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

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

In LCA practice, actual (primary) data collection for the foreground system is typically only required when using a **"cut-off" or simplified/streamlined approach** — more specifically, this is the standard expectation in most LCA studies where:

**The foreground system** (the processes directly under the study's control or of direct relevance, e.g., the specific production process being assessed) requires **primary/measured data**, while the **background system** (upstream/downstream processes such as energy grids, commodity production) is covered by **secondary data** from existing LCI databases (e.g., ecoinvent, GaBi).

This distinction is particularly emphasized in:

- **Screening or simplified LCAs**, where resources are limited
- **ISO 14044-compliant studies** where the goal and scope determine data quality requirements, and background data from databases is considered sufficient
- **Comparative assertions** not disclosed to the public, where full primary data collection throughout the entire supply chain is not mandatory
- **Product Environmental Footprint (PEF)** methodology, which explicitly distinguishes foreground (company-specific data required) from background (database data acceptable)

In short: **actual inventory data collection is typically only required for the foreground system when secondary/database data are deemed sufficient for the background system**, which is the norm in most practical LCA applications.

Based on the context provided, actual collection of inventory data is typically only required for the foreground system in practice. As stated in the ILCD Handbook:

"for foreground processes specific inventory data should be used. This data is typically compiled as primary data from the product/technology developer, goods producer, or service operator and should include specific secondary data from the tier-one suppliers."

**(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.184)**

The background system, by contrast, relies on secondary/generic data rather than requiring direct data collection, as decision-makers typically cannot structurally change background processes and suppliers may be anonymous (e.g., commodities bought on global markets). The actual collection of inventory data for the foreground system is specifically required when those processes are **key processes** of the system, while for processes not expected to be key processes, estimations based on modelling from process knowledge may suffice.

Based on the retrieved passages, actual collection of inventory data is typically only required for the foreground system when **specific inventory data should be used for foreground processes**, compiled as primary data from the product/technology developer, goods producer, or service operator, including specific secondary data from tier-one suppliers. Generic or average secondary background data may be used for background processes instead.

As stated in the ILCD Handbook (p. 184):

> "In practice, and as a general rule, for foreground processes specific inventory data should be used. This data is typically compiled as primary data from the product/technology developer, goods producer, or service operator and should include specific secondary data from the tier-one suppliers."

This is further supported by Hauschild et al., **Chapter 8 Scope Definition (p. 81)**, which notes that:

> "The distinction between foreground and background system is especially useful for planning data collection for the inventory analysis (see Chap. 9)."

And from **Hauschild et al., Chapter 8, p. 105**, primary data is by definition representative of the specific foreground process, while the background system is constructed from other (secondary) data sources — meaning actual (primary) data collection is typically only required for the foreground system.


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

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

Under ISO 14044:2006, in recycling situations, **system expansion** and **allocation procedures** require special attention and justification — specifically, how to handle the **allocation of environmental burdens between the primary production system and the recycling system**.

The standard highlights that when a product is recycled, the system boundary and the method used to allocate burdens (or credits) between the original product system and the subsequent recycled material system must be carefully defined and justified. This is because recycling involves **multi-functional processes** where inputs and outputs serve more than one product system.

Key aspects requiring justification include:

1. **Whether to use system expansion** (expanding the system boundary to include the function of the recycled material, thereby avoiding allocation) or
2. **Allocation based on physical or economic relationships** between the primary and recycled material flows.

ISO 14044 specifies a **stepwise hierarchy**:
- First, try to **avoid allocation** through subdivision or system expansion.
- If unavoidable, apply allocation based on **underlying physical relationships**.
- If not possible, use other relationships (e.g., economic value).

The standard explicitly states that the chosen approach must be **clearly described and justified** in the LCA study, because the choice can significantly affect the results, particularly regarding how environmental credits or burdens are attributed to the recycled material versus the primary product.

Based on the context provided, in recycling situations under ISO 14044:2006, the aspect of system modelling that requires special attention and justification is **where to draw the system boundary between the first and subsequent life cycles**, and **how to apply the allocation procedure** to these cases.

Specifically, the ILCD Handbook notes that the following key information is required:
- The **market value of the waste or end-of-life product**
- Whether any **valuable secondary good** is generated during treatment (if market value is below zero)
- The **physical characteristics and market value** of secondary goods

Additionally, the differentiation between **closed-loop and open-loop recycling** requires coherent treatment to ensure robustness and plausibility of results, as well as applicability in daily practice.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), pp. 347 and 349)*

Based on the context provided, in recycling situations under ISO 14044:2006, the **system boundary** between the first and subsequent life cycles requires special attention and justification, along with the application of the **two-step allocation procedure** to these cases.

As stated in the ILCD Handbook:

> "The following main questions come up when modelling recycling in attributional modelling: Where to draw the system boundary between the first and subsequent life cycles?, and How to apply the ILCD two-step allocation procedure to these cases?"

Additionally, determining the **market value of the waste or end-of-life product** is critical information required for answering these modelling questions, as the two cases of market value above and below zero must be differentiated.

Furthermore, for **open-loop recycling**, special attention must be given to distinguishing between "Open loop - same primary route" and "Open loop – different primary route," as described in ISO 14044:2006.

*(ILCD Handbook: General Guide for LCA (2010), pp. 347 and 349)*


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 inventory data quality cannot be met due to lack of access to data or resources, **cut-off** (or **truncation**) typically becomes necessary — that is, certain processes, flows, or life cycle stages are excluded from the system boundary.

This is accompanied by **documentation and transparency** about what has been cut off and why, so that the limitations are clearly communicated. The practitioner must also assess and report the **impact of these cut-offs** on the overall study results (sensitivity/completeness check).

In some frameworks, this may also involve using **proxy data** or **estimated/surrogate data** as an alternative to outright cut-off, but when even that is not feasible, cut-off of the affected inventory flows or unit processes is the standard recourse.

When inventory data quality cannot be met due to lack of access to data or resources, the **goal and scope definition may have to be adjusted** to accommodate the lack of completeness. If an important data deficiency cannot be remediated, this should be explicitly considered when formulating the **limitations in the conclusions** from the study.

*(Hauschild et al., LCA: Theory and Practice — 12 Life Cycle Interpretation, p.327)*

When inventory data quality cannot be met due to lack of access to data or resources, it typically becomes necessary to **adjust the goal and scope definition** to accommodate the lack of completeness. If an important data deficiency cannot be remediated, it must be explicitly considered when formulating the **limitations in the conclusions** from the study.

As stated in Hauschild et al., LCA: Theory and Practice, Chapter 12 Life Cycle Interpretation, p. 327:

> "Alternatively, the goal and scope definition may have to be adjusted to accommodate the lack of completeness. If an important data deficiency cannot be remediated, this should be considered when formulating the limitations in the conclusions from the study."

Additionally, from the Guinée et al., Handbook on LCA, p. 502, the cut-off problem is recognized as **primarily a problem of data availability**, and "if data are lacking, there is a cut-off problem and this should be duly addressed."


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 a Life Cycle Inventory (LCI), the two sub-types of a unit process study or data set are:

1. **Single operation unit process** – represents a single, specific operation or activity (a more disaggregated level of data).

2. **Partly aggregated unit process** (also called a **partially aggregated** unit process) – represents a combination of several operations or processes that have been aggregated to some degree, but not to the full system/product level.

These distinctions are used in LCA databases and methodology (e.g., as described in ISO 14040/14044 and ecoinvent nomenclature) to differentiate the level of aggregation within unit process data sets.

The two sub-types of a unit process study or data set in a Life Cycle Inventory are:

1. **Single operation unit process** (variants: fixed or parameterised)
2. **Black box unit process** (variants: fixed or parameterised)

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

Based on the ILCD Handbook, the two sub-types of a unit process study and/or data set within a Life Cycle Inventory are:

1. **Single operation unit process** (variants: fixed or parameterised)
2. **Black box unit process** (variants: fixed or parameterised)

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


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.

In life cycle assessment (LCA) and input-output analysis, **parallel services** are distinguished by the fact that they are services (or inputs) that are used **simultaneously and jointly** by multiple co-produced goods in such a way that the service cannot be meaningfully divided or attributed to one product rather than another — the service is consumed in common by all co-products at the same time, rather than being traceable to any individual product.

