Question: In an LCA where one process contributes about 95% of the overall Climate change impact potential due to high methane emissions, why is high data quality important for that emission?

Because that single process dominates the Climate change impact potential (about 95%) due to its high methane emission, so the accuracy of the result depends heavily on the quality of the data for that emission.

Because that methane emission is the dominant driver of the result.

If one process contributes ~95% of the total climate change impact, then:

- **The overall LCA result is highly sensitive to that single emission**
- **Any error or uncertainty in that methane data will strongly affect the total footprint**
- **Poor-quality data could lead to wrong conclusions about hotspots, comparisons, or improvement options**
- **Methane has a high global warming potential**, so even relatively small inaccuracies in the emission quantity can cause large changes in the climate impact result

In short: **when one input dominates the impact, its data quality largely determines the reliability of the whole LCA outcome.**

High data quality is important because when one emission dominates the result, inaccuracies in that emission strongly affect the overall impact result. The ILCD Handbook notes that if a specific emission is the main contributor, “it is very important to have a high quality on the data for this emission,” whereas less important emissions may be less precise if they contribute little to total impact.
— European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.137 (chapter on data quality)

Because when one emission dominates the result, the overall impact hinges on the quality of the data for that emission. The ILCD Handbook states that if a process/emission is the major contributor, “it is very important to have a high quality on the data for this emission,” whereas emissions that contribute little to the total impact can be less precise. In practice, only a limited number of emissions and processes often contribute relevantly to overall impacts, so these must be correctly identified and prioritized for high-quality data collection (European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010, chapter on data quality, pp. 136–137, esp. p. 137).


Question: When filling data gaps in a life cycle inventory, what minimum quality level must individual data sets used for the estimates achieve?

They must be at least equivalent to a "Data estimate" quality level.

When using estimated data to fill gaps in a life cycle inventory, the individual data sets used for those estimates should be of **at least “fair” quality**.

In practice, this means they should be sufficiently representative and documented so the estimate is credible, even if not based on the highest-quality primary data.

Individual data sets used to fill data gaps shall achieve at least the “Data estimate” quality level.
Cited in: ILCD Handbook: General Guide for LCA (2010), p.254 (also p.291; Hauschild et al., p.1025).

Individual data sets used to fill data gaps in a life cycle inventory must achieve at least the **“Data estimate” quality level** (ILCD Handbook, Chapter 7, p. 254).

Supporting wording: “the individual data or data set's quality shall be equivalent to at least the ‘Data estimate’ quality level” (ILCD Handbook, Chapter 7, p. 254).


Question: How should the energy content of energy resource flows be expressed, and in what reference unit?

It should be expressed as the Lower calorific value of the water-free resource, measured in the reference unit MJ.

In LCA, the **energy content of energy resource flows** should be expressed as **their lower heating value (LHV)**, unless a method specifically requires otherwise.

- **Reference unit:** usually **MJ** (megajoules)
- For electricity and heat, use the **delivered energy amount** in **MJ** or **kWh**, depending on the inventory convention

So, for fuels and other energy carriers, report the flow based on **energy content in MJ, referenced to LHV**.

The energy content of energy resource flows should be expressed as **Lower calorific value** and measured in the reference unit **MJ**.

**Citation:** European Commission – JRC, *ILCD Handbook: General Guide for LCA* (2010), **p.221**.

The ILCD Handbook states that named energy resources should be inventoried directly as elementary flows, “expressed as Lower calorific value and measured in the reference unit MJ.” (European Commission – JRC, ILCD Handbook: General Guide for LCA, chapter 7.4.3.6.1, p.221)

A related passage also notes that for combustible materials, inputs and outputs can be transformed into energy by multiplying by the relevant heat of combustion, and it must be reported whether the higher or lower heating value is used. (Guinée et al., Handbook on LCA, p.523)


Question: In consequential LCA modelling, how is a "short-term marginal" best represented when the effect is considered, and to what attributional data is it equivalent?

It is best represented by the "average market consumption mix" of the processes/systems, which is the same as the average background data used in attributional modelling.

In consequential LCA, a **short-term marginal** is best represented by the **current production technology or supplier mix that can respond without capacity changes**.

It is therefore equivalent to **average attributional data for the suppliers/market affected in the short term** — essentially the **current average technology mix**, not a future or fully optimized marginal technology.

In consequential LCA, if the effect is considered “small” after the primary consequence, the “short-term marginal” is best represented by the “average market consumption mix” of the processes/systems, i.e. the same as the average background data used in attributional modelling.
(Source: European Commission – JRC, ILCD Handbook: General Guide for LCA, chapter 7 Life Cycle Inventory analysis, p.171)

In consequential LCA, if the effect is considered “small” after accounting for the primary consequence, the “short-term marginal” is best represented by the “average market consumption mix” of the processes/systems. This is equivalent to “the same as the average background data in attributional modelling” (European Commission – JRC, ILCD Handbook: General Guide for LCA, p.171).


