Space heating emissions

Space heating is the main driver of energy consumption in buildings, and therefore of greenhouse emissions. This plugin estimates annual building space heating emissions by combining open data from various sources. In its current, first version, the plugin relies primarily on gridded data (100-m resolution) from the German 2022 census, and includes only residential buildings.

We also provide the simulated daily emission estimates for a self-defined year. This estimate is simulated based on the demand_ninja model and our emission factors.

Data sources

Spatial data

Gridded data from the German 2022 census can be downloaded here. We use the four following datasets:

1. Population counts ("Bevölkerungszahlen in Gitterzellen")
2. Living space per capita ("Durchschnittliche Wohnfläche je Bewohner in Gitterzellen")
3. Building year of construction ("Gebäude nach Baujahr in Mikrozensus-Klassen in Gitterzellen")
4. Heating energy carriers in residential buildings ("Gebäude mit Wohnraum nach Energieträger der Heizung in Gitterzellen")

Energy consumption rates

We use energy consumption values for buildings of different age classes from co2online, which are based on measurements from over 300 thousand buildings across Germany, and are adjusted by temperature differences. Co2online gave us permission to use their data in this plugin.

Age class	Energy consumption (kWh/m²)	Building standard
Before 1919	134.6
1919 to 1948	134.6
1949 to 1978	135.7
1979 to 1990	126.2	WSchVO 1
1991 to 2000	93.3	WSchVO 3
2001 to 2010	78.5	EnEV 2002
2011 to 2019	74.1	EnEV 2007*
Since 2019	74.1	EnEV 2007*

Emission factors

We use carbon dioxide emission factors from the Probas database for gas, oil, and coal. Note that we use emission factors for "unit processes", meaning that they include emissions related to burning the fuels for heat, but do not include other upstream and downstream emissions in the lifecycle of the fuels.

Energy carrier	Emission factor (kg of CO₂/kWh)	Source / Notes
Gas	0.20029	Probas
Oil	0.26793	Probas
Coal	0.33661	Probas
Wood pellets	0.34000	Note 1
Biomass/Biogas	0.20029	Note 1
District heating	0.00000	Note 2
Electricity	0.00000	Note 2
Solar/Geothermal/Environmental Heat	0.00000	Note 2

• Note 1: The model estimates emissions associated with heating without accounting for the full lifecycle of the energy carriers. This means that we estimate the CO2 released while burning biomass without considering that carbon was only recently captured through photosynthesis. Hence, we used emission factors similar to gas and coal for biogas/biomass and wood pellets, respectively.

• Note 2: The model estimates territorial (scope 1) emissions. Since no emissions are generated directly at buildings heated with electricity, heat pumps, and district heating, these emission factors are 0. Heating these buildings likely still generates emissions (for example, for electricity generation), but these happen elsewhere (e.g., at power or district heating plants).

• Note 3: For buildings with unknown energy carrier, we use the average emission factor across the 8 categories above, weighting by the number of buildings with each carrier across all of Germany.

Weather data

We use ERA5 reanalysis weather data for the heating demand simulation by demand_ninja model. The weather data-based heating demand estimates are further used for the daily emission estimation.

• 2m_temperature
• specific humidity calculated by 2m_dewpoint_temperature and surface_pressure (reference: 7.2.1(b))
• wind speed calculated by 10m_u_component_of_wind and 10m_v_component_of_wind
• surface_solar_radiation_downwards

Development setup

To run your plugin locally requires the following setup:

1. Set up the infrastructure locally in devel mode
2. Copy your .env.base_template to .env.base and update it
3. Run poetry run python {plugin-name}/plugin.py

If you want to run your plugin through Docker, refer to the Plugin Showcase.

Further requirements

Make sure you have the following installed on your machine:

• psql (PostgreSQL) v16.10 or higher
• postgis v3.4.2 or higher

Testing

We use pytest as a testing engine. Ensure all tests are passing by running poetry run pytest.

Coverage

To get a coverage report of how much of your code is run during testing, execute poetry run pytest --ignore test/core/ --cov. We ignore the test/core/ folder when assessing coverage because the core tests run the whole plugin to be sure everything successfully runs with a very basic configuration. Yet, they don't actually test functionality and therefore artificially inflate the test coverage results.

To get a more detailed report including which lines in each file are not tested, run poetry run pytest --ignore test/core/ --cov --cov-report term-missing

Releasing a new plugin version

To release a new plugin version

1. Update the CHANGELOG.md. It should already be up to date but give it one last read and update the heading above this upcoming release
2. Decide on the new version number. Once your plugin outgrew the 'special versions' dummy and mvp, we suggest you adhere to the Semantic Versioning scheme, based on the changes since the last release. You can think of your plugin methods (info method, input parameters and artifacts) as the public API of your plugin.
3. Update the version attribute in the pyproject.toml (e.g. by running poetry version {patch|minor|major})
4. Create a release on GitLab, including a changelog

Docker

To build docker images, you need to give the engine access to the climatoology repository. Create a file CI_JOB_TOKEN that contains your personal access token to the climatoology repository.

Build

The tool is also Dockerised. Images are automatically built and deployed in the CI-pipeline.

In case you want to manually build and run locally (e.g. to test a new feature in development), execute

docker build --secret id=CI_JOB_TOKEN . --tag repo.heigit.org/climate-action/heating-emissions:devel

Note that this will overwrite any existing image with the same tag (i.e. the one you previously pulled from the Climate Action docker registry).

To mimic the build behaviour of the CI you have to add --build-arg CI_COMMIT_SHORT_SHA=$(git rev-parse --short HEAD) to the above command.

Run

If you have the Climate Infrastructure running (see Development Setup) you can now run the container via

docker run --rm --network=host --env-file .env.base --env-file .env repo.heigit.org/climate-action/heating-emissions:devel

Deploy

Deployment is handled by the GitLab CI automatically. If for any reason you want to deploy manually (and have the required rights), after building the image, run

docker image push repo.heigit.org/climate-action/heating-emissions:devel