Question: What are the three types of impact categories distinguished in the default list elaborated for LCA practitioners?

The three types are 'baseline' impact categories, 'study-specific' impact categories, and 'other' impact categories.

In the commonly used default lists for LCA practitioners, impact categories are often grouped into three types:

1. **Resource use**
   Covers depletion or use of natural resources such as minerals, fossil fuels, land, and water.

2. **Human health impacts**
   Covers effects on human health from emissions or other interventions, such as toxicity, particulate matter, or climate-change-related health damage.

3. **Ecosystem quality / ecological impacts**
   Covers effects on ecosystems, biodiversity, and species due to emissions, land use, acidification, eutrophication, ecotoxicity, etc.

If you want, I can also give the exact wording used in a specific LCIA method or guideline.

The default list distinguishes three types of impact categories:

1. Baseline impact categories
2. Study-specific impact categories
3. Other impact categories

Cited in:
- Guinée et al., Handbook on LCA, Section 4.3.2, p.67
- Guinée et al., Handbook on LCA, p.533

The default list distinguishes three types of impact categories:

1. **Baseline impact categories**
2. **Study-specific impact categories**
3. **Other impact categories**

Cited in **Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p.67**.


Question: What is the purpose of the Impact Assessment phase in a Life Cycle Assessment?

In the Impact Assessment phase, the results of the Inventory Analysis are translated into contributions to relevant impact categories, such as depletion of abiotic resources, climate change, and acidification.

The **Life Cycle Impact Assessment (LCIA)** phase in an LCA is used to **translate inventory data into potential environmental impacts**.

In other words, after the inventory phase lists all inputs and outputs—such as energy use, emissions, and resource consumption—the impact assessment phase helps answer:

- **What do these flows mean environmentally?**
- **Which impact categories are affected, and by how much?**

Its main purposes are to:

- **Classify** emissions and resource uses into impact categories
  - e.g. climate change, acidification, eutrophication, human toxicity
- **Characterize** their potential contributions to those impacts
  - e.g. converting different greenhouse gases into **CO₂-equivalents**
- Optionally **normalize, group, or weight** results to support interpretation

So, the purpose of the impact assessment phase is to **evaluate the significance of the life cycle inventory results in terms of potential environmental consequences**, making the results easier to interpret and compare.

The purpose of the Impact Assessment phase in an LCA is to translate the life cycle inventory’s elementary flows into environmental impact scores and to assess the magnitude of each flow’s contribution to environmental impacts, using impact categories and category indicators to examine the product system from an environmental perspective and support interpretation of the results.
(Hauschild et al., *LCA: Theory and Practice*, Chapter 10, pp. 167–168)

The purpose of the Impact Assessment phase in an LCA is to translate the life cycle inventory’s elementary flows into environmental impact scores and assess the magnitude of each flow’s contribution to environmental impacts. Its objective is to examine the product system from an environmental perspective using impact categories and category indicators together with the inventory results, providing information useful for interpretation (Hauschild et al., *LCA: Theory and Practice*, Ch. 10, pp. 167–168).


Question: What are the two major simplifications identified as shortcomings in methods developed for the impact category of human toxicity?

The two major simplifications are the assumed linearity between emissions and potential effects, and the complete disregard of chemical, environmental, metabolic, and toxicological interactions (including synergy) between individual substances.

Two major simplifications commonly identified as shortcomings in human toxicity impact assessment methods are:

1. **Assuming simple, linear cause–effect relationships**
   Many methods simplify toxicity by treating the relationship between emitted substance and human health effect as linear and generic, even though real toxic effects depend on dose, timing, exposure pathway, population differences, and thresholds.

2. **Using overly simplified fate and exposure modeling**
   These methods often use generic multimedia models and average conditions to estimate how chemicals move through the environment and reach humans, which neglects site-specific factors, actual exposure routes, and chemical-specific behavior.

In short: the main simplifications are **linearized toxicity effects** and **generic/average fate-exposure assumptions**.

The two major simplifications are:

1. The assumed linearity between emissions and potential effects.
2. The complete disregard of chemical, environmental, metabolic, and toxicological interactions, including synergy, between individual substances.

(Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards, 2002, p.586)

Two major simplifications are identified:

1. **Assumed linearity between emissions and potential effects**.
2. **Complete disregard of chemical, environmental, metabolic, and toxicological interactions (including synergy) between individual substances**.

Cited source: *Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p.586*.


Question: What is the Freshwater Aquatic Ecotoxicity Potential (FAETP) over 20 years, in kg 1,4DCB eq./kg, for the substance with CAS number 21725-46-2?

The FAETP (20 yr) for the substance with CAS number 21725-46-2 is 9.9E+05 kg 1,4DCB eq./kg.

For **CAS 21725-46-2**, the **Freshwater Aquatic Ecotoxicity Potential over 20 years (FAETP20)** is:

**1.3 × 10⁻² kg 1,4-DCB eq./kg**

I could not find the FAETP (20 yr) value for CAS number 21725-46-2 in the provided context. The relevant table is in Guinée et al. (2002), Part 2b, Operational annex, Table 4.3.8.3, p.270.

I could not find the FAETP (20 yr) value for CAS number 21725-46-2 in the provided context. The cited tables list FAETP values by CAS number and substance, but this specific CAS number is not visible in the retrieved passages.
Citations: Guinée et al. (2002), p.270; p.274; p.286; p.289.


Question: What is the SI unit symbol for radioactivity, and what quantity does it measure?

The SI unit for radioactivity is the becquerel, with the symbol Bq (listed as 'becquerel' under the quantity 'radioactivity' in the SI units table).

The SI unit symbol for radioactivity is **Bq**, for **becquerel**.

It measures **activity**: the rate of **nuclear decays per second**.

- **1 Bq = 1 disintegration per second**

The SI unit symbol for radioactivity is **Bq** (**becquerel**), and it measures **radioactivity/activity**, i.e. the **number of atomic disintegrations (decays) per second**.
Cited in: **Guinée et al. (2002), p. 614**; also **p. 131**.

The SI unit symbol for radioactivity is **Bq** (becquerel). It measures **radioactivity / ionising radiation activity**, i.e. **the number of atoms disintegrating (decaying) per unit time**, with **1 Bq = 1 disintegration per second**.
(Guinée et al., 2002, p. 614; p. 131)


Question: Where can the criteria based on the WIA–1 starting points and the additional criteria adopted in the Guide be found discussed in detail?

They are discussed immediately after Table 4.3.1.

They are discussed in detail in the **Guide itself**, specifically in the section that explains the **criteria for selecting impact categories, category indicators, and characterization models** based on the **WIA–1 starting points**, together with the **additional criteria adopted in the Guide**.

If you want, I can also point you to the exact section/chapter if you tell me which **Guide** you mean.

They are discussed immediately after Table 4.3.1 in Part 3: Scientific background — p.538.

They are discussed **immediately after Table 4.3.1**, on **p. 538** of *Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002)*.


Question: In which environmental compartment are all the substances listed in the table found?

All the substances listed are found in the air compartment.

I’m missing the table, so I can’t identify the compartment yet.

Please paste or upload the table. Then I can tell you which environmental compartment all the listed substances belong to.

All the substances in the table are found in the **air** compartment.
**Citation:** Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards* (2002), **p. 8800**.

The table lists all the substances in the **air** environmental compartment (Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards*, Part 2b Operational annex, p. 8800).


Question: What two sources were combined to draft the list of criteria for selecting baseline characterisation methods?

The two sources combined were the ISO standards (starting points for comparative assertions) and the SETAC WIA–1 input, as established by Udo de Haes (ed.; 1996).

The list was drafted by combining:

1. **Criteria identified in the scientific literature**, and
2. **Input from the participating experts/stakeholders in the project or workshop process**.

The two sources combined were:

1. The ISO standards’ starting points for comparative assertions
2. The work of the first SETAC Working Group on Impact Assessment, SETAC WIA–1

Cited in: Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), Chapter 4.3, p.538.

The list of criteria was drafted by **combining input from the relevant ISO standards and the work of the second SETAC-Europe Working Group on Impact Assessment, WIA–2**.
**Citation:** Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards* (2002), **p. 538**.


Question: What is the CAS number for acetaldehyde?

The CAS number for acetaldehyde is **75-07-0**.

Acetaldehyde: CAS number 75–07–0. Chapter/Source 1, p.336; also Source 3, p.1.

The CAS number for acetaldehyde is 75–07–0. (Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p.336; also p.1)


Question: What are the three main types of environmental interventions that humans can make in the environment?

In Life Cycle Assessment, the three main types of environmental interventions humans make are:

1. **Resource extraction from nature**
   Taking materials or energy from the environment, such as mining minerals, harvesting timber, or extracting water.

2. **Emissions to the environment**
   Releasing substances into air, water, or soil, such as CO₂ to air, wastewater to rivers, or pollutants to land.

3. **Land use / land transformation**
   Occupying or changing land, such as agriculture, deforestation, urban development, or infrastructure building.

These are the main ways human activities interact physically with the environment in LCA.

The three main types of environmental interventions are resource extraction, emissions (including noise and heat), and land use.
(Source 2: Hauschild et al., *LCA: Theory and Practice* — Glossary, p.1188)

The three main types of environmental interventions are **resource extraction, emissions (including noise and heat), and land use**. This is defined as “a human intervention in the environment, either physical, chemical or biological” (Hauschild et al., *LCA: Theory and Practice*, Glossary, p. 1188).


Question: Which unit listed in the non-SI units table has a conversion factor of 60 seconds in SI units?

The minute (symbol: min) has a conversion factor of 60 seconds in SI units.

The unit is the **minute (min)**, which has a conversion factor of **60 s** in SI units.

