product systems to be compared on an equivalent basis. Different products may have different reference flows even when serving the same functional unit (e.g., a more efficient paint might require less grams per m²).

A reference flow represents the quantified physical flow required for a product system to fulfil the function expressed by the functional unit. It is a product-specific flow that has the functional unit as a common denominator, and is generally associated with the use phase of a product system, representing one specific way (i.e., by a specific product alternative) of obtaining the functional unit.

For example, if the functional unit is "1000 hours of light," the reference flow would be "1000 hours of light with an incandescent lamp" or "1000 hours of light with a fluorescent lamp" — each being a specific product alternative for achieving the same functional unit.

*(Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), pp. 37, 475)*

A reference flow is a measure of the outputs from processes in a given product system which are required to fulfil the function expressed by the functional unit. It is product-specific and has the functional unit as a common denominator. For example, if the functional unit is "1000 hours of light," the reference flow for system 1 would be "1000 hours of light with an incandescent lamp" and for system 2 "1000 hours of light with a fluorescent lamp" — representing one specific way (i.e., by a specific product alternative) of obtaining the functional unit.

(Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), pp. 37 and 475)


Question: When extending an existing LCIA method with characterisation factors for not yet covered elementary flows, at what stage should this need be identified, and why?

It should be identified as part of the scope definition, in order to identify the required information on elementary flows prior to the inventory analysis.

## Identifying the Need to Extend an LCIA Method

### When Should This Be Identified?

This need should be identified during the **inventory analysis (LCI) phase** — specifically when compiling and reviewing the life cycle inventory — and confirmed/addressed during the **impact assessment (LCIA) phase setup**, before characterisation factors are applied.

More precisely, the identification should occur:

1. **During inventory compilation/review** – when elementary flows in the LCI are being checked against the characterisation factors available in the chosen LCIA method.
2. **Before results are calculated and interpreted** – so that gaps do not silently lead to underestimated impacts.

---

### Why at This Stage?

- **Completeness check**: The LCI provides the full list of elementary flows actually occurring in the system. Only at this point is it known which flows require characterisation factors.
- **Avoiding silent omissions**: If uncovered flows are not identified early, they are simply **excluded from impact scores without warning**, leading to **underestimation of impacts** and potentially misleading conclusions.
- **Transparency and quality**: ISO 14044 requires that flows without characterisation factors be **documented and reported**. Identifying the gap early allows practitioners to decide whether to:
  - Derive/adapt new characterisation factors,
  - Use a proxy/surrogate factor,
  - Or explicitly acknowledge the omission as a limitation.
- **Iterative nature of LCA**: Early identification allows the study to loop back and address the gap before final interpretation, maintaining **methodological consistency**.

---

### Summary

> The gap should be identified **at the end of the LCI phase / beginning of the LCIA phase**, during the **crosscheck of inventory flows against available characterisation factors**, because this is the moment when the actual environmental exchanges are known and can be matched to the method — ensuring no impact contributions are silently lost.

Based on the context provided, the need to extend an existing LCIA method with characterisation factors for not yet covered elementary flows **should be identified as part of the scope definition**.

The reason for this timing is **in order to identify the required information on elementary flows prior to the inventory analysis**.

As noted, however, this may sometimes only be possible based on insights gained after the first or second iteration of the LCI data collection, modelling, impact assessment and interpretation.

*(Source 1: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.111)*

Based on the retrieved context, the need to extend an existing LCIA method with characterisation factors for not yet covered elementary flows **should be identified as part of the scope definition**, in order to identify the required information on elementary flows **prior to the inventory analysis**.

The reasoning given is that this allows the necessary information on elementary flows to be gathered before the inventory analysis begins. However, it is also noted that this may only be possible based on insights gained **after the first or second iteration of the LCI data collection, modelling, impact assessment and interpretation**.

> *"In other cases, existing LCIA methods may have to be extended with characterisation factors for not yet covered elementary flows that are of special relevance for the analysed system. If this is the case, this need shall be identified as part of the scope definition, in order to identify the required information on elementary flows prior to the inventory analysis. Note that this may be possible only based on insights gained after the first or second iteration of the LCI data collection, modelling, impact assessment and interpretation."*

— European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), **p. 111**


Question: What does the abbreviation KEPI stand for in the context of Ecodesign and simplified LCA?

Key Environmental Performance Indicators.

In the context of Ecodesign and simplified LCA, **KEPI** stands for **Key Environmental Performance Indicator**.

It refers to a set of selected environmental metrics or indicators used to quickly assess and communicate the most significant environmental impacts of a product during the design phase, without conducting a full, detailed LCA. KEPIs help designers focus on the most relevant environmental aspects in a simplified and practical way.

**KEPI** stands for **Key Environmental Performance Indicators**.

*(Source 1: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.30; Source 2: ILCD Handbook, p.56; Source 6: Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.967)*

**KEPI** stands for **Key Environmental Performance Indicators**.

This is stated in the ILCD Handbook (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.30 and p.56), where one of the listed LCA applications is described as:

> "Identification of **Key Environmental Performance Indicators (KEPI)** of a product group for Ecodesign / simplified LCA"

It is also confirmed in Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.967, which lists the same application:

> "Identification of **Key Environmental Performance Indicators (KEPI)** of a product group for Ecodesign/simplified LCA"


Question: In which city is PRé Consultants located?

PRé Consultants (the developers of SimaPro) is located in **Amersfoort**, the Netherlands.

