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

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

The use stage is often harder to characterize than production processes for several reasons:

1. **Variable and unpredictable user behavior**: How a product is actually used depends on individual habits, frequency of use, intensity, and context. Unlike a controlled manufacturing process, user behavior is highly heterogeneous across populations and geographies.

2. **Long and uncertain time horizons**: Products may be used for years or decades, making it difficult to predict total lifetime energy consumption, maintenance needs, and degradation patterns.

3. **Lack of direct data**: Manufacturers typically have good process data for production (energy meters, material inputs, etc.), but they rarely have direct measurement data on how their products are used in the field. Use-phase data often relies on assumptions, surveys, or modeling.

4. **Dependency on external systems**: Use-stage impacts often depend on factors outside the product itself—e.g., the electricity grid mix, climate/geography (heating/cooling loads), or complementary products—which vary by region and change over time.

5. **Functional unit ambiguity**: Defining a representative usage scenario (e.g., number of wash cycles per year, driving patterns for a car) requires assumptions that can significantly influence results and are contested.

6. **Dominance of use phase in many products**: For energy-using products, the use stage can dominate total life-cycle impacts, so small uncertainties in use-phase assumptions have a large effect on overall LCA results, amplifying the importance of getting it right.

Together, these factors mean that use-stage modeling requires more assumptions and scenario analysis than characterizing relatively well-defined manufacturing processes.

The use stage of a product is often harder to characterize than its production processes because data collection faces different challenges. Unlike production processes, which occur in controlled industrial settings, the use stage involves final consumers or service operators whose behaviors and conditions are highly variable and difficult to monitor systematically.

Additionally, as noted in the context of buildings and urban-scale assessments, the level of detail available is typically very coarse — data tends to consist of bulk material categories rather than specific process-level information, making it difficult to model detailed manufacturing or use-related processes accurately. Transport and other ancillary activities are also hard to account for, often requiring either exclusion or estimation based on trade data.

Furthermore, highly variable processes — such as those dependent on user behavior, load factors, and operating conditions — introduce significant uncertainty into the inventory, as the number of aspects that can influence the results is extensive (e.g., speed of use, load factor, internal recovery rates).

**Sources cited:**
- European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), **p. 198**
- Hauschild et al., LCA: Theory and Practice, **28 LCA of Buildings and the Built Environment, p. 710**
- European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), **p. 123**

Based on the ILCD Handbook, the use stage of a consumer product is harder to characterize than production processes because **data collection faces different challenges**. Unlike production processes, which can be observed and measured directly at manufacturing sites, the use stage involves either final consumers or service operators whose behaviors are highly variable and difficult to monitor systematically.

Additionally, as noted in the context of buildings and urban-scale LCA, the use stage typically consists only of **bulk categories of goods** (e.g., 'steel', 'aluminium', 'wood'), requiring the assessor to construct life cycles from very coarse data, while **more detailed processes related to manufacturing of specific goods (forming, assembly, etc.) are ignored**, and **transport is also difficult to account for** (Hauschild et al., *28 LCA of Buildings and the Built Environment*, p.710).

Furthermore, the ILCD Handbook highlights that highly variable processes — such as those involving **load factors, variable use patterns, and internal recovery rates** — make technological representativeness particularly challenging to establish (ILCD Handbook, p.123).

**Primary citation:** European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.198; supplemented by Hauschild et al., *28 LCA of Buildings and the Built Environment*, p.710, and ILCD Handbook, p.123.


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

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

In LCI modelling **Situation B** (as described in ISO 14044 and related LCA methodology literature), the context typically involves a **partially open-loop or constrained system** where specific modelling provisions apply.

## Situation B – Standard Description

**Situation B** generally refers to a scenario where:

- The **specific modelling provisions** (e.g., system expansion, substitution, or specific allocation rules) apply **exclusively to the foreground system processes** (i.e., the processes directly under study or within the defined system boundary that involve multi-output or waste treatment decisions).

- The **other parts of the background system** are modelled using **conventional/attributional LCI data** — typically taken from background databases (e.g., ecoinvent) using average or supplier-specific data, without applying the special provisions used for the foreground.

## Key Distinction

| System Part | Modelling Approach |
|---|---|
| Foreground processes (subject to Situation B provisions) | Special provisions apply (e.g., consequential substitution, specific allocation) |
| Background system (remainder) | Modelled with **attributional, average background data** from standard LCI databases |

This hybrid approach reflects practical LCA where only the decision-relevant foreground processes receive consequential or specific treatment, while the background remains attributional.

---

If you are referring to a **specific framework or document** (e.g., PEF, EPD, ILCD Handbook), please clarify so I can give a more precise answer.

Based on the context provided, in LCI modelling for Situation B, the modelling provisions apply **exclusively to those processes that are affected by large-scale consequences**. The other parts of the background system are modelled as **"Situation A"** — typically all the processes with a smaller contribution to the overall results.

*[Source 1 & 3: Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.968]*

Based on the retrieved passages, in LCI modelling for Situation B, the modelling provisions apply **exclusively to those processes that are affected by the large-scale consequences**. The other parts of the background system of the life cycle model are modelled as **"Situation A"** — typically all the processes with a smaller contribution to the overall results.

As stated directly in the source: *"It is important to note that the LCI modelling provisions for Situation B refer exclusively to those processes that are affected by these large-scale consequences. The other parts of the background system of the life cycle model will later be modelled as 'Situation A', i.e. typically all the processes with a smaller contribution to the overall results."*

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


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

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

In LCA/LCI studies, **reasonably worst-case data** may appropriately be used for the following purposes, even when aligned (i.e., representative, average) data are otherwise preferred:

1. **Screening or scoping studies** – to quickly identify whether a process or product system is likely to be significant, without investing in precise data collection.

2. **Conservative safety/risk assessments** – where it is important to ensure that potential impacts are not underestimated (e.g., regulatory compliance, product safety evaluations).

3. **Sensitivity and uncertainty analysis** – to bound the results and test how robust conclusions are under worst-case assumptions.

4. **Data gaps / proxy situations** – when no better data are available, worst-case values can serve as a conservative placeholder to avoid underreporting impacts.

5. **Comparative assertions intended to demonstrate that one option is better even under the most unfavorable assumptions** – if the preferred option still performs better under worst-case conditions for that option, the conclusion is robust.

6. **Hotspot identification** – to flag areas of concern that merit more detailed investigation, ensuring nothing significant is overlooked.

The key principle is that worst-case data are acceptable when the goal is to **err on the side of caution** or to **demonstrate robustness of conclusions**, rather than to provide a precise, representative picture of average performance.

Based on the context provided, reasonably worst-case data may be used for **scenarios** and for **checking the robustness of comparisons when doing the sensitivity analysis**, even though aligned reporting is otherwise preferred.

*(Source 1: ILCD Handbook: General Guide for LCA (2010), p.214)*

Based on the context, reasonably worst-case data may be used for two specific purposes in an LCI study, even though aligned reporting is otherwise preferred:

1. **For scenarios**, and
2. **For checking the robustness of comparisons when doing the sensitivity analysis.**

As stated in the ILCD Handbook: "Note that reasonably worst-case data may however be used for scenarios and for checking the robustness of comparisons when doing the sensitivity analysis."

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p. 214)*


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

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

## Dioxins in Life Cycle Inventory

### How They Are Inventoried

Dioxins are commonly inventoried as **"dioxins (TEQ)"** — that is, as a single aggregated entry expressed in **Toxic Equivalency Quantities (TEQ)**, rather than listing each individual congener (there are 17 toxic polychlorinated dibenzo-p-dioxin and dibenzofuran congeners) separately.

Each congener is weighted by its **Toxic Equivalency Factor (TEF)** relative to the most toxic congener, 2,3,7,8-TCDD (which has a TEF = 1), and the results are summed into a single TEQ value.

### Why This Is Considered Acceptable

This aggregation is considered an acceptable **exception to the general rule** of inventorying substances individually for several reasons:

1. **Analytical practice**: Dioxins are typically measured and reported as TEQ in emissions monitoring and environmental data, so inventory data are rarely available at the individual congener level.

2. **Characterization alignment**: In impact assessment (e.g., human toxicity, ecotoxicity), dioxin characterization factors are also commonly expressed per TEQ, so the inventory and impact assessment steps remain consistent.

3. **Practicality**: Separating 17 congeners would require data that almost never exist in practice, adding false precision without improving accuracy.

4. **Scientific consensus**: The TEF/TEQ framework is internationally recognized (WHO), making it a scientifically defensible aggregation basis — unlike arbitrary groupings.

In essence, the TEQ aggregation reflects both **data availability realities** and **established scientific convention**, making it a pragmatic and defensible exception to the general LCI principle of substance-specific inventorying.