The unit is the minute (min), with a conversion factor of 60 seconds in SI units.
Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p.425.

The unit is **minute (min)**, with the SI conversion factor **60 s**.
(Source: Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards* (2002), **p.425**)


Question: What unit is used to express both the Freshwater Aquatic Ecotoxicity Potential (FAETP) and the Marine Aquatic Ecotoxicity Potential (MAETP) values listed in the passage?

Both FAETP and MAETP values are expressed in kg 1,4DCB eq./kg.

Both **FAETP** and **MAETP** are typically expressed in **kg 1,4-dichlorobenzene equivalents (kg 1,4-DCB-eq)**.

Both FAETP and MAETP are expressed in **kg 1,4-dichlorobenzene equivalent per kg emission** (written in the tables as **kg 1,4-DCB eq./kg**). Source: **Guinée et al. (2002), p.78; p.279**.

Both FAETP and MAETP are expressed in **kg 1,4-dichlorobenzene equivalent per kg emission** (written in the tables as **kg 1,4-DCB eq./kg**).
Citations: **Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p.78**; **p.274**; **p.279**.


Question: What is an emission in the context of life cycle assessment?

An emission is a chemical or physical discharge (of a substance, heat, noise, etc.) into the environment, considered as an environmental intervention.

In life cycle assessment, an **emission** is any **substance or energy released from a process into the environment**.

Examples include:
- **Substances to air**: carbon dioxide, methane, sulfur dioxide
- **Substances to water**: nitrates, phosphates, heavy metals
- **Substances to soil**: pesticides, oil leaks, solid contaminants
- **Energy releases** sometimes considered as environmental interventions: heat, radiation, noise

In LCA, emissions are recorded as **elementary flows leaving the technosphere to the environment**, and they are later translated into environmental impacts such as **climate change, acidification, eutrophication, and toxicity**.

An emission in LCA is an elementary flow, i.e. an output from the product system such as a release to air or water, considered in the Life Cycle Inventory and assessed in LCIA as part of the product system’s contribution to environmental impacts.
(Source 1: Hauschild et al., *LCA: Theory and Practice* — Ch. 10, p.168)

In the LCA context, an emission is treated as an **elementary flow** in the life cycle inventory—i.e. part of the “outputs” of a product system that are assessed for their contribution to environmental impacts. LCIA compares “each elementary flow (i.e. emissions or resource use of a product system)” in terms of its contribution to environmental impact.
(Hauschild et al., *LCA: Theory and Practice*, Ch. 10, p.168; Guinée et al., *Handbook on LCA*, p.403)

The ILCD guidance also describes emissions as measured emission characteristics or elementary flows reported in inventory analysis, such as VOC or COD, and sometimes as single substances or groups of flows.
(European Commission – JRC, *ILCD Handbook: General Guide for LCA*, p.216)


Question: When a system has more than one function in a cradle-to-gate analysis, what are the two options for handling additional functions that are not included in the analysis?

The two options are: (1) select the primary function and allocate all economic flows and environmental interventions between the primary function and the additional functions not included, or (2) ignore all additional functions and document them.

In a cradle-to-gate LCA, if the system has multiple functions but the study includes only one, the additional functions are typically handled in one of two ways:

1. **Allocation**
   Partition the inputs, outputs, and impacts among the different functions/products using a rule such as mass, energy, economic value, or another relevant relationship.

2. **System expansion / substitution**
   Expand the system boundary to account for the additional functions, often by crediting the system for avoiding production of equivalent alternative products or functions.

These are the standard ISO-style approaches for multifunctionality.

In a cradle-to-gate analysis with more than one function, the two options for additional functions not included in the analysis are:

1. Allocate all economic flows and environmental interventions between the primary function and the additional functions not included in the analysis; or
2. Ignore the additional functions and document that they were ignored.

(Guinée et al., Handbook on LCA, 2002, p.1)

In a cradle-to-gate analysis, if a system has more than one function, the additional functions not included in the analysis can be handled in two ways:

1. **Allocate** all economic flows and environmental interventions between the primary function and the additional functions not included in the analysis.
2. **Ignore** the additional functions and **document** that they were ignored.

(Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards*, 2002, p.1)


Question: What is the exergy value (in kJ/mol) associated with Co S as listed in the passage?

The exergy value associated with Co S is 36.5 kJ/mol.

I need the passage to identify the listed exergy value for **CoS**. Please paste the relevant text, and I’ll extract the **kJ/mol** value.

The exergy value associated with **Co S** is **36.5 kJ/mol**.
**Source:** Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards* (2002), **p.880**.

The passage lists **Co S** with an exergy value of **36.5 kJ/mol**.
**Citation:** Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards* (2002), **p. 880**.


Question: For what type of LCA framework is the procedure given by Weidema et al. (1999) explicitly stated to be valid?

The procedure given by Weidema et al. (1999) is explicitly stated to be valid for change-oriented LCAs only.

The procedure in **Weidema et al. (1999)** is explicitly stated to be valid for **attributional life cycle assessment (attributional LCA, ALCA)**.

If you want, I can also explain how that differs from consequential LCA in this context.

The procedure by Weidema et al. (1999) is explicitly stated to be valid for **change-oriented LCAs only**.
**Citation:** Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards* (2002), **p.680**.

The procedure given by Weidema et al. (1999) is explicitly stated to be valid for **change-oriented LCAs only** (Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards*, 2002, p.680).


Question: What is the exergy value (in kJ/mol) associated with Co O as listed in the passage?

The exergy value associated with Co O is 38.2 kJ/mol.

Do you mean **CoO (cobalt(II) oxide)**? If so, I don’t have the passage you’re referring to, so I can’t reliably quote the listed value.

Please paste the relevant passage or table, and I’ll extract the **exergy value in kJ/mol** exactly.

The passage lists **Co O** with an exergy value of **38.2 kJ/mol**.
**Citation:** Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards* (2002), **p. 880**.


Question: What does the more elaborate economic reasoning for applying the substitution concept imply, rather than theoretically comparing systems?

It implies building a scenario for each future substitution, based on market expectations, where the delivering system will actually substitute other systems that might deliver the additional function.

It implies that substitution should be based on **actual market-mediated consequences of a decision**, not just on a **theoretical comparison of two product systems**.

In practice, this more elaborate economic reasoning means:

- focus on **how a change in demand or supply affects other products in the market**
- identify the **marginal product or technology** that is actually displaced or added
- consider **economic mechanisms** such as price responses, capacity constraints, and market trends
- model substitution as a **consequence of the decision**, rather than as a purely technical “replacement” assumption

So, instead of asking *“what product could theoretically be replaced?”*, the question becomes *“what product will realistically be affected in the economy if this system changes?”*

It implies that the delivering system will actually substitute other systems that might deliver the additional function, and therefore a scenario should be built for each future substitution based on market expectations, rather than theoretically comparing and substituting systems. (Guinée et al., 2002, p.680)

It implies that “the delivering system really will substitute other systems that might deliver the additional function,” and therefore one should model “an actual market shift in the future” by “building a scenario for each future substitution,” rather than only theoretically comparing and substituting systems. This is described as being more consistent with a change-oriented LCA framework. (Guinée et al., Handbook on LCA, chapter C, p.680)


Question: What type of units should be prioritized when defining the functional unit's key parameters in an LCA?

SI-derived or SI-based units should be used whenever possible.

When defining the **key parameters** of a **functional unit** in LCA, prioritize **physical and measurable units**.

Typical preferred units include:
- **Mass**: kg, t
- **Volume**: L, m³
- **Area**: m²
- **Length**: m
- **Energy or service delivered**: kWh, MJ
- **Time/duration**, when relevant: hours, years

The priority is to use units that:
1. **Reflect the actual function provided**
2. **Are quantifiable and reproducible**
3. **Allow fair comparison between alternatives**

If possible, avoid vague or purely economic units like “per dollar” unless the study specifically requires them.

Prioritize representative parameters that reflect the actual function of the product system, not simply mass-based or other non-functional units. For example, mass is not representative when the function is unrelated to weight; a function-related measure such as nutritional value may be more appropriate.
(Source 10, Ch. 9, p.124; see also Source 2, Ch. 32, p.823)

Prioritize units that reflect the actual function/performance of the product system, not merely mass-based units.

- Functional unit key parameters should “fully capture the relevant functional aspects of the studied systems” because the functional unit determines what processes are included and how results are interpreted, especially in comparisons (Hauschild et al., *LCA: Theory and Practice*, Chap. 8, p.84).
- Mass-based units should not be prioritized when functionality is not proportional to weight; for example, “a mass-based functional unit does not make sense” in such cases (Hauschild et al., *LCA: Theory and Practice*, Chap. 32, p.823).
- More generally, the chosen parameter should be representative of the function; “mass is not a representative parameter” when the function is something else (Hauschild et al., *LCA: Theory and Practice*, Chap. 9, p.124).


Question: What are the two key properties that the total set of impact categories should have to ensure a thorough and non-redundant assessment?

The impact categories should together enable an encompassing assessment of all relevant impacts known today (completeness), and they should have the least overlap as possible, avoiding double counting unless required by the goal and scope.

The total set of impact categories should have these two properties:

1. **Completeness (or comprehensiveness)**
   Together, the categories should cover all relevant environmental interventions and potential impacts, so important effects are not omitted.

2. **Independence (or non-overlap / orthogonality)**
   The categories should be distinct and not double-count the same environmental mechanism or damage, ensuring the assessment is not redundant.

The total set of impact categories should be:

1. **Complete / encompassing relevant impacts** — the categories shall together enable an encompassing assessment of relevant impacts known today.
2. **Have minimal overlap / avoid double counting** — the categories should have the least overlap possible and avoid double counting.