PRé Consultants is located in **Amersfoort (The Netherlands)**.

*(Source 1: ILCD Handbook: General Guide for LCA (2010), p.385)*

PRé Consultants is located in **Amersfoort (The Netherlands)**.

(Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.385)


Question: In which LCA phase and step is the actually achieved consistency checked?

It is checked as part of the evaluation step in the interpretation phase.

In the **Interpretation** phase, specifically in the step of **checking completeness, sensitivity, and consistency** (i.e., the **consistency check**).

According to ISO 14040/14044, the interpretation phase includes three checks:
1. Completeness check
2. Sensitivity check
3. **Consistency check** – where it is verified whether the assumptions, methods, and data have been applied consistently with the goal and scope definition throughout the study.

So the actually achieved consistency is checked in the **Interpretation phase**, during the **consistency check** step.

The actually achieved consistency is checked in the **interpretation phase**, as part of the **evaluation step** (specifically the consistency check, chapter 9.3.4).

*Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.52*

Based on the sources provided, the actually achieved consistency is checked as part of the **evaluation step in the interpretation phase**.

As stated in Source 3 (ILCD Handbook, p.52):

> "The actually achieved consistency is to be checked as part of the evaluation step in the interpretation phase (see chapter 9.3)"

This is further supported by Source 9 (Guinée et al., p.110), which defines the **consistency check** as:

> "a step of the Interpretation phase to verify whether assumptions, methods and data have been applied consistently throughout the study and in accordance with the Goal and scope definition"

The specific provisions for this consistency check are detailed in **chapter 9.3.4** of the ILCD Handbook (Source 6, p.300).


Question: When conducting an LCI or LCA study on a part that operates within a larger system, what must be explicitly considered in the system boundary definition?

The technical interaction between the analysed part and the system and its other parts must be explicitly considered.

## System Boundary Definition for a Part Within a Larger System

When conducting an LCI/LCA study on a **component part** that operates within a larger system, the system boundary definition must explicitly consider:

### 1. **Functional Unit & Reference Flow**
- How the part's function is defined *in relation to* the larger system's output
- The part's contribution to the overall system function (e.g., lifespan, performance share)

### 2. **Upstream Flows**
- Raw material extraction and processing
- Manufacturing of the part itself
- Transportation to the assembly point

### 3. **System Interactions / Integration Interfaces**
- **Energy and material inputs from the larger system** consumed by or attributed to the part
- **Shared processes** (e.g., assembly, maintenance routines) — which burdens are allocated to the part vs. the system
- **Co-functions**: if the part enables or constrains system performance, those effects must be scoped

### 4. **Use Phase Context**
- Operating conditions dictated by the larger system (duty cycle, energy consumption)
- Whether impacts are attributable to the part or the system as a whole (**allocation or system expansion** may be needed)

### 5. **End-of-Life**
- Disassembly from the larger system
- Whether the part can be reused/recycled independently or only with the system

### 6. **Cut-off Criteria**
- Explicit justification for flows excluded, especially shared infrastructure of the larger system

### 7. **Avoided Burdens / Credits**
- If the part replaces another component or enables system-level efficiency gains

---

**Key principle:** The boundary must clearly distinguish which environmental flows are *attributable to the part* versus *to the system*, with transparent documentation of any allocation decisions or system expansion approaches used.

The technical interaction between the analysed part and the system and its other parts must be explicitly considered in the system boundary definition. Parts that are operating in context of a larger system cannot typically be analysed in isolation, especially not be compared with other parts that interact with the system in another way. This applies to both attributional and consequential modelling.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.101)*

When conducting an LCI or LCA study on a part that operates within a larger system, **the technical interaction between the analysed part and the system and its other parts must be explicitly considered in the system boundary definition**. Parts operating in the context of a larger system cannot typically be analysed in isolation, especially when comparing with other parts that interact with the system in a different way. This applies to both attributional and consequential modelling.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.101)*


Question: When conservative or worst-case assumptions are used for an alternative's inventory values, what requirement applies to these estimates in the final process or system model?

Conservative or worst-case estimates or processes must not remain in the final process or system model.

When conservative or worst-case assumptions are used for an alternative's inventory values, the estimates must be **clearly identified and documented** within the final process or system model. Specifically, they should be:

- **Labeled or flagged** as conservative/worst-case estimates (rather than measured or typical values), and
- **Traceable**, so that reviewers and decision-makers can distinguish actual data from bounding assumptions and understand the basis for those estimates.

This transparency requirement ensures that the influence of such assumptions on the final results can be evaluated (e.g., through sensitivity or uncertainty analysis) and that the analysis remains reproducible and defensible.

Based on the sources provided, when conservative or worst-case assumptions are used to evaluate whether the superiority of an alternative is still valid, **any form of conservative or worst-case estimates or processes must not stay in the final process or system model**.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.209)*

Based on the sources provided, when conservative or worst-case assumptions are used for an alternative's inventory values to evaluate whether the superiority of that alternative is still valid, **any form of conservative or worst-case estimates or processes must not stay in the final process or system model**.

As stated in the ILCD Handbook: "Any form of conservative or worst-case estimates or processes must however not stay in the final process or system model." (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.209)

This is further reinforced in the LCA Cookbook: "any kind of worst case or conservative data and assumptions shall not be kept in the inventory of LCI data that are foreseen to be applicable for comparisons, unless the representing process operators or system producers themselves wish so." (Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.1026)


Question: In long-term marginal LCI modelling, why is the extra supply and demand represented as a mix of processes rather than a single process?