Question: In consequential modelling, when the market direction has changed as a consequence of the analysed decision, what combination of processes makes up the superseded processes?

A specific combination of the least cost-competitive ones and partly the most cost-competitive ones.

In consequential LCA, if the **market direction changes because of the analysed decision**, the **superseded processes** are made up of:

- the **marginally affected processes before** the decision, and
- the **marginally affected processes after** the decision.

In other words, they are the processes that would have responded in the original market trend and the processes that respond after the decision-induced change in trend; the shift between these constitutes the superseded processes.

When the market direction has changed as a consequence of the analysed decision, the superseded processes are “a specific combination of the least cost-competitive ones and partly the most cost-competitive ones.” (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.180, chapter 7.2.4; also p.164–165 overview context)

When the market direction has changed as a consequence of the analysed decision, the superseded processes are “a specific combination of the least cost-competitive ones and partly the most cost-competitive ones” (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), Chapter 7.2.4, p.180).


Question: In closed-loop recycling, what does the recycled material, recovered energy, or reused part replace when it re-enters the supply chain?

It replaces the input of newly produced materials, energy, or parts.

In **closed-loop recycling**, the recycled material, recovered energy, or reused part **replaces the equivalent virgin material, primary energy, or new part** that would otherwise have been supplied to the same product system.

So, when it re-enters the supply chain, it typically displaces:

- **Virgin raw material** if material is recycled,
- **Conventional/primary energy** if energy is recovered,
- **A newly manufactured component** if a part is reused.

In closed-loop recycling, the recycled material, recovered energy, or reused part/product re-enters the supply chain “replacing the input of newly produced materials, energy or parts.” (ILCD Handbook, Annex C, p.347)

In closed-loop recycling, the recycled material, recovered energy, or reused part/product re-enters the supply chain “replacing the input of newly produced materials, energy or parts.” (ILCD Handbook, Annex C, p. 347)


Question: What GWP100 impact factor is assigned to the correction flow for delayed emission of nitrous oxide per 1 kg of nitrous oxide and 1 year of delay?

-2.98153 kg CO2-equivalents per 1 kg nitrous oxide and 1 year of delayed emission, applied only if temporary carbon storage is considered in the study.

The **GWP100 correction factor for delayed emission of nitrous oxide (N₂O)** is typically assigned as:

**0.82 kg CO₂-eq per kg N₂O per year of delay**

This is the **annualized correction flow** used to account for the reduced climate impact when N₂O emissions are postponed by 1 year within a 100-year assessment horizon.

The correction flow for delayed emission of nitrous oxide carries a GWP100 impact factor of **-2.98153 kg CO2-equivalents per 1 kg nitrous oxide and 1 year of delayed emission**.
**Source: ILCD Handbook, section 7.4.3.7, p.231**

The “Correction flow for delayed emission of nitrous oxide (within first 100 years)” carries a **GWP100 impact factor of -2.98153 kg CO2-equivalents per 1 kg nitrous oxide and 1 year of delayed emission**.
**Source:** *ILCD Handbook: General guide for Life Cycle Assessment - Detailed guidance*, **Chapter 7.4.3.7, p.231**.


Question: When assessing future situations in an LCA, which potential futures are considered relevant for investigation?

Only a number of defined possible and probable future settings are considered relevant, not the potential futures that lie in between them.

In prospective or scenario-based LCA, the relevant futures to investigate are typically those that are:

1. **Plausible** – futures that could realistically happen.
2. **Possible** – a broader set of futures that are not impossible, even if less likely.
3. **Probable / likely** – futures that are expected to happen based on current trends and assumptions.
4. **Preferable / normative** – futures that reflect desired policy or sustainability goals.

In practice, LCA studies often focus on **plausible alternative future scenarios**, sometimes including both **business-as-usual** and **policy- or technology-driven** futures to capture uncertainty and support decision-making.

Relevant futures depend on the study purpose. In future-oriented LCA, the relevant potential futures include:

- **Predictive futures**: “What will happen?” — including **forecasts** (likely scenario) and **what-if scenarios** for short-term/simple cases.
- **What-if scenarios** are relevant for comparing well-known situations in the **short term** and in **specific cases**.
- **Cornerstone scenarios** are relevant when aiming to indicate a **possible long-term development direction**.

Citations: **Hauschild et al., Ch. 21, p.508** and **p.513**.

In future-oriented LCA, the relevant potential futures depend on the study purpose. The chapter identifies **predictive studies** such as **forecasts (“the likely scenario”)** and **what-if scenarios** as relevant future approaches; additionally, **cornerstone scenarios** are relevant when the aim is to indicate a possible long-term direction of development rather than a short-term comparison.
(Hauschild et al., *LCA: Theory and Practice*, Ch. 21, pp. 508, 513)


Question: In comparative LCAs, what can result from inconsistent scoping of the different systems or products being compared?

It can easily lead to misleading results and conclusions.

In comparative LCAs, inconsistent scoping can make the comparison invalid or misleading.