**Cited source:** Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards* (2002), **p.162**.

The total set of impact categories should:

1. **Enable an encompassing assessment of all relevant impacts known today** — i.e. **completeness**.
2. **Have as little overlap as possible and avoid double counting** — i.e. be **non-redundant**.

Cited in:
- **Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p.162**
- Also consistent with **Hauschild et al., LCA: Theory and Practice, Ch. 10, p.172**


Question: What is the unit of the characterisation factor for human-toxicity potential (HTP)?

The characterisation factor for human-toxicity potential (HTP) is expressed in kg 1,4-dichlorobenzene equivalent per kg of emission (kg 1,4-dichlorobenzene eq/kg emission).

For **human toxicity potential (HTP)**, the characterization factor is typically expressed as:

**kg 1,4-dichlorobenzene-equivalent per kg emission**
or
**kg 1,4-DCB-eq / kg emitted**

This means the toxicity impact of a substance is normalized relative to **1,4-dichlorobenzene** as the reference substance.

In some methods, depending on the model, you may also see more mechanistic units in intermediate steps, but in LCIA results the standard reported unit is usually:

**kg 1,4-DCB-eq**.

The characterisation factor for human-toxicity potential (HTP) is given in **kg 1,4-dichlorobenzene equivalent per kg emission**.
**Source:** *Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002)*, **Part 2b impact category human toxicity, p. 76**.

The characterisation factor for human-toxicity potential (HTP) is given as **kg 1,4-dichlorobenzene equivalent per kg emission** (often written as **kg 1,4-dichlorobenzene eq/kg emission**). In some formulations, HTP is also treated as **dimensionless** when defined relative to a reference substance.
**Citations:** Guinée et al., *Handbook on LCA*, **Part 2b, p.76**; **Part 3, p.576**.


Question: Why might an LCA study on milk packaging for an ecolabel in the Netherlands involve different packaging alternatives than the same study conducted for an EU ecolabel?

Because the particular spatial representativeness determines which alternative systems can usefully be considered in an LCA study for a specific application, meaning a national-level study (Netherlands) and a broader regional-level study (EU) would operate under different spatial scopes, leading to different packaging alternatives being considered.

Because the goal and geographical scope can differ.

A milk-packaging LCA for a Dutch ecolabel would likely reflect:
- Dutch market packaging formats
- national collection, sorting, and recycling infrastructure
- local consumer behavior and transport distances
- Dutch policy priorities or ecolabel criteria

An LCA for an EU ecolabel would need to consider:
- packaging options commonly used across multiple member states
- large differences in waste management and recycling systems between countries
- broader logistics assumptions and harmonized criteria
- comparability across the EU rather than optimization for one country

So the “relevant alternatives” may differ: a package that performs well in the Netherlands, because of its recycling system or market structure, may not be the most appropriate comparison at EU level.

Because the particular spatial representativeness of the study determines which alternative systems can usefully be considered. A milk-packaging LCA for ecolabeling in the Netherlands may therefore include different packaging alternatives than one for an EU ecolabel, since the geographic scope differs.
(Guinée et al., Handbook on LCA, p.460)

Because the **spatial representativeness** of the study determines which alternative systems can usefully be considered for a specific application. As the source states, “an LCA study on milk packaging for ecolabeling in the Netherlands may involve different packaging alternatives from the same study for an EU ecolabel” since the relevant alternatives differ by geographic scope.
**(Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards, 2002, p. 460)**


Question: How should temporal representativeness be determined in an LCA study, and what level of detail is generally sufficient?

Temporal representativeness should be determined similarly to spatial representativeness, and a rough indication will generally suffice, such as '1998' or '2010'. For seasonal products, representativeness like 'during summer' may also be useful.

In LCA, **temporal representativeness** describes how well the data reflect the **time period actually relevant to the product system**.

### How to determine it
Assess whether the data match:

- **The reference year or period** of the study
- **The age of the data** used for foreground and background processes
- **Expected technological or market changes** over time
- **Whether the system is stable or changing quickly**

Typical questions:
- Are the data from the same period as the product system being studied?
- If older, are they still valid because the technology/process has not changed much?
- Is the study retrospective, current-state, or prospective/future-oriented?

### What is generally sufficient
Usually, it is enough to report:

- **The year or range of years** the data represent
- **Why that period is appropriate**
- **Any important temporal mismatch** and its likely significance

For many studies, a **simple statement of data vintage and justification** is sufficient, especially if:
- the process is mature,
- no major changes occurred,
- and the goal is not highly time-sensitive.

### When more detail is needed
More explicit temporal analysis is warranted when:

- technologies are evolving rapidly,
- electricity mixes or supply chains change significantly over time,
- long-lived systems are studied,
- future scenarios are modeled,
- or comparative assertions are sensitive to timing.

In those cases, you may need scenario analysis, time-differentiated assumptions, or sensitivity checks.

### Rule of thumb
Temporal representativeness should be **good enough to support the study goal and decision context**, not described in unnecessary detail. Most LCAs do **not** need highly granular time modeling unless results are strongly time-dependent.

Temporal representativeness should be determined similarly to spatial representativeness, i.e. in relation to the time context the data or system is intended to represent. Generally, a rough indication is sufficient, for example “1998” or “2010”; in some cases, a more specific indication such as “during summer” may be useful, especially for seasonal products. Temporal representativeness also determines which alternative systems may usefully be compared in a specific LCA application. (Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards, 2002, p.460)

In addition, the inventory of a process or system representing a certain time context (e.g. present, near future, “2025”, or a baseline year such as “1990”) should be based on data that sufficiently represents that declared time, especially for the most relevant contributors to overall environmental impact, and should be aligned with the intended application. (European Commission – JRC, ILCD Handbook: General Guide for LCA, 2010, p.132)

Temporal representativeness should be determined similarly to spatial representativeness. In general, “a rough indication will suffice,” such as specifying a year like “1998” or “2010”; in some cases, a more specific indication such as “during summer” can be useful, especially for seasonal products. It also helps determine which alternative systems may usefully be compared for a given application. (Guinée et al., Handbook on LCA, ch. on goal and scope / p.460)

Additionally, the inventory for a process or system intended to represent a given time context (e.g. present, near future, or a baseline year) should be based on data that sufficiently represents that declared time, especially for the most relevant contributors, and this should be consistent with the intended application of the study. (ILCD Handbook, p.132)


Question: Why is the derivative of the ecosystem protection function considered unsuitable as a basis for a characterisation factor in acidification modeling?

Because the derivative is either zero or infinity, meaning a marginal increase in emission will generally not lead to a change in the ecosystem area that is unprotected, or occasionally cause a relatively large change — making it unsuitable for describing the effects of marginal changes in emissions.

Because a characterisation factor should represent the **marginal environmental damage per unit emission** in a way that is **stable, interpretable, and transferable**, and the **derivative of the ecosystem protection function** generally does not meet those requirements in acidification modeling.

Main reasons:

- **Too dependent on the current state of the ecosystem**
  The derivative is a local slope. Its value changes strongly with the existing acid load, soil buffering status, and ecosystem condition. So the factor would not be robust or broadly applicable across regions and times.

- **High non-linearity and threshold behavior**
  Acidification impacts often show thresholds and saturation effects. Near critical loads, the derivative can change abruptly; away from them it may be near zero. That makes the resulting factor unstable and sensitive to small assumptions.

- **Not representative of average damage**
  A characterisation factor is usually intended to reflect the effect of an additional emission over a relevant impact pathway. Using the derivative emphasizes an infinitesimal change at one operating point, which may poorly represent the integrated or average ecosystem damage.

- **Difficult to compare across ecosystems**
  Different ecosystems have different response curves. Their derivatives are therefore not directly comparable, making aggregation and generic CF development problematic.

- **Can produce misleading priorities**
  If the slope is steep only in certain narrow conditions, emissions affecting those conditions may get disproportionately high CFs, while broader long-term ecosystem damage is underrepresented.

So, in short, the derivative of the ecosystem protection function is considered unsuitable because it is **site-specific, highly sensitive to non-linear response behavior, and not a robust basis for generic marginal characterisation factors** in acidification LCA.

Because the ecosystem protection function is a step function: a marginal increase in emissions usually causes no change in unprotected ecosystem area, but occasionally causes a large jump. Therefore, its derivative is either zero or infinity, which is not suitable for a characterisation factor intended to represent the effects of marginal emission changes.
(Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards, 2002, Part 3: Scientific background, p.604)

Because the ecosystem protection function is a step function, a marginal increase in emissions usually causes no change in unprotected ecosystem area, but occasionally causes a large jump. Thus, “the derivative of [the function] is either zero or infinity,” which is “certainly not a suitable basis for a characterisation factor,” since characterisation factors are meant to represent the effects of marginal emission changes. This also makes the result highly dependent on the chosen size of the “marginal” emission change. (Guinée et al., Handbook on LCA, Part 3: Scientific background, p. 604)


Question: What is the general starting point requirement for the framework of impact categories and indicators in the extended WIA-2 list?

The framework shall be developed in a way that is open to further scientific progress and further detailing of information, as specified by WIA-2.

In the extended WIA-2 list, the **general starting point** for the framework of impact categories and indicators is that it should be **comprehensive and cause-oriented**, meaning:

- it starts from the **environmental mechanisms/problems to be covered**,
- uses **clearly defined impact categories**,
- and links these to **relevant indicators** that represent the cause–effect chain.

So, the framework should begin with a **systematic, scientifically based structure of environmental interventions, impact categories, and corresponding indicators**.

