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

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

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

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

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

The product receives a credit of **1 kg avoided disposal** (“**−1 kg**” avoided disposal) when the additional demand diverts 1 kg of secondary material from landfill.
**Citation:** European Commission – JRC, *ILCD Handbook: General Guide for LCA* (2010), **p.355**.


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

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

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

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

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

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

Using not fully technologically representative data is justifiable for LCI and LCIA data sets or non-comparative LCI/LCA studies only if using it “is not relevantly changing the overall LCIA results compared to using fully representative data”; otherwise, the lower achieved representativeness must be documented in the data set/report. (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.127)


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

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

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

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

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

The starting point is **the central process and the amount(s) of the system’s functional unit(s) or reference flow(s)**, from which all other processes are stepwise, relatively scaled. **(Hauschild et al., *LCA: Theory and Practice*, Chap. 37 “LCA Cookbook”, p. 1032)**


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

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

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

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

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

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

The two sub-types are:

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

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

The two sub-types are:

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

Source: **ILCD Handbook, p.59** (“I.a.i) Unit process study and/or data set, with two sub-types: I.a.i.1) Single operation unit process … I.a.i.2) Black box unit process …”).


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

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

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

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

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

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

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

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

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

(ILCD Handbook, p.129)


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

Some revisions will typically need to be made.

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

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

When the required inventory data quality cannot be met because data or resources are lacking, it typically becomes necessary to apply a cut-off and explicitly address it as a data availability problem: “If data are lacking, there is a cut-off problem and this should be duly addressed” (Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards, p. 502).


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

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

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

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

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

(ILCD Handbook, p.260)

Parallel services are services that “serve at the same time in parallel several co-products and that relate to the co-products in a similar way.” One example given is “storage facilities” (also transport equipment, manufacturing halls, and production equipment are listed). (European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010, p.260)


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

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

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

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

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

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

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

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

In such comparative studies, the results and recommendations must be presented **with an explicit statement that comparability is not assumed per se, but lies with the individual preference and judgement**. Relevant limitations should also be given jointly with the recommendations to avoid misinterpretation.
— **ILCD Handbook, Chapter 9 “Life cycle interpretation,” p. 304**; see also **p. 63**.


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

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

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

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

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

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

When alternatives differ only in specific parts of otherwise identical or similar systems, and the sole purpose is to identify which has the lowest environmental impact, the identical parts “can be left out when drawing the system boundaries.” The benefit is that this “can drastically reduce the effort for the LCA study” (European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010, p. 144, ch. 6.10.6).

Hauschild et al. state similarly that “in comparative studies it is justified to exclude identical processes if they deliver identical quantities of services” (LCA: Theory and Practice, Ch. 8 Scope Definition, p. 102).


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

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

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

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

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

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

- time-related representativeness
- geographical representativeness
- technological representativeness

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

In life cycle interpretation, consistency of inventory data representativeness should be checked for the three interrelated aspects of **geographical, time-related, and technological representativeness** (Hauschild et al., *LCA: Theory and Practice*, Chap. 8, p.106).


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

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

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

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

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

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

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

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

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

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

Cited in:
- **Chapter 7.9.3, p.271**: “the market value of the pre-treated waste / End-of-life product is below zero and that hence the **absolute value of its (negative) market price** should be used when calculating the allocation key”
- **Annex C / 14.4, p.354**: “the market value of the pre-treated waste / end-of-life product … is below zero and that hence the **absolute value of its (negative) market price** shall be used when allocating between the first and second life cycle.”


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

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

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

So the market value used for allocation is:

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

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

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

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


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

The "short-term marginal" processes or systems.

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

So the affected units are typically:

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

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

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

When the effect size is classified as **“small,”** it is understood to **affect only the extent of operation of one or more existing processes**; therefore, **the short-term marginal process(es)** are identified as the affected/superseded processes (European Commission – JRC, *ILCD Handbook: General Guide for LCA*, ch. 7, p. 178).

Relatedly, in **Situation A / micro-level decision support**, such small effects mean there are **no or only small-scale consequences in the background system or on other systems** (European Commission – JRC, *ILCD Handbook: General Guide for LCA*, ch. 5, p. 46).