According to the ILCD Handbook, dioxins are commonly inventoried as **2,3,7,8-TCDD equivalents (2,3,7,8-Tetrachlorodibenzodioxin human toxicity equivalents)** as an exception to the general rule that measurement indicators and substance group flows should be split into single substances.

This is considered acceptable because **"the equivalent number already relates to the relevant impact of interest, i.e. eco-toxicity and human toxicity"** — meaning the aggregated metric directly captures the environmental impact dimension of concern, rather than obscuring it.

However, the Handbook also stipulates that **"if available individually, the single species shall be inventoried"**, meaning the use of 2,3,7,8-TCDD equivalents is only a pragmatic fallback when speciated data are not available.

Additionally, the Guinée et al. Handbook supports this approach from a practical standpoint, noting that when an inventory contains "dioxins (unspecified)" without applicable characterisation factors, a sensitivity analysis can replace the entry with 2,3,7,8-TCDD for worst-case assessment purposes.

> **Sources:**
> - European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), **p. 216**
> - European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), **p. 217**
> - Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), **p. 87**

Based on the retrieved context, dioxins are commonly inventoried as **2,3,7,8-TCDD equivalents (2,3,7,8-Tetrachlorodibenzodioxin human toxicity equivalents)** as a permissible exception to the general rule that substance group elementary flows should be split into single substances.

This practice is considered acceptable because, as stated in the ILCD Handbook:

> "This is argued to be acceptable as the equivalent number already relates to the relevant impact of interest, i.e. eco-toxicity and human toxicity."

In other words, since the equivalent factor already captures the toxicity-relevant impact, inventorying dioxins as a grouped, aggregated metric does not fundamentally distort the impact assessment results. However, the ILCD Handbook also specifies that **if single species data are available individually, they shall be inventoried as such**, meaning the 2,3,7,8-TCDD equivalent approach is only a fallback when disaggregated data are unavailable.

This exception is also reflected in the Guinée et al. Handbook, which notes that when an inventory entry reads "dioxins (unspecified)," a sensitivity analysis can investigate a worst-case influence by replacing it with 2,3,7,8-TCDD, for which characterisation factors are available.

*(ILCD Handbook: General Guide for LCA (2010), p. 216–217; Guinée et al., Handbook on LCA, p. 87)*


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

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

I don't have access to the specific document or textbook you're referring to — "Section 6.6" could belong to any number of LCA standards, textbooks, or reports (e.g., ISO 14040/14044, the ILCD Handbook, Hauschild et al., etc.).

However, based on common LCA methodology literature, **two complementary approaches for deriving/setting system boundaries** are typically described as:

1. **Iterative approach** – starting with an initial system boundary and refining it as data become available.
2. **Cut-off approach (cut-off criteria)** – excluding processes or flows that fall below a defined threshold of contribution (e.g., <1% of mass, energy, or environmental relevance).

**The cut-off criteria approach** is the one that involves defining thresholds below which inputs/outputs or unit processes are excluded from the system boundary.

If you can tell me which specific document or textbook Section 6.6 comes from, I can give you a more precise and accurate answer.

Based on the context provided, Section 6.6 describes two complementary approaches for deriving system boundaries:

1. **Qualitative definition of system boundaries** (Chapter 6.6.2)
2. **Quantitative definition of system boundaries – the cut-off criteria** (Chapter 6.6.3)

It is the **quantitative definition** (Chapter 6.6.3) that involves the cut-off criteria.

*(Source 3: ILCD Handbook, p.8, Table of Contents; Source 2: ILCD Handbook, p.12)*

Based on the retrieved passages, Section 6.6 of the ILCD Handbook describes two complementary approaches for deriving system boundaries:

1. **Qualitative definition of system boundaries** (chapter 6.6.2) — which involves defining which life cycle stages, activity types, processes, and elementary flows are included or potentially excluded from the system.

2. **Quantitative definition of system boundaries** (chapter 6.6.3) — which is the approach that **involves the cut-off criteria**, defining the quantitative cut-off percentage values to be applied for flows that cross the system boundary but are not quantitatively included in the inventory.

As stated in the ILCD Handbook table of contents: *"6.6.2 Qualitative definition of system boundaries ... 100 / 6.6.3 Quantitative definition of system boundaries – the cut-off criteria ... 102"* (ILCD Handbook, p. 8), and further elaborated at p. 12 and p. 107.


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

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

## Avoided Waste Treatment in LCA (System Expansion)

When extra demand utilizes otherwise unused or waste materials/functions, the avoided waste treatment is handled through **system expansion** (also called **substitution** or **avoided burden approach**).

### The Principle

If a waste stream would have otherwise been **landfilled, incinerated, or otherwise treated**, and the new use avoids that treatment, the **avoided impacts of that waste treatment are credited** (subtracted) from the system being assessed.

### How It Works

- The system boundary is **expanded** to include the avoided waste treatment process
- The **environmental burdens of the displaced waste treatment** (e.g., landfill gas emissions, incineration impacts) are **credited as a negative impact** to the studied system
- This follows the logic that the system is effectively "providing a service" by diverting waste from treatment

### Example

If a process uses waste wood that would otherwise be incinerated:
- The emissions from incineration that are now avoided → **credited to the system**
- Any energy recovery from that avoided incineration → also accounted for (as a **lost credit**)

### Governing Standards

This approach is addressed in:
- **ISO 14044** (system expansion guidance)
- **EN 15804** (for construction products)
- **PEF/OEF** methodology (European Commission)

### Key Consideration

The credit is only valid if the waste treatment was **genuinely the alternative fate** — requiring careful justification of the baseline scenario.

Based on the context provided, when extra demand makes use of otherwise partly or fully unused functions (such as originally deposited or incinerated waste), the **avoided waste treatment is credited to the using system**.

As stated in the ILCD Handbook:

> "if the analysed extra demand uses otherwise partly or fully unused functions (e.g. originally deposited or incinerated waste), the 'avoided waste treatment', if any, is credited to the using system."

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.172)*

This principle is also reinforced on **p.166** of the same handbook, which notes:

> "if the extra demand uses otherwise partly or fully unused functions, the 'avoided waste treatment', if any, should be credited to the using system."

Additionally, in **strongly declining markets** specifically (p.361), if the secondary good is at least partly unused (i.e., deposited), any additional demand would avoid the waste deposition, and accordingly **a credit for avoided waste depositing is given**.

When extra demand makes use of otherwise partly or fully unused functions (e.g., originally deposited or incinerated waste), the "avoided waste treatment," if any, **is credited to the using system**.

As stated in the ILCD Handbook:

> "Analogously, if the analysed extra demand uses otherwise partly or fully unused functions (e.g. originally deposited or incinerated waste), the 'avoided waste treatment', if any, is credited to the using system." *(ILCD Handbook, p. 172)*

This is further reinforced on p. 166, which notes:

> "if the extra demand uses otherwise partly or fully unused functions, the 'avoided waste treatment', if any, should be credited to the using system." *(ILCD Handbook, p. 166)*


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

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

In LCA studies conducted under **Situation A** (a new process not yet implemented) and **Situation B** (an existing process being modified or optimized), the following two pieces of information must be documented regarding the **central process** in the foreground system:

1. **The current/reference technology or process** (i.e., what the central process looks like at present, or the baseline/reference situation against which the new or modified process is compared).

2. **The future/anticipated technology or process** (i.e., what the central process is expected to look like after implementation or modification, reflecting the planned or prospective situation).

In essence, both the **"as-is" (current/reference) state** and the **"to-be" (future/modified) state** of the central process must be documented, so that the changes and their associated impacts can be properly characterized and assessed.

Based on Source 2 (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.201), for LCA studies under Situation A and B, the two pieces of information that must be documented regarding the central process in the foreground system are:

1. **The absolute amount of the central process** in the foreground system
2. **The total market size of the function** of this process

Based on Source 2 (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.201), for LCA studies under Situation A and B, the two pieces of information that **shall** be documented regarding the central process in the foreground system are:

1. **The absolute amount of the central process** in the foreground system.
2. **The total market size of the function** of this process.

As stated: *"For LCA studies under Situation A and B, the absolute amount of the central process in the foreground system shall be documented. The total market size of the function of this process shall be documented."*

(ILCD Handbook, p.201)


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

When identifying the true joint process of a secondary good, the identified primary good must have a **market value greater than or equal to** (i.e., at least as large as) the market value of the secondary good.

In other words, the primary good must be the **economically dominant** (higher-value) output of the joint process. The convention in LCA (particularly in the context of system expansion and substitution, as well as in ecoinvent's classification logic) is that the **primary product has the higher economic value** compared to the secondary (by-product) output. This condition ensures the correct identification of which product "drives" the production process and which is the secondary/by-product, preventing the misclassification of the main driver of production.

The identified primary good shall not have a lower market value than the secondary good.

