3rd & 4th iTEM intercomparisons

Projections data

A single file “data.csv” (CSV format) or “data.xlsx” (Microsoft Excel format).

An example template is available in CSV and Microsoft Excel formats.

The data file:

• must have the index columns Model, Scenario, Region, Variable, Mode, Technology, and Fuel.
• must have one or more data columns with year headers (e.g. “2015”, “2020”, …).
• If in Microsoft Excel format, must contain data in only the first worksheet.
• should have a Unit column. [3]

Because of the variety of iTEM models, modeling teams:

• may either leave cells blank, or delete rows with no data, if the measure is not produced by their model, or is not produced at a particular level of disaggregation.

• should report data at the greatest level of disaggregation possible. If the categories used natively in a model are more detailed than the ones in the example template, then data should be submitted at this level of detail. For example, modeling teams should report the following conceptual differences, for any variables for which their models distinguish values:

  • Light-duty vehicles that are privately-owned or part of (ride-sourcing) fleets.
  • Light-duty vehicles that are fully autonomous or human-driven.
  • Biofuels produced by certain technologies.

• may report totals or averages across any dimension(s), if these are provided by their model. [4]

• may refer to the following files in the iTEM database tool source code:

  • concepts.yaml: gives the codes, names, and descriptions of different groups of hierarchical concepts.
  • measures.yaml: gives the codes, names, descriptions, and units of particular measures of transport quantities.
  • spec.yaml: specifies the combinations of measures and concepts that appear in the template.

  …as well as the full, 13-dimension indices of the simplified variables in the template, available in CSV and Microsoft Excel formats.

Model and scenario metadata

The file “metadata.yaml” is required. This file must be in YAML format, with the following top-level keys:

• “citation:” (required): preferred citation (first entry) and extra citations (additional entries) for modelling methods, in BibTeX format. Example:

  citation:
  - |
    @article{yeh-2016,
      author = {Sonia Yeh and Gouri Shankar Mishra and Lew Fulton and Page Kyle
                and David L. McCollum and Joshua Miller and Pierpaolo Cazzola and
                Jacob Teter},
      doi = {10.1016/j.trd.2016.11.001},
      journalsubtitle = {Transport and Environment},
      journaltitle = {Transportation Research Part D},
      keywords = {Transportation scenarios; Transportation behaviors; Energy use;
                  Climate mitigation; GHG emissions; Transportation demand; Model
                  comparison},
      title = {Detailed assessment of global transport-energy models’
               structures and projections},
      year = {2016}
    }
  - |
    @online{item-web,
      accessdate = {2019-05-10},
      author = {iTEM organizing team},
      url = {https://transportenergy.org},
    }
  

• “scenario:” (required): for each value appearing in the Scenario`` column of data.{csv,xlsx}, a one-sentence description of the scenario, and (optionally) a reference to a more detailed description. Example:

  scenario:
    Reference:
      description: This is the reference scenario in Model ABC.
      reference: author-2017  # Refers to citation.bib
    NP:
      description: A new-policies scenario.
      reference: https://example.com/scenario-descriptions/np
    15 OS:
      description: This scenario achieves 1.5°C of warming, with overshoot.
      # (no reference)
  

• “consent:” (optional): statement of consent for the Analysis team to redistribute part or all of the projection data in the future. If this permission is limited, the limits must be clearly specified. Example:

  consent: >
    The Model ABC team consents for the iTEM MIP3 steering group to redistribute
    part or all of the data included with this submission, with the exception of:
    - All values for the variable 'Sales' in region 'AFR'.
  

“regions.yaml” (optional): a file in YAML format describing the regional aggregation used by the model. If included, this file must be in the following format, and must describe every region label appearing in data.{csv,xlsx}:

Argentina:  # Label appearing in the 'Region' column of 'data.{csv,xlsx}'
  countries:
  - ARG  # One or more ISO 3166 alpha-3 codes.
  # See https://en.wikipedia.org/wiki/List_of_ISO_3166_country_codes

Australia_NZ:
  countries:
  - AUS
  - NZL

Central America and Caribbean:
  countries:
  - ABW
  - AIA
  - ANT
  - ATG
  # (many more)

# (etc.)

Input assumptions

New for MIP 3, modeling teams are asked to include, if possible, information about specific technology and policy assumptions.

This information will be used to:

1. Improve nuance in interpretation of differences in model projections, by attributing them to (or ruling out) differences in these key drivers of transport energy use.
2. Aid scenario-setting for MIP 4. Consensus or disagreement on these inputs will inform one or more specified values for MIP 4.

Because these data and their use may vary widely across models, there is no specific requirement on format or structure.