Package 'edgeTransport'

Title: Prepare EDGE Transport Data for the REMIND model
Description: EDGE-T is a fork of the GCAM transport module https://jgcri.github.io/gcam-doc/energy.html#transportation with a high level of detail in its representation of technological and modal options. It is a partial equilibrium model with a nested multinomial logit structure and relies on the modified logit formulation. Most of the sources are not publicly available. PIK-internal users can find the sources in the distributed file system in the folder `/p/projects/rd3mod/inputdata/sources/EDGE-Transport-Standalone`.
Authors: Johanna Hoppe [aut, cre] (ORCID: <https://orcid.org/0009-0004-6753-5090>), Alois Dirnaichner [aut], Marianna Rottoli [aut], Jarusch Muessel [aut] (ORCID: <https://orcid.org/0000-0002-1857-7866>), Alex K. Hagen [aut] (ORCID: <https://orcid.org/0000-0003-4793-8664>), Robert P. Pietzcker [aut] (ORCID: <https://orcid.org/0000-0002-9403-6711>)
Maintainer: Johanna Hoppe <[email protected]>
License: GPL-3
Version: 3.22.0
Built: 2026-10-15 17:28:56 UTC
Source: https://github.com/pik-piam/edgeTransport

Help Index

Generate EDGE-Transport Input Data for the REMIND model, madrat interface.

Description

calcEdgeTransportSA() is a wrapper for toolEdgeTransportSA() to make use of madrat caching.

Usage

calcEdgeTransportSA(
  SSPscen,
  transportPolScen,
  isICEban = c(FALSE, FALSE),
  demScen = c("default", "default"),
  startyear = 2021,
  gdxPath = NULL,
  outputFolder = NULL,
  isStored = FALSE,
  isTransportReported = TRUE,
  isTransportExtendedReported = FALSE,
  isREMINDinputReported = FALSE,
  isAnalyticsReported = FALSE,
  testIterative = FALSE
)

Arguments

SSPscen

SSP or SDP scenario
transportPolScen

EDGE-T transport policy scenario
isICEban

optional enabling of ICE ban
demScen

Demand scenario, used to apply reduction factors on total demands from the regression
startyear

Year after which policy differentiation sets in
gdxPath

Path to a GDX file to load price signals from a REMIND run
outputFolder

Path to folder for storing output data
isStored

Optional saving of intermediate RDS files
isTransportReported

Optional transport reporting in MIF format
isTransportExtendedReported

Optional extension of transport reporting providing more detailed variables
isREMINDinputReported

Optional reporting of REMIND input data
isAnalyticsReported

Optional reporting of analytics data (e.g. variables over iterations)
testIterative

development setting: make standalone and iterative scripts comparable, sets sectorESdemand = REMINDsectorESdemand and iterations = 1 (cost module)

Value

Transport input data for REMIND

Author(s)

Jarusch Muessel, Johanna Hoppe, Alex K. Hagen

Calculate shares based on discrete choice model.

Description

Function works for the use of generic preferences as well as for inconvenience costs. If no preferences are provided the function sets them to one which is equivalent to pure description by inconvenience costs.

Usage

calculateSharesDiscreteChoice(totPrice, lambda, pref = NULL)

Arguments

totPrice

total price of an option in a branch of the decision tree
lambda

exponent that determines the price sensitivity of the decision model
pref

optional use of generic preference fators

Value

share of option in a branch of the decision tree

Author(s)

Johanna Hoppe

Check a data.table for NAs and duplicates and throw an error if needed

Description

Check a data.table for NAs and duplicates and throw an error if needed

Usage

checkForNAsDups(dt, varname, codePosition)

Arguments

dt

data.table to be checked
varname

name of the variable
codePosition

position in the code to find the bug

Author(s)

Johanna Hoppe

List associated univocalNames for any entry of the decision tree

Description

List associated univocalNames for any entry of the decision tree

Usage

getFilterEntriesUnivocalName(categories, decisionTree)

Arguments

categories

vector of categories to filter
decisionTree

decision tree that contains the univocalNames associated to the category

Value

list of categories and their associated univocalNames e.g. trn_pass as list entry containing a vector c("Compact Car", "HSR", ..)

