Question: Which three alternative window types currently offered in the market were used for benchmarking?

Wood (W), wood/aluminium (W/ALU), and PVC windows.

The prototype wood/composite (W/C) window was benchmarked against three market alternatives: wood (W), wood/aluminium (W/ALU), and PVC windows.
(Source 1, p.1059)

The three market-available alternative window types used for benchmarking were:

1. **PVC-framed windows**
2. **Aluminum-framed windows**
3. **Wood-framed windows**

If you want, I can also provide the typical LCA comparison criteria used for benchmarking these window types.


Question: What geographic and population base is used as the reference for the percentage of total impact scores in this comparison?

The reference is **EU27** as the geographic base for the percentage of total impact scores; the provided context does **not specify a population base** for that comparison.

**Cited source:** Hauschild et al., *LCA: Theory and Practice*, Chapter 39, **p.1094**.

The percentage of total impact scores is typically referenced against the **total impacts of the selected geographic region and its population** used in the normalization step.

In most LCA comparison contexts, this means:

- a **specific geography** such as **Europe, EU-27, a country, or the world**
- for a **defined reference year**
- based on the **total annual environmental impacts caused by the population** in that region

So, for example, if the method uses **Europe** as the normalization reference, the percentage expresses how much of the **total annual impact of the European population** your product system represents.

If you want, I can also explain how to identify the exact normalization reference in a specific LCA method like ReCiPe, EF, or IMPACT World+.


Question: Which three window frame material types for a 3-layered pane are compared in the impact score breakdown shown?

The three types compared are wood (W), wood with aluminium (W/ALU), and PVC.

The impact score breakdown for a 3-layered pane compares these three window frame material types: **W (wood), W/ALU (wood/aluminium), and PVC (polyvinyl chloride)**.

**Source:** Hauschild et al., *LCA: Theory and Practice*, Ch. 39, p.1094.

The impact score breakdown compares three window frame material types for a 3-layered pane:

- **PVC frame**
- **Wood frame**
- **Wood/Aluminium frame**


Question: Which two human toxicity impact categories are distinguished in the list of assessed impact categories?

Human toxicity (cancer) and human toxicity (non-cancer) are distinguished.

The context provided does not state which two human toxicity impact categories are distinguished in the list of assessed impact categories.

Cited context: Guinée et al. (2002), p.68; p.76; p.586.

The two human toxicity impact categories typically distinguished are:

- **Human toxicity, cancer effects**
- **Human toxicity, non-cancer effects**


Question: Which two types of characterisation methodologies are presented and compared in the chapter?

Midpoint and endpoint characterisation methodologies.

The chapter presents and compares two types of characterisation methodologies: empirical models and mechanistic models.

Cited source: Hauschild et al., *LCA: Theory and Practice* — Chapter 10, p.235.

The chapter presents and compares two types of characterisation methodologies:

1. **Midpoint characterisation methodologies**
2. **Endpoint characterisation methodologies**

Midpoint methods assess impacts at an intermediate stage in the cause-effect chain, while endpoint methods evaluate damages at the level of areas of protection such as **human health, ecosystems, and resources**.


Question: In the LC-Impact method, how are chronic effects treated in relation to acute mortality for photochemical ozone formation?

LC-Impact uses a constant damage factor for acute mortality and disregards chronic effects due to lack of empirical evidence.

In LC-Impact, photochemical ozone formation includes a constant damage factor for acute mortality, while chronic effects are disregarded due to lack of empirical evidence.

Chapter 40, p.1166.

In **LC-Impact**, for **photochemical ozone formation**, **chronic health effects are not treated as a separate endpoint from acute mortality**. Instead, they are **converted into equivalent acute mortality damage** and included in the **human health damage assessment** on that basis.

So, in short: **chronic effects are accounted for by expressing them relative to acute mortality, rather than modeling them independently.**


Question: Which characterisation model is recommended by the ILCD/PEF/OEF method for photochemical ozone formation effects?

The ILCD/PEF/OEF method recommends the ReCiPe 2008 model.

ILCD/PEF/OEF recommends the LOTOSEUROS fate model as used in ReCiPe, with the resulting indicator being tropospheric ozone concentration increase (no effects).
Chapter 40, p.1154.

For **photochemical ozone formation**, the **ILCD / PEF / OEF** framework recommends the **LOTOS-EUROS model** as the basis for characterisation, typically expressed in **kg NMVOC-equivalent**.

In practice, this corresponds to the indicator often called:

- **Photochemical ozone formation, human health**
- with characterisation factors based on **LOTOS-EUROS**

If you want, I can also list the recommended models for the other ILCD/PEF impact categories.


Question: What is the main focus of the chapter authored by Ralph K. Rosenbaum regarding LCIA methods?

It provides an overview and systematic comparison of a selection of the most used Life Cycle Impact Assessment (LCIA) methods, focusing on methods that have been implemented and made available in LCA software.

The chapter’s main focus is to provide “an overview and a systematic comparison of a selection of the most used Life Cycle Impact Assessment (LCIA) methods,” especially those “implemented and made available in LCA software,” and to present and qualitatively compare available midpoint and endpoint characterisation methodologies. (Hauschild et al., *LCA: Theory and Practice*, Chapter 40 “Overview of Existing LCIA Methods—Annex to Chapter 10,” p. 1147)

Ralph K. Rosenbaum’s chapter on LCIA methods mainly focuses on **characterization modeling in Life Cycle Impact Assessment (LCIA)**—that is, how emissions and resource uses are translated into **impact category indicators**.

In particular, Rosenbaum is well known for emphasizing:

- the **scientific basis of LCIA models**
- how **characterization factors** are developed
- the distinction between **midpoint and endpoint approaches**
- key issues like **spatial and temporal differentiation, uncertainty, and model assumptions**
- improving the **comparability and robustness** of LCIA methods

So, the chapter’s core focus is typically on **how LCIA methods are built and applied to quantify environmental impacts in a scientifically consistent way**.


Question: In which journal was Owens' 2001 article on water resources in life-cycle impact assessment published?

Journal of Industrial Ecology (J. Ind. Ecol.).

The answer is not in the provided context. The relevant citation appears only in a reference list excerpt from Guinée et al., Handbook on LCA (2002), p.654, but Owens’ 2001 water resources article and its journal are not shown there.

Owens’ 2001 article on **water resources in life-cycle impact assessment** was published in the **Journal of Industrial Ecology**.


Question: How are the specific properties of the LCIA methods compared in the chapter?

They are qualitatively compared in detailed tables.

In Chapter 40, the specific properties of the LCIA methods are compared qualitatively in detailed tables.

Cited source: Hauschild et al., LCA: Theory and Practice — “40 Overview of Existing LCIA Methods—Annex to Chapter 10,” p. 1147.

In the chapter, the **specific properties of LCIA methods** are typically compared using a **structured set of evaluation criteria**, rather than only listing impact categories. The comparison usually focuses on things like:

- **Environmental scope**
  Which impact categories are covered (e.g., climate change, acidification, eutrophication, toxicity, resource use).

- **Level of modeling**
  Whether the method is mainly **midpoint** or **endpoint**, and how far cause–effect chains are modeled.

- **Geographical and temporal validity**
  Whether characterization factors are global, regional, or site-specific, and how time dependence is treated.

- **Scientific basis and transparency**
  The robustness of the underlying models, assumptions, documentation, and reproducibility.