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

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

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

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

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

Under the ILCD allocation provisions, the allocation formula must **additionally consider the amount of each co-function provided by the multifunctional process/system**, i.e. not only the criterion itself but also the quantitative output of the co-functions when solving multifunctionality by allocation.
(ILCD Handbook, chapter 7.9.3, p.257)


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

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

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

Detailed and differentiated provisions are still needed because they:

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

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

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

Detailed and differentiated method provisions are still necessary because the simplifications “draw on” them and because there are “a few cases” where the detailed provisions must be “fully applied”; in those cases, they are “essential,” so they need to be retained even if simplified provisions are used in most studies. The simplified provisions reduce effort “while not relevantly harming the accuracy or robustness of the results,” but they depend on the underlying detailed method. (European Commission – JRC, *ILCD Handbook: General Guide for LCA* (2010), p.8)

Guinée et al. add that simplifications such as omitting economic mechanisms or spatial detail make LCA easier, but they “reduce the quality of the analysis results,” which is why a detailed LCA is kept as a reference standard. (Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards* (2002), p.414)


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

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

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

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

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

The two techniques are subdivision and virtual subdivision.

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

The two techniques are **subdivision** and **virtual subdivision**.

- “**Avoiding black box unit processes by subdivision and virtual subdivision**” (European Commission – JRC, *ILCD Handbook: General Guide for LCA (2010)*, **p.10**).
- It “shall be aimed at avoiding black box unit processes … by **subdivision or virtual subdivision**” (European Commission – JRC, *ILCD Handbook: General Guide for LCA (2010)*, **p.88**).


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

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

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

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

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

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

A default period of **20 years** shall be assumed, representing the time until **about 90% of the main CO2 losses or binding** in soil organic carbon toward the new land-use equilibrium have occurred.
**Citation:** ILCD Handbook, chapter 7 Life Cycle Inventory analysis, **p.100** (European Commission – JRC, 2010).


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

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

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

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

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

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

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

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

No relevant other flows should cross that boundary, as far as possible. The ILCD states: “Next to the reference flow(s) that provide the functional unit(s) and permissible waste flows … no relevant other flows shall cross the boundary between the analysed system(s) and the rest of the technosphere” (European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010, p.105).


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

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

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

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

The technique is **subdivision** (and, where needed, **virtual subdivision**).

The ILCD Handbook explicitly names chapter **“7.4.2.2 Avoiding black box unit processes by subdivision and virtual subdivision”** and states that **“It shall be aimed at avoiding black box unit processes … by subdivision or virtual subdivision”** and that if a process turns out to be a black box unit process, **“one should check whether it can be split by subdivision before data collection or virtual subdivision afterwards.”**
**(European Commission – JRC, ILCD Handbook: General Guide for LCA, chapter 7.4.2.2, pp. 190, 192; see also p. 10)**


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

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

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

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

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

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

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


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

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

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

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

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

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

In attributional modelling:

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

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

In attributional modelling:

- **Foreground system:** use **site- or producer/provider-specific data**; for links from foreground to background, use **supplier-specific data**. Generic or average data can be used in parts of the foreground only if they are **more accurate, precise, and complete** than the available specific data.
- **Background system:** use **average market consumption mix data**.

Citations: ILCD Handbook, p.131; see also p.124.


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

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

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

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

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

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

The preferable option is **actual data collection** for the included unit processes. An alternative basis is **using knowledge about the involved processes** to split the multifunctional process data and assign inventory items to the included unit processes, e.g. knowing that certain emissions or inputs can only come from specific subprocesses.
**(ILCD Handbook, Chapter 7, p.193)**


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

The Système international d'unités (SI) units.

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

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

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

By default, results in an LCI or LCA study shall be reported using **SI units (Système international d’unités)**.
Citations: **ILCD Handbook, p.308**; also stated for LCI reporting on **p.204**.


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

Collecting data, modelling the system, and calculating results.

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

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

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

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

The chapter identifies three main activities as part of life cycle inventory analysis: **collecting data, modelling the system, and calculating results** (European Commission – JRC, *ILCD Handbook: General Guide for LCA* (2010), Chapter 7, p.153; table of contents shown on p.9).


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

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

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

In practice, this means:

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

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

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

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

When an impact category is excluded via cut-off rules, the omission **must be quantitatively justified as insignificant for the overall environmental impact**, considering **the goal definition, especially the intended applications, and the cut-off defined for the LCI/LCA study**. Any relevant exclusion must also be **explicitly considered during interpretation**, as it may limit conclusions and recommendations.
**(European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010, p.110)**


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

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

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

The provided context does not state who the editors were for **ecoinvent Report No. 1, *Overview and Methodology for the ecoinvent database v. 2.0*** (Swiss Centre for Life Cycle Inventories, 2007), so I can’t answer from the supplied sources.

Relevant citation showing only the report series, not the editors: **Hauschild et al., *LCA: Theory and Practice*, Ch. 29, p.751**.


