Question: What are the main reasons for the forming of markets as described in life-cycle assessment?

Markets form mainly for political reasons (legislation on competition and product requirements such as material bans, product safety, technical and other standards, taxes, and subsidies) and cultural reasons (markets recognised by producers and service providers).

According to the ILCD Handbook, the main reasons for the forming of markets are:

- **Political** reasons, including legislation especially on competition and product requirements such as material bans, product safety, technical and other standards, taxes, and subsidies.
- **Cultural** reasons, specifically recognised markets by producers and service providers.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.128)*

In Life Cycle Assessment (LCA), **markets** (particularly in the context of system expansion and allocation, especially within the ecosphere/technosphere framework used in consequential LCA) form for specific conceptual reasons. Here are the main reasons for the forming of markets as described in LCA:

## Main Reasons for Market Formation in LCA

1. **Multi-output processes / Co-production**
When a process produces more than one useful output (co-products), a market forms because multiple producers and consumers interact around those outputs, necessitating decisions about how to handle allocation or system expansion.

2. **Supply and demand interactions**
Markets emerge because multiple suppliers can provide a functionally equivalent product/service, and multiple consumers demand it. In consequential LCA, identifying the **marginal supplier** in a market is essential to determine what activity is actually affected by a change in demand.

3. **Substitutability / Functional equivalence**
Products that are interchangeable (substitutes) naturally form a common market. LCA uses this to determine which processes are constrained (determining) or unconstrained in supply.

4. **Economic mechanisms**
Price signals coordinate production and consumption decisions, which LCA (especially consequential LCA) must reflect when modeling real-world consequences of decisions.

5. **By-product and waste handling**
The fate of by-products creates secondary markets, influencing which processes are displaced or expanded as a consequence of the studied system.

These concepts are particularly central to **consequential LCA methodology**.


Question: In life cycle assessment, what distinguishes the 'Accounting' approach from meso/macro-level decision support?

The 'Accounting' approach is purely descriptive documentation of a system's life cycle (e.g. a product, sector, or country) without considering potential additional consequences on other parts of the economy, whereas meso/macro-level decision support addresses strategic decisions assumed to have structural consequences outside the decision-context, such as changing available production capacity.

Based on the provided sources, the **'Accounting'** approach and **meso/macro-level decision support** are distinguished as follows:

**Accounting** is described as a "purely descriptive documentation of the system's life cycle under analysis (e.g. a product, sector, or country), **without being interested in any potential additional consequences on other parts of the economy**." It is retrospective in nature — documenting what has happened or will happen based on extrapolating forecasting — with no interest in additional consequences the analysed system may have on background or other systems (Source 5, p.969).

In contrast, **meso/macro-level decision support** provides "life cycle based decision support at a strategic level (e.g. raw materials strategies, technology scenarios, policy options)" and crucially, these decisions **"are assumed to have structural consequences outside the decision-context, i.e. they are supposed to change available production capacity"** (Source 1, p.6).

In summary, the key distinction is that **Accounting** focuses solely on describing the system in isolation without considering wider economic consequences, while **meso/macro-level decision support** explicitly accounts for structural consequences and capacity changes **beyond** the immediate decision context.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.6; Hauschild et al., LCA Cookbook, p.969)*

## Accounting vs. Meso/Macro Decision Support in LCA

### Accounting Approach

The **Accounting** (or **attributional**) approach aims to describe the environmentally relevant physical flows to and from a specific product system **as it actually exists** within the economy. Key characteristics:

- Uses **average/market data** to reflect current supply chains
- Allocates burdens proportionally based on physical or economic relationships
- Answers: *"What share of total environmental burden is attributable to this product/process?"*
- Suitable for **reporting, benchmarking, and labeling** (e.g., carbon footprinting, EPDs)
- System boundaries are defined by the product's actual material flows

---

### Meso/Macro Decision Support

This approach (broadly **consequential** thinking scaled up) focuses on the **environmental consequences of decisions** affecting larger systems:

- Models **marginal or systemic changes** in the economy resulting from a policy or investment decision
- May use **input-output analysis** (MRIO) to capture economy-wide effects
- Answers: *"What will actually change in the environment if this decision is made?"*
- Appropriate for **policy analysis, taxation, sectoral strategy**
- Requires identifying **affected processes and markets**, including indirect/rebound effects

---

### Core Distinction

| Feature | Accounting | Meso/Macro Decision Support |
|---|---|---|
| Perspective | Retrospective/descriptive | Prospective/consequential |
| Data | Average flows | Marginal/systemic changes |
| Purpose | Attribution of burden | Predicting real-world consequences |
| Scale | Product level | Sector/economy level |

The fundamental distinction is between **attributing existing burdens** versus **predicting the consequences of change** at broader scales.


Question: What four aspects must a functional unit be identified and specified across in detail?

The function provided (what), in which quantity (how much), for what duration (how long), and to what quality (in what way and how well the function is provided).

Based on Source 1 (Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.974), a functional unit shall be identified and specified in detail across the following four aspects:

1. **Function provided** (what)
2. **In which quantity** (how much)
3. **For what duration** (how long)
4. **To what quality** (in what way and how well is the function provided)

*(Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.974)*

A functional unit must be identified and specified in detail across these four aspects:

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


Question: Which organizations collaborated to produce the 2008 "Guide to PAS 2050 - How to assess the carbon footprint of goods and services"?

BSI British Standards, together with DEFRA and the Carbon Trust.

Based on Source 1, the 2008 "Guide to PAS 2050 - How to assess the carbon footprint of goods and services" was produced through collaboration between **BSI British Standards, DEFRA (Department for Environment, Food & Rural Affairs), and the Carbon Trust**.

*(Source 1: European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.391)*

The 2008 "Guide to PAS 2050 - How to assess the carbon footprint of goods and services" was produced through a collaboration between the **Carbon Trust** and the **Department for Environment, Food and Rural Affairs (Defra)**, with the involvement of the **British Standards Institution (BSI)**, which developed the PAS 2050 specification itself.

To be more precise, the Guide was produced by the **Carbon Trust** and **Defra**, supporting the BSI's PAS 2050 standard.


Question: In a Carbon footprint study or other study that applies a limited set of impact indicators, to what does the cut-off relate?

It relates only to the considered indicators (e.g. covering 90 % of the Climate change impacts).

Based on Source 2 (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.104):

In a Carbon footprint study or other study that applies a limited set of impact indicators, **the cut-off will relate to the considered indicators only** — for example, "covering 90% of the Climate change impacts."

In a carbon footprint study or similar study applying a limited set of impact indicators, the **cut-off** relates to the **potential environmental impacts or issues that are not covered (excluded)** by the chosen indicators.

Specifically, it refers to the fact that by selecting only certain impact categories (e.g., greenhouse gas emissions / global warming potential in a carbon footprint), other potentially significant environmental impacts — such as water use, toxicity, land use, resource depletion, acidification, eutrophication, etc. — are **cut off** (i.e., ignored or excluded from the assessment).

The cut-off therefore represents the **limitation in scope** of the study: the study does not claim to represent the full environmental profile of the product or system, and decision-makers should be aware that impacts beyond the selected indicators are not accounted for. This is typically required to be clearly communicated as part of the study's **limitations and scope**, in line with ISO 14044/14067 requirements for transparency.


Question: When filling data gaps in an LCA, what type of data sets shall be used, and under what condition may methodologically not fully consistent data sets be used instead?

Data gaps shall be filled with methodologically consistent data sets; gaps of low relevance may be filled with methodologically not fully but sufficiently consistent data sets, provided they are developed following the document's guidance and meet the overall quality requirements.

Based on the sources provided:

When filling data gaps in an LCA, data sets of at least **"Data estimate" quality level** shall be used, provided they increase the overall quality of the final inventory of the analysed system (Source 1, p.254).

Specifically, the following options exist for filling data gaps:
- Calculation from other known information
- Using information from similar processes or regions
- Expert-based estimates
- Generic or average data
- Correlation with more complete, high-quality data from other sources (Source 4, p.251)

Regarding **methodologically not fully consistent data sets**, these may be used when they are nevertheless **"sufficiently consistent"** — this applies mainly to LCI data sets used for **background purposes** (Source 2, p.207). The condition is that such data must still contribute adequately to the required completeness and precision of the system's LCI, and their use must be documented (Source 2, p.207; Source 3, p.247).

Any remaining data gaps, as well as the approaches used to fill them and the resulting lack of representativeness, precision, or methodological consistency, must be **clearly documented** and explicitly considered when declaring the achieved data set quality or drawing conclusions from the LCA study (Source 1, p.254).

