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

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

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

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

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

In practice, this means:

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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


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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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

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

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

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


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

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

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

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

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


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

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

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

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

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

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

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

The key differences are:

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

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

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

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

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


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

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

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

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

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

So a market may be:

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

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

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


Question: What term describes a process in which additional demand does not change market direction and does not result in additional or reduced capacity of affected processes or systems?

The term is “short-term marginal” processes / systems.

Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.166.

The term is **background process** in consequential LCA terms, or more specifically a **non‑marginally affected process/system**.

But the standard phrase for your definition is usually:

**A constrained market / inelastic supply situation**, where **additional demand does not affect production capacity or market direction**.

In consequential LCA, this means the process is **not marginal** — it does **not respond to the extra demand**.


Question: In modelling reuse, recycling, and energy recovery, between what two things is allocation strictly performed?

Allocation is performed between the two co-functions that the reused, recycled, or recovered good performs—once for the primary product and once for further products as the secondary good—rather than between the first and second life cycles.

Allocation is strictly performed not between the first and second life cycle, but between the two co-functions that the reused, recycled, or recovered good performs: once for the primary product and once for further products as the secondary good.
(Source 1: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.345)

In LCA modelling of reuse, recycling, and energy recovery, allocation is strictly performed **between the product system under study and the other product system(s) connected to it through the reused/recycled/recovered material or energy flow**.

So, the allocation is **between interconnected product systems**, not between waste and product as such.


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

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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


Question: In which part of an LCA data set should the documentation of review findings be placed, and why?

It belongs to the "Review" part, because it does not relate to the documentation of the object of the data set.

The documentation of review findings should be placed in the “Review” part of the LCA data set, because it does not relate to documenting the object of the data set itself.

Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), Chapter 10.3, p.336.

In an LCA data set, the **documentation of review findings** should be placed in the **validation / review section** of the data set documentation, typically alongside:

- the **type of review** performed,
- the **reviewer(s)**,
- the **date**,
- the **scope and method of the review**, and
- the **conclusions / findings**.

### Why there?
Because review findings are part of the data set’s **quality assurance and credibility documentation**, not the inventory results themselves. Placing them in the review or validation section:

- keeps a clear distinction between **modeled data** and **external evaluation**,
- makes the data set more **transparent and traceable**,
- helps users assess **reliability, compliance, and fitness for use**,
- supports consistency with common LCA/EPD data documentation practices.

If the format has no dedicated review field, the findings should be included in the **general administrative/documentation metadata**, with a clear label indicating they are **review results**.


Question: What two crosscutting requirements on LCA must be explicitly checked, referred to in subsequent work, and documented?

Consistency of methods, assumptions, and data; and reproducibility.

The two crosscutting requirements are:

- Consistency of methods, assumptions, and data (chapter 6.2.1)
- Reproducibility (chapter 6.2.2)

Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.51.

The two **crosscutting requirements** that must be **explicitly checked**, **carried through later phases**, and **documented** in LCA are:

1. **Consistency**
2. **Completeness**

These need to be verified early, referenced throughout the study, and reported in the documentation.


Question: Why are errors of below one order of magnitude considered worse, and what are two sources of such errors?

They are worse because they can more easily pass unnoticed while still making the data and conclusions invalid. Two sources are the differing regional handling of the "." and "," decimal separators, and unit conversion errors from using different unit systems (e.g., Imperial to SI).

Errors of below one order of magnitude are considered worse because they can much more easily pass unnoticed while still rendering the data and conclusions invalid. Two sources of such errors are:
1. Different handling of “.” and “,” as decimal separators across regions and countries.
2. Unit conversion errors from using different unit systems, e.g. Imperial to SI.

(Source 1: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.203)

In LCA, errors **below one order of magnitude** are often considered “worse” because they are **harder to detect**. If a result is off by a factor of 2–5, it can still look plausible and may mislead decisions, whereas an error of 10× or more is more likely to be noticed as obviously wrong.

Two common sources of these sub-order-of-magnitude errors are:

1. **Parameter/data uncertainty**
   - Inaccurate emission factors, energy use, yields, transport distances, etc.

2. **Modeling choices / methodological assumptions**
   - System boundary selection, allocation rules, cut-off criteria, temporal or geographic mismatches.

So the issue is not that smaller errors are numerically larger, but that they are **more dangerous in practice because they often go unnoticed**.


Question: In the context of consequential modelling within LCA, what issue is addressed in the section on solving multifunctionality of processes?

Solving the multifunctionality of processes in consequential modelling.

In consequential modelling, the section addresses the task of solving multifunctionality as part of identifying and describing processes, because it directly affects which processes are included in the model rather than being a subsequent step as in attributional modelling (ILCD Handbook, Chapter 7 Life Cycle Inventory analysis, p.255).

In consequential LCA, the section on **solving multifunctionality of processes** addresses the problem that **one process delivers several functions/products at the same time**, making it unclear **which environmental consequences should be attributed to the studied product**.

The issue is therefore how to handle **co-products, by-products, recycling, and other multifunctional systems** so that the model reflects **the actual consequences of a change in demand**. In consequential modelling, this is typically addressed by **system expansion or substitution**, rather than simple allocation.


Question: How must a reminder flow be treated with respect to impact assessment factors?

Reminder flows shall not carry an LCIA impact factor; they are excluded from impact assessment.

A reminder flow shall be excluded from impact assessment: it must not carry any LCIA impact factor or characterisation factor.

Citations:
- ILCD Handbook, chapter 7.4.3.8, p.233: “Reminder flows shall not carry an LCIA impact factor.”
- ILCD Handbook, chapter 7.4.3.2 / reference to 7.4.3.8, p.217: “Reminder flows shall later be excluded from the impact assessment, i.e. have no characterisation factors.”

A reminder flow must be treated consistently with the characterization model of the impact category:

- It should be linked to the correct impact assessment factor set for the same flowable, compartment, and reference unit.
- If it represents an elementary flow, use the corresponding characterization factor directly.
- If no matching factor exists, its contribution to that impact category is effectively zero unless a justified mapping or substitution is defined.
- You should not assign arbitrary factors; any mapping must be documented and methodologically consistent.

If you want, I can also explain this specifically in the context of LCIA databases like ecoinvent/openLCA or ILCD/EF.


Question: In life-cycle assessment, what does the term "cut-off" refer to?

The omission of not relevant life cycle stages, activity types (e.g. investment goods, storage), specific processes and products (e.g. re-granulating of internally recycled polymer production waste before re-melting), and elementary flows from the system model.

In LCA, “cut-off” refers to the omission from the system model of life cycle stages, activity types, specific processes and products, and elementary flows that are judged not relevant or insignificant to the study outcome. It is quantified by the share of total environmental impact approximated to be excluded.
(European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010, p.102; p.103)

In LCA, **cut-off** refers to a **rule or threshold used to exclude certain flows, processes, or life-cycle stages from the study** because they are considered insignificant.

Common cut-off criteria are based on:
- **mass**
- **energy**
- **environmental relevance** (e.g., expected contribution to impacts)

Example: a practitioner might exclude materials that make up less than 1% of total mass, unless they are environmentally important.

The idea is to simplify the assessment, but **cut-off choices must be clearly justified and documented**, because they can affect results.

