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.

Because the year 0 emissions are treated as the “peak” inventory of the transformation itself, analogous to a production inventory, so they are shared linearly over the following land uses. Emissions occurring over a longer period are still related to the transformation but depend on the specific land use in each subsequent year and can be influenced by management, so they are treated as operational emissions and inventoried as and when they occur, which leads to a triangular allocation that gives a higher share to the land use directly following the transformation.

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

Because they represent two different kinds of causation over time:

- **Year-0 emissions** happen essentially **instantly at transformation**. They are caused by the act of converting the land, not by any one later year of occupation more than another. So if you allocate them across the affected land-use period, a **linear sharing** is used: each year gets an equal share.

- **Emissions spread over many years** (for example, gradual soil carbon loss) are different: they are **triggered by the transformation but occur mainly because of the new land use established right after conversion**. The effect is strongest early, when the transformed land is first occupied, and later years are less directly responsible. So allocation is often done with a **triangular weighting**, giving more to the land use immediately following transformation and progressively less to later years.

In short:

- **Instant transformation pulse** → equal responsibility over the benefit/use period → **linear allocation**
- **Delayed, time-distributed emissions** → stronger causal link to the first post-transformation use → **triangular allocation**

This reflects the idea that the first land use after conversion is the main driver of longer-term transformation-related emissions.


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.

The cadmium emission in the flue gas could be allocated among the co-incinerated waste streams according to their cadmium content in the materials.
Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), chapter 7.4.3.2, p.265.

A practical LCA allocation basis is the **cadmium input carried by each waste stream**.

So, allocate flue-gas Cd emissions among the co-incinerated household waste fractions according to:

- **mass of each waste fraction × its cadmium concentration**

This reflects the physical causality best, since cadmium emissions mainly depend on how much Cd enters the furnace with each material.

Formally, for waste stream \(i\):

\[
\text{Allocated Cd emission}_i
=
\text{Total flue-gas Cd emission}
\times
\frac{m_i \cdot c_{Cd,i}}{\sum_j m_j \cdot c_{Cd,j}}
\]

where:
- \(m_i\) = mass of waste stream \(i\)
- \(c_{Cd,i}\) = cadmium content of waste stream \(i\)

If detailed Cd-content data are unavailable, a weaker fallback could be allocation by **waste mass**, but this is less representative.


Question: What three types of evaluation are involved in the interpretation phase of an LCI/LCA study?

A completeness check, a sensitivity check (combined with scenario analysis and potentially uncertainty analysis), and a consistency check.

The three types of evaluation are:

- Completeness check
- Sensitivity check
- Consistency check

Cited in:
- European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.288
- Hauschild et al., LCA: Theory and Practice, Chapter 12, p.323
- Guinée et al., Handbook on LCA (2002), p.450

In the **interpretation phase** of an **LCA/LCI study**, the three main types of evaluation are:

1. **Completeness check**
   - Verifies that all relevant data, processes, and impact information needed to meet the goal and scope are included.

2. **Sensitivity check**
   - Examines how changes in data, assumptions, or methods affect the results.

3. **Consistency check**
   - Ensures that methods, data, assumptions, and procedures are applied uniformly and are consistent with the goal and scope of the study.

These are defined in the ISO 14044 framework for LCA interpretation.


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.

The problem is that the sum cannot be determined: “Unknown kg plus 0.00026 kg = ?” This illustrates the difficulty of combining partial data gaps with known inventory data during LCI result calculation. (ILCD Handbook, p.209, chapter on handling data gaps)

You can’t validly sum them.

In LCI, amounts must be numeric and known. If one lead-to-air emission is unknown, then adding it to a known value like **0.00026 kg** gives an **undefined / incomplete total**. The problem is:

- the total lead emission cannot be calculated reliably,
- the inventory result becomes uncertain or potentially underestimated if the unknown is ignored,
- and the data quality/completeness is compromised.

So the issue is essentially **missing quantitative data preventing proper aggregation**.


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.

The two especially highlighted aspects are:

1. “Accuracy” and the distinction between “variance” and “variability” — ILCD Handbook, Chapter 12.2, p.323.
2. “Reliability” as a key data quality indicator, often assessed together with completeness and representativeness — Hauschild et al., Chapter 11, p.294.

Two especially highlighted aspects of **data reliability** are:

1. **The way the data are collected**
   - e.g., whether measurements, calculations, estimates, or secondary sources are used.

2. **The representativeness and verifiability of the data**
   - i.e., how well the data reflect the actual system and whether they can be checked or confirmed.


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.

In the recycling formula, **r** is the **average recycling rate [0...1)**, and it incorporates both **collection efficiencies** and **processing efficiencies**.
**Citation:** European Commission – JRC, *ILCD Handbook: General Guide for LCA* (2010), **Annex C, p.2**.

In that recycling formula, **r** represents the **overall recycling efficiency**.

It incorporates two efficiencies:

1. **Collection efficiency** — how much of the material is successfully collected for recycling.
2. **Reprocessing efficiency** — how much of the collected material is actually converted into usable secondary material.

So, **r = collection efficiency × reprocessing efficiency**.


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.

Data from one process can be considered representative of a similar process operated elsewhere when the other location has similar production conditions and the process/material and technology are the same or closely related. More specifically, representative data may include:
- 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 on the same processes and materials from different enterprises,
- data on the same processes and materials but with different technology,
- or data on related processes/materials with the same technology.

Source: Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p.5 (section 3.6, “Background process data”).

Data from one process can be considered representative of a similar process elsewhere only if the two are sufficiently similar in the key dimensions that affect the inventory results. In practice, this usually means checking:

- **Technology equivalence**: same or very similar process design, equipment, scale, efficiency, and operating conditions.
- **Functional equivalence**: same function, product quality/specification, and reference flow.
- **Temporal representativeness**: data are from a time period that still reflects current operation.
- **Geographical relevance**: similar location-dependent conditions such as energy mix, climate, transport distances, infrastructure, and regulation.
- **Input/output similarity**: comparable raw materials, utilities, emissions control, yields, waste treatment, and coproduct handling.
- **Operational context**: similar load factors, maintenance practices, and management/skill levels.
- **Methodological consistency**: same system boundaries, allocation rules, cut-off criteria, and data collection methods.
- **Acceptable uncertainty**: differences between the original and target process are small relative to the goal and scope of the study, or are shown by sensitivity/uncertainty analysis not to change conclusions.

So, transfer is acceptable when the expected differences are minor, transparent, and justified for the study’s intended use.


Question: In which city is PRé Consultants located?

PRé Consultants is located in Amersfoort, The Netherlands.
Cited: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.385.

PRé Consultants is located in **Amersfoort, the Netherlands**.


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 physical output or product-specific flow required from a product system to fulfil the function expressed by the functional unit—i.e. one specific way of obtaining that functional unit.

Citations:
- Guinée et al. (2002), p.37: “The 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.”
- Guinée et al. (2002), p.475: “Reference flow: quantified flow generally associated with the use phase of a product system and representing one way … of obtaining the functional unit.”
- Hauschild et al., p.896: “The functional unit refers to a quantified description of the primary function of the system under study, while the reference flow refers to the physical flow required for the system to fulfil its function.”

In LCA, the **reference flow** is the **amount of product or service needed to fulfill the functional unit**.

- **Functional unit** = the quantified performance being assessed
  - Example: “washing 1 kg of clothes”
- **Reference flow** = the actual inputs/outputs tied to that function
  - Example: “0.15 kWh electricity, 50 L water, 75 g detergent per 1 kg clothes washed”

So, the reference flow **translates the functional unit into measurable product flows** that can be modeled in the life cycle inventory.


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.

In a comparative LCA study, the interpretation phase shall discuss the appropriateness of the generic LCIA methods. If further differentiation—especially spatial or temporal—can be argued or approximated to lead to significantly/substantially different results, this may limit the conclusions and recommendations that can be drawn from the study. Non-generic LCIA results should then be presented separately from the generic/default ones and discussed jointly.

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

In the interpretation phase of a comparative LCA, you must discuss the use of generic LCIA methods and whether their level of differentiation is appropriate for the compared systems.

If it can be argued that using a more specific or further differentiated LCIA method would lead to significantly different results, then the comparison is not robust enough as presented. In that case, the study should not support comparative assertions until the assessment is refined accordingly.


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.