## Data Gap Filling in LCA

### Preferred Data Sets

When filling data gaps, **representative data sets** shall be used — ideally data that are:
- Geographically, technologically, and temporally representative of the process being modeled
- Consistent with the methodological choices of the study (e.g., same system boundary approach, allocation method, characterization factors)

### Condition for Using Methodologically Inconsistent Data Sets

Methodologically **not fully consistent** data sets may be used when:

> **No other data are available** (i.e., no representative, methodologically consistent data exist to fill the gap)

In such cases, the use of inconsistent data sets should be:
- **Documented** transparently
- **Justified** in the study
- Subject to a **sensitivity analysis** to assess the potential influence on the results

---

### Basis (ISO 14044 / ILCD context)

This guidance is rooted in **ISO 14044:2006 (§4.3.3.4)** and elaborated in guidance documents such as the **ILCD Handbook**, which specify that fully consistent data are the standard, but pragmatic compromises are permitted under data scarcity — provided uncertainty and limitations are clearly communicated.


Question: What are the data quality indicators defined under the ILCD approach for classifying the achieved data quality of LCI data?

Overall data quality, technological representativeness, geographical representativeness, time-related representativeness, completeness, precision/uncertainty, and methodological appropriateness and consistency.

Based on the ILCD Handbook, the data quality indicators defined under the ILCD approach for classifying the achieved data quality of LCI data are:

1. **Technological representativeness**
2. **Geographical representativeness**
3. **Time-related representativeness**
4. **Completeness** (of environmental impacts covered by the inventory)
5. **Precision / uncertainty** (of the collected or modelled inventory data)
6. **Methodological appropriateness and consistency** (especially on the system level)

These indicators relate directly to the key characteristics of LCI data sets that describe their quality. As noted in the handbook, **"the weakest of the quality indicators generally weakens the overall quality of the data set,"** and the overall Data Quality Rating (DQR) is calculated by summing up the achieved quality rating for each of the quality components.

*(Sources: ILCD Handbook, Annex A, Chapter 12.3, p. 329; p. 1–2; and Section 6.8.1, p. 122)*

Under the **ILCD (International Reference Life Cycle Data System)** approach, the data quality of LCI (Life Cycle Inventory) data is assessed using the following **data quality indicators (DQIs)**:

---

### 1. **Technological Representativeness**
How well the data reflect the actual technology used in the process being modeled (e.g., state-of-the-art vs. outdated technology).

### 2. **Geographical Representativeness**
How well the data reflect the geographical location or region relevant to the study (e.g., local, national, regional, global).

### 3. **Time-Related Representativeness**
How current or up-to-date the data are relative to the time period of the study.

### 4. **Completeness**
The degree to which all relevant flows (inputs and outputs) of a process are included and reported.

### 5. **Precision / Uncertainty**
The degree of variability or uncertainty in the data values (e.g., measured, estimated, or assumed data).

### 6. **Methodological Appropriateness and Consistency**
How well the data conform to accepted LCA methods and are internally consistent.

### 7. **Overall Data Quality Rating**
A summary/aggregated quality score derived from the above indicators.

---

These indicators are typically scored on a **5-level scale** (1 = very good to 5 = very poor), and they align with the **ILCD Handbook** guidelines developed by the European Commission's Joint Research Centre (JRC). The overall data quality classification feeds into the **ILCD compliance levels** (Level I, II, or III) for datasets.


Question: When analysing long-term emissions in an LCA, what time boundary is used to separate the two sets of results that are then jointly discussed?

Interventions within the first 100 years from the time of the study are analysed separately from those beyond that 100-year time limit, and the two sets of results are then jointly discussed.

100 years from the time of the study. Results for interventions within the first 100 years are analysed separately from those beyond that time limit, and both are then jointly discussed. (ILCD Handbook, p.304; Hauschild et al., LCA Cookbook, p.1036)

In LCA, when handling long-term emissions (particularly relevant for waste disposal, landfills, or slow-releasing processes), the conventional time boundary used is **100 years**.

Emissions occurring **within 100 years** are typically included in the standard impact assessment results, while emissions released **beyond 100 years** are reported separately. The two sets of results are then presented and discussed jointly (rather than aggregated into a single score), acknowledging the uncertainty and ethical questions around how to weight near-term versus very long-term impacts.

This approach is reflected in guidance such as the **ILCD Handbook** and is common practice in waste-related LCA methodology (e.g., following the approach developed by Doka, ecoinvent, and others for landfill modeling).


Question: When analyzing a decision, what threshold percentage should be used as the cap when the annual additional demand or supply exceeds the capacity of the annually replaced installed capacity?

If that percentage is bigger than 5%, 5% should be used instead.

**5%**

If the annual additional demand or supply triggered by the analysed decision exceeds the capacity of the annually replaced installed capacity, and that percentage is over 5%, then **5% should be assumed instead** as the cap.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p. 42)*

In LCA and related decision-support contexts (particularly in consequential LCA methodology), the threshold commonly used is **100%** — meaning that when the annual additional demand or supply exceeds the capacity of the annually replaced (retired/decommissioned) installed capacity, the cap is set at **100%** of that replaced capacity.

However, if you are referring to a specific methodological guideline or framework (e.g., the ecoinvent consequential approach or a particular policy analysis tool), the specific threshold is often cited as **100%** of the constrained capacity as the upper bound for attributing marginal supply to the retiring/replaced technology.

Could you provide more context about the specific framework or document you are referencing? That would allow me to give a more precise answer, as different guidelines may specify different thresholds (e.g., some use **50%** as a decision threshold for determining the marginal supplier).


Question: In an open-loop recycling example where a 0.2 kg polymer product can only be used for energy recovery, what is the secondary good produced and how much of the primary-produced electricity is credited?

The secondary good is the 0.28 kWh of electricity generated from incinerating the plastic waste and fed into the grid, and the full 0.28 kWh of primary-produced electricity is substituted (credited).

The secondary good produced is **0.28 kWh of electricity** generated from the incinerated plastic waste that is fed into the grid. This electricity is technically equivalent to, and assumed to have the same market price as, average large producer electricity. As a result, the **full 0.28 kWh** of primary-produced electricity would be substituted ("credited").

*(ILCD Handbook: General Guide for LCA, 2010, p. 360)*

## Open-Loop Recycling: Energy Recovery Example

### Secondary Good Produced
The secondary good produced is **electricity** (generated from the combustion/incineration of the waste polymer for energy recovery).

### Credit Calculation

In open-loop recycling for energy recovery, the system receives a credit for the **avoided production** of primary electricity that the recovered energy displaces.

For a **0.2 kg polymer product** undergoing energy recovery:

The credit is calculated based on:
- The **energy content** of the polymer (calorific value)
- The **conversion efficiency** of the energy recovery process
- The **substitution factor** for the displaced primary electricity

Using typical values applied in LCA energy recovery crediting (following the substitution/system expansion approach):

The system receives a credit equivalent to the **primary-produced electricity avoided**, calculated as:

> **Credit = mass × net calorific value × energy conversion efficiency**

For example, with a polymer calorific value of ~40 MJ/kg and a conversion efficiency of ~25–30%, approximately **2–2.4 MJ (or ~0.6 kWh)** of primary electricity production is credited per 0.2 kg polymer.

### Key Principle
The **primary production of electricity is credited** (subtracted from the system's burden) because the energy recovered from the waste polymer **displaces** the need to generate that electricity from primary sources (e.g., the grid mix or a specific reference technology).

If you are working from a specific textbook example (e.g., Weidema's or the ISO 14044 guidelines), the exact credit figure will depend on the assumed calorific value and efficiency stated in that source.


Question: When developing unit process data sets as deliverables of LCA work, why is the calculation of LCI results required?

Because it serves, together with subsequent characterisation, to quantify the overall completeness and approximate the overall uncertainty of the data set per impact category.

The calculation of LCI results is required when developing unit process data sets as deliverables of LCA work because **it serves, together with subsequent characterisation, to quantify the overall completeness and approximate the overall uncertainty of the data set per impact category**. If normalisation and weighting are included in the definition of the cut-off rules, these are also to be applied.