It can result in:
- unfair comparisons between systems,
- biased conclusions,
- apparent differences that are caused by scope choices rather than real environmental performance,
- omission or double-counting of important life cycle stages, processes, or impacts,
- wrong decision-making by users of the study.

In short: if the systems are not scoped consistently, you may end up comparing “apples and oranges.”

In comparative LCAs, inconsistent scoping of the different systems or products can “easily lead to misleading results and conclusions.” This can happen, for example, if the products do not provide the same functionality because of an inappropriate functional unit definition, or if different modelling principles are applied to the compared systems.
(Source 1: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), Annex D, Section 15.2.6, p.371)

In comparative LCAs, inconsistent scoping of the systems/products can “easily lead to misleading results and conclusions.” This can happen, for example, if the compared products do not provide the same functionality because of an inappropriate functional unit definition, or if different modelling principles are applied to the compared systems.
(Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.371, Annex D, 15.2.6)


Question: From an LCA perspective, what condition determines that a recycling situation is best captured as closed-loop recycling, regardless of whether the secondary good is used in the same or another product?

As long as the secondary good does not change its inherent technical properties and provides the same functional unit, closed-loop recycling best captures the situation.

From an LCA perspective, the key condition is:

**the recycled material can be considered to replace virgin material in a functionally equivalent way, without relevant loss of inherent material properties or quality.**

So, **closed-loop recycling** is appropriate when the secondary material effectively returns to the technosphere as **the same material quality**, even if it is used in a **different product system**.

In short: **what matters is functional/material equivalence, not whether it goes back into the exact same product.**

A recycling situation is best captured as closed-loop recycling when the secondary good does not change its inherent technical properties and provides again the same functional unit, regardless of whether it is used in the same or another product.

Cited source: ILCD Handbook, Annex C, p. 347.

From an LCA perspective, a recycling situation is best captured as **closed-loop recycling** **as long as the secondary good does not change its inherent technical properties and provides the same functional unit**, regardless of whether it is used in the same or another product.
(Source: **European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), Annex C, p. 347**)


Question: Under what condition can virtual subdivision be applied in consequential modelling without distorting the substitution?

Only if it results in complete separation of the inventory of the analysed function.

In consequential modelling, **virtual subdivision** can be used **only if the subdivision does not change the marginal technology or market response being substituted**.

So the key condition is:

- the co-products or fractions created by the virtual split must **all lead to the same substituted product/service**, in the **same market**, with the **same marginal supplier**, and
- the split must not alter the amount or nature of the avoided burden.

If virtual subdivision changes which product is assumed to be displaced, or changes the substitution ratio, then it **distorts the substitution** and should not be used.

Virtual subdivision can be applied in consequential modelling without distorting the substitution only if it does not “cut” through a physically not subdividable multifunctional joint process; partial subdivision that cuts through a multifunctional process should be avoided because it distorts substitution.

Cited:
- ILCD Handbook, chapter 7.2.4.6, p.174
- ILCD Handbook, p.174

Virtual subdivision can be applied in consequential modelling provided it does not “cut” through a physically not subdividable multifunctional joint process. The ILCD states that “under consequential modelling, virtual subdivision shall not be done if it ‘cuts’ through a physically not subdividable multifunctional joint process. This would distort the substitution” (ILCD Handbook, chapter 7.2.4.6, p.174).


Question: What general order of preference should be followed when sourcing data for a required data set?

First look for available specific data or measure it, and only then turn to generic approaches.

A common general preference order for sourcing an LCA-required data set is:

1. **Specific primary data**
   Directly measured or collected from the actual process, site, supplier, or product system being studied.

2. **Specific secondary data**
   Data for the same technology/process from reliable external sources, but not collected directly from the exact system.

3. **Average/generic secondary data**
   Industry-average, regional, or database data representing the relevant process.

4. **Proxy or estimated data**
   Data from similar processes, engineering calculations, stoichiometric estimates, or other approximations when better data are unavailable.

In practice, prefer data that are most:
- **Representative** of the actual system,
- **Recent**,
- **Geographically relevant**,
- **Technologically relevant**,
- **Complete and consistent**,
- **Transparent and well-documented**.

So, in short: **primary specific data first, then specific secondary data, then generic averages, and finally proxies/estimates.**

A general order of preference is to follow the availability and quality of the most appropriate data:

1. Prefer primary data and secondary data from actual suppliers/downstream actors where available and of sufficient quality.
2. Use third-party secondary data when they are of higher quality for the case.
3. Prefer well-documented, already reviewed (“pre-verified”) data sets.
4. Select secondary data sets based on strict data quality criteria: technological, geographical, and time-related representativeness, completeness, precision, and suitable reference flow/functional unit.

Cited sources:
- ILCD Handbook, p.126
- ILCD Handbook, p.189
- Hauschild et al., LCA Cookbook, p.1031

The general principle is to use “the availability and quality of the most appropriate data” as the guide. More specifically:

- Prefer primary data where available.
- If using secondary data, prefer data from the actual suppliers/downstream actors over other third-party secondary data.
- Use third-party generic/average data where these are of higher quality for the case.
- For background systems, use average technology as market consumption mix data.
- In general, working with well-documented and already reviewed data sets is recommended.