The need should be identified during the scope definition, so the required information on the relevant elementary flows can be identified before the inventory analysis. The ILCD Handbook notes this may only become fully apparent after the first or second iteration of LCI data collection, modelling, impact assessment, and interpretation.
(Source 1: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.111)

This need should be identified as early as possible during the **goal and scope definition** and confirmed during **life cycle inventory (LCI) modelling / flow mapping**.

**Why:**
- You need to know whether relevant elementary flows in the product system are **missing characterisation factors** in the chosen LCIA method.
- If this is discovered late, the assessment may be **incomplete, biased, or inconsistent**, and results may need reworking.
- Early identification allows you to:
  - check **method coverage** against expected emissions/resource flows,
  - decide whether to **extend the LCIA method**,
  - ensure the added factors are **scientifically consistent** with the method,
  - document assumptions and maintain **comparability and transparency**.

In short: identify the gap **before impact assessment is carried out**, ideally already when selecting the LCIA method and reviewing the expected elementary flows.


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

KEPI stands for “Key Environmental Performance Indicators.”
Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.30; p.56.

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


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.

Experts in the following domains should be involved in identifying and modelling large-scale consequences:

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

Policy scenario experts are also recommended, regarding their function as setting constraints.

Source: Hauschild et al., LCA: Theory and Practice, Ch. 37 “LCA Cookbook”, p.1003.

In consequential LCA, identifying and modelling **large-scale consequences** should be done **interdisciplinarily**, because effects can propagate far beyond the immediate product system.

Key domains of expertise typically needed are:

- **LCA / environmental systems analysis**
  To structure the assessment and link consequences to environmental interventions.

- **Economics**, especially:
  - **microeconomics** for market-mediated effects, substitution, elasticities
  - **macroeconomics / general equilibrium modelling** for economy-wide rebound and structural effects

- **Sector-specific technical experts**
  To judge what technological changes, constraints, and substitutions are actually feasible in the relevant industry.

- **Energy / resource system modelling experts**
  Important when consequences affect electricity, fuels, materials, land, or other large infrastructure/resource systems.

- **Policy and regulatory experts**
  Since large-scale consequences often depend on regulations, taxes, subsidies, trade rules, and governance responses.

- **Social scientists / behavioural experts**
  To address adoption, consumption changes, behavioural rebound, and institutional responses.

- **Land-use / agriculture / ecology experts**
  Especially where indirect land-use change, biodiversity, or ecosystem effects may occur.

In short: **no single discipline is sufficient**. Large-scale consequential modelling should involve **LCA practitioners together with economists, relevant engineers/sector experts, policy experts, and where relevant social and ecological specialists**.


Question: In consequential modelling, how should reduced technical properties of an application-unspecific secondary good be corrected in the accredited inventory?

By using the market price ratio (value correction) of the secondary good to the primary produced replaced function.

In consequential modelling, for application-unspecific secondary goods, reduced technical properties should be corrected in the accredited inventory by using the market price ratio (value correction) of the secondary good to the primary produced replaced function.

Cited in:
- ILCD Handbook, 7.2.4 Identifying processes in consequential modelling, XIII.b.vi), p.183
- Hauschild et al., LCA: Theory and Practice, 13:2:6, p.1011

In consequential modelling, if an application-unspecific secondary good has **reduced technical properties** compared with the equivalent primary good, this should be corrected in the **accredited inventory** by applying a **quality correction**.

Practically, this means:

- adjust the amount of secondary good to reflect its **lower functional performance** (e.g. more material is needed to deliver the same service), or
- equivalently, scale the substituted primary production by a **substitution ratio below 1:1**.

So the inventory should credit only the **functionally equivalent amount** of avoided primary good, not the physical amount of secondary good output.


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.

For each identified limitation, its relevance and importance for the study’s goal and conclusions must be evaluated.

Cited: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.301.

For each identified limitation in an LCA study, you should evaluate:

- **Its nature/source** — what the limitation is and why it exists
- **Its magnitude/significance** — how important or severe it is
- **Its effect on the results and conclusions** — whether it could change findings, comparisons, or decision support
- **The extent of uncertainty introduced** — including implications for reliability and robustness

In short, each limitation should be assessed for **how much it matters and how it influences the study outcomes**.


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.

In long-term marginal LCI modelling, extra supply and demand is represented as a mix because in real, non-monopolised markets there is generally not only one marginal supplier/process, and it is often difficult to identify a single correct long-term marginal process. Therefore, a mix of possible long-term marginal processes is recommended, also to reflect uncertainty and support sensitivity analysis.

Citations:
- European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.166
- Hauschild et al., LCA: Theory and Practice, Chapter 8, p.99
- Hauschild et al., LCA: Theory and Practice, Chapter 8, p.97
- European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.172

Because in long-term marginal LCI, a small change in demand is usually met by the market, not by one fixed supplier.

So the “extra” supply or demand is modeled as a mix of processes because:

- **Several producers can respond** to the change, not just one.
- **Capacity adjusts over time**: firms invest, expand, reduce, or retire capacity differently.
- **Markets are interconnected**: the marginal effect is distributed across technologies, regions, and suppliers.
- **Competition matters**: the suppliers that are marginal are those able and willing to change output in the long run.
- **Substitution on the demand side** can also involve multiple products or users, not one single process.

In short, long-term marginal modelling tries to represent the **actual market response to a demand change**, and that response is typically a **weighted mix of marginal processes**, not a single unit process.


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. They may be used to test robustness, but “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 (chapter 7.4.2.11.3 context)

If you use **conservative / worst-case assumptions** for an alternative’s inventory values, those estimates must still be **physically and technically realistic and representative enough for the final process or system model’s goal and scope**.

In practice, this means they should be:

- **Plausible and justifiable**, not arbitrary
- **Documented transparently**
- **Consistent with the modelling rules and intended application**
- Often **tested in sensitivity analysis** if they could influence conclusions

So, worst-case estimates are allowed, but they must remain **credible for the modeled system** and not distort the final results beyond what can reasonably occur.


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.

The actually achieved consistency is checked in the Interpretation phase, as part of the evaluation step — specifically the consistency check.

Cited source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), chapter 9.3, p.52:
“The actually achieved consistency is to be checked as part of the evaluation step in the interpretation phase (see chapter 9.3) …”

More specifically:
European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), chapter 9.3.4 “Consistency check”, p.300.

The **actually achieved consistency** is checked in the **Interpretation phase**, specifically in the step called **consistency check**.

So:

- **Phase:** **Life Cycle Interpretation**
- **Step:** **Consistency check**

This step verifies whether the methods, data, assumptions, and level of detail used across the study are consistent with the **goal and scope definition**.


Question: For quasi-permanent CO2 storage to be inventoried as having no emissions, for how long must the storage form be able to guarantee, according to current scientific knowledge and independent qualified expert review, that the substance is not emitted?

At least 100,000 years (a number set by convention).

At least 100,000 years.
Cited: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.229, chapter 7.4.3.7.4.

For **at least 1,000 years**.

In LCA/GHG accounting, **quasi-permanent CO₂ storage** can be treated as having **no emissions** only if, based on **current scientific knowledge** and **independent qualified expert review**, the storage form can guarantee that the CO₂ will not be emitted for **1,000 years or more**.


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.

The technical interaction between the analysed part and the larger system and its other parts must be explicitly considered in the system boundary definition. (European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010, p.101)

You must explicitly define the **interface between the part and the larger system** within the **system boundary**.

In practice, that means clearly stating:

- **What functions are provided by the part**
- **What exchanges cross the boundary** between the part and the rest of the system, such as:
  - energy
  - materials
  - emissions
  - signals/controls, if relevant
  - maintenance, auxiliaries, or supporting flows
- **Which upstream and downstream processes are included or excluded**
- **How shared functions or burdens are allocated** if the part interacts with other components

In short: when a part is studied inside a larger system, the boundary must explicitly capture **its relationship and exchanges with the surrounding system**.


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

Existing benefits outside the analysed system are considered in Situation C1.
Cited: ILCD Handbook, p.86, chapter 6 Scope definition / Situation C (“For the two sub-types of Situation C, the key difference is whether existing benefits outside the analysed system are considered or not: In Situation C1, this is the case”).

In **Situation C2**.

This sub-type explicitly considers **existing benefits outside the analysed system** that are affected by the decision, such as changes in other product systems or markets beyond the directly analysed system boundary.


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.

