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Guidelines for variable definitions

The variable column specifies which type of information is provided in the specific timeseries. This can be for example quantities of energy, prices, economic activity, or specifications of technology parameters.

Overview

The variable hierarchy

This column implements a "semi-hierarchical" structure using the | character (pipe, not l or i) to indicate the depth. Semi-hierarchical means that a hierarchy can be imposed, e.g., a user can enforce that the sum of Emissions|CO2|Energy and Emissions|CO2|Other must be equal to Emissions|CO2 (if there are no other Emissions|CO2|… variables). However, this is not mandatory, e.g., the sum of Primary Energy|Coal, Primary Energy|Gas, Primary Energy|Fossil and other Primary Energy|… data should not be expected to equal Primary Energy because this would double-count fossil fuels.

Naming convention for variables

When suggesting/adding new variables, please follow these rules:

  • A | (pipe) character indicates levels of hierarchy
  • Do not use spaces before and after the | character, but add a space between words (e.g., Primary Energy|Non-Biomass Renewables)
  • All words must be capitalised (except for 'and', 'w/', 'w/o', etc.)
  • Do not use abbreviations (e.g, 'PHEV') unless strictly necessary
  • Add hierarchy levels where it might be useful in the future, e.g., use Electric Vehicle|Plugin-Hybrid instead of 'Plugin-Hybrid Electric Vehicle'
  • Do not use abbreviations of statistical operations ('min', 'max', 'avg') but always spell out the word
  • Do not include words like 'Level' or 'Quantity' in the variable, because this should be clear from the context

Common terms

Variables are usually constructed with a top-level category/indicator (e.g, Primary Energy, Capacity) followed by a number of categories and specification (e.g., <Fuels>, <Sectors>). Examples for common values used to construct variables are given below. These are not intended to be hierarchical or mutually exclusive; instead, it is the responsibility of the modelling team to choose an appropriate subset of variables in their reporting workflows.

  • Fuels: Fossil, Coal, Oil, Gas, Nuclear, Non-Biomass Renewables, Hydro, Solar, PV, Biomass, Electricity[1], Heat, ...
  • Sectors: Industry, Residential and Commercial, Transportation, Non-Energy Use, ...
  • Specifications: Offshore, Onshore, w/ CCS, w/o CCS,Freight, ...

[1] Please use Electricity instead of Power for consistency (except for "power plant")

Categories of variables

This section provides an overview of the top-level categories or indicators of the variable dimension, i.e., <Category>|.... The detailed explanation and codelists are provided in the subfolders.

Production, generation and consumption of energy (fuels)

This category includes three top-level indicators related to the energy supply chain (also called reference energy system):

  • Primary Energy
  • Secondary Energy
  • Final Energy

More information

Characteristics of (energy) technologies

This section defines variables and indicators related to characteristics and specifications of (energy) technologies including power plants, transmission lines and pipelines.

  • Capacity
  • Capital Cost
  • Investment

More information

Emissions, carbon sequestration and climate

This section defines variables and indicators related to emissions, carbon sequestration and the impact of emissions on the climate (i.e., temperature).

  • Emissions
  • Carbon Sequestration
  • Temperature

More information

Macro-economic indicators and societal drivers

This section defines variables and indicators related to the economy and societal drivers such as population.

  • Discount Rate
  • Population
  • GDP
  • Consumption
  • Price
  • Policy Cost

More information

Definition of units

The list of variables (codelists) defined in the yaml-files in the subfolders contain an attribute unit, which specifies the recommended unit for this indicator.

It is currently still under discussion whether the recommended unit should be interpreted as mandatory.

For unit conversion as part of the pre- or postprocessing in the model workflow, the Python package pyam provides an intuitive and low-level interface; see this tutorial for more information.