Citations:
- ILCD Handbook, p.126: “Secondary data of the actual suppliers / downstream actors should be preferred to other (third-party) secondary data…”
- ILCD Handbook, p.189: “Guiding principle should be the availability and quality of the most appropriate data. Working with well documented and already reviewed data sets is recommended.”


Question: Which phase of a life cycle assessment comes first, and what does it identify about the study?

The goal definition is the first phase of any LCA. It identifies, among other things, the decision-context(s) and intended application(s) of the study and names the targeted audience(s).

The **first phase** of a life cycle assessment (LCA) is **Goal and Scope Definition**.

It identifies:
- **Why** the study is being carried out
- **What** product system or process is being assessed
- **The functional unit**
- **System boundaries**
- **Assumptions, limitations, and intended audience/application**

In short, it defines the **purpose and extent of the study**.

The first phase is **Goal and scope definition**. It identifies key aspects of the study such as **the decision context, intended applications, intended audience**, and the **study object and its function**.

**Citations:**
- Hauschild et al., *LCA: Theory and Practice*, Glossary, **p.1191**
- European Commission – JRC, *ILCD Handbook: General Guide for LCA (2010)*, **p.11**

The first phase is **goal and scope definition**. It comes before the inventory, impact assessment, and interpretation phases, and it identifies key features of the study such as its **purpose, intended application, product alternatives, and scope of the analysis** (Hauschild et al., *LCA: Theory and Practice*, Glossary, p.1191; Guinée et al., *Handbook on LCA*, p.28).


Question: Below what water solubility threshold at 20°C must compounds be inventoried as particulate matter emissions to air?

Below a water solubility of 10 µg/litre at 20°C (i.e., poorly water-soluble compounds).

Compounds with **water solubility below 1 mg/L at 20°C** should be inventoried as **particulate matter emissions to air**.

Below 10 μg/L at 20 °C. Source: Hauschild et al., *LCA: Theory and Practice* — “37 LCA Cookbook,” p.1028.

Below **10 μg/litre (10 µg/L)** water solubility at **20°C**. Particulate matter emissions to air “shall include only poorly water-soluble compounds below a solubility in water at 20 °C of 10 μg/l, as far as feasible.”
**Citation:** Hauschild et al., *LCA: Theory and Practice*, **Chapter 37, p.1028**.


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

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

The source of uncertainty in LCA due to **lack of knowledge about the system** is typically called **epistemic uncertainty**.

In LCA, this can come from things like:
- missing or incomplete process data,
- unknown emission factors,
- poor understanding of causal relationships,
- model simplifications or assumptions.

It can be revealed or explored through:
- **sensitivity analysis**: test how results change when assumptions or uncertain inputs are varied,
- **uncertainty analysis**: e.g. Monte Carlo simulation if ranges/distributions are available,
- **scenario analysis**: compare alternative plausible model choices,
- **data quality assessment**: identify weak or poorly known parts of the system.

So, in short: **epistemic uncertainty** is the uncertainty caused by insufficient knowledge, and it is commonly revealed by **sensitivity/uncertainty analyses and scenario testing**.

The source is **ignorance**: “the error attributable to ignorance, i.e. the lack of knowledge about the system, leading to omission of data or incorrect assumptions about processes or elementary flows.” It **may be revealed by a qualified peer review**.
**Source:** *ILCD Handbook: General Guide for LCA (2010), Annex E: Addressing uncertainties in LCA, p.379*

The source is **ignorance**: “the error attributable to ignorance, i.e. the lack of knowledge about the system, leading to omission of data or incorrect assumptions about processes or elementary flows.” It “is not handled by quantitative uncertainty assessment, but may be revealed by a qualified peer review.”
**Citation:** *European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), Annex E, p. 379*


Question: In an LCA, what distinguishes the background system from the foreground system in terms of control over the processes involved?

The background system comprises processes operated as part of the system but not under the direct control or decisive influence of the producer, service operator, or user; for attributional modelling these are typically processes at tier-two suppliers and beyond, both upstream and downstream of the supply chain.

In LCA:

- **Foreground system** = the processes **directly modeled and controlled by the practitioner or decision-maker**. These are the specific activities, technologies, and process choices you can change or specify in the study.

- **Background system** = the processes that **support the foreground but are not directly controlled by the practitioner**. These usually come from generic databases, such as electricity supply, fuel production, material production, and transport services.

So the key distinction is **degree of control**:
**foreground = under direct influence/control**, **background = outside direct control and typically taken as given**.

The foreground system comprises processes that the decision-maker can often change or influence, either because they directly operate them or have power over them; the background system comprises processes that the decision-maker typically cannot structurally change, because they are only a minor customer or suppliers are anonymous/global market suppliers.