*(Source 1: ILCD Handbook: General Guide for LCA (2010), p. 273)*

## Why LCI Results Calculation is Required When Developing Unit Process Data Sets

When developing unit process data sets as LCA deliverables, calculating **LCI (Life Cycle Inventory) results** is required for several important reasons:

### 1. **Validation and Quality Assurance**
- Calculating LCI results allows the practitioner to verify that the unit process data set is **mass and energy balanced** correctly
- It helps detect errors, inconsistencies, or missing flows within the unit process before delivery

### 2. **Verification of Completeness**
- Running the LCI calculation confirms that all relevant elementary flows (emissions, resource extractions) are **properly accounted for** across the supply chain connected to the unit process
- It ensures no significant flows have been omitted

### 3. **Compliance with ISO 14044 Requirements**
- ISO 14044 requires that LCI results be calculated and reported as part of a complete LCA study, meaning unit processes must be functional within a **complete system boundary**

### 4. **Enabling Peer Review and Transparency**
- LCI results provide a **traceable and verifiable output** that reviewers and clients can assess against benchmarks or comparable data sets

### 5. **Demonstrating Fitness for Purpose**
- Calculating results confirms the unit process data set **performs correctly** when integrated into a product system, making it usable by others (e.g., in background databases)

### 6. **Identifying Hotspots**
- Even at the unit process development stage, LCI results reveal which flows or upstream processes are most significant, informing **data quality priorities**

---

In essence, the LCI calculation serves as a **functional test** of the unit process data set, ensuring it is complete, balanced, and fit for use in broader LCA studies.


Question: When a unit process inventory item has missing qualitative information, under what condition should the respective flow be created and used in the regular inventory?

Only if it is a product or waste flow; little specified elementary flows (e.g., "Metals to air") shall not be kept in the regular inventory and must be documented in another way.

Based on the context provided, the respective flow should be created and used in the regular inventory **only if it is a product or waste flow**.

*(Source 1, p.209; Source 2, p.213)*

When a unit process inventory item has missing qualitative information, the respective flow should be created and used in the regular inventory **only if the missing qualitative information is not essential for characterizing the flow's environmental relevance or impact** — more specifically, when the flow can still be reasonably identified and quantified, and the missing qualitative data (such as chemical composition, physical state, or geographic/temporal context) does not prevent its proper linkage to an impact assessment method or does not materially affect the accuracy of the inventory result.

In practical ecoinvent/ISO terms, the condition is typically:

**The flow should be created and included in the regular inventory when the available quantitative and qualitative information is sufficient to uniquely identify the substance/flow and assign it to the correct compartment/category, even if some ancillary qualitative descriptors are absent.** If the missing information makes it impossible to distinguish the flow from others or to correctly characterize its impact, it should instead be treated as an unspecified or proxy flow, or flagged for further data collection rather than introduced into the regular inventory.


Question: What is the purpose of the sensitivity check in an LCA study, and which quantitative methods support it?

The sensitivity check assesses the reliability of the final results and, if included, the conclusions and recommendations of the LCA study. It is supported quantitatively by scenario analysis and uncertainty calculations.

The purpose of the sensitivity check in an LCA study is **to assess the reliability of the final results and — if included — of the conclusions and recommendations of the LCA study**. Expert judgement and previous experiences contribute to the sensitivity analysis.

The two quantitative methods that support the sensitivity check are:

1. **Scenario analysis**
2. **Uncertainty calculations**

In the interpretation step, the sensitivity analysis is used together with information about the uncertainties of significant issues among inventory data, impact assessment data, and methodological assumptions and choices to assess the reliability of the final results and conclusions.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p. 295)*

## Purpose of Sensitivity Analysis in LCA

Sensitivity analysis in an LCA study serves to **assess how changes in key assumptions, data, or methodological choices affect the final results and conclusions**. Its core purposes are:

1. **Identify critical parameters** – Determine which inputs (e.g., emission factors, allocation factors, system boundaries, transport distances) have the greatest influence on outcomes.
2. **Test robustness of conclusions** – Verify whether the comparative assertions or hotspot findings remain valid under alternative assumptions.
3. **Prioritize data collection** – Focus effort on refining data for parameters that matter most, avoiding unnecessary precision on inconsequential inputs.
4. **Increase transparency and credibility** – Demonstrate that results are not artifacts of arbitrary methodological choices (required under ISO 14040/14044 for comparative studies subject to critical review).
5. **Support decision-making under uncertainty** – Inform stakeholders of the confidence level associated with conclusions.

---

## Quantitative Methods Supporting Sensitivity Analysis

| Method | Description |
|--------|-------------|
| **One-at-a-time (OAT) / differential sensitivity analysis** | Varies one parameter at a time by a fixed percentage (e.g., ±10%) and observes the change in results; simple but ignores parameter interactions |
| **Scenario analysis** | Compares discrete alternative scenarios (e.g., different energy mixes, end-of-life routes, allocation methods) |
| **Monte Carlo simulation** | Propagates probability distributions across all uncertain parameters simultaneously; yields a distribution of output results and confidence intervals |
| **Latin Hypercube Sampling (LHS)** | A more efficient stratified sampling variant of Monte Carlo for large parameter spaces |
| **Global sensitivity analysis (e.g., Sobol indices)** | Variance-based method that decomposes output variance to attribute shares to individual parameters and their interactions |
| **Contribution analysis / perturbation analysis** | Mathematically derives partial derivatives of the result with respect to each inventory parameter |
| **Fuzzy set / interval arithmetic** | Propagates imprecise or range-based data without assuming probability distributions |

---

**In practice**, ISO 14044 requires sensitivity analysis to be performed and reported; Monte Carlo simulation (supported by tools such as SimaPro and openLCA) is the most widely used rigorous quantitative approach.


Question: When a data set combines data from different sources or otherwise lacks representativeness, what action must be taken?

This shall be stated in the data set and any accompanying documentation, if published.

When a data set combines data from different sources or otherwise lacks representativeness, **this must be clearly stated in the data set and any accompanying documentation, if published**. The documentation should clarify in a prominent place the degree to which the data set represents the intended scope (e.g., a market mix), even if it does not cover all technologies or routes.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p. 195)*

When a data set combines data from different sources or otherwise lacks representativeness, **disaggregation** must be taken — the data must be **disaggregated** (separated/broken down) into its constituent parts to ensure that each data point accurately represents the specific process, region, or time period it is meant to model.

In LCA practice, this means that combined or aggregated data that does not adequately represent the specific system being studied must be separated so that appropriate, representative data can be assigned to each unit process. If disaggregation is not possible, the lack of representativeness must be clearly documented and its potential impact on results assessed (e.g., through sensitivity or uncertainty analysis).


Question: When a functional unit cannot generally be given for materials and other application-unspecific products, what should be provided instead?

Only the reference flow that includes the main technical specification of the product should be provided.

For materials and other application-unspecific products where a functional unit cannot generally be given, **only the reference flow that includes the main technical specification of the product should be provided**. In this case, the reference flow is also the **declared unit**, but not the functional unit.

*(Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.975, item 7:1)*

When a functional unit cannot generally be given for materials and other application-unspecific products, a **declared unit** should be provided instead.

The declared unit is a quantified unit of a product for use as a reference unit in an EPD (Environmental Product Declaration) for products where a functional unit cannot be determined because the product's ultimate application and function are not known at the time of the LCA study. It simply describes the amount of product being assessed (e.g., 1 kg of material, 1 m² of material at a specified thickness) without referencing a specific function or performance level.


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

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

Based on the context provided, failing to base a comparison on a valid functional unit that reflects the service provided can lead to **seriously misleading results**.

As stated in the ILCD Handbook:

> "When the functional unit does not reflect the service provided or the reference flows are not based on a functional unit, **seriously misleading results may occur**."

The functional unit must give an unambiguous definition of the service or function that the compared products must provide, and only from this definition can the reference flows of the products be properly determined.

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

## Consequence of Not Using a Valid Functional Unit in Comparative LCA

If a comparison is not based on a valid functional unit that reflects the service provided, the comparison becomes **invalid and potentially misleading**, specifically:

### Core Problem: Comparing "Apples to Oranges"
The systems being compared will not be delivering **equivalent function or service**, meaning any environmental conclusions drawn are fundamentally flawed.

### Specific Consequences

1. **Biased results** – One product may appear environmentally superior simply because it delivers *less* service per unit analyzed, not because it is genuinely more efficient.

2. **Incorrect decision-making** – Stakeholders (consumers, policymakers, businesses) may make suboptimal or counterproductive choices based on misleading data.

3. **Underestimation or overestimation of impacts** – If quantities are not normalized to equivalent service delivery, total life cycle impacts will not be comparable.

4. **Loss of study validity** – Per ISO 14044, the functional unit must be clearly defined and measurable; failure to do so undermines the scientific and methodological integrity of the study.

5. **Potential for greenwashing** – Results could be selectively reported to favor a particular product without a legitimate basis for comparison.

### Classic Example
Comparing **1 kg of plastic bag** vs. **1 kg of cotton bag** without accounting for the number of uses each provides — the functional unit should reflect *carrying goods a defined number of times*, not just mass.

