This article gives you all the relevant information on how we create (spin up) ClimateBoards for cities, including the board structure, the parameters and their sources.
When creating a ClimateBoard for a city, we spin it up based on a template board for the country the city is in, and pre-populate its parameters. The spun-up ClimateBoards give you a good starting point for your climate journey and should be modified and refined to display your municipality's path to net-zero more accurately.
All boards start with a universal logic structure. This structure covers its main and sub-sectors of emissions (e.g. transportation), Transition element groupings (e.g. travel avoidance), all the way down to the pre-selected Transition elements from our template board. This universal ClimateBoard structure and the choice and weightings of Transition elements are based upon on a typical city's pathway to net-zero in your country.
Personalise your ClimateBoard
To personalise, you can add additional groups and layers to the structure (or remove irrelevant ones) and add or remove Transition elements as well as modify the weighting of the Transition elements. In order to fully personalise your ClimateBoard, you will also need to modify the parameters.
The pre-populated parameter values are estimated values calculated from data based on national and sometimes international statistics for that country/region. We have structured our data in a hierarchical manner to be able to support regional, national and global data. When spinning up a board, we take the parameter value with the closest regional match to your city.
For example, looking at the data hierarchy for Germany: If we do have a parameter value for the Bundesland (regional level) of your city, that value is the first choice. If that does not exist, we fall back to the value for Germany (national level). Should that not exist either, we fall back to the global value.
Some values are largely free of variance across different geographies, others are very locally specific. Therefore, depending on the type of parameter, the need for local or regional data will vary, and global data is sufficient. For example, the amount of kilometres driven in petrol cars per person will vary between cities and regions, whereas the emission factors for burning diesel will be very similar across the globe.