- **Normalization and weighting availability**
  Whether the method includes optional steps for interpretation and aggregation.

- **Practical applicability**
  Data requirements, software availability, ease of use, and compatibility with LCA standards.

- **Treatment of uncertainty**
  How uncertainty, value choices, and model limitations are addressed.

So, the chapter compares LCIA methods by setting their **methodological characteristics side by side** in a table or narrative discussion, highlighting their **strengths, weaknesses, scope, and intended application areas**.


Question: Which publisher and location are associated with the IDA Desalination Yearbook 2009–2010?

I do not see the publisher and location for the *IDA Desalination Yearbook 2009–2010* in the provided context.

Relevant citation context: Hauschild et al., *LCA: Theory and Practice* — Chapter 33 “LCA of Drinking Water Supply,” p.856.

The **IDA Desalination Yearbook 2009–2010** is associated with the publisher **Media Analytics Ltd.** and the location **Oxford, UK**.


Question: Who published the IDA Desalination Yearbook 2009–2010, and in what year?

Pankratz, T. published the IDA Desalination Yearbook 2009–2010 in 2010. (Hauschild et al., LCA: Theory and Practice, Ch. 33, p.858)

The **IDA Desalination Yearbook 2009–2010** was published by the **International Desalination Association (IDA)** in **2009**.


Question: What characterisation model and reference is associated with the EPS 2000 method for photochemical ozone formation?

EPS 2000 uses the POCP characterisation model from Lindfors et al. (1994).

The provided context does not state the photochemical ozone formation characterisation model and reference for the EPS 2000 method.

Relevant context only identifies POCP-based modelling generally:
- “Methods based on POCPs … The POCPs of Derwent & Jenkin (1990) … updated in 1996, 1998 and 1999” and “based on a 5-day trajectory model of VOC transportation above Europe” (Guinée et al., 2002, Part 3, p.598).
- See also the alternative methods table mention: “Table 4.3.9.2: Alternative MIRs, MOIRs and EBIRs for characterising photo-oxidant forming releases to the air” (Guinée et al., 2002, p.335).

In **EPS 2000**, the impact category **photochemical ozone formation** is typically characterized using a **photochemical oxidant creation / tropospheric ozone formation model** based on **POCP-type equivalency factors**.

A commonly associated reference is:

- **Steen, B. (1999). *A systematic approach to environmental priority strategies in product development (EPS). Version 2000 – General system characteristics*. CPM Report 1999:4, Chalmers University of Technology, Sweden.**

If you need, I can also provide the **exact characterization unit/substance reference used in EPS 2000** for this category.


Question: What is the English translation of the title of Petersen's work 'Central Sekundavandsopsamling'?

Centralized rainwater harvesting for non-potable purposes.

“Centralized rainwater harvesting for non-potable purposes” (Hauschild et al., LCA: Theory and Practice, Ch. 33 “LCA of Drinking Water Supply,” p. 858).

The English translation of **“Central Sekundavandsopsamling”** is:

**“Central Secondary Water Collection”**

If you want, I can also suggest a more natural English rendering depending on the context, since Danish technical titles sometimes translate more idiomatically than literally.


Question: What is the subtitle or focus of Owens' 2001 article on water resources in life-cycle impact assessment?

Considerations in choosing category indicators.

The provided context does not contain Owens’ 2001 article on water resources in life-cycle impact assessment, so the subtitle or focus cannot be determined from it.

Chapter/page cited: Guinée et al., Handbook on LCA (2002), p.654

Owens’ 2001 article is commonly cited as focusing on **addressing water resources within life-cycle impact assessment (LCIA)**—essentially, **how to characterize or assess water resource use/depletion as an LCIA impact category**. If you want, I can also give the full citation.


Question: What was the topic of the 2011 life cycle assessment study by Pasqualino, Meneses, and Castells?

Urban wastewater reclamation and reuse alternatives.

The 2011 life cycle assessment study by Pasqualino, Meneses, and Castells is not described in the provided context, so the topic cannot be determined from it.
Chapter 33, p.856

The 2011 life cycle assessment study by **Pasqualino, Meneses, and Castells** focused on the **environmental assessment of the treatment of sewage sludge from wastewater treatment plants**, using **LCA to compare sludge management options**.


Question: On what page range does the Pasqualino et al. (2011) article appear in the Journal of Industrial Ecology?

The context provided does not contain the page range for the Pasqualino et al. (2011) article in the Journal of Industrial Ecology.

The Pasqualino et al. (2011) article in the *Journal of Industrial Ecology* appears on pages **596–606**.


Question: Which country was the focus of Núñez, Pfister, Vargas, and Antón's 2015 work on water use impact assessment characterisation factors?


Question: What two types of specificity did the 2015 Núñez et al. characterisation factors for water use impact assessment in Spain address?

They addressed **spatial and temporal specificity**.

**Citation:** Hauschild et al., *LCA: Theory and Practice*, Chapter 33 “LCA of Drinking Water Supply,” **p. 858**.

The 2015 Núñez et al. water-use characterisation factors for Spain addressed two kinds of specificity:

1. **Spatial specificity** — differences by **river basin / watershed location** in Spain.
2. **Temporal specificity** — differences by **time period / season (monthly variation)** in water scarcity.

So, the CFs were **region-specific and time-specific**.


Question: What is the purpose of the annexes in this LCA document?

The annexes provide detail on broader relevant issues that would disturb the reading flow if kept in the main text, such as the ILCD data quality concept, modelling of waste and end-of-life product reuse, recycling and energy recovery, and how to avoid misleading LCA studies.

The annexes are intended to provide detailed or broader issues that are relevant but would interrupt or disturb the reading flow of the main text/report if included there. They also serve as a technical reference for practitioners, including items such as raw data, assumptions, and full LCI results.

Citations:
- European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.6
- European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.313

In an LCA document, the annexes usually serve to provide **supporting detail that is too extensive for the main report**, while keeping the core findings readable.

Typical purposes of annexes include:

- **Documenting methodology** in more depth
- **Providing detailed inventory data** and assumptions
- **Listing system boundaries, scenarios, and calculation rules**
- **Showing characterization factors, emission factors, or background datasets**
- **Including sensitivity, uncertainty, or scenario analyses**
- **Providing supplementary tables, figures, and references**
- **Supporting transparency, reproducibility, and critical review**

So, in short, the annexes are there to **back up the main LCA results with detailed evidence and technical documentation**.


Question: In LCA, what distinguishes a complete life cycle inventory in terms of the flows it ultimately shows?

All other product and waste inputs and outputs are completely modelled until the final inventories exclusively show elementary flows.

A complete life cycle inventory ultimately shows “a list of quantified elementary flows crossing the system boundary of the studied life cycle.”
— Hauschild et al., *LCA: Theory and Practice*, Chap. 9, p.118

A **complete life cycle inventory (LCI)** ultimately shows the **elementary flows** of the product system:

- **Inputs from nature**: raw materials, land use, water, energy resources, etc.
- **Outputs to nature**: emissions to air, water, and soil, waste heat, etc.

What distinguishes it is that all intermediate exchanges between processes have been resolved, so the final inventory is expressed at the level of **flows crossing the boundary between the technosphere and the environment**, not just flows between industrial processes.


Question: When a site is modelled as part of the technosphere, which portion of applied fertilisers and agrochemicals such as fungicides should be inventoried as emissions to air or water?

