To create our heating impact database, we follow the same layered approach as for electricity, but with four primary source categories:
District – industrial, natural gas
District – industrial, other than natural gas
Central small-scale – natural gas
Central small-scale – other than natural gas
These four high-level categories form the basis of our heating emission factors per geography.
1. Source and fuel composition
Each category references a specific set of Ecoinvent datasets and underlying exchanges.
For other than natural gas categories, alternative fuels can be present, such as:
biogas
thermal heating
biomass
waste heat
To handle this, we apply the same normalization approach as in electricity.
Example:
A region reports that 50% of non-natural gas heating is biogas
Within that 50%, there may be multiple biogas types and technologies
We normalize this in two steps:
Normalize the share of the alternative source to 100% when the user specifies it (e.g., “100% biogas”)
Preserve internal variation, such as:
biogas production pathways
combustion technologies
equipment differences
This allows users to select:
regional averages, or
specific heating source configurations
while still maintaining fidelity to the underlying datasets.
2. Equipment differentiation
Heating systems vary significantly by unit type.
To support realistic modeling, we also distinguish between:
boilers
power engines
combined heat units
Each unit has different:
efficiencies
fuel inputs
emission profiles
By using the exchanges behind each dataset, we can calculate:
the average boiler impact when the user only knows “boiler”
the specific unit impact when the user selects a concrete technology
This enables both high-level and detailed footprinting.
3. Scope allocation logic
Unlike electricity, heating datasets do not include an explicit scope split table.
Therefore, we classify emissions directly from the dataset structure in Ecoinvent.
Each Ecoinvent reference includes:
technical intermediate exchanges
elementary flows
We apply the following rule:
Elementary flows = emissions from fuel combustion → always Scope 1
CO₂
CH₄
N₂O
etc.
Technical exchanges = upstream processes → always Scope 3
fuel extraction
distribution
equipment production and maintenance
boiler infrastructure
This approach provides a consistent, methodology-aligned scope assignment for heating.
4. Unit conversion (MJ ↔ m³ natural gas)
Heating datasets are expressed in MJ of heat produced, but users often provide:
cubic meters (m³) of natural gas
To support this, we extract the conversion directly from the dataset exchanges.
Example:
A heating process dataset shows:
0.1 m³ natural gas consumed per 1 MJ of heat
This gives us:
1 m³ natural gas = 10 MJ heat
We can then convert user inputs:
If a user uploads 500 m³ natural gas
we derive heat energy: 500 × 10 MJ
and calculate emissions accordingly
This enables flexible input formats without losing accuracy.
Summary
The heating methodology combines:
regional source mixes
fuel normalization
equipment differentiation
scope allocation via dataset structure
automatic unit conversion
This creates a scalable and accurate utility emission factor system for heating, similar in design to the electricity module.