Spend management methodology

At Lune, we adhere to the highest standards for emissions calculation, ensuring full transparency and accuracy across every purchase and expenditure. Our methodology aligns with the Greenhouse Gas (GHG) Protocol, the globally recognised standard for spend-based emissions calculation.Our approach has been audited and ISO-certified (ISO 14064), delivering reliable results that support both corporate reporting and climate action goals.This document outlines how Lune applies these standards and methodologies to generate high-quality emissions estimates for you and your customers.

How to use this document

To get the most out of this methodology guide, we recommend a basic understanding of concepts such as emission factors and CO₂e.

Section 1: OverviewUnderstand the core parameters that underpin Lune’s emissions calculations, applicable across all expenditure and activity types.
Section 2: Expenditure typesLearn how emissions are calculated for specific expenditure types (for example, card transactions, and invoices).
Section 3: Activity typesLearn how emissions are calculated for specific activity types (for example, office use, and passenger transport).
Section 4: Emissions dataSee all the emission factors included and maintained in Lune's database.

If you have any questions, or require further clarification, please get in touch ([email protected])!

1. Overview

Lune calculates emissions by determining the best emission factor and multiplying by the monetary value or unit of activity.Lune references a range of emission factor databases, including EXIOBASE, EPA, BEIS, and ADEME, ecoInvent, CBAM. These databases are aligned with the GHG protocol, and reviewed on a quarterly basis to ensure they are up to date.The primary output is the tonnes of CO₂e emitted by the purchase or activity. Where available, individual greenhouse gas contributions, such as methane, nitrous oxide, and water vapor, are also included.

2. Expenditure types

Lune calculates spend-based emissions using the following equation:Emission factor × Monetary valueThe selected emission factor depends on the expenditure category and the currency used. If needed, Lune converts the source currency to match the emission factor’s currency using a daily updated exchange rate.Lune currently supports emissions calculations for the following expenditure types:

Transactions with or without a Merchant Category Code (MCC).
Line items on transaction documents (for example, invoices, purchase orders, and receipts).

The primary output is the CO₂e of each purchase. Where available, individual greenhouse gas contributions, such as methane, nitrous oxide, and water vapor, are also included.

2.1 Transactions

For transaction-based inputs, the transaction is mapped to an emission factor, which is then multiplied by the monetary value.When relevant to the purchase category, Lune applies dietary multipliers based on the purchaser’s reported dietary habits. This methodology is based on research by Peter Scarborough and colleagues published in 2014.Options for dietary habits:

Meat eater (low, medium, or high meat consumption)
Fist eater
Vegetarian
Vegan

2.1.1 Card transactions

For card transactions that include a Merchant Category Code (MCC), Lune maps the MCC to the most appropriate emission factor in our database. This mapping is maintained through a rigorous review process and is regularly audited and updated.

2.1.2 Transactions without an MCC

To determine the most appropriate emission factor for transactions without an MCC, Lune uses two pieces of customer-provided data:

Search term (required): a description of the item or service.
Category (optional): a broader classification that the search term belongs to, helping to contextualise and refine the selection of the emission factor.

Lune Category matching:Using this information, Lune applies a semantic search algorithm to identify the best-fitting Lune category from a predefined list.Each Lune category is mapped both to a broad emission factor for the entire category, and to a set of products and services within the category, each with their respective emission factors.Emission factor selection:With the customer input and the mapped Lune category, Lune determines the most accurate emission factor using a fallback approach, progressively applying different methods depending on the confidence level achieved at each step. The methods are described below, starting with a category constrained search, then moving on to a full database search, and finally falling back on the broad category emission factor if the first two searches do not reach the required confidence level.Note: The confidence score used in the fallback process is based on the cosine similarity between the customer provided search term and the matched emission factor.Methods for emission factor selection:

Category constrained search: The first search uses the customer provided search term to identify the most likely product or service (and associated emission factor) within the identified Lune category set.A result is selected if the vector distance is below 0.16. If a match is found, the corresponding emission factor is returned.
Full database search: If no sufficiently confident match is found within the category, Lune broadens the search across the entire emission factor database, ignoring the category constraint.Due to the larger search space, a higher confidence threshold is applied (vector distance must be below 0.1). If a match is found here, the corresponding emission factor is returned.
Fallback to broad category emission factor: If no high-confidence match is found through either search, Lune falls back to the broad emission factor associated with the Lune category.This ensures that every expenditure still has a reasonable and reliable emission estimate, without introducing false positives.

Models used:

gte-small for semantic search

If enabled, the final emission factor may be impacted by regional fallbacks. See below for an explanation of this strategy.

2.2 Documents

Lune can calculate emissions from documents (for example, invoices, receipts) at the line item level. When line item extraction is not possible, we fallback to a document-level calculation.Process:

Extract document data: total amount spent, country, currency, and category (if available).
Extract line items: description and category of goods or services rendered, amount purchased, monetary value, currency, and country.
Validate totals: If the sum of the line items differs from the total by more than ±10%, we use a document-level calculation. This accounts for potential refunds, currency conversions, and rounding errors.
Run semantic matching: Each item is matched to an emission factor and multiplied by its monetary value to determine the emissions. The final emission factor may be impacted by regional fallbacks, as explained below.