If a process is identified as a black box unit process during data planning or raw data collection, it should be checked whether it can be split by subdivision before data collection or by virtual subdivision afterwards, and whether this would ease review, improve accuracy and applicability, and avoid multifunctionality/allocation problems. The aim is to identify and model separately the included sub-processes, ideally isolating a process-chain that provides only the required functional output. This can improve data quality, support quality control and review, reduce distortion in results, and help avoid allocation problems. If full subdivision is not possible, partial subdivision should still be done, as it can still substantially improve data accuracy.
(ILCD Handbook, pp.190, 192, 256, 76, 193)

Check whether the “black box” process can be **split into smaller, more elementary unit processes**.

Doing so can bring several benefits:
- **Higher transparency**: you can see which sub-steps drive impacts.
- **Better data quality and allocation**: inputs/outputs can be linked more accurately to specific functions or co-products.
- **Greater flexibility and reusability**: sub-process data can be reused in other studies or modeled differently.
- **Improved consistency and comparability**: easier to align with the system model and other datasets.
- **More targeted improvement analysis**: hotspots and optimization options become easier to identify.

So, during planning or collection, verify whether disaggregation is feasible and useful before keeping the process as a single black box.


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.

Alongside the description of the analysed system, the passage recommends providing a brief description of the product group under study, describing the studied product or service alternatives and justifying the choice, and commenting on or listing relevant alternatives not covered; if none exist, this should also be stated. For consumer products specifically, the functional unit should be unambiguously defined to reflect the actual function in a measurable and quantitative way. (Guinée et al., Handbook on LCA, p.475)

Alongside the description of the analysed system, the passage recommends providing **a clear visual or technical representation of the product/system**. For **consumer products**, this is especially suggested in the form of **a photograph or similar illustration**, so readers can easily understand what product is being assessed.


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 part-system relationship should include possible changes to the battery mount and wiring, and the resulting change in the car’s total weight, which can affect the car’s acceleration properties.

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

When comparing **starter batteries with substantially different weight**, consider not just the battery itself, but these **knock-on effects on the car**:

- **Fuel/energy use during driving**
  A heavier battery slightly increases vehicle mass, which can raise fuel consumption (or electricity use in EV/hybrid contexts), especially in stop-and-go driving.

- **Vehicle lifetime impacts from extra mass**
  More weight can marginally increase wear on:
  - tires
  - brakes
  - suspension
  In most passenger cars this effect is usually small unless the weight difference is large.

- **Mounting and structural compatibility**
  A much heavier battery may require:
  - stronger battery tray or clamp
  - different mounting hardware
  - checks for crash safety and vibration resistance

- **Weight distribution / handling**
  Because the battery is often placed at one end of the car, a significant weight change can slightly affect:
  - front/rear axle load
  - steering/handling
  - ride height
  Usually minor, but worth noting if the difference is substantial.

- **Charging system interaction**
  If weight difference comes from a different battery technology, also consider whether the:
  - alternator charging profile
  - battery management strategy
  - start-stop system compatibility
  are still appropriate.

- **Transport and maintenance impacts**
  Heavier batteries can increase:
  - handling effort in servicing
  - transport impacts in supply chain
  - replacement difficulty/safety risk

For **LCA**, the main car-level knock-on effect is usually the **operational energy penalty from extra mass**, while wear-and-tear and structural effects are typically secondary unless the mass difference is very large.


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.

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

- technology development forecasting, including learning curves and experience curves
- scenario development
- market cost and market forecasting
- technology cost modelling
- general-equilibrium modelling
- partial-equilibrium modelling

If policy scenarios are explicitly analysed, policy scenario experts are also recommended.

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

Beyond LCA method knowledge, identifying detailed consequences and marginal processes in a consequential model often needs domain-specific expertise such as:

- **Economics / market analysis**
  To understand price effects, supply-demand responses, substitution, market constraints, and which suppliers are marginal.

- **Industry / process engineering**
  To know how specific production systems actually operate, their flexibility, bottlenecks, efficiencies, and likely technology shifts.

- **Energy systems expertise**
  Especially when electricity, fuels, or heat are involved, since marginal mixes depend on dispatch, capacity expansion, and grid dynamics.

- **Agronomy / forestry / land-use science**
  Important for bio-based systems, where indirect land-use change, yield responses, crop substitution, and soil effects may matter.

- **Policy and regulatory expertise**
  Regulations, subsidies, mandates, and trade rules can strongly affect what consequences occur and which actors respond.

- **Supply chain and logistics knowledge**
  To identify transport changes, sourcing shifts, capacity limits, and regional supplier responses.

- **Behavioral / consumer research**
  Useful when consequences depend on user behavior, rebound effects, or adoption patterns.

- **Systems modeling / operations research**
  For modeling constrained systems, optimization, capacity expansion, and scenario-based consequences.

- **Geography / regional expertise**
  Marginal suppliers and consequences are often location-specific.

In short, consequential modeling is often interdisciplinary: you need not just LCA expertise, but also expertise in the affected markets, technologies, policies, and systems.


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.

Relying entirely on readily available third-party background data can undermine LCA results because, if those data are not checked for quality or data gaps, the study’s results and conclusions may lack quality and robustness. The ILCD Handbook therefore recommends anticipating the need to specifically collect or obtain high-quality data also for key background processes.

Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), Chapter 7 “Life Cycle Inventory analysis - collecting data, modelling the system, calculating results,” p.188.

Relying entirely on generic third-party background data can undermine LCA results because those datasets are often only an approximation of your real system.

Key reasons:

- **Lack of representativeness**: Background datasets may differ from your case in geography, time period, technology, energy mix, scale, and supplier practices.
- **Hidden assumptions**: Database processes include modeling choices, allocation rules, cut-offs, and system boundaries that may not match your goal and scope.
- **Loss of specificity**: Product differences in materials, yields, transport, recycled content, or end-of-life can be masked by average data.
- **Inconsistent quality**: Readily available data may be outdated, incomplete, or based on secondary sources with high uncertainty.
- **Black-box risk**: Users may treat software data as “true” without understanding how it was built, making interpretation and critical review weaker.
- **Bias in comparisons**: If the background data fit one alternative better than another, comparative assertions can become misleading.
- **Poor decision support**: LCA is meant to inform decisions about a specific product or system; overly generic data can point to the wrong hotspots and wrong improvement actions.

In short, convenient background data are useful and often necessary, but if used uncritically they reduce the validity, transparency, and decision relevance of the LCA. The best practice is to check data quality and replace the most influential datasets with more representative, case-specific data where possible.


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.

Unless the deliverable of the LCA is a unit process data set, product flows connecting the foreground system with the background system require “a detailed specification including of their function and functional unit.”
(Source 1: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), chapter 7.2.4.7, p.175)

For product flows that connect the foreground system to the background system, you should specify the **provider of the product flow** unless the LCA deliverable is a **unit process data set**.

In other words: the **supplying process / source process** for each such connecting product flow needs to be identified.


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.

The third, alternative solution is allocation (partitioning), to be used when subdivision / virtual subdivision and the other preceding options are not possible or feasible.
— ILCD Handbook, chapter 7.9.2, p.255; see also the ISO hierarchy summary in Hauschild et al., chapter 9.2.2, p.121.

In the ILCD Handbook’s multifunctionality hierarchy, the **third, alternative solution** is **allocation**.

More specifically: if subdivision or system expansion/substitution are not possible or feasible, the process inputs and outputs are **partitioned between the co-products according to a relevant allocation key** (preferably based on a physical relationship, and if that is not appropriate, another justified relationship such as economic value).


Question: In recycling situations under ISO 14044:2006, what aspect of system modelling requires special attention and justification?

The drawing of the system boundary between the first and subsequent life cycles.

In recycling situations under ISO 14044:2006, special attention and justification are required for **where to draw the system boundary between the first and subsequent life cycles** and **how to apply the ILCD two-step allocation procedure**.
**Source:** European Commission – JRC, *ILCD Handbook: General Guide for LCA* (2010), **chapter 14.4.1.1, p.349**.

Under ISO 14044:2006, in recycling situations **special attention must be given to the allocation procedures** used in system modelling.

Because recycling creates **shared burdens and benefits across multiple product systems**, the practitioner must **justify how inputs, outputs, and environmental loads are allocated** between:

- the original product system,
- the recycling process, and
- the subsequent product system using the recycled material.

In short: **allocation in open-loop/closed-loop recycling needs particular care and clear justification.**


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.

Some aspects that distinguish a niche market from the main market are:
- price
- life-style and value-system related issues
- high quality, durability / longevity
- practicality and/or time-saving

Chapter 5, p.33 (ILCD Handbook: General Guide for LCA, 2010).