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

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

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

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

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

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

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

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

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

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

The main reasons for the forming of markets are:

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

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

The main reasons for the forming of markets in LCA are:

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

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


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

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

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

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

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

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

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

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

“Accounting” is a **purely descriptive documentation** of the life cycle of the system under analysis (e.g. product, sector, country), **without interest in additional consequences on other parts of the economy**. In contrast, **meso/macro-level decision support** is **strategic-level life cycle based decision support** (e.g. raw material strategies, technology scenarios, policy options) where the decisions are assumed to have **structural consequences outside the decision context**, i.e. they are supposed to **change available production capacity**.
(Source: **ILCD Handbook: General Guide for LCA, p.6**)


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

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

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

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

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

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

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

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


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

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

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

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

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

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

The typical value range is **10 to 0.00001** for normalised impacts of goods and services, improving communication and interpretation.
**Source:** Hauschild et al., *LCA: Theory and Practice*, **Chapter 37 “LCA Cookbook,” p. 994**.


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

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

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

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

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

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

Source 1, p.391.

The 2008 Guide to PAS 2050 was produced by **BSI British Standards, with DEFRA and the Carbon Trust**.
**Citation:** European Commission – JRC, *ILCD Handbook: General Guide for LCA (2010)*, **p. 391**.


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

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

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

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

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

For filling data gaps, **data sets that increase the overall quality of the final inventory** shall be used; the individual data or data set’s quality shall be at least the **“Data estimate” quality level**.
**Methodologically not fully consistent data** may be used instead **if they are sufficiently consistent**; this mainly refers to **LCI data sets for background use**.
(ILCD Handbook, chapter 7, p.254; p.207)


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

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

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

In practice:

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

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

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

When the **LCI data set is intended to be used in subsequent comparisons**. In that case, modelling of assumption scenarios changes from being **recommended only** to a **“shall” requirement**.
**Citation:** ILCD Handbook, **chapter 6.5.4.4, p.86**.


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

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

Under the ILCD approach, the data quality indicators for classifying the achieved quality of LCI data sets are:

- technological representativeness
- geographical representativeness
- time-related representativeness
- completeness of environmental impacts covered by the inventory
- achieved precision of the data
- appropriate and consistent application of LCI methodologies

This is stated in ILCD Handbook, Annex A, Chapter 12.3 “Data quality indicators” (p.329): “The ILCD data quality indicators relate directly to those key characteristics of LCI data sets that describe their quality… These are: technological, geographical and time-related representativeness, completeness … precision … and appropriate and consistent application of LCI methodologies.”

A matching summary is also given in Chapter 6.8.1 (p.122), where LCI data quality is structured by representativeness, completeness, precision/uncertainty, and methodological appropriateness and consistency.


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

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

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

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

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

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

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

The secondary good is 0.28 kWh of electricity generated from incinerating the 0.2 kg plastic waste and fed into the grid, and the full 0.28 kWh of primary-produced electricity is credited. (ILCD Handbook, p.360, Source 1)


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

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

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

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

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

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

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

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

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

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

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

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

1. **Function provided (what)**
2. **Quantity (how much)**
3. **Duration (how long)**
4. **Quality (in what way and how well the function is provided)**

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


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

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

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

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

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

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

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

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

The sensitivity check in an LCA study is used “to assess the reliability of the final results and – if included – of the conclusions and recommendations of the LCA study.” It is applied in the interpretation step together with information on uncertainties in inventory data, impact assessment data, and methodological assumptions and choices. The quantitative methods that support it are “scenario analysis and uncertainty calculations.” (European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010, chapter 9.3.3, p.295)


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

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

Use **100%** as the cap.

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

Use **5%** as the cap.

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

Use **5% as the cap**.

Source: **European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.42**: “if that percentage is over **5 %**, **5 %** should be assumed instead.”


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

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

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

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

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

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

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

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


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

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

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

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

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

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

When a unit process inventory item has **missing qualitative information**, the respective flow should be **created and used in the regular inventory only if it is a product or waste flow** (European Commission – JRC, *ILCD Handbook: General Guide for LCA* (2010), ch. 7.4.2.11.3, p. 213; see also p. 209).


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

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

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

Key reasons:

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

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

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

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

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

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

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

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

When developing **unit process data sets as deliverables**, the **calculation of LCI results is required** because it is used, **together with subsequent characterisation, to quantify the overall completeness and to approximate the overall uncertainty of the data set per impact category**.
(Source: **European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), chapter 7.8 / p.273**)


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

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

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

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

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

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

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

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

It must be **clearly stated in the data set and any accompanying documentation**, including **what the data set represents** and any **lack of representativeness** from combining different sources.
**Citation:** European Commission – JRC, *ILCD Handbook: General Guide for LCA* (2010), **p. 195**.


Question: Which types of activities should in principle be inventoried in a system, depending on the included life cycle stages and the system boundary?