Author(s)

Johanna Hoppe

EDGE-Transport iterative

Description

Run in the REMIND output folder in between iterations

Usage

iterativeEdgeTransport()

Author(s)

Johanna Hoppe, Alex K. Hagen

Function to adjust the sector ES demand on CES level to the covid decrease in 2020

Description

The adjustment is based on the relative differences of our FE model projections in 2020 to the FE IEA data averaged over five years. In addition, the deviation in 2020 is corrected for earlier diferences between IEA data and model projections, such that only the decrease in deviation due to covid is regarded here. The final differences used for this correction are stored in inst/extdata/IEAdemandDifferencesCovid.csv For more context, the full data and calculations can be found in /p/projects/edget/adjustmentDataFiles/IEAdemandDifferencesCovid_compHistoricREMIND.xlsx

Usage

toolAdjustDemandCovid(demandData)

Arguments

demandData

the energy service demand on CES level: trn_aviation_intl, trn_shipping_intl, trn_freight, trn_pass

Details

The demand projections are constantly shifted by an absolute difference from 2020 onwards

This function is applied in toolDemandRegression() directly after the regression calculation as a default

Value

demandData adjusted for the decrease in demand related to covid

Apply ICE ban on vehicle types that feature preference factors

Description

Apply ICE ban on vehicle types that feature preference factors

Usage

toolApplyICEbanOnPreferences(preferenceTab, helpers, ICEbanYears)

Arguments

preferenceTab

data.table including preferences for all levels of the decision tree
helpers

list of helpers
ICEbanYears

sequence of years in which ICEban is applied

Value

Preferences in accordance to the ICE ban policy

Author(s)

Johanna Hoppe, Alex K. Hagen

toolApplyMixedTimeRes

Description

Applies two different temporal resolutions on a data.table object in the edgeTransport data structure and do a linear approximination for the highRes data that is not available

Usage

toolApplyMixedTimeRes(data, helpers, idcols = NULL)

Arguments

data

data.table containing data in in the edgeTransport data structure (at least featuring univocalName, period, value)
helpers

list containing several helpers used throughout the model. It includes dtTimeRes, a data.table containing the temporal resolution for different univocalNames
idcols

optional supply of idcols for using approx_dt

Value

data.table

Author(s)

Johanna Hoppe

Apply scenario specific adjustments to the preference trends

Description

Apply scenario specific adjustments to the preference trends

Usage

toolApplyScenPrefTrends(
  baselinePrefTrends,
  scenParPrefTrends,
  GDPpcMER,
  allEqYear,
  GDPcutoff,
  helpers
)

Arguments

baselinePrefTrends

Baseline preference trends
scenParPrefTrends

Scenario parameters to be applied on the preference trends
GDPpcMER

Per capita GDP based on market exchange rate
allEqYear

Year after which scenario differentiation sets in
GDPcutoff

Threshold used to categorize countries into different mitigation groups based on their GDP
helpers

List containing several helpers used throughout the model

Value

Scenario specific preference trends

Author(s)

Johanna Hoppe, Alex K. Hagen

Apply demand scenario specific adjustments to the energy intensity

Description

Apply demand scenario specific adjustments to the energy intensity

Usage

toolApplyScenSpecEnInt(enInt, scenParEnergyIntensity, allEqYear, helpers)

Arguments

enInt

Energy intensity input data supplied by mrtransport
scenParEnergyIntensity

Transport policy scenario specific energy intensity improvement factors
allEqYear

Year after which scenario differentiation sets in
helpers

List with helpers

Value

data.table with scenario specific energy intensity input data

Author(s)

Johanna Hoppe, Alex K. Hagen

Apply demand scenario specific adjustments to the load Factor

Description

Apply demand scenario specific adjustments to the load Factor

Usage

toolApplyScenSpecLoadFactor(loadFactor, scenParLoadFactor, allEqYear, helpers)

Arguments

loadFactor

load factor input data supplied by mrtransport
scenParLoadFactor

Scenario specific parameters to be applied on the baseline load factor
allEqYear

Year after which scenario differentiation sets in
helpers

List with helpers

Value

Scenario specific load factor

Author(s)

Johanna Hoppe, Alex K. Hagen

Assemble the list of helpers shared across the EDGE-T model

Description

Derives filterEntries (associated univocalNames per category) and the mixed temporal resolution, then collects them together with the structural mappings into the helpers list used throughout the model. Shared by toolLoadInputs() (full input preparation) and toolReLoadInputs() (reload from RDS in iterative mode) so that the two paths cannot drift apart.