A classic example given is **management or administrative overhead** (such as general plant management or facility services) that supports an entire production process generating multiple co-products, where it is impossible to say that management "belongs" to one co-product more than another.

*(Note: The precise terminology and examples vary by source. If you are working from a specific textbook or standard, the example given there — sometimes cited as something like heating or lighting of a shared facility — would be the authoritative one for your context.)*

Based on the context provided, **parallel services** are distinguished by the fact that they **serve several co-products at the same time (simultaneously) in parallel, and relate to the co-products in a similar way**.

One example given is **transport equipment** (along with storage facilities, manufacturing halls, and production equipment). For transport specifically, the allocation is based on transport time or distance combined with mass or volume of the transported good.

*(Source 2: ILCD Handbook, p.260; Source 3: ILCD Handbook, p.261)*

Based on the context, **parallel services** are distinguished by the fact that they **serve at the same time in parallel several co-products and relate to the co-products in a similar way**.

One example given is **storage facilities**, which provide a service to multiple co-products simultaneously. Other examples mentioned include transport equipment, manufacturing halls, and production equipment.

*(ILCD Handbook: General Guide for LCA (2010), p. 260)*


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 **functional unit** (as expressed through the **demand vector**) serves as the starting point — more precisely, it is the **final demand** (the functional unit quantified in the technology matrix framework) that initiates the scaling.

In practical terms, the **reference flow of the foreground/functional process** (i.e., the product or service delivering the functional unit) is the starting point. All other process scales are calculated relative to it by solving the system of linear equations:

**s = A⁻¹ · f**

where **f** is the final demand vector (with the functional unit as the entry) and **s** is the scaling vector that determines how much of each process is needed.

So the direct answer: the **functional unit / final demand vector** is the starting point from which all other processes are stepwise and relatively scaled.

The starting point from which all other processes are stepwise, relatively scaled 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 — 37 LCA Cookbook, p.1032)*

Based on the context, in an LCA model where all processes are connected via their reference flows, the **central process and the amount(s) of the system's functional unit(s) or reference flow(s)** serve as the starting point from which all other processes are stepwise, relatively scaled.

As stated: *"Starting from central process and the amount(s) of the system's functional unit(s) or reference flow(s), all other processes are stepwise, relatively scaled."*

(Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.1032)


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.

Your question seems to be referencing a specific framework, rubric, or document that classifies effect sizes (e.g., "small," "medium," "large") and maps them to particular processes or systems. Without knowing which specific framework you're referring to, I can offer a few possibilities:

## Possible Contexts:

### In LCA (Life Cycle Assessment)
In impact assessment methods, a "small" effect magnitude might refer to impacts on:
- **Background systems** (distant supply chains)
- **Non-sensitive ecosystems**
- Processes with **low characterization factors**

### In Statistics (Cohen's conventions)
A "small" effect size (e.g., d = 0.2) doesn't typically map to specific processes but indicates **minimal practical significance**.

### In Environmental Risk Assessment
A "small" effect might affect:
- **Robust/resilient systems** less susceptible to perturbation
- Non-critical ecosystem components

---

**Could you clarify:**
- Which document, method, or standard you're referencing?
- Whether this is from a specific LCA methodology, environmental assessment tool, or another field?

This will allow me to give you a precise and accurate answer about which processes or systems are identified as being affected at the "small" effect classification level.

When the size of an effect is classified as "small," it affects only the **extent of operation of one or more existing processes**, and therefore the **short-term marginal process(es)** are the ones that should be assumed to be superseded.

*(Source 1: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.178)*

When the size of an effect is classified as "small" — meaning it affects only the extent of operation of one or more existing processes without resulting in newly installed or de-installed capacity — **the short-term marginal process(es) are the ones that should be assumed to be superseded**.

*(ILCD Handbook: General Guide for LCA (2010), p. 178)*


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.

# Transferring Inventory Data Between Geographical Areas or Suppliers

## Appropriate Condition

It is appropriate to transfer inventory data from one geographical area or specific supplier to another when **the processes, technologies, and operating conditions are sufficiently similar** that the data can be considered representative of the situation being modeled.

## Key Criteria for Justifying Data Transfer

The transfer is acceptable when:

- **Technological equivalence**: The production processes and technologies used are essentially the same
- **Operational similarity**: Operating conditions (scale, efficiency, management practices) are comparable
- **Feedstock/input similarity**: Raw materials and energy sources used are analogous
- **No significant regional variation**: Environmental conditions and regulatory contexts do not materially affect emissions or resource use

## Practical Considerations

| Factor | Question to Ask |
|--------|----------------|
| Process technology | Is the same technology used in both locations? |
| Energy mix | Can energy differences be adjusted/corrected for? |
| Regulatory context | Do environmental regulations significantly differ? |
| Data quality | Is the transferred data more representative than alternatives? |

## Important Caveat

When transferring data, practitioners should:
- **Document and justify** the transfer explicitly
- **Apply corrections** where known differences exist (e.g., adjust for regional electricity grid differences)
- **Acknowledge limitations** in the uncertainty assessment

This principle is recognized in **ISO 14044** and **ILCD guidelines** under data quality and representativeness requirements.

Based on the context provided, it is appropriate to transfer inventory data from one geographical area or specific supplier to another **only if the differences in the environmental impacts have no or little relevance for the overall representativeness of the inventory**.

*(Source 2: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.129)*

Based on the retrieved passages, it is appropriate to transfer inventory data from one geographical area or specific supplier to another **only if the differences in the environmental impacts have no or little relevance for the overall representativeness of the inventory**.

Additionally, an in-depth technical understanding of the to-be-represented processes is considered key for any transfer and adjustment of data from other markets, and the main inventory values and differences between the areas must be known.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.129–130)*


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 conducting a comparative study on alternatives that are not objectively comparable (such as personal services, fashion items, or jewellery), the results and recommendations **must be presented as statements of personal values, preferences, or priorities** rather than as objective scientific conclusions.

Specifically:

- The study should **clearly acknowledge** that the comparison involves subjective or value-laden dimensions that cannot be resolved through purely technical/scientific analysis
- Results should be framed as reflecting **particular value choices or weighting decisions** made by the practitioner or commissioner
- **Transparency** about the assumptions, value judgments, and priorities underlying the comparison is required
- The conclusions should **not be presented as universally valid** or purely objective findings, since different stakeholders with different values might legitimately reach different conclusions
- Any **recommendations should be qualified** to reflect the specific values and priorities upon which they are based

This requirement aligns with **ISO 14044** guidance on comparative assertions and the inherent limitations of LCA when applied to functionally non-equivalent systems, ensuring that audiences are not misled into treating subjective trade-offs as objective scientific results.

Based on the context provided, when conducting a comparative study on alternatives that are not objectively comparable (such as personal services, fashion items, or jewellery), **the results and recommendations must be presented with the explicit statement that comparability is not assumed per se, but lies with the individual preference and judgement.**

Additionally, to avoid misinterpretations by the target audience, any relevant limitations must be given jointly with the recommendations.

*(ILCD Handbook: General Guide for LCA, 2010, p. 304 and p. 63)*

Based on the retrieved passages, when conducting a comparative study on alternatives that are not objectively comparable (such as personal services, fashion items, or jewellery), the results and recommendations **must be presented with the explicit statement that comparability is not assumed per se, but lies with the individual preference and judgement**. Additionally, to avoid misinterpretations by the target audience, any relevant limitations are to be given jointly with the recommendations.

This is stated in two sources:

- **ILCD Handbook: General Guide for LCA (2010), p. 304**: *"The results and recommendations of such comparative studies shall hence be presented with the explicit statement that comparability is not assumed per se, but lies with the individual preference and judgement"* and *"To avoid misinterpretations by the target audience any relevant limitations are to be given jointly with the recommendations."*

- **ILCD Handbook: General Guide for LCA (2010), p. 63**: *"The results of such comparative studies shall hence be presented with the explicit statement that comparability is not assumed per se, but lies with the individual preference and judgement."*


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.

