2nd iTEM intercomparison

The iTEM database is a collection of outputs from the iTEM participants. The full database is currently not made public.

This page briefly outlines the content of the database. The capitalized words MAY, MUST, SHOULD, etc. have the meanings described here; groups submitting data to iTEM MUST understand and follow these guidelines. Contact the organizing team with any questions.


Download an example data reporting template: CSVOpenDocumentMicrosoft Excel.

Data are collected in series of annual data points between a start year and end year. Series SHOULD include every fifth (2025) or tenth (2030) year; other intermediate years MAY be included.

Each data series is labelled along the following conceptual and physical dimensions. The labels for each dimension are text strings. Commas in the labels SHOULD be avoided; where this is not possible, the fields MUST be quoted when the data is provided in CSV format.

1. Model

The model that produced the data series.

2. Scenario

Each modeling group's internal name for a distinct set of assumptions or policies. Scenarios are loosely grouped as "Reference" and "Policy".

  • Participants MAY submit one or more "Policy" scenarios, but MUST submit at least one "Reference" scenario.
  • Participants MUST submit, separately from the data files, a description of each scenario included. This description MAY take the form of a reference to existing publications.

3. Region

Particular region of the world (group of countries, country, or subnational division) described by a series, or the value Global. See Regional downscaling, below.

4. Variable

The conceptual quantity described by the series.

  • Data submissions MUST include a field Unit describing the units for the data.
  • Participants SHOULD submit data in the suggested units; where this is not possible, they MUST supply an unambiguous description of the units used.

5. Mode

The mode or modes of transport described by the series, or the value All.

6. Technology

The energy technology described by the series, or the value All.

Each Mode may be disaggregated to one or more Technology. For instance, the light duty vehicles (LDV) mode may be powered by an internal combustion engine (Liquids) or a plug-in hybrid-electric (PHEV) powertrain technology.

7. Fuel

The energy source or carrier described by the series, or the value All.

Each Technology may employ one or more Fuel. For instance, LDVs with PHEV powertrain technology may use both gasoline, diesel or biofuels (Liquids) or Electricity.

Submission & analysis software

Submitting data in a common format facilitates automated comparison by the analysis software co-developed by the iTEM organizing team.

iTEM data can be voluminous; for iTEM2, some participants submitted over 10,000 series. Participants are strongly encouraged to script or otherwise automate the conversion of data from their models' native output formats to the format described here. This has two benefits:

  • Unlike a manual process, the code can easily be checked for errors, and its outputs replicated.
  • Data may be revised with little effort as model development progresses and projections change.

Regional downscaling

The global transportation models in this study sub-divide the world into between 7 and 32 regions, where a model region is composed of one or more countries. As these models were developed by different institutions with different purposes, the regional boundaries used differ significantly between models. In order to conduct analysis at the region level, with common regional definitions, in this study we first "downscale" each model's output for all quantity variables (i.e., annual flows) to the country level, and then re-aggregate to the 16 study regions. Derived variables such as intensities are then computed from the aggregated quantity variables.

The key to the downscaling method used here is the selection of an appropriate downscaling proxy, defined as the variable used to apportion multi-country regional quantities down to the country level. The key criteria for a downscaling proxy are as follows:

  1. the information needs to be available at the country level, and
  2. the data needs to be a reasonable approximation of the particular variable being downscaled.

The following are the key datasets and methods used for downscaling:

  • SSP Socioeconomics database: The Shared Socioeconomic Pathways database's population and GDP-PPP projections for the SSP2, or "Middle of the Road", scenario were used for the downscaling of population and GDP from model regions to countries. These projections cover all reporting years from 2000 to 2100, for about 180 countries.
  • CDIAC: Fossil-Fuel CO2 Emissions by Nation: The CDIAC CO2 emissions by nation are used to downscale reported CO2 emissions by all sectors (for models with whole-economy coverage), in the years 2005-2010. For 2015-2100, the country-level 2010 CO2 emissions quantities are multiplied by the PPP-GDP ratio from 2010 to each year, so that region-level emissions are apportioned increasingly to countries that account for increasing shares of the region's economy. Similar methods are used for transportation energy, and are documented below with examples.
  • IEA Energy Balances: The IEA Energy Balances present estimates of energy consumption by fuel for eight transportation modes, two of which are not used (pipeline transport and non-specified), and the two aviation categories (domestic aviation and international aviation) are added together and named "AIR" in the table below. For downscaling we use the energy consumption by all fuels (total). The mapping from iTEM2 transportation modes to the IEA's categories (flows) are shown below.