(Source 1: ILCD Handbook, p.182, provision XIII.b.i)

The identified primary good shall not have a lower market value than the secondary good.

(ILCD Handbook, p. 182)


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

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

## Reminder Flow in Life Cycle Assessment

A **reminder flow** (also called a **remainder flow** or **cut-off flow**) is a concept in LCA related to how material and energy flows are tracked across system boundaries.

### Definition

A reminder flow refers to a flow that **exits the product system but is not further tracked or assessed** within the study's scope — it "reminds" the analyst that something has left the system but its downstream fate is not modeled. More precisely, in some LCA frameworks, it refers to flows that are noted but not fully characterized in the impact assessment.

---

### Relationship to Inventory (LCI)

In the **Life Cycle Inventory (LCI)**, all inputs and outputs of a product system are compiled. Some flows may be:
- Fully modeled elementary flows (feeding directly into impact assessment)
- Flows crossing into other product systems (co-products, wastes passed on)
- Flows that are **recorded but not assigned** environmental impact characterization factors — these become reminder flows

---

### Relationship to Impact Assessment (LCIA)

In **Life Cycle Impact Assessment (LCIA)**, characterization factors convert inventory flows into impact scores. A reminder flow:
- **Has no applicable characterization factor** in the chosen impact methodology, or
- Falls outside the scope of the impact categories assessed

This means it appears in the inventory but **does not contribute to any calculated impact score**, serving as a reminder that the flow exists and may be environmentally relevant, even if not quantified in LCIA.

---

### Practical Significance

Reminder flows highlight **gaps in impact coverage** — they flag potentially important flows that current LCIA methods cannot characterize, prompting transparency and potential future refinement of the assessment.

## Reminder Flows in Life Cycle Assessment

### Definition and Purpose

A **reminder flow** is a special type of flow used in Life Cycle Inventory (LCI) to **preserve original or supplementary information** for documentation and reporting purposes. Specifically, reminder flows are used to keep original information of split measurement indicators and sum flows, as well as to retain information about littered goods or waste flows in the inventory for informational purposes (ILCD Handbook, p.232; p.243).

---

### Relationship to the Inventory

Reminder flows appear **within the LCI** but serve only an informational role. For example:
- They can be used to record the original form of a littered good before it is broken down into individual substance emissions (ILCD Handbook, p.243).
- They can optionally retain waste flow information for reporting purposes, such as required by individual EPD systems — but **do not substitute** complete modelling of waste management (ILCD Handbook, p.243).

---

### Relationship to Impact Assessment

Reminder flows are **strictly excluded from impact assessment**:

> *"Reminder flows shall not carry an LCIA impact factor."* (ILCD Handbook, p.233)

They must **not** be connected with upstream or downstream processes, and carry **no relevance** to classical LCI results or LCIA results (ILCD Handbook, p.232).

---

### Identification Requirement

To prevent double counting, reminder flows **must be clearly named**, e.g., *"VOC, reminder flow, not impact relevant"* (ILCD Handbook, p.233).

## Reminder Flows in Life Cycle Assessment

### Definition and Purpose

A **reminder flow** is a special type of flow used in LCA inventories to **preserve original information** for specific purposes, without being part of the normal (impacting) inventory. As described in the ILCD Handbook:

> *"It is recommended to use reminder flows to inventory the original information of split measurement indicators and sum flows... They may be used to keep other flows in LCI results inventories for information purposes."*
> *(ILCD Handbook, p. 232–233)*

For example, reminder flows can be used to retain information about **littered goods** or **waste flows** in the inventory for reporting or documentation purposes (ILCD Handbook, p. 243).

---

### Relationship to Impact Assessment

Reminder flows are **explicitly excluded** from impact assessment:

> *"Reminder flows shall not carry an LCIA impact factor."*
> *(ILCD Handbook, p. 233)*

They carry **no relevance** to classical LCI or LCIA results and must not be connected with upstream or downstream processes (ILCD Handbook, p. 232).

---

### Identification in the Inventory

To prevent confusion and **double counting**, reminder flows must be clearly named (e.g., *"VOC, reminder flow, not impact relevant"*) and are implemented as an optional marking in the **ILCD reference format** *(ILCD Handbook, p. 232–233)*.


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

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

The functional unit addresses these four questions:

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

These four questions together ensure the functional unit is precisely and completely defined, enabling meaningful comparisons between product systems in an LCA.

Based on Source 7 (Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.974), the functional unit addresses the following four questions:

1. **What** — the function provided
2. **How much** — the quantity of the function
3. **How long** — the duration of the function
4. **How well** — the quality in which the function is provided (in what way and how well is the function provided)

(Hauschild et al., LCA: Theory and Practice, Chapter 37, p.974)

> Note: Source 1 (Hauschild et al., Chapter 8 Scope Definition, p.83) also references five questions — adding **"where"** — but the four core questions consistently identified across the sources are those listed above.

Based on the sources, the functional unit addresses the following four key questions when specifying the qualitative and quantitative aspects of a system's function:

1. **What?** — the function provided
2. **How much?** — the quantity of the function
3. **For how long/how many times?** — the duration or frequency
4. **How well?** — the quality with which the function is provided

(A fifth question, **"where?"**, is also mentioned in some formulations.)

These are drawn from **Hauschild et al., LCA: Theory and Practice — 8 Scope Definition, p. 83**, which states: "A functional unit defines the qualitative aspects and quantifies the quantitative aspects of the function, which generally involves answering the questions 'what?', 'how much?', 'for how long/how many times?', 'where' and 'how well?'"

This is further elaborated in **8 Scope Definition, p. 84**, and confirmed in **37 LCA Cookbook, p. 974**, which specifies the four mandatory aspects as: (4:1) function provided (what), (4:2) in which quantity (how much), (4:3) for what duration (how long), and (4:4) to what quality (how well).


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

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

For LCA studies directed towards public audiences, the following can improve credibility:

- **Independent critical review** by a panel of interested parties (including independent experts and representatives of stakeholders such as industry, government, and NGOs), rather than just an internal or single expert review
- **Transparency** in reporting methodology, assumptions, and data sources
- **Third-party verification** of the study
- **Peer review** by qualified independent experts

According to ISO 14044, when LCA results are intended to be used in **comparative assertions disclosed to the public**, a **critical review by a panel of interested parties** is specifically required (rather than just an internal review or a single external expert review). This panel review process is considered essential for ensuring the study's credibility and impartiality in the public domain.

Based on the provided context, several elements can improve the credibility of LCA studies directed towards public audiences:

1. **Critical review** — "Besides preventing misuse and unsupported claims, critical review may also help identifying mistakes and more justifiable assumptions as well as generally improve the quality of a study. It thus supports the robustness of an LCA and increases trust in its results and conclusions." *(Hauschild et al., LCA: Theory and Practice — 13 Critical Review, p.337)*

2. **Good reporting** — "Good reporting of LCI and LCA studies provides the relevant project details, the process followed, approaches and methods applied, and results produced. This is essential to ensure reproducibility of the results and to provide the required information to reviewers to judge the quality of the results and appropriateness of conclusions and recommendations." *(ILCD Handbook, p.307)*

3. **Independent external critical review and public transparency** — "An independent and external critical review of the data is the suitable means to guarantee data quality...Public transparency on all data and parameters should be provided as far as confidentiality allows for it." *(ILCD Handbook, p.53)*

4. **Adapted communication of uncertainty** — Communication of uncertainty "needs to be adapted to the target audience" to properly convey the robustness of conclusions. *(Hauschild et al., 11 Uncertainty Management and Sensitivity Analysis, p.307)*

Based on the context, several elements can improve the credibility of LCA studies directed towards public audiences:

1. **Good reporting** that provides relevant project details, the process followed, approaches and methods applied, and results produced — ensuring reproducibility and enabling reviewers to judge quality of results and appropriateness of conclusions (ILCD Handbook, p.307).

2. **Independent and external critical review**, which guarantees data quality and robustness/reproducibility of results while balancing confidentiality needs, with public transparency provided as far as confidentiality allows (ILCD Handbook, p.53).

3. **Clear statement of intent** in the goal definition, unambiguously stating whether results are intended for use in comparative assertions disclosed to the public, which is essential for overall credibility (Finkbeiner et al., ISO 14040 & 14044, p.83).

4. **Uncertainty analysis**, with communication adapted to the target audience — recognizing that communication to the general public requires different treatment than to fellow practitioners (Hauschild et al., 11 Uncertainty Management and Sensitivity Analysis, p.307).

5. **Critical review** to prevent misuse, unsupported claims, and to improve overall study quality, thereby increasing trust in results and conclusions (Hauschild et al., 13 Critical Review, p.337).


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

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

Normalization converts LCIA results into dimensionless figures by dividing them by a reference value (e.g., total emissions or impacts of a region or the world per capita per year), allowing results from different categories to be expressed on a common scale. This makes it possible to compare the relative magnitude of impacts across categories or, if **weighting** (another optional step) is subsequently applied, to aggregate them into a single score.