Usage

toolBuildHelpers(decisionTree, timeResDataBase)

Arguments

decisionTree

data.table of the full edgeTransport decision tree
timeResDataBase

data.table with univocalName and period used to derive the temporal resolution (see toolDeriveTimeRes())

Value

list of helpers used throughout the model

Author(s)

Alex K. Hagen

Calculate annuity for different vehicle types

Description

Calculate annuity for different vehicle types

Usage

toolCalculateAnnuity(annuityCalc, helpers)

Arguments

annuityCalc

input data for interest rate and service Life from edgeTransport
helpers

list with helpers

Author(s)

Johanna Hoppe

Calculate composition of the fleet

Description

This function calculates the fleet composition based on the energy service demand, the composition of new sales each year and the service life of vehicles. With the help of the annual mileage, the absolute numbers of vehicles is calculated. If the remaining stock is sufficient to meet the energy service demand, 10

Usage

toolCalculateFleetComposition(
  ESdemandFVsalesLevel,
  vehDepreciationFactors,
  vehSalesAndModeShares,
  annualMileage,
  loadFactor,
  helpers
)

Arguments

ESdemandFVsalesLevel

Energy service demand on fuel vehicle level for new sales
vehDepreciationFactors

Factors for the depreciation of vehicles in each year of the maximum service Life
vehSalesAndModeShares

Vehicle sales and mode shares for the decision tree
annualMileage

Annual kilometers driven by a vehicle
loadFactor

Persons or tons loaded on each vehicle
helpers

List of helpers

Value

list of fleet composition data

Author(s)

Johanna Hoppe

toolCalculateFS3share

Description

Calculates fuel subsector L3 shares

Usage

toolCalculateFS3share(
  endoCostData,
  timesteps,
  timeValue,
  preferences,
  lambdas,
  helpers
)

Arguments

endoCostData

data.table containing all cost components on technology level
timesteps

years for which to calculate FS3 shares
timeValue

data.table containing mode specific time value costs based on speed and gdp
preferences

data.table containing preference trends
lambdas

data.table containing exponents for discrete choice calculation
helpers

list containing helpers like mappings, decisionTree etc.

Value

data.table containing all cost components on technology level and their respective FS3 shares

Author(s)

Johanna Hoppe

Calculate eneryg service demand on fuel vehicle level

Description

Calculate eneryg service demand on fuel vehicle level

Usage

toolCalculateFVdemand(
  sectorESdemand,
  salesAndModeShares,
  helpers,
  histESdemand = NULL,
  baseYear = NULL
)

Arguments

sectorESdemand

energy service demand on CES node level
salesAndModeShares

vehicle sales and mode shares
helpers

List with helpers
histESdemand

historcial energy service demand for iterative EDGE-T
baseYear

ned year of historical energy service demand

Value

data.table

Author(s)

Johanna Hoppe

Apply regional differences for inconvenience cost start values based on ICE cost differences

Description

Apply regional differences for inconvenience cost start values based on ICE cost differences

Usage

toolCalculateInitialIncoCost(
  combinedCost,
  incoCostStartVal,
  annuity,
  loadFactor,
  annualMileage,
  helpers
)

Arguments

combinedCost

total cost of ownership
incoCostStartVal

start values for inconvenience costs
annuity

calculated annuity for different vehicle types
loadFactor

load factor data
annualMileage

annual mileage data
helpers

list with helpers

Value

data.table including initial inconvenience costs from 1990-2020 for LDV 4W US$/(p|t)km

Author(s)

Johanna Hoppe

Calculate shares for all levels of the decision tree for a variable on FV level

Description

Calculate shares for all levels of the decision tree for a variable on FV level

Usage

toolCalculateSharesDecisionTree(dtVariable, helpers)

Arguments

dtVariable

data.table of a transport variable, e.g., energy service demand with data at the most detailed level of of the decision tree (FV = fuel-vehicle)
helpers

list of helpers

Value

data.table including shares of the variable for all levels of the decision tree

Author(s)

Johanna Hoppe

Calculate vehicle depreciation factors for fleet tracking

Description

Calculate vehicle depreciation factors for fleet tracking

Usage

toolCalculateVehicleDepreciationFactors(annuityCalc, helpers)

Arguments

annuityCalc

parameters for annualization
helpers

list with helpers

Value

data.table including vehicle depreciation factors over service life#'

Author(s)

Johanna Hoppe

Calibrate the logit share weights to historical data.