Only the amounts that leave the site (i.e. the field, plantation, managed forest, etc.) should be inventoried as emissions to air or water.

When the site is modelled as part of the technosphere, only the amounts of applied fertilisers and agrochemicals that leave the site (e.g. the field, plantation, managed forest) should be inventoried as emissions to air or water.
(Source 1: European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010, p.238)

Only the fraction that leaves the technosphere and enters the environment should be inventoried as emissions.

So, if the site is modelled as part of the technosphere:

- **Do not** report the full applied amount of fertilisers or agrochemicals as emissions.
- **Do** report only the portion that is actually released to:
  - **air**: e.g. ammonia volatilisation, N₂O, spray drift to air
  - **water**: e.g. nitrate leaching, phosphate runoff, pesticide runoff/drainage
  - and, where relevant, **soil** if your modelling framework treats agricultural soil as environment rather than technosphere.

The remainder is considered to stay within the technosphere/site system, at least initially.

In short: **inventory the emitted fraction, not the applied amount.**


Question: What should the goal definition state firstly, and in what manner?

It should firstly state the intended application(s) of the LCA results in a precise and unambiguous way.

Firstly, the goal definition should state and justify the goal of the LCA study—i.e., explain the goal (aim or objective) of the study—and it should be defined as unambiguously as possible.
(Guinée et al., Handbook on LCA, 2002, p.34)

In an LCA, the **goal definition should first state the intended application** of the study, **clearly and unambiguously**.

Typically, it should be expressed in a way that makes clear:
- **why** the study is being carried out,
- **who** will use the results,
- and **whether** the results are intended for **comparative assertions disclosed to the public**.

So, firstly: **the purpose/intended application**, stated **explicitly and clearly**.


Question: What is the DOI of the 2016 Núñez et al. article on life cycle impact assessment methods for freshwater use consequences on ecosystems?

The 2016 Núñez et al. article is:

**Núñez, M. et al. (2016). “Life cycle impact assessment methods to model the consequences of freshwater use on ecosystems: an overview.” _The International Journal of Life Cycle Assessment_.**

**DOI:** **10.1007/s11367-015-1017-1**


Question: Under the ILCD Handbook, how many requirements must an LCI or LCA study meet to claim compliance when there are contradictions among provisions or inapplicability of a provision?

Three requirements. ILCD Handbook, section 2.4, p.17–18: “an LCI or LCA study can claim compliance with the ILCD Handbook if the following three requirements are met by the study.”

Under the ILCD Handbook, an LCI or LCA study may claim compliance only if it meets **all applicable requirements**.

So, when there are contradictions among provisions or a provision is not applicable, the study must still satisfy **all remaining applicable, non-contradictory requirements**.


Question: Why does ISO 14044:2006 impose stricter requirements on studies that compare systems and make assertions about their superiority, inferiority, or equality?

To strengthen the interests of affected stakeholders and avoid the misuse of LCA in market competition.

ISO 14044:2006 imposes stricter requirements on comparative studies “to strengthen the interests of the affected stakeholders, avoiding the misuse of LCA in market competition.”
— European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.141

It also helps avoid “inappropriately claiming equality of compared alternatives, based on unbalanced or poor quality data that result in insignificance of differences.”
— European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.303

ISO 14044:2006 is stricter for **comparative assertions disclosed to the public** because those studies carry **higher risk of misuse and harm**.

### Why the stricter requirements exist
1. **Comparisons can strongly influence decisions**
   Public claims like “product A is better than product B” can affect:
   - consumer choices,
   - purchasing and procurement,
   - regulation and policy,
   - brand reputation and competition.

2. **LCA results are highly sensitive to assumptions**
   Comparative outcomes can change depending on:
   - functional unit,
   - system boundaries,
   - allocation methods,
   - data quality,
   - impact categories chosen,
   - assumptions and value choices.

   Because of this, superficial or biased studies could produce misleading claims.

3. **Need to ensure fairness and equivalence**
   To compare systems properly, the study must show that:
   - the systems deliver the **same function**,
   - data and methods are **consistent**,
   - differences in results are not just artifacts of modeling choices.

4. **Greater transparency and credibility are needed**
   ISO therefore requires more rigorous:
   - methodological consistency,
   - sensitivity and completeness checks,
   - documentation and transparency,
   - interpretation of limitations.

5. **Independent review is especially important**
   For public comparative assertions, ISO requires a **critical review by an external panel** to help ensure the study is scientifically and technically valid, transparent, and not misleading.

### In short
ISO 14044 imposes stricter requirements because **public comparative claims based on LCA can be powerful but easily distorted by methodological choices**. The extra rigor is meant to protect **credibility, fairness, and informed decision-making**.


Question: In the ILCD Handbook's goal definition stage, why might the involvement of trade partners in the review process be wanted for a planned national legislation?

To improve international acceptance of the study.

For improving international acceptance of the planned national legislation, involvement of trade partners in the review process may be wanted.

Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), chapter 5.2.4, p.34.

Because a planned national legislation can affect international markets and may create trade barriers or competitive impacts beyond the country itself, involving trade partners in the ILCD goal definition review helps to:

- improve transparency and credibility,
- identify cross-border concerns early,
- ensure the study assumptions and interpretation are acceptable internationally,
- reduce the risk of disputes or accusations of discrimination,
- support consistency with trade agreements and international practice.

In short, trade partners may be included when the LCA will underpin legislation that could influence imports, exports, or market access.


Question: For biomass taken from nature, how is the physically embodied energy measured?

As the Lower calorific value of the water-free (oven-dry) substance.

For biomass taken from nature, the physically embodied energy is measured as the **Lower calorific value of the water-free substance**, i.e. **as if the biomass were oven-dry**.

**Source:** European Commission – JRC, *ILCD Handbook: General Guide for LCA* (2010), **Chapter 7, p.221**.

For **biomass taken from nature**, the **physically embodied energy** is usually measured as its **intrinsic energy content** in the material itself, not the energy used to extract or process it.

In practice, this is quantified by:

- **Mass of biomass** × **heating value**
- Heating value is typically:
  - **Lower Heating Value (LHV)** or
  - **Higher Heating Value (HHV)**

Common units:
- **MJ/kg dry matter**
- **GJ/tonne**
- Sometimes per functional unit in LCA

Important distinctions:
- **Dry vs. wet biomass:** moisture strongly affects energy content, so it is often reported on a **dry basis**.
- **Embodied energy in LCA** can mean different things:
  - **Physical embodied energy of the biomass itself** = chemical energy stored via photosynthesis
  - **Cumulative energy demand** = all external energy inputs required to grow, harvest, transport, and process it

So if you mean **biomass as a natural resource input**, its physically embodied energy is generally its **gross or net calorific value** based on composition and moisture content.


Question: On what page does the provision concerning the emission of particles to air begin?

ILCD Handbook: General Guide for LCA (2010), p.11 — “7.4.3.4 Emission of particles to air” begins on p. 218.


Question: In landfill modelling, how are emissions occurring beyond the first 100 years treated in the LCIA results compared to emissions within the first 100 years?

Emissions within the first 100 years undergo the same LCIA impact assessment as all other interventions, while emissions beyond 100 years are excluded from the general LCIA results calculation and aggregation and are instead calculated, presented, and discussed as separate LCIA results.