Because in real, non-monopolised markets there is never only one marginal process or supplier, so the extra supply and demand is modelled as a mix of long-term marginal processes/systems for a given time and market.

## Why Extra Supply and Demand is Represented as a Mix of Processes

In **long-term marginal LCI modelling**, the extra supply and demand is represented as a **mix of processes** (rather than a single marginal process) for the following key reasons:

### 1. **Uncertainty About Which Process Will Respond**
In the long run, it is genuinely uncertain which specific technology or supplier will respond to a change in demand. Multiple technologies may plausibly expand or contract, so using a mix reflects this uncertainty more honestly than arbitrarily selecting one process.

### 2. **Multiple Processes Are Likely to Respond Simultaneously**
Over longer time horizons, structural changes in a market may involve **several technologies adjusting together** — e.g., a combination of new renewables, gas plants, and efficiency improvements responding to increased electricity demand — rather than a single technology at the margin.

### 3. **The Marginal Supplier Is Not Identifiable**
Unlike short-term marginal modelling (where the "last unit" supplier can sometimes be identified), long-term changes trigger **market-wide adjustments** across multiple suppliers, making a single marginal process an oversimplification.

### 4. **Reflects System-Level Change**
Long-term marginal modelling aims to capture how the **entire system restructures** over time, which inherently involves a portfolio of processes rather than one technology.

### 5. **Consistency with Market Equilibrium Logic**
In equilibrium, price signals cause **many producers to adjust** at their margins, so a weighted mix better represents the aggregate response.

---

### In Practice
The mix is often constructed using **market share data**, **scenario projections**, or **expert judgment** about which processes are likely to expand or contract, weighted accordingly.

This approach acknowledges that **long-term consequences are diffuse across the system**, not attributable to a single process.

In long-term marginal LCI modelling, the extra supply and demand is represented as a mix of processes rather than a single process because **in real, non-monopolised markets there is never only one marginal process or supplier**. Multiple processes or systems can be similarly cost-competitive, making it inappropriate to restrict the model to a single marginal process.

As the ILCD Handbook states: "To restrict the model to a single marginal process or system is only justifiable if there are no other, similarly cost-competitive processes or systems and hence the use of a single one is more appropriate." (European Commission – JRC, ILCD Handbook, p.172)

Furthermore, it is often difficult to identify a single long-term marginal process with certainty, which is why a mix of *possible* long-term marginal processes is pragmatically recommended. As noted in Hauschild et al.: "identifying the correct long-term marginal processes in Situation B can be challenging and this is why it is pragmatically recommended to use a mix of possible long-term marginal processes, rather than actual long-term marginal processes." It is also advised to model the LCI using a range of different mixes to analyse sensitivity of results to the estimated mix. (Hauschild et al., LCA: Theory and Practice, Chapter 8, p.99)

In long-term marginal LCI modelling, the extra supply and demand is represented as a mix of processes rather than a single process because **in real, non-monopolised markets there is never only one marginal process or supplier**. As the ILCD Handbook states, a single marginal process or system is only justifiable "if there are no other, similarly cost-competitive processes or systems" (ILCD Handbook, p.172).

This is further reinforced by the practical difficulty of identifying a single long-term marginal process. As Hauschild et al. explain: "Often it is difficult to identify a single long-term marginal process, which is why a mix of potential long-term marginal processes is often used" (Hauschild et al., LCA: Theory and Practice, p.97). Similarly, because "identifying the correct long-term marginal processes in Situation B can be challenging… it is pragmatically recommended to use a mix of possible long-term marginal processes, rather than actual long-term marginal processes" (Hauschild et al., p.99).

The ILCD Handbook (p.166) correspondingly defines the LCI model of extra supply and demand as a **"mix of 'long-term marginal' processes/systems of given time and market"**, reflecting both the competitive reality of markets and the uncertainty inherent in identifying which single process would actually respond to the change in demand.


Question: Why does relying entirely on readily available third-party background data, such as that included in LCA software, undermine LCA results?

Because such data are often used without checking for their quality or for data gaps (where other third-party data may be needed), which contributes to a lack of quality in the results and reduces the robustness of conclusions.

## Why Relying Entirely on Third-Party Background Data Undermines LCA Results

Relying entirely on readily available third-party background data undermines LCA results for several interconnected reasons:

### 1. **Representativeness Issues**
- Background datasets are often **geographically generic** (e.g., global or European averages) and may not reflect the actual supply chain or production context of the study
- **Temporal gaps** exist — datasets may be years or decades old, failing to capture technological change or grid decarbonization
- **Technological mismatch** — average industry datasets may not represent the specific technology, scale, or efficiency of the actual process being studied

### 2. **Loss of Specificity (Foreground-Background Confusion)**
- Treating what should be **foreground system data** (directly controlled or measurable processes) as background data introduces significant inaccuracy
- Key processes with high environmental significance get treated as generic rather than measured

### 3. **Masking the Actual Environmental Hotspots**
- Generic data can obscure where real impacts occur, leading to **incorrect conclusions** about improvement priorities or comparative performance

### 4. **Reduced Credibility and Defensibility**
- Results become difficult to defend under **critical review** if primary data collection was not attempted for significant processes
- ISO 14044 requires data quality assessment and emphasizes **fitness for purpose**

### 5. **Propagation of Systematic Errors**
- Errors embedded in third-party databases are inherited uncritically, and **uncertainty compounds** across multiple background processes

### Summary
The fundamental problem is that **generic data averages out variability** that may be highly significant for a specific product system, undermining the ability to draw valid, decision-relevant conclusions.