Description

Calibrate the logit share weights to historical data.

Usage

toolCalibratePreferences(
  sharesToBeCalibrated,
  combinedCosts,
  timeValueCost,
  lambdas,
  helpers
)

Arguments

sharesToBeCalibrated

Shares at each level of the decision tree for which preferences are to be calibrated
combinedCosts

Annualized total cost of ownership
timeValueCost

Time value cost for passenger transport modes
lambdas

Exponents for discrete choice function
helpers

list with helpers

Value

data.table with calibrated historical preferences

toolCheckAllLevelsComplete

Description

Checks whether data is complete for all levels of decision tree

Usage

toolCheckAllLevelsComplete(data, decisionTree, name)

Arguments

data

data.table containing data in all levels format that should be checked
decisionTree

data.table containing full edgeTransport decision Tree
name

name of variable to be checked

Value

data.table

Author(s)

Johanna Hoppe

Function that converts CAPEX and OPEX into US$/(p|t)km and provides them combined in a structured format

Description

Function that converts CAPEX and OPEX into US$/(p|t)km and provides them combined in a structured format

Usage

toolCombineCAPEXandOPEX(
  CAPEXtrackedFleet,
  nonFuelOPEXtrackedFleet,
  CAPEXother,
  nonFuelOPEXother,
  fuelCosts,
  subsidies,
  energyIntensity,
  loadFactor,
  annualMileage,
  annuity,
  helpers
)

Arguments

CAPEXtrackedFleet

CAPEX data for vehicle types that feature fleet tracking: Cars, trucks, busses
nonFuelOPEXtrackedFleet

non-fuel OPEX data for vehicle types that feature fleet tracking: Cars, trucks, busses
CAPEXother

CAPEX data for other vehicle types
nonFuelOPEXother

non-fuel OPEX data for other vehicle types
fuelCosts

fuel cost data
subsidies

purchase price subsidy data
energyIntensity

energy intensity data
loadFactor

load factor data
annualMileage

annual mileage data
annuity

calculated annuity for different vehicle types
helpers

list with helpers

Value

data.table including total costs of ownership in US$/(p|t)km

Function that converts CAPEX and OPEX into US$/(p|t)km and provides them combined in a structured format

Description

Function that converts CAPEX and OPEX into US$/(p|t)km and provides them combined in a structured format

Usage

toolCombineCAPEXandOPEXiterative(
  CAPEXandNonFuelOPEX,
  fuelCosts,
  energyIntensity,
  loadFactor,
  annualMileage,
  helpers
)

Arguments

CAPEXandNonFuelOPEX

non-fuel OPEX and CAPEX data as combined in the previous EDGET run and loaded from RDS file
fuelCosts

updated fuel cost from REMIND
energyIntensity

energy intensity data
loadFactor

load factor data
annualMileage

annual mileage data
helpers

list with helpers

Value

data.table including total costs of ownership in US$/(p|t)km

Energy service demand projection

Description

Energy service demand projection

Usage

toolDemandRegression(
  historicalESdemand,
  GDPperCapitaPPP,
  POP,
  genParDemRegression,
  scenParDemRegression,
  scenParRegionalDemRegression,
  scenParDemandFactors,
  baseYear,
  allEqYear,
  helpers
)

Arguments

historicalESdemand

Historical energy service demand
GDPperCapitaPPP

GDP per capita based on purchase power parity
POP

Population data
genParDemRegression

General regression factors
scenParDemRegression

Scenario specific general regression factors
scenParRegionalDemRegression

Scenario specific regionally differentiated regression factors
scenParDemandFactors

Demand scenario factors
baseYear

End year of historical energy service demand data
allEqYear

Start year of scenario differentiation
helpers

list with helpers

Value

Scenario specific energy service demand for all model years on CES level

Author(s)

Johanna Hoppe

Derive the mixed temporal resolution used across EDGE-T from a reference data.table

Description

Vehicle types that feature fleet tracking are resolved annually (highRes), all other modes use a coarser resolution (lowTimeRes). The split is based on which univocalNames provide data for the full set of highRes periods. Shared by toolLoadInputs() and toolReLoadInputs() so the full-load and reload paths cannot drift apart.