Emissions within the first 100 years are included in the normal LCIA and aggregated with other system interventions, while emissions occurring beyond 100 years are inventoried and calculated separately as long-term emissions and are not included in the general LCIA results aggregation.

Cited:
- European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.225
- Hauschild et al., LCA: Theory and Practice, Ch. 37, p.1036

In most landfill LCIA modelling, **all modeled emissions are included in the inventory**, even those occurring **after 100 years**.
However, in the **LCIA results**, emissions are typically treated differently depending on the impact assessment method:

- **Emissions within the first 100 years** are generally **characterized normally** and contribute fully to the reported impact scores.
- **Emissions occurring beyond 100 years** are often **reported separately** as **long-term emissions** or may be **excluded from characterization**, depending on the method and database convention.

So, compared with emissions in the first 100 years, **post-100-year emissions usually do not contribute in the same way to the main LCIA totals**; they are commonly flagged as **long-term** and handled separately.


Question: In a life-cycle assessment, when a lack of quality is found for some significant issues, what actions can be taken to improve robustness for data issues, LCIA issues, and method issues respectively?

Revisit the inventory analysis and/or impact assessment phases to improve the concerned data (for data issues), improve impact factors (for LCIA issues), and try to qualify and discuss the sensitive assumption or choice (for method issues).

When a lack of quality is found for some significant issues, robustness can be improved by:

- **Data issues:** revisit the **inventory analysis** phase to improve the concerned data.
- **LCIA issues:** revisit the **impact assessment** phase to improve the concerned **impact factors**.
- **Method issues:** **qualify and discuss the sensitive assumption or choice**.

**Source:** European Commission – JRC, *ILCD Handbook: General Guide for LCA* (2010), Chapter 9 Life cycle interpretation, **p.299**.

When significant issues have poor quality in an LCA, robustness can be improved in different ways depending on the type of issue:

- **Data issues:**
  Improve the inventory data by collecting better site-specific or more recent data, filling gaps, checking consistency, and testing uncertainty/sensitivity of key parameters.

- **LCIA issues:**
  Strengthen the impact assessment by using better-suited or updated characterization models/factors, checking coverage of relevant impact categories, and comparing results with alternative LCIA methods where needed.

- **Method issues:**
  Improve the study design and assumptions by refining system boundaries, allocation rules, functional unit, cut-off choices, and scenario definitions; then test these choices through sensitivity, scenario, or uncertainty analysis.

In short:
**better data**, **better/alternative impact assessment models**, and **better-tested methodological choices**.


Question: Which two ISO standards (and their year) must the provisions of the document comply with under the ISO compliance principle?

The document’s provisions must comply with:

- ISO 14040:2006, *Environmental Management—Life Cycle Assessment—Principles and Framework*
- ISO 14044:2006, *Environmental Management—Life Cycle Assessment—Requirements and Guidelines*

Cited in: Finkbeiner et al. (2006), p.1 (Source 6).

Under the ISO compliance principle, the document’s provisions must comply with:

- **ISO 14040:2006** — *Environmental management — Life cycle assessment — Principles and framework*
- **ISO 14044:2006** — *Environmental management — Life cycle assessment — Requirements and guidelines*


Question: In the context of waste or end-of-life treatment, which process is considered the superseded and to-be-substituted one, and what is excluded from it?

It is the electricity mix of the market (e.g., country, region, or sub-grid) where the waste/end-of-life treatment takes place, excluding the to-be-substituted electricity source.

In waste or end-of-life treatment, the superseded and to-be-substituted process is “the electricity mix of the market (e.g. country, region, sub-grid) where the waste / end-of-life treatment takes place,” excluding “the to-be-substituted electricity source.”
— European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.84

In end-of-life or waste treatment, the **superseded process** is the **average market process for producing the substituted product/material/energy** that the recovered output replaces.

What is **excluded** from that superseded process:
- the **waste treatment/end-of-life operations themselves**
- the **recovery/recycling process under study**
- any **burdens already attributed to the waste-generating product system before it became waste**

So, only the **avoided conventional production** of the substituted function is counted as the process being replaced.


Question: In a C1-type analysis of 1990 rapeseed-based biodiesel production, why could the co-product glycerine be credited with avoiding petro-based glycerine production?

Because in 1990 the co-product glycerine was entirely absorbed by the market, thereby avoiding petro-based glycerine production.

Because in 1990 the co-product glycerine was entirely absorbed by the market and therefore displaced alternative petro-based glycerine production; the market could absorb the small amount produced, so it was considered to avoid petro-based glycerine.
(Source: European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010, p.44)

Because in a C1-type consequential/substitution treatment, the glycerine from biodiesel is treated as a marketable co-product that displaces the product it would otherwise replace.

For a 1990 rapeseed biodiesel system, that meant:

- biodiesel production generated crude glycerine,
- glycerine already had an established industrial market,
- the relevant alternative supply at the time was largely petrochemical (petro-based) glycerine,
- so the biodiesel co-product could be credited with “avoiding” the production of an equivalent amount of petro-based glycerine.

In short: the credit comes from the assumption that co-product glycerine substitutes for conventional market glycerine, which in 1990 was predominantly fossil-derived.


Question: What are some of the alternative names used for the consequential life cycle inventory modelling principle?

It is also called "change-oriented", "effect-oriented", "decision-based", "market-based", and (in older, incomplete or misleading usage) "marginal" or "prospective".

The consequential life cycle inventory modelling principle is also called “change-oriented,” “effect-oriented,” “decision-based,” “market-based,” and, in older usage, “marginal” or “prospective” (European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010, p.71).

Some alternative names commonly used for the consequential life cycle inventory modelling principle are:

- **Market-based modelling**
- **Effect-oriented modelling**
- **Decision-oriented modelling**
- **Change-oriented modelling**
- **Marginal modelling**
- **Prospective modelling** (sometimes used, though not always as a strict synonym)

These terms all point to the same general idea: modelling the **environmental consequences of a change in demand or decision**, rather than describing the average burdens of an existing product system.


Question: Which three influence factors are combined to determine the native soil carbon stock in calculating CO2 emissions from land transformation, and from what source are these factors derived?

The native soil carbon stock is varied by the native soil carbon stock factors (climate region and soil type), the land use factor (land use type, temperature regime, and moisture regime), and the management factor (specific land management for cropland and grassland, along with the related input level factor). These factors are derived from the most recent available IPCC reports.

The three combined influence factors are:

1. Native soil carbon stock
2. Land use factor
3. Management factor

These are derived from “the most recent available related IPCC reports.”

Citation: ILCD Handbook, Annex B: Calculation of CO2 emissions from land transformation, p.2.

The **native soil carbon stock** is determined by combining three influence factors:

1. **Climate**
2. **Soil type**
3. **Land use / vegetation type**

These factors are typically derived from the **IPCC guidelines**, especially the default factors and methods provided in the **IPCC Guidelines for National Greenhouse Gas Inventories**.


Question: In life-cycle assessment, how is comparability achieved when one or more of the systems being compared have additional functional units?

Comparability is achieved by system expansion.

Comparability is achieved by rendering the systems “equivalent” through system expansion/substitution: for alternatives that do not provide the additional function, an added system is included that delivers that function, and then this added function system is subtracted to avoid multiple functional units. This can be interpreted as substitution.
— Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p.679

Similarly, system expansion renders systems comparable by adding to the respective product systems co-products that are deemed equivalent.
— Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p.508

In LCA, comparability is achieved by making sure all compared systems deliver the **same quantified service**, i.e., are expressed on an equivalent **functional unit** basis.

