Resources.

An Emission Factor is a multiplier that converts a unit of economic activity into an environmental impact. Each Emission Factor is specific to a business activity, country, year and the impact being measured. For example, if the business activity is 100 USD of printing services in Singapore in 2025, the corresponding Emission Factor is 0.633 kg CO2e per USD. To determine the Scope 3 emissions of that business activity, simply multiply 100 USD by 0.633 kg CO2e per USD to get 63.3 kg CO2e.

Scope 1 refers to direct emissions due to activities controlled by an entity like a company.
Scope 2 emissions are indirect and occur when an organisation consumes secondary energy like electricity or heat, that is generated outside of its boundaries
Scope 3 emissions are all indirect emissions (not included in Scope 2) that occur in the value chain of the reporting company, including both upstream and downstream.
This includes purchased goods and services. Scope 3 spend-based emission factors provided by FootprintLab include all the upstream emissions in the supply chain of a good or service and can be connected to records of expenditure to derive the Scope 3.1 Purchased Goods and Services component of a carbon inventory.

The GHG Protocol refers to five dimensions of 'Data Pedigree':

1. geographic match between the Emission Factor and the business activity.

2. temporal match between the Emission Factor and the business activity. As a rule of thumb, there should be less than 3 years between the primary data year of the Emission Factor, and the business activity.

3. completeness. This refers to how much of the supply chain is included in the calculations and is often a key trade-off between bottom up methods and top down methods.

4. technical fit between the product, sector or service the Emission Factor covers.

5. methodology quality, including whether the primary data and calculation methods are documented.

For each Emission Factor used, it's a good idea to document the 5 dimensions behind it for smoother audits.

The GHG Protocol refers to a Data Quality 'Hierarchy':

1. Supplier specific activity-based data.

2. Generic activity-based data

3. Spend-based emission factors.

Where possible - especially for Scope 1 and 2 calculations - activity-based emission factors should be used. However, there are other considerations:

• Activity-based emission factors are not widely available for all products and services. Both the 'data burden' and cost of data collection is therefore high, particularly for supplier-specific data.

• Activity-based emission factors depend on the availability of activity business data, e.g. knowing the number of hotel-nights or kg of paper used, which may not be readily available to sustainability accounting teams. This is particularly the case for services, such as plumbing or accounting services, where the activity unit is difficult to determine.

• Activity-based emission factors may have higher data pedigree when it comes to product fit, but suffer in other data pedigree dimensions. For example, you may find activity-based emission factors from a different country, or that are more than 5 years old.

A good approach is to use spend-based factors as a foundation, and focus efforts to obtain activity-based emission factors for the business activities with a higher estimated carbon footprint (ie, improve data hierarchy and data pedigree where materiality is highest).

Available for all IELab Emission Factors using Geometric Square Standard Deviation.

Primary supplier data is the long-term direction. The GHG Protocol's 2026 revision moves the standard that way, and FootprintLab works alongside companies building toward it. When supplier data is available, spend-based Emission Factors are used to benchmark the data and contextualise it according to industry and country trends. For most value chains, primary data won't be available for most suppliers for some time. While that gap exists, spend-based Emission Factors with good data pedigree are what you can actually defend.

A Consumer Price Index (CPI) adjusts Emission Factors to the year of the business year. There is generally a 2 to 3 year time lag between the primary data behind an Emission Factor, and the business activity you apply it to.

CPIs are widely available from national statistics offices and international institutions. They can be applied to Emission Factors in a highly granular way by adjusting the CPI according to the location, date and sector of the business activity. On the other end of the spectrum, users might select a single CPI for a country and year, across all sectors uniformly.

The workflow is:
Emission Factor ( impact per $(primary data year) )
÷ CPI ( $(business activity year) / $(primary data year)
= Emission Factor ( impact per $(business activity year)

One of things we are aware of is that there are many possible margins added to the price paid for goods and services: freight, wholesale markup, retail mark up, taxes and insurance (in fact, there are something like 18 different kinds of markup than can be in play). This affects the price paid in different circumstances.

However, FootprintLab usually provide emissions factors in only two formats: basic price (BP), which excludes any margins, and purchaser price (PP) that includes all margins. We don't make emissions factors for every possible mark-up combination but, generally, the closer to the 'farm gate' or the 'factory door' that the purchase was made, the more appropriate it is to use basic price emissions factors. The closer the purchase is made to an actual store front, the more appropriate it is to use purchaser price emissions factors.

For example, it's possible you could buy "Iron ore" from a shop but we think it's much more likely to be a basic price emissions factor direct from a mine. Similarly, most meals and entertainment purchases would include all the markups, so here it is more appropriate to use the purchaser price emissions factor.

FootprintLab has an exclusive commercialization agreement with IELab. We can ensure the latest data because of IELab's systematic handling of the primary data it compiles from national statistical offices and the IEA, OECD, CommTrade, FAO, EDGAR, and the ILO. Although primary sources are updated at different intervals, IELab systematically collates whatever are the latest data and reconciles these into a single coherent database annually.

The data compilation and updating processes are described in these peer-reviewed academic publications:

Economic Systems Research, (2017) 29, 158-186. doi.org/10.1080/09535314.2017.1301887

Economic Systems Research, (2017) 29(2), 275-295. https://doi.org/10.1080/09535314.2017.1315331

Science of The Total Environment, (2014) v 485-486, 241-251, https://doi.org/10.1016/j.scitotenv.2014.03.062.

Outside of IELab we source and provide data from publicly available sources or under license.

FootprintLab takes credibility and transparency of data sources seriously. If we didn't make it or know exactly where it comes from, we don't serve it. The Australian and international IELab data that FootprintLab supplies has been selected by the United Nations (UN) to monitor progress on Sustainable Development Goals (SDG 8 and 12). Data has been used elsewhere by the UNEP's Sustainable Consumption and Production project, and are among the core data that already supports Australia's Climate Active accreditation program.

We are aware of a lot more data out there than we have loaded into our database and, generally, if you have a request we can find it.

So, for example, we know that there's freely available Australian emissions intensity data on a bunch of waste flows and fuel/energy uses here. You could get that yourself but sometimes the data is not consistently presented, has technical nuances or is embedded in text in PDFs.

We can easily extract this and ingest it into our database, verify the source, add notes about the age of the data, scope, and appropriate use, and then you could access that data and all your other data through the one service: our API.

We keep track of where our data comes from, and also categorize that by UN Global regions. For example, if you can't find data for Australia, first look at what else we might have for "Australia and New Zealand" as the relevant global region containing Australia.

Be aware that your use of data from another country or region, applied to a different jurisdiction, changes the 'geographical correlation' in the statement of data pedigree – see the table of how we think about this here.

ISAPC is a way of categorising Australian product classes of goods and services. They relate to the fundamental "Input Output Product Class" used by the Australian Bureau of Statistics. They were developed in 2009 by IELab who have used that classification to supply emission factors to the Australian Government's Climate Active scheme and others for several years. More information on the history, changes and definition of ISPAC emission factors can be found here – see the appendix of the documentation for a dictionary on ISAPC.

Data is hosted in Australia. Encrypted in transit and at rest with SSL/TLS. ISO 27001 certification is in progress. GDPR-aligned for European customers.

Both. FootprintLab provides a REST API that returns verified emission factors and spend-based carbon data as JSON, secured with OAuth2 and hosted in Australia. It is available from the Core plan upward. For small datasets or in special circumstances, excel or csv formats are available.