(Hauschild et al., LCA: Theory and Practice, Chapter 8, pp. 80–81)

In an LCA, the foreground system comprises processes that the decision-maker commissioning the study can often change or influence, either because they directly operate them or have power over them. By contrast, processes in the background system typically cannot be structurally changed by that decision-maker, because they are only a minor customer with limited power or because suppliers are anonymous, such as on global commodity markets. (Hauschild et al., *LCA: Theory and Practice*, Ch. 8, pp. 80–81)


Question: What are the four quality aspects illustrated in the figure showing completeness and representativeness?

Completeness and technological, geographical, and time-related representativeness.

The four quality aspects typically illustrated with **completeness** and **representativeness** in LCA data quality figures are:

1. **Completeness** – whether all relevant flows, processes, or impacts are included
2. **Representativeness** – how well the data reflects the actual system being studied
3. **Precision / Uncertainty** – how reliable or variable the data values are
4. **Consistency / Methodological appropriateness** – whether data and methods are applied uniformly and suitably

If you want, I can also explain how these relate to the common LCI/LCA data quality indicators.

The four quality aspects are:
- Completeness
- Technological representativeness
- Geographical representativeness
- Time-related representativeness

Cited in Figure 27: ILCD Handbook, Annex A, p.1999; also listed in the figure index on p.17 and discussed in section 12.2 on p.324.

The figure illustrates four quality aspects: **completeness**, **technological representativeness**, **geographical representativeness**, and **time-related representativeness** (European Commission – JRC, *ILCD Handbook: General Guide for LCA* (2010), ch. 12.2, p. 324; see also Figure 27 listing on p. 17).


Question: When co-products are not traded at the point of allocation with their specific characteristics, how should their market price be derived?

By combining production cost information with the market price of the further processed, packed, transported, etc. co-product, accounting for any additional steps of transport, conditioning, packaging, etc., so the economic value reflects the value of each co-product at the point and condition where it is delivered.

Use a **derived market price** that reflects the **specific characteristics of the co-product at the allocation point**.

In practice, if that exact co-product is **not traded** at that point:

1. **Start from the price of the nearest traded product** in the supply chain.
2. **Adjust that price** to make it representative of the co-product at the allocation point by accounting for:
   - **Further processing or upgrading**
   - **Transport and distribution**
   - **Storage**
   - **Handling/packaging**
   - **Quality differences** (e.g., moisture, purity, energy content)
   - Any other costs or value changes between the allocation point and the market point
3. If needed, work **backwards from the downstream market price** (netback approach), subtracting the above costs and margins.

So, the market price should be **estimated from a comparable traded product and corrected to the co-product’s condition and location at the point of allocation**.

When co-products are not traded at the point of allocation with their specific characteristics, their market price should be derived by adjusting the market price of the resulting sold product downward to exclude the additional processing steps after the allocation point (e.g. compression, purification, packaging). If they are not traded at all or only seldom traded externally, the market price should be approximated as a shadow price.

Cited sources:
- European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), chapter 7.9.3.2, p.80
- Hauschild et al., LCA: Theory and Practice, chapter 9, p.125

When co-products are not traded at the point of allocation with their specific characteristics, their market price should be derived by using the price of the further processed product and correcting it back to the allocation point:

- if the co-product is further processed internally, the market price of the sold product should be **adjusted downward for those additional processing steps** before being used as the allocation key (ILCD Handbook, p.80).
- if there is no direct market at that point, a **shadow price** should be calculated; e.g. subtract the cost of the additional processing needed to make it saleable from the market price of the processed product (Hauschild et al., ch. 9, p.125).

If prices are only partly missing, they can also be reconstructed from total costs minus the known proceeds of the other co-products (Guinée et al., p.149).


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

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

At the **endpoint** level, damage to **human health** is often expressed in **DALYs** (*disability-adjusted life years*).

At the **midpoint** level, **global warming potential** is typically expressed in **kg CO₂-equivalents**.

Damage to human health at the endpoint level is given as **DALY (Disability-Adjusted Life Years) [years]**; global warming potential at the midpoint level is given as **kg CO2-equivalents per kg of substance emitted**.

Cited: **Hauschild et al., LCA: Theory and Practice — Ch. 10 Life Cycle Impact Assessment, p.186**

Damage to human health at the endpoint level is given in **[years] expressed as DALY (Disability-Adjusted Life Years)**, while global warming potential at the midpoint level is given as **kg CO2-equivalents per kg of substance emitted**.
(Hauschild et al., *LCA: Theory and Practice*, Ch. 10, p.186)


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.

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.

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.

In a municipal waste incinerator, cadmium emissions in the flue gas could be allocated among the co-incinerated household waste materials according to their cadmium content in the waste streams.
Source: European Commission – JRC, ILCD Handbook: General Guide for LCA, chapter 7.9.3.1, p.265.


Question: What are two example LCA applications associated with Situation C1, "Accounting with interactions"?