There is also a specific use of “cut-off” in some database/system-model contexts, especially for **recycling allocation**: a **cut-off approach** assigns the burdens of producing virgin material to the first product system, while the recycled material enters the next product system with little or no upstream burden.


Question: In life cycle inventory analysis, where can confidential information be placed so that only critical reviewers can access it under confidentiality?

In a separate "confidential report" that is made accessible only to the critical reviewers under confidentiality.

Confidential information can be placed in a separate “confidential report” made accessible only to the critical reviewers under confidentiality.

Cited sources:
- ILCD Handbook, p.204: “confidential information can be documented in a separate ‘confidential report’ that is made accessible only to the critical reviewers under confidentiality.”
- ILCD Handbook, p.318, Chapter 10.3: “VI) MAY - Confidential report … shall however be made available to the critical reviewers under confidentiality.”
- ILCD Handbook, p.205, Chapter 7.4.2.11.1: information in a separate “confidential report” accessible only to the critical reviewers under confidentiality.

In a **confidential annex/appendix to the LCI report**.

This allows the sensitive inventory data to be made available **only to the critical review panel or reviewers under a confidentiality agreement**, while keeping it out of the public report.


Question: What principle should guide the level of differentiation of flow data sets in an inventory relative to LCIA methods?

The differentiation of flow data sets should be no finer than supported by state-of-the-art LCIA methods and no coarser than required to capture differences in the LCIA results, keeping the number of flows as low as reasonably possible without relevantly affecting impact assessment.

The level of differentiation of flow data sets in the inventory should be guided by compatibility with the applied LCIA methods: collect only the degree of differentiation that the LCIA method requires, especially spatial differentiation where relevant. If spatially differentiated LCIA methods are used, the inventory should include compatible spatial information for elementary flows.

Cited: Hauschild et al., *LCA: Theory and Practice*, Chapter 8, p.113; European Commission – JRC, *ILCD Handbook*, p.245.

Use the **goal‑ and impact‑oriented principle**: differentiate inventory flow data sets **only as far as needed to support the intended LCIA methods and the study goal**.

In practice, this means:

- **Differentiate more** when LCIA distinguishes between flows by **substance, compartment, location, form/species, timing, or emission context** and those differences materially affect results.
- **Avoid unnecessary detail** when the LCIA methods do not use that distinction or it is irrelevant to the study goal.

So, the **level of flow differentiation in the LCI should be consistent with, and sufficient for, the sensitivity and requirements of the selected LCIA methods**—no less, and ideally no more than necessary.


Question: In life-cycle assessment, what condition causes the provision of an additional good or service to fall under Situation B rather than Situation A?

When the annually provided amount is larger than the average replacement rate of the installed capacity of the superseded alternative good or service.

It falls under Situation B when the additional good or service causes large-scale consequences outside the foreground system, i.e. the analysed decision alone is big enough to overcome thresholds or other constraints and changes installed production capacity; more specifically, “large-scale (‘big’) consequences shall generally be assumed if the annual additional demand or supply … exceeds the capacity of the annually replaced installed capacity of the additionally demanded or supplied process, product, or broader function.”
— European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.48, p.91, p.92

In consequential LCA, an additional good or service falls under **Situation B** when its provision is **constrained**—that is, the supplier cannot simply increase output in response to a small change in demand.

So:

- **Situation A:** suppliers are unconstrained and can adjust production.
- **Situation B:** a **capacity, regulatory, physical, or other limiting constraint** prevents that direct adjustment, so the change is handled differently in the market/system.


Question: What two situations can cause an LCA study to produce misleading conclusions?

When elements of the goal and scope definition are performed in a way that leads to misleading results, and when the results are interpreted in a way that is not in accordance with the goal of the study or how the analysis was scoped.

Two situations that can cause misleading conclusions are:

1. Omitting relevant impact categories or life-cycle aspects when comparing technologies with different impact patterns — this can seriously mislead results, e.g. focusing only on water and energy use while ignoring detergent-related impacts.
(Source 2, European Commission – JRC, ILCD Handbook, p.368)

2. Interpreting negative impacts from the disposal stage without proper relation to the goal and system boundaries — these can mislead interpretations of LCA studies.
(Source 3, Hauschild et al., *LCA: Theory and Practice*, Ch. 35, p.907)

Two common situations can make an LCA give **misleading conclusions**:

1. **Poorly defined goal, scope, or functional unit**
   If the systems being compared do not deliver the same function, or the system boundaries are inconsistent, the comparison can be unfair and the results misleading.

2. **Shifting burdens by looking at too little or too few impacts**
   If the study ignores important life cycle stages or only focuses on one impact category, it may appear that one option is better while it actually causes greater impacts elsewhere.


Question: When unit process, partly terminated system, and LCI results data sets are the deliverables of a study, for what purpose does provision 8.2 on the calculation of LCIA results apply?

It applies only to quantify the achieved completeness and precision, since these need to be evaluated from the system's perspective.

Provision 8.2 applies “only to quantify the achieved completeness and precision,” since these “need to be evaluated from the system’s perspective.”
(Chapter 8.2, p.280)

Provision 8.2 applies to the purpose of **using those delivered datasets to calculate LCIA results later by the dataset user**.

In other words, if a study delivers **unit process**, **partly terminated system**, or **LCI results** datasets rather than final impact results, provision 8.2 is relevant for ensuring they are suitable for **subsequent LCIA calculation and use in other studies/tools**.


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

In Situation C1, existing interactions with other systems (such as recycling benefits or avoided production for co-products) are included in the LCI model, whereas Situation C2 accounts for the analysed system in isolation.

Situation C1 is “accounting with interactions”: it describes an existing system and includes its interactions with other systems, e.g. crediting avoided burdens from recycling. Situation C2 is “accounting without interactions”: it describes an existing system in isolation and does not include interactions with other systems. Also, for multifunctionality, C1 uses substitution/system expansion, while C2 uses allocation.

Citations:
- Hauschild et al., LCA: Theory and Practice, Ch. 37 “LCA Cookbook”, p.969
- Hauschild et al., LCA: Theory and Practice, Ch. 7 “Goal Definition”, p.71
- European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.8–9, p.92

In **life-cycle inventory (LCI) accounting**, **Situation C1** and **Situation C2** are distinguished by **how multifunctionality is handled when recycling or recovery occurs at end of life**.

- **Situation C1**:
  The recycled material is treated as a **valuable co-product** that **displaces primary production** in a subsequent system.
  - The product system may receive a **credit** for supplying recyclable material.
  - This corresponds to an **open-loop / market-substitution** logic.

- **Situation C2**:
  The recycled material is treated as **waste leaving the system**, and the original product system is generally assigned only the burdens up to the point of waste management.
  - The benefits of using the recycled material are assigned to the **next product system**, not the original one.
  - This corresponds more to a **cut-off / recycled content** logic.

So, the key distinction is:

- **C1 gives the original system credit for avoided virgin production from recycling**
- **C2 does not; it cuts off at disposal/recycling and assigns downstream benefits elsewhere**


Question: When aggregating unit processes to LCI results, how should product and waste flows be handled if background data of sufficient quality is not available for them?

These flows shall remain in the aggregated inventory, making the data set a "partly terminated system," and the user must be explicitly informed in a prominent place that these parts still need to be completed or the gap considered in further use and interpretation.