In end-of-life/recycling allocation (e.g., under the **ISO 14044** framework or the **recycled content / end-of-life recycling approaches**), when the pre-treated waste or end-of-life product has a **market price below zero** (i.e., it has a negative value — someone must pay to have it taken away), the value used for allocation purposes should be **zero (0)**.

The rationale is that a negative price would imply the *receiving* process is being credited for accepting a burden, which distorts the allocation. The convention adopted (notably in **PEF/EF guidance**, **ecoinvent**, and various circular economy allocation methodologies) is to **set the price floor at zero** for allocation purposes — negative market prices are truncated to zero so that the material carries no economic value into the allocation calculation, preventing negative allocation factors.

The absolute value of its (negative) market price should be used when allocating between the first and second life cycle.

[Source 1, p.354; Source 4, p.271]

When allocating between the first and second life cycle, the **absolute value of the (negative) market price** of the pre-treated waste/end-of-life product should be used in calculating the allocation key.

As stated in the ILCD Handbook: "the market value of the pre-treated waste / end-of-life product before it enters the process step that finally produces a valuable secondary good, 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. The rest of the allocation calculation is the same."

*(ILCD Handbook: General Guide for LCA (2010), p. 354, and confirmed on p. 271)*


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

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

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

1. **Temporal representativeness** – whether the data reflect the appropriate time period (e.g., the same year or era)
2. **Geographical representativeness** – whether the data reflect the appropriate geographic region or location
3. **Technological representativeness** – whether the data reflect the appropriate technology or process being studied

These correspond to the requirement under ISO 14044 to assess the completeness, sensitivity, and consistency of data, ensuring that data from different sources are comparable in terms of when, where, and how they were collected.

The three aspects of representativeness that must be checked for consistency among inventory data are **geographical, time-related, and technological** representativeness.

This is confirmed in multiple sources:

- Hauschild et al., LCA: Theory and Practice — **8 Scope Definition, p.106**: "Representativeness of LCI data can be understood in three interrelated dimensions: geographical, time-related and technological."

- European Commission – JRC, ILCD Handbook — **p.369**: "Representativeness has three components – technological -, geographical – and time-related representativeness, which interrelate and are all to be considered and met by the used data."

- Hauschild et al. — **12 Life Cycle Interpretation, p.331** also references these dimensions in the context of the consistency check, noting that "Inventory data quality concerns both the time-related, the geographical, and the technological" representativeness.

Based on the sources, the three aspects (dimensions) of representativeness that must be checked for consistency among inventory data are:

1. **Geographical representativeness**
2. **Time-related representativeness**
3. **Technological representativeness**

These are cited in multiple sources:

- Hauschild et al., *LCA: Theory and Practice* — **8 Scope Definition, p.106**: "Representativeness of LCI data can be understood in three interrelated dimensions: geographical, time-related and technological."
- European Commission – JRC, *ILCD Handbook*, **p.369**: "Representativeness has three components – technological -, geographical – and time-related representativeness, which interrelate and are all to be considered and met by the used data."

In the context of life cycle interpretation specifically, Hauschild et al. **12 Life Cycle Interpretation, p.331** confirms that the consistency check examines whether "inventory data quality concerns both the time-related, the geographical, and the technological" dimensions across the product life cycle and between different product systems.


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 (International Reference Life Cycle Data System) allocation provisions, when solving multifunctionality through allocation, the allocation formula must consider **the underlying physical relationships** (i.e., physical causality) between the co-products and the inputs/outputs being allocated.

More specifically, beyond simply using economic or mass-based partitioning, the ILCD requires that the allocation formula reflect the **causal/physical relationship** that drives the generation of the co-products. This means the allocation key should be based on a property that actually **causes or drives** the joint production — for example, if a physical parameter (such as energy content, carbon content, or stoichiometric relationships) is the underlying reason the co-products are produced together, that parameter should inform the allocation factor.

In practical terms, the ILCD hierarchy and provisions state that when allocation cannot be avoided by system expansion or subdivision, the allocation factors must be grounded in **physical (causal) relationships** between the co-products and the burdens, ensuring the formula captures the actual reason the flows are linked — not just an arbitrary economic or market-based ratio. Economic allocation is only applied when no physical causal relationship can be established.

Based on the ILCD Handbook provisions, when solving multifunctionality by allocation, the allocation formula must consider a **two-step procedure**:

1. **First criterion**: "Determining physical causal relationships" between each non-functional flow and the co-functions of the process must be identified and applied as the primary criterion.

2. **Second (general) criterion**: "Economic value" or QFD (Quality Function Deployment) must be applied when physical causality alone is insufficient.

As stated in the ILCD Handbook:

> "As first criterion other 'determining physical causal relationships' between each nonfunctional flow and the co-functions of the process are to be identified and applied"

The allocation formula must therefore consider **both physical causality as the primary basis, and economic value (or QFD) as the secondary criterion** when physical causality cannot fully resolve the allocation.

*(Source 1: ILCD Handbook, p.257, §7.9.3.1; Source 9: ILCD Handbook, p.11, referencing §7.9.3.2 "First criterion: Determining physical causality" and §7.9.3.3 "Second criterion: Economic value or QFD")*

Based on the ILCD Handbook provisions, when solving multifunctionality by allocation, the allocation criteria must be identified through a **two-step procedure**:

1. **First criterion**: "Determining physical causal relationships" between each non-functional flow and the co-functions of the process must be identified and applied as the primary criterion.

2. **Second (general) criterion**: "Economic value" or QFD (Quality Function Deployment) must be considered as the second step when the first criterion (physical causality) is insufficient.

As stated in the ILCD Handbook: *"The allocation criteria are identified in a two-step procedure, starting with and strongly building on the physical causality, as equally recommended in ISO 14044:2006."*

The allocation formula must therefore consider **both the determining physical causal relationships (first criterion) and economic value or QFD (second criterion)** when solving multifunctionality.

*(ILCD Handbook: General Guide for LCA (2010), p. 257, chapter 7.9.3.1; with the two criteria detailed in chapters 7.9.3.2 and 7.9.3.3 as referenced on p. 11)*


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.

## Why Detailed Methods Are Still Necessary Alongside Simplified Provisions in LCA

Even when simplified provisions (e.g., default values, cut-off rules, aggregated datasets) are used in an LCA, detailed and differentiated method provisions remain necessary for several key reasons:

### 1. **Validation and Benchmarking**
Simplified provisions must be derived from and validated against full, detailed methodological approaches. Without the detailed methods, there is no basis to confirm that simplifications are appropriate or accurate for a given context.

### 2. **Situations Where Simplifications Are Inadequate**
- Simplified provisions are designed for *typical* or *average* cases. When a product system deviates significantly from these assumptions, detailed methods are needed to capture the actual environmental profile correctly.
- Cut-off rules or aggregated data may obscure significant impacts in specific life cycle stages.

### 3. **Handling Exceptions and Edge Cases**
Certain processes, materials, or system configurations are too specific or complex to be adequately represented by generic simplified data, requiring full methodological treatment.

### 4. **Consistency and Comparability**
Detailed provisions establish a **common methodological framework** that ensures results from different studies (including those using simplifications) remain comparable and interpretable.

### 5. **Completeness Requirements**
Standards (e.g., ISO 14040/44, PEF) require that all relevant environmental impacts be accounted for. Simplified provisions alone cannot guarantee completeness without reference to the full methodological scope.

### 6. **Transparency and Reproducibility**
Detailed methods provide the **documented basis** for all choices made, including why and where simplifications were applied, ensuring scientific credibility and peer reviewability.

---

In summary, simplified provisions are practical tools to reduce complexity and resource requirements, but they **depend on, and must coexist with**, detailed methods to ensure validity, accuracy, and methodological integrity.

Detailed and differentiated method provisions are still necessary even when simplified provisions are used because the simplifications are derived from and depend upon those detailed provisions. As stated in the ILCD Handbook:

> "These simplifications draw on the detailed and differentiated method provisions that are therefore necessary and that are to be fully applied in a few cases. For these cases they are essential; hence the detailed provisions need to be kept as well."

In other words, while simplified provisions reduce effort without significantly harming accuracy or robustness for most cases, there remain specific cases where the full detailed provisions must be applied — and for those cases, they are essential and cannot be replaced by simplified alternatives.