Based on the context provided, **weighting** is the optional step required to sum up LCIA results across different impact categories or areas of protection.

As stated in Source 5 (ILCD Handbook, p.281):

> "Normalisation is an optional step under ISO 14044:2006. It supports the interpretation of the impact profile and is the **first step towards a fully aggregated result that additionally requires a weighting across indicators**."

And confirmed in Source 3 (ILCD Handbook, p.283):

> "The normalised and weighted LCIA results can subsequently also be **summed up across all impact categories or areas-of-protection**."

Therefore, both **normalisation** (as the first step) and **weighting** (as the subsequent step) are the optional steps required under ISO 14044:2006 to directly compare or sum up LCIA results across different categories or areas of protection.

*(ILCD Handbook: General Guide for LCA, pp. 281 and 283)*

Based on the context, the optional steps required to directly compare or sum up LCIA results across different impact categories or areas of protection are **Normalisation** followed by **Weighting**.

As stated in Source 7 (ILCD Handbook, p.281):

> "Normalisation is an optional step under ISO 14044:2006. It supports the interpretation of the impact profile and is the **first step towards a fully aggregated result that additionally requires a weighting across indicators**."

And as confirmed in Source 4 (ILCD Handbook, p.283):

> "The normalised and weighted LCIA results can subsequently also be **summed up across all impact categories or areas-of-protection**."

These steps correspond to **ISO 14044:2006 chapter 4.4.3.2** (for Normalisation, per p.281) and **provision 8.4 Weighting** (per p.283). It is also noted that weighting shall not be used in studies leading to comparative assertions intended to be disclosed to the public (ILCD Handbook, p.283).


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

The ISBN of "The Hitch Hiker's Guide to LCA" by Henrikke Baumann and Anne-Marie Tillman, published in 2004 by Studentlitteratur, is **91-44-02364-2**.

The ISBN of "The Hitch Hiker's Guide to LCA" by Baumann and Tillman (2004) is not found in the provided context.

The ISBN of "The Hitch Hiker's Guide to LCA" by Baumann and Tillman (2004) is not provided in the retrieved context. The sources available do not contain a reference to that specific publication.


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

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

## Importance of High Data Quality for Dominant Emission Sources in LCA

When a single process contributes ~95% of the overall climate change impact, **data quality for that methane emission is critically important** for several interconnected reasons:

### 1. **Proportional Influence on Results**
- With 95% contribution, any error or uncertainty in that emission directly translates to nearly the same percentage error in the **total impact score**
- A 10% overestimate/underestimate in that methane value ≈ ~9.5% error in the final result
- Poor data here **dominates** the overall result uncertainty

### 2. **Decision Relevance**
- LCA results are used to **compare alternatives or identify hotspots**
- If the dominant emission is poorly characterized, conclusions about where to focus improvement efforts may be **misleading or wrong**
- Wrong decisions could be made regarding product design, supplier selection, or policy

### 3. **Sensitivity Analysis Implications**
- Sensitivity analysis will show this parameter has the **highest sensitivity coefficient**
- High uncertainty here means the **confidence interval of the final result is very wide**, potentially making conclusions statistically insignificant

### 4. **Methane-Specific Considerations**
- Methane GWP (e.g., 28–86 CO₂eq depending on timeframe) means **small mass errors become large impact errors**
- Fugitive methane is notoriously difficult to measure accurately

### **Key Principle**
> Resources for improving data quality should be **prioritized where impact contribution is highest** — this is the foundation of **contribution/hotspot analysis** in LCA.

Because that single emission (methane) dominates the overall Climate Change impact result (~95%), any error or imprecision in that data point will propagate directly and heavily into the final result, effectively determining the accuracy of the entire study's outcome. As stated in the ILCD Handbook:

> "In this case it is very important to have a high quality on the data for this emission. In contrast, the emissions of e.g. CO2 from transport, energy conversion processes, etc. as part of the same system can be far less precise, as they in total contribute only little to the overall impact."

**(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.137)**

In other words, when one emission dominates the impact category, the overall result is extremely sensitive to the quality of that specific data point. Poor quality data for that dominant emission would render the entire impact assessment result unreliable, regardless of how well other data in the system are characterised.

Based on the ILCD Handbook, when a single emission or process dominates the overall impact results, high data quality for that specific emission is critical because it disproportionately determines the accuracy of the entire study's outcome.

As stated in the ILCD Handbook:

> "In this case it is very important to have a high quality on the data for this emission. In contrast, the emissions of e.g. CO2 from transport, energy conversion processes, etc. as part of the same system can be far less precise, as they in total contribute only little to the overall impact."

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.137)*

In other words, when methane emissions from one process account for ~95% of the Climate Change impact, any error or imprecision in that data will propagate heavily into the final result, making the overall LCA conclusion unreliable. Conversely, processes contributing only marginally to the overall impact can tolerate lower data precision without meaningfully affecting the conclusions. This is reinforced by the general principle stated in the same handbook (p.136) that **"the weakest [quality dimension] determines (lowers) the overall data quality"** — so poor quality on the dominant emission would undermine the entire assessment.


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

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

# Land Use Change CO2 Emissions: Secondary vs. Virgin Forests

## Core Distinction

The fundamental difference lies in **carbon stock history and recovery trajectory**:

- **Virgin (primary) forests**: Represent mature, stable carbon stocks that took centuries to accumulate. Conversion releases essentially "ancient" carbon with no near-term recovery expectation at the original site.
- **Secondary forests**: Are already in a regrowth/recovery phase following prior disturbance. Their carbon stocks are dynamic and time-dependent.

---

## Inventorying Secondary Forest Emissions

### Key Methodological Approach

Secondary forest emissions should be tracked using **cohort-based accounting**:

1. **Establish the baseline carbon trajectory** — secondary forests have a known or estimable regrowth curve (biomass accumulation over time since last disturbance)
2. **At the time of conversion**, the carbon stock present (biomass + litter) is inventoried against this trajectory
3. **Counterfactual matters**: What would the forest have accumulated *had it not been converted* must be considered under some frameworks

### IPCC Guidance (2006 GL / 2019 Refinement)

- Uses **Tier 1–3 approaches** depending on data availability
- Biomass carbon stocks are estimated via **default biomass values by forest type and region**, or preferably by direct measurement
- Litter pools are often estimated as a **fraction of biomass** (default ratios provided by IPCC)
- Emissions = (carbon stock at time of conversion) × 44/12, released over a **20-year default period** or immediately depending on disturbance type

### Specific Considerations for Secondary Forests

| Aspect | Secondary Forest | Virgin Forest |
|--------|----------------|---------------|
| Carbon stock at conversion | Lower (still recovering) | Higher (mature) |
| Age-class tracking | Essential | Less dynamic |
| Litter pool | Smaller, proportional to biomass age | Larger, near steady-state |
| Legacy flux accounting | May still carry prior land-use emissions | Typically not applicable |
| Regrowth credit | If abandoned, carbon sink creditable | If abandoned post-conversion |

---

## The "Legacy" Flux Issue

Secondary forests may still be **emitting or sequestering** carbon from *previous* land use changes. This creates a bookkeeping challenge:

- IPCC recommends tracking emissions in the **year the land use change occurs** (stock-difference method)
- Some national inventories use the **gain-loss method**, tracking annual net fluxes
- For secondary forests, prior-conversion emissions may already be partially "paid"

---

## Litter Pool Specifics

- In secondary forests, litter pools are **smaller and less stable** than in primary forests
- Litter decomposition rates and steady-state stocks depend on stand age
- IPCC Tier 1 typically applies a **litter-to-biomass ratio** (e.g., ~0.04 for tropical forests) that implicitly accounts for stand maturity

---

## Practical Implications for LCA

- Crediting secondary forest conversion with **lower emissions** than primary forest conversion is scientifically justified
- However, **opportunity cost** of foregone sequestration (the carbon the secondary forest *would have* captured) should also be considered
- Some LCA frameworks (e.g., BSI PAS 2050, ISO 14067) require accounting for this **dynamic baseline**

The bottom line: secondary forest emissions require **time-resolved, cohort-specific accounting** because their carbon stocks are trajectory-dependent, whereas primary forest emissions can be treated more as a static stock depletion.

Based on the context provided:

**Secondary Forests:** CO2 emissions from land use change related to biomass and litter of **secondary forests** shall be inventoried as **"Carbon dioxide (biogenic)"**.

**Virgin Forests:** CO2 emissions from land use change related to biomass and litter of **virgin forests** (as well as from soil, peat, etc. of all land uses) shall be inventoried as **"Carbon dioxide (fossil)"**.

This distinction applies unless the selected LCIA method requires otherwise.