> **Bottom line:** Without a valid functional unit, the LCA comparison is scientifically meaningless and cannot reliably support conclusions about relative environmental performance.


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

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

Based on the sources provided, the types of activities that should in principle be inventoried in a system include:

- **Mining**
- **Processing**
- **Manufacturing**
- **Use, repair and maintenance**
- **Transport**
- **Waste treatment**
- **Other purchased services** such as cleaning and legal services, marketing
- **Production and decommissioning of capital goods**
- **Operation of premises** such as retail, storage, administration offices
- **Staff commuting and business travel**

This applies to all activities under normal and abnormal operating conditions related to the analysed system, while **excluding accidents, spills and the like**.

Any exclusion of individual processes or activity types must be justified using cut-off criteria.

*(Sources: ILCD Handbook, p.155; Hauschild et al., LCA Cookbook, p.1008)*

# Activities to be Inventoried in an LCA System

## Core Principle
The system boundary defines which unit processes are included, and **all activities within that boundary must be inventoried** to ensure completeness.

---

## Activities by Life Cycle Stage

### 1. **Raw Material Extraction (Cradle)**
- Mining, drilling, harvesting of natural resources
- Land use and transformation
- Primary processing of raw materials

### 2. **Material Processing & Manufacturing**
- Processing of intermediate materials
- Energy conversion and supply
- Manufacturing/fabrication of the product
- Auxiliary material production

### 3. **Transport & Distribution**
- Transport between all life cycle stages
- Warehousing and storage operations
- Packaging production and use

### 4. **Use Phase**
- Direct energy/water consumption during use
- Consumables and auxiliary inputs
- Maintenance and servicing activities
- Repair and refurbishment

### 5. **End-of-Life (Grave)**
- Collection and transport
- Recycling processing
- Waste treatment (incineration, landfill)
- Reuse activities

---

## Cross-Cutting Activities (all stages)
- **Energy production** (electricity, heat generation)
- **Water supply and treatment**
- **Infrastructure** (buildings, machinery) — if relevant
- **Human labor** — typically excluded but debated
- **Capital goods** — included if significant

---

## Key Considerations for System Boundary
| Boundary Type | Included Stages |
|--------------|-----------------|
| Cradle-to-gate | Extraction → Manufacturing |
| Cradle-to-grave | Extraction → End-of-life |
| Cradle-to-cradle | Includes recycling loops |
| Gate-to-gate | Single process/site only |

---

## Principle of Completeness
Activities should be included when they contribute **meaningfully to environmental impacts**; cut-off criteria (e.g., mass, energy, or environmental relevance thresholds) are applied to exclude negligible processes, but these must be **documented and justified**.


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

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

Based on the context provided, when describing a modelled unit process in more detail, the following types of representativeness and quantitative information should be included:

**Types of Representativeness:**
- **Technological representativeness**
- **Geographical representativeness**
- **Time-related representativeness**

These three dimensions are closely interrelated and are the classical perspectives from which representativeness is evaluated (Source 2, p.122).

**Quantitative and Qualitative Information:**
- **Functional unit(s)** and **reference flow(s)**
- Other quantitative and qualitative information relevant to the process

Additionally, the unit process should be characterised regarding its **technology**, and the description must clearly state what the data set represents, including:
- Operating conditions
- Assumptions made
- Use of data from other sources
- Data gaps
- Achieved completeness and precision of the inventory

If combining data from different sources or having lack of representativeness, this **shall** be explicitly stated in the data set and any accompanying documentation.

*(Sources: ILCD Handbook, p.195 — Provisions 7.4.2.3; ILCD Handbook, p.122 — Chapter 6.8)*

When describing a modelled unit process in detail, the following types of **representativeness** and **quantitative information** should be included:

## Representativeness

1. **Geographical representativeness** – the region(s) or country/countries the data represents (e.g., national, regional, global average)
2. **Temporal representativeness** – the time period or year(s) the data reflects
3. **Technological representativeness** – the technology mix or specific technology type represented (e.g., best available technology, average market mix, specific process)
4. **Completeness** – the degree to which all relevant flows have been included
5. **Sample representativeness** – how well the data sources represent the actual population of processes (sample size, data sources)

## Quantitative Information

1. **Data quality indicators** – numerical scores or ratings for reliability, completeness, temporal, geographical, and technological correlation (e.g., using the Pedigree Matrix / data quality scoring system)
2. **Uncertainty information** – standard deviations, variance, or uncertainty ranges for individual flows/parameters
3. **Reference flow / functional unit** – the quantitative basis to which all flows are normalized
4. **Mass/energy/economic allocation factors** – where applicable
5. **Cut-off criteria applied** – thresholds used and percentage of flows excluded

## Key Standards/Frameworks
These requirements align with guidance in **ISO 14044**, the **ecoinvent** documentation standards, and the **ILCD Handbook** (European Commission).


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

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

Based on Source 1 (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.365), the goal definition must be very clear on three elements to avoid misleading results interpretation:

1. **The comparative character of the LCA study** (e.g., comparing fuel-type A and fuel-type B), including whether assertions about environmental superiority or equality are made and foreseen to be published.

2. **The reasons to carry out the study, including the decision-context** (e.g., supporting governmental decisions on the introduction of new fuel-types).

3. **Who commissioned the study** (e.g., the National Ministry for Transportation).

In an LCA study, a goal definition must clearly state:

1. **The intended application** – the specific purpose or use for which the study is being conducted (e.g., product improvement, comparative assertion, policy support)

2. **The intended audience** – who the results are directed at (e.g., internal decision-makers, general public, regulatory bodies), as this influences the level of detail, transparency, and critical review required

3. **The reasons for carrying out the study** – the motivation or context behind the study, which helps frame how results should be interpreted and communicated

These three elements are required by ISO 14040/14044 to ensure transparency and prevent misuse or misinterpretation of LCA results.


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

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

Based on Source 1 (ILCD Handbook, p.373), burdens can plausibly be allocated to secondary goods **only at the process step where a valuable secondary good is produced (Pn)** — that is, the final step where the treated waste crosses the "zero market value" border.

The reasoning given is twofold:

1. The waste or end-of-life product is **generated by the first system**, and a waste *per se* cannot carry any burden of treatment.
2. It is considered **inappropriate to attribute all preceding waste treatment processes** (P1 through Pn-1) to the eventually produced secondary good.

Therefore, allocation of burdens to secondary goods can only be justified at that specific process step (Pn) where value is created, not throughout the earlier treatment steps. The procedure requires first modelling the waste/end-of-life management processes until the treated waste crosses the "zero market value" border, and then applying the two-step allocation procedure at that point.

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

## Allocation of Burdens to Secondary Goods

Burdens (environmental impacts) can plausibly be allocated to secondary goods at the **end-of-life (waste treatment / recycling) process step**.

### Why This Step?

At end-of-life, a process typically handles a **primary function** (waste treatment, disposal) while simultaneously producing a **secondary output** (recovered materials, energy, scrap metals, etc.). Two rationales support allocation here:

1. **The secondary good has economic value** – The recycled/recovered material displaces virgin material production, creating a real incentive and market. It is therefore reasonable to assign a share of the processing burden to it, since the process would not be operated (or optimized) without the value of that secondary output.

2. **The process serves two purposes simultaneously** – Waste treatment both disposes of waste *and* produces usable secondary materials. Neither function is entirely "free"; the burden is jointly caused.

### Common Approaches

| Approach | Logic |
|----------|-------|
| **System expansion** (preferred by ISO 14044) | Avoid allocation by crediting the secondary material with avoided burdens of the displaced primary production |
| **Physical allocation** (e.g., mass, energy content) | Split burdens proportionally between primary waste treatment function and secondary material output |
| **Economic allocation** | Split by market value of the secondary good relative to the service |

### Key Point
ISO 14044 discourages arbitrary allocation and recommends **system expansion** first; but when allocation is unavoidable, end-of-life recycling processes are the classic context where allocating *some* burden to the secondary (recovered) good is physically and economically defensible, because the secondary good is a genuine co-product of that step.


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

Based on the context provided, solving multifunctionality of processes in attributional modelling is covered in **chapter 7.4.2.2** (as referenced in Source 2, p.75 of the ILCD Handbook), which provides "more details on subdivision, partial subdivision, and virtual subdivision."

Additionally, the topic is addressed under the broader **chapter 7** (Life Cycle Inventory analysis), specifically in the section on **"Solving multifunctionality"** as noted on **p.255** of the ILCD Handbook (Source 1), which references the ISO hierarchy detailed in **chapter 6.5.3**.