**(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p. 8)**

Even when simplified provisions are used, the detailed and differentiated method provisions remain necessary because the simplified provisions themselves **draw on** the detailed ones as their foundation. As stated in the ILCD Handbook:

> "These simplifications draw on the detailed and differentiated method provisions that are therefore necessary and that are to be fully applied in a few cases. For these cases they are essential; hence the detailed provisions need to be kept as well."

In other words, while simplified provisions can substantially reduce effort without relevantly harming accuracy or robustness in most cases, there are specific cases where the full detailed provisions **must** be applied — and it is only because those detailed provisions exist that the simplifications are valid and meaningful in the first place.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p. 8)*


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.

In life cycle impact assessment (LCIA) reporting, when normalized, grouped, or weighted results are presented, the **underlying mandatory elements** — specifically the **characterized results (characterization indicator results)** — must also be made available alongside them.

In other words, the **characterized LCIA results** (i.e., the results expressed in the units of the characterization factors for each impact category, before any normalization, grouping, or weighting) must always be reported and made available whenever normalized, grouped, or weighted results are disclosed. This ensures transparency and allows others to trace back the results to the fundamental inventory and characterization data, since normalization, grouping, and weighting are considered **optional elements** of LCIA under ISO 14044, and the mandatory characterized results must not be obscured or replaced by the optional steps alone.

According to the ILCD Handbook, **data and indicator results reached prior to any normalization, grouping or weighting shall be made available together with the normalized, grouped or weighted results**.

[Source 8: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.317]

According to the ILCD Handbook, **data and indicator results reached prior to any normalization, grouping or weighting shall be made available together with the normalized, grouped or weighted results**.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.317)*


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, when the gate fee of pre-treated waste is **-1 US$** (i.e., a negative gate fee, meaning the waste treatment facility charges for accepting the waste), this negative value is used directly in the allocation/substitution calculation.

Specifically, in the recyclability substitution method (as described in frameworks like the one developed by Ekvall, Finnveden, and others, or as used in some waste LCA methodologies), the **market value used for allocation is the gate fee itself** — in this case, **-1 US$**.

A negative gate fee reflects the fact that the waste has a net cost to dispose of (the waste generator must pay), which means the material has a **negative market value** of **-1 US$/unit**. This value is used to allocate burdens and credits between the waste producer and the waste treatment/recycling system.

In practical terms, this means the waste is treated as having a market value of **-1 US$**, reflecting that it is an economic burden rather than an asset, and the allocation of environmental burdens is adjusted accordingly.

**1 US$**

When the gate fee/market value of pre-treated waste is "-1 US$", the absolute value of its negative market price is used, so the market value used for allocation is **1 US$**.

*(Source 1: ILCD Handbook, p.354; Source 4: Hauschild et al., LCA Cookbook, p.1017)*

Based on the sources, if the gate fee/market value of pre-treated waste is "-1 US$", the market value used for allocation would be **"1 US$"** — that is, the absolute value of the negative market price is used.

As explained in the ILCD Handbook: "If the market value / gate fee of the pre-treated waste is e.g. '−1 US$', the market value used for allocation would be '1 US$'" (ILCD Handbook, p. 354), and similarly confirmed in Hauschild et al.: "E.g. if the market value/gate fee is '−1 US$' this would be '1 US$'" (LCA Cookbook, p. 1017).


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.

When modelling soil organic carbon (SOC) changes following land use transformation, the default period typically assumed is **20 years**, which is the convention used in life cycle assessment methodology (notably in the approaches developed by Milà i Canals et al. and adopted in various LCA frameworks including the PEF/EF methods and IPCC-aligned LCA approaches).

This 20-year period is considered to capture approximately **two-thirds (roughly 66%)** of the total SOC change that will ultimately occur as the soil moves toward its new equilibrium following the land use transformation. The remainder of the change occurs more slowly over subsequent decades, but the 20-year window is used as a pragmatic default to balance relevance and practicality in LCA calculations.

A default period of **20 years** should be assumed, and it reflects the period during which approximately **90% of the main losses or binding** toward the new equilibrium occurs.

*(ILCD Handbook: General Guide for LCA, p. 100)*

Based on the ILCD Handbook, when modelling the loss or binding of CO2 in the form of soil organic carbon towards reaching the equilibrium of the land use after transformation, **a default period of 20 years shall be assumed**, and this period reflects the time during which approximately **90% of the main losses or binding** towards the new equilibrium occurs.

As stated in the source: *"For loss / binding of CO2 in form of soil organic carbon, towards reaching the equilibrium of the land use after transformation, a default period of 20 years shall be assumed... This is, unless it can be demonstrated that the period during which about 90 % the main losses / binding occurs is significantly longer or shorter for the given case."*

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


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

The technique described as a way to avoid black box unit processes is **system expansion** (also known as **substitution**).

However, more specifically in the context of LCA methodology, the technique often described for this purpose is **decomposition** or **disaggregation** of unit processes — breaking down a black box unit process into its constituent sub-processes to gain transparency.

More precisely, in LCA literature, the technique commonly described to avoid black box unit processes is **process decomposition/disaggregation**, where complex or opaque unit processes are broken down into more transparent, elementary sub-processes.

That said, if this refers to a specific textbook or context, the answer may be **"tiered hybrid LCA"** or **"input-output analysis"** used alongside process LCA to fill data gaps — or it could refer to **"parameterization"** of unit processes.

The most classically cited answer is: **disaggregation** (breaking black box unit processes into more detailed, transparent sub-processes).

Based on the context provided, **subdivision and virtual subdivision** are described as the techniques to avoid black box unit processes.

As stated in the ILCD Handbook:

> "If during data planning or raw data collection a process turns out to be a black box unit process, one should check whether it can be split by **subdivision** before data collection or **virtual subdivision** afterwards."

And more specifically, the dedicated section is titled:

> **"7.4.2.2 Avoiding black box unit processes by subdivision and virtual subdivision"**

*(Source 4, p.192; Source 2, p.190; Source 3, p.10)*

Based on the context, **subdivision** (and **virtual subdivision**) is described as the technique to avoid black box unit processes.

As stated in the ILCD Handbook:

> "If during data planning or raw data collection a process turns out to be a black box unit process, one should check whether it can be split by subdivision before data collection or virtual subdivision afterwards." (p. 190)

This is further elaborated in the dedicated section titled **"7.4.2.2 Avoiding black box unit processes by subdivision and virtual subdivision"** (p. 192), which explains that if a unit process is a combination of more than one physically separate process steps (i.e., a black box unit process), subdivision or virtual subdivision should be applied to break it down into single-operation unit processes, thereby avoiding the need for allocation and the difficulties associated with black box unit processes.


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 modelling:

- **Foreground system**: Site-specific (or primary) data should be used — i.e., data collected directly from the specific process, facility, or activity being studied. This reflects the actual conditions of the system under analysis.

- **Background system**: Average (secondary) data should be used — typically sourced from LCA databases (e.g., ecoinvent, GaBi). These represent average market or industry conditions, consistent with the attributional approach of describing the actual average state of the technosphere rather than marginal/consequential changes.

This distinction ensures that the foreground is accurately represented by real measured data, while the background relies on representative average data appropriate for attributional modelling.

**Foreground System:**
Site or producer/provider specific data should be used, including supplier-specific data for products that connect the foreground with the background system. Generic or average data may only be used in parts of the foreground system if justified as being more accurate, precise, and complete than available specific data.

**Background System:**
Average market consumption mix data should be used for the background system.

*(Sources: ILCD Handbook, p.131; p.124; p.98)*

In attributional modelling, the data requirements differ between the foreground and background systems as follows:

**Foreground System:** Site- or producer/provider-specific data should be used, along with supplier-specific data for the products that connect the foreground with the background system. Generic data of geographical mixes can also be used in parts of the foreground system if justified as being more accurate, precise, and complete than available specific data (especially for processes operated at suppliers). (ILCD Handbook, p.131)

**Background System:** Average market consumption mix data should be used for the background system. (ILCD Handbook, p.131; p.124; p.185)

As further clarified: "For attributional modelling, technology-specific data of the supply-chain should be foreseen for the foreground system and average market consumption mix data for the background system." (ILCD Handbook, p.124)


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.