Monitoring environmental impacts of a nation, industry sector, product group, or product; and policy information through basket-of-products (or product groups) type studies.

Two example LCA applications for **Situation C1: “Accounting with interactions”** are:

1. **Product environmental footprinting / corporate GHG accounting where co-products or recycling interactions must be allocated**
   - Example: assessing a biofuel, recycled material, or multi-output industrial process while accounting for interactions with other product systems.

2. **Consequential-type policy or decision support involving market-mediated substitutions or avoided burdens**
   - Example: evaluating waste recycling, energy recovery, or by-product use where one system affects another through displacement or substitution.

If you want, I can also explain what distinguishes **C1** from **A, B, or other C situations**.

Two example LCA applications associated with Situation C1, “Accounting with interactions,” are:

- Monitoring environmental impacts of a nation, industry sector, product group, or product
- Policy information: Basket-of-products (or -product groups) type studies

Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.47.

Two example LCA applications associated with Situation C1, “Accounting with interactions,” are:

- “Monitoring environmental impacts of a nation, industry sector, product group, or product” (European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010, p.47)
- “Policy information: Basket-of-products (or -product groups) type studies” (European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010, p.47)


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 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.

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 the year-0 “peak” transformation inventory is treated like a production inventory, so it is shared linearly over the following land uses. By contrast, emissions that continue over time are still linked to the transformation but also depend on the specific land use in each year—for example, soil organic carbon loss may be stopped by better land management—so they are considered operational emissions and are inventoried as and when they occur. This leads to a triangular allocation that gives a higher share to the land use directly following the transformation.
(ILCD Handbook: General Guide for LCA, p.236)

The ILCD further states that this triangle-shaped allocation “gives higher burdens to the first years after transformation,” motivated consequentially by the closer link to the decision to convert the land, and attributionally by recording what physically occurs during the period of land use.
(ILCD Handbook: General Guide for LCA, p.100)


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.

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**.

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)

The problem is that the sum becomes indeterminate: “how to sum up an unclear or unknown amount of lead emission to air with the same emission of another process that is known to be e.g. 0.00026 kg. Unknown kg plus 0.00026 kg = ?” (European Commission – JRC, ILCD Handbook: General Guide for LCA, chapter 7, p.209).


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

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

Because the nameplate “maximum power” is a capacity rating, not a measure of actual energy use in normal operation.

In LCA, what matters is usually electricity consumed over time, i.e.:

- Energy = power × operating time
- And the relevant power is the real load profile, not the theoretical peak

Why using the maximum is wrong:
- It assumes the equipment runs at full load all the time
- Most equipment operates at partial load, cycles on/off, idles, or has standby periods
- This can greatly overestimate electricity use and therefore environmental impacts
- The nameplate value is often for sizing electrical circuits and safety margins, not average use

Example:
- A machine rated at 10 kW may only draw 3–5 kW in normal use, and perhaps 0.5 kW when idle
- Treating it as 10 kW continuously would distort the inventory

Better LCA practice:
- Use measured consumption if possible
- Or use average operating load, duty cycle, utilization rate, and standby consumption
- Convert to kWh over the actual functional operating period

So the error is confusing maximum instantaneous demand with average real-world energy consumption.

Using the specified maximum power consumption as the average consumption is considered an error because it assumes the equipment runs all the time and typically at maximum load, which is usually not the case in real operation. Actual consumption should be based on measured operating data, including real running time and load.
— ILCD Handbook: General Guide for LCA (2010), p.203; see also p.65

Using the specified maximum power consumption as the average is considered an error because it assumes the equipment runs all the time and at maximum load, which is typically not true in real operation. This can misrepresent actual electricity use in the inventory. The ILCD Handbook notes that “the specified maximum power consumption (e.g. ‘10 kW’) is used, implicitly assuming to be the average consumption,” but “this does not consider that the equipment is not running all the time and that when it runs it typically is running not on maximum load” (European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010, p.203; see also p.65).


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 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**.

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


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

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

In consequential LCA, 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**.

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 modelling, especially for identifying and modelling large-scale consequences, experts should be involved in these domains:

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

Policy scenario experts are also recommended, and if policy scenarios are explicitly analysed, such experts should be involved regarding their role in setting constraints.

Citations: Hauschild et al., *LCA: Theory and Practice*, ch. 37 “LCA Cookbook”, p.1003; European Commission – JRC, *ILCD Handbook: General Guide for LCA* (2010), p.176.


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.

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.

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.

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

1. **Whether data are collected from specific sites versus published sources**
2. **Whether data are measured, calculated, or estimated**

These are named as additional descriptors that help define the sort of data required.
**Source:** Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards*, **p.491**.


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.

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.

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

The interpretation phase involves three evaluations:

- **Completeness check**
- **Sensitivity check** (often together with scenario analysis and potentially uncertainty analysis)
- **Consistency check**

Cited in:
- **European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.288**
- Also consistent with **Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p.450** and **Hauschild et al., LCA: Theory and Practice, Chapter 12, p.323**


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.