In the context of LCA methodology documents (such as the ILCD Handbook or ISO 14044), the section covering **solving multifunctionality of processes in attributional modelling** is typically found in the section on **system boundary definition and allocation**, often referred to in the ILCD Handbook as:

**Section on "Multifunctionality and allocation"** — specifically dealing with the hierarchy of approaches:
1. Subdivision/disaggregation
2. System expansion (avoided burden)
3. Allocation based on physical relationships
4. Allocation based on economic value

In the **ILCD Handbook (EC, 2010)**, this is addressed in **Section 7.3** (or the equivalent subsection on multifunctionality under inventory modelling).

If you are referring to a **specific document**, please clarify the title, as the exact section numbering varies by source (e.g., ISO 14044, ILCD Handbook, EPA guidelines, ecoinvent documentation).


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

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

Based on the provided sources, the key distinction between Situation C1 and Situation C2 is whether interactions with other systems are included in the model.

**Situation C1** accounts for interactions the analysed system has with other systems — for example, crediting existing avoided burdens from recycling. It is used for life cycle-based monitoring that includes indirect effects and existing interactions with background systems (Hauschild et al., p.969; ILCD Handbook, p.43).

**Situation C2** describes an existing system **in isolation**, without accounting for interactions with other systems. It is only used when specifically prescribed by the commissioner's goal of the study (Hauschild et al., p.969; ILCD Handbook, p.71).

A further modelling difference is that for **Situation C2**, multifunctionality **shall always be solved via allocation**, whereas Situation C1 follows the same modelling approach as Situation A (which may use substitution) (ILCD Handbook, p.9).

Both situations are retrospective/accounting in nature and cannot be directly used to inform forward-looking decisions such as purchase decisions or "what if" policy scenarios (ILCD Handbook, p.44).

**Sources:** Hauschild et al., p.969; ILCD Handbook, pp. 8–9, 43–44, 71.

In life-cycle assessment (LCA), particularly within the context of **system expansion and substitution** (as described in ISO 14044 and related guidance documents, including work by Weidema and others on multi-output processes), **Situation C1** and **Situation C2** refer to different circumstances for handling co-production or waste treatment allocation:

---

## Situation C1 vs. Situation C2

These situations are described in the context of **determining whether outputs are co-products or wastes**, and how to handle system boundaries accordingly:

### **Situation C1**
- The co-product (or secondary output) **can replace an existing product** on the market (i.e., there is a marginal supplier that is displaced).
- System expansion is straightforward: the system is **credited** for avoiding the production of the substituted product.
- The determining factor is that the **substitute has a clearly identifiable alternative production route**.

### **Situation C2**
- The co-product **cannot be assumed to substitute** a specific existing product, or the market situation is such that the co-product **creates its own demand** (e.g., it is a new product with no direct substitute).
- System expansion is more complex or less applicable, and **allocation by physical or economic parameters** may be more appropriate.
- The distinguishing factor is the **lack of a clearly displaced alternative product**.

---

**In essence**, the key distinction is whether a **substitutable alternative production route exists** (C1) or not (C2), which determines whether system expansion/substitution credit is applicable.

If you are referring to a specific publication or framework, please clarify, as terminology can vary by source.


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

**"Short-term marginal"** processes/systems.

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

The term you're describing is **"marginal demand"** in the context of LCA — more specifically, this concept is referred to as **"non-marginal"** or, more precisely, the situation describes a **"non-affected"** or **"inframarginal"** condition.

However, in LCA methodology, the specific term used is:

**"Average/non-marginal"** — but the most precise answer here is that this describes a **"constrained market"** or, in consequential LCA terminology, a process/supplier that is **"non-determining"** (i.e., not the marginal supplier).

More specifically, in the context of consequential LCA and market modeling, when additional demand **does not change market direction and does not result in additional or reduced capacity**, this is described as a situation where the technology or process is operating under **"constrained conditions"** — and the relevant concept is that it is **not the marginal technology**.

The precise term most commonly used in this context (particularly in ecoinvent and consequential LCA methodology) is:

**"Constrained market"** — where the marginal supplier is a **constrained/non-expandable** technology, meaning demand shifts to another supplier rather than expanding capacity.

The term specifically describing the condition where additional demand causes **no change** in capacity is a **"non-responsive"** or **"inelastic"** supply situation.


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

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

Allocation is strictly performed not between the first and second life cycle, but between the **two co-functions** that the reused, recycled, or recovered good performs — once for the primary product and once for further products as the secondary good.

*(ILCD Handbook: General Guide for LCA, 2010, p. 345)*

In modelling reuse, recycling, and energy recovery, allocation is strictly performed between the **previous life cycle** (or the product system providing the recyclable material) and the **subsequent life cycle** (or the product system using the recovered material/energy).

In other words, allocation is performed between the **first use** (or supplying system) and the **second use** (or receiving system) of the material or energy.


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

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

In LCA, a market is defined as **"the unit that allows buyers and sellers to exchange any type of goods and services"** (European Commission – JRC, ILCD Handbook, p.128).

This definition is explicitly distinguished from strict geographical or political boundaries such as countries or regions in several important ways:

1. **Markets may be geographically equal to, smaller than, or larger than a country** — they do not automatically align with national borders (ILCD Handbook, p.128–129).

2. **Market boundaries are not exact**, as imports and exports occur across market borders. A useful delimitation is that no *relevant* amounts of such cross-border flows occur, or that they are accounted for when modelling consumption mixes (ILCD Handbook, p.128).

3. **Markets are shaped by multiple factors** beyond geography, including:
   - Political factors (legislation, standards, taxes, subsidies)
   - Cultural recognition by producers and service providers
   - Natural geographical barriers (islands, large distances, climate)
   - Transportation costs relative to product value (Hauschild et al., *LCA: Theory and Practice*, Chapter 9, p.137)

4. Markets can also be **differentiated temporally** (e.g., intraday or seasonal segmentation), not just geographically (ILCD Handbook, p.128).

Thus, while countries or regions are administrative/geographical constructs, markets in LCA are defined by **economic exchange relationships**, which may or may not align with political or geographical boundaries.

In LCA, a **market** is defined primarily by the conditions under which products (goods or services) are exchanged and substituted for one another, rather than by administrative or geographical boundaries.

Key distinguishing characteristics:

## Definition of a Market in LCA

A market is essentially **a set of suppliers and consumers connected by the possibility of substitution** — where one supplier's output can replace another's for the same functional purpose. It is defined by:

1. **Fungibility/substitutability**: Products within a market are interchangeable for a given function. Electricity on a synchronized grid is the classic example — consumers cannot distinguish between suppliers.

2. **Physical or contractual connectivity**: Suppliers and consumers must actually be able to transact, meaning there must be infrastructure (pipelines, grids, transport routes) or trade relationships linking them.

3. **Marginal supply logic**: The market determines which supplier is **marginal** (i.e., responds to changes in demand), which is critical for consequential LCA.

## How This Differs from Geography

| Concept | Basis |
|---|---|
| Country/Region | Administrative or political boundaries |
| Market | Substitution possibilities and connectivity |

- A **single country** may contain **multiple distinct markets** (e.g., isolated regional grids)
- A **single market** may span **multiple countries** (e.g., the European electricity grid, North American natural gas pipelines)
- Two physically adjacent areas may belong to **different markets** if no substitution pathway exists between them

## Practical Implication

This distinction matters especially in **consequential LCA**, where identifying the correct market determines which technology or supplier is marginal and thus which process actually responds to a change in demand. Using a country boundary as a proxy for a market can introduce significant error if the actual substitution space is broader or narrower than national borders.


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

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

Based on the provided sources, the identification of the technology mix of marginal processes depends on the following factors:

- **Market conditions/market direction**
- **Cost-competitiveness of the potential marginal processes**

Additionally, constraints must be identified that may partly or fully prevent the marginal process mix from being directly used in the system model, including factors such as monopolies (lack of choice of supplier or technology) and other constraints that increase, decrease, or block a primary or secondary consequence.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.124 and p.127; European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.177)*

The identification of the technology mix of marginal processes depends on several key factors:

## Key Factors

### 1. **Time Horizon**
- **Short-term**: The marginal process is typically the one most easily adjusted (e.g., the least efficient plant currently operating)
- **Long-term**: New capacity investments determine the marginal technology, often reflecting planned or projected future technologies

### 2. **Geographic/Market Scope**
- The relevant electricity market or regional grid boundaries
- Whether the analysis is local, national, or international in scope
- Interconnections between markets

### 3. **Demand Changes**
- The **magnitude** of the demand change being assessed (small vs. large incremental change)
- Whether demand is increasing or decreasing
- Peak vs. off-peak conditions

### 4. **Market Structure and Policy Context**
- Regulatory frameworks and energy policies
- Renewable energy targets and subsidies
- Carbon pricing mechanisms
- Capacity markets vs. energy-only markets

### 5. **Technological and Economic Characteristics**
- Merit order of existing plants (dispatch order based on variable costs)
- Load curves and capacity constraints
- Planned investments and retirements

### 6. **Data Availability and Modeling Approach**
- Use of consequential vs. attributional modeling
- Econometric or engineering-based approaches
- Forecasting methods and scenarios

### 7. **System Constraints**
- Grid infrastructure limitations
- Storage availability
- Flexibility requirements

The identification is particularly important in **consequential LCA**, where the goal is to capture the actual system response to a change in demand.