When aggregating unit processes to LCI results, product and waste flows for which sufficient-quality background data is not available shall remain in the aggregated inventory, making the dataset a “partly terminated system.” The user must be explicitly informed prominently that these parts still need to be completed or the gap considered in further use and interpretation.
— European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.210

When aggregating unit processes to LCI results, product and waste flows without sufficiently reliable background data should generally be treated as follows:

- **Do not include them with poor-quality or speculative background data** just to make the dataset appear complete.
- **Keep them as unlinked / open flows** in the aggregated LCI if no adequate background dataset exists.
- **Document them transparently**, including:
  - flow identity and amount,
  - whether they are a product output or waste flow,
  - the reason they could not be linked,
  - data gaps and expected relevance.

This avoids introducing misleading results. If the flows are environmentally relevant, they should be highlighted as a limitation and, where possible, addressed later through improved data, proxy data, or sensitivity analysis.


Question: How can relevant specific emissions that escape previous assessment steps, such as particle or dioxin emissions, mostly be identified?

By including all legally regulated emissions for that or similar process types, or through expert judgement drawing on know-how for these or similar processes.

They can mostly be identified by including all legally regulated emissions for that or similar process types, or by expert judgement drawing on know-how for these or similar processes.

Chapter/page: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p. 292.

Mostly by doing a **systematic process inventory review** of the specific technologies and substances involved, especially:

- **Check unit process data and emission lists** in the LCI for unusual or toxic releases.
- **Review process-specific literature, permits, and BAT/BREF documents** to identify known emissions from that technology.
- **Use expert judgment / hotspot analysis** to flag processes where such emissions are likely, e.g. combustion, incineration, metal processing, chlorine chemistry.
- **Consult emission measurement databases and regulatory reporting** for site- or sector-specific pollutants.

In short: these emissions are usually found through a **detailed, process-specific examination beyond aggregated impact results**, supported by technical and regulatory sources.


Question: When a final exclusion is made from the study's inventory, what must be done and what consequences may result?

The final exclusion must be justified by referring to the cut-off criteria, and it may limit the applicability of the resulting data set or the conclusions that can be drawn from a comparative study.

A final exclusion must be justified by referring to the cut-off criteria, and the exclusion list should be updated to reflect the final situation. Such an exclusion may limit the applicability of the resulting data set or the conclusions that can be drawn from a comparative study. (Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.983)

Also, exclusions should be stated and justified together with a consideration of their significance for the study outcome. (Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards, 2002, p.482)

If a **final exclusion** is made from the LCA study inventory, you should:

1. **Clearly document and justify it**
   - State what was excluded and why.
   - Show that the exclusion meets the study’s cut-off/completeness rules.

2. **Assess and report its significance**
   - Evaluate whether the exclusion could materially affect the results, conclusions, or comparisons.
   - Mention any assumptions and uncertainties introduced.

3. **Declare it transparently in the report**
   - Especially important for comparative assertions or critical review.

**Possible consequences:**
- **Reduced completeness** of the inventory
- **Higher uncertainty**
- **Potential bias or distortion** of impact results
- **Weaker comparability/credibility** of the study
- In serious cases, it may **limit the validity of conclusions** or require **qualification by reviewers** if the exclusion is not negligible.


Question: When combining data sets from different sources, what reference elementary flows should be used to ensure compatible inventories and avoid multiple occurrences of the same flows in joint or aggregated inventories?

The ILCD reference elementary flows should be used wherever possible and relevant, to ensure compatible inventories and avoid multiple occurrences of the same flows in joint or aggregated inventories.
(Source 1: European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010, p.153)

Use **one consistent, authoritative elementary flow list as the common reference** for all datasets.

In practice, inventories from different sources should be **mapped to the same elementary flows** defined by a recognized **LCIA/LCI nomenclature** or **reference flow list** so that identical emissions/resource flows are not counted multiple times under different names.

Typical choices include:
- the **ecoinvent/ecoinvent-compatible elementary flow list**
- **ILCD/EF reference elementary flows**
- another **single harmonized master list** used consistently across the combined database

Key point:
**Every dataset must be converted to the same flow identity, compartments, and units** before aggregation. This avoids duplicate occurrences like the same substance appearing under slightly different names or compartment definitions.


Question: What environmental topic did Finnveden's 2008 editorial in the International Journal of Life Cycle Assessment address regarding electricity production?

It addressed electricity production in consequential assessments in a world with CO2 caps.

The provided context does not contain Finnveden’s 2008 editorial or its topic regarding electricity production, so I cannot determine it from the sources given. (No relevant chapter/page in the provided context)

Finnveden’s 2008 editorial addressed the **choice of environmental assessment perspective for electricity production**, specifically the debate around **attributional vs. consequential life cycle assessment (LCA)** and how electricity should be modeled in LCA studies.


Question: In the incineration of different wastes that produces electricity and steam, when a study aims to calculate the inventory for the electricity or steam, which co-functions are relevant and how is the inventory handled under allocation?

The electricity and steam are the relevant co-functions, and the inventory is allocated between these two only, with all other flows including the waste treatment services treated as non-functional product flows.

When the study aims to calculate the inventory for the electricity or the steam, the relevant co-functions are electricity and steam. In case of allocation, the inventory is allocated only between these two; all other flows, including the waste treatment services, are treated as non-functional product flows only.

(Source 1, ILCD Handbook, p.73, chapter “Multifunctional processes with multiple sets of co-functions”)

For **waste incineration with energy recovery**, the process is typically **multifunctional**. If your study’s goal is to calculate the inventory for the **electricity** or **steam** produced, the relevant **co-functions** are:

- **Waste treatment / disposal** of the incoming waste, and
- The other **energy product(s)** from the plant:
  - if studying **electricity**, then **steam/heat** is a co-function,
  - if studying **steam**, then **electricity** is a co-function.

## Under allocation
If you choose **allocation**, the total inventory of the incineration process is **partitioned among all co-functions** according to the selected allocation rule, for example:

- **Energy-based allocation**,
- **Exergy-based allocation**,
- **Economic allocation**,
- or another justified physical relationship.

So the inventory assigned to **electricity** is only the allocated share of:
- emissions,
- auxiliary fuel/material use,
- residues,
- etc.,

and likewise for **steam**.

## Important point
The **waste input itself** is usually not treated simply like an ordinary fuel input without considering the **waste treatment function**. In LCA, incineration often provides both:
1. **a waste management service**, and
2. **energy products**.

So if allocation is used, you need to allocate the burdens across these **co-functions**, not just between electricity and steam.


Question: Why are accidents and accident-type leakages and spills excluded from the normal life cycle inventory, and what related emissions are nevertheless included?

They are excluded because they are fundamentally different in nature from the production- or operation-related normal and abnormal operating conditions that LCA relates to; however, fugitive emissions through sealings and other "engineered losses" are included in LCA.

Accidents and accident-type leakages and spills are excluded from the normal life cycle inventory because they are fundamentally different from normal and abnormal operating conditions considered in LCA. Their modelling would require frequencies and cause–effect chains to assign them to the responsible unit processes, and this is treated as a separate methodological area still under development. Nevertheless, fugitive emissions through sealings and other “engineered losses” are included in LCA.

Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), chapter 6 “Scope definition – what to analyse and how,” p.95.

In LCA, the **normal life cycle inventory (LCI)** is meant to represent **expected, routine flows** from the product system under **normal operating conditions**.