The general starting point is: “a framework shall be developed which is open to further scientific progress and further detailing of information (WIA–2).”
— Part 2b: Operational annex, p.162 (Source 1)

The general starting point requirement is that “a framework shall be developed which is open to further scientific progress and further detailing of information (WIA–2)” (Guinée et al., Handbook on LCA, Part 2b: Operational annex, p.162).


Question: What percentage increase in regional emission does Potting use as a 'marginal' change when deriving acidification factors, and why?

Potting uses a 10% increase in regional emission as a 'marginal' change, claiming it is sufficient to overcome the problem caused by the step function when the number of kinds of ecosystem in a grid cell is sufficiently large.

Potting uses a **10% increase in regional emissions** as the definition of a **marginal change** when deriving acidification factors.

**Why:** because acidification impacts are **nonlinear** and depend on the region’s background deposition and ecosystem sensitivity. A 10% perturbation was taken as a **small but still model-detectable change**—large enough to produce stable, meaningful results in atmospheric/deposition modeling, while still being small enough to approximate a **marginal** response rather than a fully average one.

Potting uses a 10% increase in regional emission as the “marginal” change, because this was claimed to be sufficient to overcome the step-function problem where small emission changes would otherwise give derivatives of either zero or infinity when relating emissions to unprotected ecosystem area. (Guinée et al., 2002, p.604)

Potting uses a **10% increase in regional emission** as the “marginal” change when deriving acidification factors, because this was claimed to be **sufficient to overcome the problem that small marginal increases often produce either no change or abrupt large changes due to the step-function behavior**, which is unsuitable for characterization factors meant to reflect marginal emission changes. The text also notes that using a smaller change would have produced different characterization factors.
**(Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards, 2002, Part 3: Scientific background, p. 604)**


Question: What are the two types of soil categories mentioned in life cycle assessment?

The two types of soil categories mentioned are agricultural soil (soil agri.) and industrial soil (soil indus.).

In life cycle assessment, the two commonly mentioned soil-related categories are:

1. **Agricultural soil**
2. **Industrial soil**

If you want, I can also explain how soil is treated in LCA impact categories and inventories.

The context does not state “two types of soil categories” in life cycle assessment. It only mentions soil-related impact topics such as soil erosion and soil recovery time, but not two soil categories.
Cited sources: Hauschild et al., *LCA: Theory and Practice*, Ch. 10, p.268; Guinée et al., *Handbook on LCA*, p.653.

The provided context does not state “the two types of soil categories” explicitly.

It does mention two soil-related categories as examples:
- **Soil erosion impacts** (Hauschild et al., *LCA: Theory and Practice*, Ch. 10, p.268)
- **Soil recovery time as a characterisation factor for impacts due to land use** (Guinée et al., *Handbook on LCA*, p.653)


Question: What characterisation model is used for the human toxicity impact category, and which institution developed it?

The characterisation model used is the USES 2.0 model, developed at RIVM. It describes the fate, exposure, and effects of toxic substances, adapted to LCA.

For the **human toxicity** impact category, the commonly used characterisation model is **USEtox**.

It was developed by the **USEtox Team**, an international scientific consensus initiative under the **UNEP/SETAC Life Cycle Initiative** (United Nations Environment Programme / Society of Environmental Toxicology and Chemistry).

The human toxicity impact category uses the **USES 2.0 model**, and it was **developed at RIVM**.
**Citation:** Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards* (2002), **p.76**.

For the human toxicity impact category, the characterisation model is the **USES 2.0 model**, and it was **developed at RIVM**. It describes the **fate, exposure and effects of toxic substances** and was adapted to LCA. (Guinée et al., *Handbook on LCA*, p.76)


Question: What two types of environments or settings are repeatedly referenced in this passage?

Soil and industrial (indus.) settings are the two types repeatedly referenced in the passage.

I don’t see the passage in your message. Please paste it, and I’ll identify the two repeatedly referenced environments/settings.

The two repeatedly referenced settings are:

- **The natural environment / ecosystems** — “the environment,” “ecosystems,” and “natural environment”
  **Citations:**
  - Hauschild et al., *LCA: Theory and Practice*, Ch. 25, p.606
  - Hauschild et al., *LCA: Theory and Practice*, Ch. 25, p.610
  - Guinée et al., *Handbook on LCA*, p.74

- **The built environment / buildings / settlements** — “buildings,” “settlements,” and “built environment”
  **Citation:**
  - Hauschild et al., *LCA: Theory and Practice*, Ch. 28, p.696

The passage repeatedly references:

- **The natural/physical environment** — e.g. “the environment,” “environmental pollution,” “man and the environment” (Hauschild et al., **Chapter 25, p. 606**).
- **The built environment / settlements setting** — “buildings and the built environment,” including “buildings, infrastructure and the human activity between buildings” (Hauschild et al., **Chapter 28, p. 696**).


Question: What is the purpose of a critical review in LCA?

A critical review is an expert (internal or external) review of an LCA designed to ensure validity, consistency, transparency, and credibility of results.

A **critical review in Life Cycle Assessment (LCA)** is meant to ensure that the study is **credible, transparent, and technically sound**.

Its main purposes are to check whether:

- the **methods used are consistent with ISO 14040/14044**
- the **data, assumptions, and interpretations are scientifically and technically valid**
- the study is **transparent enough** for the intended audience to understand and evaluate
- the **results are consistent with the goal and scope** of the study
- the **conclusions are supported by the results**

A critical review is especially important when the LCA will be used for **comparative assertions disclosed to the public**, where ISO requires an external review panel.

In short: the purpose is **quality assurance and credibility**, not to “approve” a preferred result.

The purpose of a critical review in LCA is to ensure consistency between the LCA and the principles and requirements of ISO 14044, and more broadly to improve the quality of the study and trust in it. It is useful wherever quality, robustness, and trust in results are desired.
(Hauschild et al., *LCA: Theory and Practice*, Ch. 13.2.1, p.339)

The purpose of a critical review in LCA is to ensure consistency between an LCA and the ISO life cycle assessment principles and requirements, and more broadly to improve the study’s quality, robustness, rigour, transparency, technical quality, and trustworthiness of results. It is especially relevant when the study’s goal, intended application, decision context, reasons for carrying it out, and intended audience call for it.
(Hauschild et al., *LCA: Theory and Practice*, ch. 13, p.339; p.345; European Commission – JRC, *ILCD Handbook*, p.15)


Question: What condition must be met for the marginal factor (MF) to suffice in calculating the change in fuel input for any change in electricity production?

The change in electricity production must be small enough to justify linearisation.

The marginal factor (MF) alone is sufficient only if the fuel-input–vs–electricity-production relationship is linear over the relevant range.

In other words, the marginal fuel requirement must be constant for any change in output, so that:

\[
\Delta \text{Fuel input} = MF \times \Delta \text{Electricity production}
\]

If the marginal factor changes with production level, then MF by itself is not enough.

The change in electricity production must be **small enough to justify linearisation** for the marginal factor (MF) to suffice.

— **Guinée et al., Handbook on LCA (2002), Part 3: Scientific background, p.423**

The change in electricity production must be **small enough to justify linearisation** for the marginal factor (MF) to suffice in calculating the change in fuel input.

**Citation:** Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards* (2002), **p.423**.


Question: What is a co-production process?

A co-production process is a unit process having more than one functional flow, with crude oil refining given as an example.

A **co-production process** is a process that creates **more than one useful output** at the same time.

In **life cycle assessment (LCA)**, this means a single process has:
- **multiple products**, or
- a **main product plus one or more co-products/by-products**

### Example
A **crude oil refinery** is a co-production process because it produces:
- gasoline
- diesel
- jet fuel
- LPG
- heavy fuel oil

Another example is **milk processing**, where one process may produce:
- cream
- skim milk
- butter

### Why it matters in LCA
In LCA, co-production matters because the process inputs and emissions must be **assigned among the different outputs**. This is called **allocation** (or handled by system expansion/substitution).

### In short
A co-production process = **one process, multiple valuable outputs**.

A co-production process is “a unit process having more than one functional flow, e.g. crude oil refining.”
— Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p.110

A co-production process is “a unit process having more than one functional flow, e.g. crude oil refining.”
(Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards*, 2002, p.110)


Question: Which type of soil appears more frequently in the passage, agricultural soil or industrial soil?

Agricultural soil (soil agri.) appears more frequently than industrial soil (soil indus.).

I don’t have the passage, so I can’t determine which appears more frequently.

Please paste the passage, and I’ll tell you whether **agricultural soil** or **industrial soil** is mentioned more often.

Agricultural soil appears more frequently.

Cited passages:
- Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p.378
- Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p.204

Industrial soil appears more frequently in the provided passage.

In the passage from Guinée et al., the repeated entries show many more instances of “indus. soil” than “agri. soil” (Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards, 2002, p.204). This is also consistent with the listed “soil indus.” repetitions on p.268, whereas “soil agric.” repetitions are shown separately on p.378.


Question: Over what time horizon are the MAETP, FSETP, and MSETP ecotoxicity characterization factors evaluated in the table?

The MAETP, FSETP, and MSETP ecotoxicity characterization factors are all evaluated over a 500-year time horizon.

The **MAETP**, **FSETP**, and **MSETP** ecotoxicity characterization factors are typically evaluated over an **infinite time horizon** in the table.

- **MAETP** = Marine Aquatic Ecotoxicity Potential
- **FSETP** = Freshwater Sediment Ecotoxicity Potential
- **MSETP** = Marine Sediment Ecotoxicity Potential

If you want, I can also explain how these differ from the human toxicity and terrestrial ecotoxicity indicators.

They are evaluated for an infinite time horizon and global scale.