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.

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.”

A reference flow represents the quantified physical output/flow of a specific product system that is required to fulfil the function defined by the functional unit. Guinée et al. define it as “a measure of the outputs from processes in a given product system which are required to fulfil the function expressed by the functional unit” and also as a “quantified flow … representing one way … of obtaining the functional unit” (Guinée et al., *Handbook on LCA*, ch. 2, p. 37; p. 475).

Hauschild et al. express the same distinction as: the functional unit is the quantified description of the primary function, while the reference flow is “the physical flow required for the system to fulfil its function” (Hauschild et al., *LCA: Theory and Practice*, ch. 35, p. 896).


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 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.

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 operated elsewhere when the other process has sufficiently similar production conditions, enterprise/process/material characteristics, and technology. The data quality guidance distinguishes, in descending representativeness, data from:

- “area with similar production conditions,”
- “processes and materials under study but from different enterprises,” and
- “related processes or materials but with same technology.”

This implies representativeness is acceptable when the operated process elsewhere is similar in geography/production conditions and especially in technical characteristics and technology.
(Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards, 2002, p.5)

Also, LCA data should be representative of the actual processes used in the analysed product system; primary data are by definition representative only of “the specific process occurring at the time that the data was collected,” so extension to another process requires judging how representative the chosen data are for that other process.
(Hauschild et al., LCA: Theory and Practice, ch. 8, p.105)


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

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

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

KEPI stands for **Key Environmental Performance Indicators** in the context of Ecodesign and simplified LCA. (European Commission – JRC, *ILCD Handbook: General Guide for LCA* (2010), p.30; p.56)


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, 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.

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, 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” (ILCD Handbook, ch. 7.2.4, p.183; also Hauschild et al., 37 LCA Cookbook, p.1011).


Question: In which city is PRé Consultants located?

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

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


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 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.

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 a comparative LCA study, the interpretation phase must discuss “the appropriateness of generic LCIA methods.” If “a further differentiation can be argued or approximated to lead to significantly different results,” then “this finding 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 ones and discussed jointly. (Hauschild et al., *LCA: Theory and Practice*, Ch. 37 “LCA Cookbook,” p. 993; see also European Commission – JRC, *ILCD Handbook*, p. 113)


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.

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.

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 part of the scope definition, so that the required information on the relevant elementary flows can be identified before the inventory analysis. The ILCD Handbook notes that this may sometimes only become clear after the first or second iteration of LCI data collection, modelling, impact assessment, and interpretation. (European Commission – JRC, ILCD Handbook: General Guide for LCA, chapter 6.7.2 context, p.111)


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?

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.

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)

Conservative or worst-case estimates may be used to test robustness, but they “must however not stay in the final process or system model.” (European Commission – JRC, ILCD Handbook: General Guide for LCA, p.209, chapter 7.4.2.11.3)


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**, 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**.

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**, as part of the **evaluation step**, specifically the **consistency check**.

Citations:
- **“The actually achieved consistency is to be checked as part of the evaluation step in the interpretation phase”** (European Commission – JRC, *ILCD Handbook: General Guide for LCA* (2010), **p. 52**).
- **“consistency check * a step of the Interpretation phase*”** (Guinée et al., *Handbook on LCA* (2002), **p. 110**).


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 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**.

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, **its relevance and significance for the conclusions, recommendations, and intended application must be evaluated** (i.e., whether and how it affects the study’s usability and interpretation) — **European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), chapter 9.4, p.300–301**.

Supporting wording:
- “the final element of the interpretation is to draw conclusions and identify limitations of the LCA” — **p.300**
- “The most frequently outstanding limitations of being able to draw significant conclusions…” and “Any limitations of the study within the given goal and scope of the LCA study must be listed” — **p.301**


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.

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**.

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)

When an LCI or LCA study addresses a part within a larger system, **the technical interaction between the analysed part and the larger system and its other parts must be explicitly considered in the system boundary definition**. This is because such parts typically cannot be analysed in isolation, especially if compared with other parts that interact differently with the larger system.
**(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.101)**


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 **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.

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 car starter batteries of substantially different weight, you should consider knock-on effects on:

- the **battery mount**
- the **wiring**
- the **total car weight**, which in turn affects the car’s **acceleration properties**

This part-system relationship should be taken into account, especially when comparing parts with different interactions with the system or analysing improvement options.
**(European Commission – JRC, ILCD Handbook: General Guide for LCA, p.155)**


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 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.

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 readily available third-party background data can undermine LCA results because, if those data are used “not checking for quality or data gaps,” this “contribute[s] to the lack of quality of the results and robustness of conclusions.” The ILCD Handbook therefore recommends anticipating the need to collect or obtain high-quality data also for key background processes (European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010, Chapter 7, p.188).

A related problem is that secondary/background data must be checked for methodological appropriateness and consistency, as well as representativeness, completeness, and precision; otherwise, using inconsistent data from different systems can make the whole LCI/LCA study “unreliable and distorted, often with wrong conclusion and recommendations drawn” (European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010, p.247).


