Frequently Asked Questions

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 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 can be connected to records of expenditure to derive this component of a carbon footprint.

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 ANZSIC codes only, we have also created a ‘Scope_3_Default’, which is where we have used our expert judgement on the likely place of purchase. 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 the Scope_3_Default there is 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.

The ‘life cycle” scope of the Scope 3 spend-based emissions factors we provide is upstream “cradle to shelf”. Downstream of the “shelf” are other emissions involved in the use of a product (importantly, this applies to purchased transport fuels) and emissions associated with end-of-life (waste).

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.