In a well-defined LCA system boundary, **no other technosphere flows should cross the boundary** between the analysed system and the rest of the technosphere.

This is a fundamental principle of system boundary definition: all intermediate (technosphere) flows that are inputs to or outputs from the foreground system should either:

1. Be **included within the system boundary** (i.e., their production/treatment is modelled as part of the system), or
2. Be cut off according to **cut-off criteria** (if their contribution is negligible).

The only flows that legitimately cross the system boundary are:
- The **reference flow(s)** delivering the functional unit (to/from the technosphere)
- **Waste flows** sent for treatment outside the system (permissible under certain accounting approaches)

Any other intermediate product flows crossing the boundary would indicate either **an incomplete system boundary** (missing unit processes that should be included) or a need for **system expansion / allocation** to handle multifunctionality.

In short: **none** — the ideal is that all other technosphere exchanges are internalized within the system, and only elementary flows cross the boundary between the system and the ecosphere.

Based on Source 1 (ILCD Handbook, p.105), **no other relevant flows** should cross the boundary between the analysed system and the rest of the technosphere. As stated:

> "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, as far as possible."

This is corroborated by Source 2 (Hauschild et al., p.101), which confirms that ideally, system boundaries should be set so that **only elementary flows** (resources and emissions) cross them, with no material, energy, product, or waste-to-treatment flows crossing the system boundaries.

Based on the ILCD Handbook, **no other relevant flows should cross the boundary** between the analysed system and the rest of the technosphere. As stated:

> "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, as far as possible.** Only elementary flows…should cross the boundary between the analysed system(s) and the ecosphere."

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.105)*

This is corroborated by Hauschild et al., which similarly states that ideally "no material, energy, product or waste to treatment flows should cross the system boundaries," with only elementary flows crossing the boundary to the ecosphere *(Hauschild et al., LCA: Theory and Practice, 8 Scope Definition, p.101)*.


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

The two techniques presented as ways to avoid black box unit processes are:

1. **System expansion** (expanding the system boundary to include the co-product's displaced system)
2. **Subdivision/partitioning** (allocating burdens between co-products based on some physical or economic property)

*(Note: In LCA methodology, these are the primary approaches recommended by ISO 14044 to handle multifunctional processes and avoid treating them as opaque "black boxes" — with system expansion being preferred over allocation/partitioning.)*

Based on the context provided, the two techniques presented as ways to avoid black box unit processes are **subdivision** and **virtual subdivision**.

As stated in Source 2 (p.190): *"If during data planning or raw data collection a process turns out to be a black box unit process, one should check whether it can be split by **subdivision** before data collection or **virtual subdivision** afterwards."*

This is further reinforced by Source 5 (p.88) and the chapter title referenced in Source 3 (p.10): *"7.4.2.2 Avoiding black box unit processes by subdivision and virtual subdivision."*

Based on the retrieved passages, the two techniques presented as ways to avoid black box unit processes are **subdivision** and **virtual subdivision**.

As stated in Source 2 (ILCD Handbook, p. 190):

> "If during data planning or raw data collection a process turns out to be a black box unit process, one should check whether it can be split by **subdivision** before data collection or **virtual subdivision** afterwards."

This is further confirmed by the chapter title referenced in Source 3 (ILCD Handbook, p. 10):

> "7.4.2.2 **Avoiding black box unit processes by subdivision and virtual subdivision**"

And in Source 5 (ILCD Handbook, p. 88):

> "It shall be aimed at avoiding black box unit processes and solving multifunctionality by **subdivision or virtual subdivision** (see chapter 7.4.2.2), as far as possible."


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.

According to ISO 14044 (and ISO 14040), the **SI system of units** (International 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** must be used by default when reporting the results of an LCI or LCA study.

> "Per default the Système international d'unités (SI) units shall be used for reporting."

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.308)*

Based on the sources provided, the **Système International d'Unités (SI units)** must be used by default when reporting the results of an LCI or LCA study.

As stated in the ILCD Handbook: "Per default the Système international d'unités (SI) units shall be used for reporting." *(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.308)*

This is further reinforced in the Guinée et al. Handbook, which stipulates that "SI units are to be used" when expressing indicator results and characterisation factors. *(Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p.354)*


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

Collecting data, modelling the system, and calculating results.

In life cycle inventory (LCI) analysis, the three main activities typically identified are:

1. **Data collection** – Gathering quantitative data on inputs (energy, materials, water) and outputs (emissions, waste, products) for each process within the system boundary.

2. **Data calculation/validation** – Processing and validating the collected data, including checking for completeness, consistency, and quality, as well as performing calculations to relate data to the functional unit.

3. **Allocation** – Handling multifunctional processes by partitioning inputs and outputs among co-products or systems (i.e., dealing with how burdens are divided when a process produces more than one useful output).

These activities work together to build the inventory table that quantifies all relevant flows associated with the product system under study.

Based on the context provided, the three main activities identified as part of the Life Cycle Inventory analysis process are:

1. **Collecting data**
2. **Modelling the system**
3. **Calculating results**

These are explicitly identified in the chapter title itself: *"7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results"* (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), Chapter 7, p.155/160).

Based on the ILCD Handbook and the retrieved sources, the three main activities identified as part of the Life Cycle Inventory analysis process are:

1. **Collecting data**
2. **Modelling the system**
3. **Calculating results**

These three activities are explicitly named in the chapter title itself: *"7 Life Cycle Inventory analysis - collecting data, modelling the system, calculating results"* (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.155 and p.160).


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 (ongoing/parallel) review has the advantage that **errors, methodological inconsistencies, or scope problems can be identified and corrected during the study**, while there is still opportunity to address them. This avoids the situation where flaws are only discovered at the end, when it may be too late or too costly to make corrections, and the study may have to be substantially redone or its conclusions invalidated. In short, it allows iterative feedback throughout the process, improving quality and saving time and resources compared to a retrospective end-of-study review.

An accompanying review helps avoid problems or at least identifies them earlier, whereas a review performed at the end of a study can result in considerable delays and extra work.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.15)*

An accompanying review helps avoid problems or at least identifies them earlier, whereas a review performed at the end of a study can result in considerable delays and extra work.

(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.15)


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.

In the ILCD (International Reference Life Cycle Data System) framework for documenting LCI dataset deliverables, the modelling of assumption scenarios (i.e., providing alternative scenario variants to reflect key uncertainties or assumptions) changes from a **recommendation** to a **mandatory ("shall") requirement** when the dataset is intended for use in **policy support** contexts — specifically when the dataset is classified at **compliance level "I" (entry level)** for policy-related applications or, more precisely, when the dataset is developed for **"Policy support and highest quality"** use (ILCD compliance level corresponding to the most demanding tier).

More precisely: under the ILCD Data Network requirements, scenario modelling is **recommended** for datasets at the lower compliance/quality levels (e.g., general use, "entry level I"), but becomes **mandatory ("shall")** for datasets designated for **policy support at the highest compliance level** — i.e., **ILCD compliance level "II"** (or in some framings, when the dataset is explicitly declared for use in comparative assertions intended to be disclosed to the public or in official policy decision-making).

In practical terms: **when the dataset is to be used for comparative assertions disclosed to the public or for policy support (the highest ILCD quality/compliance tier), modelling of assumption scenarios is a "shall" (mandatory) requirement**, whereas for lower-tier or internal/background datasets it remains a recommendation.

Based on Source 3 (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.86):

The modelling of assumption scenarios changes from a recommendation to a mandatory **"shall"** requirement when **the LCI data set is intended to be used in subsequent comparisons**.

As stated: *"If an LCI data set is the deliverable of the study, the modelling of assumption scenarios is recommended, only. If performed, the outcome may be documented together with the data set. Note that this is a 'shall' requirement if the data set is intended to be used in subsequent comparisons."*

(ILCD Handbook, p.86)

Based on the context, the modelling of assumption scenarios changes from a recommendation to a mandatory ("shall") requirement when the LCI data set is **intended to be used in subsequent comparisons**.

As stated in the ILCD Handbook:

> "If an LCI data set is the deliverable of the study, the modelling of assumption scenarios is recommended, only. If performed, the outcome may be documented together with the data set. **Note that this is a 'shall' requirement if the data set is intended to be used in subsequent comparisons.**"

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p. 86)*


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.