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

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

Situation A refers to **"Micro-level decision support"** — it applies when an LCA is used to support decisions where the consequences on the background system are **none or small-scale**. It covers decision support directly or indirectly related to informing the purchase of products already offered in the market, or informing the design/development of products foreseen to enter the market. In this context, the product can be assumed to be produced only as a consequence of the decision being supported by the LCA study.

Situation A applies to applications such as:
- Identification of Key Environmental Performance Indicators (KEPI) for Ecodesign/simplified LCA
- Weak point analysis of a specific product
- Detailed Ecodesign/Design-for-recycling

In general, Situation A is best suited for analysing **short-term effects** at an operational level (e.g., comparison of two specific products).

*(Sources: Hauschild et al., LCA: Theory and Practice — 37 LCA Cookbook, p.967–968; European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.39; Hauschild et al., LCA: Theory and Practice — 27 LCA of Electromobility, p.676)*

In the context of LCA decision support, **Situation A** refers to a decision situation involving **changes within an existing system** — typically a **marginal or small-scale change** where the decision affects only part of the production system without fundamentally restructuring it.

More specifically, Situation A (as described by Weidema and others in the context of consequential LCA and decision support frameworks) refers to situations where:

- The decision concerns a **small change** relative to the total market/system
- **Marginal suppliers** (i.e., the suppliers most sensitive to changes in demand) are the relevant ones to model
- The system is essentially **constrained** or operating within existing capacity

This is contrasted with **Situation B**, which involves **large-scale or structural changes** that affect the entire system or market, where average or transformational data may be more appropriate.

The distinction is important for determining which type of LCI data (marginal vs. average) and which modeling approach (attributional vs. consequential) is most appropriate for the decision at hand.


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

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

Based on the context provided, in end-of-life recycling models, the total inventory assigned to the analysed system includes **all processes up to the level of the quality of the primary material, energy carrier or part as obtained also later via recycling, plus all recycling and waste treatment steps** (including the repeated recycling processes and the disposal that contribute to the total inventory).

However, it explicitly **excludes** any of the processes from the manufacture and use of the products made from the material, energy carrier or part, because those processes are not physically related to the production of the later reused/recycled/recovered material, energy carrier, or part.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.1, Source 6)*

In end-of-life (EoL) recycling models, the process steps whose inventories are assigned to the analysed system depend on the specific recycling approach used, but the general principle is:

## Process Steps Assigned to the Analysed System

### Collection & Sorting/Pre-processing
- **Collection** of end-of-life products
- **Sorting and dismantling**
- **Pre-processing / shredding / separation**

These steps are consistently assigned to the system producing the waste (the analysed system), as they are necessary to handle the material at end of life.

### Recycling/Reprocessing (partially, depending on model)
- In some models (e.g., the **Recycled Content method / cut-off approach**), the reprocessing step is **not** assigned to the analysed system — the system "cuts off" at the point of waste generation.
- In other models (e.g., **End-of-Life recycling credit / avoided burden approach**), the reprocessing **and** the credit for avoided primary production are assigned to the analysed system.

## Summary by Common Model

| Model | Steps assigned to analysed system |
|---|---|
| **Cut-off (recycled content)** | Collection, sorting, pre-processing (up to scrap handover) |
| **Avoided burden / EoL credit** | Collection, sorting, pre-processing, reprocessing **minus** credit for substituted primary material |
| **Closed-loop substitution (EN 15978 / ISO 21930)** | Collection, sorting, reprocessing, with substitution credit |

The **key boundary** is typically at the point where the waste material becomes a **secondary raw material** (the "end of waste" point), after which responsibility may shift to the next system.


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

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

Based on the ILCD Handbook, the documentation of review findings should be placed in the **"Review" part** of an LCA data set, and **not** in the documentation section.

As stated in the ILCD Handbook (p.336):

> "The documentation of the review findings belongs to the 'Review' part, since it does not relate to the documentation of the object of the data set."

The rationale is clear: review findings concern the **review process itself**, not the substance or content of what the data set describes. Since documentation is meant to capture information about the object of the data set (supporting transparency and reproducibility of the inventory), review findings are categorically distinct and therefore belong under the "Review" part of the data set structure.

## Placement of Review Findings in an LCA Data Set

Review findings should be documented in the **administrative information** section (or "data set information / review" section) of an LCA data set — for example, in the `<review>` field within the **ecospold2** format or the equivalent structure in **ILCD format** (`<dataSetInformation>` → `<reviewers>` / `<review>`).

### Why This Placement?

1. **Transparency and traceability**: Administrative information is the designated location for provenance, quality, and validation metadata. Placing review findings here ensures users can immediately assess the data set's credibility and review status without confusing it with the underlying process or flow data.

2. **Separation of concerns**: LCA data set formats distinguish between:
   - *Technical content* (process inputs/outputs, exchanges, characterization factors)
   - *Administrative/quality metadata* (who created it, when, under what review)

   Review findings belong to the latter category.

3. **Conformance with standards**: Both **ISO 14044** and data format standards (ILCD, ecospold) explicitly provide dedicated fields for review information in the administrative section, ensuring interoperability and consistent interpretation across tools and databases.

4. **User guidance**: Decision-makers and practitioners typically check administrative metadata first to judge data quality; locating review findings there supports informed data selection.

In summary, the administrative information section is the correct location because it is structurally designated for quality and validation metadata, keeping technical data separate while ensuring full transparency.


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

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

Based on Source 1 (European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.51), the two crosscutting requirements on LCA that must be explicitly checked, referred to in subsequent work, and documented are:

1. **Consistency of methods, assumptions, and data** (chapter 6.2.1)
2. **Reproducibility** (chapter 6.2.2)

In LCA methodology (as set out in ISO 14040/14044), the two crosscutting requirements that must be explicitly checked, referred to in subsequent work, and documented are:

1. **Completeness check** – verifying that all relevant information and data needed to address the goal and scope are available and sufficiently complete.

2. **Consistency check** – verifying that the assumptions, methods, and data are applied consistently throughout the study and are consistent with the goal and scope.

(A **sensitivity check** is also required in ISO 14044, but the two that are specifically flagged as crosscutting requirements to be explicitly documented and referred to in subsequent phases are completeness and consistency.)


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

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

Errors below one order of magnitude are considered worse because **they can much more easily pass unnoticed, while still rendering the data and conclusions invalid**.

Two sources of such errors are:

1. **The use of "." and "," as decimal separators**, which are handled differently in different regions and countries.
2. **Unit conversion errors** related to using different unit systems (e.g., Imperial system to SI).

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

## Errors Below One Order of Magnitude in LCA

### Why They Are Considered Worse

Errors below one order of magnitude (i.e., errors less than a factor of 10) are considered **more insidious and dangerous** than larger errors because:

- **Large errors are obvious** — mistakes of multiple orders of magnitude are typically caught during review, sanity checks, or comparison with literature values, as they produce clearly unrealistic results.
- **Small-to-moderate errors are plausible** — errors within one order of magnitude fall within a range that *looks reasonable*, making them much harder to detect. They can pass unnoticed through quality checks while still significantly distorting results and conclusions.

In other words, they are "worse" not in absolute magnitude but because they are **invisible errors** that silently undermine the reliability of the study.

---

### Two Common Sources of Such Errors

1. **Incorrect or imprecise activity data** — Using proxy data, outdated figures, or data from a different geography/technology that differs from the actual system by a factor of 2–5 (e.g., using average grid electricity instead of the actual energy mix used by a facility).

2. **System boundary errors / missing processes** — Omitting processes or life cycle stages (e.g., upstream raw material extraction or end-of-life treatment) that each contribute modestly but cumulatively shift results by a factor of 2–10 without triggering obvious implausibility.


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

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

"Cut-off" refers to the omission of not relevant life cycle stages, activity types (e.g. investment goods, storage), specific processes and products (e.g. re-granulating of internally recycled polymer production waste before re-melting) and elementary flows from the system model. Cut-offs are quantified in relation to the percentage of environmental impacts that is approximated to be excluded via the cut-off (e.g. "95%" relates to cutting off about 5% of the total environmental impact (or of a selected impact category)).