So **accidents, major leaks, and spills** are usually excluded because they are:

- **Abnormal / non-routine events**
- **Highly site- and time-specific**
- **Difficult to predict statistically for a specific product system**
- Often better treated in **risk assessment** rather than standard LCI

However, some **related emissions are still included** if they are part of normal operation, such as:

- **Fugitive emissions** from regular equipment leakage
- **Evaporative losses**
- **Diffuse emissions** during handling, storage, loading/unloading
- **Small routine spills/leakages** that are inherent to standard operations and occur with expected regularity

So the rule of thumb is: **exclude accidental, exceptional releases; include routine, expected non-point emissions.**


Question: If a product made from 1 kg of material is recycled indefinitely at a recycling rate of 95%, what is the total amount of use obtained, and what formula gives this result?

The total amount of use is 20 kg, obtained using the formula U = p/(1-r), where p = 1 kg and r = 0.95.

According to the European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), Annex C, p.1, for 1 kg of material with a recycling rate of 95% recycled indefinitely, the total amount of use is 20 kg.

The formula is:
U = p / (1 − r)

where:
- U = total amount of use
- p = primary use
- r = recycling rate

Citation: Annex C: Modelling reuse, recycling, and energy recovery, p.1 (Source 2).

If 1 kg of material is recycled indefinitely with a **95% recycling rate**, the total cumulative use is the sum of a geometric series:

\[
1 + 0.95 + 0.95^2 + 0.95^3 + \dots
\]

This equals:

\[
\frac{1}{1-0.95} = \frac{1}{0.05} = 20
\]

So the **total amount of use obtained is 20 kg-equivalents of product use** from the original 1 kg of material.

### General formula
For recycling rate \(r\) (with \(0<r<1\)):

\[
\text{Total use} = \sum_{n=0}^{\infty} r^n = \frac{1}{1-r}
\]

If starting with \(M\) kg of material:

\[
\text{Total use} = \frac{M}{1-r}
\]

For \(M=1\) kg and \(r=0.95\):

\[
\frac{1}{1-0.95}=20
\]


Question: In the data quality rating system where two quality indicators may be only "good" rather than "very good," what happens to a data set if more than two indicators are only "good"?

The data set is downgraded to the next quality class.

If more than two quality indicators are only “good,” the data set is downgraded to the next quality class.
— European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), Table 8, p. 1 (source excerpt)

In that rating rule, a data set can still qualify only if at most two quality indicators are rated merely “good” instead of “very good.”

If more than two indicators are only “good,” the data set no longer meets that top-quality classification and is downgraded to a lower data quality level/rating.


Question: What are the three levels of reporting that should be distinguished in an LCA study?

Reports or data sets for internal use; reports or data sets for external use (made available to a limited, well-defined list of recipients including at least one organisation that did not participate in the study); and comparative assertion reports intended to be made available to the (non-technical) public.

The ILCD Handbook distinguishes three reporting levels in an LCA study:

1. Internal use by commissioner of study
2. Third party, i.e. a limited, well-defined list of recipients with at least one organisation that has not participated in the study
3. Comparative studies to be disclosed to the public

Source: Hauschild et al., LCA: Theory and Practice — Chapter 38 “Report Template”, p. 1051.

In an LCA study, three reporting levels are typically distinguished:

1. **Internal report**
   Detailed documentation for the practitioner, commissioner, or internal reviewers. It contains all assumptions, data sources, methods, calculations, and results.

2. **Third-party report / critical review report**
   Documentation prepared for or by external reviewers to assess whether the LCA complies with relevant standards, such as ISO 14040/14044.

3. **Public report**
   A report intended for external communication, often with summarized results and conclusions suitable for customers, stakeholders, or the general public.

These distinctions are important because the level of detail, transparency, and review required differs by audience and intended use.


Question: When correcting for functional differences of a secondary good, to what must the percentage value relate, and what are some examples of appropriate properties and units?

It must relate to the appropriate property and unit of the secondary good, for example mass in kg for recycled materials, lower calorific value in MJ for recovered energy, and pieces in number for reused parts.

When correcting for functional differences of a secondary good, the percentage value must relate to the appropriate property and unit of the secondary good.

Examples:
- Mass in kg for recycled materials
- Lower calorific value in MJ for recovered energy
- Pieces in number for reused parts

Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.182.

The percentage correction must relate to a **specific, quantifiable property of the secondary good that reflects its function**, compared against the corresponding property of the substituted reference good.

So the % should not be arbitrary—it should be tied to a **measurable performance/quality parameter**.

Examples of appropriate properties and units include:

- **Mass or dry matter content** — kg, % dry matter
- **Energy content** — MJ/kg, kWh
- **Nutrient content** — kg N, kg P, protein content (%)
- **Material quality/composition** — purity (%), metal content (%), fibre content (%)
- **Mechanical performance** — strength (MPa), durability (years)
- **Volume or concentration** — m³, g/L, % concentration
- **Heating value / calorific value** — MJ/kg
- **Chemical functionality** — active ingredient content (%), molarity, reactivity measures

In short: the correction percentage should be based on the **relevant functional property and its unit**, so the secondary good is adjusted according to how well it actually delivers the same function as the product it replaces.


Question: In the described data quality framework, which three complementary aspects do not reflect the actual data quality itself but support it?

Documentation (providing information for reproducibility), Review (assurance of quality), and Nomenclature (supporting data consistency through e.g. the same elementary flows and units of measurement).

The three complementary aspects are:

- Documentation
- Review
- Nomenclature

Source: ILCD Handbook, Chapter 12.2, p.323.

The three complementary aspects are:

1. **Data collection process**
2. **Documentation / metadata**
3. **Review / validation**

These do not describe the intrinsic quality of the data itself, but they help **support, demonstrate, and ensure** data quality.


Question: For general processes involving capital goods such as processing machines (but not buildings) that input directly to multifunctional processes, what reference flow properties may be used?

Time (duration) of use OR mass, volume, or length of the produced good.

For general processes by other capital goods’ input directly to multifunctional processes (e.g. processing machines themselves, but not buildings), the reference flow properties may be:

- time (duration) of use, or
- mass, volume, or length of produced good.

Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), chapter 7.9.3 context, p.268.

For capital goods like processing machines that directly input to multifunctional processes, the reference flow property is typically based on the machine’s **function over time**, not mass or energy.

Common reference flow properties you may use are:

- **Unit of equipment** (e.g. 1 machine)
- **Time of service / operating life** (e.g. machine-hour, year of use, lifetime use)
- **Production capacity over lifetime** (e.g. kg processed, m³ treated, operating hours delivered over its life)

In practice, the most appropriate property is usually a **service-based measure** such as:

- **machine-hour**
- **capacity × time**
- **one machine over its lifetime**

This allows the capital good to be allocated to the multifunctional process according to the share of its useful service consumed.


Question: Why might the conclusions from an LCA study conducted in Country X not be valid for another country, based on factors like untreated wastewater discharge and an inefficient canteen dishwasher?

Because these aspects may not be representative of the situation in the other country, so the conclusions from Country X will most likely not be valid there.

Because those aspects are site-specific and may not be representative elsewhere. If another country does not have untreated wastewater discharge of detergent from washing ceramic cups directly into rivers, or does not use an inefficient canteen dishwasher, then the conditions driving the results differ, and “the conclusion from country X will most likely not be valid here.”
— European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), Annex D, p. 375

Because many LCA results are highly context-specific.