Usage

toolDeriveTimeRes(dt)

Arguments

dt

data.table containing at least univocalName and period columns (e.g. energy intensity data) that spans the full temporal resolution of the model

Value

list with dtTimeRes (unique univocalName/period combinations) and lowTimeRes (the coarse resolution periods)

Author(s)

Johanna Hoppe, Alex K. Hagen

Calculate vehicle sales shares and mode shares for all levels of the decisionTree.

Description

Function that traverses each level of the decision tree and calculates the shares according to the discrete choice approach

Usage

toolDiscreteChoice(input, lambdasDiscreteChoice, updatedEndoCosts, helpers)

Arguments

input

dataset for discrete choice module
lambdasDiscreteChoice

lambdas from the general model parameters
updatedEndoCosts

updated endogenous costs
helpers

list of helpers

Value

calculated shares

Author(s)

Johanna Hoppe

Energy Demand Generator (EDGE)- Transport Model

Description

The Edge Transport Model includes the transport specific input data preparation, a choice model to determine transport mode and technology shares, a demand regression and a fleet tracking for cars, busses and trucks

Usage

toolEdgeTransportSA(
  SSPscen,
  transportPolScen,
  isICEban = c(FALSE, FALSE),
  demScen = c("default", "default"),
  startyear = 2021,
  gdxPath = NULL,
  outputFolder = NULL,
  isStored = TRUE,
  isTransportReported = TRUE,
  isTransportExtendedReported = FALSE,
  isREMINDinputReported = FALSE,
  isAnalyticsReported = FALSE,
  testIterative = FALSE
)

Arguments

SSPscen

SSP or SDP scenarios
transportPolScen

EDGE-T transport policy scenarios
isICEban

optional enabling of ICE ban
demScen

Demand scenarios, used to apply reduction factors on total demands from the regression
startyear

First time point in which policy differentiation sets in, cm_startyear in REMIND
gdxPath

Path to a GDX file to load price signals from a REMIND run
outputFolder

Path to folder for storing output data
isStored

Optional saving of intermediate RDS files
isTransportReported

Optional transport reporting in MIF format
isTransportExtendedReported

Optional extension of transport reporting providing more detailed variables
isREMINDinputReported

Optional reporting of REMIND input data
isAnalyticsReported

Optional reporting of analytics data (e.g. variables over iterations)
testIterative

development setting: make standalone and iterative scripts comparable, sets sectorESdemand = REMINDsectorESdemand and iterations = 1 (cost module)

Value

Transport input data for REMIND

Author(s)

Johanna Hoppe, Jarusch Müßel, Alois Dirnaichner, Marianna Rottoli, Alex K. Hagen

Distribute common parameters to standalone and iterative EDGET script

Description

Distribute common parameters to standalone and iterative EDGET script

Usage

toolGetCommonParameters(startyear, ICEbanBefore = FALSE, ICEbanAfter = FALSE)

Arguments

startyear

First time point in which policy differentiation sets in, cm_startyear in REMIND
ICEbanBefore

Boolean to determine if ICEban is in effect before startyear
ICEbanAfter

Boolean to determine if ICEban is in effect from startyear on

Value

a list of parameters: allEqYear, ICEbanYears, GDPcutoff, baseYear, hybridElecShare

Author(s)

Alex K. Hagen

Read and build the complete structure of the edgeTransport decision tree

Description

Read and build the complete structure of the edgeTransport decision tree

Usage

toolLoadDecisionTree(regionAggregation = "iso")

Arguments

regionAggregation

choose one of the different options for regional aggregation (iso|regionCode21|regionCode12)

Value

data.table of full spatially extended edgeTransport decision tree

Author(s)

Johanna Hoppe

Load all inputs that are required to run the model

Description

Load all inputs that are required to run the model

Usage

toolLoadInputs(SSPscen, transportPolScen, demScen, hybridElecShare, allEqYear)