A **niche market** differs from the **mainstream market** in several common ways:

- **Specific customer segment**: It serves a narrowly defined group with particular needs, preferences, or identities.
- **Specialized needs**: Products or services are often tailored, customized, or highly focused rather than broadly appealing.
- **Smaller market size**: Demand is usually lower in volume compared with the main market.
- **Less direct competition**: Fewer firms may serve the segment, especially if the niche is highly specialized.
- **Higher willingness to pay**: Customers may accept premium prices because the offering closely matches their needs.
- **Stronger customer loyalty**: Buyers in niche markets often value expertise, authenticity, or unique features, leading to repeat business.
- **Targeted marketing**: Promotion is usually more focused and community-based rather than mass-market.
- **Lower economies of scale**: Production and distribution may be less efficient due to smaller volumes.
- **Higher sensitivity to changes in preferences**: Because the customer base is narrow, shifts in trends can affect the market more strongly.
- **Barriers based on expertise or identity**: Success may depend on deep knowledge of the segment, specialized technology, or brand credibility.

In short, a niche market is usually **smaller, more specialized, and more focused** than the main market, which aims for **broad appeal and larger-scale demand**.


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

The two most commonly omitted categories are **services** and **investment goods**.

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

In LCA practice, the two process categories most often omitted without adequate justification are:

1. **Capital goods / infrastructure processes**
   - e.g., construction of factories, machinery, buildings, roads, equipment.

2. **Human labor and related services**
   - e.g., employee activities, administrative/support services, and other service inputs.

These are frequently excluded because they are seen as secondary or difficult to model, but often without a strong cutoff justification.


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).

The product receives a credit of **1 kg avoided disposal** (“**-1 kg**” avoided disposal).
**Source 1, p.355 (ILCD Handbook, Annex C)**

It receives a credit equal to the avoided disposal of that 1 kg of secondary material to landfill.

In other words, the product is credited for preventing 1 kg of waste from being landfilled, typically modeled as the avoided landfill treatment burden for 1 kg of that material.


Question: What action typically becomes necessary when inventory data quality cannot be met due to lack of access to data or resources?

Some revisions will typically need to be made.

When the required inventory data quality cannot be met because data or resources are lacking, it typically becomes necessary to use data estimates or average/generic data, provided they increase the overall inventory quality; if important deficiencies remain, they must be explicitly addressed in the interpretation and limitations of the study. (Hauschild et al., *LCA: Theory and Practice*, Ch. 37, p.1025; European Commission – JRC, *ILCD Handbook*, p.186; Hauschild et al., *LCA: Theory and Practice*, Ch. 12, p.327)

When required inventory data quality cannot be achieved because data or resources are unavailable, it typically becomes necessary to **revise the goal and scope of the LCA study**—for example, by narrowing the system boundary, changing data quality requirements, or simplifying the study assumptions.


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 such a descriptive, accounting/monitoring study, “the LCI model will describe the system as it can be measured.” (ILCD Handbook, section 5.3.3, p.37)

In a descriptive, accounting/monitoring study, the LCI model represents the system as it actually exists or is observed, i.e. with average, historical, or current supply-chain relationships and flows.

So the model is generally:
- retrospective/descriptive rather than predictive,
- based on average data and average market mixes,
- intended to account for environmental burdens of the studied system, not to model consequences of decisions.


Question: When is an actual collection of inventory data typically only required for the foreground system?

When all data in the background system can be sourced from available background databases.

Actual collection of inventory data is typically only required for the foreground system when specific inventory data should be used for foreground processes, typically compiled as primary data from the product/technology developer, goods producer, or service operator, including specific secondary data from tier-one suppliers. Generic or average secondary background data may be used initially instead.
(Source 1, European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010, p.184)

In LCA, **actual, process-specific inventory data are typically only collected for the foreground system** when you are using a **hybrid/attributional setup with background data taken from databases**.

That is, you usually gather primary data only for the **processes directly under the decision-maker’s control or specific to the study**—for example:

- the company’s own operations,
- key suppliers if they are highly relevant,
- unique or influential processes.

The **background system**—generic upstream and downstream processes like electricity, fuels, transport, common materials, waste treatment—is then usually modeled with **secondary data from LCI databases** rather than new data collection.

So in short: **primary inventory collection is typically only required for the foreground when the background can be adequately represented by existing generic database data.**


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.

Using not fully technologically representative data is justifiable only if it does not relevantly change the overall LCIA results compared to using fully representative data; otherwise, the lower achieved representativeness must be documented in the data set/report.
— European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), chapter 6.8.2 III.c.i, p.127

Using data that are **not fully technologically representative** can be justified **only if the resulting uncertainty is explicitly considered and shown not to compromise the goal of the study**.

In practice, this means it may be acceptable for:
- **non-comparative studies**, or
- cases where **the intended application does not require full technological representativeness**,

**provided that** the limitations are transparently documented and their influence on the results is assessed.


Question: In the allocation of service inputs to co-produced goods, what distinguishes parallel services and what is one example given of them?

Parallel services serve several co-products at the same time in parallel and relate to those co-products in a similar way. Examples include the services provided by storage facilities, transport equipment, manufacturing halls, and production equipment.

Parallel services serve several co-products at the same time in parallel and relate to the co-products in a similar way. One example given is transport equipment.

(ILCD Handbook, p.260)

Parallel services are service inputs that can be separately attributed to each co-product without needing shared allocation across them. They run “in parallel” for different outputs rather than serving the joint process as a whole.

Example: separate packing or storage operations for different co-produced goods.


Question: In an LCA model where all processes are connected via their reference flows, what serves as the starting point from which all other processes are stepwise, relatively scaled?

The central process and the amount(s) of the system's functional unit(s) or reference flow(s).

The starting point is the central process and the amount(s) of the system’s functional unit(s) or reference flow(s). (Hauschild et al., LCA: Theory and Practice, Ch. 37, p. 1032)

The starting point is the **functional unit** (more precisely, the **reference flow that fulfills the functional unit**).

In an LCA model, all linked processes are then **scaled stepwise relative to that demand**, via their reference flows and exchange amounts.


Question: What are the two sub-types of a unit process study or data set in a Life Cycle Inventory?

Single operation unit process and black box unit process (each available in fixed or parameterised variants).

The two sub-types are:

1. Single operation unit process
2. Black box unit process

Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), Table 3, p.59.

In Life Cycle Inventory, a **unit process** study or dataset is commonly divided into two sub-types:

1. **Single-operation unit process**
   - Covers one specific operation or step in the system.

2. **Aggregated unit process**
   - Combines several operations into one dataset while still representing a unit-process level description.

If you want, I can also explain how these differ from **system process** datasets.


Question: When comparing alternatives in specific parts of otherwise identical systems solely to determine which system has the lowest environmental impact, what can be done with the identical parts when drawing system boundaries, and what is the benefit?

All parts of the systems that are identical can be left out when drawing the system boundaries, which can drastically reduce the effort for the LCA study.

When comparing alternatives in specific parts of otherwise identical or similar systems, and the sole purpose is to determine which has the lowest environmental impact, the identical parts of the systems can be left out when drawing the system boundaries. The benefit is that this can drastically reduce the effort required for the LCA study.

Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), section 6.10.6, p. 144.

When the systems are identical except for certain parts, you can **exclude the identical parts from the system boundaries** and focus only on the **differing components/processes**, provided those excluded parts truly contribute equally in all alternatives.

**Benefit:** this **simplifies the assessment** and reduces data collection and modeling effort, while still allowing a valid comparison of **which alternative has the lower environmental impact**.


Question: When conducting a comparative study on alternatives that are not objectively comparable, such as personal services, fashion items, or jewellery, how must the results and recommendations be presented?

They must be presented with the explicit statement that comparability is not assumed per se, but lies with the individual's preference and judgement.

For alternatives that are not objectively comparable, the results and recommendations must be presented with an explicit statement that comparability is not assumed per se, but lies with the individual’s preference and judgement; and any relevant limitations must be given jointly with the recommendations to avoid misinterpretation.

Cited: ILCD Handbook, chapter 6.10.3 / p.304; see also p.63.

When the alternatives are **not objectively comparable**—for example **personal services, fashion goods, or jewellery**—the study **must not present one option as environmentally better overall in a way that implies a general recommendation**.