Relying entirely on readily available third-party background data undermines LCA results because it involves **not checking for quality or data gaps** in those data. When practitioners fail to verify the quality of background data sourced from LCA software or other third parties, and do not identify where other or better third-party data may be needed, it contributes to a **lack of quality in the results and robustness of conclusions**.

To address this, it is recommended to always anticipate that **high quality data may need to be specifically collected or obtained for key background processes**, rather than defaulting to whatever is most conveniently available.

*(ILCD Handbook: General Guide for LCA, European Commission – JRC, 2010, p. 188)*

Relying entirely on readily available third-party background data included in LCA software undermines LCA results because it involves not checking for quality or data gaps in those data. This approach contributes to a lack of quality in the results and a lack of robustness in the conclusions. It is therefore recommended to always foresee that high-quality data may need to be specifically collected or obtained for key background processes, rather than assuming that whatever is readily available in software packages is sufficient.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p. 188)*


Question: When documenting the limitations of an LCA study, what must be evaluated for each identified limitation?

The type and magnitude of consequences each limitation has for the conclusions and intended applications of the study.

When documenting the limitations of an LCA study, **each identified limitation must be evaluated for its potential influence on the study results and conclusions**.

Specifically, for each limitation you must assess:

- **The significance/magnitude** of the potential impact on the results
- **The direction** of the potential bias (whether it could over- or underestimate impacts)
- **Whether it affects the validity** of the conclusions drawn from the study

This is required under ISO 14044, which states that limitations should be identified and their **potential influence on the results and conclusions** must be discussed. This ensures transparency and allows decision-makers to properly interpret the reliability and applicability of the study findings.

Based on the context provided, when documenting the limitations of an LCA study, the following must be evaluated:

From **ILCD Handbook, p. 301**, any limitations of the study within the given goal and scope must be listed, and the conclusions must be checked for consistency with the requirements. The most frequently outstanding limitations to be evaluated include:

- The **system boundary / cut-off settings** (and that they have been actually met by the LCI data and model)
- The **achieved LCI data quality and consistency**, as required by the goal
- The **uncertainty of the LCIA methods**
- **Specific predefined assumptions** of the goal phase
- Other **specific methodological and study limitations** relevant to the given case

Additionally, from **ILCD Handbook, p. 304**, all assumptions and related limitations noted during the course of the study must be considered, and any **impacts outside the LCA scope** must be named and addressed.

From **ILCD Handbook, p. 32**, limitations arising from applied **methodology, assumptions made, or limited impact-coverage** must be clearly identified and prominently reported, noting that "the identification and appreciation of such limitations needs a relevant degree of expertise and experience."

Based on the context provided, when documenting the limitations of an LCA study, the conclusions must be evaluated for **consistency with the requirements** of the goal and scope. Specifically, the following must be assessed for each identified limitation:

- The **system boundary / cut-off settings** (and whether they have been actually met by the LCI data and model)
- The **achieved LCI data quality and consistency**, as required by the goal
- The **uncertainty of the LCIA methods**
- **Specific predefined assumptions** of the goal phase
- Any other **specific methodological and study limitations** relevant to the given case

If the conclusions are consistent with the requirements, they can be reported as final conclusions; otherwise, they must be re-formulated and checked again. Additionally, any limitations of the study within the given goal and scope **must be listed**, and assumptions and related limitations noted during the course of the study must all be considered.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), pp. 301 and 304)*


Question: Besides LCA expertise, what types of expertise may be required to identify the detailed consequences and marginal processes in a consequential model?

Expertise in technology development forecasting (learning curves, experience curves), scenario development, market cost and market forecasting, technology cost modelling, general-equilibrium modelling, and partial-equilibrium modelling.

In consequential LCA, identifying detailed consequences and marginal processes often requires expertise beyond LCA methodology itself, including:

## Economic Expertise
- **Market analysis** to determine whether markets are constrained or unconstrained
- **Microeconomics** to identify marginal suppliers and price elasticities
- **Agricultural/commodity economics** for understanding market dynamics

## Engineering/Technology Expertise
- **Process engineering** to understand production technologies and capacity constraints
- **Energy systems expertise** to identify marginal electricity suppliers
- **Industrial ecology** knowledge of production systems

## Industry/Sector-Specific Knowledge
- Understanding of specific industries (chemicals, metals, agriculture, etc.)
- Knowledge of production capacities and expansion possibilities
- Supply chain expertise

## Statistics and Econometrics
- For analyzing market trends and identifying long-run marginal suppliers
- Forecasting demand and supply changes

## Systems Analysis
- **Operations research** for modeling complex system interactions
- Understanding of feedback loops and indirect effects

## Policy/Regulatory Knowledge
- Understanding regulations that may constrain markets (e.g., cap-and-trade systems)
- Policy scenarios affecting technology choices

The core challenge is that consequential LCA requires **interdisciplinary collaboration**, as no single expert can fully master all the economic, technical, and systemic dimensions needed to reliably identify true marginal processes across different product systems.