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.102)*

In life cycle assessment (LCA), **"cut-off"** refers to a criterion or rule used to decide which processes, flows, or life cycle stages can be excluded from the system boundary without significantly affecting the results.

Specifically, it involves setting thresholds (based on **mass, energy, or environmental relevance**) below which inputs or outputs are omitted from the study. For example, if a material contributes less than 1% of the total mass and is not considered environmentally significant, it may be "cut off" (excluded) from the inventory.

It also refers specifically to the **"cut-off approach"** (or cut-off allocation method) used in handling **recycled materials**: under this convention, the original producer of a material bears no credit or burden for recycling at end-of-life, and the user of recycled/secondary material receives it "burden-free" (i.e., the upstream burdens of primary production are cut off). This is one of the most common approaches in databases like ecoinvent.


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

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

Reminder flows **shall not carry any LCIA impact factor (characterisation factor)** and must be excluded from impact assessment entirely.

Additionally, the fact of being a reminder flow shall be clearly identified in the flow name (e.g., "VOC, reminder flow, not impact relevant").

*(ILCD Handbook: General Guide for LCA (2010), p.233, Section 7.4.3.8, Provisions II and III; also confirmed on p.217)*

# Reminder Flow Treatment in LCA Impact Assessment

## Key Principle

A **reminder flow** (also called a **remainder flow**) must be treated as an **unallocated co-product or residue** that carries its proportional share of environmental burdens from the system.

## Treatment Requirements

### 1. **Allocation Perspective**
- Reminder flows must be subject to the **same allocation rules** applied to the main product
- They cannot simply be ignored or set to zero impact
- Burdens must be **partitioned** between the main flow and the reminder flow using consistent allocation criteria (mass, energy, economic value, etc.)

### 2. **Impact Assessment**
- The reminder flow must be characterized using **appropriate characterization factors** if it enters the environment
- If it re-enters a technical system, it may require **system expansion** or substitution credit treatment
- Elementary flows associated with the reminder flow must be correctly **attributed to the functional unit**

### 3. **Boundary Considerations**
- If the reminder flow is **released to the environment**, it becomes an elementary flow requiring characterization
- If it is **further processed**, it should either expand the system boundary or trigger allocation

## Important Note
The treatment must ensure **mass and energy balance** is maintained, and the choice of method must be **consistent** throughout the LCA study per ISO 14044 requirements.

Would you like more detail on a specific aspect?


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

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

Based on the context provided, two situations that can cause an LCA study to produce misleading conclusions are:

1. **Narrowing the scope/omitting relevant impact categories**: When a study is defined too narrowly (e.g., as only a carbon footprint study or energy flow analysis), serious misleading may occur by omitting some of the impacts that the system has. This is particularly problematic when two technologies that differ in their pattern of environmental impacts are being compared. *(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.368)*

2. **Transferring conclusions beyond their valid context**: When conclusions drawn from a specific country or situation are applied to another country or context where the underlying conditions (e.g., wastewater treatment practices, appliance efficiency) are not representative, the conclusions will most likely not be valid. *(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.375)*

In Life Cycle Assessment (LCA), two key situations that can cause misleading conclusions are:

1. **Incorrect or inappropriate system boundary definition** – If the system boundaries are drawn too narrowly (cut-off errors) or inconsistently, important upstream or downstream processes may be excluded, leading to an incomplete picture of the true environmental burdens. This can make a product or process appear more (or less) environmentally favorable than it actually is.

2. **Poor or unrepresentative inventory data (data quality issues)** – If the life cycle inventory (LCI) data used are inaccurate, outdated, geographically inappropriate, or not representative of the actual processes being studied, the results will be skewed. This can lead to incorrect conclusions about which product or system has lower environmental impacts.

These two issues—flawed scope/boundaries and poor data—are widely recognized as the primary sources of misleading LCA results.


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

Solving the multifunctionality of processes in consequential modelling.

In the context of consequential modelling within LCA, the section on solving multifunctionality addresses the identification of **relevant consequences and related processes** for cases of multifunctionality. Specifically, it covers multifunctionality in Situations A, B, and C1, processes affected by "big" changes (large-scale consequences) in Situation B, and "assumption scenarios" in Situation B where consequential modelling is included.

Notably, the ILCD Handbook explains that in consequential modelling, solving multifunctionality is addressed at the stage of **identifying and describing processes** (rather than as a subsequent step as in attributional modelling), because this step directly affects which processes are to be included in the model.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p. 255 and p. 164)*

In consequential LCA, the section on solving multifunctionality of processes addresses the issue of **how to handle processes that deliver more than one function (co-products or by-products)** — i.e., how to determine which other processes in the background system are affected when demand for one of the functions changes.

Specifically, the core problem is: when a process produces multiple outputs (e.g., a refinery producing both gasoline and diesel, or a slaughterhouse producing meat and hide), and you only need one of those outputs for your study, you must decide **which processes are displaced or expanded** as a consequence of changing demand for that one function.

The key issues addressed include:

1. **Identifying whether co-products are constrained by the system** (i.e., whether increasing one output necessarily increases the other) — distinguishing between **constrained/coupled** and **unconstrained** co-production.
2. **Determining the appropriate substitution/displacement approach** — identifying which alternative process supplies the same function in the market, so that the displaced process can be credited or debited.
3. **Avoiding allocation** (which is a attributional approach) in favor of **system expansion or substitution**, consistent with the consequential paradigm.
4. **Handling waste treatment** and whether a process is a supplier or receiver of a function.

The fundamental principle is that multifunctionality is resolved by identifying the **marginal supplier** of each co-product and modeling the **economic consequences** of changes in demand, rather than mathematically partitioning impacts between co-products.


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

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

The level of differentiation of flow data sets in an inventory should be guided by the principle of **compatibility** between the collected elementary flows and the LCIA methods to be applied. Specifically, if spatially differentiated LCIA methods are chosen, spatial information for elementary flows (e.g., nation name, watershed ID, or GIS grid cell coordinates) must be collected in the inventory in a manner compatible with those methods. Conversely, if spatially generic LCIA methods are used, such differentiation may not be necessary.

*(Hauschild et al., LCA: Theory and Practice — 8 Scope Definition, p.113)*

The principle that should guide the level of differentiation of flow data sets in an inventory relative to LCIA methods is **consistency** (or correspondence) between the inventory and the impact assessment method — specifically, the inventory flows should be differentiated **to the same degree (but not more) as the LCIA characterization factors can distinguish between them**.

More precisely:

- If an LCIA method applies **different characterization factors** to a substance depending on certain properties (e.g., emission compartment, geographic region, temporal profile, chemical species), then the inventory flows should be **differentiated accordingly** to capture those distinctions.
- Conversely, if the LCIA method assigns the **same characterization factor** regardless of a particular distinction (e.g., it does not differentiate between emissions to urban air vs. rural air), there is **no benefit** in differentiating the inventory flows to that level — it adds complexity without affecting results.

This principle avoids both **under-differentiation** (losing information that LCIA methods could use) and **over-differentiation** (creating unnecessary complexity with no impact on results). It ensures that the granularity of elementary flow data sets is **fit for purpose** relative to the characterization factors available.


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

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

Based on Source 1 (ILCD Handbook, p.280), when unit process, partly terminated system, and LCI results data sets are the deliverables of a study, provision 8.2 on the calculation of LCIA results applies only **to quantify the achieved completeness and precision**, as these need to be evaluated from the system's perspective.

(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.280)

When unit process, partly terminated system, and LCI results data sets are the deliverables of a study, **provision 8.2 on the calculation of LCIA results applies only for quality check purposes** — that is, to verify the correctness and consistency of the data sets — rather than as the primary goal of the study.


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

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

Confidential information can be placed in a **separate "confidential report"** that is made accessible only to the critical reviewers under confidentiality.

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.204 and p.318; Hauschild et al., LCA: Theory and Practice, p.1000 and p.144)*

In life cycle inventory analysis, confidential information can be placed in a **confidential annex** (or confidential appendix) to the LCA report. This annex is made available only to critical reviewers (third-party reviewers) under confidentiality agreements, while the main report accessible to the public or other stakeholders contains only non-confidential information.

This approach is consistent with ISO 14044 guidelines, which allow for the protection of sensitive business data (such as proprietary process data, trade secrets, or commercially sensitive inventory data) while still enabling a rigorous and transparent critical review process.


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

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

Based on the sources provided, the condition that causes a provision to fall under **Situation B** rather than Situation A is when **the consequences of the analysed decision alone are large enough to overcome related thresholds and/or other constraints, resulting in large-scale consequences in the installed production capacity outside the foreground system**.

