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

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

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

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

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

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

## Identifying the Need to Extend an LCIA Method

### When Should This Be Identified?

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

More precisely, the identification should occur:

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

---

### Why at This Stage?

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

---

### Summary

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


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

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

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

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

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

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

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


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

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

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

*(Source 1: ILCD Handbook, p.183, Provision XIII.b.vi; Source 2: Hauschild et al., LCA Cookbook, p.1011, §13:2:6)*

## Correcting Reduced Technical Properties of Secondary Goods in Consequential LCA

In consequential modelling, when a secondary (recycled) material has **reduced technical properties** compared to the primary material it substitutes, the inventory must be corrected to reflect the **actual functional equivalence**.

---

### The Core Principle

The substitution (system expansion) approach in consequential LCA requires that only **functionally equivalent** quantities are credited/debited. If the secondary good cannot perform the same function on a 1:1 basis, a direct substitution credit would overstate the benefit.

---

### Correction Method

The correction is applied through a **quality ratio (or equivalence factor)**:

**Correction factor = Technical performance of secondary material / Technical performance of primary material**

The credited avoided production (or the quantity of secondary material modelled as substituting primary) is **scaled down** proportionally:

> *Effective substitution credit = Quantity of secondary material × (Technical property_secondary / Technical property_primary)*

This means the system receives a **reduced avoided burden credit**, reflecting that more secondary material may be needed per unit of function, or that it can only partially replace primary material.

---

### Practical Example

- Recycled steel has 90% of the tensile strength of virgin steel.
- The quality correction factor = 0.90.
- The avoided production credit is multiplied by 0.90 — i.e., only 0.9 tonnes of virgin steel production is avoided per tonne of recycled steel supplied.

---

### Key Point

This correction ensures the **functional unit** is maintained and that the consequential model reflects **marginal real-world substitution** accurately, avoiding artificial inflation of recycling benefits.


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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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

---

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

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


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

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

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

*(Source 1: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p. 229)*

For quasi-permanent CO₂ storage to be credited as having no emissions in LCA (specifically under standards such as those developed by the **ISO 14067** framework and related guidance, including work by the **IPCC** and **PAS 2050/2060** approaches), the storage must be demonstrated to last for a period of **at least 100 years**.

This 100-year timeframe is the conventional threshold used in life cycle assessment and carbon accounting frameworks (consistent with the 100-year global warming potential time horizon) to qualify storage as effectively permanent for accounting purposes, provided it is verified by current scientific knowledge and independent qualified expert review that the CO₂ will not be re-emitted during that period.


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

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

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

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

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

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

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

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

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

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

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

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

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

---

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


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

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

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

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

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

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

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

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

Specifically, for each limitation you must assess:

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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


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

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

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

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

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

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

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

## Direct Vehicle Effects

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

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

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

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

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

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


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

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

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

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

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

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

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


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

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

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

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

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

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

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

## Black Box Unit Process: Checking for Splitting Opportunities

### What Should Be Checked

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

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

### Benefits of Splitting

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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


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

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

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

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

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


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

Allocation is the third, alternative solution.

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

As stated in the ILCD Handbook:

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

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

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

More specifically, in the ILCD hierarchy:

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

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

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


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

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

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

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

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

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

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

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


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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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


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.

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.

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.


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.

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)*

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.


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

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

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

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

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

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


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

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

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

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

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

More precisely, this is justifiable when:

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

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


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

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

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

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

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

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

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


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, 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, **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.


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.

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)*

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.)*


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

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

The two sub-types 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)*

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.


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.

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)*

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.


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

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

The starting point 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)*

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.


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.

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)*

# 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.


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.

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]

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.


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

The "short-term marginal" processes or systems.

When the 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)*

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.


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

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

When comparing alternatives in specific parts of otherwise identical or similar systems, **the identical parts of the systems can be left out when drawing the system boundaries**.

The benefit is that **this can drastically reduce the effort for the LCA study**, since only the differing parts need to be assessed to determine which system has the lowest environmental impact.

This applies in cases such as comparing material alternatives for parts of a product, or comparing alternative electricity sources in the energy efficiency in the use stage of an electricity-using product, where the sole purpose is to identify the environmentally preferable option (e.g., for ecodesign or procurement decisions).

*(Source 1: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.144)*

This is further supported by Hauschild et al., which states that "in comparative studies it is justified to exclude identical processes if they deliver identical quantities of services (energy, materials or treatment of waste) in the systems studied."

*(Source 2: Hauschild et al., LCA: Theory and Practice — 8 Scope Definition, p.102)*

## Handling Identical Parts in Comparative LCA

When comparing alternatives in specific parts of otherwise identical systems, the **identical parts can be excluded from the system boundary** (i.e., they can be "cut out" or omitted from the analysis).

### What is done:
The system boundary is drawn to include **only the parts that differ** between the alternatives — the components, processes, or subsystems that actually vary. The identical portions of the system are simply not modeled or inventoried.

### The benefit:
This **significantly reduces the scope and complexity** of the study:

- **Saves time and resources** — there is no need to collect inventory data, model, or assess impacts for processes that are the same in both systems and would cancel out in the comparison anyway
- **Focuses the analysis** — attention is directed precisely where the differences (and therefore the meaningful results) lie
- **Reduces data burden** without compromising the validity of the comparative conclusion, since identical contributions would affect both alternatives equally and thus have **no influence on the relative outcome**

### Important condition:
This simplification is only valid when the **sole purpose** is to determine which alternative has the lower impact — it would **not** be appropriate if the goal requires knowing the absolute environmental impact of the full system.

This approach is sometimes referred to as identifying the **relevant system boundary** or performing a **differential/marginal comparison**.


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.

**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)*

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.


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.

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.

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.


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

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

Detailed and differentiated method provisions are still necessary 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)**

## 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.


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.

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.

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.


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.

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")*

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.


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.

**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 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.


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.

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]

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.


Question: What technique is described as a way to avoid black box unit 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)*

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).


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.

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).

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.


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

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."*

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.)*


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.

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)*

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.


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

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

A default period of **20 years** 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)*

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.


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

The provided context does not 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 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.


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

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

The omission must be **quantitatively justified as 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)*

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.**


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.

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)*

## 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**.


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

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

An accompanying review 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 (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.


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.

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)

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.


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

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

The typical value range 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)*

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.