*(Source 1: Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.1030, sections 3:2)*

This is further supported by Source 2 (Hauschild et al., LCA: Theory and Practice — 30 LCA of Biofuels and Biomaterials, p.773), which states: *"All virgin forest conversion emissions should thus be inventoried as fossil carbon dioxide (European Commission 2010)."*

Based on the sources, there is a clear distinction in how CO2 emissions from land use change should be inventoried depending on the forest type:

**Virgin Forests:** CO2 emissions from biomass and litter of virgin forests shall be inventoried as **"Carbon dioxide (fossil)"** (Hauschild et al., LCA Cookbook, p.1030; confirmed by Hauschild et al., p.773: "All virgin forest conversion emissions should thus be inventoried as fossil carbon dioxide").

**Secondary Forests:** Emissions from biomass and litter of secondary forests shall be inventoried as **"Carbon dioxide (biogenic)"** (Hauschild et al., LCA Cookbook, p.1030).

The key distinction is therefore:
- Virgin forest → **fossil CO2**
- Secondary forest biomass and litter → **biogenic CO2**

This differentiation applies "unless the selected LCIA method requires otherwise" (Hauschild et al., LCA Cookbook, p.1030).

Additionally, for soil, peat, etc. of **all land uses**, emissions shall be inventoried as "Carbon dioxide (fossil)" regardless of forest type (Hauschild et al., LCA Cookbook, p.1030).

**Citations:** Hauschild et al., *LCA: Theory and Practice* — 37 LCA Cookbook, p.1030; and 30 LCA of Biofuels and Biomaterials, p.773.


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

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

When filling data gaps in a life cycle inventory, individual data sets used for the estimates must achieve at least a **"Fair"** data quality level (sometimes described as the minimum acceptable quality rating in LCA data quality frameworks).

More specifically, under widely used guidance (such as that from ecoinvent or the EPA/ILCD data quality frameworks), data used to fill gaps should meet at least a **"fair" (or equivalent level 3 out of 5)** quality rating across the relevant data quality indicators (technological representativeness, geographical representativeness, temporal representativeness, completeness, and reliability), so that the resulting estimates do not introduce unacceptable uncertainty into the inventory.

In practical terms, this means the surrogate or proxy data used must be sufficiently representative of the process being modeled — it should not be so poorly matched in technology, geography, or time period that it renders the gap-filling estimate unreliable.

Individual data or data sets used to fill data gaps must achieve at least the **"Data estimate" quality level**.

*(Source 1, ILCD Handbook, p.254; Source 3, Hauschild et al., LCA Cookbook, p.1025)*

Based on the retrieved passages, when filling data gaps in a life cycle inventory, individual data or data sets used for estimates must achieve at least the **"Data estimate" quality level**.

As stated in the ILCD Handbook:

> "Only data that increase the overall quality of the final inventory of the analysed system shall be used to fill data gaps. That means that the individual data or data set's quality shall be equivalent to at least the **'Data estimate' quality level**."

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p. 254)*

This is further reinforced in the Hauschild et al. LCA Cookbook (p. 1025), which adds that this quality assessment "shall include both the quality of the used data estimate and of the amount of the flow," and that the determination shall be based on "at least an individually, briefly justified expert judgement, explicitly considering the named shortcomings."


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

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

## Short-Term Marginal in Consequential LCA

### Definition and Representation

In consequential LCA, a **short-term marginal** technology represents the supplier (or technology) that will realistically respond to a small change in demand **in the short term** — i.e., within a timeframe where capacity is fixed and only variable costs/outputs can adjust.

The short-term marginal is best represented by the **technology currently on the margin of being constrained** — typically:

- The **last unit of production** from an existing plant operating below full capacity
- Often identified as the **most expensive/least efficient operating unit** that would be the first to be turned on or off in response to demand changes (the "swing producer")

In electricity systems, this is classically the **marginal power plant** (e.g., a gas peaker plant) that adjusts output to meet fluctuating demand.

---

### Equivalence to Attributional Data

The short-term marginal is considered equivalent to **current average (attributional) data** from the specific marginal technology — more precisely:

> It corresponds to the **unit process data** (attributional inventory) of the **specific marginal supplier**, representing its current operating performance.

In practice, this means using the **actual current operating data** of the identified marginal technology — which is essentially **attributional unit process data** for that specific technology, not a system average.

---

### Key Distinction

| | Short-term marginal | Long-term marginal |
|---|---|---|
| Capacity | Fixed | Can expand |
| Equivalent to | Current attributional data of the marginal unit | Data for the technology expected to be built (future/prospective) |

The short-term marginal thus bridges consequential intent with **attributional unit-level data** for the currently constrained/swing technology.

When the effect is considered "small" (i.e., considering only the primary market consequence), the **"short-term marginal" is best represented by the "average market consumption mix"** of the processes/systems. This is equivalent to the **average background data used in attributional modelling**.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.171)*

Based on the ILCD Handbook source, when the size of the effect — considering only the primary market consequence — is **"small"**, the **"short-term marginal" is best represented by the "average market consumption mix"** of the processes/systems.

This is explicitly stated to be equivalent to the **average background data used in attributional modelling**.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.171)*


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

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

In consequential modelling, when the market direction has changed as a consequence of the analysed decision, the superseded processes are made up of a combination of the **marginal supplier** (the supplier that would otherwise have been the one to respond to changes in demand — typically the constrained supplier) and the **average supplier**.

More specifically, when the market direction changes (i.e., the decision causes a shift from a declining to a growing market or vice versa), the superseded technology is represented by a **combination of the most competitive supplier and the average supplier** — or more precisely, a mix of the **constrained marginal supplier** and the **average existing supplier** — reflecting the fact that the market transition means neither a purely marginal nor a purely average approach fully captures the situation alone.

To be more precise in standard consequential LCA terminology: when market direction changes, the superseded process is a **combination of the unconstrained (average) supplier and the constrained marginal supplier**, weighted to reflect the mixed market conditions during the transition period.

Based on the context provided, when the market direction has changed as a consequence of the analysed decision, the superseded processes are **a specific combination of the least cost-competitive ones and partly the most cost-competitive ones**.

This is stated in two sources:
- **Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.1007** (provision 5:4:2)
- **European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.180** (chapter 7.2.4.5)

Based on the sources, when the market direction has changed as a consequence of the analysed decision, the superseded processes are **a specific combination of the least cost-competitive ones and partly the most cost-competitive ones**.

This is stated in two sources:
- ILCD Handbook, p.180 (Section 7.2.4)
- Hauschild et al., LCA Cookbook, p.1007 (Section 5:4:2)


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

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

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

1. **Internal reporting** – for use within the commissioning organization only, with the least stringent disclosure requirements

2. **Disclosed to interested parties (but not publicly)** – shared with specific stakeholders under controlled conditions, with intermediate requirements

3. **Public disclosure** – the most stringent level, requiring full transparency and compliance with all reporting and documentation requirements, including critical review obligations

Each successive level demands greater completeness, transparency, and verifiability of the LCA study and its results.

Based on the available context, the sources do not explicitly list the three levels of reporting under ISO 14044:2006 with their distinct requirements in detail. However, the ILCD Handbook references three levels of reporting requirements that align with ISO 14044:2006:

According to the **ILCD Handbook (p.309)**:

> "In accordance with the ISO 14044:2006 standard, this handbook operates with **three levels** of the classical reporting with different (increasing) requirements. These relate to both project reports and data set files."

The three levels of reporting are described in terms of **accessibility/audience**, as outlined in **Hauschild et al., LCA: Theory and Practice (p.1000)**:

1. **Internal** reporting
2. **External** (but limited, well-defined recipients)
3. **Third-party report, publicly accessible**
4. **Report on comparisons, publicly accessible**

Additionally, the **forms** of reporting are described in the **ILCD Handbook (p.151)**:
- Detailed report (including non-technical executive summary)
- Data set
- Data set plus detailed report
- Non-technical executive summary

The full detail of the three levels and their specific requirements is referred to in **Chapter 10.3** of the ILCD Handbook (p.309), but the complete breakdown of those requirements is not provided in the available context.

Based on the retrieved sources, the three main levels of reporting under ISO 14044:2006 are described in the LCA Cookbook (Hauschild et al., Chapter 37, p.1000) as:

1. **Internal** reporting
2. **External** (but limited, well-defined recipients)
3. **Third-party report, publicly accessible**
4. **Report on comparisons, publicly accessible**

*(Note: The source at p.1000 actually lists four levels, not three.)*

The **three levels** framing comes from the ILCD Handbook (p.309), which states:

> "In accordance with the ISO 14044:2006 standard, this handbook operates with **three levels** of the classical reporting with different (increasing) requirements. These relate to both project reports and data set files." — *ILCD Handbook, p.309 (refers to ISO 14044:2006 chapters 5.1.2, 5.2, and 5.3)*

However, the specific names/definitions of those three levels are not fully detailed in the retrieved passages beyond what is cited above. The ILCD Handbook notes that the full description of these levels is presented in **chapter 10.3** of that handbook (as referenced on p.151).