More specifically, this occurs when the annual additional demand or supply triggered by the analysed decision **exceeds the capacity of the annually replaced installed capacity** of the additionally demanded or supplied process, product, or broader function.

As stated in the ILCD Handbook:

> "Where a study cannot initially be clearly assigned to either Situation A or B...the guiding criteria shall be whether the consequences of the analysed decision alone are big enough to overcome related thresholds and/or other constraints and result in large-scale consequences in the installed production capacity outside the foreground system. Then: Situation B. If not: Situation A."

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.48)*

Additionally, regarding multifunctionality specifically: if no functionally equivalent alternative processes exist, or they are not operated to a sufficient extent, "the study is in fact a Situation B type study, as this implies large-scale consequences on other systems."

*(European Commission – JRC, ILCD Handbook: General Guide for LCA (2010), p.91)*

In LCA, the distinction between **Situation A** and **Situation B** (as defined in ISO 14044 and elaborated in guidance documents like the ILCD Handbook) hinges on whether the background system can respond to a change in demand:

- **Situation A** applies when the **background (market) system is able to respond** to the additional demand — i.e., there is a marginal supplier or unconstrained capacity, so the additional good or service is provided by an **unconstrained, expandable process**. The system can deliver more output without being limited by a fixed capacity constraint.

- **Situation B** applies when the **background system is constrained** and **cannot increase its total output** to meet the additional demand. The supply of the co-product or good is **fixed** (e.g., by a fixed-output process or a constrained market), so obtaining more of it means **diverting it from another use** — i.e., it displaces or substitutes for what that diverted supply would otherwise have provided. The additional provision therefore causes a substitution effect rather than an expansion of production.

In short, the condition that places the situation in **Situation B** is that the **production volume of the process providing the good is fixed (constrained)**, so an increased demand for one of its outputs cannot be met by expanding production but only by reallocating output away from another user, triggering a substitution/displacement effect.


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

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

Based on the provided sources, the key distinction between Situation C1 and C2 is:

**Situation C1 ("Accounting with interactions")** accounts for an existing system **including** its interactions with other systems — for example, crediting avoided burdens from recycling or indirect effects under Environmental Management Systems. It applies system expansion/substitution to handle multifunctionality, independently of the absolute amount involved (Hauschild et al., p.969; ILCD Handbook, p.9).

**Situation C2 ("Accounting without interactions")** describes an existing system **in isolation**, without accounting for any interaction with other systems. Multifunctionality in C2 is always solved via **allocation** rather than substitution (Hauschild et al., p.969; ILCD Handbook, p.9).

As stated in the ILCD Handbook (p.9): *"For Situation C2, the life cycle is equally modelled as in Situation A, but multifunctionality shall always be solved via allocation."*

C1 is the default unless C2 is specifically prescribed by the commissioner's goal of the study (Hauschild et al., p.71).

In life-cycle inventory (LCI) accounting, the distinction between **Situation C1** and **Situation C2** relates to how **waste processing** and **recyclability** are handled at the end of a product's life — particularly in the context of the **European Commission's Product Environmental Footprint (PEF)** / **EF (Environmental Footprint)** methodology and related ISO/CEN frameworks for circular economy allocation.

---

### **Situation C1 – Waste processing with no recyclable material recovered**
In C1, the material undergoes **waste treatment/processing** (e.g., incineration, landfill) **without recovery of a secondary material or energy that displaces a primary equivalent**. The burden of waste processing is borne by the product system. There is no credit given because nothing of value is recovered to substitute for a virgin/primary input elsewhere.

---

### **Situation C2 – Waste processing that yields a recyclable/recoverable output**
In C2, the waste processing step **produces a secondary material or energy** that can substitute for a primary material or energy source elsewhere in the economy. Here, the system must account for:
- The **burden** of the waste treatment process itself, AND
- A **credit** (avoided burden) for the secondary material/energy that displaces primary production.

The allocation formula (often the **circular footprint formula, CFF**) is applied to split burdens and credits between the supplying product system and the receiving (next use) system.

---

### **Key Distinction**
| | C1 | C2 |
|---|---|---|
| Output of waste processing | No recoverable secondary material/energy | Recoverable secondary material or energy |
| Credit applicable? | No | Yes (avoided primary production) |
| CFF allocation | Not applicable (pure end-of-life burden) | Applied to share burdens/credits |

In essence, **C1 is a pure waste/disposal situation**, while **C2 involves value recovery**, triggering allocation between the current product system and future users of the recovered material.


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

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

Relevant specific emissions that escape previous assessment steps can mostly be identified by **including all legally regulated emissions for that or similar process types**, or are **identified by expert judgement, drawing on know-how for these or similar processes**.

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

## Identifying Specific Emissions Escaping Previous Assessment Steps

Relevant specific emissions such as **particle emissions** or **dioxin emissions** that escape earlier assessment steps can mostly be identified through:

### **Site-Specific Measurements / Direct Emission Measurements**
- Stack measurements and monitoring at emission sources
- Continuous or periodic emission monitoring programs
- Ambient air quality measurements near facilities

### **Key Approaches:**

1. **Literature and Database Review**
   - Reviewing substance-specific emission factors from scientific literature
   - Consulting specialized databases (e.g., EMEP/EEA emission inventory guidebooks)

2. **Material/Substance Flow Analysis**
   - Tracking specific substances through the production chain to identify where they may escape accounting

3. **Regulatory Emission Inventories**
   - National/regional pollutant release and transfer registers (e.g., E-PRTR in Europe)
   - Industry-reported emission data

4. **Characterization Factor Databases**
   - Consulting impact assessment databases that flag substances with high characterization factors but low reporting frequency

5. **Expert Knowledge & Process Knowledge**
   - Engaging process engineers or industry experts familiar with specific emission sources
   - Technology-specific emission profiles

### **Practical Note**
In LCA, these emissions are often captured through **site-specific data collection** or **primary data gathering** when generic background databases fail to represent specific process conditions adequately.

The key principle is that **direct measurement** at the process level remains the most reliable method for capturing such otherwise overlooked emission categories.


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

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

Based on the provided sources, when applying LCI modelling and method approaches, the aspects that differ between Situations A, B, and C — given that the unit process inventory is basically identical across them — are:

1. **Which processes are included within the system boundary**, especially in the background system
2. **How the processes are combined to represent the life cycle model**
3. **How multifunctionality is solved**

As stated in Source 1 (ILCD Handbook, p.87):
> "What differs is which processes are within the system boundary, especially in the background system...and how the processes are combined to represent the life cycle model and how multifunctionality is solved."

This is further confirmed in Source 2 (ILCD Handbook, p.8):
> "The main difference between Situation A, B and C lies hence in the selection of the processes that are included in the system boundary and how the life cycle is modelled by connecting them."

Additionally, some specific additional information may be required depending on the Situation, such as **market size and the amount of products involved**, but the core unit process inventories remain essentially the same across all three Situations (ILCD Handbook, p.8 and p.87).

## Key Differences Between Situations A, B, and C in LCI Modelling

When the physical inventory of a unit process is essentially the same, the differences between Situations A, B, and C lie in the **system boundary definition and how co-products/multifunctionality are handled**, particularly regarding **allocation and substitution approaches**. These situations come from the ISO 14044 / ILCD framework for handling multifunctional processes.

---

### Situation A
- The process delivers **one functional output** (no co-products or waste issues)
- No multifunctionality problem exists
- The inventory is directly attributable to the single function
- **No allocation needed**

---

### Situation B
- The process delivers **multiple co-products** (multifunctionality)
- The system can be **expanded** or co-products can be handled by **subdivision**
- Preference is given to **system expansion / substitution** (avoided burden approach)
- If subdivision is not possible, allocation by **physical or economic relationships** is applied

---

### Situation C
- Involves **waste treatment** or processes where one input is a waste (negative economic value)
- The question of **which function bears the burden** differs from Situation B
- The waste-generating system may be credited, or the treatment process carries its own burdens
- Allocation reasoning differs because one "product" is actually a **waste to be treated**

---

### Summary Table

| Aspect | Situation A | Situation B | Situation C |
|--------|------------|------------|------------|
| Multifunctionality | None | Co-products | Waste treatment |
| Allocation needed | No | Yes (or system expansion) | Yes (specific rules for waste) |
| System boundary | Simple | Expanded or partitioned | Waste-specific boundary |
| Substitution credit | N/A | Possible | Specific to waste handling |

The **core inventory data** may be identical, but the **attribution of burdens and credits** changes the final LCI results depending on which situation applies.