The Emissions Reporting app uses several data sources to calculate emissions estimates. For an overview of the data sources, see the table below.
Data sources
Best quality: Direct Metadata
Direct Measurement | Good quality: Proxy Data
Indirect Measurement | Lowest quality: Complete Asset Metadata
Complete Asset Metadata |
Direct measurement provides the optimal calculation method through measured data inputs using a standard function for either fuel consumed for combustion engine assets, or electricity consumed for electric powered assets.
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| If direct measurement data points are not available, other sources of indirect data such as load, RPM, and fuel levels are used to derive fuel or electricity consumption.
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| Information such as Make, Model, Serial Number, and other fields are used when looking up reference usage data for estimations, as well as helping specific use cases for various asset types, like scissor lifts.
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Calculation Method
Depending on the type of data available for each asset, the calculation methods are as follows.
Direct Measurement Calculation |
The calculation is based on the consumption of fuel or electricity. If asset is electric powered:
If asset is internal combustion engine powered:
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Indirect Measurement Calculation |
Without directly measured fuel / electricity consumption data available, we analyze the available usage data, based on historical information matched to the available asset specifications to estimate fuel / electricity usage. If asset is electric powered:
If asset is internal combustion engine powered:
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What is the source for emissions factors?
CO2 emission factors are sourced from the Environmental Protection Agency (EPA), Table 2 ‘Mobile Combustion CO2’ which details the conversion of energy into equivalent CO2.
How are emissions estimates generated?
When asset metadata is received in Trackunit Manager (CAN or ISO feed):
Data Source rates the quality of received metadata and suggests improvements
Fuel Type selects the emissions factor based on the fuel reported
As outlined above, for indirect sources, emissions are estimated by leveraging Trackunit Iris. The Trackunit predictive model factors inputs from more than 8,000 makes & models of construction machines to ensure emissions estimates are backed by substantive real-world data. This continuous assessment also advances the Trackunit predictive model overall to eliminate downtime in construction.
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