In your example, two country-dependent factors can strongly change the impacts:

- **Untreated wastewater discharge:** If Country X has poor wastewater treatment, emissions to water and related eutrophication/toxicity impacts may be much higher than in a country with effective sewage treatment. So a process that looks environmentally harmful in Country X might perform differently elsewhere.
- **Inefficient canteen dishwasher:** If the dishwasher in Country X uses unusually high amounts of water, energy, or detergent, the product system will reflect those local inefficiencies. In another country, with more efficient equipment or different operating practices, those impacts could be much lower.

More generally, LCAs may not transfer well across countries because of differences in:
- electricity mix
- infrastructure efficiency
- waste and wastewater treatment
- technology performance
- user behavior and operating conditions
- background databases and emission factors
- environmental sensitivity and regulations

So conclusions from Country X may not be valid elsewhere unless the inventory data and assumptions are adapted to the new local conditions.


Question: In consequential modelling, what approach is used to achieve sufficient comparability when aspects of the functional unit differ significantly between compared systems?

In consequential modelling, sufficient comparability is achieved by system expansion.
Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.147 (chapter 6.10.2); also confirmed on p.979 in Hauschild et al., LCA: Theory and Practice.

In consequential modelling, if the compared systems differ a lot in functional-unit-related aspects, sufficient comparability is typically achieved by using **system expansion**.

This means broadening the functional unit and/or product system so that **all relevant functions and consequences** of the alternatives are included, rather than forcing a narrow one-to-one comparison.

In short: **consequential LCA generally uses system expansion to handle multifunctionality and major functional differences between systems.**


Question: In an LCA, which phase generally constitutes the main part of the work in terms of duration and resources used?

The Life Cycle Inventory (LCI) work generally constitutes the main part of an LCA in terms of duration and resources used.
Cited in:
- European Commission – JRC, *ILCD Handbook: General Guide for LCA* (2010), p.12
- Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards* (2002), p.477

In most LCAs, the **inventory analysis (LCI) phase** is generally the most time- and resource-intensive.

This is because it involves:
- collecting large amounts of data,
- modeling all relevant inputs and outputs across the life cycle,
- checking data quality and gaps,
- and often iterating with system boundaries and assumptions.

So while goal/scope definition and impact assessment are important, **LCI usually constitutes the main part of the work**.


Question: Why is carrying out an LCI or LCA study described as an iterative process?

Because as more information becomes available during the data collection, impact assessment, and interpretation phases, the initial scope settings typically need to be refined and sometimes revised.

Carrying out an LCI or LCA study is described as iterative because after the goal is defined and the initial scope settings are derived, new information obtained during inventory data collection, impact assessment, and interpretation typically requires those initial scope settings to be refined or sometimes revised. The phases also use results from each other, which improves comprehensiveness and consistency.
(Source 1, p.25; Source 2, p.83)

LCI/LCA is described as **iterative** because you rarely get everything right or complete on the first pass. As the study develops, earlier choices often need to be revisited and refined.

Typical reasons:

- **Goal and scope may need adjustment**
  Once data collection starts, you may realize the original system boundary, functional unit, or level of detail is not practical or appropriate.

- **Data collection reveals gaps or inconsistencies**
  In the LCI phase, missing, poor-quality, or conflicting data often force revisions to assumptions, boundaries, allocation rules, or data sources.

- **Modeling choices affect results**
  Decisions about allocation, cut-off criteria, recycling, coproduct treatment, and impact assessment methods can change outcomes, so practitioners test and revise them.

- **Interpretation feeds back into earlier phases**
  If results seem surprising, insignificant, or too uncertain, you may go back to improve inventory data, refine the model, or even redefine the study question.

- **Sensitivity and uncertainty analysis require rework**
  These analyses often identify the most influential assumptions or processes, prompting additional data collection or model changes.

So, the process is iterative because **each phase informs the others**, and improving the study usually means cycling back through goal and scope, inventory, impact assessment, and interpretation until the results are robust enough for the intended use.


Question: Why is using the direct market value for allocation considered somewhat distorting for waste and end-of-life products?

Because waste and end-of-life products with a negative market value can still have a "value"—namely when their value is higher (i.e., the waste fee is less negative) than the default option such as discarding the waste without benefit, e.g., landfilling without energy recovery.

Using the direct market value is considered somewhat distorting because waste and end-of-life products can still have a “value” even when their market value is negative: if their value is higher than the default disposal option, such as discarding or landfilling without energy recovery. Therefore, direct market value alone does not capture the cost difference relative to waste disposal fees.

Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), chapter 7 Life Cycle Inventory analysis, p.266.

Using **direct market value** for allocation can be distorting for **waste and end-of-life products** because their price often does **not reflect their actual environmental relevance or physical role**.

Main reasons:

- **Waste may have zero, negative, or highly unstable prices**
  A waste stream can have:
  - **negative value** if someone must pay to have it treated,
  - **zero value** if it is just discarded,
  - **positive value** if it becomes a recyclable material.
  These price shifts can drastically change allocation results without any real change in the underlying physical flows.

- **Prices are driven by market conditions, not causation**
  Market value depends on demand, subsidies, disposal fees, regulation, and scarcity. These factors may be only loosely related to the processes that caused the environmental burdens.

- **Small price differences can cause large burden shifts**
  If one output has very low value, economic allocation may assign it almost no burden, even if it is physically significant or requires substantial treatment.

- **End-of-life products are often not normal “products”**
  Their market value may reflect the **cost of getting rid of them** rather than their usefulness or the share of resources embedded in them.

- **Can create arbitrary or counterintuitive results**
  For example, a recyclable waste gaining temporary market value could suddenly receive a larger share of upstream burdens, simply because scrap prices changed.

So, direct market value is considered somewhat distorting because for waste and end-of-life materials it is often **volatile, policy-dependent, and disconnected from physical or environmental causality**.


Question: In the ILCD Handbook provisions, how must the life cycle model be modelled for Situation A ("Micro-level decision support")?

It must be modelled as an attributional model, i.e. depicting the existing supply-chain processes.

For Situation A (“Micro-level decision support”), the life cycle model shall be modelled as an attributional model, i.e. depicting the existing supply-chain processes.
(ILCD Handbook, section 6.5.4.2 / 6.5.4.2 I.a.i, p. 88)

For **Situation A: Micro-level decision support**, the ILCD Handbook says the life cycle model should be **consequential** in nature.

In practice, this means it should be modelled to reflect:

- **Market-mediated consequences of a decision**
- **Processes that are expected to change as a result of the decision**
- **Marginal suppliers / marginal technologies**
- **Specific cause-effect chains triggered by the decision**

So, rather than describing the average, status-quo life cycle, the model should represent the **environmental consequences of the decision being studied**.


Question: In which phase of an LCA does the identification of the specific processes take place, after the question of which activities relate to a product or process is answered?

The identification of specific processes takes place in the **Life Cycle Inventory (LCI) phase**.

Cited source: **European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.12** — “**As first step of the LCI phase … identify the to-be-included processes within your system boundaries.**”

This takes place in the **Life Cycle Inventory (LCI) phase**.

