Auditability

At Climatiq, we strive to not be a black box, ensuring that your calculations are both accurate and credible. Our goal is for our calculations to be transparent enough to help you and your end-users trust the results you receive.

For many requests, like those to the Basic Estimate Endpoint, providing this transparency is simple. These calculations involve direct use of one specific emission factor, with the provided activity data, and so Climatiq simply returns the used emission factor.

The Estimation Model returned by many Climatiq endpoints, contain details about the used emission factor, including details about constituent gases, and additional indicators such as biogenic emissions. This gives you a solid overview of the emission factor used, and the returned activity_data parameter allows you to see exactly what input data the emission factor was used with, and replicate the calculation if you need to double-check.

Source Trail

For some of Climatiq’s more complex calculation, a simple list of emission factors is insufficient. In these instances, Climatiq’s “Source Trail” feature shows you the relevant emission factors and data points considered during the computation process. While the source trail doesn’t enable you to replicate the calculations, it does provide a window into the data involved, enhancing your confidence in the results.

Source Data Point

Endpoints that support Source Trail will contain one or more lists of source data points.

Source data points detail the individual data points utilized in estimations. This information signals the credibility, recency, and relevance of the data to the users. We don’t include data for universal constants like conversion factors between pints and liters, and we don’t include the numeric value of the data points.

Attribute	Type	Description
name	`string` or `null`	The name describing the data point.
source	`string` or `null`	The source of the data point, e.g., a scientific journal, research institute, or government agency.
source_dataset	`string` or `null`	If the source publishes multiple datasets, this field clarififies which dataset the field comes from. This could be a dataset published by a governmental body or a research institute, or a citation of a scientific paper.
year_most_applicable	`string` or `null`	The years that the source judged their data to be most valid for.
region	`string` or `null`	The region code where the data point is most relevant. Note that data points may be applied outside their primary region.
region_name	`string` or `null`	The full name of the `region` field.
data_category	`string` or `null`	The category of the data point. Currently, this is either `null` or `"emission_factor"`. Future updates may introduce more categories.
id	`string` or `null`	The UUID of the emission factor if `data_category` is `"emission factor"`, otherwise `null`. Future updates may introduce other IDs for other categories.

Consider an example of the Business Travel Feature used with rail travel, with a partly electrified rail journey. To make that calculation Climatiq takes into account:

How many kg diesel or kWh is used per passenger-km for diesel or electrified rail.
How much of the railroad network in the traveled country is electrified
The emission factors for diesel or electricity generation in the traveled country.

That means the endpoint will return a list of data points under source_trail that looks like this.


{
    // ... other fields
    "source_trail": [
        {
            "data_category": null,
            "name": "Average kg diesel used per passenger-km for diesel rail",
            "region": "EU",
            "region_name": "European Union",
            "source": "Fraunhofer Institute",
            "source_dataset": "Methodology for GHG Efficiency of Transport Modes",
            "year": "2020"
        },
        {
            "data_category": null,
            "name": "Average kilowatt-hours used per passenger-km for electric rail",
            "region": "EU",
            "region_name": "European Union",
            "source": "Fraunhofer Institute",
            "source_dataset": "Methodology for GHG Efficiency of Transport Modes",
            "year": "2020"
        },
        {
            "data_category": null,
            "name": "Average railroad electrification for country",
            "region": "DK",
            "region_name": "Denmark",
            "source": "EUROSTAT",
            "source_dataset": null,
            "year": "2021"
        },
        {
            "data_category": "emission_factor",
            "id": "679d61bc-8dc2-4068-943e-931a62f79fb4",
            "name": "Diesel (100% mineral diesel)",
            "region": "GB",
            "region_name": "United Kingdom",
            "source": "BEIS",
            "source_dataset": "Greenhouse gas reporting: conversion factors 2023",
            "year": "2023"
        },
        {
            "data_category": "emission_factor",
            "id": "81260232-5079-42b4-ba71-1c816f366362",
            "name": "Electricity supplied from grid - production mix",
            "region": "DK",
            "region_name": "Denmark",
            "source": "AIB",
            "source_dataset": "European Residual Mix",
            "year": "2022"
        }
    ]
}