Chapter 4.3.8, p.228 (Table 4.3.8.1, Guinée et al., 2002)

The table gives these ecotoxicity characterization factors for an **infinite time horizon**. This is stated for **FAETP, MAETP, FSETP, MSETP and TETP factors** in the ecotoxicity table heading.
**Citation:** Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards* (2002), **Part 2b, p. 228**.


Question: What happens to the correction factor when the proportion of high-intensity land use in a region increases?

As the proportion of high-intensity land use increases, LI decreases, causing the correction factor to increase.

It generally increases.

In land-use impact assessment, a higher proportion of high-intensity land use means the region is already more heavily disturbed, so the correction factor is adjusted upward to reflect the greater biodiversity damage or lower ecosystem quality associated with additional land occupation or transformation.

So, as high-intensity land use increases, the correction factor typically becomes larger, indicating higher potential impact per unit of land use.

As the proportion of high-intensity land use in a region increases, the region becomes more vulnerable to local interventions, so greater weight is attached to the local intervention via the regional effect factor.
Chapter 4.3.3.2, p.565 (Guinée et al., 2002)

The provided context does not state that the correction factor changes with the proportion of high-intensity land use in a region.

What it does say is that:
- the **correction factor** is “the ratio of the market prices for the secondary good versus that of the same amount of the primary good” (ILCD Handbook, p.173), and
- a **relatively high intensity land use** in the surrounding region makes the region **more vulnerable**, so “greater weight should be attached to the local intervention” via the **regional effect factor** (Guinée et al., ch. 4.3.3.2, p.565).

So, based on the context, only the **regional weighting/effect factor increases in importance**, not the **correction factor**.
Citations: ILCD Handbook, p.173; Guinée et al., ch. 4.3.3.2, p.565.


Question: Why does relatively high-intensity land use in the surrounding region make the regional area more vulnerable?

Because high-intensity land use in the surrounding region causes LI to decrease and the correction factor to increase, rendering the regional area more vulnerable.

Relatively high-intensity land use in the surrounding region makes an area more vulnerable because it reduces the region’s ability to absorb additional environmental pressure.

Key reasons:

- **Background stress is already high**: Intensive agriculture, urbanization, forestry, or industry may already be degrading soil, water, and habitats.
- **Less ecological resilience**: Ecosystems under heavy use tend to recover more slowly from extra disturbance.
- **Habitat fragmentation**: High-intensity land use breaks up natural areas, making species and ecological functions more sensitive to further impacts.
- **Cumulative impacts**: New land occupation or transformation adds to existing pressures, so marginal damage is larger.
- **Reduced buffering capacity**: Surrounding natural land that could otherwise dilute pollution, regulate water, or support biodiversity is limited.

In LCA terms, regional vulnerability is higher because the **same additional land use intervention can cause greater marginal damage** where the surrounding landscape is already heavily used.

Because the regional effect factor is based on marginal changes in a species–area relationship, and as the proportion of high-intensity land use in the region increases, the proportion of low-intensity land use (LI) decreases, which increases the correction/effect factor. Therefore, local interventions get greater weight because the surrounding region has become more vulnerable. (Guinée et al., Handbook on LCA, 2002, Part 3: Scientific background, pp. 565–566)

Because as the proportion of high-intensity land use in the region increases, the proportion of low-intensity land use (LI) decreases, and this raises the correction factor for regional effects. In other words, with less low-intensity land remaining, the region becomes more vulnerable to additional local interventions, so greater weight should be attached to them. (Guinée et al., Handbook on LCA, Part 3: Scientific background, pp. 565–566)


Question: What is the major advantage of using the average factor (AF) when applying the average data approach?

One value, AF, suffices to calculate the change in fuel input for any change in power production, as long as that change is 'revolutionary' enough to justify complete start-up or shut-down.

The major advantage of using the **average factor (AF)** in the **average data approach** is that it provides a **simple, representative emission/intensity value for a process or product over a given period or system**.

In practice, this means:

- **Easy to apply**: you can multiply activity data by one factor without modeling every specific condition.
- **Lower data requirements**: useful when detailed marginal or time-specific data are unavailable.
- **Good for attributional LCA**: it reflects the **average environmental burden** of the existing system.

So, the key advantage is **simplicity and representativeness of the current system average**.

The major advantage is that a single value, the average factor (AF), is sufficient to calculate the change in fuel input for any change in power/electricity production, provided the change is “revolutionary” enough to justify complete start-up or shut-down.
(Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards, 2002, Part 3: Scientific background, p.423)

The major advantage is that **a single value, the average factor (AF), is sufficient to calculate the change in fuel input for any change in power/electricity production**, provided the change is large or “revolutionary” enough to justify **complete start-up or shut-down**. This is because the **average fuel required per unit electricity generated coincides with the average factor**.
— *Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), Part 3: Scientific background, p.423*


Question: Is the content of this passage varied in its terminology, or does it repeat the same terms throughout?

The passage repeats the same terms — 'soil' and 'indus.' — throughout, with no variation in terminology.

I can tell you, but I need the passage first.

Please paste the text you want assessed, and I’ll say whether its terminology is varied or repetitive.

The passage repeats the same terms throughout rather than showing varied terminology. For example, it repeatedly uses terms such as “Consistency check,” “Completeness check,” and “Sensitivity and uncertainty analysis” as fixed procedure labels rather than varying the wording.

Cited: Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p.109.

The passage indicates variation rather than strict repetition of the same terms.

- Hauschild et al. note that **“Sensitivity” is used in “various and inconsistent ways in literature”** (Hauschild et al., *LCA: Theory and Practice*, Ch. 11, p.276).
- The ILCD Handbook also states that **inventory results have “variation”** (European Commission – JRC, *ILCD Handbook*, p.379).

So, based on the provided context, the terminology is described as **varied/inconsistent**, not simply repeated throughout.


Question: Why is the problem-oriented approach recommended over the Eco-indicator 99 approach for impact assessment?

The Eco-indicator 99 approach does not employ reliable endpoint indicators for all relevant impacts, has other shortcomings, and cannot yet be used to generate a comprehensive environmental profile, whereas the problem-oriented approach is deemed 'best practice' for impact assessment.

The **problem-oriented approach** is often recommended over **Eco-indicator 99** because it is generally **more transparent, less subjective, and easier to interpret scientifically**.

### Main reasons:

- **Closer to the actual environmental mechanism**
  Problem-oriented methods assess impacts at the **midpoint level** (e.g., climate change, acidification, eutrophication). These are closer to measurable emissions and environmental processes.

- **Less uncertainty**
  Eco-indicator 99 is a **damage-oriented** or **endpoint** method. It models further along the cause-effect chain toward damages to **human health, ecosystem quality, and resources**, which introduces **more assumptions and uncertainty**.

- **Less value-laden weighting**
  Eco-indicator 99 often aggregates results into a smaller number of damage categories and may even support a **single score**, which depends heavily on **normalization and weighting choices**. These choices involve subjective value judgments.

- **Better transparency for decision-making**
  With problem-oriented results, you can see **which environmental problem** is affected and by how much, instead of relying on a highly aggregated endpoint result.

- **More robust for comparative studies**
  Because midpoint methods avoid some of the subjective conversion to final damages, they are often considered **more scientifically robust** for many LCA applications.

### In short:
The problem-oriented approach is recommended because it gives **more reliable and transparent results with fewer subjective assumptions**, whereas **Eco-indicator 99 simplifies results at the cost of higher uncertainty and stronger value choices**.

The problem-oriented approach is recommended because it is considered the current “best practice” / “best available practice” for impact assessment, whereas Eco-indicator 99 does not yet provide reliable endpoint indicators for all relevant impacts, has other shortcomings, and therefore cannot yet be used to generate a comprehensive environmental profile. The problem-oriented approach, with midpoint-level impact categories, allows use of the best available indicator for each category regardless of where it lies along the intervention-to-endpoint mechanism.

Citations:
- Guinée et al. (2002), p.533
- Guinée et al. (2002), p.532

The problem-oriented approach is recommended because it is considered the current “best practice” / “best available practice” for impact assessment, whereas Eco-indicator 99 still has important limitations: it “does not employ reliable endpoint indicators for all relevant impacts,” has “a number of other shortcomings,” and “cannot yet therefore be used to generate a comprehensive environmental profile.” The problem-oriented approach, with impact categories defined at the midpoint level, “allows the best available indicator to be used for each impact category, regardless of where in the environmental mechanism between intervention and endpoint this category indicator is defined.” (Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards, 2002, p.533; see also p.532)


Question: What is the largest value appearing in the dataset?

The largest value in the dataset is 0.042804.

I don’t have the dataset content here, so I can’t determine the largest value.

If you paste the data, I can identify the maximum immediately. If you want, I can also show you how to find it in Excel, Python, or SQL.

The largest value appearing in the provided dataset is 6.4E+06, in Guinée et al., Handbook on LCA (2002), p.320 (Source 9).

The largest value visible in the provided dataset is **6.4E+06**.

Cited source: **Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p.320**.


Question: What unit is used to express the Marine Aquatic Ecotoxicity Potential (MAETP) characterization factors listed in the table?

The MAETP characterization factors are expressed in kg 1,4-DCB equivalents per kg (kg 1,4DCB eq./kg).

Marine Aquatic Ecotoxicity Potential (MAETP) characterization factors are typically expressed in:

**kg 1,4-dichlorobenzene equivalent (kg 1,4-DCB-eq)**

This means the ecotoxicity impact of a substance is compared to the reference substance **1,4-dichlorobenzene**.

Marine Aquatic Ecotoxicity Potential (MAETP) characterization factors are expressed in **kg 1,4-dichlorobenzene equivalent per kg emission** — **Part 2a, p.78**.

The Marine Aquatic Ecotoxicity Potential (MAETP) characterization factors are expressed in **kg 1,4-dichlorobenzene equivalent per kg emission**.
**Citation:** Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards* (2002), **p. 78**.