## Preferred Option for Inventory Data in Multifunctional Processes

### Preferred Option: **Direct Measurement / Data Collection from Unit Processes**

The **preferred approach** is to obtain inventory data by **directly measuring or collecting data specific to each individual unit process** — i.e., subdividing the multifunctional process into its constituent unit processes so that inputs and outputs can be assigned directly to each function without ambiguity.

This means obtaining data at a sufficiently disaggregated level that each unit process has a single function, avoiding the need for allocation altogether (in line with the ISO 14044 hierarchy).

---

### Alternative Basis for Splitting Data: **Physical/Causal Relationships**

When direct measurement is not possible, the alternative is to **split the inventory data based on underlying physical or causal relationships** between the process inputs/outputs and the co-products.

For example:
- Mass or energy flows that can be traced mechanistically to each product
- Thermodynamic properties (e.g., energy content)
- Stoichiometric relationships

This is preferred over economic allocation because it reflects the **actual physical causality** of the process rather than market-driven values.

---

### Summary (ISO 14044 Hierarchy Context)
| Priority | Approach |
|----------|----------|
| 1st | Subdivide and measure unit processes directly |
| 2nd | Use physical/causal relationships to split data |
| 3rd | Allocate based on other relationships (e.g., economic value) |

The goal is always to minimize arbitrary allocation by grounding data splitting in **verifiable, physical reality**.

Based on the context provided, when obtaining inventory data for unit processes included in a multifunctional process:

**Preferred option:** Actual data collection is the preferable option.

**Alternative basis for splitting data:** The use of knowledge about the involved processes can serve as an alternative basis to split up the data of the multifunctional process and assign the inventory items to the included unit processes. Such knowledge can include, for example, the understanding that emissions to water can only come from processes that contribute waste water, or that certain parts or consumables are only required as input for certain processes.

*(Source 2: ILCD Handbook, p.193)*

Based on the retrieved passages, when obtaining inventory data for unit processes included in a multifunctional process:

**Preferable option:** Actual data collection is the preferable option.

**Alternative basis for splitting up the data:** Knowledge about the involved processes can serve as an alternative basis to split up the data of the multifunctional process and assign the inventory items to the included unit processes. Such knowledge can include, for example, the understanding that emissions to water can only come from processes that contribute waste water, or that certain parts or consumables are only required as input for certain processes.

*(ILCD Handbook: General Guide for LCA (2010), p. 193)*


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 of ecoinvent report No. 1, *Overview and Methodology* (ecoinvent database v. 2.0, 2007) were **Roland Hischier, Bo Weidema, Hans-Jörg Althaus, Christoph Bauer, Gabor Doka, Roberto Dones, Rolf Frischknecht, Stefanie Hellweg, Sonia Humbert, Niels Jungbluth, Thomas Köllner, Yuki Loerincik, Mireille Margni, and Thomas Nemecek** — though the primary/lead editors credited on that report were **Rolf Frischknecht and Niels Jungbluth**.

To be more precise, the report was edited by **Rolf Frischknecht** and **Niels Jungbluth**, with the other named authors contributing as co-authors of various sections.

The provided context does not contain information about the editors of ecoinvent report No. 1, 'Overview and Methodology for the ecoinvent database v. 2.0.' While ecoinvent Reports No. 1–25 are briefly referenced in Source 1 (Hauschild et al., LCA: Theory and Practice — 29 LCA of Food and Agriculture, p.751), no details about the editors of that specific report are provided.

The provided context does not contain information about the editors of ecoinvent Report No. 1, 'Overview and Methodology for the ecoinvent database v. 2.0.' The sources only reference the ecoinvent Reports No. 1–25 as a collective citation (Ecoinvent Centre: Ecoinvent Reports No. 1–25. Swiss Centre for Life Cycle Inventories, Dübendorf (2007)) without listing the editors of individual reports (hauschild:29 LCA of Food and Agriculture, p.751).


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.

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

## Required Justification

1. **Quantitative demonstration**: You must show that the excluded impact category contributes only a negligible share (typically **≤1–5%**, depending on the study's cut-off criteria) of the total impact across all assessed categories or of the overall environmental burden.

2. **State the criterion explicitly**: The cut-off rule applied (e.g., "impact categories contributing less than 1% of total weighted impact are excluded") must be **clearly defined and documented** in the report.

3. **Document the exclusion transparently**: The omitted category and the reason for its exclusion must be **reported explicitly** so reviewers and readers can assess the decision.

4. **Ensure no significant impact is missed**: You must confirm that the exclusion does **not bias conclusions** — i.e., the omitted category does not dominate for any specific life cycle stage or scenario even if small in aggregate.

5. **Reference to goal and scope**: The justification must be consistent with the **goal and scope** of the study, including the intended application and audience.

## Underlying Standard Requirement (ISO 14044)
ISO 14044 requires that any such omissions be **clearly stated and justified**, and that they do not compromise the **integrity and completeness** of the study for its intended purpose.

In essence: **document, quantify, and demonstrate that the omission does not materially affect the conclusions.**

The omission must be **quantitatively justified as being insignificant for the overall environmental impact**, taking into account the goal definition, especially the intended applications, and the cut-off defined for the LCI/LCA study. Furthermore, any relevant exclusion must be explicitly considered during interpretation, as it can lead to limitations for conclusions and recommendations.

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

Based on the ILCD Handbook, when applying cut-off rules to exclude an impact category deemed of little overall relevance, **the omission must be quantitatively justified as being insignificant for the overall environmental impact**, taking into account:

1. The **goal definition** of the study,
2. The **intended applications**, and
3. The **cut-off defined for the LCI/LCA study**.

Furthermore, **any relevant exclusion must be explicitly considered during interpretation** and can lead to limitations for conclusions and recommendations.

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


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.

In Life Cycle Assessment (LCA), normalisation is used to put impact category results into a common perspective. After normalisation, the results are typically brought down to values in the range of **10⁻¹⁴ to 10⁻¹⁰** (or more broadly expressed as very small dimensionless numbers, often on the order of **10⁻¹³ to 10⁻¹¹**), since individual product systems represent a tiny fraction of the total annual reference system (e.g., European or global annual impacts per person or per year).

These small normalised values are often then further processed through **weighting** to make them more interpretable, or they are compared relative to each other (rather than in absolute terms) to identify which impact categories are most significant for a given product system.

The typical value range that normalised impacts for goods and services are brought down to for better communication and interpretation is **10 to 0.00001** (instead of 1E-7 to 1E-14).

*(Source 1: Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.994; Source 2: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.120)*

The typical value range that normalised impacts for goods and services are brought down to for better communication and interpretation is **10 to 0.00001** (instead of 1E-7 to 1E-14).

This is cited in both:
- Hauschild et al., LCA: Theory and Practice — **37 LCA Cookbook, p.994**
- European Commission – JRC, ILCD Handbook: General Guide for LCA (2010) — **p.120**


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 similar study applying a limited set of impact indicators, the **cut-off** relates to the **potential environmental impacts or issues that are not covered (excluded)** by the chosen indicators.

Specifically, it refers to the fact that by selecting only certain impact categories (e.g., greenhouse gas emissions / global warming potential in a carbon footprint), other potentially significant environmental impacts — such as water use, toxicity, land use, resource depletion, acidification, eutrophication, etc. — are **cut off** (i.e., ignored or excluded from the assessment).

The cut-off therefore represents the **limitation in scope** of the study: the study does not claim to represent the full environmental profile of the product or system, and decision-makers should be aware that impacts beyond the selected indicators are not accounted for. This is typically required to be clearly communicated as part of the study's **limitations and scope**, in line with ISO 14044/14067 requirements for transparency.

Based on Source 2 (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.104):

In a Carbon footprint study or other study that applies a limited set of impact indicators, **the cut-off will relate to the considered indicators only** — for example, "covering 90% of the Climate change impacts."

In a Carbon footprint study or other study that applies 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)*


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.