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

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

For delayed emissions in LCA, a correction flow accounts for the fact that a greenhouse gas emission is postponed. The impact factor for a delayed emission correction is calculated based on the derivative of the characterization factor with respect to time.

For nitrous oxide (N₂O) with GWP100, the correction flow impact factor for 1 kg of N₂O delayed by 1 year is:

**-0.0063 kg CO₂ eq. per kg N₂O per year of delay**

This value comes from the approach used in dynamic LCA / delayed emission characterization, where the correction factor equals the negative of the annual rate of change of the cumulative radiative forcing (i.e., the time derivative of the GWP characterization factor). For N₂O with a GWP100 of approximately 265–298, the per-year correction reflects the marginal change in impact when the emission is shifted by one year.

Specifically, in the ecoinvent or similar LCA database convention, the correction flow for delayed N₂O emission is approximately **−0.0063 kg CO₂-eq per kg N₂O · year**, reflecting that delaying an N₂O emission by one year reduces its 100-year GWP impact by roughly 0.63% of its instantaneous GWP100 value.

The GWP100 impact factor assigned to the correction flow for delayed emission of nitrous oxide is **-2.98153 kg CO2-equivalents** per 1 kg nitrous oxide and 1 year of delayed emission.

*(ILCD Handbook: General Guide for LCA, 2010, p. 231)*

The GWP100 impact factor assigned to the "Correction flow for delayed emission of nitrous oxide (within first 100 years)" is **-2.98153 kg CO₂-equivalents** per 1 kg nitrous oxide and 1 year of delayed emission.

*(ILCD Handbook: General Guide for LCA (2010), p. 231)*


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

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

In Life Cycle Assessment (LCA), the energy content of energy resource flows should be expressed in terms of **energy units**, specifically:

- **Megajoules (MJ)** is the standard reference unit used for energy resource flows.

The energy content should be expressed as:

- **Higher Heating Value (HHV)** — also known as **Gross Calorific Value (GCV)** — or
- **Lower Heating Value (LHV)** — also known as **Net Calorific Value (NCV)**

depending on the convention used (though LHV/NCV is more commonly used in European LCA practice, and the choice should be clearly stated and applied consistently).

The **reference unit is MJ**, and flows are typically characterized on a **per MJ of energy content** basis (e.g., MJ of crude oil, MJ of natural gas extracted), allowing consistent accounting of energy resources in the inventory and enabling calculation of **cumulative energy demand (CED)** and related impact categories.

Based on the context provided, the energy content of energy resource flows should be expressed as the **Lower Calorific Value (LCV)**, measured in the reference unit **MJ**.

Specifically, for renewable energy resources (such as solar energy), they are inventoried as elementary flows expressed as Lower Calorific Value in MJ.

*(Source 1: ILCD Handbook: General Guide for LCA (2010), p.221)*

Additionally, for combustible materials (e.g., oil, gas, or coal), inputs and outputs can be transformed into an energy input or output by multiplying by the relevant heat of combustion, and it must be reported whether the **higher heating value or lower heating value** is used.

*(Source 7: Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p.523)*

Based on the context, the energy content of energy resource flows should be expressed as the **Lower Calorific Value (LCV)**, measured in the reference unit **MJ**.

This is specified in the ILCD Handbook, which states that energetic resources "shall be inventoried directly as the respective elementary flows, e.g. 'Solar energy' as 'Renewable energy resources from air', expressed as Lower calorific value and measured in the reference unit MJ."

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

Additionally, ISO 14041 notes that when transforming inputs/outputs related to combustible materials into energy inputs or outputs, "it shall be reported if the higher heating value or the lower value is used," and the ILCD Handbook further specifies that unit process energy flows use "lower or upper calorific values."

*(Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p. 523)*


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

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

From an LCA perspective, the condition that determines a **closed-loop recycling** situation is that the **secondary material (recyclate) is functionally equivalent to the primary/virgin material it displaces** — i.e., it substitutes for the same quality or type of material without significant loss of properties or change in application.

More precisely, the key condition is:

> **The recycled material retains sufficient quality to replace virgin material on a like-for-like (or near like-for-like) basis**, so that no quality adjustment or "quality correction factor" is needed, and the system can be treated as a closed loop in terms of material quality and substitutability.

This means it does **not** matter whether the recyclate re-enters the *same* product system or a *different* product system — what matters is that **no downcycling occurs** (or that any minor quality change is negligible). When this condition holds, the recycling credit can be handled by simply **avoiding virgin material production** without needing to account for quality differences, and the system is modeled as closed-loop (e.g., the 0:100 or 100:0 cut-off approach, or the closed-loop approximation in ISO 14044).

In contrast, if the recyclate substitutes a *different quality* or *different type* of material (downcycling), an open-loop allocation approach with quality correction (such as the method in EN 15804 or PAS 2050) is required instead.

According to the ILCD Handbook, the condition that determines a recycling situation is best captured as closed-loop recycling is that **the secondary good does not change its inherent technical properties and provides the same functional unit** — regardless of whether it is used in the same or another product.

As stated: *"as long as the secondary good is not changing its inherent technical properties and provides the same functional unit, closed-loop recycling best captures the situation."*

[Source: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p. 347]

According to the ILCD Handbook, the condition that determines a recycling situation is best captured as closed-loop recycling — regardless of whether the secondary good is used in the same or another product — is that **the secondary good does not change its inherent technical properties and provides the same functional unit**.

As stated in the source:

> "as long as the secondary good is not changing its inherent technical properties and provides the same functional unit, closed-loop recycling best captures the situation."

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


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

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

Based on common LCA methodology frameworks, the four quality aspects typically illustrated in figures showing completeness and representativeness are:

1. **Temporal representativeness** – whether the data reflects the relevant time period
2. **Geographical representativeness** – whether the data reflects the relevant geographical location
3. **Technological representativeness** – whether the data reflects the relevant technology or process
4. **Completeness** – whether all relevant flows and processes are included

These four aspects are commonly used in data quality assessment frameworks in LCA (such as those described in ISO 14044 and ecoinvent documentation) to evaluate how well inventory data represents the actual system being modeled.

Based on the sources provided, the four quality aspects illustrated in Figure 27 are:

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

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p. 1999 and p. 325)*

Based on the sources, the four quality aspects illustrated in Figure 27 are:

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

As stated in the ILCD Handbook: "Figure 27 The four quality aspects completeness and technological, geographical and time-related representativeness" (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.325/p.1999).


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

It can easily lead to misleading results and conclusions.

In comparative LCAs, inconsistent scoping of the different systems or products being compared can result in **biased or unfair comparisons**, specifically:

- **Systematic bias** favoring one product/system over another, because more lifecycle stages, processes, or burdens are included for one system than the other
- **Misleading conclusions**, where one option appears environmentally superior not because it genuinely is, but because its scope was defined more narrowly (excluding significant upstream or downstream impacts)
- **Invalid results**, undermining the credibility and usefulness of the study for decision-making

This is why ISO 14044 requires that compared systems share the **same functional unit** and **equivalent system boundaries** — ensuring that like is compared with like. Inconsistent scoping is a common source of **methodological error** (sometimes deliberate in advocacy/marketing LCAs) and is a key issue reviewers and critical reviewers look for.

Based on the context provided, inconsistent scoping of different systems or products in comparative LCAs can lead to **misleading results and conclusions**. Specifically, this can occur:

- If the compared products A and B do not provide the same functionalities due to an inappropriate definition of the functional unit
- If different modelling principles are applied in the analysis of the compared products A and B

Additionally, inconsistencies can arise from differences in data sources, data accuracy, technology coverage, and time-related coverage between the compared options, which can make a comparison invalid or biased.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.371; Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p.637; Hauschild et al., LCA: Theory and Practice — 12 Life Cycle Interpretation, p.333)*

Based on the retrieved passages, inconsistent scoping of systems in comparative LCAs can easily lead to **misleading results and conclusions**. Specifically, this can occur:

- If the compared products A and B do not provide the same functionalities due to an inappropriate definition of the functional unit
- If different modelling principles are applied in the analysis of the compared products A and B

Additionally, inconsistencies such as differences in data sources (e.g., one option based on literature while another uses primary data), differences in data accuracy, differences in technology coverage, or differences in time-related coverage can render a comparison **invalid** or **biased**.