If one system has **additional functions** beyond the main one, you handle this through **multifunctionality resolution**, typically by:

1. **Subdivision** – separate the processes if possible.
2. **System expansion / substitution** – expand the comparison so all systems include the same additional functions, or credit the multifunctional system for displacing equivalent products/services.
3. **Allocation** – if expansion or subdivision is not possible, partition inputs/outputs among functions using a justified basis (e.g., mass, energy, economic value).

So, when one or more systems have additional functional units/functions, comparability is achieved by **adjusting the systems so they are functionally equivalent before comparing impacts**.


Question: Why is spatial or temporal differentiation of characterization models rarely feasible in practice for LCA, despite being foreseen by ISO 14044:2006?

Because there is a lack of spatially or temporally differentiated LCI data and, especially, of corresponding LCIA methods, making such differentiation not or rarely feasible for the time being.

ISO 14044:2006 foresees considering spatial and temporal differentiation, but in practice it is “not or rarely feasible” because there is a lack of spatially or temporally differentiated LCI data and, especially, a lack of corresponding LCIA methods. Therefore, using non-generic differentiated LCIA methods requires scientific justification that they lead to significantly different LCIA results.
(Source: European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010, section 6.7.5, p.112)

Because it usually demands far more specific data than most LCAs can realistically provide.

In principle, ISO 14044 allows characterization factors to vary by place and time, but in practice this is rarely feasible because:

- **Inventory data are too aggregated**: LCI data often lack exact information on where and when emissions or resource uses occur.
- **Supply chains are complex and global**: A product system may involve many processes across multiple regions, making spatially explicit modeling very difficult.
- **Temporal information is weak**: Emissions are usually reported as total amounts, not with timing, duration, or seasonality.
- **Characterization models become data- and computation-intensive**: Regionalized or time-dependent fate, exposure, and effect modeling requires much more modeling effort.
- **Available CFs are limited and inconsistent**: Robust, widely accepted regionalized or dynamic characterization factors do not exist for all impact categories and locations.
- **Uncertainty may not decrease**: Adding spatial or temporal detail can create an impression of precision without enough reliable supporting data.

So, although spatial and temporal differentiation is methodologically desirable, most practical LCAs rely on **generic, average characterization factors** because they are more compatible with available data, methods, and effort.


Question: What two subtopics are covered under the section on quality of LCA methods, assumptions, and data in the ILCD Handbook?

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

The two subtopics are:

1. Consistency of methods, assumptions and data
2. Reproducibility

Source: ILCD Handbook: General Guide for LCA (2010), chapter 6.2.1 and 6.2.2, p.8.

Under the ILCD Handbook, the section on **quality of LCA methods, assumptions, and data** covers two subtopics:

1. **Data quality**
2. **Quality of the LCA model and methodological choices**


Question: When resolving the multifunctionality of processes and systems, which approach must be applied in preference to substitution?

Subdivision and virtual subdivision shall be applied in preference to substitution.

Source: Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.1008.

When resolving multifunctionality, **subdivision or system expansion by avoiding allocation where possible** is preferred, and **if allocation cannot be avoided, allocation based on underlying physical relationships** should be applied **in preference to substitution**.

So, the preferred approach over substitution is:

**Allocation according to physical causality (physical relationships), if subdivision is not possible.**

This follows the ISO 14044 hierarchy:
1. Avoid allocation by subdivision or expanding the system.
2. If not possible, allocate based on physical relationships.
3. Only then use other relationships, such as economic allocation.


Question: On the level of LCIA results and LCA studies, which component can generally be assumed to have the highest uncertainty?

The LCIA methods can generally be assumed to have the highest uncertainty.

The LCIA component can generally be assumed to have the highest uncertainty at the level of LCIA results and LCA studies. Hauschild et al. state that “for some impact categories, this contribution may be much larger than that of the LCI.”
— Hauschild et al., *LCA: Theory and Practice*, Chapter 10 “Life Cycle Impact Assessment,” p.186

At the **LCIA result / whole-study level**, the component that is **generally assumed to have the highest uncertainty is the characterization modeling**, especially for **impact categories beyond climate change**.

In practice:

- **Inventory data uncertainty** is often important, but
- **LCIA model uncertainty** tends to dominate many category results because of:
  - incomplete scientific understanding,
  - simplified cause-effect chains,
  - spatial/temporal variability,
  - value choices in indicators and models.

This is particularly true for categories like:
- toxicity,
- ecotoxicity,
- land use,
- water scarcity/use impacts,
- eutrophication/acidification in some contexts.

By contrast, **climate change results (GWP)** are usually considered among the **more robust / less uncertain** LCIA results.

So, if you need one general answer: **the LCIA characterization/modeling component usually has the highest uncertainty at the LCIA/study level.**


Question: When inventorying biomass from nature, how should the embodied energy be measured?

As the Lower calorific value of the water-free (oven-dry) substance, expressed in MJ.

For biomass from nature, the embodied energy should be measured as the amount physically embodied, using the **Lower calorific value of the water-free substance**—i.e. as if the biomass were **oven-dry**.

**Source:** European Commission – JRC, *ILCD Handbook: General Guide for LCA* (2010), **p.223**.

For **biomass taken directly from nature**, the **embodied energy is generally not counted as the biomass’s calorific/solar energy content** in LCI.

Instead, in LCA it is usually handled as:

- **A resource flow from nature**: biomass extraction is recorded in **mass units** (e.g., kg dry matter, m³ wood).
- **Embodied energy** refers to the **human-controlled energy inputs** needed to make that biomass available:
  - harvesting
  - collection
  - drying
  - transport
  - processing

So the key distinction is:

- **Do not assign the inherent chemical energy of naturally grown biomass as an upstream energy burden** of the technosphere product.
- **Do include the lower/higher heating value only if you are describing the fuel/product itself**, for example for energy balance, combustion modeling, or feedstock energy content.

In short:
**Measure biomass from nature primarily by physical amount, and count as embodied energy only the anthropogenic energy used to extract and supply it, not the energy stored in the biomass by nature.**


Question: Why should the collection of black box unit processes generally be avoided in favor of single operation unit processes?

Because black box unit processes cause difficulties to review and often create multifunctionality problems, which require extra information and effort to solve and in any case distort the results to some degree.

The collection of black box unit processes should generally be avoided because they “cause difficulties to review and often in addition multifunctionality problems.” These problems “require extra information and effort to be solved and in any case distort the results to some degree.” Therefore, unless it is an explicit aim of the study, data collection should aim at single operation unit processes instead of black box unit processes.
Source: ILCD Handbook, chapter 7.4.2.2, p.190.

Because black-box unit processes hide the internal inputs, outputs, and allocation logic, they reduce transparency and flexibility in LCA.

Single-operation unit processes are generally preferred because they:

- **Improve transparency**: you can see what happens at each step.
- **Allow better allocation and system modeling**: multifunctionality, recycling, and by-products can be handled more appropriately.
- **Support consistency checks**: easier to verify mass/energy balances and data quality.
- **Enable reuse and adaptation**: processes can be recombined for different products, technologies, regions, or scenarios.
- **Reduce double counting or omissions**: clearer links between process stages.
- **Make uncertainty and hotspot analysis more meaningful**: impacts can be traced to specific operations.

