Navigating Our Database and Supported Data Sources

We provide the largest database  of scientifically vetted emission factors available today, with more than 40 sources, 80 datasets, and 300 regions. All our data and our ingestion methodology is compliant with GHG Protocol and other national (and international) standards and is third-party audited accordingly.

This guide explains the types of data sources available through our platform, how to choose between them, and best practices for building reliable carbon footprints. For full details on how we vet, normalize, and quality-assure our data, see the Methodology Hub .

Types of Data Sources

Our database integrates emission factors from a range of data providers. They fall into several broad categories, each suited to different use cases.

Government sources are published by national agencies as part of their regulatory frameworks. They are free, regularly updated, and designed for domestic GHG reporting. Examples include BEIS/DEFRA (UK), EPA (US), ADEME (France), UBA (Germany), DISER (Australia), or the Government of Canada.

MRIO / spend-based sources derive emission factors from large-scale economic models (Environmentally Extended Input-Output tables) that link monetary flows to environmental impacts across sectors and regions. EXIOBASE is the most widely used, covering 44 countries and 200+ product sectors. We also include CEDA (Watershed), OpenIO-Canada, and EPA/USEEIO, now called Cornerstone. These are essential for scope 3.1 (purchased goods and services) and scope 3.2 (capital goods) when activity data is unavailable.

LCA databases provide process and activity-level emission factors across the full lifecycle of products and services, from raw material extraction through manufacturing and end-of-life. ecoinvent is the most prominent, with thousands of activities covered globally. Other LCA sources include AusLCI, Agribalyse (food), OEKOBAUDAT (construction), and Plastics Europe. These are the go-to choice for product carbon footprints (PCFs) and detailed lifecycle assessments.

Industry-specific sources are tailored to a particular sector or compliance domain. Examples include GLEC (freight and logistics), CCF (cloud computing), PCAF (financed emissions), CBAM (EU-regulated materials), Greenview (hospitality), and AIB (European electricity residual mix). They offer the highest relevance within their domain but are narrow in scope.

Academic and institutional sources provide reference factors, methodological guidance, or observation-based data. Examples include the IPCC, GHG Protocol, Climate TRACE, and UNEP. They often serve as foundational references alongside more specific sources.

A complete list of all supported sources is available in the Data Explorer .

Core vs. Premium Data Sources

We distinguish between core and premium (add-on) data sources.

Core data sources are included with all Climatiq plans. With over 40 sources, including government agencies, sector-specific datasets, and institutional sources, they provide broad coverage for corporate carbon footprinting (CCF), regulatory compliance, and screening-level scope 3 assessments.

Premium sources require a separate license and provide deeper granularity for specialized use cases such as product carbon footprints, detailed LCA studies, and material-level calculations.

Premium data source overview

Earlier versions of EXIOBASE (v3.8.2 and below) remain available as standard data. For ecoinvent, IEA, Carbon Minds, and sustamize, there is no free equivalent at the same level of granularity; standard sources cover many of the same activities but at a less detailed level.

Use cases by study type

Premium data sources are accessible through the Data Explorer, Climatiq for Excel / Google Sheets, and our API, as well as for PCF calculations. For licensing and pricing information, visit the data licensing page .

Activity-Based vs. Spend-Based Emission Factors

We provide both activity-based and spend-based emission factors. Understanding the difference is fundamental to choosing the right approach.

Activity-based factors link physical measurements (kWh of electricity, km traveled, kg of material) to emissions. They are more accurate because they reflect actual activity. Most sources in our database are activity-based, including BEIS/DEFRA, EPA, ecoinvent, GLEC, and IEA.

Spend-based factors link monetary expenditure to emissions using Environmentally Extended Input-Output (EEIO) models. They are derived from sources like EXIOBASE, CEDA, and EPA/USEEIO. Spend-based factors are widely used for scope 3.1 (purchased goods and services) because nearly every organization already has expenditure data, even when detailed activity data is not available.

When to use which

Example: office furniture procurement (€50,000)

With a spend-based approach, you multiply the total spend by an EXIOBASE “Manufacture of furniture” emission factor. This takes minutes, but the result reflects industry averages. It cannot distinguish between a supplier using recycled materials and one using virgin wood.

With an activity-based approach, you collect data on the actual materials and quantities (e.g. 200 kg steel, 500 kg particleboard, 50 kg fabric) and apply emission factors from ecoinvent or another data source to each. This is more accurate and reveals material-level hotspots, but requires detailed procurement data.