*(ILCD Handbook: General Guide for LCA, p.371; Guinée et al., Handbook on LCA: Operational Guide to the ISO Standards (2002), p.637; Hauschild et al., LCA: Theory and Practice — 12 Life Cycle Interpretation, p.333)*


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

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

In LCA, when assessing future situations (prospective LCA), the following potential futures are generally considered relevant for investigation:

## Key Types of Potential Futures

1. **Baseline/Reference future** – A "business-as-usual" scenario assuming current trends continue without major disruptions.

2. **Optimistic/Best-case future** – Favorable developments (e.g., rapid decarbonization of the grid, technological improvements, high efficiency gains).

3. **Pessimistic/Worst-case future** – Unfavorable developments (e.g., slow transition, continued fossil fuel dependence).

4. **Policy-driven futures** – Scenarios aligned with specific regulatory targets (e.g., Paris Agreement pathways, national energy plans).

5. **Technological futures** – Scenarios reflecting anticipated technology development (e.g., next-generation processes, circular economy adoption).

## Common Scenario Frameworks Used

- **IAM (Integrated Assessment Model) scenarios** – e.g., SSPs (Shared Socioeconomic Pathways) and RCPs (Representative Concentration Pathways)
- **IEA scenarios** (e.g., Stated Policies, Announced Pledges, Net Zero by 2050)
- **Ecoinvent prospective databases** (e.g., via tools like **premise**)

## Key Principle

Typically, **at least two contrasting scenarios** are explored to capture the **range of uncertainty**, rather than relying on a single projected future — acknowledging that the future is inherently uncertain.

Based on the context, when assessing future situations in an LCA, the following potential futures are considered relevant for investigation:

- **Predictive approaches** ("What will happen?"): suited for simple objects and short-term studies, including **forecasts** (the likely/business-as-usual scenario) and **what-if** scenarios (conditioned on specific events).

- Beyond predictive approaches, **explorative scenarios** are also relevant, particularly for medium- to long-term assessments, which aim to point out potential directions in future development rather than predicting a single outcome.

Specifically regarding scenario types:
- **What-if scenarios** are relevant for comparisons of well-known situations in the **short-term**.
- **Cornerstone scenarios** aim to point out a potential direction in future development with a **long-term perspective**.

*(Hauschild et al., LCA: Theory and Practice — 21 Future-Oriented LCA, pp. 508 and 513)*

Based on the context, when assessing future situations in an LCA, the following potential futures are considered relevant for investigation:

**Predictive approaches** answer "What will happen?" and are suited for simple objects and short-term studies. These include:
- **Forecasts** (the likely scenario)
- **What-if** scenarios (conditioned on specific events)

These predictive approaches aim at describing the most likely futures and generally involve forecasting current trends into the future, creating 'surprise-free' or 'business as usual' images of the future.

Beyond predictive approaches, **what-if scenarios** are relevant for comparing well-known situations in the short-term, while **cornerstone scenarios** aim to point out potential directions in future development with a long-term perspective.

Future orientation is also categorised by time horizon:
- **Short-term** (1–5 years)
- **Medium-term** (3–15 years)
- **Long-term** (more than 20 years)
- **Very long-term** (more than 50 or 100 years)

In practice, future-oriented LCA studies often look towards the future in the long-term or medium-term, but set up possibilities for action and recommendations in the short-term.

*(Hauschild et al., LCA: Theory and Practice — 21 Future-Oriented LCA, pp. 508–509, 513)*


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

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

In consequential LCA, **virtual subdivision** (splitting a multi-output process into single-output sub-processes) can be applied without distorting the substitution **only when the co-products are independently variable** — i.e., when the production volumes of the individual co-products can vary independently of one another in response to changes in demand.

More precisely, virtual subdivision is valid when:

- The outputs of the process are **not jointly produced in fixed proportions** (i.e., they are not true joint products constrained by a fixed ratio), AND
- The marginal supplier can actually **adjust the output of each co-product separately**, so that a change in demand for one product does not automatically force a proportional change in another.

If the co-products are produced in **fixed, inseparable proportions** (classic joint production, e.g., some chemical or refining processes where the ratio is technologically fixed), virtual subdivision would misrepresent the system because increasing one output necessarily increases the other — you cannot substitute for one without affecting the other. In that case, subdivision distorts the substitution logic, and **system expansion** (or proper marginal-change analysis) must be used instead.

**In summary:** Virtual subdivision is permissible in consequential modelling when the co-products are **not constrained to vary in fixed ratios** — that is, when each co-product's output level is independently adjustable at the margin.

Based on the provided context, virtual subdivision can be applied in consequential modelling without distorting the substitution **only when it does not "cut" through a physically non-subdividable multifunctional joint process**.

As stated in the ILCD Handbook:

> "under consequential modelling, virtual subdivision shall not be done if it 'cuts' through a physically not subdividable multifunctional joint process. This would distort the substitution"

*(Source 1, ILCD Handbook, p. 174)*

This is further reinforced in the introduction to solving multifunctionality under consequential modelling:

> "partial subdivision that cuts through a multifunctional process should be avoided as it renders the substitution (see below) distorted."

*(Source 3, ILCD Handbook, p. 174)*

Therefore, virtual subdivision can be applied without distorting substitution in consequential modelling **only when the subdivision does not cut through a physically inseparable (non-subdividable) multifunctional joint process** — i.e., when the process can be genuinely and cleanly separated into distinct sub-processes.

Based on the retrieved passages, virtual subdivision can be applied in consequential modelling without distorting the substitution **only when it does not "cut" through a physically non-subdividable multifunctional joint process**.

As stated in the ILCD Handbook:

> "under consequential modelling, virtual subdivision shall not be done if it 'cuts' through a physically not subdividable multifunctional joint process. This would distort the substitution"
*(ILCD Handbook, p. 174)*

And further confirmed:

> "partial subdivision that cuts through a multifunctional process should be avoided as it renders the substitution (see below) distorted."
*(ILCD Handbook, p. 174)*

In other words, virtual subdivision can be validly applied in consequential modelling **only when the subdivision does not cut through a physically inseparable (non-subdividable) joint multifunctional process** — i.e., when the process can be genuinely separated into distinct sub-processes without artificially splitting an inherently unified joint production process.


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

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

When sourcing data for a required dataset in LCA, the general order of preference is:

1. **Primary data** (site-specific, measured data from the actual process/facility being studied) — most preferred
2. **Secondary data from databases** specific to the same technology, geography, and time period
3. **Secondary data from databases** with similar technology or geography (proxy data)
4. **Literature/published data** from comparable processes or industries
5. **Estimated/modeled data** (e.g., stoichiometric calculations, engineering estimates)
6. **Surrogate/proxy data** from dissimilar processes — least preferred

The key considerations driving this hierarchy are:
- **Representativeness** (technological, geographical, and temporal)
- **Reliability** and verifiability
- **Specificity** to the system being studied

Generally: **primary/specific > secondary/regional > generic/estimated**, always prioritizing data that best matches the actual process, location, and time frame of the study.

Based on the sources provided, the general order of preference for sourcing data for a required data set is:

1. **Primary data from actual suppliers/downstream actors** in the foreground system should be preferred first.
2. **Secondary data from actual suppliers/downstream actors** should be preferred over other third-party secondary data.
3. **Technology-specific, generic or average data from third parties** should be used where it is of higher quality (more accurate, precise, complete) than available primary or secondary data from suppliers/downstream actors.
4. For the **background system**, average technology as market consumption mix data should be used.

The guiding principle throughout is **"the availability and quality of the most appropriate data,"** with a recommendation to work with **well-documented and already reviewed data sets**.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.126 and p.189)*

Additionally, when selecting secondary data sets, they should be chosen according to their **technological, geographical, and time-related representativeness, completeness, and precision**, with preference given to already critically reviewed ("pre-verified") data sets to limit review effort.

*(Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.1031)*

Based on the retrieved sources, the general order of preference for sourcing data for a required data set is:

1. **Primary data** from actual suppliers/downstream actors in the foreground system should be preferred first.
2. **Secondary data from actual suppliers/downstream actors** should be preferred over other (third-party) secondary data.
3. **Technology-specific, generic or average data from third parties** should be used where this is of higher quality (more accurate, precise, complete) than available primary or secondary data from suppliers/downstream actors.
4. For the **background system**, average technology as market consumption mix data should be used.

Additionally, **pre-verified/already reviewed data sets** should be preferred, as this limits the review effort — only the appropriate use of these data sets needs to be reviewed.

The guiding principle overall is **the availability and quality of the most appropriate data**, with well-documented and already reviewed data sets recommended to support correct use, sound documentation, and review.

*(ILCD Handbook, p.126; p.189; Hauschild et al., LCA Cookbook, p.1031)*


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

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

In LCA, the key distinction is:

**Foreground system**: Processes over which the decision-maker or study commissioner has **direct control or influence** — these are the processes that are directly affected by the decision being modeled (e.g., the specific production facility, technology choice, or process being assessed). Data is typically collected specifically for these processes (primary data).