Black-box collections are only useful when detailed data are unavailable or confidentiality prevents disclosure, but they should generally be avoided because they limit interpretability and robustness of the study.


Question: When defining the actual composition of a polymer material in an LCA, which components should be considered besides the main resin?

Fillers, colouring agents, stabilisers, and curing agents.

Besides the main resin, the actual composition of a polymer material should also consider the individual components of the material composition, i.e. additives and other constituent substances, and these should be inventoried as individual substances if data are available or estimated via composition information if needed.
Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.279.

Besides the main polymer resin, the **full material recipe** in an LCA should usually include all relevant **non-resin constituents**, especially if they are present in meaningful amounts or affect impacts. Typical components are:

- **Additives**
  - stabilizers
  - plasticizers
  - flame retardants
  - lubricants / processing aids
  - UV absorbers / antioxidants
  - antistatic agents

- **Fillers and reinforcements**
  - talc, calcium carbonate, silica
  - glass fibers, carbon fibers, mineral fillers

- **Colorants**
  - pigments, dyes, masterbatch carriers

- **Blowing/foaming agents** if applicable

- **Residual monomers / solvents / moisture** if relevant and significant

- **Recycled content**
  - post-industrial or post-consumer recycled polymer fractions

- **Coatings, adhesives, laminates, or compatibilizers**
  - if the “material” is actually a compound, blend, multilayer, or composite

In practice, include components that:
1. contribute non-negligible **mass**,
2. have potentially high **environmental burdens** per kg, or
3. influence **end-of-life behavior** or emissions.

So the composition should reflect the **actual formulated compound**, not just the base resin.


Question: Why is the use stage of a product often harder to characterize than its production processes?

Because the way a product is used is often much less homogeneous and much less well-defined, with many different use scenarios existing.

The use stage is often harder to characterize because, when it is included within the system boundaries, “the data collection faces different challenges than for production processes.” This reflects that use-phase situations are more variable and context-dependent than production processes.

European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.198.

Because the **use stage depends heavily on real-world behavior and context**, while production is usually **more controlled, measurable, and standardized**.

Key reasons:

- **User behavior varies**: Different people use the same product differently: frequency, intensity, maintenance, lifetime, settings, misuse.
- **Operating conditions differ**: Climate, electricity mix, water availability, local infrastructure, and surrounding systems can all change impacts.
- **Time dependence**: Use-phase impacts may occur over years, and assumptions about durability, repairs, and replacement strongly affect results.
- **Data is harder to collect**: Manufacturers often know their production inputs well, but they usually have limited visibility into how customers actually use products.
- **Scenario sensitivity**: Small changes in assumptions, like number of wash cycles, driving style, or charging behavior, can greatly change results.
- **Interaction with other systems**: The use phase may involve consumables, maintenance, energy supply, transport, or end-user choices outside the product itself.

In LCA terms, the use stage often requires **more assumptions, scenarios, and uncertainty handling** than production, which makes it harder to characterize robustly.


Question: In the LCI modelling for Situation B, which processes do the modelling provisions apply to exclusively, and how are the other parts of the background system modelled?

The Situation B LCI modelling provisions apply exclusively to those processes that are affected by the large-scale consequences; the other parts of the background system of the life cycle model are later modelled as Situation A.

In Situation B, the LCI modelling provisions apply exclusively to those processes that are affected by the large-scale consequences. The other parts of the background system are modelled as “Situation A,” typically the processes with a smaller contribution to the overall results.

Chapter 37, p.968 (Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook)

For **Situation B**, the **modelling provisions apply exclusively to the foreground processes**—i.e. the processes that are **directly affected by the decision or demand change being studied**.

The **rest of the background system** is modelled using **average / attributional background data** (the **unconstrained/marginally unaffected parts** of the system are not modelled consequentially).

So in short:

- **Apply the special Situation B provisions only to the affected foreground processes**
- **Model all other background processes with average background datasets**


Question: Under what condition is the use of not fully time-representative data justifiable for LCI/LCIA data sets or non-comparative LCI/LCA studies?

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

The use of not fully time-representative data is justifiable only if this does not relevantly change the overall LCIA results compared to using fully time-representative data; otherwise, the lower achieved time-representativeness shall be documented in the data set/report.
— Hauschild et al., *LCA: Theory and Practice*, Ch. 37 “LCA Cookbook”, p. 988

Using data that are **not fully time-representative** is justifiable **when the expected temporal variation is not environmentally significant for the goal of the study**.

In practice, this means it can be used if:
- the data still reasonably reflect the process or system,
- no better time-representative data are available,
- and the lack of full time representativeness does **not materially affect the conclusions** of the LCI/LCIA dataset or the **non-comparative** LCI/LCA study.

Typically, this should be **documented and justified transparently**.


Question: In life-cycle inventory practice, how are Dioxins commonly inventoried as an exception for substance groups, and why is this considered acceptable?

Dioxins are commonly inventoried as 2,3,7,8-TCDD (2,3,7,8-Tetrachlorodibenzodioxin) human toxicity equivalents. This is considered acceptable because the equivalent number already relates to the relevant impact of interest, namely eco-toxicity and human toxicity. However, if individual species are available, they shall be inventoried individually.

Dioxins are commonly inventoried as **2,3,7,8-TCDD equivalents** (“2,3,7,8-Tetrachlorodibenzodioxin human toxicity equivalents”) as an exception for substance groups. This is considered acceptable because **the equivalent number already relates to the relevant impact of interest, i.e. eco-toxicity and human toxicity**. If individual species are available, they should still be inventoried separately.
**Source:** European Commission – JRC, *ILCD Handbook: General Guide for LCA* (2010), **p.216**.

Dioxins are a common exception to the usual “inventory individual substances” rule.

In LCI practice, they are typically inventoried as a grouped emission, usually reported as:

- **“Dioxins and furans”**
- often aggregated into a single indicator such as **toxic equivalents (TEQ)**

This is considered acceptable because:

1. **They occur as complex mixtures** of many closely related congeners rather than one easily separable substance.
2. **Measurement and reporting are commonly done in aggregated form** in emissions data.
3. **Their impacts are evaluated using equivalency factors** relative to a reference compound, so summing them as **TEQ** is scientifically established and practical for LCIA.

So, unlike most substance groups, dioxins are often inventoried collectively because both monitoring practice and impact assessment methods are built around that aggregation.


Question: What two complementary approaches does Section 6.6 describe for deriving system boundaries, and which one involves the cut-off criteria?

Section 6.6 describes a qualitative definition of system boundaries and a quantitative definition of system boundaries; the quantitative definition involves the cut-off criteria.

Section 6.6 describes two complementary approaches:

1. Qualitative definition of system boundaries — Section 6.6.2
2. Quantitative definition of system boundaries — the cut-off criteria — Section 6.6.3

The approach that involves the cut-off criteria is the quantitative definition of system boundaries.

Citation: ILCD Handbook, Section 6.6, p.8 (table of contents listing 6.6.2 and 6.6.3); see also p.12.

Section 6.6 describes two complementary ways to derive system boundaries:

1. **From the function/product system outward** — starting with the studied product system and identifying which unit processes to include based on relevance to the function.
2. **From environmental significance inward** — starting from the potential environmental contributions and deciding which processes matter enough to include.