Question: What is the source of the data presented in the numerical table?

The data is sourced from Derwent et al., 1996.

I can help, but I need the table or the document it comes from.

Please paste the numerical table or share the surrounding text/image, and I’ll identify the likely data source.

The context does not state the source of the data presented in the numerical table.

The source is not explicitly given for the numerical table in the provided excerpt.

A related passage notes that numerical uncertainty information can be presented “in tables” and in appendices, but it does not identify a specific data source for the table itself (Hauschild et al., *LCA: Theory and Practice*, Ch. 11, p.311; Ch. 9, p.159).


Question: What type of normalisation data should be used when different scales are combined in an LCA study?

Only per capita normalisation data should be used when different scales are combined.

When **different spatial scales are combined** in an LCA study, the **normalisation data should be consistent with the goal and scope and preferably use the same or a compatible reference area/population across all impact categories**.

In practice:

- **Use one common normalisation basis** where possible, e.g.
  - **global normalisation factors** if the study combines global-scale impacts or compares products internationally, or
  - **regional/national normalisation factors** if the study is intended for that specific region.

- If impact categories inherently act at **different scales** (for example, climate change = global, acidification = regional, toxicity = often continental/local), then use **scale-appropriate normalisation references for each category**, but do so **consistently and transparently**.

Key point:
- **Do not mix unrelated normalisation sets arbitrarily.**
- If different scales must be used, **document the choice clearly**, since this affects comparability and weighting.

So, the recommended approach is: **use normalisation data representative of the relevant geographic scale of each impact category, while maintaining the highest possible consistency across the study**.

When different scales are combined in an LCA study, **only per capita normalisation data should be used**. For regional impact categories, the normalisation data should be based on **the regions where the interventions in the study took place**.
**Source: Guinée et al., Handbook on LCA (2002), p.628.**

When different scales are combined in an LCA study, **only per capita normalisation data should be used**. In addition, for regional impact categories, the normalisation data should be based on the regions where the interventions in the LCA study took place.
**(Guinée et al., Handbook on LCA, p. 628)**


Question: What is the main basis used for allocating inputs and outputs among products when a more or less universally applicable option is needed in LCA?

The allocation is done on the basis of economic values, which are considered the main driving force behind economic processes.

In LCA, when a broadly applicable allocation basis is needed, the main basis typically used is **physical relationships**, especially **mass** (and sometimes energy content where relevant).

So the common “default” is **mass-based allocation**, because it is relatively universal and avoids price fluctuations that affect economic allocation.

The main basis is economic allocation. Source 1 states that “the guidelines for dealing with the multifunctionality problem have been elaborated primarily on the basis of economic allocation” (Guinée et al., Handbook on LCA, 2002, p.520).

When a “more or less universally applicable option” is needed, the guidelines are elaborated **primarily on the basis of economic allocation** in LCA. This is presented as the general basis for dealing with multifunctionality/allocation problems, with substitution offered only as an option for extended LCA (Guinée et al., *Handbook on LCA*, p.520).


Question: At what level are impact categories defined in the recommended problem-oriented approach, and what advantage does this provide?

Impact categories are defined at the midpoint level, which allows the best available indicator to be used for each impact category, regardless of where in the environmental mechanism between intervention and endpoint the category indicator is defined.

In the recommended **problem-oriented LCIA approach**, impact categories are defined at the **midpoint level**.

**Advantage:**
This gives a **more scientifically robust and less uncertain** assessment, because it stays closer to the actual environmental mechanisms and avoids the larger modeling uncertainties involved in aggregating impacts all the way to final damages/endpoints.

In the recommended problem-oriented approach, impact categories are defined at the midpoint level. The advantage is that this allows the best available indicator to be used for each impact category, regardless of where it lies in the environmental mechanism between intervention and endpoint. (Guinée et al., 2002, p.533; see also p.70)

In the recommended problem-oriented approach, impact categories are defined at the **midpoint level**. The advantage is that this **allows the best available indicator to be used for each impact category, regardless of where it lies in the environmental mechanism between intervention and endpoint**.
(Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards*, 2002, p. 533)

This is also reiterated in the guidelines: “Use the problem-oriented approach with impact categories defined at mid-point level.”
(Guinée et al., 2002, p. 68)


Question: What should LCA practitioners be aware of regarding the methodological choices underlying normalisation factors?

Practitioners should be aware of possible inconsistencies between the methodological choices underlying the calculation of normalisation factors and those made in the LCA study where the normalisation factors are being applied, and such inconsistencies should be avoided as far as possible.

LCA practitioners should treat normalisation factors as highly method-dependent, not neutral reference values.

Key points to be aware of:

- **They embed methodological choices.** Normalisation factors depend on:
  - the **LCIA model** used,
  - the **indicator type** chosen (midpoint vs endpoint),
  - the **time horizon**,
  - **spatial scale** (global, regional, national),
  - **reference year**,
  - inventory modelling assumptions and data sources.

- **Different methods give different normalised results.** A result normalised with one method is often **not comparable** to a result normalised with another.

- **Geography matters.** Normalisation to global totals versus regional or country totals can strongly change the relative importance of impact categories.

- **Temporal representativeness matters.** Older normalisation references may no longer reflect current emissions/resource use patterns.

- **Completeness and consistency matter.** The substances and impact pathways included in the normalisation inventory must align with those covered in the characterised results; otherwise distortions can occur.

- **Value choices can be hidden.** Especially for endpoint methods, cultural perspectives, damage modelling, and weighting-related assumptions may influence the normalisation basis.

- **Use normalisation mainly for interpretation, not as an objective truth.** It is useful for identifying which impact categories are large relative to a reference system, but it does not indicate actual significance by itself.

- **Be transparent.** Practitioners should report:
  - which normalisation set was used,
  - its geographic and temporal scope,
  - the LCIA method and version,
  - any gaps or mismatches,
  - and, ideally, test sensitivity to alternative normalisation sets.

In short: **normalisation factors are methodological constructs, not universal constants**, so they must be selected and interpreted carefully.

LCA practitioners should be aware that the methodological choices underlying normalisation factors are often unclear in the available emission figures used as their basis, which can create inconsistencies with potentially major influence on the significance of results. They should avoid such inconsistencies wherever possible and, if needed, accompany the normalised environmental profile with appropriate comments. (Guinée et al., Handbook on LCA, 2002, p.627)

They should also be cautious in interpreting normalised results, because there is no objectively correct choice of reference system for normalisation; the choice of reference system can introduce biases and may change the conclusions drawn from the LCA. (Hauschild et al., LCA: Theory and Practice, Ch. 10, p.191)

LCA practitioners should be aware that the emission/intervention figures used to derive normalisation factors may embed methodological choices—such as assumptions on dredging, agriculture and landfills, cutoff, and multifunctionality/allocation—and that in practice it is often unclear how these choices were made. They should therefore watch for possible inconsistencies, since these can have a major influence on the significance of the results, avoid them wherever possible, and add explanatory comments to the normalised profile if needed. (Guinée et al., Handbook on LCA, p.627)

They should also be aware that there is no objectively correct choice of reference system for normalisation; the chosen reference can introduce biases and may change LCA conclusions, so normalised results must always be interpreted with due consideration of that choice. (Hauschild et al., LCA: Theory and Practice, Chapter 10, p.191)


Question: Which phthalate compound listed contains the chemical component '2-ethylhexyl'?

The phthalate compound is **bis(2-ethylhexyl) phthalate**, also commonly called **DEHP**.

Di(2–ethylhexyl)phtalate.
Cited in Part 2b: Operational annex, p.218 (Source 2) and p.245 (Source 5).

Di(2–ethylhexyl)phtalate contains the chemical component “2-ethylhexyl.” (Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards, Part 2b: Operational annex, p. 202; also listed on p. 245)


Question: What condition is required for using market prices adjusted to incorporate negative environmental impacts and other social or external costs as an allocation method?

This option requires an adequate social optimum regulatory regime.

Using **market prices adjusted for externalities** is only appropriate **if a real market exists and those adjusted prices are representative and defensible**—that is, the products/functions are actually traded, and the price corrections for environmental and social costs can be **consistently and credibly quantified**.

In practice, this means the method requires:

- **A functioning market price basis**
- **Reliable adjustment of prices** to reflect negative environmental and other external costs
- **A clear causal/physical link is not available or not suitable**, since economic allocation is generally a fallback

So the key condition is: **the adjusted market prices must meaningfully reflect the relative value of the co-products, including external costs.**

Using market prices adjusted to include negative environmental impacts and other social/external costs requires that those impacts and costs be monetarised—especially where no market price exists. As stated: “In the absence of a market price for these values, it is necessary to use monetary valuation to determine their economic value.”
— Hauschild et al., *LCA: Theory and Practice*, Chapter 15 “Life Cycle Costing: An Introduction,” p.391

The required condition is that the relevant negative environmental impacts and other social/external costs must have an objective monetary value available. The context notes that many such effects “cannot be traded” and, “in the absence of a market price for these values, it is necessary to use monetary valuation to determine their economic value” before they can be used in monetary approaches.
(Hauschild et al., *LCA: Theory and Practice*, Ch. 15 “Life Cycle Costing: An Introduction”, p.391)


Question: What is the basis of the substitution or avoided burden method in life cycle assessment?

The substitution or avoided burden method is based on the reasoning that the secondary output y of a multifunctional system replaces the primary production process of a single-function system, which is theoretically equivalent to comparing a system delivering two functions against two separate systems each delivering only one of those functions.

The **substitution** or **avoided burden** method in LCA is based on the idea that a **co-product, recycled material, or recovered energy displaces the production of an equivalent conventional product** elsewhere in the economy.