    Mode FLOW
    2W and 3W ROAD
    Aviation AIR
    Bus ROAD
    Domestic Shipping DOMESNAV
    Freight Rail RAIL
    Freight Rail and Air and Ship RAIL
    International Shipping MARBUNK
    Passenger Rail RAIL
    Road ROAD
    Rail RAIL

For each reported iTEM2 annual flow by model region (e.g., energy consumption or emissions, but not intensities or emissions factors), the country-within-model-region share is computed directly from the IEA's data for the period from 2005-2010. For subsequent years, these shares are calculated from the IEA's 2010 energy consumption quantities multiplied by each country's GDP-PPP ratio (SSP2 scenario) from 2010 to 2100.

One of the key features of the downscaling method is that the country-within-model-region shares evolve over time according to the GDP pathways assumed in SSP2. Thus, the quantities of energy assigned to the countries reflects that some countries may follow a different development trajectory than their parent region. While South Africa is not an iTEM model region, it nevertheless illustrates the issue well: in 2010, it accounts for 20% of the GDP of Africa, and in 2100 in the SSP2 scenario it accounts for 2%. The following figure shows the evolution of Brazil, Mexico, and Other Latin America as a share of the total reported quantities for the Latin America (LAM) region in the MESSAGE model for the Reference scenario.

Latin America Downscaling Shares

As shown, the GDP shares (solid lines) of Brazil and Mexico are 34% and 24% in 2010, respectively, and decline to 23% and 25% by 2100 in the SSP2 scenario. The shares of energy consumption, passenger kilometers travelled, and all other transportation-related quantity variables also follow the GDP trends in going from 2010 to 2100. In this way, Brazil and Mexico data are downscaled from data reported for Latin America as a whole, in a way that is consistent with the sub-regional dynamics of the SSP2 scenario.


The following table gives the number of (Scenario, Region, Mode, Technology, Fuel) combinations for each Variable in the iTEM 2 data base, and thus a general sense of which variables are reported by which models.

For all models except ITF, variables with _percapita in the name are derived from the corresponding absolute value and the population for the corresponding region.

Variable BP EPPA5 ExxonMobil GCAM GET ITF MESSAGE MoMo Roadmap Shell Statoil WEPS+
CO2 Concentration 2 7 2
CO2 Emissions (all sectors) 17 17 34 7 34 2
CO2eq Concentration 2
Carbon Price 1 7 2
GHG Emissions (all sectors) 17 34 34
PPP-GDP 17 17 17 34 119 34 28 34 34 17
Population 17 17 34 119 34 28 34 34 17
Radiative Forcing 2 2
ef_bc 672 136
ef_co2 17 34 2070 2109 2006 1190 4
ef_co2_fuel 17 34 3414 198 3662 3864 1598 24
ef_co2_service 33 2342 254 2278 2600 1836 4
energy 977 51 19 4296 1001 4614 4786 4114 188 320 703
energy_percapita 168 102 16 804 154 618 684 656 680 292 402
intensity 170 51 2688 339 2584 3264 102 320 85
intensity_new 17 318 340 48
intensity_service 133 2876 309 2856 3172 4658 68 184
load_factor 149 3748 426 3672 3808 34 102
pkm 185 2422 2992 2620 3332 117
pkm_percapita 166 536 618 544 440 544 234
sales 714 1158 1530 1190 176
stock 714 34 1158 1530 1190 176
stock_percapita 102 68 204 272 170
tkm 128 1326 680 1352 1326 68 67
tkm_percapita 64 336 136 220 204 136 134
ttw_bc 2016 544
ttw_ch4 472 544
ttw_co2 17 34 3414 3662 4964 1598 24
ttw_co2_percapita 34 68 672 360 548 600 544 28
ttw_co2e 17 34 3414 3662 4964 1598 24
vkt 238 51 3492 3332 3264 102 352 85
vkt_percapita 68 102 804 618 612 408 204 170
wtt_co2e 3414 4964
wtw_co2e 3414 4964