After defining which activities are relevant to the product system, the LCI phase identifies the **specific unit processes** involved and quantifies their inputs and outputs.


Question: What is a black box unit process, and why can it cause difficulties during review?

A black box unit process is a unit process that combines more than one physically separate process step. It can cause difficulties to review, especially when developed as a generic process, because the reviewer would be better able to judge the single process steps than an integrated chain of steps.

A black box unit process is “a unit process that includes more than one single-operation unit processes” (Source 2, p.23). It is also described as a unit process that is “a combination of more than one, physically separate process steps” (Source 3, p.192).

It can cause difficulties during review because black box unit processes “cause difficulties to review” (Source 1, p.190; Source 3, p.192), especially for generic processes, since a reviewer can better judge “on the level of the single process steps than on an integrated chain of steps” (Source 3, p.192). In addition, they often create multifunctionality problems, which “require extra information and effort to be solved and in any case distort the results to some degree” (Source 1, p.190). Allocation of black-box unit processes can also distort results if they include multifunctional processes (Source 6, p.74).

A **black box unit process** is a process in an LCA dataset where only the **aggregated inputs and outputs** are provided, while the **internal process details, assumptions, and sub-processes** are hidden or not documented.

### Why it causes difficulties during review
- **Lack of transparency:** Reviewers cannot see how results were built up.
- **Hard to verify data quality:** It is difficult to assess representativeness, completeness, allocation choices, cut-offs, or assumptions.
- **Limited reproducibility:** Others cannot recreate or test the process.
- **Risk of hidden errors or double counting:** Important modeling choices may be buried inside the black box.
- **Poor suitability for adaptation:** Reviewers cannot tell whether the process is appropriate for a different goal, scope, region, or technology.

So, black box processes reduce **traceability and credibility**, making critical review much harder.


Question: In ILCD multifunctionality allocation, what procedure is applied directly when the market price of the waste or end-of-life product is equal to or above zero?

The two-step allocation procedure is applied directly between the process step that generates the waste or end-of-life product and the true joint process.

When the market price of the waste or end-of-life product is equal to or above zero, the two-step allocation procedure shall be applied directly between the process step that generates the waste or end-of-life product and the true joint process.
— ILCD Handbook, chapter 7.9.3, p.271

In ILCD multifunctionality allocation, if the market price of the waste or end-of-life product is **equal to or above zero**, the procedure applied directly is **allocation at the point of substitution / market value-based allocation**.

In practice, this means the waste or end-of-life product is treated as having **economic value**, so the burdens and credits are assigned using its **market price** rather than applying the “waste with negative value” approach.


Question: In Situation C1, how is the multifunctionality of processes and systems solved, and how does this differ from Situation A?

It is solved with substitution via system expansion, as in Situation A, but independently of the absolute amount of the not (function).

In Situation C1, multifunctionality of processes and systems is solved by substitution via system expansion, as in Situation A, but independently of the absolute amount of the additionally demanded or supplied function/product. This differs from Situation A because Situation A considers the scale of consequences, including whether they are large-scale depending on the annual additional demand or supply relative to replaced installed capacity.

Cited sources:
- ILCD Handbook, chapter 6.5.4.4, p.92
- ILCD Handbook, chapter 5.3.7, p.43

In **Situation C1**, multifunctionality is solved by **expanding the system** so that **all functions of the multifunctional process/system are included in the study**. Instead of allocating burdens between co-products, you model the larger system and compare **functionally equivalent systems** that provide the same bundle of functions.

### In short for C1:
- **Approach:** **System expansion**
- **What happens:** Include all co-functions/co-products in the functional unit or comparison
- **Goal:** Avoid allocation by comparing alternatives that deliver the **same overall functions**

### How this differs from **Situation A**:
In **Situation A**, the issue is usually addressed by **subdivision** or by **collecting more specific process data** so that the unit process can be separated into parts attributable to the different functions. The idea is to **avoid multifunctionality by splitting the process**, rather than broadening the system.

### Key difference:
- **Situation A:** solve it by **separating the process**
- **Situation C1:** solve it by **expanding the product system**

If you want, I can also give the ISO 14044 decision-tree framing for A, C1, and C2.


Question: Why should normalised LCIA results not be summed directly, and what does doing so implicitly assume?

Because directly summing normalised LCIA results is equivalent to choosing an equal weight for all impact categories, meaning a weighting is always at least implicitly involved; if summing or comparison is intended, it should include an explicit weighting step (with equal weights).

Normalised LCIA results should not be summed directly because they only show the contribution of the analysed product relative to a reference, not the severity or relevance of the total impact. Direct summation would therefore amount to aggregating across impact categories without an explicit weighting step.

Doing so implicitly assumes equal weighting of all impact categories.

Cited sources:
- ILCD Handbook, Chapter 8 “Life Cycle Impact Assessment – calculating LCIA results”, p.282
- Hauschild et al., LCA: Theory and Practice, Chapter 37.5.3 “Weighting”, p.1038

Normalised LCIA results should **not be summed directly** because normalization only puts different impact category results on a **common relative scale** (e.g., “fraction of annual impacts per person/region”). It does **not** make them scientifically interchangeable or equally important.

If you sum them, you are implicitly assuming that:

1. **All impact categories are equally important**, and
2. **One unit of normalized score in one category is fully substitutable with one unit in another**.

In other words, direct summation of normalized results is effectively a form of **weighting with equal weights**, even if no explicit weighting step was stated. That is why it is generally considered a **value choice**, not a purely objective calculation.


Question: Which institute is Stefan Diederichs affiliated with, as listed in the passage?

The Johann Heinrich von Thünen-Institut, Federal Research Institute for Rural Areas, Forestry and Fisheries, Institute for Wood Technology and Wood Biology.

Stefan Diederichs is affiliated with the Stefan Johann Heinrich von Thünen-Institut, Federal Research Institute for Rural Areas, Forestry and Fisheries, Institute for Wood Technology and Wood Biology (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.388).

Stefan Diederichs is affiliated with the **Karlsruhe Institute of Technology (KIT), Institute for Technology Assessment and Systems Analysis (ITAS)**.


Question: On which page does the section about optionally extending the goal begin?

Section 5.5 “Optionally extending the goal” begins on p.50 (European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010).

The section on **optionally extending the goal** begins on **page 29**.


Question: Why are the substances emitted by an analysed system into a context system described as formally not being emissions, even though they are called emissions for simplicity?

Because they are emitted inside the technosphere (the context system) rather than into the environment, making them formally equivalent to untreated emissions such as raw gas or raw wastewater rather than true emissions; they are still called emissions when viewed from the perspective of the analysed system.

They are described as formally not being emissions because they are released “inside the technosphere, i.e. the context system,” and are therefore equivalent to intermediate untreated flows such as raw gas or raw wastewater rather than true emissions to the environment. They are nevertheless called emissions for simplicity when viewed from the perspective of the analysed system.

Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.156.

In LCA and general systems theory, “emission” is used informally for anything leaving the analysed system and entering its context (environment, technosphere, another system). Formally, though, that flow is just a cross-boundary output.

It is “not really an emission” in the strict sense because:

- **Emission is context-dependent**: whether a substance counts as an environmental emission depends on what the receiving system is.
- **It may enter another managed system** rather than the natural environment, so formally it is an **output flow** or **elementary/product/waste flow**, not necessarily an emission.
- **The term “emission” implies release to the environment**, while system modeling only requires that the substance leaves the studied system boundary.