Activities such as mining, processing, manufacturing, use, repair and maintenance, transport, waste treatment, and other purchased services linked to the analysed system.

In principle, you should inventory all activities that are part of the product system within the chosen life cycle stages and system boundary. That typically means:

- **Raw material acquisition and preprocessing**
  - extraction, harvesting, refining, intermediate material production

- **Manufacturing and assembly**
  - component production, processing, packaging, plant operations

- **Transport and distribution**
  - all relevant freight, storage, retail-related logistics if included

- **Use stage**
  - operation, maintenance, repair, replacement, consumables, energy/water use

- **End-of-life**
  - collection, sorting, reuse, recycling, recovery, incineration, landfill, waste transport

Also include supporting activities if they are inside the boundary, such as:

- **Energy supply**
  - electricity, fuels, heat, steam, compressed air

- **Auxiliary materials and services**
  - lubricants, solvents, cleaning agents, treatment chemicals

- **Waste and emission management**
  - wastewater treatment, off-gas treatment, solid waste handling

- **Capital goods/infrastructure**
  - machinery, buildings, roads, vehicles, if the study includes them

So the general rule is: **inventory every unit process that significantly contributes to delivering the functional unit and that lies within the defined boundary**, including foreground and relevant background processes.

In principle, all processes operated as a consequence of the analysed system/decision should be inventoried, depending on the included life cycle stages and the system boundary. This includes activities such as mining, processing, manufacturing, use, repair and maintenance, transport, waste treatment, and other purchased services such as cleaning and legal services, marketing, production and decommissioning of capital goods, operation of premises (retail, storage, administration offices), staff commuting, and business travel.

Cited:
- ILCD Handbook: General Guide for LCA, Chapter 7 “Life Cycle Inventory analysis - collecting data, modelling the system, calculating results,” p.155
- Hauschild et al., LCA: Theory and Practice, Chapter 37 “LCA Cookbook,” p.1008

In principle, all processes operated as a consequence of the analysed system/decision should be inventoried, depending on the system boundary and included life cycle stages. This includes activities such as mining, processing, manufacturing, use, repair and maintenance, transport, waste treatment, and other purchased services, e.g. cleaning and legal services, marketing, production and decommissioning of capital goods, operation of premises (retail, storage, administration offices), staff commuting, and business travel.
(ILCD Handbook, ch. 7, p. 155; Hauschild et al., *LCA Cookbook*, ch. 37, p. 1008)


Question: When describing a modelled unit process in more detail, what types of representativeness and quantitative information should be included?

Information on its actual technological, geographical, and time-related representativeness, and especially the functional unit(s) and reference flow(s), along with other quantitative and qualitative information.

When documenting a modelled unit process in more detail, include both **representativeness information** and **key quantitative descriptors**.

## Representativeness to report
Describe how well the process reflects reality in terms of:

- **Technological representativeness**
  What technology, equipment, process route, or operating conditions are modelled.

- **Geographical representativeness**
  Country, region, site conditions, grid mix, transport context, climate, etc.

- **Temporal representativeness**
  Reference year, period of data collection, and whether the data reflect current, historical, or projected conditions.

Sometimes it is also useful to mention:
- **Scale representativeness**: lab, pilot, industrial scale
- **Market/sector representativeness**: typical plant, best available technology, industry average, supplier-specific process

## Quantitative information to include
Provide the main numerical characteristics of the process, such as:

- **Amount of product/service represented by the unit process**
- **Reference flow and functional basis**
- **Input and output quantities**
  Materials, energy, water, emissions, waste, co-products

- **Process yields, efficiencies, and losses**
- **Capacity or throughput**
- **Operating conditions**, where relevant
  e.g. temperature, pressure, load factor, runtime

- **Transport distances and modes**, if included in the process
- **Allocation parameters**, if multi-output
  e.g. mass, energy, economic shares

- **Data coverage and uncertainty indicators**, if available
  e.g. share of measured vs estimated data, ranges, standard deviations, pedigree-type quality indicators

In short: document **how representative the process is technologically, geographically, and temporally**, and provide the **main numerical parameters that define the process and its exchanges**.

When describing a modelled unit process in more detail, it should include its actual technological, geographical, and time-related representativeness, and especially the functional unit(s) and reference flow(s), along with other quantitative and qualitative information.
(Source: European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010, p.195, chapter 7.4.2.3)

When describing a modelled unit process in more detail, the description should include its:

- **technological representativeness**
- **geographical representativeness**
- **time-related representativeness**
- **especially the functional unit(s) and reference flow(s)**
- **other quantitative and qualitative information**

This detailed description supports inventory data collection and quality control.
**Source:** ILCD Handbook, **chapter 7.4.2.3**, **p. 195**.


