Configuration and input data
This page describes the structure and format of inputs required for building MESSAGEix-Transport.
Both input data and configuration are stored in files under message_ix_models/data/transport/
.
In most cases, these files are read from a subdirectory like …/data/transport/nodes/
, where nodes denotes the message_ix_models
node code list—for instance, “R12”—for which MESSAGEix-Transport will be built.
This value is retrieved from the model.Config.regions
setting on a Context
object.
If the file data or configuration settings have a node (\(n\)) dimension, the file must be placed in such a subdirectory. Data for one node list is not usable for base models using a different node list.
For other data, a node list–specific file may be used. If none exists, the file of the same name in
…/data/transport/
is used as a default. For example,…/data/transport/R12/set.yaml
is used if it exists; if not, then…/data/transport/set.yaml
is used.
Configuration
General (config.yaml
, required)
The contents of this configuration file exactly map to the attributes of the class transport.Config
.
The class stores all the settings understood by the code for building, solving, and reporting MESSAGEix-Transport.
The class also defines the default values for each setting (there is no file …/data/transport/config.yaml
containing defaults.)
It also has methods for reading the configuration from file; see the detailed description of Config.from_context()
.
The following is the configuration file for a base model with R12 nodes:
→ View data/transport/R12/config.yaml on GitHub
Technology code list (technology.yaml
)
This file gives the code list for the MESSAGE technology
concept/set/dimension.
Some annotations (iea-eweb-flow
, input
, report
) and the child
hierarchy give information about technologies’ grouping according to transport modes.
→ View message_ix_models/data/transport/technology.yaml on GitHub
Code lists for other MESSAGE sets (set.yaml
)
This file gives code lists for other MESSAGE concepts/sets/dimensions.
→ View message_ix_models/data/transport/set.yaml on GitHub
Input data files
transport.files.FILES
gives a list of all data files.
Through transport.build.main()
(ultimately, transport.build.add_exogenous_data()
), each of these files is connected to a genno.Computer
used for model-building, and its contents appear at the key given in the list below.
Example
Contents of the file freight-mode-share-ref.csv
are available at the key freight mode share:n-t:ref
.
The indicates the dimensionality of this quantity is \((n, t)\).
The file has column headers “node”, “technology”, and “value”.
Not all files are currently or always used in model-building computations.
Some submodules of transport
use additional data files via other mechanisms.
Most of the files have a header comment including a precise description of the quantity, source of the data, and units of measurement.
In some cases extended information is below (where a header comment would be too long).
The git history of files, or the GitHub “blame” view can also be used to inspect the edit history of each file, line by line.
ldv-activity.csv
→ ldv activity:n:exo
- Measure
Activity (driving distance) per light-duty vehicle
- Units
kilometre / year
Notes
- node = R12_AFR [1]
Obtained from literature, based on estimates from South Africa. The reported value for South Africa is lower (18000 km/year, source) than the one for Kenya (22000 km/year, source).
- node = R12_FSU [1]
Based on Russia estimates (source).
- node = R12_NAM [1]
Based on US estimates (source <https://afdc.energy.gov/data/10309>`__), Canada estimates tend to [be] lower in general.
- node = R12_PAO [1]
Estimates for AU is 11000 in 2020, it’s a sharp decrease from 12600 in 2018 (maybe a Covid effect?). Whereas JP is 8532 (source) in 2016.
- node = R12_PAS [1]
Based on Singapore by Chong et al. (2018).
- node = R12_SAS [1]
Based on India, mainly Delhi estimate by Goel et al. (2015).
ldv-fix_cost.csv
, ldv-inv_cost.csv
, ldv-fuel-economy.csv
Data on costs and efficiencies of LDV technologies.
Formerly this data was read from ldv-cost-efficiency.xlsx
, a highly-structured spreadsheet that performs some input calculations.
The function get_USTIMES_MA3T()
reads data from multiple sheets in this file.
To understand the sheet names and cell layout expected, see the code for that function.
As the name implies, the data for MESSAGE (V)-Transport was derived from the US-TIMES and MA³T models.
pdt-cap-ref.csv
→ pdt:n:capita+ref
- Measure
Passenger distance travelled per capita in the model base year
- Dimensions
\((n)\)
- Units
km / year
Notes
- node = R12_CHN [4]
Based on the vehicle activity method Liu, et al. 2022 estimate the total PDT for R12_CHN for year (2017) is 9406 billion pkm. This is the latest corrected estimate available from Liu, et al. 2022. Based on similar estimates for 2013 & 2015, I estimate the average growth of PDT to be 8% per year. Using the growth rate and 2017 estimate, the total PDT for year (2020) comes out to be 11848.9 billion pkm.
R12_CHN population estimate from IMAGE: 1.4483 billion
the PDT/capita = 11848.9/1.4483
Javaid, message_data#538 (comment).
pdt-elasticity.csv
→ pdt elasticity:scenario-n:exo
- Measure
“Elasticity” or multiplier for GDP PPP per capita
- Dimensions
\((n, \text{scenario})\). ‘scenario’ identifiers are partial URNs for codes in the
SSP_2024
code list.- Units
dimensionless
- Where/how used
Other files
demand-scale.csv
→demand scale:n-y:exo
disutility.csv
→disutility:n-cg-t-y:per vehicle
energy-other.csv
→energy:c-n:transport other
freight-activity.csv
→freight activity:n:ref
freight-mode-share-ref.csv
→freight mode share:n-t:ref
fuel-emi-intensity.csv
→fuel emi intensity:c-e:exo
ikarus/availability.csv
→ikarus availability:source-t-c-y:exo
ikarus/fix_cost.csv
→ikarus fix_cost:source-t-c-y:exo
ikarus/input.csv
→ikarus input:source-t-c-y:exo
ikarus/inv_cost.csv
→ikarus inv_cost:source-t-c-y:exo
ikarus/technical_lifetime.csv
→ikarus technical_lifetime:source-t-c-y:exo
ikarus/var_cost.csv
→ikarus var_cost:source-t-c-y:exo
input-base.csv
→input:t-c-h:base
ldv-class.csv
→ldv class:n-vehicle_class:exo
ldv-new-capacity.csv
→cap_new:nl-t-yv:ldv+exo
load-factor-ldv.csv
→load factor ldv:n:exo
load-factor-nonldv.csv
→load factor nonldv:t:exo
ma3t/attitude.csv
→ma3t attitude:attitude:exo
ma3t/driver.csv
→ma3t driver:census_division-area_type-driver_type:exo
ma3t/population.csv
→ma3t population:census_division-area_type:exo
mer-to-ppp.csv
→mer to ppp:n-y:exo
population-suburb-share.csv
→population suburb share:n-y:exo
Other data sources
transport
makes use of the tools.exo_data
mechanism to retrieve data from common (not transport-specific) sources.
DataSourceConfig
, transport.Config.ssp
, and other settings determine which sources and quantities are used.
These include:
GDP and population from the
project.ssp
data sources or other sources including the ADVANCE project, the Global Energy Assessment project, the SHAPE project, etc.Note
Formerly, file
gdp.csv
was used.This is no longer supported; instead, use databases via
exo_data.prepare_computer()
or introduce quantities with the same dimensions and units into theComputer
used for model building/reporting.Energy from the IEA Extended World Energy Balances.