After the calculation, an estimate of tonnes CO₂e is returned. If line items cannot be extracted or validation fails, the estimate returns an emission estimate for the document as a whole, rather than by line item.Models used:

llama-3.1-8b-instant for primary text extraction.
llama-3.3-70b-versatile as fallback.
gte-small for semantic search.

Regional fallback

To ensure the highest accuracy in emission estimates, Lune uses a database of country-specific emission factors. When an EF is unavailable for the specific item-country combination, we apply a regional fallback strategy. This is an opt-in startegy, only applied if specifically requested. This approach selects a comparable country to stand in for the missing EF.Comparable countries are determined using data from Climate Watch and are curated by Lune for each emissions category (such as electricity, heat, forestry, and land use). As a result, the emission factor returned may correspond to a country not explicitly listed on the invoice or transaction. The set of comparable countries amongst each emission factor is rigorously vetted to ensure its validity as a proxy. This is based on a cross-validation of similar emission factors across countries, and average national GHG emissions across sectors.For example, for manufacturing and construction, the following countries are grouped together: Switzerland, Spain, France, United Kingdom, Luxembourg, Norway, and Sweden. This means that an expense originated from Spain may result in an emission factor from Switzerland or France.

3. Activity types

Lune calculates activity-based emissions using the following equation:Emission factor × Activity specific measurementExamples of activity specific measurement include:

Person per kilometre for air travel.
Number of items for car parts or electrical equipment.
Rooms per night for a hotel stay.

3.1 Passenger transport

Lune calculates emissions for passenger transport by car, air, or rail, using BEIS emission factors and mode specific methodologies.The emission is calculated as:Emission factor × Distance travelledDistance can be provided directly or inferred using source and destination data.

3.1.1 Car

The distance travelled by car, if not explicitly provided, is calculated using Mapbox. Additional details are used to determine the most appropriate emission factor for the method such as the vehicle type. Diesel is used as the fuel type.Vehicle type options:

Mini
Medium sized
Executive
Luxury car
Sports car

3.1.2 Air

Different emission factors are applied based on flight distance, as shorter flights emit more on average per kilometre.Flight distances are grouped into three bands:

Short-haul: less than 1,500 km.
Medium-haul: 1,500 km to less than 2,500 km.
Long-haul: greater than 2,500 km.

Linear interpolation is used to calculate the final emission factor based on the specific distance travelled. If the customer does not provide a distance, Lune automatically calculates it using the Great Circle Distance method.Additional inputs, such as cabin class and number of passengers, are also used to further refine the emission factor.

3.1.3 Rail

If distance is not provided, Lune calculates it using the Google Maps routing engine. Emission factors are based on the UK BEIS industry averages for passenger rail.

3.2 Other activities

Lune maintains a set of activity-based emission factors that are not directly tied to monetary spend or distance. These cover scenarios such as:

Hourly office space use
Laptop operation
Use of other equipment or services

Customers can map their activity data to these factors for basic emissions estimates.

4. Emissions data

Lune collects and maintains over 100,000 emission factors. These emission factors are sourced from only the most-up-to-date databases created by governments, leading academic research, industry experts, and propriety models.

Source	Description	Geography	License
EXIOBASE	Spend-based category-level emission factors published by the Exiobase consortium.	Global coverage; country-specific for 44 countries	Creative Commons Attribution-ShareAlike 4.0 International License.
BEIS	Emission factors published yearly by the United Kingdom’s Department of Business, Energy, and Industrial Strategy.	United Kingdom	Open Government Licence v3.0
EPA	Emission factors published by the United States’ Environmental Protection Agency.	United States of America	US Public Domain License
ecoInvent	Not-for-profit organisation dedicated to promoting and supporting the availability of environmental data worldwide.	Global: region granularity	Commercial Lune license
GLEC	The global method for calculation and reporting of logistics emissions. In alignment with GHG Protocol, Global Green Freight Action Plan; CDP reporting.	Global	N/A
ADEME	ADEME (Agence de la transition écologique). These emission factors are used to estimate greenhouse gas emissions associated with various activities, such as energy consumption and transportation.	France, Global	Open source
IDEMAT	IDEMAT (short for Industrial Design & Engineering MATerials database) is a compilation of LCI data of the Sustainable Impact Metrics Foundation, SIMF, a non-profit spinn-off of the Delft University of Technology. It is designed for the need of designers, engineers and architects in the manufacturing and building industry.	Global	CC BY-NC
CBAM	Greenhouse gas emission intensities of the steel, fertilisers, aluminium and cement industries in the EU and its main trading partners	Global	CC BY-NC-SA 4.0
Lune	Electric vehicle, truck, and train emission factors. Based on GLEC vehicle consumption data, local electricity grid mix from WorldInData, and electricity emission factors from RTE France.	Global	N/A