Based on the context provided, besides LCA expertise, the following types of expertise may be required to identify the detailed consequences and marginal processes in a consequential model:

1. **Technology development forecasting** (e.g., learning curves, experience curves)
2. **Scenario development**
3. **Market cost and market forecasting**
4. **Technology cost modelling**
5. **General-equilibrium modelling**
6. **Partial-equilibrium modelling**

Additionally, **policy scenario experts** are recommended, particularly regarding their function in setting constraints.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.165 and p.176)*

Based on the ILCD Handbook, besides LCA expertise, the following types of expertise are required to identify the detailed consequences and marginal processes in a consequential model:

- **Technology development forecasting** (e.g. learning curves, experience curves)
- **Scenario development**
- **Market cost and market forecasting**
- **Technology cost modelling**
- **General-equilibrium and partial-equilibrium modelling**
- **Policy scenario expertise** (recommended regarding constraints set by policy scenarios)

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), pp. 165 and 176)*


Question: In which sub-type of Situation C are existing benefits outside the analysed system considered?

In Life Cycle Assessment (LCA), particularly in the context of end-of-life (EoL) recycling and the handling of recycled materials, **Situation C** deals with open-loop recycling scenarios.

The sub-type where **existing benefits outside the analysed system are considered** is **Situation C2** (also referred to as the case where the recycled material substitutes for a primary material in another system, and the credit/benefit for that substitution is accounted for outside the system boundary of the product being studied).

Specifically, in **Situation C2**, the benefits accruing to the external/outside system (i.e., the avoided burden from substituting virgin/primary material in another product system) are taken into account, even though they occur outside the analysed system boundary.

**Situation C1** considers existing benefits outside the analysed system.

As stated in the ILCD Handbook: "In Situation C1, this is the case (e.g. the benefit of a process of the analysed system is producing a co-product that actually supersedes another product). This is hence to be credited." *(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.87)*

In contrast, Situation C2 "accounts for the analysed system in isolation, i.e. interactions with other systems are not accounted for." *(p.43)*

In **Situation C1**, existing benefits outside the analysed system are considered (e.g., credits for existing recycling benefits or where a co-product of the analysed system supersedes another product).

*(ILCD Handbook, p. 86)*


Question: When comparing car starter batteries of substantially different weight, what knock-on effects on the car should be considered?

A changed battery mount, wiring, and an altered total car weight, which in turn affects the car's acceleration properties.

When comparing car starter batteries of substantially different weight, the following knock-on effects should be considered:

## Direct Vehicle Effects

**Handling & Dynamics**
- Battery weight contributes to overall vehicle mass, affecting acceleration, braking distances, and fuel/energy consumption
- Position matters: a heavier battery affects weight distribution and corner weights, potentially altering handling balance
- Unsprung vs. sprung weight considerations if location changes

**Fuel/Energy Consumption**
- Every kg of added mass increases fuel consumption (roughly 0.3–0.7% per 100 kg depending on drive cycle)
- Cumulative effect over vehicle lifetime can be significant in LCA terms

## Structural & Mechanical
- Battery tray, brackets, and fixing points are typically sized for a specific weight range — a significantly heavier battery may stress mountings
- Vibration damping requirements may differ

## Functional Considerations
- Cold cranking amps (CCA) and reserve capacity may differ between weight classes, affecting reliability
- A lighter battery may have shorter service life, meaning more frequent replacement (important in lifecycle assessment)
- Heavier batteries often have greater capacity, potentially reducing depth of discharge stress

## LCA-Specific Knock-On Effects
- Manufacturing burden scales roughly with mass (more lead, electrolyte, casing)
- End-of-life recycling — lead-acid batteries are highly recycled, so mass affects recycled material flows
- Logistics/transport emissions scale with weight

The **fuel consumption penalty** and **replacement frequency** are typically the most significant knock-on effects in a lifecycle context.

When comparing car starter batteries of substantially different weight, the following knock-on effects on the car should be considered:

1. **Changes to peripheral components** — a substantially different battery weight will result in a changed battery mount, wiring, etc.
2. **Total vehicle weight** — the differing component weights will affect the overall weight of the car.
3. **Performance characteristics** — the resulting different total weight of the car will also affect the acceleration properties of the car.

These knock-on effects mean that the part-system relationship must be included in the analysis, particularly when making comparisons, as the battery alone cannot perform its ultimate function in isolation.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p. 155)*

When comparing car starter batteries of substantially different weight, the following knock-on effects on the car should be considered:

1. **Changed battery mount and wiring** – a substantially different battery weight will result in changes to the battery mount and wiring.
2. **Total car weight** – the different battery weight alters the total weight of the car.
3. **Acceleration properties** – the resulting different total weight of the car will affect the acceleration properties of the car.

These part-system relationships should be taken into account especially when comparing parts with somewhat different interactions with the system or when analysing improvement options.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.155)*


Question: What additional documentation does the passage recommend providing alongside the description of the analysed system, particularly for consumer products?

A detailed description of the analysed system plus photos, especially in the case of consumer products.

It seems you're referring to a specific passage or document, but no passage has been shared in our conversation. Could you please provide the passage you're referring to? That way, I can accurately answer your question about what additional documentation it recommends providing alongside the description of the analysed system for consumer products.

Based on the context provided, for consumer products' **use stage**, the passage recommends complementing the product description with **surveys and studies that analyse the average or typical user behaviour**, alongside product specifications and user manuals. Additionally, information provided in **product category rules (PCR)** can be supporting documentation.

*(Source 1: Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.1022)*

Based on the retrieved passages, for consumer products the documentation recommends providing **use stage information**, specifically surveys and studies that analyse the average or typical user behaviour to complement **product specifications and user manuals**. Information provided in **product category rules (PCR)** can also be supporting, particularly regarding representativeness of operation conditions.

