Overview
Altruistiq's Calculation Engine finds the best Environmental Factor (EF) for each activity data point by matching across three dimensions:
Geography - Where the activity happens
Time - When the activity occurs
Technology - What type of activity it is
Geographic Matching
Geographic matching compares your activity data location with available environmental factor locations to find the most accurate match.
Finding available geographies
Your activity data links to a specific geography - this might be your facility location for utilities data, or your supplier's location for purchases. Environmental factors also apply to specific geographies, from individual countries to global averages.
The Calculation Engine filters environmental factors to show only those valid for your activity data location. For example, a Global Average environmental factor works for activity data from any location.
Example: Purchase data for potatoes from a French supplier matches with potato environmental factors from France, Western Europe, EU-27, and Global sources.
Geography selection hierarchy
From available geographies, we select the most specific match for highest accuracy:
Preference | Geography Type | Definition | Example |
1 | Sub-Country | Specific to a region within a country | Ohio, a US state |
2 | Country | Relevant for a given country | United States |
3 | Region | For a region larger than a country | North America |
4 | Global | Applicable anywhere globally | World |
Special case: When multiple regions match (like Caribbean and North America), we choose the smaller region with fewer countries.
Time Period Matching
Time period matching ensures environmental factors align with when your activity data was recorded.
Checking valid time periods
Each environmental factor has a validity period - the timeframe it's designed to cover. This might match a reporting year or the data collection period for the underlying research.
The Calculation Engine excludes environmental factors that start after your activity date. This prevents using newer factors for older activity data, which could underestimate emissions as factors typically improve over time due to decarbonisation efforts.
Example: For 2020 baseline activity data, we won't use 2024 environmental factors as they would likely underestimate your 2020 emissions.
Prioritising between valid factors
When multiple environmental factors match on geography and technology with valid time periods, we select the closest matching validity period. If there's no exact match, we choose the most recent available version.
Example: For 2024 electricity purchases without a 2024 environmental factor available, we'd select the most recent factor from 2023.
Technology Matching
Technology matching measures how well an environmental factor represents your specific activity data after geographic and time filtering.
How technology matching works
Altruistiq categorises every environmental factor using a structured system that:
Defines characteristics at the calculation method level
Matches contextual data fields from your activity data (both required and optional)
Uses hierarchical "tags" to classify environmental factors
Example: When adding new electric van environmental factors from BEIS and HBEFA for 2024, we characterise them with standard fields:
Vehicle type: Van (Class III)
Fuel type: Battery electric vehicle
Traffic scenario: Urban
Vehicle load: Left blank (undefined)
Data enrichment process
When your activity data enters the calculation engine:
Exact matches: Environmental factor applies automatically
Non-exact matches: System enriches your data to match our classification system, with human review for lower-confidence matches
Example: "Grapefruit juice" purchase data gets enriched to match "Apple juice" environmental factors with ISIC Class 1030 (fruit and vegetable processing). This requires human review due to lower confidence, then calculation re-runs.
This approach lets you analyse data two ways:
Using your original categories and descriptions
Using enriched categorisation based on environmental factor selection
This maintains complete data lineage for audit purposes while expanding analysis options.
Environmental Factor Source Priority
After geographic and time filtering, when multiple environmental factor sources match your technology requirements, we apply this hierarchy:
Priority | Source Type |
Highest | Primary Product Carbon Footprint data from supply chain engagement |
Medium | Custom environmental factor sources you've added |
Standard | Altruistiq's standard environmental factor sources |
This prioritisation ensures your most specific and relevant environmental data takes precedence in calculations.