Source Trail

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 straightforward requests, like those to the Basic Estimate Endpoint, providing this transparency is simple. These calculations involve direct application of specific emission factors to the activity data you provide, and so Climatiq simply returns the used emission factor.

For more complex calculations, however, 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 this feature doesn't enable you to replicate the calculations, it does provide a window into the data involved, enhancing your confidence in the results.

Source Trail and Audit Trail

Source Trail is somewhat analogous to the audit trail feature already offered by Climatiq. The main difference lies in the intent: audit trails enable auditors to replicate calculations, whereas Source Trail offers insights into the components of the calculation without enabling replication.

Unlike the audit trail, which is an add-on feature, Source Trail is included at no extra cost for supported endpoints.

Source Data Point

Endpoints 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.

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",
            "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",
            "name": "Electricity supplied from grid - production mix",
            "region": "DK",
            "region_name": "Denmark",
            "source": "AIB",
            "source_dataset": "European Residual Mix",
            "year": "2022"
        }
    ]
}

Audit Trail Data Versioning