Instead, results should be presented **only as information on the environmental aspects of each alternative**, with clear explanation that:

- the products/services fulfill **different subjective preferences or functions**,
- they are **not strictly comparable on an objective functional basis**, and
- any conclusions are **limited and conditional**, not a universal recommendation.

In practice, this means avoiding statements like **“Option A should be chosen over Option B”** and instead reporting the environmental profiles transparently so users can make their own value-based judgment.


Question: Under what condition is it appropriate to transfer inventory data from one geographical area or specific supplier to another?

It is appropriate only when the differences in environmental impacts have no or little relevance for the overall representativeness of the inventory.

It is appropriate only **if the differences in the environmental impacts have no or little relevance for the overall representativeness of the inventory**.

**Source:** European Commission – JRC, *ILCD Handbook: General Guide for LCA* (2010), **p.129**.

It is appropriate only when the **source process is sufficiently representative of the target situation**.

In practice, transfer inventory data from one geography or supplier to another only if you can justify that the transferred data is a good proxy in terms of things like:

- **Technology/process similarity**
- **Energy mix and fuels used**
- **Raw material inputs**
- **Emission controls and regulations**
- **Transport distances/logistics**
- **Scale, efficiency, and operating conditions**
- **Time period/data age**

So the condition is essentially **demonstrated equivalence or acceptable similarity**, with any differences assessed, documented, and ideally shown to have no significant effect on the LCA results. If not, you should adapt the data or use a different dataset.


Question: When allocating between the first and second life cycle, what value should be used for the pre-treated waste/end-of-life product whose market price is below zero before it enters the process step producing a valuable secondary good?

The absolute value of its negative market price should be used.

Use the absolute value of its negative market price when calculating the allocation key between the first and second life cycle.

Cited: ILCD Handbook, chapter 7.9.3, p.271; Annex C, p.354.

Use a **negative value** for the pre-treated waste/end-of-life product if its market price is below zero at the point it enters the recycling or recovery step.

In LCA allocation between first and second life cycles, the input to the process that produces the secondary material should reflect its **actual economic value at the handover point**:

- **If the waste has a positive market price:** use that positive value.
- **If the waste has zero value:** use zero.
- **If the waste has a negative market price:** use the **negative price**.

This means the holder of the waste is effectively **paying someone to take it**, so the material carries a negative economic value into the next process. That negative value should be used in any **economic allocation/substitution formula** based on market value at that stage.

If you want, I can also explain how this is handled in specific frameworks like **PEF, EN 15804, or ecoinvent/APOS**.


Question: In life cycle interpretation, what three aspects of representativeness must be checked for consistency among inventory data?

The time-related, geographical, and technological representativeness of the data.

In life cycle interpretation, the three aspects of representativeness that must be checked for consistency among inventory data are:

- time-related representativeness
- geographical representativeness
- technological representativeness

Cited in:
- Hauschild et al., *LCA: Theory and Practice*, Chapter 12, p.331
- Hauschild et al., *LCA: Theory and Practice*, Chapter 8, p.106

In life cycle interpretation, consistency of inventory data should be checked for representativeness in three main aspects:

1. **Time-related representativeness** – whether the data refer to the same or appropriate time period.
2. **Geographical representativeness** – whether the data reflect the same or relevant location/region.
3. **Technological representativeness** – whether the data correspond to the same or comparable technology/process conditions.

These are checked to ensure the inventory data are consistent and comparable across the study.


Question: When the size of an effect is classified as "small," which processes or systems are identified as being affected?

The "short-term marginal" processes or systems.

When the effect is classified as “small,” it is understood as “affecting only the extent of operation of one or more existing processes”; therefore, the affected ones are existing processes, and the short-term marginal process(es) are assumed to be superseded.
(Source 1, ILCD Handbook, section 7.2.4, p.178)

In impact/significance classification, a **“small” effect** usually means the impact is limited to **individual processes or local systems**, rather than affecting the whole product system, sector, or broader environment.

So the affected units are typically:

- **Single processes**
- **Sub-processes or unit operations**
- **Localized parts of a system**

In other words, the effect is noticeable but **confined in scope**.


Question: In the recyclability substitution approach, if the gate fee of pre-treated waste is "-1 US$", what market value is used for allocation?

"1 US$" is used as the market value for allocation.

If the gate fee / market value of the pre-treated waste is “−1 US$”, the market value used for allocation is “1 US$” (absolute value of the negative price).
Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.354; also p.361.

In the recyclability substitution approach, a gate fee of **−1 US$** for pre-treated waste means the material has **negative market value**.

So the market value used for allocation is:

**−1 US$ per functional unit or per unit of waste considered**.

In other words, the recycler is **paid to take the waste**, rather than purchasing it as a valuable secondary material.


Question: Why are detailed and differentiated method provisions still necessary even when simplified provisions are used in an LCA?

Because the simplifications draw on the detailed provisions, and in a few cases the detailed provisions are essential and must be fully applied; hence they need to be kept as well.

Detailed and differentiated method provisions are still necessary because the simplified provisions are derived from them and are appropriate only for most cases; in a few cases, the detailed provisions must be fully applied, where they are essential to avoid harming accuracy or robustness. Also, some specific aspects apply only in selected cases or processes within a study.
(Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.8)

Because simplified LCA provisions are only shortcuts, not replacements for methodological rigor.

Detailed and differentiated provisions are still needed because they:

- **Set the reference rules**: simplifications only make sense if there is a full method defining what is being simplified.
- **Protect consistency and comparability**: without detailed rules, different practitioners may simplify in different ways and get non-comparable results.
- **Define when simplifications are acceptable**: they help determine the limits, conditions, and materiality thresholds for using simplified approaches.
- **Handle important exceptions**: some processes, products, or impact categories are too sensitive or complex for generic simplifications.
- **Support transparency and review**: detailed provisions allow others to understand, verify, and judge whether the simplifications were appropriate.
- **Improve accuracy where needed**: if a simplified result could influence decisions significantly, the detailed method is needed to refine it.

In short, simplified provisions reduce effort, but detailed and differentiated provisions ensure the LCA remains credible, consistent, and fit for decision-making.


Question: Under the ILCD allocation provisions, what must the allocation formula consider in addition when solving multifunctionality?

It must consider the change in the inherent properties of the secondary good.

Under the ILCD allocation provisions, the allocation formula must consider, in addition, the “determining physical causal relationships” between each non-functional flow and the co-functions of the process as the first criterion in the two-step procedure for solving multifunctionality by allocation.
(ILCD Handbook, chapter 7.9.3.1, p.257)

Under the ILCD allocation provisions, when allocation is used to solve multifunctionality, the **allocation formula should also take into account the relevant underlying physical relationships** between the inputs/outputs and the co-products or functions.

In other words, beyond simply splitting burdens, it should reflect **how the processes are causally linked**, and only if no adequate physical relationship can be established should **other relationships, such as economic value**, be used.


Question: What technique is described as a way to avoid black box unit processes?

Subdivision or virtual subdivision is described as the way to avoid black box unit processes.
Chapter 7.4.2.2, p.192 (see also contents listing on p.10 and provisions on p.194).

In LCA, a common technique to avoid “black box” unit processes is **disaggregation** (sometimes called **process disaggregation** or using **foreground unit-process data**).

Instead of modeling a process as one aggregated block, you break it into **smaller, transparent unit processes** so the inputs, outputs, and emissions are visible and traceable.


Question: In attributional modelling, what type of data should be used for the foreground system versus the background system?

Technology-specific data of the supply-chain should be used for the foreground system, while average market consumption mix data should be used for the background system.

In attributional modelling:

- Foreground system: use site- or producer/provider-specific data; supplier-specific data for products linking foreground and background. Generic/average data may be used only if justified as more accurate, precise, complete, or sufficiently representative than available specific data. (ILCD Handbook, ch. 6.5.4 / p.131; also p.184)

- Background system: use average market consumption mix data. (ILCD Handbook, ch. 6.5.4 / p.131; also ch. 7.3.3 / p.185, and p.124)

In **attributional LCA**, you typically use:

- **Foreground system:** **specific/primary data**
  Data that directly represent the processes you are studying and can influence or know in detail (e.g. your own production site, supplier-specific data if available).

- **Background system:** **generic/secondary average data**
  Data from databases or literature representing average market/industry conditions for upstream and downstream processes you do not control directly (e.g. electricity mixes, material production, transport datasets).

So, in short: **foreground = specific process data; background = generic average database data.**


Question: When modelling the loss or binding of CO2 as soil organic carbon toward the equilibrium of land use after transformation, what default period should be assumed, and approximately what proportion of the main losses or binding does it reflect?