The **cut-off criteria** belong to the **second approach**, where processes or flows can be excluded if they are below defined mass, energy, or environmental relevance thresholds.


Question: For what purposes may reasonably worst-case data be used in an LCI study, even though aligned reporting is otherwise preferred?

Reasonably worst-case data may be used for scenarios and for checking the robustness of comparisons when doing the sensitivity analysis.

Reasonably worst-case data may be used for scenarios and for checking the robustness of comparisons in the sensitivity analysis.

Cited source: ILCD Handbook, chapter 7.4.3, p.214.

Reasonably worst-case data may be used in an LCI study for limited, conservative purposes such as:

- **Screening or preliminary assessments** when specific aligned data are not yet available
- **Filling temporary data gaps** to complete the inventory
- **Sensitivity or scenario analysis** to test how results change under conservative assumptions
- **Risk-averse decision support**, where it is preferable to avoid underestimating impacts
- **Hotspot identification**, especially to ensure potentially important contributors are not overlooked

Even in these cases, **aligned reporting remains preferred** for the main study results, and use of worst-case data should be **clearly justified, documented, and transparent**.


Question: For LCA studies under Situation A and B, what two pieces of information must be documented regarding the central process in the foreground system?

The absolute amount of the central process in the foreground system and the total market size of the function of that process must be documented.

For LCA studies under Situation A and B, two items must be documented for the central process in the foreground system:

1. The absolute amount of the central process
2. The total market size of the function of this process

Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.201.

For both **Situation A and Situation B** LCA studies, the documentation for the **central process in the foreground system** must include:

1. **The market trend / capacity situation of the process**
   - e.g. whether the process is **capacity-constrained** or **not**, and how it responds to changes in demand.

2. **The resulting type of process response / supplier affected**
   - i.e. whether the process change leads to a **specific marginal supplier or technology being affected**, and which one.

In short: document **the capacity/market condition of the central process** and **which marginal process/supplier is thereby affected**.


Question: Why can the performance of windows only be assessed within the context of the whole building?

Because correctly assessing the windows' use stage requires including the building's heating and/or cooling system, solar gains that depend on the window area and orientation, and other aspects.

Because a window’s use-stage performance depends on whole-building factors that must be included for a correct assessment, such as the building’s heating and/or cooling system, solar gains depending on window area and orientation, and related system interactions. Therefore, windows can only be compared from a system perspective within the context of the whole building.
(Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.156)

Because a window’s impact is highly **context-dependent** and interacts with the **entire building system**.

Key reasons:

- **Heat losses and gains depend on the whole envelope**
  A window’s U-value matters, but its effect depends on the insulation level of walls, roof, floor, and airtightness. The same window performs differently in a poorly insulated building versus a high-performance one.

- **Solar gains can be beneficial or problematic**
  Windows can reduce heating demand by admitting sunlight, but they can also increase cooling demand and overheating. This depends on:
  - climate
  - orientation
  - shading
  - glazing type
  - building thermal mass
  - occupancy patterns

- **Daylighting affects lighting energy use**
  Larger or better-placed windows can reduce artificial lighting demand, but this benefit depends on room depth, layout, controls, and user behavior.

- **Ventilation and comfort are building-related**
  Operable windows influence natural ventilation, indoor air quality, and thermal comfort, but only in relation to the building’s ventilation strategy and design.

- **Trade-offs cannot be judged in isolation**
  A window with very low heat loss may also have lower solar transmittance or daylight admission. Whether that is good or bad depends on the building’s energy concept and use.

- **Life cycle assessment is comparative at building level**
  In LCA, the relevance of a window’s embodied impacts must be compared with the operational energy savings or penalties it causes in the whole building over its lifetime.

So, windows cannot be meaningfully evaluated as standalone products only by their own properties; their true performance emerges from how they interact with the **building, climate, and use conditions**.


Question: When extra demand makes use of otherwise partly or fully unused functions, such as originally deposited or incinerated waste, how is the avoided waste treatment handled in the assessment?

The avoided waste treatment, if any, is credited to the using system.

When the extra demand uses otherwise partly or fully unused functions, the “avoided waste treatment,” if any, is credited to the using system.

Cited in:
- European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.166
- European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.172

When extra demand uses a function that would otherwise remain unused—like waste that would otherwise be landfilled or incinerated—the **avoided waste treatment is normally counted as an avoided burden (credit)** in the assessment.

In practice:

- The product/system using the waste takes over the waste’s function.
- The treatment that would otherwise have happened is no longer needed, fully or partly.
- Therefore, the environmental impacts of that displaced treatment are **subtracted** from the system being assessed.

Typical examples:
- If waste is used as a fuel, the assessment may credit the system for **avoided incineration or landfill**.
- If only part of the original treatment is avoided, then only that **fraction** is credited.

Important caveat:
- This is appropriate only if the extra demand truly **changes what happens marginally** to the waste. If the waste would still be treated anyway, then no such avoided treatment credit should be given.

So, the key rule is: **credit the system for the waste treatment that is actually displaced by the new use of the otherwise unused waste function.**


Question: Which four questions does the functional unit address when specifying the qualitative and quantitative aspects of a system's function?

"What", "how much", "how well", and "for how long".

The functional unit addresses: “what?”, “how much?”, “for how long/how many times?”, and “how well?” — plus also “where” in the cited source.

Source: Hauschild et al., *LCA: Theory and Practice*, Chapter 8 “Scope Definition,” p.83.

In LCA, the functional unit specifies a product system’s function by answering four key questions:

1. **What?** — What function or service is being provided?
2. **How much?** — What quantity of that function is delivered?
3. **How well?** — What quality or performance level is required?
4. **For how long?** — What duration or lifetime must the function be provided?

These four aspects define the qualitative and quantitative basis for comparison.


Question: For LCA studies directed towards public audiences, what can improve the study's credibility?

An interactive review process conducted at various stages of the LCA.

For LCA studies directed towards public audiences, credibility can be improved by an independent external critical review, which supports data quality and the robustness/reproducibility of results, especially for comparative studies disclosed publicly (European Commission – JRC, ILCD Handbook: General Guide for LCA, p.53; Hauschild et al., *LCA: Theory and Practice*, Ch. 13 “Critical Review,” pp.336–337).

For public-facing LCA studies, credibility is improved by **independent third-party critical review**.

Especially important are:
- **External expert review** by qualified, independent reviewers
- **Transparent reporting** of assumptions, data sources, system boundaries, and limitations
- **Compliance with recognized standards** such as **ISO 14040/14044**
- If comparative assertions are disclosed publicly, **ISO requires a critical review panel**

In short: **an independent critical review and transparent, standards-based reporting** are key to improving credibility.


Question: What is a reminder flow in life-cycle assessment, and how does it relate to the impact assessment and inventory?

A reminder flow is not its own category of flow but an additional classification applicable to any flow. It excludes the flow from impact assessment and system modelling while keeping it in the inventory as a "reminder," even when creating LCI results.

