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Freight v2 Quickstart

Intermodal Freight Quickstart

A step-by-step guide on how to make your first estimation using Climatiq's Intermodal Freight API.

Prerequisites

  • Sign up for Climatiq and have your API key ready.

Introduction to Intermodal Freight

With the Intermodal Freight API, you can calculate the carbon footprint of shipping goods worldwide across multiple transport modes, including air, road, rail, and sea. For added accuracy, you can also include pre- and post-leg journeys.

The Intermodal Freight API can answer questions like:

  1. How much CO2e is emitted when shipping a container from Shanghai to Los Angeles by sea, including the pre- and post-legs?
  2. How much CO2e is emitted when transporting 10 tons of goods from Berlin to Madrid, combining both rail and truck?
  3. How much CO2e is emitted when flying 500 kg of refrigerated cargo from New York to London?

Your First Intermodal Freight Estimate

The Climatiq Intermodal Freight API determines the most likely route for your shipment by identifying key transit hubs, such as ports, terminals, or airports, then breaking the shipment into pre- and post-leg segments. The pre-leg is the journey before the main transport, and the post-leg is what comes after, ending at the delivery destination. This approach reflects how intermodal freight shipments typically move in the real world, ensuring accurate emissions estimates even when full route details aren’t available.

With just a few basic inputs such as origin, destination, shipment type, and weight, you’ll receive precise carbon estimates based on advanced routing logic, complete with source transparency and GLEC-compliant methodology.

API Request

For example, let’s estimate the emissions for transporting 500 kg of tea leaves by air freight from Birmingham airport in England to Berlin airport in Germany.

In order to make a call to the Intermodal Freight API, we need to specify the location details of the shipment, such as the shipment origin and destination. Due to the start and end locations being airports, it makes most sense for IATA codes to be used. These can be specified in the code line "location": { "iata": "your_iata_code_here" }.

It is also crucial to specify the transport_mode, such as air, road, rail, or sea, as well as the weight of the cargo.

The Intermodal Freight API uses the shipment details you input into the request to calculate the emissions and returns a detailed breakdown of the results.

Ready to get into the code?

Open the terminal on your computer, then copy and paste the code below to run it:

curl --request POST \
  --url https://api.climatiq.io/freight/v2/intermodal \
  --header "Authorization: Bearer $CLIMATIQ_API_KEY" \
  --data '{
    "route": [
      {
        "location": { "iata": "BHX" }
      },
      {
        "transport_mode": "air"
      },
      {
        "location": { "iata": "BER" }
      }
    ],
    "cargo": {
      "weight": 500,
      "weight_unit": "kg"
    }
  }'

Make sure to:

API Response

Based on your API request, the API returns a structured breakdown of the estimated carbon emissions for the intermodal freight shipment (snippet below).

At the top level, you’ll see the total emissions (co2e) for the shipment are 1257.0 kg CO₂e. This is the combined result of emissions from vehicle operation, energy provision, and handling at logistics hubs.

The distance covered (distance_km) is reported as 1032.0 km, representing the most likely travel route between Birmingham, England and Berlin, Germany.

The response includes the calculation methodology (co2e_calculation_method), here specified as ipcc_ar6_gwp100, which refers to the IPCC’s 100-year Global Warming Potential (GWP) method under Assessment Report 6, ensuring a standardized and transparent calculation approach.

Each segment of the shipment (locations and transport legs) has its corresponding emissions and a source_trail that provides full traceability of the emission factors used, clearly outlining the underlying source and dataset. This includes references to GLEC datasets and scientific sources such as the Int. J. Life Cycle Assess for radiative forcing.

There is also a notices section, which clearly states if any assumptions or adjustments were made that could impact the end result.