A default period of 20 years should be assumed, reflecting about 90% of the main losses or binding.

A default period of 20 years shall be assumed, reflecting the period during which about 90% of the main CO2 losses or binding as soil organic carbon occurs toward the new equilibrium.

Chapter 7, p.100 (ILCD Handbook: General Guide for LCA, 2010).

Use a **default period of 20 years** after the land-use transformation.

This is generally assumed to capture **about 95% of the total soil organic carbon loss or sequestration** toward the new land-use equilibrium.


Question: In life cycle impact assessment reporting, what form of data and indicator results must be made available alongside normalized, grouped, or weighted results?

The data and indicator results reached prior to any normalization, grouping, or weighting must be made available together with the normalized, grouped, or weighted results.

Data and indicator results reached prior to any normalization, grouping, or weighting must be made available alongside the normalized, grouped, or weighted results.
Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.317.

Alongside any normalized, grouped, or weighted LCIA results, you must also make available the **unweighted characterization results**:

- the **LCI/elementary flow data used**, and
- the **indicator results after characterization**, **before** normalization, grouping, or weighting.

In short: report the **raw characterized indicator results** in addition to any normalized/grouped/weighted results.


Question: Besides the reference flows providing the functional unit and permissible waste flows, what other flows should cross the boundary between the analysed system and the rest of the technosphere?

As far as possible, no other relevant flows should cross that boundary.

No relevant other flows should cross the boundary between the analysed system and the rest of the technosphere, as far as possible.
Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), chapter 6.6.1, p.105.

Besides the **reference flows** (delivering the functional unit) and **permissible waste flows**, the system boundary to the rest of the **technosphere** should also include:

- **Intermediate product flows** crossing the boundary
  - materials, components, semi-finished goods, auxiliaries, packaging, etc. entering from or leaving to other technosphere systems

- **Energy flows** crossing the boundary
  - electricity, fuels, steam, heat, compressed air, cooling, etc. supplied by or sent to other technosphere processes

- **Service/operation-related technosphere inputs** where relevant
  - transport services, maintenance, capital goods or infrastructure inputs if included in the goal and scope

In short: **any input or output exchanged with other human-controlled technical systems** that is required to model the product system, not just the final reference product and waste.


Question: What two techniques are presented as ways to avoid black box unit processes?

The two techniques are subdivision and virtual subdivision.

Cited in:
- ILCD Handbook, chapter 7.4.2.2, p.10: “Avoiding black box unit processes by subdivision and virtual subdivision”
- ILCD Handbook, p.190: “one should check whether it can be split by subdivision before data collection or virtual subdivision afterwards.”

Two commonly presented ways to avoid black-box unit processes in LCA are:

1. **Disaggregation / more detailed unit-process modeling**
   Break aggregated processes into transparent sub-processes with explicit inputs and outputs.

2. **System expansion (or substitution / avoiding allocation)**
   Expand the system boundary to include additional functions and connected processes rather than hiding them inside aggregated datasets.

If you want, I can also explain how these differ from allocation.


Question: Which system of units must be used by default when reporting the results of an LCI or LCA study?

The Système international d'unités (SI) units.

By default, the **Système international d'unités (SI units)** must be used for reporting the results of an LCI or LCA study.
**Cited:** ILCD Handbook: General Guide for LCA (2010), **p.308**; also stated in the inventory guidance on **p.204**.

By default, **SI units (the International System of Units)** should be used when reporting **LCI or LCA** results.

This is the standard approach under **ISO 14040/14044**, to ensure consistency, transparency, and comparability of results. If other units are used, they should be clearly justified and documented.


Question: In the chapter on life cycle inventory analysis, what three main activities are identified as part of the process?

Collecting data, modelling the system, and calculating results.

In the chapter on life cycle inventory analysis, the process is described as “collecting data, modelling the system, calculating results.” (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), Chapter 7, p.155; see also table of contents wording on p.9)

The three main activities in life cycle inventory (LCI) analysis are:

1. **Data collection** – gathering input and output data for the product system.
2. **Data calculation/quantification** – converting the collected data into inventory flows linked to the functional unit.
3. **Data allocation and validation/interpretation** – assigning flows appropriately among co-products or processes and checking the consistency and quality of the results.

If you want, I can also give the ISO-style wording often used in LCA texts.


Question: When obtaining inventory data for the unit processes included in a multifunctional process, which option is preferable, and what is an alternative basis for splitting up the data?

Actual data collection is the preferable option; an alternative is using knowledge about the involved processes (e.g., that emissions to water can only come from processes contributing waste water, or that certain parts or consumables are only required as input for certain processes) to split up the data and assign inventory items to the included unit processes.

The preferable option is actual data collection for the included unit processes. An alternative basis is the use of knowledge about the involved processes to split up the data of the multifunctional process and assign inventory items to the included unit processes.

Chapter 7, p.193 (European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010)

Prefer **collecting or deriving data for the specific subprocesses directly** so each unit process is represented separately, rather than starting from aggregated multifunctional data.

If that is not possible, an alternative is to **split the multifunctional inventory on the basis of the underlying physical relationships** between inputs/outputs and the different functions/products, for example by mass, energy content, or other causal physical parameters.


Question: Who were the editors of the ecoinvent report No. 1, 'Overview and Methodology for the ecoinvent database v. 2.0,' published by the Swiss ecoinvent Centre in 2007?

The provided context does not state who the editors were for ecoinvent Report No. 1, “Overview and Methodology for the ecoinvent database v. 2.0,” published in 2007. It only mentions “Ecoinvent Centre: Ecoinvent Reports No. 1–25. Swiss Centre for Life Cycle Inventories, Dübendorf (2007)” without listing the editors. Source 1, p.751.

The editors were **Rolf Frischknecht** and **Niels Jungbluth**.


Question: When applying cut-off rules to exclude an impact category deemed of little relevance, what must be done to justify the omission?

The omission must be quantitatively justified as being insignificant for the overall environmental impact in view of the goal definition, the intended applications, and the cut-off defined for the LCI/LCA study.

The omission must be quantitatively justified as insignificant for the overall environmental impact, considering the goal definition, intended applications, and the cut-off defined for the LCI/LCA study.

Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.110.

To justify omitting an impact category under cut-off rules, you should **demonstrate and document that its exclusion does not significantly affect the overall conclusions of the study**.

In practice, this means:

- **Provide a clear rationale** for why the category is considered of little relevance.
- **Show evidence**—for example through screening, contribution analysis, or sensitivity checks—that the omitted category is negligible.
- **Document the omission transparently** in the goal and scope / methodology, including the **criteria used** for the cut-off.

So the key requirement is **transparent justification backed by evidence that the omission is insignificant to the results and conclusions**.


Question: What advantage does an accompanying review have over a review performed at the end of an LCA study?

An accompanying review can help avoid problems such as considerable delays and extra work, or at least identify them earlier.

An accompanying review can help avoid considerable delays and extra work, or at least identify problems earlier than a review performed at the end of the study. (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.15)

An accompanying review is done in parallel with the LCA, so it can improve the study while it is still being developed.

Main advantage:
- **Problems are found early**, when they can still be corrected easily.

Compared with an end-of-study review, this means:
- **Methodological choices can be adjusted in time**
- **Data gaps or inconsistencies can be fixed before finalization**
- **Iterations are more efficient and less costly**
- **The final study is usually more robust and credible**

By contrast, a review only at the end may identify important issues when major rework is already difficult, time-consuming, or expensive.


Question: When documenting an LCI data set deliverable, under what condition does modelling of assumption scenarios change from a recommendation to a mandatory ('shall') requirement?

It becomes a 'shall' requirement when the data set is intended to be used in subsequent comparisons.

When the LCI data set is intended to be used in subsequent comparisons, modelling of assumption scenarios changes from recommended to a mandatory “shall” requirement.

Chapter/page cited: ILCD Handbook, chapter 6.5.4.4, p.86 (Source 3).

It becomes a **mandatory (“shall”) requirement** when the **commissioner or goal-and-scope of the study requires assumption scenarios to be documented and modelled** because they are **relevant for interpretation or decision-making**.

In practice:

- If assumption scenarios are only potentially useful, modelling them is a **recommendation**.
- If the **intended application, contractual requirements, or review/decision context** depends on those assumptions, then they **shall be modelled and documented** as part of the LCI data set deliverable.


Question: What are the main reasons for the forming of markets as described in life-cycle assessment?