*(Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p. 1022)*


Question: When a process is identified as a black box unit process during data planning or raw data collection, what should be checked regarding splitting it, and what benefits can that bring?

One should check whether the process can be split by subdivision and whether doing so would ease review, improve accuracy and applicability, and avoid multifunctionality.

## Black Box Unit Process: Checking for Splitting Opportunities

### What Should Be Checked

When a process is identified as a **black box unit process**, you should check whether it can be **split into two or more separate unit processes**. Specifically, examine:

- Whether the black box actually contains **distinguishable sub-processes** or stages that could be separated
- Whether **independent data** exists (or can be obtained) for each sub-process
- Whether the internal flows and activities within the black box can be meaningfully disaggregated
- Whether different sub-processes have **different co-products, waste outputs, or environmental profiles** that warrant separation

### Benefits of Splitting

1. **Improved allocation handling** – Splitting allows allocation to be applied at the most appropriate point, rather than forcing a single allocation approach across a lumped process, potentially reducing allocation errors

2. **Better accuracy and transparency** – Each sub-process can be characterized with more precise data, improving the overall quality of the inventory

3. **Enhanced applicability** – Disaggregated unit processes are more reusable in other LCA studies with different system boundaries or functional units

4. **Easier data collection** – Smaller, well-defined processes are often easier to collect specific, verifiable data for

5. **Clearer identification of hotspots** – Environmental burdens can be attributed to the correct sub-process rather than being obscured within an aggregate

6. **Avoidance of inappropriate averaging** – Prevents masking variability between genuinely different activities or technologies within the black box

In essence, splitting a black box unit process moves the study closer to a **transparent, disaggregated inventory** that better reflects physical reality and supports more defensible conclusions.

When a process is identified as a black box unit process during data planning or raw data collection, the following should be checked and considered:

**What to Check:**
One should check whether the process can be split by **subdivision** — specifically, whether it contains physically distinguishable sub-process steps and whether it is theoretically possible to collect data exclusively for those sub-processes. It should also be assessed whether subdivision can solve any multifunctionality of the black box unit process (i.e., whether a process or process-chain within the black box can be identified and modelled separately to provide only the one required functional output). *(p. 192, 256)*

**When to Perform It:**
Subdivision is generally done **before data collection**, or as **virtual subdivision afterwards**. *(p. 192)*

**Benefits It Can Bring:**
- **Eases review** and improves quality control, as inventories no longer combine several processes in a black box *(p. 192, 76)*
- **Improves accuracy and applicability** of results *(p. 192)*
- **Avoids or resolves multifunctionality problems**, which otherwise require extra effort and distort results to some degree *(p. 190, 76)*
- Allows for better **weak-point analysis or ecodesign purposes** by singling out individual contributors *(p. 192)*

*(Sources: ILCD Handbook, pp. 190, 192, 256, 76)*

When a process is identified as a black box unit process during data planning or raw data collection, one should check **whether the process can be split by subdivision**, and specifically whether that subdivision would:

1. **ease review**
2. **improve accuracy and applicability**
3. **avoid multifunctionality**

As stated in the ILCD Handbook:

> "if during data planning or raw data collection a process turns out to be a black box unit process, one should check whether the process can be split by subdivision and whether that would ease review, improve accuracy and applicability, and avoid multifunctionality. Subdivision is generally done before data collection or virtual subdivision afterwards."
*(ILCD Handbook, p. 192)*

This is reinforced on p. 190, which notes that:

> "Black box unit processes cause difficulties to review and often in addition multifunctionality problems. The latter require extra information and effort to be solved and in any case distort the results to some degree. If during data planning or raw data collection a process turns out to be a black box unit process, one should check whether it can be split by subdivision before data collection or virtual subdivision afterwards."
*(ILCD Handbook, p. 190)*

Additionally, p. 76 notes that singling out true unit processes has advantages for **quality control and review**, as inventories no longer combine several processes in a 'black box', and that allocation done on black box unit processes regularly **distorts/produces incorrect results**.
*(ILCD Handbook, p. 76)*


Question: In LCA practice, which two categories of processes are most commonly omitted without sufficient justification?

In LCA practice, the two categories of processes most commonly omitted without sufficient justification are:

1. **Capital goods (infrastructure/equipment)** – The production, maintenance, and disposal of machinery, buildings, and equipment used in the foreground system are frequently excluded, often with the rationale that their contribution is negligible, without actually verifying this through sensitivity or cut-off analysis.

2. **Ancillary inputs / auxiliary materials** – Chemicals, lubricants, cleaning agents, packaging of inputs, and other minor materials used in production processes are routinely left out, again typically without a documented justification based on mass, energy, or environmental significance cut-off criteria.

These omissions are specifically flagged in ISO 14044 as requiring explicit justification, yet in practice they are among the most common undocumented exclusions in published LCA studies.

Based on Source 1 (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.162):

The two categories of processes most commonly omitted without sufficient justification are **services** and **investment goods**.

Based on the retrieved passages, the two categories of processes most commonly omitted without sufficient justification in LCA practice are **services** and **investment goods** (also referred to as capital goods).

As stated in the ILCD Handbook:

> "Among these processes, services and investment goods are the most common ones."

(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.162)


Question: For product flows connecting the foreground system with the background system, what specification is required unless the deliverable of the LCA is a unit process data set?

A detailed specification including their function and functional unit is required.