{
    "co2e": 1257.0,
    "hub_equipment_co2e": 0.6,
    "vehicle_operation_co2e": 1110.0,
    "vehicle_energy_provision_co2e": 146.6,
    "co2e_unit": "kg",
    "co2e_calculation_method": "ipcc_ar6_gwp100",
    "cargo_tonnes": 0.5,
    "distance_km": 1032.0,
    "route": [
        {
            "type": "location",
            "co2e": 0.3,
            "name": "Birmingham International Airport",
            "co2e_calculation_method": "ipcc_ar6_gwp100",
            "source_trail": [
                {
                    "name": "Freight logistics - transshipment - ambient",
                    "source": "GLEC",
                    "dataset": "GHG emission intensity values v3.0"
                }
            ]
        },
        {
            "type": "leg",
            "co2e": 1257.0,
            "transport_mode": "air",
            "distance_km": 1032.0,
            "vehicle_operation_co2e": 1110.0,
            "vehicle_energy_provision_co2e": 146.6,
            "co2e_calculation_method": "ipcc_ar6_gwp100",
            "source_trail": [
                {
                    "name": "Radiative Forcing uplift",
                    "source": "Int J Life Cycle Assess (2019)"
                },
                {
                    "name": "Air freight (<1500 km) - unknown - RP1678",
                    "source": "GLEC",
                    "dataset": "GHG emission intensity values v3.0"
                }
            ],
            "notices": [
                {
                    "message": "A Radiative Forcing Index of 2 applied to operational emissions of air leg."
                }
            ]
        },
        {
            "type": "location",
            "co2e": 0.3,
            "name": "Berlin Brandenburg Airport",
            "co2e_calculation_method": "ipcc_ar6_gwp100",
            "source_trail": [
                {
                    "name": "Freight logistics - transshipment - ambient",
                    "source": "GLEC",
                    "dataset": "GHG emission intensity values v3.0"
                }
            ]
        }
    ]
}

Adding pre- and post- legs

One of the key strengths of the Freight API is its intermodal intelligence—it models multi-leg journeys across diverse transport modes.

The Intermodal Freight API can handle different levels of location granularity. If we change up the previous example discussed by putting an exact location of the tea farm in Coventry, and changing our destination to a more high level location such as “Lisbon”, we can evaluate how the results might change.

We can specify that to get from the tea farm to the nearest airport, the tea will be transported by road by simply adding "transport_mode": "road" in the request. The API automatically identifies the nearest suitable transport hubs (such as airports) and inserts the necessary road segments to connect them. There’s no need to manually define intermediate legs—the API handles routing, hub detection, and mode transitions based on the input locations.

Here’s how the request would be updated to reflect the journey:

curl --request POST \
  --url https://api.climatiq.io/freight/v2/intermodal \
  --header "Authorization: Bearer $CLIMATIQ_API_KEY" \
  --data '{
  "route": [
    {
      "location": {
        "query": "Somers Rd, Meriden, Coventry CV7 7PL, United Kingdom"
      }
    },
    {
      "transport_mode": "road"
    },
    {
      "transport_mode": "air"
    },
    {
      "location": {
        "query": "Lisbon"
      }
    }
  ],
  "cargo": {
    "weight": 500,
    "weight_unit": "kg"
  }
}'

To illustrate how the API response handles intermodal freight, below is a simplified snippet showcasing the detailed multi-leg routing output.

The API identified Birmingham International Airport (BHX) as the closest departure point to the origin address in Coventry. As a result, the first leg of the journey was modeled as a road transport segment, using an articulated truck. This leg was estimated to produce 0.4548 kg CO₂e over a distance of 7.84 km, based on emissions data from GLEC.

For the second leg, Lisbon Airport (LIS) was selected as the nearest viable arrival point for the destination city, triggering an air transport leg from BHX to LIS. This segment accounted for 1,187 kg CO₂e over 1,623 km, incorporating both GLEC data and a radiative forcing uplift to account for the increased climate impact of high-altitude air travel.

{
    "co2e": 1189.0,
    "distance_km": 1631.0,
    "route": [
        {
            "type": "leg",
            "transport_mode": "road",
            "from": "CV7 7PL, Coventry, United Kingdom",
            "to": "Birmingham International Airport",
            "distance_km": 7.841,
            "co2e": 0.4548,
            "source": "GLEC",
            "name": "Articulated truck <34t - Average/mixed load - Diesel"
        },
        {
            "type": "leg",
            "transport_mode": "air",
            "from": "BHX",
            "to": "LIS",
            "distance_km": 1623.0,
            "co2e": 1187.0,
            "source": "GLEC + Radiative Forcing uplift"
        }
    ]
}

Conclusion

Whether by air, rail, road, or sea, Climatiq’s Intermodal Freight API delivers detailed carbon emission insights with just a few basic shipment details. Its flexible and customizable input parameters let you define the level of detail you need. Plus, full transparency around data sources and methodologies ensures your calculation results are accurate, reliable, and audit ready.

Next Steps

QuickstartUpload and Use Private Emission Factors