Arguments

SSPscen

SSP or SDP scenarios
transportPolScen

EDGE-T transport policy scenarios
demScen

Demand scenario, used to apply reduction factors on total demands from the regression
hybridElecShare

Share of electricity in Hybrid electric vehicles
allEqYear

Year after which scenario differentiation sets in, last year in which scenarios are the same

Value

list with different input data sets

Author(s)

Johanna Hoppe, Alex K. Hagen

Load GDP and Population data from mrdrivers

Description

Load GDP and Population data from mrdrivers

Usage

toolLoadmrdriversData(SSPscen, helpers, allEqYear)

Arguments

SSPscen

SSP scenarios for which the mrdrivers input data shall be loaded
helpers

list containing several helpers used throughout the model. It includes dtTimeRes, a data.table containing the temporal resolution for different univocalNames
allEqYear

Year after which policy differentiation sets in

Value

list of data.tables containing mrdrivers input data

Load input data from the mrtransport package

Description

Load input data from the mrtransport package

Usage

toolLoadmrtransportData(SSPscen = "SSP2")

Arguments

SSPscen

SSP scenario for which the mrtransport input data should be loaded

Value

list of data.tables with the mrtransport input data

Load input data from the edgeTransport package and choose data according to SSP and transport policy scenario

Description

Load input data from the edgeTransport package and choose data according to SSP and transport policy scenario

Usage

toolLoadPackageData(SSPs, transportPolS, demScenario = NULL)

Arguments

SSPs

SSP scenarios for which the package data should be loaded
transportPolS

transport policy scenarios for which the package data should be loaded
demScenario

demand scenario for which the package input data should be loaded

Value

list of data.tables with the package input data

Creates RDS files for iterative REMIND-EDGE runs from csv input files. Existing files are overwritten silently. Does not return anything.

Description

Creates RDS files for iterative REMIND-EDGE runs from csv input files. Existing files are overwritten silently. Does not return anything.

Usage

toolLoadRDSinputs(edgeTransportFolder, inputFiles)

Arguments

edgeTransportFolder

directory in the output folder of the REMIND run that stores EDGET RDS files
inputFiles

list of filename strings that should be read in

Load REMIND energy service demand

Description

Load the energy service demand from a REMIND fulldata.gdx on sector level (CES level) in [bn (p|t)km/yr] and the requested temporal resolution

Usage

toolLoadREMINDesDemand(gdxPath, helpers)

Arguments

gdxPath

path to REMIND fulldata.gdx
helpers

list of helpers

Load fuel prices from a REMIND fulldata.gdx in [US$/MJ] and map them on to the edgeTransport decision tree. The output is provided in the same spatial resolution as the transferred gdx file and the temporal resolution is set according to the param yrs.

Description

Load fuel prices from a REMIND fulldata.gdx in [US$/MJ] and map them on to the edgeTransport decision tree. The output is provided in the same spatial resolution as the transferred gdx file and the temporal resolution is set according to the param yrs.

Usage

toolLoadREMINDfuelCosts(
  gdxPath,
  hybridElecShare,
  helpers,
  transportFolder = ".",
  iterationNumber = NULL
)

Arguments

gdxPath

path to REMIND fulldata.gdx
hybridElecShare

Share of electricity in Hybrid electric vehicles
helpers

list with helpers
transportFolder

folder to search for fuel prices from previous iterations for averaging
iterationNumber

iteration number to store fuel costs for later iterations

Value

fuel costs on technology level

Function to load a parameter set of inconvinience start costs for a specific scenario(combination) from the csv file in the package

Description

Function to load a parameter set of inconvinience start costs for a specific scenario(combination) from the csv file in the package

Usage

toolLoadScenParIncoCost(SSPs, transportPolS)

Arguments

SSPs

SSP scenarios
transportPolS

transport policy scenarios

Value

list with different parameter sets, one for each scenario

Author(s)

Alex K. Hagen

Normalize preferences so that the maximum in each branch of the decision tree equals 1

Description

Normalize preferences so that the maximum in each branch of the decision tree equals 1

Usage

toolNormalizePreferences(preferenceTab)

Arguments

preferenceTab

data.table including preferences for all levels of the decision tree

Value

Normalized preferences

Author(s)