Markets form mainly for political reasons (legislation on competition and product requirements such as material bans, product safety, technical and other standards, taxes, and subsidies) and cultural reasons (markets recognised by producers and service providers).

The main reasons for the forming of markets are:

- Political reasons: legislation, especially on competition and product requirements such as material bans, product safety, technical and other standards, taxes, and subsidies
- Cultural reasons: recognised markets by producers and service providers

(Source: European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010, p.128)

In life-cycle assessment, “markets” are used to represent how products and by-products are supplied and demanded. The main reasons markets form are:

1. **To match supply and demand**
   Producers make goods, users need goods, and markets connect them.

2. **To handle multiple suppliers of the same product**
   A product often comes from several technologies, regions, or producers. The market mixes these sources into an average or marginal supply.

3. **To allocate by-products and co-products**
   When one process produces several outputs, markets help determine whether a by-product displaces other production or is just absorbed by demand.

4. **To reflect geographic trade**
   Products are rarely consumed only where they are produced. Markets represent imports, exports, and regional supply mixes.

5. **To model economic substitution in consequential LCA**
   If demand changes, markets help identify which producer is likely to respond and what production is displaced.

6. **To distinguish product quality and substitutability**
   Separate markets may form for products that are only partly interchangeable because of differences in function, purity, grade, or performance.

So, in LCA, markets are mainly formed to represent **exchange, substitution, competition among suppliers, and regional/product-specific differentiation** in a way that links processes realistically.


Question: In life cycle assessment, what distinguishes the 'Accounting' approach from meso/macro-level decision support?

The 'Accounting' approach is purely descriptive documentation of a system's life cycle (e.g. a product, sector, or country) without considering potential additional consequences on other parts of the economy, whereas meso/macro-level decision support addresses strategic decisions assumed to have structural consequences outside the decision-context, such as changing available production capacity.

“Accounting” is a purely descriptive documentation of the life cycle of the system under analysis, without interest in additional consequences on other parts of the economy. In contrast, “meso/macro-level decision support” is strategic decision support and assumes structural consequences outside the decision context, i.e. changes in available production capacity.

Cited source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.6.

In LCA, **Accounting** and **meso/macro-level decision support** differ mainly by **purpose, system perspective, and how results are interpreted**:

- **Accounting approach**
  - Describes the **environmental burdens associated with a product, organization, or system as it currently exists**.
  - Focuses on **inventorying and allocating** impacts within a defined boundary.
  - Typically supports **reporting, benchmarking, footprinting, and tracking performance**.
  - It is mostly **descriptive**: “What impacts are attributable to this system?”

- **Meso/macro-level decision support**
  - Used for **decisions affecting larger systems** such as sectors, regions, national policy, infrastructure, or markets.
  - Looks beyond attribution to **system-wide consequences**, interactions, and possible structural changes.
  - Often considers **market effects, rebound, substitution, indirect effects, and scenario analysis**.
  - It is more **decision-oriented**: “What will happen environmentally if this policy or large-scale change is implemented?”

So, the key distinction is that **accounting assigns impacts to an existing system**, while **meso/macro decision support evaluates the broader consequences of interventions at larger scales**.


Question: When filling data gaps in an LCA, what type of data sets shall be used, and under what condition may methodologically not fully consistent data sets be used instead?

Data gaps shall be filled with methodologically consistent data sets; gaps of low relevance may be filled with methodologically not fully but sufficiently consistent data sets, provided they are developed following the document's guidance and meet the overall quality requirements.

To fill data gaps, data sets that increase the overall quality of the final inventory shall be used, and the individual data or data set’s quality shall be at least equivalent to the “Data estimate” quality level. Methodologically not fully consistent data may be used instead if they are still sufficiently consistent, mainly for background LCI data sets.
(ILCD Handbook, pp. 254, 207)

For **filling data gaps in an LCA**, you should use **data sets that are methodologically consistent with the rest of the study**.

If that is **not possible**, then **methodologically not fully consistent data sets may be used**, but only **as a substitute when consistent data are unavailable** and this choice should be **documented and justified**.


Question: In a Carbon footprint study or other study that applies a limited set of impact indicators, to what does the cut-off relate?

It relates only to the considered indicators (e.g. covering 90 % of the Climate change impacts).

In a Carbon footprint study or other study with a limited set of impact indicators, the cut-off relates to the considered indicators only, e.g. “covering 90% of the Climate change impacts.”
— European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.104

In a **carbon footprint study** or any study using a **limited set of impact indicators**, the **cut-off** relates to the **environmental relevance of omitted inputs or processes with respect to the selected indicators**.

So, exclusions are judged based on whether they are **insignificant for the impact categories being studied** (for example, climate change in a carbon footprint), **not for all possible environmental impacts**.

Example:
- In a **carbon footprint study**, a flow may be cut off if its contribution to **GHG emissions** is negligible, even if it might matter for other impacts like toxicity or eutrophication.

In short: **cut-off is indicator-specific and tied to the goal and scope of the study.**


Question: What four aspects must a functional unit be identified and specified across in detail?

The function provided (what), in which quantity (how much), for what duration (how long), and to what quality (in what way and how well the function is provided).

A functional unit shall be identified and specified in detail across these four aspects:

1. Function provided (“what”)
2. In which quantity (“how much”)
3. For what duration (“how long”)
4. To what quality (“in what way and how well is the function provided”)

Source: Hauschild et al., *LCA: Theory and Practice*, Ch. 37 “LCA Cookbook”, p. 974.

In LCA, a **functional unit** should be identified and specified across four key aspects:

1. **What** – the **function/service provided**
   - What exactly the product system delivers.

2. **How much** – the **quantity of that function**
   - The amount of service provided.

3. **How well** – the **required quality or performance level**
   - The standard, effectiveness, or technical performance expected.

4. **For how long** – the **duration or lifetime** of the service
   - The time period over which the function is delivered.

A common shorthand is: **what, how much, how well, and for how long**.


Question: Which organizations collaborated to produce the 2008 "Guide to PAS 2050 - How to assess the carbon footprint of goods and services"?

BSI British Standards, together with DEFRA and the Carbon Trust.

The 2008 Guide to PAS 2050 was produced by BSI British Standards, with DEFRA and the Carbon Trust.

Source 1, p.391.

The 2008 **“Guide to PAS 2050: How to assess the carbon footprint of goods and services”** was produced through collaboration between:

- **BSI (British Standards Institution)**
- **Carbon Trust**
- **UK Department for Environment, Food and Rural Affairs (Defra)**

These were the main organizations behind PAS 2050 and its accompanying guide.


Question: What is the typical value range that normalised impacts for goods and services are brought down to for better communication and interpretation?

A typical value range of 10 to 0.00001, instead of 1E-7 to 1E-14.

The typical value range is **10 to 0.00001**.

Cited in:
- **Hauschild et al., LCA: Theory and Practice, Ch. 37, p. 994**
- **European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p. 120**

A common rule of thumb is that **normalised LCA results are expressed so they fall roughly in the 0.001 to 10 range**, ideally around **0.01 to 1** for easy interpretation.

This is why normalization is used in communication: it converts very different impact categories into values of a similar, more intuitive order of magnitude relative to a reference (for example, a person’s annual contribution or a regional total).


Question: In an open-loop recycling example where a 0.2 kg polymer product can only be used for energy recovery, what is the secondary good produced and how much of the primary-produced electricity is credited?

The secondary good is the 0.28 kWh of electricity generated from incinerating the plastic waste and fed into the grid, and the full 0.28 kWh of primary-produced electricity is substituted (credited).

In this open-loop recycling case, the secondary good is 0.28 kWh of electricity generated from incinerating the 0.2 kg plastic waste, and the full 0.28 kWh of primary-produced electricity is credited. (ILCD Handbook, Annex C: Modelling reuse, recycling, and energy recovery, p.360)

The **secondary good** is **electricity** produced from **energy recovery/incineration** of the 0.2 kg polymer.

In the open-loop recycling allocation, the system gets credited for the **avoided primary electricity production** equal to the amount of electricity generated from that recovered polymer. So the credited amount is:

- **the quantity of electricity generated from the 0.2 kg polymer**,
- expressed as an equivalent amount of **primary-produced electricity avoided**.

If you want, I can also show this in the usual open-loop recycling formula form.


Question: When analysing long-term emissions in an LCA, what time boundary is used to separate the two sets of results that are then jointly discussed?