So the text says they are called emissions **for simplicity**, but formally they are more generally **substances crossing from the analysed system into the context system**.


Question: Why is reporting considered a vital element of any LCA, and what can happen without clear documentation and communication?

Reporting is vital because without clear and effective documentation to experts and communication to decision makers, LCAs can be subject to erroneous and misleading use and will not contribute to improving environmental performance.

Reporting is considered vital because LCA results depend strongly on methods, data, assumptions, exclusions, and choices made during the study, so they must be documented objectively, transparently, and communicated clearly to experts and decision makers. Without clear and effective documentation and communication, LCAs can be used erroneously and misleadingly, will be of very limited value, and will not contribute to improving environmental performance. (European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010, p.149; Guinée et al., Handbook on LCA, 2002, p.27; p.426)

Reporting is vital in any Life Cycle Assessment because it is the main way the study’s methods, assumptions, data sources, system boundaries, and results are made transparent and understandable to others.

Good reporting is important because it:

- **Ensures transparency**: others can see how the LCA was done.
- **Supports credibility**: decision-makers can trust the results.
- **Allows review and replication**: practitioners or reviewers can check, reproduce, or compare the study.
- **Prevents misinterpretation**: clear explanations help users understand what the results do and do not mean.
- **Enables proper decision-making**: stakeholders can use the findings appropriately.

Without clear documentation and communication:

- results may be **misunderstood or misused**,
- important assumptions may be **hidden or overlooked**,
- the study may **lose credibility**,
- comparisons with other LCAs may become **invalid**,
- and decisions based on the LCA may be **wrong or misleading**.

In short, without strong reporting, even a technically sound LCA can become unreliable or unusable in practice.


Question: In attributional modelling of a joint process, what allocation guidance is applied to handle multifunctionality?

The two-step allocation guidance is applied, as for all multifunctional processes (detailed in chapter 14.4.1).

In attributional modelling of a joint process, the ILCD applies the two-step allocation guidance used for all multifunctional processes: “For attributional modelling that means that the two-step allocation guidance is applied as for all multifunctional processes.” (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.346, chapter 14.4.1)

The two-step procedure is specified as:
1. First, identify and use the “determining physical causal relationships” as allocation criterion.
2. If allocation is to be done, share the environmental burden between co-functions by allocation.
(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.266, section 7.9.3.2)

In attributional LCA, multifunctionality in a joint process is typically handled using the ISO 14044 hierarchy:

1. **Avoid allocation where possible**
   - Subdivide the process, or
   - Expand the product system only to isolate separately attributable functions.

2. **If allocation cannot be avoided, allocate based on underlying physical relationships**
   - e.g. mass, energy content, stoichiometry, causality.

3. **If no meaningful physical relationship exists, use another relationship**
   - most commonly **economic allocation**.

So, for attributional modelling of a joint process, the guidance is generally: **first try to avoid allocation; if not possible, use physical allocation; if that is not appropriate, use economic or other non-physical allocation.**


Question: What purpose does identifying the target audience of an LCA study serve?

It helps identify the critical review needs and the appropriate form and technical level of reporting, by specifying to whom the results are intended to be communicated.

Identifying the target audience serves to define to whom the LCA results will be communicated and to shape how the study is documented, reported, interpreted, and communicated. It influences the required level of detail, the technical level of reporting, and how results and uncertainties are presented, especially when audiences are unfamiliar with LCA or when results are communicated publicly.

Citations:
- Hauschild et al., LCA: Theory and Practice, Ch. 7 “Goal Definition”, p.72
- European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.307
- Hauschild et al., LCA: Theory and Practice, Ch. 11 “Uncertainty Management and Sensitivity Analysis”, p.307
- European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.36

Identifying the target audience of an LCA study helps ensure the study is designed, communicated, and reviewed appropriately.

Main purposes:
- **Shapes the goal and scope**: Different audiences—internal managers, customers, regulators, the public—need different levels of detail, system boundaries, and impact categories.
- **Determines methodological rigor**: If results will support public comparisons or external claims, stricter standards and documentation are usually required.
- **Guides communication of results**: The way findings are presented should match the audience’s technical background and decision needs.
- **Defines review requirements**: For studies disclosed externally, especially comparative assertions, an independent critical review may be necessary.
- **Improves relevance and usability**: Knowing who will use the study helps make sure the results actually support the intended decisions.

In short, identifying the target audience ensures the LCA is fit for purpose and credible for its intended use.


Question: What form of misleading interpretation occurs when conclusions for a specific country or technology are treated as valid for broader cases, and what example illustrates the appropriate scope-limited goal?

It occurs when results are interpreted beyond what the chosen scope definition supports—generalising conclusions for specific cases (e.g., a specific technology, use scenario, or country) to broader cases (a family of technologies, all uses, or globally). The example is a comparative diaper study whose goal is defined as the comparison of reusable and single-use diapers in Country X.

This is the form of misleading interpretation of **interpreting results beyond what is supported by the chosen scope definition**, i.e. **generalising conclusions from specific cases** such as a specific technology, use scenario, or country to broader cases.

Illustrative scope-limited example: **“Comparison of reusable and single-use diapers in Country X.”**

**Source:** European Commission – JRC, *ILCD Handbook: General Guide for LCA* (2010), **p. 371**.

This is **overgeneralization** (or **misleading extrapolation**): taking conclusions that are valid only for a specific country, region, time, or technology and presenting them as if they apply more broadly.

**Example of an appropriate scope-limited goal:**
A study framed as **“comparing the environmental impacts of electricity generation technologies in Switzerland”** is valid for **Switzerland-specific conditions** only, not automatically for all countries or all power systems.


Question: What does geographical representativeness measure in an inventory data set?

The degree to which the data set reflects the true population of interest regarding geography (e.g., location/site, region, country, market, continent), including for any included background data sets.

Geographical representativeness measures the degree to which an inventory data set reflects the true population of interest regarding geography—i.e. the relevant location such as site, region, country, market, or continent where the process is operated, produced, or consumed. It expresses how well the data represents the actual process with respect to location-specific conditions and parameters.
(Source 4, p.329; Source 2, p.128; Source 1, p.106)

Geographical representativeness measures how well an inventory data set reflects the actual geographic area where the process occurs or where the results will be applied.

It looks at whether the data is appropriate for the region in terms of things like:

- technology used
- energy mix
- transport distances
- raw material sources
- climate and environmental conditions
- local regulations and practices

For example, electricity production data from France may have poor geographical representativeness for a study about electricity use in India, because the energy mixes are very different.

In short: it indicates the degree to which the location-specific conditions of the data match the intended system or study area.


Question: In the matrix approach, what condition must be met for the connection of product and waste flows between processes to be done automatically?

All product and waste flows on the output and input sides of all processes that are to be connected must be identically named.

In the matrix approach, connection is done automatically only if all product and waste flows to be connected on the output and input sides of all processes are identically named.
(Source 1: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.252)

In the matrix approach, **product and waste flows can be connected automatically only if they match exactly across processes** — typically meaning:

- the **same flow identity/name**
- the **same direction/type** (product vs. waste, input vs. output as required)
- and usually the **same unit/flow property**

So, automatic linking requires that the output flow of one process and the input flow of another refer to the **same defined flow**.


