See how Ducky calculates climate footprints for citizens in a region, and see the research and methodology behind the numbers
Ducky aims to give municipalities and county municipalities an overview of the footprint of their inhabitants, and thus provide a starting point for climate plans for indirect emissions. In the long run, there will be tools for making analyzes of possible measures, and the footprint will go back in time, so that one can follow developments.
Ducky wants to offer the world's most advanced tools for mapping consumption-based greenhouse gas emissions. Unlike much of the emissions reporting that uses a production-based approach, consumption-based climate accounting links all global emissions to individuals' consumption of products and services. These emissions have been undocumented for a number of years. In order to achieve the climate goals, Western society must address the issue from both a production-based and consumption-based approach.
A broad public-private partnership is the basis for these calculations, and the figures will be continuously updated with more and better data sources. The data sources are statistical, i.e. we collect general information about how people consume, but never data about individuals. Examples of data sources are: Statistics Norway and public registers such as the National Population Register and the Employer and Employee Register, and private sources such as consumption data from banks aggregated at a high level with consent.
Data and methodologies have been developed with funding from the Research Council, in collaboration with Asplan Viak and Tietoevry.
General information on how footprints are calculated
Ducky calculates the average climate footprint per inhabitant per region, in kg CO2e / year. Eventually you will be able to see variations in footprint over time, but so far the footprint is based on the latest available data.
Footprints are divided into five different categories: food and drink, energy, transport, consumption of goods and services, and the population's share of public consumption. The method for finding the footprint in each category is described in the following categories in this document. Each of these categories uses different calculation methods to arrive at different values, but in the end they are all converted to CO2 equivalents. Read more in the article Questions and answers about climate footprints.
This means that all the greenhouse gases connected to your emissions are converted into the amount of carbon dioxide (CO2) that would have the same effect on the climate over a 100-year period. This is the unit most commonly used to measure the impact on climate change, and is used by the UN Climate Panel.
To calculate footprints, you need to know how much of something (meat, kilometres driven by car, electricity, etc.) is consumed, and how much consumption corresponds to CO2e. The latter we call CO2 intensity.
We calculate CO2 intensity in collaboration with Asplan Viak, and with two different methods:
- Environmentally extended cross-sectional analyses are an analysis of the entire economy, where emissions from production, imports and exports are calculated at national level. We link these analyses to the Consumption Survey from Statistics Norway to find CO2e-intensity per krone consumed in a number of categories. You can read more about this method in Steen-Olsen et. al (2016).
- Life cycle analysis is used to analyse the environmental consequences of individual products. These are used in areas where cross-sectional analyses do not provide such good accuracy, for example when we look at the emissions associated with food. We use the life cycle studies that are most appropriate for Norwegian consumption.
The advantage of using both of these methods is that they can be used to check each other and thus increase accuracy. For example, if we know from life cycle analyses how much CO2e a steak emits, and multiply this by the amount of steak sold in Norway each year, this should be relatively similar to the figure obtained for the steak industry from environmentally extended cross-sectional analyses.
To find the average footprint of an individual and aggregated for all citizens, we do the following steps:
Using statistical data, we simulate all households in a basic district/municipality/county. This means that based on, for example, population statistics, living space and car ownership, we construct different compositions of households.
The footprint of the simulated households is calculated in each of the categories (food, transport, energy, and consumption of goods and services). We run the calculation with many different simulated households, and find the footprint that is most likely based on this.
The footprint of the simulated households is added up at basic district/municipality/county level and divided by the number of inhabitants.
Our calculations are continuously updated based on new research and not least new consumption statistics. The figures calculated are always as accurate as possible based on the research available today. The challenge with footprints is that it is indirect, and thus can not be measured. The only way to check that we have the right footprint is to make sure that we have the most up-to-date methods and datasets possible at all times.
The quality of the data we collect has a lot to say about how accurate the footprint is. A data source that is accurate, gives us high-resolution statistics (for example, down to the basic circuit level, and preferably age groups) and has few gaps or deficiencies. Many of the data sources we use, such as income statistics, have relatively high standard deviations. This means that we do not calculate the footprin tof a single inhabitant, we can only say something about the probable average footprint.
To explain how good data we use, we have introduced a data quality indicator that you can see in the map. The levels are as follows:
Level 1 - the calculation is based only on income and population data. Available for all municipalities.
Level 2 - the calculation is based on several public registers such as the Land Registry and the Motor Vehicle Register. Available for pioneer municipalities that have not yet signed a data processing agreement.
Level 3 - the calculation is based on public registers and registers that require a data processor agreement, such as the Population Register and the Employer and Employee Register. The data processor agreement allows us to connect several registers to link e.g. information on the number of people living in a household with house size and main heating source, so that the accuracy of the calculation increases considerably. Available for pioneer municipalities that have signed a data processing agreement.
Level 4 - the calculation uses aggregated data from private sources such as energy and banking. Currently not available.
Level 5 - will be defined as we use even more data sources, such as statistics on food consumption, reuse, etc. Currently not available.
To ensure the quality of the data, we take several measures. Sensitivity analyzes are used to find out which parameters have the most impact, so that we can focus on getting these data sources as good as possible. We also do a quality check, where we calculate which factors dominate the footprint of an area. In this way, we can provide information about why an area has a high or low footprint.
Calculations and data sources per category
A complete overview of our data sources can be found here. Click on the links below to learn more about the calculations and data sources per main category of the footprint: