DATA DISCLAIMER: We have used our reasonable efforts to ensure the accuracy of the data used in this tool, yet data should be read as indicative of magnitude rather than exact figures. Note that data is drawn from a variety of publicly available datasets. Not all figures are reported across all geographies.
Due to different data frameworks and availability across different regions the approach for gathering, preparing and showcasing information is organised geographically. Please navigate to your region of interest to review the method. Note that where relevant, methods or data may be duplicated across regions.
Should you wish to upload bottom up data for your city, then please contact us.
UN - IRP
GHS Urban Centre Database 2015
We utilise employment data (FTE) from Eurostat at a nuts3 level aggregated by sector from 2016.
We utilise data from the EEA for emissions per sector at a national level and scale it down using nuts3 gross value added data.
We utilise gross value added data at a nuts3 level as a proxy for the city that is contained within the nuts3 region.
Material flow data is not available at a city level, and even less so as specified by sector or specific material stream. Various methods exist for this estimation and there are different implications for the figures depending on which material data is used, and which scaling down method is used.
We create country level profiles of material usage per industry, in kilograms aggregated by material flow.
This is done by using national figures of material flows (kg) aggregated according to industry standard for material definitions. We utilise RME/product by final consumption at the EU level to derive country level product consumption in kg by material category. Then we utilise economic figures regarding industry use of products to derive industry use of products in terms of material categories in kg.
We utilise waste data from Eurostat to profile waste streams per sector. We classify waste streams into their matching input flow to estimate quantities and proportions of waste per sector per material flow.
We utilise gross value added data about sectors at a nuts3 level as a first scaling down step from national figures. We match cities to their corresponding nuts3 region as a proxy, and scale down from nuts3 using population.
Should you wish to learn more and /or join our working group for Circular City Profiling in the EU then please contact us.
Consistent data for employment per sector at the required level of granularity is not available. Please contact us if you would like to share employment data for your region of interest.
The data on PM2.5 and CO2 emissions are derived from the European Commission's inhouse Emissions Database for Global Atmospheric Research (EDGAR v4.3.2), which
estimates anthropogenic greenhouse gas and particulate air pollutant emissions for the
years 1970 to 2012 (Crippa et al., 2018). The calculation of the emissions includes all
human activities, except large scale biomass burning and land use, land-use change, and
forestry. The results are comparable between countries thanks to the bottom-up
compilation methodology of sector-specific emissions applied consistently for all world
countries. (GHS Urban Centres, 2015)
Please contact us if you would like to share emissions data for your region of interest.
Consistent data for GVA per sector at the required level of granularity is not available. Please contact us if you would like to share gross value added data for your region of interest.
We utilise exiobase to obtain country level tables of industry use of products, and then we map those products to corresponding material flow profiles in order to obtain industry use of material flows at a national level. Because exiobase is from several years back, we use the industry material usage per country proportions and apply it to the most recent global materials flow database from the IRP.
In order to create the combined MFA table, multiple transformations are required for both data sources before they can be joined and used for analytical purposes. Exiobase has only product to industry, which means that product needs to be converted to material. Both data sources end up using different material codes, which need to be categorized to a simplified representation of both. Finally, both data sources have different geographical coverage although exiobase has catch-all "countries" for all continents.
Transforming exiobase into a material use per industry table requires some external data sources. The key component in this conversion is an RME coefficients table provided by Eurostat (https://ec.europa.eu/eurostat/documents/1798247/6874172/Documentation+of+the+EU+RME+model/). These RME coefficients are frequently used to convert between products and materials. Because Eurostat employs CPA codes for products, the exio product codes are first recategorized into CPA codes (where possible, certain categories need to be aggregated into parent categories on both sides before a clean join can be achieved). The new CPA codes can easily be converted to RME codes, but not all unit conversions between the expected RME conversion and the unit in exiobase are possible (especially tedious because we are using the hybrid use table). There are 5 combinations between the RME and exio units, of which two need external sources to enable the transformation:
Tonnes -> tonnes
Nothing to do.
Million euros -> thousand euros
Easily achieved by multiplying by a thousand.
TJ -> TOE
Achieved by converting from TJ to GJ to TOE.
Tonnes -> euro
Converting between tonnes and euros is achieved by applying a conversion ratio calculated based on EU aggregate comext data. Comext is the international trading database from Eurostat, which reports data in both tonnes and euros per CPA. Because this database represents all trade with EU countries (not just between) it has pretty decent coverage. However, it still only reports countries importing or exporting with European countries, which means that two countries trading outside the EU are not visible in this database. Because of this limitation, we simply take the EU aggregate average of the total traded sum of products in tonnes and euros per CPA and use this to get a ratio between tonnes and euros for a particular CPA per year. This ratio is applied equally to all countries.
Tonnes -> TOE
In some cases the RME conversion expects energy (TOE) where exio provides the material in tonnes. For example, coking coal (hard coal in CPA) is represented in tonnes of coal, whereas the RME conversion expects the energy equivalent (TOE). These cases are converted using a calorific conversion table (https://unstats.un.org/unsd/energy/balance/2013/05.pdf) and then converted from TJ to TOE.
After applying each conversion we now have an exio use table in materials per industry per country. We do the same for final demand, and join it with use to get materials per industry (and final demand) per country. Because the next step is to get industry material use per country proportions, it is important to first add final demand so that the totals from IRP are not applied to just the industries. The proportions are created by taking the individual material / industry values and dividing it by the total within exiobase per country.
Finally, the exio proportions are applied to the IRP global material flows database, which has almost full coverage. Because exio is nowhere near global coverage and only contains a handful of countries, in most cases the exio rest of world (continent aggregates) proportions are applied.
We utilise exiobase to obtain country level tables of waste streams per industry use of products, and then we map those products to corresponding material flow profiles in order to obtain industry use of material flows at a national level.
This national level data is scaled down to city level using population data (in the absence of more representative scaling factors).
In the absence of more representative scaling factors, we utilise population data to create city level estimates of population and use these to scale from national. In case of the global MFA, the aim is to have as much baseline coverage as possible and then work to improve individual regions with higher granularity data or even bottom up data.
All non European countries use the same scaling system based on income and population. The first scaling factor is income per region, to scale country to subnational regions (usually province). From province the data is scaled down to the urban metropolitan area using population.
It is preferred to utilise economic data, such as employment or value added data, to scale down physical use tables.
Should you wish to join our working group for Circular City Profiling in your region, or if you want to reach out on the research, data methods, or opportunities for this in your city or region, then please contact us.
Thank you for reading!