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

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

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

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

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

For materials and other application-unspecific products, **the reference flow should be provided instead, including the main technical specification of the product**. In that case, **the reference flow is also the declared unit, but not the functional unit** (Hauschild et al., *LCA: Theory and Practice*, ch. 37 “LCA Cookbook”, p. 975).

The ILCD Handbook states similarly that for **application-unspecific products**, one should use **the reference flow as “declared unit” plus product specification instead of a functional unit** (European Commission – JRC, *ILCD Handbook: General Guide for LCA*, p. 65).


Question: In an LCA intended to compare two or more products, what is the consequence of not basing the comparison on a valid functional unit that reflects the service provided?

Seriously misleading results may occur, because the functional unit must give an unambiguous definition of the service or function the products provide, and the reference flows must be based on it.

If the comparison is not based on a valid functional unit that reflects the actual service provided, the LCA results become **not meaningfully comparable**.

In practice, this means:

- you may compare **different amounts or qualities of service** rather than equivalent performance,
- the conclusion can be **misleading or biased**,
- one product may appear environmentally better **only because the basis of comparison is wrong**, not because it truly has lower impacts.

So the main consequence is an **invalid comparison** and potentially **wrong decision-making**.

Not basing the comparison on a valid functional unit that unambiguously reflects the service provided can lead to seriously misleading results, because the reference flows may not correctly represent the compared products’ functions.

Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.366.

Not basing the comparison on a valid functional unit that unambiguously reflects the service provided can lead to “seriously misleading results,” because the reference flows may then not correctly represent the compared products’ functions.
(European Commission – JRC, *ILCD Handbook: General Guide for LCA* (2010), p. 366)


Question: When applying the LCI modelling and method approaches, which aspects differ between Situations A, B, and C, given that the inventory of a unit process is basically identical across them?

What differs is which processes are within the system boundary—especially in the background system—and how the processes are combined to represent the life cycle model and how multifunctionality is solved.

In ecoinvent-style LCI modelling, the **unit process inventory itself is largely the same** across **Situations A, B, and C**. What changes is mainly **how exchanges are interpreted and linked in the product system**, not the physical inputs/outputs recorded at the process level.

The key differences are:

- **Market/reaction to demand**
  - **Situation A**: no strong market consequences considered; attributional framing.
  - **Situation B**: small-scale, marginal change; consequential framing with marginal suppliers affected.
  - **Situation C**: large-scale or constrained change; consequential modelling may include capacity changes, constraints, or market shifts.

- **Choice of suppliers / system linking**
  - **A** uses average/representative suppliers.
  - **B** uses marginal suppliers expected to respond to a small change.
  - **C** may require broader market modelling, affected technologies, or structural changes.

- **Multifunctionality treatment**
  - **A** typically applies allocation or substitution according to attributional rules.
  - **B/C** more often use system expansion/substitution based on consequences of changed demand.

- **Interpretation of by-products, waste, and recycling**
  - Same flows may be classified differently depending on whether the modelling is attributional or consequential and whether the material is constrained, burden-free, avoided burden, etc.

So, although the **foreground unit process inventory is basically identical**, **the modelling approach, linking logic, supplier selection, and multifunctionality/recycling treatment differ between A, B, and C**.

Between Situations A, B, and C, what differs is:

- which processes are included within the system boundary, especially in the background system,
- how the processes are combined/connected to represent the life cycle model, and
- how multifunctionality is solved.

The unit process inventory itself is basically identical, apart from some additional required information in specific situations (e.g. market size, amount of products involved).

Citations:
- ILCD Handbook, p.87, chapter on scope definition / LCI modelling and method approaches
- ILCD Handbook, p.8, section 2.2.3 “Overview of differences in the provisions for the Situations A, B, and C”

The inventory of a unit process is “basically identical” across Situations A, B, and C; what differs is:

- **Which processes are included in the system boundary**, “especially in the background system”
- **How the processes are combined/connected to represent the life cycle model**
- **How multifunctionality is solved**
- In some cases, **additional situation-specific information** is needed, e.g. **market size** or “the amount of products involved and the size of the respective market”

This is stated in:
- **ILCD Handbook, p.87**: “What differs is which processes are within the system boundary, especially in the background system … and how the processes are combined to represent the life cycle model and how multifunctionality is solved.”
- **ILCD Handbook, p.8**: “The main difference between Situation A, B and C lies hence in the selection of the processes that are included in the system boundary and how the life cycle is modelled by connecting them.”


Question: At which process step can burdens plausibly be allocated to secondary goods, and why?

Burdens can plausibly be allocated only at the process step where a valuable secondary good is produced (Pn), because a waste cannot itself carry any treatment burden and it is inappropriate to attribute all preceding waste treatment processes to the eventually produced secondary good.

A plausible point is **after the process step where the secondary good becomes a distinct, usable output with a market or technical function**—often called the **point of separation** or **split-off point**.

Why:
- **Before that step**, the inputs and emissions belong to the joint production process and cannot meaningfully be attributed to one output versus another.
- **At or after that step**, the secondary good is no longer just waste/residue within the system; it is a **co-product or by-product** with identifiable value or function.
- This makes allocation defensible because the product relationship is now clear, and burdens can be shared using a rule such as **mass, energy, economic value, or other physical causality**.

In short: burdens can plausibly be allocated **from the stage where the secondary good is intentionally recovered and becomes a valuable output**, because only then does it make sense to treat it as bearing part of the environmental load.

Burdens can plausibly be allocated to secondary goods only at the process step where a valuable secondary good is produced, i.e. process step Pn. The reason is that the waste or end-of-life product is generated by the first system, a waste per se cannot carry any treatment burden, and it is considered inappropriate to attribute all preceding waste treatment processes to the eventually produced secondary good.

Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.373.

Burdens can plausibly be allocated to secondary goods only at the process step where a valuable secondary good is actually produced, i.e. at process step **Pn**. The reason is that the waste or end-of-life product is generated by the first system, and “a waste can per se not carry any burden of treatment”; moreover, it is considered inappropriate to attribute all preceding waste treatment processes to the eventually produced secondary good. Therefore, modelling should first cover the waste/end-of-life treatment until the treated waste crosses the “zero market value” border, and only then apply the allocation procedure at that step.
**(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.373)**


Question: Which section of the document covers solving multifunctionality of processes in attributional modelling?

It’s covered in the section on **multifunctionality / allocation in attributional modelling** — typically titled something like:

- **“Solving multifunctionality in attributional modelling”**
- or **“Allocation and multifunctional processes”**

If you want, send me the document title or table of contents and I can point to the exact section number.

European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), chapter 7.4.2.2, p.75.

The section is in the **ILCD Handbook, chapter 7 “Life Cycle Inventory analysis”**, specifically the subsection **“Solving multifunctionality”** for attributional modelling, cited at **p. 255**.


Question: What three elements must a goal definition clearly state to avoid misleading results interpretation in an LCA study?

The comparative character of the study (including any assertions about environmental superiority or equality intended for publication), the reasons for carrying out the study including the decision-context, and who commissioned the study.

To avoid misleading interpretation in an LCA, the **goal definition** should clearly state at least these three elements:

1. **Intended application**
   What the study will be used for.

2. **Reasons for carrying out the study**
   Why the assessment is being done.

3. **Intended audience / whether results will be used in comparative assertions disclosed publicly**
   Who will use the results, and especially whether they will support public comparisons between products.

These are core ISO 14040/14044 goal-definition requirements and help ensure the results are interpreted in the right context.

To avoid misleading results interpretation, the goal definition must be very clear on these three elements:

1. The comparative character of the LCA study, including whether assertions of environmental superiority or equality are to be published
2. The reasons for carrying out the study, including the decision-context
3. Who commissioned the study

Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p. 365.

To avoid misleading results interpretation, the goal definition must be very clear on these three elements:

1. **The comparative character of the LCA study**, including whether assertions about environmental superiority or equality are made and intended for publication.
2. **The reasons for carrying out the study, including the decision-context**.
3. **Who commissioned the study**.

Source: **European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.365**.


Question: What distinguishes Situation C1 from Situation C2 in life-cycle assessment?

Whether existing benefits outside the analysed system (such as recycling credits) are considered—in Situation C1 they are considered, while in Situation C2 they are not.

In **life-cycle assessment (LCA)**, **Situation C1** and **Situation C2** usually refer to different **decision contexts** in consequential thinking:

- **Situation C1**:
  A **small-scale change** in demand or supply that does **not alter the overall production capacity** or market structure.
  - The affected suppliers are typically the **marginal suppliers** already responding within existing capacity.
  - Example: a modest increase in demand for a material that existing producers can meet without building new plants.

- **Situation C2**:
  A **large-scale or long-term change** that is big enough to **influence capacity expansion or reduction**.
  - The change may trigger **investment decisions**, shifts in technology, or structural market changes.
  - Example: a policy-driven increase in biofuel demand that leads to construction of new production facilities.

### Key distinction
The main difference is whether the change:
- stays **within existing capacity** (**C1**), or
- **changes capacity/market structure** (**C2**).

If you want, I can also explain how these relate to **marginal technologies** and **consequential LCA modeling**.

Situation C1 and C2 are both “accounting” situations, but they differ in whether interactions with other systems are included:

- Situation C1: describes an existing system and accounts for its interactions with other systems, e.g. crediting existing avoided burdens from recycling.
- Situation C2: describes an existing system in isolation, without accounting for interactions with other systems.

Source: Hauschild et al., LCA: Theory and Practice, ch. 37 “LCA Cookbook”, p. 969.

Additionally, the decision tree states: “C1 is used unless C2 is specifically prescribed by the commissioner’s goal of the study.” Source: Hauschild et al., LCA: Theory and Practice, ch. 7 “Goal Definition”, p. 71.

Situation C1 and C2 are both “accounting” situations for describing an existing system retrospectively, but they differ in whether interactions with other systems are included:

- **Situation C1**: describes an existing system **and accounts for interactions with other systems**, e.g. “crediting existing avoided burdens from recycling.” It is used for “the ‘normal’ full life cycle” in monitoring/accounting studies.
- **Situation C2**: describes an existing system **in isolation without accounting for interactions with other systems**. In modelling, “multifunctionality shall always be solved via allocation.”

Sources: Hauschild et al., *LCA: Theory and Practice*, ch. 37, p. 969; European Commission – JRC, *ILCD Handbook*, pp. 44 and 9.


Question: In life-cycle assessment, how is a market defined, distinguishing it from geographical concepts like countries or regions?

A market is the unit that allows buyers and sellers to exchange any type of goods and services, and it often has a different delimitation than geographical concepts such as countries or regions.

In LCA, a **market** is defined by **economic exchange and substitutability**, not by political geography.

- A **market** is the set of **suppliers and consumers that can realistically trade a given product or service** under similar conditions.
- It is determined by factors like:
  - **product equivalence/substitutability**
  - **transportability**
  - **trade barriers and regulations**
  - **price signals**
  - **temporal availability**
  - **technology and quality requirements**

So a market may be:

- **smaller than a country** if trade is local (e.g., district heating, municipal waste treatment),
- **equal to a country** if national conditions dominate,
- **larger than a country or region** if products are globally traded (e.g., crude oil, metals, some crops).

A **country** or **region** is just a **geographical or administrative area**. A **market** is a **functional-economic boundary**: the area over which supply and demand interact for the product in question.

In consequential LCA especially, defining the market correctly matters because it determines **which suppliers are affected by a change in demand**.

In LCA, a market is defined as “the unit that allows buyers and sellers to exchange any type of goods and services.” It is not the same as a country or region: markets are differentiated geographically, temporally, and by customer segments, and their geographical scope “can geographically be equal, smaller or larger than a country.” Thus, countries or regions may or may not coincide with the relevant market.
— ILCD Handbook: General Guide for LCA, chapter 7.4.4, pp. 128–129 (esp. p.128)

In LCA, a **market** is defined as “**the unit that allows buyers and sellers to exchange any type of goods and services**.” It is distinguished from purely geographical units because markets are delimited by exchange relationships and can be differentiated **geographically, temporally, and by customer segments**, while their **geographical scope is typically not exact** due to imports and exports. Markets may therefore be **equal to, smaller than, or larger than a country** rather than coinciding with countries or regions. Political, cultural, and natural barriers can shape them.
— **European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.128–129**

Related guidance adds that the appropriate geographical resolution in LCA depends partly on the **spatial extent of markets**, since “**some markets are very local, while others are global**.”
— **Hauschild et al., *LCA: Theory and Practice*, Ch. 8 “Scope Definition”, p.106**

Also, for some products there may be **many small local markets**, while for others there may be **one global market**; thus “**the spatial nature of a market has to be established as a first task**.”
— **Hauschild et al., *LCA: Theory and Practice*, Ch. 9 “Life Cycle Inventory Analysis”, p.137**


Question: On what factors does the identification of the technology mix of marginal processes depend?

It depends, among others, on the market direction and the cost-competitiveness of potential marginal processes.

In consequential LCA, the **technology mix of marginal processes** depends mainly on:

1. **The time horizon of the change**
   - **Short term:** constrained existing capacity responds.
   - **Long term:** capacity can expand or contract, so competitive technologies are affected.

2. **The market trend**
   - **Growing market:** marginal supply is usually from technologies that are expanding.
   - **Stable/declining market:** marginal effects may come from the least competitive or shrinking technologies.

3. **Production constraints**
   - Whether suppliers can actually change output or capacity.
   - Technical, regulatory, resource, and contractual constraints matter.

4. **Geographical market scope**
   - Local, regional, or global market boundaries determine which suppliers can respond.

5. **The size of the demand change**
   - Small changes often affect the current marginal supplier.
   - Large changes can shift the market and involve several technologies.