For product flows connecting the foreground system with the background system, a **unit process** specification is required — specifically, the flow must be linked to (or representable by) a **unit process data set** — unless the deliverable of the LCA is itself a unit process data set.

More precisely, the requirement is that such connecting product flows must be specified with a **reference to a background system process (i.e., an LCI/aggregated process dataset)**, but the key specification required is that the flow be associated with a **system process (aggregated/LCI dataset)** from the background database — *unless* the LCA deliverable is a unit process data set, in which case the foreground unit processes can remain disaggregated without needing to aggregate into a system process.

In standard LCA methodology guidance (e.g., the ILCD Handbook), the specific requirement is that product flows at the boundary between foreground and background must be **quantitatively specified** with a corresponding **background dataset (system process/LCI dataset)**, ensuring the background system is fully represented — the exception being when the study deliverable is itself a unit process data set (where full aggregation is not the goal).

To put it simply: **a system process (LCI/aggregated) dataset specification** is required for those connecting flows, unless the deliverable is a unit process data set.

A detailed specification including their **function and functional unit** is required for product flows connecting the foreground system with the background system.

*(Source 1: ILCD Handbook: General Guide for LCA (2010), p. 175)*

Unless the deliverable of the LCA is a unit process data set, **a detailed specification including the function and functional unit** of those product flows that connect the foreground system with the background system is required.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.175)*


Question: What are some of the aspects that distinguish a niche market from the main market?

Price (investment cost or life cycle cost/total-cost-of-ownership), life-style and value-system related issues (such as a "green" or "social" image, or being fashionable, modern, prestige, etc.), high quality and durability/longevity, and practicality and/or time-saving.

Here are the key aspects that distinguish a niche market from the main (mass) market:

## Size & Scope
- **Smaller, more defined** customer base
- Serves a **specific segment** rather than the general population
- Limited geographic or demographic reach

## Customer Characteristics
- More **specialized needs** and preferences
- Higher degree of **customer loyalty**
- Customers often willing to pay **premium prices**
- Shared specific interests, values, or problems

## Competition
- **Fewer competitors** operating in the space
- Higher **barriers to entry** due to specialized knowledge required
- Less price competition overall

## Products/Services
- **Highly tailored** offerings
- Greater **depth** rather than breadth of products
- Often addresses **underserved or overlooked** needs

## Business Approach
- **Specialized expertise** is typically required
- Targeted, focused marketing strategies
- Often relies on **word-of-mouth** and community
- Lower marketing costs due to precise targeting

## Profitability
- Potentially **higher profit margins** despite lower volume
- Less price sensitivity from customers
- Sustainability through **differentiation** rather than scale

## Examples of contrast:
| Niche | Mass Market |
|-------|-------------|
| Vegan pet food | General pet food |
| Left-handed tools | Standard tools |
| Gluten-free baking | General baking products |

The core distinction is **specificity vs. breadth** in serving customer needs.

Based on the context provided, the aspects that distinguish a niche market from the main market include:

- **Price** (i.e., investment cost of a good or life cycle cost / total-cost-of-ownership)
- **Life-style and value-system related issues** (e.g., "green" image such as "locally produced," "bio-based," "recycled," "recyclable," "ecolabelled," or "social" image such as "fair-traded," "free-of-childwork," or aspects such as "fashionable," "modern," "prestige," "young," etc.)
- **High quality, durability / longevity**
- **Practicality and/or time-saving**

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p. 33)*

Based on the ILCD Handbook, the aspects that separate a niche market from the main market include:

- **Price** (i.e. investment cost of a good or life cycle cost / total-cost-of-ownership)
- **Life-style and value-system related issues** (e.g. "green" image in general or more specific such as "locally produced", "bio-based", "recycled", "recyclable", "ecolabelled", etc., or "social" image in general or more specific such as "fair-traded", "free-of-childwork", etc., or aspects such as "fashionable", "modern", "prestige", "young", etc.)
- **High quality, durability / longevity**
- **Practicality and/or time-saving**

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p. 33)*


Question: In the ILCD Handbook's hierarchy for handling multifunctional processes, what is the third, alternative solution to be used when other options are not possible or feasible?

Allocation is the third, alternative solution.

In the ILCD Handbook's hierarchy for handling multifunctional processes, the third alternative solution is **system expansion** (expanding the system boundary to include the additional functions), or more precisely, **substitution** (also referred to as system expansion by substitution) — where the co-products are credited by subtracting the burdens of an equivalent product that would otherwise be produced.

More specifically, in the ILCD hierarchy:

1. **Subdivision / Avoid multifunctionality** by subdividing the process
2. **System expansion** (expanding the system boundary)
3. **Substitution** (a form of system expansion using substitution/credit for avoided products)
4. **Allocation** based on physical relationships (e.g., mass, energy)
5. **Allocation** based on economic value

The **third** option — when subdivision and system expansion are not feasible — is **allocation based on physical causality** (e.g., mass or energy content), before falling back on economic allocation.

To be precise per the ILCD Handbook's stepwise hierarchy, the **third step** is **allocation based on physical relationships** (such as mass, energy, or other physical properties) when system expansion/substitution is not possible or feasible.

Based on the ILCD Handbook's guidance on handling multifunctional processes, when subdivision/virtual subdivision is not possible or feasible, and when other options have been exhausted, the third alternative solution is **allocation (partitioning)**.

As stated in the ILCD Handbook:

> "If this is principally impossible or other reasons make it practically impossible, **allocation (partitioning)** is the next possible step (see chapter 6.5.3)."

