Methodology
Explanation of impact measurement methodology
The following sections explain the methodological foundations behind Nios impact measurements.
Key notes on analysis scope
Value chain stages and impact groups covered.
Scientific frameworks and data
Underlying databases and scientific frameworks used.
Note on alignment to global standards
Explanation of how the Nios methodology is aligned to global standards.
Limitations and future work
Explanation of methodology limitations and planned work.
Key notes on analysis scope
Value chain stage coverage
The assessment scope covers upstream value chain stages, resulting in cradle-to-gate system boundaries from raw material extraction through end-product manufacturing.
For the lowest available value chain stage, Nios applies cradle-to-gate intensity factors, which capture all upstream value chain impacts (analogous to upstream Scope 3 in GHG accounting).
For all higher-level stages, Nios applies gate-to-gate intensity factors, which include only direct, stage-specific impacts (analogous to Scope 1 in GHG accounting).
This approach ensures that upstream impacts are fully accounted for at the most disaggregated stage while avoiding double counting across higher-level stages. Downstream stages such as product use and end-of-life are not yet included.
Impact group coverage
Nios has chosen impact groups based on their relevance to known regulations, standards, and science-based frameworks such as the Planetary Boundaries and IPBES.
| Impact Group | Impact Categories | Metric | Explanation |
|---|---|---|---|
| Biodiversity | Climate change Land use Water consumption Pollution | PDF·yr (Potentially Disappeared Fraction of species over time) | Category expressing the fraction of species at risk of local extinction over time due to human pressures. Based on spatially explicit LC-IMPACT factors. Sub-categories highlight which pressures drive biodiversity loss. |
| GHG emissions | Not further subdivided | kg CO₂e (kilograms of carbon dioxide equivalents) | Represented as aggregated greenhouse gases characterized into CO₂ equivalents using IPCC 100-year global warming potentials. |
| Land use | Annual crops Permanent crops Pastures Extensive forestry Intensive forestry | m² (square meters of land occupied) | Group measuring the area of land used. Categories reflect land-use types with distinct ecological impacts (e.g., cropland vs. forestry). |
| Water use | Blue water (agriculture) Blue water (non-agriculture) | m³ H₂Oe (cubic meters of water equivalents) | Group capturing the absolute physical volume of freshwater withdrawn or consumed. "H₂Oe" normalizes different water flows (e.g., surface, groundwater) into a common comparable unit. Reflects demand regardless of local scarcity. |
| Water stress | Agriculture Non-agriculture | m³ H₂Oe (cubic meters of water equivalents, adjusted for scarcity) | Group adjusting water use by local scarcity and competition factors. Using 1 m³ in water-scarce regions counts for more than in water-abundant regions. Highlights where water demand is most critical for ecosystems and people. |
| Pollution | SO₂ NOₓ NH₃ | kg of emissions (SO₂, NOₓ, NH₃) | Capture emissions that typically drive terrestrial acidification. Expressed as kilograms of each pollutant released. |
Scientific frameworks and data
Nios calculations are based on the following scientific frameworks and datasets:
| Scientific framework and data | Description | Comments |
|---|---|---|
| GLORIA | GLORIA (Global Resource Input-Output Assessment) is an Environmentally Extended Multi-Regional Input-Output (EE-MRIO) dataset constructed by The University of Sydney's IELab and commercialized by FootprintLab. Comprehensive technical description of the GLORIA database can be found here. More information about the underlying dataset used in Nios App can be found here. | GLORIA is recognized as one of the most comprehensive EE-MRIO databases globally, covering ~160 countries plus 4 Rest-of-World regions and 120 economic sectors, and providing spend- and sector-based intensity factors for GHGs, land use, water use, and pollution. It has been selected by the United Nations (UN) to monitor progress on Sustainable Development Goals (SDG 8 and 12). Data has also been used elsewhere by the UNEP's Sustainable Consumption and Production project, and is used by many multi-national companies. It has been built on reputable global data sources including OECD, EDGAR, FAO, Aquastat, and others, ensuring internationally recognized quality and consistency of environmental extensions. |
| LC-Impact | LC-Impact is a life cycle impact assessment (LCIA) methodology that translates environmental pressures into biodiversity impacts (PDF score). LC-IMPACT was launched under the European Union's FP7 funding program and is designed to align with globally recognized LCA frameworks such as UNEP-SETAC Life Cycle Initiative. | Unlike many LCIA methods, LC-IMPACT is spatially explicit, meaning characterization factors vary by ecosystem type and geographic region, making it one of the most advanced methods for assessing biodiversity loss in LCA. To calculate PDF metrics, we follow the Biodiversity impact translation scheme used in the Kulionis et al. 2024 research paper. Note: for now, we exclude N and P emission factors as this is not provided in GLORIA dataset. |
Note on alignment to global standards
At this stage, Nios is not formally certified under any single standard, but our methodology is designed to align closely with their core principles. Our methodology:
- Mimics core principles of life cycle assessment (LCA) to ensure scientific grounding and transparency.
- Prioritizes pragmatism — providing actionable insights for decision-makers today rather than waiting for perfect standardization.
- Covers environmental impacts holistically (not only GHGs), in line with the broader framing of the Planetary Boundaries Framework and IPBES drivers of nature loss.
We monitor the development of key frameworks such as CSRD ESRS, TNFD, SBTN, EU PEF, ISO, and the GHG Protocol. We aim for conceptual consistency with these standards where possible and may revisit our stance as alignment between standards matures.
Our methodological choices already overlap with the recommendations of these frameworks:
- Environmentally-extended input-output tables (EEMRIOs) are repeatedly referenced in the TNFD LEAP methodology (e.g., pages 44, 84, 89, 236, 237) as a way to model value chain impacts when company-specific data is unavailable.
- The TNFD biodiversity footprinting discussion paper explicitly refers to LC-Impact (pages 25, 42, 48).
Limitations and future work
- Exclusions: Current scope does not yet include various other impact groups and categories such as ecotoxicity, invasive species, ocean acidification, or marine biodiversity impacts.
- Averaging: Sector-based models assume uniform technologies and sectoral-level system boundaries, which may under- or over-estimate company/product-level results. The resulting values should therefore be interpreted as directional approximations rather than precise footprints. Where available, primary data or activity/process-level LCAs can provide more granular and company-specific results
- Inventory data: We will expand support for mass- and quantity-based inventory data (e.g., process-level inputs, flow quantities, and detailed impact intensity factors). Current results rely primarily on spend- or sector-based intensity factors, and future updates will improve granularity and precision.
- Chat agent limitations: Nios AI agents are non-deterministic and rely on probabilistic reasoning. While designed to act as specialists in matching and analyzing data, results may vary between runs and occasional mismatches can occur. Outputs are structured approximations meant to support — not replace — expert review. We are committed to giving users transparency into how the agents work, including their assumptions, source links, and uncertainties, and are continuously improving their accuracy, consistency, and explainability.