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  content="openccf, carbon footprint api, emission clustering api, scope 1 2 3 emissions engine, rest api clustering, carbon accounting automation, carbonsutra api"
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  content="Data Interoperatbility - Fast Emission Clustering | CarbonSutra"
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  content="Group, log, and retrieve carbon emission calculations using custom cluster labels without storing data locally."
/>
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</Head>

# Rapid Clustering

Developers can eliminate the need to store emission calculation results by assigning a self-declared tag to it. These tags are called cluster names and they can be added to any calculation within CarbonSutra. The endpoint: /cluster-data accepts the name of this tag and gives the output of all calculations in granular format in JSON.

For example, say a company calculates emissions for business travel (air and land), electricity consumption, hotel stays and their vehicle fleet, once every quarter. Using this self-defined label as an additional input parameter, these calculations can be clustered into sets and retrieved.

## Example of clustering

Say a user has assigned a cluster name of "KrugerBrent-Paints-Q1" with each of the 3 emission calculations. Entering the same tag in /cluster-data endpoint will result in data as:

```
{
  "data": [
    {
      "id": "949c6f9e-d675-7d08-ad3c-9229c7b83ea6",
      "type": "estimate-travel-flight",
      "cluster_name": "KrugerBrent-Paints-Q1",
      "attributes": {
        "type": "estimate-travel-flight",
        "add_rf": "Y",
        "co2e_gm": 8560440,
        "co2e_kg": 8560.44,
        "co2e_lb": 18872.52,
        "co2e_mt": 8.56,
        "airport_to": "Ninoy Aquino International Airport",
        "round_trip": "Y",
        "explanation": "This emission profile is computed using the UK Government's 2026 conversion factors for greenhouse gas (GHG) reporting. The calculation is for a single passenger travelling in Business class between Los Angeles International Airport(LAX) and Ninoy Aquino International Airport(MNL). The distance between these airports is estimated at 11,740 km, utilizing the Haversine equation for Great-Circle Distance (GCD). Since it is a round trip, the distance is doubled to 23,480 km. Radiative forcing uplift is included and indirect/WTT factors are added, as per inputs. Based on these operational parameters, the total footprint evaluates to 8,560,440 grams of CO2e. Standardized metric conversions render this as 8560.44 kg (rounded to two decimal places) or 8.56 metric tons. Scaled against imperial baselines using the conversion factor of 2.20462 (source: GHG conversion factors, 2026), the final metric is expressed as 18872.52 lbs.",
        "include_wtt": "Y",
        "airport_from": "Los Angeles International Airport",
        "flight_class": "Business",
        "iata_airport_to": "MNL",
        "iata_airport_from": "LAX",
        "number_of_passengers": 1
      },
      "estimated_at": "2026-09-24 09:41:48"
    },
    {
      "id": "8715bcc5-a4df-0bb9-c621-24fc749436eb",
      "type": "estimate-hotel-stay",
      "cluster_name": "KrugerBrent-Paints-Q1",
      "attributes": {
        "type": "estimate-hotel-stay",
        "co2e_gm": 66950,
        "co2e_kg": 66.95,
        "co2e_lb": 147.6,
        "co2e_mt": 0.07,
        "country": "United Kingdom",
        "city_name": "",
        "explanation": "CarbonSutra uses the Cornell Hotel Sustainability Benchmarking Index 2026 (CHSB2026) for this calculation, specifically leveraging the mean value from the CHSB validity test for Measure 1 - HCMI Room Night Emission (Carbon Category). For this itinerary of one room and 5 nights, country-level emission factors for United Kingdom have been applied. This methodology relies on the Expedia Star Rating classification system. Where regional data gaps exist, proxy assignments are made according to the transparent governance rules published on carbonsutra.com. For the hotel rating of four stars, an emission factor of 13.39 kgCO2e per room night is used. This footprint evaluates to 66,950 grams of CO2e. Standardized unit conversions render this as 66.95 kg (rounded to two decimal places) or 0.07 metric tons. Scaled against imperial baselines using the conversion factor of 2.20462 (source: GHG conversion factors, 2026), the final metric is expressed as 147.6 lbs.",
        "hotel_rating": 4,
        "number_of_rooms": 1,
        "number_of_nights": 5
      },
      "estimated_at": "2026-09-25 01:57:32"
    },
    {
      "id": "53594086-db27-4cc3-ab2a-8f9e55e63a08",
      "type": "estimate-vehicle-usage",
      "cluster_name": "KrugerBrent-Paints-Q1",
      "attributes": {
        "type": "estimate-vehicle-usage",
        "co2e_gm": 456544,
        "co2e_kg": 456.54,
        "co2e_lb": 1006.5,
        "co2e_mt": 0.46,
        "fuel_type": "Unknown",
        "explanation": "CarbonSutra computed this emission profile using the UK Government's 2026 conversion factors for greenhouse gas (GHG) reporting. This is large size car, the fuel type of which is not known. For a transit distance of 1,600 kilometers and with indirect/WTT factors added, the total footprint evaluates to 456,544 grams of CO2e. Standardized metric conversions render this as 456.54 kg (rounded to two decimal places) or 0.46 metric tons. Scaled against imperial baselines using the conversion factor of 2.20462 (source: GHG conversion factors, 2026), the final metric is expressed as 1006.5 lbs.",
        "include_wtt": "Y",
        "vehicle_type": "Car-Size-Large",
        "distance_unit": "km",
        "distance_value": 1600
      },
      "estimated_at": "2026-09-25 02:11:40"
    }
  ],
  "success": true,
  "status": 200
}
```

## Retrieval Logic

The JSON data for cluster name is retrieved for the request which has the same API key or Bearer Token as the ones which performed the calculation. The same API is being used for the interoperability format of OpenCCF.