Interventions within the first 100 years from the time of the study are analysed separately from those beyond that 100-year time limit, and the two sets of results are then jointly discussed.

The separating time boundary is **100 years from the time of the study**: results for interventions **within the first 100 years** are analysed separately from those **beyond 100 years**, and then jointly discussed.
**Cited:** European Commission – JRC, *ILCD Handbook: General Guide for LCA* (2010), **p.304**.

A common convention is to use a **100-year time boundary**.

In long-term emission analysis, results are often split into:

- **emissions within the first 100 years**, and
- **emissions occurring after 100 years**,

and then these two result sets are **reported separately but interpreted together**.


Question: What are the data quality indicators defined under the ILCD approach for classifying the achieved data quality of LCI data?

Overall data quality, technological representativeness, geographical representativeness, time-related representativeness, completeness, precision/uncertainty, and methodological appropriateness and consistency.

Under the ILCD approach, the data quality indicators for LCI data are:

- Technological representativeness
- Geographical representativeness
- Time-related representativeness
- Completeness of environmental impacts covered by the inventory
- Precision of the data
- Methodological appropriateness and consistency

Source: ILCD Handbook, Annex A, Chapter 12.3, p.329; see also Chapter 6.8.1, p.122.

Under the **ILCD (International Reference Life Cycle Data System)** approach, the achieved data quality of **LCI data** is classified using these **Data Quality Indicators (DQIs)**:

1. **Technological representativeness**
   How well the data reflects the actual technology or mix of technologies studied.

2. **Geographical representativeness**
   How well the data matches the relevant geographic area.

3. **Time-related representativeness**
   How well the data reflects the relevant time period.

4. **Completeness**
   The degree to which all relevant flows, processes, and elementary exchanges are included.

5. **Precision / Uncertainty**
   The statistical reliability of the data and its variability or uncertainty.

6. **Methodological appropriateness and consistency**
   How consistent and appropriate the methods, assumptions, allocation rules, and system modeling choices are with the goal of the study.

In ILCD, these indicators are typically scored on a **1 to 5 scale**, where **1 = very good quality** and **5 = very poor quality**, and can be combined into an overall data quality rating.


Question: What is the purpose of the sensitivity check in an LCA study, and which quantitative methods support it?

The sensitivity check assesses the reliability of the final results and, if included, the conclusions and recommendations of the LCA study. It is supported quantitatively by scenario analysis and uncertainty calculations.

The purpose of the sensitivity check is to assess the reliability of the final results and, if included, the conclusions and recommendations of the LCA study. The quantitative methods that support it are scenario analysis and uncertainty calculations.

(Source 1: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.295)

In an **LCA (Life Cycle Assessment)**, a **sensitivity check** is used to test how strongly the results depend on key **assumptions, data choices, and methodological decisions**. Its purpose is to identify whether the study conclusions are **robust** or whether small changes in inputs could significantly alter the outcome.

### Purpose of the sensitivity check
It helps to:
- assess the **robustness** of results,
- identify **influential parameters** or assumptions,
- understand the effect of **data uncertainty** and methodological choices,
- support interpretation and improve confidence in comparisons.

### Quantitative methods that support it
Common quantitative approaches include:
- **Scenario analysis**: compare results under different plausible assumptions.
- **Parameter variation / one-at-a-time sensitivity analysis**: vary one input or assumption within a defined range and observe the effect.
- **Monte Carlo simulation**: propagate uncertainty in many input parameters simultaneously using probability distributions.
- **Global sensitivity analysis**: quantify the contribution of multiple uncertain inputs to output variance, e.g. using:
  - **regression/correlation-based methods**,
  - **variance-based methods** such as **Sobol indices**.

So, in short: the sensitivity check determines how stable LCA conclusions are, and it is supported by methods ranging from simple parameter variation to Monte Carlo and global sensitivity analysis.


Question: When a unit process inventory item has missing qualitative information, under what condition should the respective flow be created and used in the regular inventory?

Only if it is a product or waste flow; little specified elementary flows (e.g., "Metals to air") shall not be kept in the regular inventory and must be documented in another way.

When a unit process inventory item has missing qualitative information, the respective flow should be created and used in the regular inventory only if it is a product or waste flow. (ILCD Handbook, p.209; also p.213, section 7.4.2.11.3 IV.a)

Create and use the flow in the regular inventory only if the missing qualitative information is not relevant to the flow’s quantitative characterization and intended use.

In practice, that means:
- the amount and identity of the flow are sufficiently clear,
- the missing metadata does not affect allocation, impact assessment, or interpretation in a significant way,
- and the flow can still be consistently linked and documented.

If the missing qualitative information could materially change meaning or use of the flow, it should not be used as a normal regular-inventory flow until clarified.


Question: When analyzing a decision, what threshold percentage should be used as the cap when the annual additional demand or supply exceeds the capacity of the annually replaced installed capacity?

If that percentage is bigger than 5%, 5% should be used instead.

Use **5%** as the cap.

**Citation:** European Commission – JRC, *ILCD Handbook: General Guide for LCA* (2010), **p. 42**.

Use **100%** as the cap.

In consequential/LCA-style decision analysis, if the **annual additional demand or supply** is larger than the **capacity of the annually replaced installed capacity**, the threshold is capped at **100% of the annually replaced capacity**. This means the change is large enough to fully affect the marginal replacement/new capacity in that period, and anything beyond that exceeds the replacement-rate basis of the threshold.


Question: When developing unit process data sets as deliverables of LCA work, why is the calculation of LCI results required?

Because it serves, together with subsequent characterisation, to quantify the overall completeness and approximate the overall uncertainty of the data set per impact category.

When developing unit process data sets as deliverables, the calculation of LCI results is required because, together with subsequent characterisation, it serves “to quantify the overall completeness and approximate the overall uncertainty of the data set per impact category.” If normalisation and weighting are included in the cut-off rules, these also have to be applied.
— ILCD Handbook: General Guide for LCA (2010), Chapter 7, p.273

Calculating LCI results is required because a unit process dataset is not just a list of raw measurements—it must be transformed into a complete, consistent, quantified inventory that can actually be used in LCA models.

Key reasons:

- **To quantify all exchanges consistently**
  Inputs, outputs, emissions, wastes, and co-products must be calculated and related to the process’s reference flow.

- **To normalize to a functional unit or reference product**
  Raw plant or supplier data often come in different bases. LCI calculation converts them into a dataset per unit of output.

- **To apply allocation and modeling rules**
  Multi-output processes, recycling, losses, and internal loops require calculation to distribute burdens correctly.

- **To ensure completeness and mass/energy balance**
  LCI calculation helps identify gaps, inconsistencies, and errors in the unit process data.

- **To create interoperable database-ready datasets**
  A unit process dataset delivered for LCA use must contain calculated elementary and intermediate flows in a form that software and downstream users can link and aggregate.

- **To support transparency and review**
  The calculated LCI results show how collected process data were converted into the final inventory, enabling verification and interpretation.

In short, calculation of LCI results is required because only through calculation do collected process data become a usable, consistent, reviewable unit process dataset for LCA.


Question: When a data set combines data from different sources or otherwise lacks representativeness, what action must be taken?

This shall be stated in the data set and any accompanying documentation, if published.

When a data set combines data from different sources or otherwise lacks representativeness, this must be clearly stated in the data set and any accompanying documentation, if published. The documentation should prominently clarify what the data set represents.

Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), chapter 7.4.2.3, p.195.

You must **clearly document and justify the limitations**.

In LCA terms, if a dataset combines different sources or is not fully representative, you should:

- **State the lack of representativeness explicitly**
- **Document the data sources and how they were combined**
- **Explain assumptions, approximations, and selection rules**
- **Assess the potential effect on results**
- If relevant, **flag the dataset as lower quality/uncertain** and consider **sensitivity or uncertainty analysis**

So the required action is essentially **transparent documentation and qualification of the dataset’s representativeness**.


Question: When a functional unit cannot generally be given for materials and other application-unspecific products, what should be provided instead?

Only the reference flow that includes the main technical specification of the product should be provided.

For materials and other application-unspecific products, instead of a functional unit, **the reference flow including the main technical specification of the product should be provided**; in this case, **the reference flow is also the declared unit**.
**Source 1, Chapter 37 “LCA Cookbook”, p.975**; see also **Source 2, p.65**.

When a functional unit cannot generally be defined for materials or other application-unspecific products, the **declared unit** should be provided instead.

This is commonly used in EPD/LCA practice for products whose final function depends on their specific application.