Johanna Hoppe

toolOrderandCheck

Description

sort data.table according to edgeTransport data structure and check for NAs If checkCompletness is activated it is further checked, if the full data set is provided. In case of fleetVars == TRUE the full data set is reduced to the vehicle types that feature fleet tracking

Usage

toolOrderandCheck(
  data,
  decisionTree,
  yrs = NULL,
  checkCompleteness = FALSE,
  fleetVars = FALSE
)

Arguments

data

data.table containing data in all levels format that should be checked
decisionTree

data.table containing full edgeTransport decision Tree
yrs

temporal resolution
checkCompleteness

if activated it is further checked, if the full data set is provided
fleetVars

if activated the full data set is reduced to vehicle types that feature fleet tracking

Value

data.table

Author(s)

Johanna Hoppe

Prepare scenario specific input data and calibrate historical preferences

Description

Applies the scenario specific changes to the raw input data, calibrates the historical preferences from the historical energy service demand and assembles the preference trends (calibrated + scenario specific, mixed time resolution, optional ICE ban, normalized). Shared by toolEdgeTransportSA() (standalone) and iterativeEdgeTransport() (first call in a REMIND run) so that the two paths cannot drift apart.

Usage

toolPrepareAndCalibrateModelInput(
  genModelPar,
  scenModelPar,
  inputDataRaw,
  commonParams,
  isICEban,
  helpers
)

Arguments

genModelPar

General model parameters
scenModelPar

Transport scenario (SSPscen + demScen + polScen) specific model parameters
inputDataRaw

Raw input data, including the (deaggregated) REMINDfuelCosts
commonParams

List of common parameters from toolGetCommonParameters(), supplying allEqYear, GDPcutoff and ICEbanYears
isICEban

Vector of length two indicating an ICE ban before/after the startyear
helpers

List with helpers

Details

The returned inputData is complete for standalone mode. The iterative caller appends CAPEXandNonFuelOPEX, which is returned separately and is the only mode specific entry left. histPrefs is returned because both callers hand it on to storeData(), which writes it to 3_Calibration; nothing else reads it. It is a calibration artefact rather than a scenario input: the calibration runs on historical periods only, and scenario differentiation starts after allEqYear (2020 at the earliest), so in standalone mode the result does not vary with the transport policy scenario. In iterative mode it does, but only through the REMIND fuel costs, which toolLoadREMINDfuelCosts() back-extrapolates from 2025 into the calibration years.

inputData is the interface that both producers of model input must satisfy: this function on the first call, and toolReLoadInputs() on every later REMIND Nash iteration. That is why annualMileage, timeValueCosts and histESdemand travel through inputData instead of being read from inputDataRaw at the point of use - inputDataRaw does not exist on a reload iteration. Keep the members in step with the file list in toolReLoadInputs().

What belongs in inputData, and what does not:

  • scenario processed data consumed by the model (scenSpecPrefTrends, scenSpecLoadFactor, combinedCAPEXandOPEX, initialIncoCosts)

  • raw data consumed unchanged by the model (annualMileage, timeValueCosts, histESdemand)

  • scenSpecEnIntensity and upfrontCAPEXtrackedFleet, which no model function reads; they travel in inputData only so that storeData() writes them to 2_InputDataPolicy

  • CAPEXandNonFuelOPEX (iterative only), which is persisted to RDS so that the next REMIND Nash iteration can recombine it with the updated fuel costs

Raw inputs that scenario processing supersedes (energyIntensityRaw, loadFactorRaw, the CAPEX and OPEX tables, subsidies) and the driver data (GDP, population) stay out of inputData. They are read from inputDataRaw where they are needed - by toolPrepareScenInputData() inside this function and by toolDemandRegression() in the caller - and reach the output path from inputDataRaw.

Note on regional resolution: the function is agnostic to the number of regions. In iterative mode the REMIND fuel costs must already be deaggregated to 21 regions and appended to inputDataRaw before this function is called, since they enter the cost combination and thereby the preference calibration.

