URMOD

URMOD provides reliable, internationally comparable answers to these questions using consumption-based indicators. URMOD is a modular environmental-economic input-output model. At its core is a highly detailed input-output table for the German economy comprising 182 production sectors. Sectors with high raw material and emission intensities are broken down in particularly fine detail and are expressed in physical units, whilst the remaining sectors (service sectors such as travel services, architectural services, social services and many more, as well as food processing, vehicles, etc.) are expressed in monetary terms. Numerous statistical data sets are incorporated into the derivation of the tables, including data on international trade, agricultural and industrial production, raw material extraction and energy use. Using the Leontief inverses, URMOD calculates cumulative raw material and emissions coefficients for consumption and investment. In doing so, it also takes into account the raw material requirements and emissions associated with the production of imports and exports, thereby highlighting the effects of shifts in production.

The model is implemented in Python and was derived from the RME model for Germany, which was developed in collaboration with Karl Schoer for the German Resource Report. It is fully harmonised with Eurostat’s EU RME model and can be linked to its raw material coefficients, so that imports are modelled using average European technology. The raw material indicators are therefore methodologically consistent with the European indicators.

Modules

• Raw materials: domestic extraction across 52 raw material categories (biomass, metal ores, non-metallic minerals, fossil fuels)
• Greenhouse gases: emissions based on the concept of Environmental Accounts (resident principle)
• LULUCF emissions: calculation of emissions from land use, land-use change and forestry based on FAO data and country-specific coefficients
• Consumption interdependence: Linkage with consumption interdependence matrices for analyses by expenditure categories such as food, housing or transport (SEA/COICOP classification) or income brackets
• Scenarios (URMOD-3R): comparative-static assessment of the impact of individual measures or future raw material and emissions pathways, depending on framework assumptions

Areas of application

• Raw material indicators in raw material equivalents: raw material input (RMI), raw material consumption (RMC), trade flows and total raw material productivity
• Direct and indirect GHG emissions in CO₂ equivalents, including upstream emissions
• Emissions and raw material consumption in individual demand sectors or economic sectors
• Monetary (spend-based) emission factors for determining Scope 3 emissions
• Time series from 2000 to 2024

Scalability

Thanks to its modular structure, URMOD can be flexibly adapted to the research question and can be expanded to include further satellite accounts (e.g. land use, biodiversity or water) or broken down by additional economic sectors.

Publications

• Ewers, B., Dittrich, M., Schoer, K. (2023): Calculating Germany’s Raw Material Footprint – Method and Results. ifeu paper 01/2023 (PDF, 800 KB)
• Estimating Raw Material Equivalents with Multi-Regional Input-Output Models: The Impact of Sectoral Disaggregation. ifeu Working Paper 1 (PDF, 2 MB)
• URMOD-3R – An MRIO-type scenario model for estimating the resource footprint of Germany, the EU and the World. ifeu paper 1/2024 (PDF, 7 MB)