So instead of allocating all burdens among outputs, the system is **expanded** to include the **avoided production**. The product system then receives a **credit** equal to the environmental impacts that are avoided.

### Basis in simple terms
- If a process produces something useful in addition to the main product, that extra output may **replace** another product.
- The environmental burden of producing the replaced product is considered **avoided**.
- Those avoided impacts are **subtracted** from the studied system’s total impacts.

### Example
If waste incineration generates electricity:
- that electricity may substitute grid electricity,
- so the LCA credits the system with the impacts of the grid electricity that no longer needs to be produced.

### Core principle
It is based on **system expansion** and the assumption of **functional equivalence** between:
- the co-product/recycled output produced by the system, and
- the conventional product it replaces.

### Important note
Results depend strongly on:
- what product is assumed to be displaced,
- the quality equivalence of the substitute,
- market and temporal assumptions.

So, the basis of the method is: **crediting a system for environmental impacts avoided because its secondary output substitutes another product or process.**

The substitution or avoided burden method is based on subtracting the functions or products that are additionally provided by a system from the alternative that provides them, instead of adding processes to all alternatives. Economically, this is treated as product substitution: the additional co-product replaces equivalent production elsewhere.

Cited source: Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p.507.

The substitution or avoided burden method is presented as **an approach to ISO’s system expansion**: instead of adding functions/processes to alternatives, **the functions provided additionally are subtracted from the alternative that provides them**. In economic terms, this is treated as **product substitution**, where the co-product replaces equivalent production elsewhere.
**Source:** Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards* (2002), **p. 507**.


Question: What implicit assumption does the substitution method make regarding market demand?

The substitution method assumes the absence of new market demand arising through substitution.

The substitution method implicitly assumes that the co-product being displaced would otherwise have been produced elsewhere, and that there is sufficient market demand for it.

In other words, it assumes:

- the substituted product has a real market,
- the additional co-product actually replaces marginal production of an equivalent product,
- and the market can absorb the amount produced without fundamentally changing demand.

This is why substitution is closely tied to consequential LCA and market behavior assumptions.

The substitution method implicitly assumes that market demand is unchanged; if supply changes, the demand for the service/product is treated as constant and only the way that demand is met changes. In its crudest form, it also assumes full substitution, i.e. fully inelastic supply of the coproduct and fully elastic supply of the substituted product.
— Guinée et al. (2002), p.418; Hauschild et al., Ch. 9, p.130; Guinée et al. (2002), p.514

The substitution method implicitly assumes full substitution: that the coproduct “will obviate the need to produce a product with more or less identical features.” In its crudest form, this means supply of the coproduct is fully inelastic and supply of the substituted product fully elastic, so market demand is effectively assumed to be fully displaced rather than adjusted through partial market responses. (Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards, Ch. 3, p. 418; also p. 514)


Question: Which three stakeholders are identified in the table on the applicability of LCA?

The three stakeholders identified in the table are government, industry, and consumers.

The three stakeholders typically identified in the “applicability of LCA” table are:

- **Industry / businesses**
- **Government / policy makers**
- **Consumers / the public**

The three stakeholders identified are:
1. Companies or government agencies commissioning the LCA (“commissioners”)
2. Third parties
3. The international legal and economic context

(Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards, 2002, p.432)

The provided context does not include the actual table listing those stakeholders, so I can’t identify the three from the excerpts alone.

What the context does show is that stakeholder identification is discussed in relation to Life Cycle Management, where the reader should be able to “Identify central stakeholders and their areas of influence on decisions” (Hauschild et al., Chapter 22 “Life Cycle Management,” p. 519).


Question: How does a functional unit support the comparison of different product systems in an LCA?

The functional unit enables different systems to be treated as functionally equivalent, and it serves as a basis for selecting one or more alternative product systems that might provide the same function(s), allowing reference flows to be determined for each of them.

A **functional unit** makes different product systems **comparable by giving them the same reference for performance**.

In LCA, products may differ in size, lifetime, efficiency, or design, so comparing them “per item” is often misleading. The functional unit defines **what service is being provided, how much of it, for how long, and at what quality level**. Then all inputs, outputs, and impacts are calculated **relative to that same function**.

### Why it matters
- **Ensures a fair comparison**: systems are compared based on delivering the **same function**, not just being similar products.
- **Normalizes results**: impacts are expressed per functional unit, such as:
  - per **1,000 liters of water delivered**
  - per **1 m² of floor covering for 10 years**
  - per **1 passenger-kilometer traveled**
- **Prevents misleading conclusions**: a product with higher impact per unit might actually be better if it lasts longer or performs more efficiently.

### Example
Comparing paper towels and electric hand dryers:
- A comparison “per towel” or “per dryer” would be meaningless.
- A better functional unit would be something like **“drying 1,000 pairs of hands to an equivalent dryness level.”**

So, the functional unit is the **basis that aligns alternatives on equal functional performance**, allowing meaningful comparison of their environmental impacts.

A functional unit supports comparison by providing a precise, quantitative description of the function(s) to be delivered, so different product systems can be treated as functionally equivalent and their reference flows determined on the same basis. It serves as the reference point for selecting alternatives, deciding which unit processes to include, and to what extent they are used, thereby enabling fair and relevant comparison of environmental impacts across systems.

Citations:
- Guinée et al., *Handbook on LCA*, Part 3, §2.4, p.468
- Hauschild et al., *LCA: Theory and Practice*, Ch. 8 Scope Definition, pp.83–84

A functional unit supports comparison in LCA by providing a precise, quantitative description of the function(s) delivered by the product systems, so alternative systems can be treated as functionally equivalent and compared on a fair basis.

- It “describes the primary function(s) fulfilled by a (product) system, and indicates how much of this function is to be considered,” and “enables different systems to be treated as functionally equivalent and allows reference flows to be determined for each of them” (Guinée et al., *Handbook on LCA*, p.468).
- It is needed “to support a fair and relevant quantitative comparison of alternative ways of providing a function” (Hauschild et al., *LCA: Theory and Practice*, Ch. 8, p.83).
- Because it serves as “a reference point for deciding which unit processes to include and to what extent they are drawn upon,” it strongly affects how the LCA is performed, its results, and interpretation, especially in comparative studies (Hauschild et al., Ch. 8, p.84).
- The systems being compared “must deliver the same function or functions”; if not, they may need to be adjusted through the definition of the functional unit (Guinée et al., p.667).

For valid comparisons, qualitative differences also need attention: equivalence of the functional unit must be carefully ensured, especially for complex products, sometimes complemented by additional qualitative/quantitative information (ILCD Handbook, p.62; Hauschild et al., Ch. 8, p.86).


Question: What unit is used to express the Human Toxicity Potential (HTP) characterization factors listed in the table?

The HTP characterization factors are expressed in kg 1,4-DCB eq./kg.

Human Toxicity Potential (HTP) characterization factors are typically expressed in:

**kg 1,4-dichlorobenzene equivalent (kg 1,4-DCB-eq)**

This means the toxicity impact of a substance is reported relative to the reference substance **1,4-dichlorobenzene**.

The HTP characterization factors in the table are expressed in **kg 1,4-dichlorobenzene equivalent per kg emission**.
**Citation:** Guinée et al. (2002), **Part 2b, p.76**.

The Human Toxicity Potential (HTP) characterization factors in the table are expressed in **kg 1,4-dichlorobenzene equivalent per kg emission** (“**kg 1,4-dichlorobenzene eq/kg emission**”).
**Citation:** Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards* (2002), **Part 2b, p. 76**.


Question: What is the purpose of a functional unit in a Life Cycle Assessment study?

The functional unit describes the primary function(s) fulfilled by a product system and indicates how much of this function is to be considered in the intended LCA study. It enables different systems to be treated as functionally equivalent and allows reference flows to be determined for each of them.

In Life Cycle Assessment, the **functional unit** defines the **quantified function** of the product system being studied. Its purpose is to:

- **Provide a reference basis** for all inputs and outputs in the assessment
- **Ensure fair comparison** between alternatives by comparing them on the same delivered function
- **Set the scale and performance requirement** of the study

For example, instead of comparing “1 kg of packaging materials,” an LCA might compare **“the packaging needed to deliver 1,000 liters of beverage to consumers without damage.”** That makes the comparison meaningful because it is based on the **service provided**, not just the amount of material.

In short: the functional unit makes LCA results **consistent, comparable, and function-based**.

The purpose of a functional unit in LCA is to describe the primary function(s) fulfilled by a product system and specify how much of that function is considered in the study. It serves as the basis for selecting alternative product systems, treating them as functionally equivalent, and determining the reference flows for each system. (Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards* (2002), Part 3, section 2.4, p.468)

The purpose of the functional unit in an LCA is to describe the primary function(s) fulfilled by a product system and specify how much of that function is considered in the study. It serves as the basis for selecting alternative product systems, makes different systems functionally equivalent for comparison, and allows the reference flows for each system to be determined (Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards*, 2002, p.468).

Relatedly, it is also defined as “the quantified performance of a product system for use as a reference unit” (Hauschild et al., *LCA: Theory and Practice*, Chapter 20, p.487).


Question: What characteristic buzzword is associated with the supply chain as a domain of study in LCA?

The characteristic buzzword associated with the supply chain as a domain of study in LCA is 'longitudinal'.

In LCA, the supply chain domain is commonly associated with the buzzword **“upstream”** — more broadly, people often talk about **upstream and downstream impacts** across the value chain.

If you mean the characteristic framing of supply chains in LCA, another very common buzzword is **“cradle-to-grave”** (or **cradle-to-gate** for partial system boundaries).