A reminder flow in LCA is an input/output flow kept in the inventory only for information purposes, for example to preserve original information such as split measurement indicators, sum flows, littered goods, or waste flows. It can be marked as “Reminder flow” in the ILCD format and should be clearly identified in the flow name, e.g. “VOC, reminder flow, not impact relevant.” It should also have its own specific name to reduce double counting risk. Reminder flows are not part of the normal impacting inventory, are not to be connected with upstream or downstream processes, and must not carry any LCIA impact factors. Therefore, they have no relevance for classical LCI results or LCIA results and shall be excluded from impact assessment.
(ILCD Handbook, ch. 7.4.3.8, pp. 232–233; p. 243)

The inventory is the basis for the subsequent impact assessment, and LCIA uses the elementary flows in the established life cycle inventory to assess environmental impacts.
(LCA: Theory and Practice, ch. 10, p. 168; ch. 37, p. 1000)

In LCA, **“reminder flow”** is not a standard term. You may be thinking of one of these:

1. **Elementary flow**
   - A material or energy flow crossing the boundary between the technosphere and the environment
   - Examples: CO₂ to air, nitrate to water, crude oil from the ground
   - These are the key link between the **life cycle inventory (LCI)** and **life cycle impact assessment (LCIA)**.

2. **Reference flow**
   - The amount of product needed to fulfill the **functional unit**
   - Used in the inventory modeling to scale all inputs and outputs.

## Relation to inventory and impact assessment

- **LCI** compiles all inputs and outputs of the product system:
  - resource use
  - emissions
  - wastes
  - energy and material flows

- Among these inventory flows, the **elementary flows** are the ones used in **LCIA**.

- **LCIA** takes those elementary flows and translates them into environmental impacts using **characterization factors**:
  - CO₂, CH₄, N₂O → climate change
  - SO₂, NOx, NH₃ → acidification
  - phosphate, nitrate → eutrophication

## In short

- If you meant **reference flow**: it belongs mainly to the **inventory modeling** stage.
- If you meant **elementary flow**: it is the connection between **inventory results** and **impact assessment**.

If you want, I can also explain the difference between **functional unit, reference flow, intermediate flow, and elementary flow** in one simple table.


Question: When identifying the true joint process of a secondary good, what condition must the identified primary good satisfy relative to the secondary good's market value?

The identified primary good shall not have a lower market value than the secondary good.
(Source 1: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.182)

The identified **primary good must have a higher market value than the secondary good**.

In other words, to determine the **true joint process** of a secondary good, the process should be one where the co-produced **primary product is more valuable in the market** than the secondary product.


Question: Under ISO 14044:2006, what optional step is required to directly compare or sum up LCIA results across different categories or areas of protection?

Under ISO 14044:2006, the optional step is **weighting**. It is used so that normalised LCIA results can be **compared across impact topics** and also **summed up across all impact categories or areas of protection**.

**Cited source:** European Commission – JRC, *ILCD Handbook: General Guide for LCA (2010)*, **p.283** (comparison and summing across categories/areas of protection) and **p.281** (normalisation is the first step toward aggregation, which additionally requires weighting).

Under **ISO 14044:2006**, the **optional step** required to directly compare or aggregate LCIA results across impact categories or areas of protection is **normalization**.

Normalization converts category indicator results to a common reference, making them comparable before any further grouping or weighting.


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

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

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

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

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

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

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


Question: What is the ISBN of "The Hitch Hiker's Guide to LCA" by Baumann and Tillman, published in 2004?

The ISBN is not provided in the supplied context.

Relevant cited context: Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p.656.

The 2004 Studentlitteratur edition of *The Hitch Hiker’s Guide to LCA* by Henrikke Baumann and Anne-Marie Tillman has ISBN **978-91-44-02364-9**.


Question: How should land use change-related CO2 emissions from biomass and litter of secondary forests be inventoried, and how does this differ from such emissions from virgin forests?

Emissions from biomass and litter of secondary forests shall be inventoried as "Carbon dioxide (biogenic)", whereas those from biomass and litter of virgin forests shall be inventoried as "Carbon dioxide (fossil)".

Land use change-related CO2 emissions from biomass and litter of secondary forests shall be inventoried as “Carbon dioxide (biogenic)”. In contrast, for virgin forests, these emissions shall be inventoried as “Carbon dioxide (fossil)”.
(Source: Hauschild et al., LCA: Theory and Practice, ch. 37 “LCA Cookbook”, p. 1030; see also ch. 30 “LCA of Biofuels and Biomaterials”, p. 773)

For **secondary forests**, CO2 emissions from **biomass and litter due to land use change** should generally be **inventoried based on the actual carbon stock change caused by the transformation**. In practice:

- account for the **loss of aboveground and belowground biomass carbon**
- account for **litter carbon losses**
- quantify emissions as the **difference between carbon stocks before and after conversion**
- distribute them according to the LCA method or inventory rules being used

### Why secondary forests are treated this way
Secondary forests are **regrown systems**, so their biomass and litter carbon stocks are typically considered part of the **managed/recoverable terrestrial carbon cycle**. If they are cleared, the associated carbon loss is attributed directly to the land use change.

### Difference from virgin forests
For **virgin (primary) forests**, the approach is often stricter because they contain **long-accumulated, non-recoverable or very slowly recoverable carbon stocks** and are associated with **broader ecological damage**. In LCA and carbon accounting, this means:

- emissions from biomass and litter removal are also counted, often as **large one-time carbon stock losses**
- but conversion of virgin forest is commonly treated as **more severe** than conversion of secondary forest
- some frameworks distinguish it explicitly through **different characterization factors, land transformation categories, or additional biodiversity/irreversibility considerations**

### In short
- **Secondary forest:** inventory CO2 from biomass and litter as the **carbon stock decrease due to conversion**
- **Virgin forest:** also inventory the stock decrease, but it is typically treated as a **more significant and less reversible loss**, sometimes with additional penalties or separate classification in the assessment framework

If you want, I can also give this in **IPCC-style accounting terms** or in **LCA database/ISO 14044 wording**.


Question: Under ISO 14044:2006, what are the three main levels of reporting, each with a different set of requirements?

Reports for internal use, third-party reports, and reports on comparative studies to be disclosed to the public.

Under ISO 14044:2006, the three main levels of reporting are:

1. internal
2. external (but limited, well defined recipients)
3. third-party report, publicly accessible
4. report on comparisons, publicly accessible

Source: Hauschild et al., *LCA: Theory and Practice*, Ch. 37 “LCA Cookbook,” p.1000.

Under ISO 14044:2006, the three main **levels of reporting/communication of LCA results** are:

1. **Internal use only**
   - Results are used within the organization and not disclosed externally.

2. **External communication without comparative assertions disclosed to the public**
   - Results are shared outside the organization, but not used to publicly compare products.

3. **External communication with comparative assertions intended to be disclosed to the public**
   - Results are used to support public comparisons between products performing the same function.
   - This level has the strictest requirements, including **critical review by an independent panel**.

If you want, I can also summarize the specific reporting requirements that differ across these three levels.


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

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

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

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

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


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

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

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

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

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

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

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


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

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

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

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

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

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


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

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

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

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

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

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


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

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

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

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

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


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

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

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

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

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

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

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

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


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

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

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

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

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

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


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

It can easily lead to misleading results and conclusions.

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

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

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

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


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

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

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

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

From an LCA perspective, the key condition is:

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

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

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


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

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

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

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

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

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

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


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

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

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

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

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

So the key condition is:

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

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


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

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

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

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


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

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

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

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

In LCA:

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

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

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


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

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

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

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

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

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

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

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

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

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

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

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


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

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

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

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

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

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

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


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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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


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

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

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

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

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

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


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

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

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

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

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

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

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

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


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

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

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

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

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

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

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

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

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

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