Value

List with inputData, the calibrated historical preferences (histPrefs) and CAPEXandNonFuelOPEX, which only the iterative caller needs

Author(s)

Alex K. Hagen

Format all cost components

Description

Format all cost components

Usage

toolPrepareDataEndogenousCosts(inputData, lambdas, helpers)

Arguments

inputData

List containing inputData
lambdas

exponents for discrete choice calculation
helpers

List with helpers

Value

data.table including all cost components

Author(s)

Johanna Hoppe

Apply transport scenario specific adjustments to input data

Description

Apply transport scenario specific adjustments to input data

Usage

toolPrepareScenInputData(
  genModelPar,
  scenModelPar,
  inputDataRaw,
  allEqYear,
  GDPcutoff,
  helpers
)

Arguments

genModelPar

General model parameters
scenModelPar

Transport scenario (SSPscen + demScen + polScen) specific model parameters
inputDataRaw

Raw input data
allEqYear

Year after which scenario differentiation sets in
GDPcutoff

GDP cutoff to differentiate between regions
helpers

List with helpers

Value

List of data.tables with scenario specific input data

Author(s)

Johanna Hoppe

called from iterativeEdgeTransport() reloads scenario specific inputData from RDS files in EDGE-T folder instead of generation via mrtransport

Description

called from iterativeEdgeTransport() reloads scenario specific inputData from RDS files in EDGE-T folder instead of generation via mrtransport

Usage

toolReLoadInputs(edgeTransportFolder)

Arguments

edgeTransportFolder

folder where the RDS files from last iterativeEdgeTransport() run are stored

Value

list with different input data sets

Author(s)

Alex K. Hagen

Function to rename scenarios between REMIND and EDGE-Transport naming conventions It is used at the interfaces REMIND-EDGETiterative, EDGETiterative-REMIND The same translations have to be done in mrremind readEDGETransport for REMIND input data generation

Description

Function to rename scenarios between REMIND and EDGE-Transport naming conventions It is used at the interfaces REMIND-EDGETiterative, EDGETiterative-REMIND The same translations have to be done in mrremind readEDGETransport for REMIND input data generation

Usage

toolTranslateTransportScenario(
  demScen,
  transportPolScen,
  direction = c("EDGEtoREMIND", "REMINDtoEDGE")
)

Arguments

demScen

demand scenario, if demScen is in mapping it will be translated otherwise returned unchanged
transportPolScen

transport policy scenario if transportPolScen is in mapping it will be translated otherwise returned unchanged
direction

one of two labels indicating the direction of the translation

Value

list of translated demScen and transportPolScen in the desired model name logic

Author(s)

Alex K. Hagen

Calculate data for the next higher level of the decision tree.

Description

Function that aggregates the data for the next higher level of the decision tree

Usage

toolTraverseDecisionTree(data, upperLevel, decisionTree)

Arguments

data

data.table containing the data of the lower level
upperLevel

name of the upper level in the decision tree
decisionTree

full edgeTransport decision tree

Value

data.table

Author(s)

Johanna Hoppe

toolUpdateEndogenousCosts

Description

Provides updates for endogenous cost components e.g. inconvenience costs for cars

Usage

toolUpdateEndogenousCosts(
  dataEndoCosts,
  depreciationFactors,
  scenParIncoCost,
  allEqYear,
  timeValue,
  preferences,
  lambdas,
  helpers,
  isICEban,
  ICEbanYears,
  vehiclesPerTech = NULL
)

Arguments

dataEndoCosts

data.table containing all cost components for cars over the full range of policy years in a yearly resolution. Exogenous CAPEX and OPEX are provided over the full range. Endogenous cost components and FS3 shares are provided until 2020. Rest is filled with NA.
depreciationFactors

data.table containing vehicle depreciation factor for each year of service Life
scenParIncoCost

data.table containing scenario specific parameters for inconvenience costs policy mask
allEqYear

year from which scenario-specific differentiation begins
timeValue

data.table containing mode specific time value costs based on speed and gdp
preferences

preference factor trends
lambdas

data.table containing exponents for discrete choice calculation
helpers

list containing helpers like mappings, decisionTree etc.
isICEban

optional enabling of ICE ban
ICEbanYears

sequence of years in which ICEban is applied
vehiclesPerTech

data.table containing total number of vehicles for all years and regions

Value

list containing data.table with endogenous cost components over the full time span and additional data.tables for model behavior analysis

Author(s)

Johanna Hoppe