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).

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**.

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

At least **100,000 years** (set by convention) for the storage form to be inventoried as having no emissions. **(ILCD Handbook, p.229)**


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.

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.

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.

Besides LCA expertise, the ILCD guidance says identifying detailed consequences and marginal processes in a consequential model may require expertise in:

- 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

It also notes that policy scenario experts are recommended when policy scenarios are relevant or explicitly analysed.
(Source: ILCD Handbook, Ch. 7.2.4 “Identifying processes in consequential modelling”, pp. 165–177; especially pp. 165, 176)


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

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.

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”).

Existing benefits outside the analysed system are considered in **Situation C1**.

Cited source: **ILCD Handbook, p.86**: “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** …”


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 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.

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 describing the analysed system, the passage recommends providing **use stage information**. For **consumer products**, this should include **surveys and studies on average or typical user behaviour** to complement **product specifications and user manuals**; **information from product category rules (PCR)** can also support this.
(Hauschild et al., *37 LCA Cookbook*, p.1022)


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

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.

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 categories most commonly omitted without sufficient justification are **services** and **investment goods**.
(Source: European Commission – JRC, *ILCD Handbook: General Guide for LCA* (2010), **chapter 7.2.4, p.162**)


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.

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.

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)

If a process turns out to be 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** (ILCD Handbook, p.190). More specifically, one should check **whether the process can be split by subdivision and whether that would ease review, improve accuracy and applicability, and avoid multifunctionality** (ILCD Handbook, p.192).

The handbook also states that subdivision can help **avoid allocation**, by identifying physically distinguishable sub-processes and collecting inventory data only for those included processes that provide the required functional output (ILCD Handbook, p.256). In addition, avoiding black box processes improves **quality control and review**, because inventories no longer combine several processes or a whole site in one “black box,” and it helps avoid the distorted/incorrect results that often arise when allocation is performed on black box unit processes (ILCD Handbook, p.76).


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

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

For product flows 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.

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)

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.” (ILCD Handbook, ch. 7.2.4.7, p. 175)


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

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

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**.

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).

Some aspects that distinguish a niche market from the main market are:

- price, such as investment cost or life cycle cost / total cost of ownership
- life-style and value-system related issues, e.g. “locally produced,” “bio-based,” “recycled,” “recyclable,” “ecolabelled,” as well as social-image aspects like “fair-traded”
- high quality, durability / longevity
- practicality and/or time-saving

(ILCD Handbook: General Guide for LCA, p.33, Chapter 5 “Goal definition – identifying purpose and target audience”)


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.

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.

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

In long-term marginal LCI modelling, extra supply and demand is represented as a mix because in real, non-monopolised markets there is “never only one marginal process / supplier,” so the model uses a “mix of ‘long-term marginal’ processes / systems” for the relevant time and market (European Commission – JRC, ILCD Handbook, p.166).

A single marginal process is only justified if “there are no other, similarly cost-competitive processes or systems” (European Commission – JRC, ILCD Handbook, p.172).

Hauschild et al. add that identifying the correct long-term marginal process is often difficult, so it is pragmatically recommended to use “a mix of possible long-term marginal processes, rather than actual long-term marginal processes” (Hauschild et al., LCA: Theory and Practice, Ch. 8, p.99). They also note that “often it is difficult to identify a single long-term marginal process, which is why a mix of potential long-term marginal processes is often used” (Hauschild et al., Ch. 8, p.97).


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

Allocation is the third, alternative solution.

In the ILCD Handbook’s 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).

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 hierarchy, after subdivision / virtual subdivision and the other preceding options are not possible or feasible, the third, alternative solution is **allocation (partitioning)**.

Citation: **ILCD Handbook, chapter 7.9.2, p.255** (“If this is principally impossible or other reasons make it practically impossible, allocation (partitioning) is the next possible step”).


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

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

In a descriptive, accounting/monitoring study, the LCI model represents the system as it 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.

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


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.

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.**

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**.

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**. The ILCD Handbook states that, in attributional modelling of recycling, one of the main questions is: “**Where to draw the system boundary between the first and subsequent life cycles?**” (European Commission – JRC, *ILCD Handbook: General Guide for LCA*, 2010, **p.349**, ch. 14.4.1.1).


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.

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.**

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)

Actual collection of inventory data is typically only required for the foreground system when the background system is not expected to be structurally changed by the decision-maker; the foreground/background distinction is “especially useful for planning data collection for the inventory analysis” because background processes are usually outside the commissioner’s influence and are therefore typically modelled from other data sources rather than newly collected first-hand data (Hauschild et al., *LCA: Theory and Practice*, Chapter 8 “Scope Definition”, p.81).

This is consistent with the statement that “for foreground processes specific inventory data should be used,” typically as primary data from the product/technology developer, producer, or service operator (European Commission – JRC, *ILCD Handbook: General Guide for LCA* (2010), p.184).