Question: In the recyclability substitution approach, what determines the size of the credit given for a secondary good, and what credit results from producing low-quality or value-less secondary goods?

The credit is determined by the reduced technical properties of the secondary good—how much of which alternative primary good it can replace—or via value correction. Producing low-quality, low-value, or value-less secondary goods results in a lower credit.

In the recyclability substitution approach, the size of the credit is determined by the amount of primary good the secondary good can actually replace and, where relevant, by a value correction reflecting its technical quality and market value. Thus, the credit reflects both the amount and the quality of the secondary good. For low-quality, low-value, or value-less secondary goods, a lower credit is given; if the additional supply merely ends in waste deposit, no credit is given and waste depositing is modelled instead.
(ILCD Handbook, p. 362, Chapter/Annex C on modelling reuse, recycling, and energy recovery)

In the recyclability substitution approach, the **size of the credit** for a secondary good is determined by the **amount of virgin production it can actually displace**. In practice, this depends on the **quality and functionality** of the secondary material relative to the primary material, and sometimes its **market value/usefulness**.

- If the secondary good is **high quality** and can fully substitute for a primary good, it receives a **larger credit**.
- If it is **lower quality** and only partially substitutes, the credit is **smaller**.

If recycling produces a **low-quality or value-less secondary good**, then it provides **little or no substitution benefit**, so the resulting credit is **small or even zero**.


Question: When claiming compliance with the ILCD Handbook, what must be identified in connection to the claim?

The applied version or edition of the ILCD "General guide for LCA" must be identified in connection to the claim.

When claiming compliance, the applied version or edition of the ILCD “General Guide for LCA” must be identified; additionally, if a newer applicable ILCD component exists but an older version is used, the name of that component and the publication date of the new version must be clearly identified in the study or deliverable claiming compliance. (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.18; p.16)

When making a **claim of compliance with the ILCD Handbook**, you must **identify the specific deliverable or object** the claim refers to — for example:

- the **method**
- the **data set**
- the **study/report**
- the **review**
- or another specific ILCD-related product

In other words, the compliance claim cannot be generic; it must clearly state **what exactly is claimed to be ILCD-compliant**.


Question: What is the name of Situation A in the LCI modelling provisions?

Situation A is named “Micro-level decision support” (Chapter 6.5.4.2, p.88; also p.81).

In the ILCD/PEF-style LCI modelling provisions, **Situation A** is typically called **“market situation A”** or **“ordinary market conditions”**.

It refers to the **normal supply-and-demand market response**, where a small change in demand is met by the **marginal suppliers/technologies** in the market.

If you want, I can also list **Situations A, B, and C** together.


Question: In the window LCA example, which component is set as level 0 after the analysed window system's levels 1 to 3 processes have been identified?

Window glass becomes the new level 0 process.

“Window glass” is set as the new level 0 process after identifying the analysed window system’s level 1 to 3 processes (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.161).

After identifying the analysed window system’s levels 1–3 processes, the **whole window system itself** is set as **level 0**.


Question: In life-cycle assessment, what are the two functions that a product to be recycled is considered to have?

Firstly, the function(s) the product is primarily made for, and secondly, the function of providing secondary resources for use in subsequent life cycles or systems.

In LCA, a product to be recycled is considered to have two functions:

1. “the function(s) the product is primarily made for”
2. “the function of providing secondary resources for use in subsequent life cycles / systems”

(Source 1: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.343)

In LCA, a product that is recycled is usually considered to have **two functions**:

1. **Its original function** in the first product system
   - e.g., a bottle serving as packaging

2. **A secondary function as a material resource** for another product system after recycling
   - e.g., the bottle providing glass or plastic feedstock for a new product

This is why recycling creates **multifunctionality / allocation** issues in LCA.


Question: In life-cycle-assessment, what characterizes Situation B as a context for life cycle based decision support?

Situation B refers to life cycle based decision support whose consequences are so extensive that they overcome thresholds and result in additionally installed or decommissioned equipment/capacity (e.g. production infrastructure) outside the foreground system of the analysed system.

Situation B is characterized as life cycle based decision support with consequences “so extensive that they overcome thresholds and result in additionally installed or additionally decommissioned equipment / capacity (e.g. production infrastructure) outside the foreground system of the analysed system.” It is associated with meso-/macro-level, strategic or policy decision support.

Cited: ILCD Handbook, ch. 5.3, p.40; Hauschild et al., *LCA: Theory and Practice*, ch. 27, p.676.

In LCA-based decision support, Situation B is characterized by **small-scale, incremental decisions where the overall market is not significantly affected**.

Key features:
- The decision causes **only marginal changes** in demand or supply.
- The surrounding production system is assumed to remain **essentially unchanged**.
- The relevant modelling approach is typically **attributional / accounting-oriented LCA**, or a simplified consequential view using **average data** when no important market consequences occur.
- Examples: choosing between materials for a product with low market influence, improving a process within an existing production setup.

In short, **Situation B = decisions with limited consequences beyond the product system, without major structural market effects**.


Question: What are the three approaches illustrated for solving the multifunctionality problem in a process?

Subdivision of the black box unit process, substitution of the not required co-functions (system expansion), and allocation of the inventory to the co-functions.

The three illustrated approaches are:

1. Subdivision of the black box unit process
2. Substitution of the not required co-functions
3. Allocation of the inventory to the co-functions

Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.16 (Figure 8, Figure 9, Figure 11).

In LCA, the three standard approaches for handling **multifunctionality** in a process are:

1. **Subdivision or system disaggregation**
   - Split the unit process into smaller subprocesses and assign inputs/outputs separately where possible.

2. **System expansion / substitution**
   - Expand the system boundary to include the additional functions, or credit the system for avoiding production of an alternative product.

3. **Allocation**
   - Partition the environmental burdens among co-products using a rule such as:
     - **Physical relationships** (e.g. mass, energy content)
     - **Economic value**

These are typically applied in that order in ISO 14044: avoid allocation where possible, then allocate if necessary.


Question: In an LCA comparing alternative systems, what does the overall data quality requirement depend on, and how can the requirement change for ecodesign decisions when one alternative has much lower impacts?

The overall data quality requirement depends on the relative difference of the overall environmental impact between the compared systems; for ecodesign decisions, the requirements may be modest if one alternative has much lower impacts than the others. These initial requirements are to be revised once the results of the first inventory and impact assessment calculation are available.

In comparative LCA studies, the overall data quality requirement depends on the relative difference in the overall environmental impact between the compared systems. For ecodesign decisions, if one alternative has much lower impacts than the others, the data quality requirements may be modest; the initial requirements should then be revised once first inventory and impact assessment results are available.
(Source: ILCD Handbook, ch. 6.10.5, p. 144)

In comparative LCA, the **required overall data quality** depends mainly on:

- the **goal of the study** and intended use,
- the **magnitude of the differences** between the alternatives,
- the **importance of the processes/flows** driving those differences,
- and the **consequences of the decision** being supported.

For **ecodesign decisions**, the requirement can be **relaxed** when one alternative has **clearly and substantially lower impacts** than the others. In that case, very high precision may not be necessary, because the conclusion is unlikely to change even with moderate data uncertainty. If the alternatives are close in performance, however, **higher data quality** is needed to distinguish them reliably.