## Data Gap Filling in LCA

### Preferred Data Sets

When filling data gaps, **representative data sets** shall be used — ideally data that are:
- Geographically, technologically, and temporally representative of the process being modeled
- Consistent with the methodological choices of the study (e.g., same system boundary approach, allocation method, characterization factors)

### Condition for Using Methodologically Inconsistent Data Sets

Methodologically **not fully consistent** data sets may be used when:

> **No other data are available** (i.e., no representative, methodologically consistent data exist to fill the gap)

In such cases, the use of inconsistent data sets should be:
- **Documented** transparently
- **Justified** in the study
- Subject to a **sensitivity analysis** to assess the potential influence on the results

---

### Basis (ISO 14044 / ILCD context)

This guidance is rooted in **ISO 14044:2006 (§4.3.3.4)** and elaborated in guidance documents such as the **ILCD Handbook**, which specify that fully consistent data are the standard, but pragmatic compromises are permitted under data scarcity — provided uncertainty and limitations are clearly communicated.

Based on the sources provided:

When filling data gaps in an LCA, data sets of at least **"Data estimate" quality level** shall be used, provided they increase the overall quality of the final inventory of the analysed system (Source 1, p.254).

Specifically, the following options exist for filling data gaps:
- Calculation from other known information
- Using information from similar processes or regions
- Expert-based estimates
- Generic or average data
- Correlation with more complete, high-quality data from other sources (Source 4, p.251)

Regarding **methodologically not fully consistent data sets**, these may be used when they are nevertheless **"sufficiently consistent"** — this applies mainly to LCI data sets used for **background purposes** (Source 2, p.207). The condition is that such data must still contribute adequately to the required completeness and precision of the system's LCI, and their use must be documented (Source 2, p.207; Source 3, p.247).

Any remaining data gaps, as well as the approaches used to fill them and the resulting lack of representativeness, precision, or methodological consistency, must be **clearly documented** and explicitly considered when declaring the achieved data set quality or drawing conclusions from the LCA study (Source 1, p.254).

Based on the retrieved passages, when filling data gaps in an LCA:

**Type of data sets to be used:** Individual data or data sets whose quality shall be equivalent to at least the **"Data estimate" quality level**. Options include: calculation from other known information, using information from similar processes or regions, estimates based on specific expertise, or generic/average data (ILCD Handbook, p.254).

**Condition for using methodologically not fully consistent data:** Methodologically not fully but **sufficiently consistent data** may be used — this applies mainly to LCI data sets **for background use** (ILCD Handbook, p.207).

In both cases, the measures taken must be documented, and any resulting lack of representativeness, precision, and methodological consistency must be clearly documented and explicitly considered when declaring the achieved data set quality or when drawing conclusions or recommendations from the LCA study (ILCD Handbook, p.254).


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

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

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

1. **What** – the function or service being performed
2. **How much** – the magnitude/quantity of the function
3. **How well** – the quality or performance level required
4. **For how long** – the duration or time frame over which the function is delivered

Based on Source 1 (Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.974), a functional unit shall be identified and specified in detail across the following 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)

*(Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.974)*

Based on Source 1 (Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.974), a functional unit shall be identified and specified in detail across the following 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)

*(Note: The source also lists a fifth aspect — changes in functional performance over time — but the core four aspects are as listed above.)*

**Citation: Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.974**


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 (LCA), **markets** (particularly in the context of system expansion and allocation, especially within the ecosphere/technosphere framework used in consequential LCA) form for specific conceptual reasons. Here are the main reasons for the forming of markets as described in LCA:

## Main Reasons for Market Formation in LCA

1. **Multi-output processes / Co-production**
When a process produces more than one useful output (co-products), a market forms because multiple producers and consumers interact around those outputs, necessitating decisions about how to handle allocation or system expansion.

2. **Supply and demand interactions**
Markets emerge because multiple suppliers can provide a functionally equivalent product/service, and multiple consumers demand it. In consequential LCA, identifying the **marginal supplier** in a market is essential to determine what activity is actually affected by a change in demand.

3. **Substitutability / Functional equivalence**
Products that are interchangeable (substitutes) naturally form a common market. LCA uses this to determine which processes are constrained (determining) or unconstrained in supply.

4. **Economic mechanisms**
Price signals coordinate production and consumption decisions, which LCA (especially consequential LCA) must reflect when modeling real-world consequences of decisions.

5. **By-product and waste handling**
The fate of by-products creates secondary markets, influencing which processes are displaced or expanded as a consequence of the studied system.

These concepts are particularly central to **consequential LCA methodology**.

According to the ILCD Handbook, the main reasons for the forming of markets are:

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

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

According to the ILCD Handbook, the main reasons for the forming of markets are:

1. **Political reasons**, including:
   - Legislation (especially on competition and product requirements such as material bans, product safety, etc.)
   - Technical and other standards
   - Taxes
   - Subsidies

2. **Cultural reasons**, including:
   - Recognised markets by producers and service providers

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


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 a collaboration between the **Carbon Trust** and the **Department for Environment, Food and Rural Affairs (Defra)**, with the involvement of the **British Standards Institution (BSI)**, which developed the PAS 2050 specification itself.

To be more precise, the Guide was produced by the **Carbon Trust** and **Defra**, supporting the BSI's PAS 2050 standard.

Based on Source 1, the 2008 "Guide to PAS 2050 - How to assess the carbon footprint of goods and services" was produced through collaboration between **BSI British Standards, DEFRA (Department for Environment, Food & Rural Affairs), and the Carbon Trust**.

*(Source 1: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.391)*

Based on the context, the 2008 "Guide to PAS 2050 - How to assess the carbon footprint of goods and services" was produced by **BSI British Standards** in collaboration with **DEFRA** (Department for Environment, Food & Rural Affairs) and the **Carbon Trust**.

*(Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.391)*


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).

## Open-Loop Recycling: Energy Recovery Example

### Secondary Good Produced
The secondary good produced is **electricity** (generated from the combustion/incineration of the waste polymer for energy recovery).

### Credit Calculation

In open-loop recycling for energy recovery, the system receives a credit for the **avoided production** of primary electricity that the recovered energy displaces.

For a **0.2 kg polymer product** undergoing energy recovery:

The credit is calculated based on:
- The **energy content** of the polymer (calorific value)
- The **conversion efficiency** of the energy recovery process
- The **substitution factor** for the displaced primary electricity

Using typical values applied in LCA energy recovery crediting (following the substitution/system expansion approach):

The system receives a credit equivalent to the **primary-produced electricity avoided**, calculated as:

> **Credit = mass × net calorific value × energy conversion efficiency**

For example, with a polymer calorific value of ~40 MJ/kg and a conversion efficiency of ~25–30%, approximately **2–2.4 MJ (or ~0.6 kWh)** of primary electricity production is credited per 0.2 kg polymer.

### Key Principle
The **primary production of electricity is credited** (subtracted from the system's burden) because the energy recovered from the waste polymer **displaces** the need to generate that electricity from primary sources (e.g., the grid mix or a specific reference technology).

If you are working from a specific textbook example (e.g., Weidema's or the ISO 14044 guidelines), the exact credit figure will depend on the assumed calorific value and efficiency stated in that source.

The secondary good produced is **0.28 kWh of electricity** generated from the incinerated plastic waste that is fed into the grid. This electricity is technically equivalent to, and assumed to have the same market price as, average large producer electricity. As a result, the **full 0.28 kWh** of primary-produced electricity would be substituted ("credited").

*(ILCD Handbook: General Guide for LCA, 2010, p. 360)*

Based on the context, in the open-loop recycling example where a 0.2 kg polymer product can only be used for energy recovery (due to material degradation after long-time use or soiling), the **secondary good is 0.28 kWh of electricity** generated from the incinerated plastic waste that is fed into the grid.

This electricity is technically equivalent to, and assumed to have the same market price as, the average large producer electricity price of 0.04 Euro per kWh. As a result, the **full 0.28 kWh of primary-produced electricity is substituted ("credited")**.

*(ILCD Handbook: General Guide for LCA (2010), p. 360)*


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.

In LCA, when handling long-term emissions (particularly relevant for waste disposal, landfills, or slow-releasing processes), the conventional time boundary used is **100 years**.

Emissions occurring **within 100 years** are typically included in the standard impact assessment results, while emissions released **beyond 100 years** are reported separately. The two sets of results are then presented and discussed jointly (rather than aggregated into a single score), acknowledging the uncertainty and ethical questions around how to weight near-term versus very long-term impacts.