6. **Competitiveness/economic attractiveness of technologies**
   - Costs, profitability, policy support, and investment conditions influence which technologies are marginal.

So, identifying the marginal technology mix depends on **time, market dynamics, constraints, geography, scale of change, and competitiveness of available technologies**.

The identification of the technology mix of marginal processes depends “among others on the market direction and the cost-competitiveness of potential marginal processes.” (European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010, p.124)

It is also stated as depending “among others on the market conditions and the cost-competitiveness of the potential marginal processes.” (European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010, p.127)

The identification of the technology mix of marginal processes depends, among others, on:

- market direction / market conditions
- cost-competitiveness of the potential marginal processes

This is stated in:
- ILCD Handbook, ch. 7.2.4, p.124: “The technology mix of marginal processes shall be foreseen to be identified depending among others on the market direction and the cost-competitiveness of potential marginal processes.”
- ILCD Handbook, p.127: “The technology mix of marginal processes should be identified, depending among others on the market conditions and the cost-competitiveness of the potential marginal processes.”


Question: What does Situation A refer to in the context of LCA decision support?

Situation A refers to decision support directly or indirectly related to informing the purchase of products already offered in the market, or to informing the design/development of products foreseen to enter the market, where the product can be assumed to be produced only as a consequence of the decision being supported by the LCI/LCA study.

In LCA decision support, **Situation A** refers to a context where the **decision can affect the overall level of production or demand** of a product system.

In practice, this means:
- the decision is **large enough to influence markets**, and
- you should consider **market consequences and changes in production volumes**.

This is typically associated with a **consequential LCA perspective**, because the study asks: *what changes in the wider system if this decision is made?*

By contrast, smaller decisions that do not change market volumes are usually treated differently.

Situation A refers to “Micro-level decision support,” i.e. decision support for cases with no or only small-scale process changes in the background system or other systems. It is used when the study supports decisions on alternatives with better or worse environmental performance and the consequences are small-scale; examples include weak point analysis, ecodesign, and simplified LCA/KEPI identification.

Cited: Hauschild et al., *LCA: Theory and Practice*, Ch. 37 “LCA Cookbook”, p.967; Ch. 7 “Goal Definition”, p.71.

Situation A refers to **“Micro-level decision support”**: decision support for choices with **none or small-scale consequences in the background system or other systems**. It covers applications such as **KEPI identification, weak point analysis, detailed ecodesign, and design-for-recycling**. (Hauschild et al., *LCA: Theory and Practice*, Ch. 37 “LCA Cookbook”, p. 967)

The ILCD Handbook further states that Situation A is **decision support directly or indirectly related to informing the purchase of products already on the market, or the design/development of products foreseen to enter the market**. (European Commission – JRC, *ILCD Handbook: General Guide for LCA*, p. 39)


Question: In end-of-life recycling models, which process steps have their related inventories assigned to the analysed system?

The process steps that condition, modify, transport, etc. the end-of-life products or waste until the valuable function (e.g. a recycled metal bar) is available in a quality and at a place where it supersedes an alternative production are within the system boundary, so their inventories are assigned to the analysed system.

In end-of-life recycling models, the analysed system is typically assigned the inventories for the recycling steps that occur because its waste enters recycling. This usually includes:

- Collection and transport of the discarded product/material
- Sorting, separation, and dismantling
- Pre-treatment and reprocessing into secondary material

It generally does not include the burdens of the original primary material production again, and whether credits or additional burdens are assigned beyond the recycling point depends on the specific allocation approach used.

In end-of-life recycling models, the analysed system is assigned the inventories of:

- all processes up to the level of the quality of the primary material, energy carrier, or part as later obtained via recycling,
- plus all recycling processes,
- plus all waste treatment/disposal steps.

It does not include the manufacture and use of the products made from that recycled material, since these are not physically related to producing the later reused/recycled/recovered material.

Cited: ILCD Handbook, p.1 and p.2 (Source 6; Source 1).

In end-of-life recycling models, the analysed system is assigned the inventories of:

- “all processes up to the level of the quality of the primary material, energy carrier or part as obtained also later via recycling,” and
- “all recycling and waste treatment steps.”

It “does not include however any of the processes from the manufacture and use of the products made from the material, energy carrier or part.” (European Commission – JRC, ILCD Handbook: General Guide for LCA, Chapter 7, p.1)

Also, if “during the waste treatment no valuable product is produced at all … all waste treatment steps are to be modelled and the inventory is fully to be assigned to the first system that has generated the waste / end-of-life product.” (European Commission – JRC, ILCD Handbook: General Guide for LCA, Chapter 7, p.1)