Our carbon footprint methodology

What is the Climate score?

The climate score we provide is our way of estimating the greenhouse gas (GHG) emissions linked to everyday transactions. It translates spending data into an easy-to-understand carbon footprint, expressed in CO₂e (carbon dioxide equivalent). To make it relatable, we also show equivalents – like kilometres driven or hours of phone charging – so people can picture the impact more clearly.

How do you calculate a footprint from a transaction?

Each purchase is categorised using Merchant Category Codes (MCCs). These codes tell us what type of goods or services were bought (for example, groceries or travel). We then map the MCCs to global emissions datasets like EXIOBASE, which contain detailed information on the climate impact of different industries and their supply chains. By combining this with the purchase value and country, we estimate the emissions for that transaction.

Is it accurate for every product?

Not yet. Today’s payment data doesn’t provide item-level detail (e.g. whether your grocery basket contained beef or broccoli). That’s why the Climate Score is an estimate at the sector level, not a precise measure of a specific product.

What makes your approach trustworthy?

We’ve built the Climate score in partnership with Nature Positive, an organisation that specialises in environmental data and impact measurement. Together, we’ve designed a methodology based on internationally recognised greenhouse gas accounting standards and trusted datasets like EXIOBASE. Importantly, it reflects not just direct emissions but also the wider supply chain, since most climate impact occurs during production and transport rather than at checkout.

How do you handle limitations?

We’re upfront that the Climate score is an indicator, not a perfect measurement. It’s designed to raise awareness, encourage sustainable choices, and connect spending to positive climate action through vetted environmental projects. As data and science evolve, we continuously update and improve the model to keep it relevant, reliable, and impactful.