The Global Resource Accounting Model (GRAM) is a global multi-regional input-output (MRIO) model for the global assessment of environmental impacts (such as emissions or raw material consumption) of domestic final demand along international supply chains. The GRAM assessment approach has already been applied by GWS to various database structures for calculating global footprint indicators.

Initially, the model was developed on the basis of the OECD input-output tables for a period from 1995 to 2010 for 62 countries and a country group ‘Rest of the World’ as well as 48 economic sectors per country or region. Combined with other data sets such as CO₂ emissions or material extractions of the economic sectors, so-called satellite data, the model enables the allocation of production-related variables to final consumption in a static input-output model.

In more recent applications, the GRAM assessment approach was at first parameterised for data structures of the EXIOBASE database. Current applications are based on the data structures of the GLORIA database, which is regularly updated by an international consortium led by the University of Sydney and is also used for footprint calculations on behalf of the United Nations International Resource Panel. Compared to the previously used OECD datasets, EXIOBASE and GLORIA are characterised in particular by a much more detailed depiction of the resource extracting (agriculture, forestry, mining) and resource processing economic sectors.

GRAM is a ‘real’ MRIO model that maps national production structures in a globally complete input-output matrix A. In the model version parameterised on the basis of the GLORIA database, this matrix has the dimension 19,680 x 19,680 (164 world regions*120 economic sectors x 164 world regions*120 economic sectors) and the matrix P of consumption-based emissions is calculated as the product of the matrix of emission intensities E, the inverted input-output matrix A and the final demand matrix Y. In order to be able to carry out these calculations for the extensive original data structures of the GLORIA database on standard desktop computers, the entire evaluation approach was parameterised in GWS's own (C++-based) object-oriented development environment solve.

In the SYMOBIO II project, the GRAM approach was partially dynamised by updating boundary variables with the corresponding results of global projections up to the year 2050. In addition, specific scenario specifications in the form of changes to cost, trade and demand structures have been implemented in GRAM, so that we can speak of a partially dynamised global MRIO with a high regional and sectoral resolution.