In practice, most organizations screen with spend-based factors first, identify the highest-impact categories, and then switch to activity-based calculations where precision matters.

Key things to watch out for

• Do not use spend-based factors for scope 1 or 2. If you only have expenditure data for fuel or electricity, convert it to physical units (liters, kWh) using average prices before applying activity-based factors.
• Check LCA boundaries on activity-based factors. Some cover cradle-to-gate only; others include use-phase emissions. Make sure what is included matches what you need.
• Spend ≠ emissions. A price discount reduces your calculated footprint without any real-world change. Be aware of this limitation when interpreting spend-based results.
• Document transitions. When moving from spend-based to activity-based for a category, recalculate prior years if possible to maintain comparability.

For a deeper comparison, see Spend-Based vs. Activity-Based  blog.

Best Practices for Selecting Data Sources

Mixing databases

Combining multiple sources in one calculation is common and acceptable, as long as each source is applied to the geography and activity it was designed for. For example, using BEIS/DEFRA for UK energy alongside EPA for US operations and EXIOBASE for global scope 3.1 is a sound approach.

Do not selectively pick the lowest factor for each item across different sources. Searching across multiple databases and selecting the lowest emission factor for each material is not a valid approach. For example, using a certain source for steel because it gives the lowest value and another source for aluminum because it gives a lower value there produces a result that is not methodologically defensible. Choose your source based on geographic fit, methodological relevance, and data quality, not on which factor produces the smallest number.

Watch for double-counting: if you calculate fuel or electricity with activity-based factors under scope 1/2, exclude those costs from your spend-based scope 3.1 estimate.

Always document your source choices per scope category, and maintain consistency across reporting periods.

Avoid mixing sources for the same activity in the same calculation, and never mix different EEIO models (EXIOBASE, CEDA, USEEIO) within the same scope 3.1 assessment. Their methodologies, sector classifications, and pricing conventions differ, which produces inconsistent results. Pick one spend-based source and apply it consistently.

For example, USEEIO (Cornerstone) and CEDA are not necessarily the same dataset. Up until last year, both datasets were built and published by two different entities, CEDA came through VitalMetrics and the dataset that’s now called Cornerstone used to be the USEEIO dataset from the US EPA.

• CEDA is a multi region input output model, while USEEIO / Cornerstone is a Single region input output model
  • Because Cornerstone is a single-region model, it has historically struggled to accurately measure the carbon footprint of components the US imports to build its domestic products.
  • CEDA, being a multi-regional model, connects the US economy to 147 other countries in a massive mathematical feedback loop. This means CEDA’s US factors inherently carry more precise global supply chain data within them, whereas Cornerstone has historically had to use broader estimates or “rest of world” averages for imports.
• CEDA and USEEIO evolved on parallel tracks. While both use core data from the US Bureau of Economic Analysis (BEA) and the EPA, their underlying algorithms for cleaning that data, allocating environmental flows, and mapping sectors developed independently, which results in different values for the same region and industries

Right now, on the official Cornerstone roadmap, one of their primary technical milestones is explicitly listed as: “Harmonizing the U.S. modeling approach between USEEIO and CEDA.” So they are actively working to swap out the underlying architecture so that the US node inside CEDA and the standalone USEEIO model share the exact same mathematical foundation. Until that unified model drops (slated for 2026), you will still see slight variations between the two datasets.

CCF vs. PCF

For a CCF, start with standard sources. Government factors cover scope 1 and 2, and EXIOBASE or CEDA handles spend-based scope 3. Premium data becomes important when you need to drill down into specific product lines or materials.

For a PCF, process-level and material-level data is essential. ecoinvent is the baseline for most PCFs; add Carbon Minds for chemicals or sustamize for metals and composites. Our PCF Studio  and PCF API  use these sources automatically when available in your plan.

Comparing databases

Not all emission factor sources are built the same way. Different databases use different methodologies, cover different regions, and are updated at different frequencies. Before selecting a source for a specific activity, it is worth checking whether it matches your geographic scope, the type of data you have available, and the reporting framework you need to comply with. When evaluating which source to use for a given activity, consider these dimensions:

Quick overview of key databases to support these calculations

Not all emission factor sources are built the same way. Different databases use different methodologies, cover different regions, and are updated at different frequencies. Before selecting a source for a specific activity, it is worth checking whether it matches your geographic scope, the type of data you have available, and the reporting framework you need to comply with. Below is a non-exhaustive overview of the key databases supported by Climatiq.

If you are unsure which source fits your use case, our science and support teams are here to help. Contact us  and we are happy to support.

Supporting resources