This approach is reflected in guidance such as the **ILCD Handbook** and is common practice in waste-related LCA methodology (e.g., following the approach developed by Doka, ecoinvent, and others for landfill modeling).

100 years from the time of the study. Results for interventions within the first 100 years are analysed separately from those beyond that time limit, and both are then jointly discussed. (ILCD Handbook, p.304; Hauschild et al., LCA Cookbook, p.1036)

Based on the ILCD Handbook and the LCA Cookbook, the time boundary used to separate the two sets of results is **100 years from the time of the study**. Results for interventions within the first 100 years are calculated and analysed separately from those beyond that 100-year limit, and both sets are then jointly discussed.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.304; Hauschild et al., LCA Cookbook, p.1036)*


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 data quality of LCI (Life Cycle Inventory) data is assessed using the following **data quality indicators (DQIs)**:

---

### 1. **Technological Representativeness**
How well the data reflect the actual technology used in the process being modeled (e.g., state-of-the-art vs. outdated technology).

### 2. **Geographical Representativeness**
How well the data reflect the geographical location or region relevant to the study (e.g., local, national, regional, global).

### 3. **Time-Related Representativeness**
How current or up-to-date the data are relative to the time period of the study.

### 4. **Completeness**
The degree to which all relevant flows (inputs and outputs) of a process are included and reported.

### 5. **Precision / Uncertainty**
The degree of variability or uncertainty in the data values (e.g., measured, estimated, or assumed data).

### 6. **Methodological Appropriateness and Consistency**
How well the data conform to accepted LCA methods and are internally consistent.

### 7. **Overall Data Quality Rating**
A summary/aggregated quality score derived from the above indicators.

---

These indicators are typically scored on a **5-level scale** (1 = very good to 5 = very poor), and they align with the **ILCD Handbook** guidelines developed by the European Commission's Joint Research Centre (JRC). The overall data quality classification feeds into the **ILCD compliance levels** (Level I, II, or III) for datasets.

Based on the ILCD Handbook, the data quality indicators defined under the ILCD approach for classifying the achieved data quality of LCI data are:

1. **Technological representativeness**
2. **Geographical representativeness**
3. **Time-related representativeness**
4. **Completeness** (of environmental impacts covered by the inventory)
5. **Precision / uncertainty** (of the collected or modelled inventory data)
6. **Methodological appropriateness and consistency** (especially on the system level)

These indicators relate directly to the key characteristics of LCI data sets that describe their quality. As noted in the handbook, **"the weakest of the quality indicators generally weakens the overall quality of the data set,"** and the overall Data Quality Rating (DQR) is calculated by summing up the achieved quality rating for each of the quality components.

*(Sources: ILCD Handbook, Annex A, Chapter 12.3, p. 329; p. 1–2; and Section 6.8.1, p. 122)*

Based on the ILCD Handbook, the data quality indicators defined for classifying the achieved quality of LCI data sets are:

1. **Technological representativeness** – how well the data reflects the actual technology being modelled
2. **Geographical representativeness** – how well the data reflects the actual geographical scope
3. **Time-related representativeness** – how well the data reflects the relevant time period
4. **Completeness** – coverage of all relevant environmental impacts via the completeness of the inventoried flows
5. **Precision / uncertainty** – the achieved precision of the collected or modelled inventory data
6. **Methodological appropriateness and consistency** – appropriate and consistent application of LCI methodologies (especially on the system level)

These indicators relate directly to the key characteristics of LCI data sets that describe their quality. The weakest of the quality indicators generally weakens the overall quality of the data set, and the **overall data quality rating (DQR)** is calculated by summing up the achieved quality rating for each of the quality components.

Additionally, complementary aspects — **documentation**, **review**, and **nomenclature** — are combined with the overall data quality indicators to form the "Overall data set quality," though these do not reflect actual data quality itself.

*(ILCD Handbook: General Guide for LCA, 2010, pp. 1, 2, 329; Annex A, Chapter 12.3)*


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.

In LCA and related decision-support contexts (particularly in consequential LCA methodology), the threshold commonly used is **100%** — meaning that when the annual additional demand or supply exceeds the capacity of the annually replaced (retired/decommissioned) installed capacity, the cap is set at **100%** of that replaced capacity.

However, if you are referring to a specific methodological guideline or framework (e.g., the ecoinvent consequential approach or a particular policy analysis tool), the specific threshold is often cited as **100%** of the constrained capacity as the upper bound for attributing marginal supply to the retiring/replaced technology.

Could you provide more context about the specific framework or document you are referencing? That would allow me to give a more precise answer, as different guidelines may specify different thresholds (e.g., some use **50%** as a decision threshold for determining the marginal supplier).

**5%**

If the annual additional demand or supply triggered by the analysed decision exceeds the capacity of the annually replaced installed capacity, and that percentage is over 5%, then **5% should be assumed instead** as the cap.

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

When the annual additional demand or supply triggered by the analysed decision exceeds the capacity of the annually replaced installed capacity, the threshold percentage that should be used as the cap is **5%**.

As stated in the ILCD Handbook: "if that percentage is over 5%, 5% should be assumed instead."

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


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.

## Purpose of Sensitivity Analysis in LCA

Sensitivity analysis in an LCA study serves to **assess how changes in key assumptions, data, or methodological choices affect the final results and conclusions**. Its core purposes are:

1. **Identify critical parameters** – Determine which inputs (e.g., emission factors, allocation factors, system boundaries, transport distances) have the greatest influence on outcomes.
2. **Test robustness of conclusions** – Verify whether the comparative assertions or hotspot findings remain valid under alternative assumptions.
3. **Prioritize data collection** – Focus effort on refining data for parameters that matter most, avoiding unnecessary precision on inconsequential inputs.
4. **Increase transparency and credibility** – Demonstrate that results are not artifacts of arbitrary methodological choices (required under ISO 14040/14044 for comparative studies subject to critical review).
5. **Support decision-making under uncertainty** – Inform stakeholders of the confidence level associated with conclusions.

---

## Quantitative Methods Supporting Sensitivity Analysis

| Method | Description |
|--------|-------------|
| **One-at-a-time (OAT) / differential sensitivity analysis** | Varies one parameter at a time by a fixed percentage (e.g., ±10%) and observes the change in results; simple but ignores parameter interactions |
| **Scenario analysis** | Compares discrete alternative scenarios (e.g., different energy mixes, end-of-life routes, allocation methods) |
| **Monte Carlo simulation** | Propagates probability distributions across all uncertain parameters simultaneously; yields a distribution of output results and confidence intervals |
| **Latin Hypercube Sampling (LHS)** | A more efficient stratified sampling variant of Monte Carlo for large parameter spaces |
| **Global sensitivity analysis (e.g., Sobol indices)** | Variance-based method that decomposes output variance to attribute shares to individual parameters and their interactions |
| **Contribution analysis / perturbation analysis** | Mathematically derives partial derivatives of the result with respect to each inventory parameter |
| **Fuzzy set / interval arithmetic** | Propagates imprecise or range-based data without assuming probability distributions |

---

**In practice**, ISO 14044 requires sensitivity analysis to be performed and reported; Monte Carlo simulation (supported by tools such as SimaPro and openLCA) is the most widely used rigorous quantitative approach.

The purpose of the sensitivity check in an LCA study is **to assess the reliability of the final results and — if included — of the conclusions and recommendations of the LCA study**. Expert judgement and previous experiences contribute to the sensitivity analysis.

The two quantitative methods that support the sensitivity check are:

1. **Scenario analysis**
2. **Uncertainty calculations**

In the interpretation step, the sensitivity analysis is used together with information about the uncertainties of significant issues among inventory data, impact assessment data, and methodological assumptions and choices to assess the reliability of the final results and conclusions.

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

The sensitivity check in an LCA study serves **"to assess the reliability of the final results and – if included – of the conclusions and recommendations of the LCA study."** Expert judgement and previous experiences contribute to it, while the quantitative methods that support it are **scenario analysis and uncertainty calculations**.

In the interpretation step, the sensitivity analysis is used together with information about the uncertainties of significant issues among inventory data, impact assessment data, and methodological assumptions and choices to assess the reliability of the final results and conclusions.

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