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.255)*

Based on the retrieved passages, the ILCD Handbook's hierarchy for handling multifunctional processes places **allocation (partitioning)** as the third, alternative solution when subdivision and system expansion (substitution) are not possible or feasible.

As stated in the ILCD Handbook:

> "This means that the first step is the subdivision of multifunctional black box unit processes to mono-functional single operation unit processes... If this is principally impossible or other reasons make it practically impossible, **allocation (partitioning) is the next possible step**."

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.255)*


Question: In the described recycling credit approach, what credit does a product receive when additional demand diverts 1 kg of secondary material from landfill?

A credit of 1 kg of avoided disposal ("-1 kg" avoided disposal).

When additional demand diverts 1 kg of secondary material from landfill, the product receives a credit equal to the **avoided burden of landfilling** that 1 kg of secondary material — i.e., the product is credited with the environmental impacts that would have occurred had that kilogram gone to landfill instead (the avoided landfill impacts/emissions).

In the recycling credit (or "avoided burden") approach, the credit is specifically the **negative of the landfill burden** for that 1 kg of secondary material — meaning the product gains a credit equivalent to the landfill impacts avoided (e.g., avoided methane emissions, leachate, etc.) by diverting that material from landfill to use as a secondary/recycled material.

Based on the context, when additional demand diverts 1 kg of secondary material X from landfill, the product receives a credit of **1 kg avoided disposal ("-1 kg" avoided disposal)**.

[Source 1: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.355]

Based on the context, when additional demand diverts 1 kg of secondary material X from landfill, **the product receives a credit of 1 kg avoided disposal ("-1 kg" avoided disposal)**.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.355)*


Question: In a descriptive study that has an accounting or monitoring character rather than providing direct decision support, how does the LCI model represent the system?

The LCI model describes the system as it can be measured.

In a descriptive (accounting/monitoring) study, the LCI model represents the system **as it is** — i.e., it describes the actual, existing system with its real-world configurations, actual supplier mixes, current technology, and observed operational conditions. The model captures the system's actual flows and processes without hypothetical modifications or marginal adjustments.

This contrasts with a change-oriented (decision-support) study, where the model would represent **marginal or consequential changes** to the system. In the descriptive/attributional approach, the system is modeled using **average or allocated data** reflecting the current state of the system, and multi-output processes are typically handled through **allocation** (partitioning) rather than system expansion, since the goal is to account for burdens as they exist rather than to simulate the consequences of a decision.

In summary: the LCI model represents the system **as it actually operates** (attributional/descriptive modeling), capturing its real physical and economic flows in their current state.

In a descriptive study of accounting or monitoring character (rather than direct decision-support), the LCI model describes the system **as it can be measured**. Specifically, it documents what has happened (or is going to happen), and the model is purely retrospective from a decision perspective, intended exclusively for accounting-type purposes.

*(Source 1, p.37; Source 2, p.44)*

In a descriptive study of accounting or monitoring character (rather than direct decision-support), the LCI model will describe the system **as it can be measured**. As stated in the ILCD Handbook:

> "in that case, the LCI model will describe the system as it can be measured."

Furthermore, in Situation C specifically, the LCI model is **only documenting what has happened** (or is going to happen in the future, e.g. during the use of long-living products that have already been produced). The study is purely retrospective from a decision perspective, and the results are intended exclusively for accounting-type purposes.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), pp. 37 and 44)*


Question: When using not fully technologically representative data for LCI and LCIA data sets or non-comparative studies, under what condition is such use justifiable?

It is justifiable only if it does not relevantly change the overall LCIA results compared to using fully representative data; otherwise the lower representativeness must be documented in the data set or report.

When using not fully technologically representative data for LCI and LCIA datasets or non-comparative studies, such use is justifiable when **the data are still sufficiently representative for the intended purpose of the study** — specifically, when the uncertainty or lack of technological representativeness does not materially affect the conclusions or the decision to be supported.

More precisely, this is justifiable when:

- The **goal and scope of the study** do not require high technological specificity (e.g., screening-level or hotspot identification studies),
- The **uncertainty introduced** by using less representative data is acknowledged, documented, and shown (e.g., through sensitivity or uncertainty analysis) to be **not significant enough to change the outcome or conclusions**,
- And/or **no better data are available** and the limitations are transparently communicated to the intended audience.

In essence, the use is justifiable as long as the **fitness for purpose** criterion is met — i.e., the data quality is adequate relative to the study's goal, and the limitations are clearly disclosed so that decision-makers are not misled.

Based on Source 2 (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.127):

The use of not fully technologically representative data for LCI and LCIA data sets or non-comparative LCI/LCA studies is justifiable **only if it is not relevantly changing the overall LCIA results compared to using fully representative data**. If it does relevantly change the results, the lower achieved representativeness shall be documented in the data set/report. Additionally, for data provided for a competitor's product, lower representativeness shall not lead to higher overall environmental impacts in the LCIA results calculated for that product.

Based on the retrieved passages, the use of not fully technologically representative data for LCI and LCIA data sets / non-comparative LCI/LCA studies is justifiable **only if this is not relevantly changing the overall LCIA results compared to using fully representative data**; otherwise, the lower achieved representativeness shall be documented in the data set / report.

Additionally, for data provided for a competitor's product, lower representativeness shall not lead to higher overall environmental impacts of the LCIA results calculated for that product.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p. 127)*