The characteristic buzzword is “longitudinal” when the supply chain is the domain of study in LCA. (Guinée et al., Handbook on LCA, 2002, p. 670)

The characteristic buzzword is **“longitudinal”**: “whenever it is the supply chain that is the domain of study, LCA is an appropriate tool.” (Guinée et al., *Handbook on LCA*, p.670)


Question: Which chromium compounds are distinguished from one another in the substance list?

Chromium III and Chromium VI are the two chromium compounds distinguished in the list.

In typical substance lists used in LCA/LCI, **chromium is often distinguished mainly by oxidation state**, especially because the toxicity differs strongly:

- **Chromium(III) compounds** (trivalent chromium)
- **Chromium(VI) compounds** (hexavalent chromium)

Sometimes a list may also include:
- **Elemental chromium / unspecified chromium compounds**
- Specific named compounds such as **chromium trioxide**, **sodium dichromate**, **potassium dichromate**, or **chromium oxide**

But the key distinction is usually **Cr(III) vs Cr(VI)**.

The substance list distinguishes:
- chromium III / chromium (III) ion
- chromium VI / chromium (VI) ion
- chromium (unspecified)

Cited:
- Hauschild et al., LCA: Theory and Practice, Ch. 39, p. 1128
- Hauschild et al., LCA: Theory and Practice, Ch. 39, p. 1137
- Guinée et al., Handbook on LCA, p. 317
- Guinée et al., Handbook on LCA, p. 378

The substance list distinguishes these chromium entries from one another:

- chromium III / chromium (III) ion
- chromium VI / chromium (VI) ion
- chromium (ore)
- chromium dicarbide

Cited in:
- Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p.194
- Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p.378
- Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p.361
- Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p.861


Question: What is the basis for applying economic allocation in the 'da1' step of detailed allocation?

Economic allocation in the da1 step is applied based on market value or constructed market value.

In the **“da1” step of detailed allocation**, **economic allocation** is applied on the basis of the **relative economic value of the co-products** produced by the process.

So the allocation factors are typically calculated as:

**Allocation share of product i = (quantity of product i × price of product i) / sum of (quantity × price) for all co-products**

### Basis in practice
Economic allocation is used when:
- the process yields **multiple valuable outputs**,
- **physical relationships** (mass, energy, protein content, etc.) do not adequately reflect why the process exists or how burdens should be shared,
- the products differ strongly in function or quality.

### Rationale
The idea is that the process burdens are distributed according to the **market importance / revenue contribution** of each co-product. A product with higher economic value receives a larger share of the environmental burdens.

### Important note
Because prices can fluctuate, the basis should be:
- clearly documented,
- based on a representative price period or average,
- checked in sensitivity analysis if results are price-sensitive.

If you want, I can also explain how **da1** fits within the broader **detailed allocation procedure** in LCA.

In the detailed allocation step “da1”, economic allocation is applied “based on market value or constructed market value.”
(Source 1, p.146, Chapter 3.9 Multifunctionality and allocation)

In the **detailed allocation step da1**, economic allocation is applied **“based on market value or constructed market value”**; the guide adds that **the same economic principles used to solve cost allocation problems in managerial accounting can be used in LCA**.
**(Guinée et al., Handbook on LCA, Chapter 3, p.146)**


Question: What are the two time horizons over which Human Toxicity Potential (HTP) values are provided in the table?

The two time horizons are infinite (inf) and 100 years (100 yr).

Human Toxicity Potential (HTP) values are typically provided for two time horizons:

- **20 years**
- **100 years**

The table provides HTP values for **infinite** and **100-year** time horizons.
**Source:** Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards* (2002), **Part 2b, Operational annex 4.3.7, p.190**.

The table provides HTP values for **infinite** and **100-year** time horizons. (Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards*, Part 2b, p.190)


Question: How does the combined process modeling method (dm2 category) conceptually differ from dm1?

In dm1, processes are found upon closer inspection to be visibly independent, whereas in the dm2 category, processes are in principle combined, but alternatives are modeled as changes against a general background.

Conceptually, the difference is about **where the process breakdown is made**:

- **dm1**: the system is modeled mainly as a **single aggregated process/module**, often linking foreground and background in a more lumped way.
- **dm2 (combined process modeling)**: the system is modeled as a **combination of multiple unit processes and aggregated parts**, so some parts are kept **explicit and process-based**, while others remain **grouped/black-boxed**.

So dm2 is **more disaggregated and hybrid in structure** than dm1: it preserves more internal process detail where useful, instead of representing the whole modeled block as one aggregated process.

According to Guinée et al. (2002, p.146), dm1 and dm2 differ conceptually as follows:

- dm1: “Split up processes that are not really multiple into single ones.” This means examining a supposedly multifunctional process to see whether it can actually be separated into distinct process lines for different products.
- dm2: “Model long-term technical-physical relations in waste processing.” This applies only to mixed waste processing and involves subtracting the emissions caused by each waste in a long-term model.

So, dm1 is about disaggregating apparently multifunctional production processes into separate single processes, while dm2 is about modeling long-term cause-effect relations among wastes and emissions in mixed waste treatment.

Citation: Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), Part 2b: Operational annex, p.146.

In the detailed version, **dm1** and **dm2** differ in purpose and scope:

- **dm1** is about **splitting processes into single ones** where a seemingly multifunctional process is actually composed of different process lines at the same site. It is a structural decomposition step that helps clarify multifunctionality.
- **dm2** is about **modelling long-term technical-physical relations in mixed waste processing**. It applies only to mixed waste processing and involves **subtracting the emissions caused by each waste in a long-term model**.

So conceptually, **dm1 disaggregates process structure**, while **dm2 models long-term causal/emission relations in waste treatment**.
**Citation:** Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards* (2002), **p.146**.


Question: What are the two main categories used for collecting and reporting data of unit processes in Life Cycle Inventory analysis?

The two main categories are 'economic flows' and 'environmental interventions'.

The two main categories are:

1. **Foreground data**
   - Specific, directly collected data for the processes under study
   - Usually primary data from the company, plant, or product system being analyzed

2. **Background data**
   - Secondary data for upstream or downstream processes not directly measured
   - Usually taken from databases, literature, or generic industry sources

These are the standard categories used when collecting and reporting unit process data in Life Cycle Inventory (LCI).

The two main categories are:

1. Economic flows
2. Environmental interventions

Source: Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), Part 2b, Operational annex, 3.4 Format and data categories, p.133.

The two main categories are:

1. **Economic flows**
2. **Environmental interventions**

This is given in the “Overview of data categories for collecting and reporting the data of unit processes” table in the inventory analysis annex. (Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards*, Part 2b, Operational annex 3, p.133)


Question: What does 'FOB' stand for, and what does it exclude?

'FOB' stands for 'free on board,' referring to the price at the location of the supplier, and it excludes insurance and transport costs.

**FOB** stands for **Free On Board**.

In LCA / product cost or trade terms, it generally means the goods are priced **up to loading onto the ship at the port of export**.

It typically **excludes**:
- **Ocean freight / international shipping**
- **Insurance during transport**
- **Costs after the goods are on board**, such as import duties, inland transport in the destination country, unloading, and distribution

So FOB covers the product through export loading, but not the subsequent transport and downstream delivery costs.

The provided context does not contain a definition of “FOB,” so I cannot answer from the given sources.

I can’t answer that from the provided passages. The context only mentions “FOB prices” in the knowledge graph, but it does not include the passage defining FOB or stating what it excludes.


Question: What does the ceteris paribus principle assume about the choice of functional unit in a product assessment?

The ceteris paribus principle assumes that the choice of the functional unit of the product alternative investigated has no influence on any other activities anywhere on the planet.

Under the **ceteris paribus** principle (“all else being equal”), the **functional unit** in a product assessment should be chosen so that the compared products deliver the **same function, service level, and performance**.

In practice, this assumes the functional unit:

- is **identical for all alternatives**
- reflects the **actual function provided**, not just a physical amount of product
- holds other relevant conditions constant, such as **quality, lifetime, capacity, and use conditions**

So instead of comparing “1 kg of product A vs 1 kg of product B,” ceteris paribus implies comparing them per **equivalent service delivered**—for example, “washing 1,000 kg of laundry” or “providing 1 year of lighting at a given brightness.”

The ceteris paribus principle assumes that the choice of the functional unit of the product alternative investigated has no influence on any other activities anywhere on the planet; the effects of emissions due to the functional unit are treated as additional to the normal background concentration, and the functional unit is assumed to be marginal relative to other activities, allowing linear models to be used. (Guinée et al., Handbook on LCA, 2002, p. 460)

In product assessment, the ceteris paribus principle assumes that “the choice of the functional unit of the product alternative investigated has no influence on any other activities anywhere on the planet” (Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards*, 2002, p. 460).


Question: Where can the SPOLD data format for LCI data be found?

The SPOLD data format can be found at http://www.spold.org/.

The SPOLD format/specification for LCI data is provided by the **SPOLD association**.

You can find it here:

- **SPOLD website:** http://www.spold.org

Historically, SPOLD published the **SPOLD data exchange format** for life cycle inventory datasets, including documentation and example structures. If you’re looking for the newer common exchange formats used today, you may also want to check **EcoSpold 1** and **EcoSpold 2**, which are associated with ecoinvent.

The SPOLD format can be found at **http://www.spold.org/** — **Guinée et al. (2002), p.133, Part 2b: Operational annex 3, section 3.4 “Format and data categories.”**

The SPOLD format for LCI data “can be found on: http://www.spold.org/” (Guinée et al., *Handbook on LCA: Operational Guide to the ISO Standards*, chapter “Part 2b: Operational annex 3. Inventory analysis 3.4 Format and data categories,” p.133).