**Background system**: Processes over which the practitioner has **no direct control** — these are upstream and downstream processes that supply energy, materials, or services to the foreground system (e.g., electricity grids, commodity chemical production, transport infrastructure). These are typically modeled using **secondary data** from generic databases (e.g., ecoinvent).

In essence, the **degree of control** is the defining criterion: you can change foreground processes through your decision, but background processes are taken as given from the broader economy and are not directly alterable by the decision-maker.

The key distinction between the foreground and background systems in terms of control lies in the decision-maker's ability to influence or change the processes involved.

The **foreground system** comprises processes that "can often be changed by the decision-maker commissioning a study (e.g. a company), either because they are directly operated by the decision-maker (e.g. at the production site) or because the decision-maker has the power to change or influence the processes." (Hauschild et al., *LCA: Theory and Practice* — Chapter 8, p.80)

In contrast, processes in the **background system** "typically cannot be structurally changed by the decision-maker commissioning a study (e.g. a company), because the decision-maker is only a minor customer and therefore can only exert limited power or because the suppliers are anonymous to the customer" — such as copper bought on the global metal market. (Hauschild et al., *LCA: Theory and Practice* — Chapter 8, p.81)

An additional distinction relates to data: the foreground system is "largely modelled using primary data, i.e. data collected first-hand by the LCA practitioner," while the background system relies on secondary data (Guinée et al., *Handbook on LCA*, p.499).

Based on the retrieved sources, the key distinction between the foreground and background systems in terms of control is as follows:

**Foreground System:** Comprises processes that are specific to the product system being studied and are **largely modelled using primary data**. From a management perspective, "processes in the foreground system can often be changed by the decision-maker commissioning a study (e.g. a company), either because they are directly operated by the decision-maker (e.g. at the production site) or because the decision-maker has the power to change or influence the processes." *(Hauschild et al., LCA: Theory and Practice — 8 Scope Definition, p.80)*

**Background System:** In contrast, "processes in the background system can typically **not** be structurally changed by the decision-maker commissioning a study (e.g. a company), because the decision-maker is only a minor customer and therefore can only exert limited power or because the suppliers are anonymous to the customer" — such as copper bought on the global metal market. *(Hauschild et al., LCA: Theory and Practice — 8 Scope Definition, p.81)*

Complementarily, the Guinée handbook distinguishes the two based on data type: foreground systems use **primary, site-specific data**, while background systems use **secondary data**. *(Guinée et al., Handbook on LCA, p.499)*


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

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

## First Phase of a Life Cycle Assessment

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

### What It Identifies

This phase establishes:

- **Goal**: The *purpose* of the study — why it is being conducted, the intended application, and the intended audience
- **Scope**: The *boundaries and depth* of the study, including:
  - The **functional unit** (the reference unit against which inputs and outputs are measured)
  - **System boundaries** (what processes and life cycle stages are included or excluded)
  - **Assumptions and limitations**
  - **Impact categories** to be assessed

### Why It Matters

This phase is critical because it determines how the entire study is structured and ensures the results are relevant, consistent, and transparent. All subsequent phases — **Inventory Analysis (LCI)**, **Impact Assessment (LCIA)**, and **Interpretation** — are guided by the decisions made here.

Based on the context provided, the **Goal and Scope Definition** phase comes first in a Life Cycle Assessment.

According to the Glossary in Hauschild et al., *LCA: Theory and Practice* (Glossary, p.1191), the four phases of an LCA are listed in order as: "**Goal and scope definition**, life cycle inventory analysis, life cycle impact assessment, life cycle interpretation."

As further supported by Guinée et al., *Handbook on LCA* (p.28), the goal and scope definition is described as "the initialising phase," where "many choices are made here, relating to **purpose, intended application, product alternatives, scope of the analysis**, and so on." It is characterized as a crucial phase that informs all subsequent steps, and since LCA is an iterative process, the choices made here are regularly checked for consistency throughout the study.

Based on the sources provided, the first phase of a Life Cycle Assessment is **Goal and Scope Definition**.

As stated in the Hauschild et al. Glossary (p.1191): *"The four phases of an LCA are: Goal and scope definition, life cycle inventory analysis, life cycle impact assessment, life cycle interpretation."*

The Goal and Scope Definition phase identifies key elements about the study including its **purpose, intended application, product alternatives, and scope of the analysis**. As noted in Guinée et al. (p.28): *"The initialising phase of defining the goal and scope is crucial in this respect. Many choices are made here, relating to purpose, intended application, product alternatives, scope of the analysis, and so on."*

Furthermore, according to Hauschild et al. (Chapter 12, p.323), the interpretation phase — the final phase — looks back at the results of *"the previous stages of goal and scope definition, inventory analysis and impact assessment,"* confirming that goal and scope definition is indeed the first phase that precedes all others.


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

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

Below a solubility in water at 20°C of **10 μg/litre**, compounds must be inventoried as particulate matter (PM) emissions to air (i.e., only poorly water-soluble compounds below this threshold are inventoried as particles).

**Sources:**
- Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.1028
- European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.218

Based on the sources, compounds must be inventoried as particulate matter emissions to air when their solubility in water at 20°C is **below 10 μg/litre (10 lg/l)**.

As stated in the ILCD Handbook: "particulate matter (PM) emissions to air shall include only poorly water-soluble compounds below a solubility in water at 20°C of 10 μg/litre" (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.218), and confirmed in the LCA Cookbook: "Particulate matter (PM) emissions to air shall include only poorly water-soluble compounds below a solubility in water at 20°C of 10 lg/l" (Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.1028).


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

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

In LCA methodology, **Situation C1 "Accounting with interactions"** (from the framework often associated with Heijungs & Suh or related LCA methodology literature) refers to situations where system interactions or co-production relationships need to be handled.

Two example applications associated with this situation are:

1. **Co-production / multi-output processes** – for example, the allocation of environmental burdens between co-products in a refinery or chemical plant, where multiple outputs are produced simultaneously and their interactions must be accounted for (e.g., crude oil refining producing gasoline, diesel, and other products together).

2. **Recycling and waste treatment systems** – for example, accounting for the interactions between a product system and a recycling loop, where recycled material re-enters the system and displaces virgin material production, requiring proper accounting of the credits and burdens across the interacting systems.

---

*Note: If you are referring to a specific framework or textbook (e.g., a particular classification by Finnveden, Weidema, or another author), please share the source so I can give a more precise answer, as the exact labeling "C1" may vary by publication.*

Based on Source 1 (ILCD Handbook, p.47), two example LCA applications associated with Situation C1, "Accounting with interactions," are:

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

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

Based on Source 1 (ILCD Handbook, p.47), two example LCA applications associated with Situation C1, "Accounting with interactions," are:

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

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


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

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

When co-products are not traded at the point of allocation with their specific characteristics, their market price should be derived from the **price of the nearest downstream product** (or the nearest traded form of the product), adjusted by subtracting the costs of the additional processing, transportation, or transformation steps required to convert the co-product at the allocation point into that traded downstream product.

In essence:

**Market price at allocation point = Price of nearest traded downstream product − Costs of further processing/transformation to reach that traded form**

This approach is sometimes referred to as working **"back from the market"** or using a **net realisable value (NRV)** approach, and it ensures that the imputed price reflects what the co-product is actually worth at the specific point where allocation is being applied, even when no direct market exists for it in that form at that location.

When co-products are not traded at the point of allocation with their specific characteristics, their market price should be derived by adjusting (reducing) the market price of the final sold product to account for any additional processing steps (such as compression, purification, or packaging) that occur before sale. This adjusted price is used as the allocation key.

For co-products that are not traded externally at all (e.g., refinery gas), market price information must be approximated.

Similarly, if a co-product needs to go through additional processing before it is sold, the LCA practitioner should calculate a **shadow price** — for example, by subtracting the cost of additional processing from the market price of the processed product to derive the value at the point where it leaves the multifunctional process.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.80; Hauschild et al., LCA: Theory and Practice, Chapter 9, p.125)*

When co-products are not directly traded at the point of allocation with their specific characteristics, their market price should be derived by adjusting (i.e. reducing) the market price of the resulting product that is sold to account for any additional processing steps (e.g., compression, purification, packaging) that occur after the point of allocation. For co-products that are not traded at all or seldom traded externally (e.g., refinery gas), market price information must be approximated.

Additionally, a **shadow price** should be calculated for co-products that require further processing before being sold. For example, as noted in Hauschild et al., the economic value of the processed co-product must have the cost of additional processing subtracted to calculate the shadow price of the co-product as it leaves the multifunctional process (e.g., the market price of baled straw minus the cost of baling = shadow price of unbaled straw).

**Sources:**
- ILCD Handbook, p. 80 (Source 1)
- Hauschild et al., 9 Life Cycle Inventory Analysis, p. 125 (Source 7)