6. Assumptions and Limitations

Every lifecycle assessment (LCA) or Product Carbon Footprint (PCF) study involves assumptions and inherent limitations due to the complexity of data collection, modelling, and reporting. This section outlines the assumptions and limitations applied within Emvide's methodological framework to ensure transparency and robustness.

These assumptions and limitations represent the starting framework for every LCA or PCF report produced by Emvide. They are designed to provide a balance between speed, scalability, and accuracy, enabling the efficient delivery of lifecycle assessments at scale.

While these starting points ensure consistency and alignment with international standards, they can be customised or refined to meet specific study requirements or to address unique aspects of a product or process. Users are encouraged to adapt these assumptions where necessary to enhance the precision and relevance of their reports.

Core Assumptions

1. Resource Modelling

Primary Data Priority: Verified LCA or Environmental Product Declaration (EPD) data are prioritised for resources.
Ecoinvent Semantic Matching: Where primary data are unavailable, Emvide’s AI performs semantic searches within the Ecoinvent database to identify the most relevant market activities.
Decomposition:
- Complex resources are broken down into sub-resources, up to three levels, for accurate modelling.
- Matches are identified for materials and transforming activities at each level of decomposition.
Proxy Data and Assumptions: When no suitable matches are found, proxy data or assumption-based modelling is applied using generalised data.

2. Process Modelling

Primary Data Overrides: If detailed primary data is provided, Emvide prioritises user-specified inputs for process modelling.
AI-Driven Semantic Matching: For processes without primary data, Emvide selects best-fit transforming activities from the Ecoinvent database.
Proxy Modelling: Proxy data or generalised assumptions are used if no matches are found for certain processes.

3. Temporal Scope

Data reflect operational practices and technologies in place during the [Reporting Year], assumed to be representative of typical operations.

4. Geographical Scope

Regional variations in energy mix, transportation, and production practices are modelled using either Ecoinvent data or user-provided region-specific inputs.

5. Completeness

All explicitly defined resources and processes are included in the system boundaries.
Organisational overheads are excluded unless specified by the user.

Core Limitations

1. Dependence on Data Quality

The accuracy of results is heavily dependent on the quality of primary data provided by users or LCA practitioners. Errors or omissions in data submissions may affect results.

2. Secondary Data and Assumptions

Where primary data is unavailable, secondary data (e.g., Ecoinvent averages) or assumptions are applied. While systematically managed, these may introduce variability or uncertainty.

3. Decomposition Depth

Resource decomposition is limited to three levels. Beyond this, the availability of relevant matches decreases, potentially requiring reliance on proxy or assumption-based data.

4. Dynamic Factors

The study does not account for:
- Future changes in technology or energy systems.
- Evolving market conditions or regulatory changes.

5. Specific Exclusions

Minor inputs, ancillary processes, or organisational overheads are excluded unless explicitly included by the user. Justifications for exclusions are provided in Emvide reporting.

Transparency in Assumptions

All assumptions and limitations are documented in the report:

Appendix: Detailed descriptions of assumptions made during data collection, modelling, and reporting.
Lifecycle Inventory (LCI): Assumptions at specific nodal points are highlighted for traceability.

Mitigating Limitations

To enhance the robustness of studies and reduce uncertainties:

Primary Data Collection: Focus on obtaining detailed operational data from clients and supply chains.
Refining Assumptions: Continuously improve proxy data and assumptions using updated datasets.
Regionalisation: Expand the use of region-specific datasets for higher accuracy in geographic modelling.
Model Enhancements: Invest in developing deeper decomposition capabilities and more advanced AI-driven matching.

Compliance with Standards

Emvide ensures alignment with internationally recognised standards to address limitations:

ISO 14067: Emphasises transparency in assumptions and limitations for carbon footprint studies.
ISO 14040/44: Requires clear documentation of any assumptions or exclusions.
GHG Protocol Product Standard: Highlights the importance of balancing completeness with practical constraints in lifecycle analysis.

Why This Approach is Effective

By clearly documenting assumptions and limitations, Emvide enables:

Transparency: Stakeholders can understand and assess the boundaries and constraints of the study.
Robustness: Systematic methods for data handling and proxy modelling reduce the impact of uncertainties.
Actionable Insights: Despite limitations, the structured approach ensures meaningful results for decision-making.